syncing up dev and default

README.rst

@@ -12,6 +12,12 @@ A few papers on how it works:
 - Parameter inference: `Mirocha, Harker, & Burns (2015) <http://adsabs.harvard.edu/abs/2015ApJ...813...11M>`_.
 - Galaxy luminosity functions: `Mirocha, Furlanetto, & Sun (2016) <http://adsabs.harvard.edu/abs/2016arXiv160700386M>`_.
 
+And some applications:
+
+- `Mirocha & Furlanetto (2019) <http://adsabs.harvard.edu/abs/2018arXiv180303272M>`_
+- `Schneider (2018) <http://adsabs.harvard.edu/abs/2018PhRvD..98f3021S>`_
+- `Mirocha et al. (2018) <http://adsabs.harvard.edu/abs/2018MNRAS.478.5591M>`_
+
 Be warned: this code is still under active development -- use at your own
 risk! Correctness of results is not guaranteed.
 

ares/populations/GalaxyEnsemble.py

@@ -290,8 +290,10 @@ def _gen_deterministic_histories(self):
         # SFR = (zform, time (but really redshift))
         # So, halo identity is wrapped up in axis=0
         # In Cohort, formation time defines initial mass and trajectory (in full)
-        histories = {'z': zall, 'w': nh, 'SFR': sfr, 'Mh': Mh,
-            'MAR': mar, 'SFE': sfe, 'nh': nh}
+        zobs = np.array([zall] * nh.shape[0])
+        histories = {'z': zall, 'zobs': zobs, 'w': nh, 'SFR': sfr, 'Mh': Mh,
+            'MAR': mar, 'SFE': sfe, 'nh': nh, 
+            'Mg_c': Mh, 'Mg_h': Mh}
 
         # Add in formation redshifts to match shape (useful after thinning)
         histories['zform'] = self.tile(zall, thin)
@@ -478,9 +480,9 @@ def _gen_indeterminate_histories(self):
         for i, zform in enumerate(all_zform):
             t, z = self.get_timestamps(zform)
 
-            print(zform, len(t))
+            #print(zform, len(t))
 
-            tdyn = self.halos.DynamicalTime(1e10, z) / s_per_yr
+            #tdyn = self.halos.DynamicalTime(1e10, z) / s_per_yr
             k = t.size
 
             # Grab the things that we can't modify (too much)
@@ -498,6 +500,19 @@ def _gen_indeterminate_histories(self):
 
             jmax = t.size - 1
 
+            # Sure would be nice to eliminate the following loop.
+            # Can only do that if there's no randomness that depends on
+            # previous timesteps.
+
+            # This can go faster...
+            if self.pf['pop_bcycling'] == False:
+                # Really want an internal feedback determinism switch.
+                pass
+
+
+
+
+
             Mh[i,0] = _Mh[0]            
             Mg_h[i,0] = 0.0
             Mg_c[i,0] = fbar * _Mh[0]
@@ -722,8 +737,9 @@ def LuminosityFunction(self, z, x, mags=True, wave=1600., band=None):
                     # For each unique object (or object bin), sum emission
                     # over past episodes of star formation.
 
-                    L[k] = np.trapz(L_per_msun * self.histories['SFR'][k,iz:],
-                        dx=dt[0:-1])
+                    L[k] = np.sum(L_per_msun * self.histories['SFR'][k,iz:] * dt)
+                    #L[k] = np.trapz(L_per_msun * self.histories['SFR'][k,iz:],
+                    #    dx=dt[0:-1])
 
             else:
                 # In this case, the time-stepping is different for each 
@@ -768,7 +784,7 @@ def LuminosityFunction(self, z, x, mags=True, wave=1600., band=None):
                     if len(ages) == 0:
                         print('ages has no elements!', k)
                         continue
-
+                                        
                     # Need to be careful about interpolating within last
                     # dynamical time.
 

ares/util/SetDefaultParameterValues.py

@@ -520,6 +520,7 @@ def PopulationParameters():
     # Cluster-centric model
     "pop_poisson": False,
     "pop_bcycling": False,
+    "pop_internal_feedback": False,
     "pop_sf_via_inflow": True,
     "pop_sf_via_reservior": False,
 

doc/example_litdata.rst

@@ -10,7 +10,7 @@ A very incomplete set of data from from the literature exist in ``$ARES/input/li
 
 If any of these papers ring a bell, you can check out the contents in the following way: ::
 
-    litdata = ares.util.read_lit('mirocha2016')  # a self-serving example
+    litdata = ares.util.read_lit('mirocha2017')  # a self-serving example
     
 or, look directly at the source code, which lives in ``$ARES/input/litdata``. Hopefully the contents of these files are fairly self-explanatory! 
 
@@ -57,23 +57,23 @@ Reproducing Models from *ares* Papers
 -------------------------------------
 If you're interested in reproducing a model from a paper exactly, you can either (1) contact me directly for the model of interest, or preferably (someday) download it from my website, or (2) re-compute it yourself. In the latter case, you just need to make sure you supply the required parameters. To facilitate this, I store "parameter files" (just dictionaries) in the litdata framework as well. You can access them like any other dataset from the literature, e.g., ::
 
-    m16 = ares.util.read_lit('mirocha2016')
+    m17 = ares.util.read_lit('mirocha2017')
     
 A few of the models we focused on most get their own dictionary, for example our reference double power law model for the star-formation efficiency is stored in the ``dpl`` variable: ::
 
-    sim = ares.simulations.Global21cm(**m16.dpl)
+    sim = ares.simulations.Global21cm(**m17.dpl)
     sim.run()
     sim.GlobalSignature()  # voila!
     
 Hopefully this results *exactly* in the solid black curve from Figure 2 of `Mirocha, Furlanetto, & Sun (2016) <http://adsabs.harvard.edu/abs/2016arXiv160700386M>`_, provided you're using *ares* version 0.2. If it doesn't, please contact me! 
 
 Alternatively, you can use the ``ParameterBundle`` framework, which also taps into our collection of data from the literature. To access the set of parameters for the "dpl" model, you simply do: ::
 
-    pars = ares.util.ParameterBundle('mirocha2016:dpl')
+    pars = ares.util.ParameterBundle('mirocha2017:dpl')
     
-This tells *ares* to retrieve the ``dpl`` variable within the ``mirocha2016`` module. See :doc:`param_bundles` for more on these objects.
+This tells *ares* to retrieve the ``dpl`` variable within the ``mirocha2017`` module. See :doc:`param_bundles` for more on these objects.
 
-`Mirocha, Furlanetto, & Sun (2016) <http://adsabs.harvard.edu/abs/2016arXiv160700386M>`_ (``mirocha2016``)
+`Mirocha, Furlanetto, & Sun (2016) <http://adsabs.harvard.edu/abs/2016arXiv160700386M>`_ (``mirocha2017``)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 This model has a few options: ``dpl``, and the extensions ``floor`` and ``steep``, as explored in the paper. 
 
@@ -95,11 +95,11 @@ To re-make the right-hand panel of Figure 1 from the paper, you could do somethi
 
     import ares
     
-    dpl = ares.util.ParameterBundle('mirocha2016:dpl')
+    dpl = ares.util.ParameterBundle('mirocha2017:dpl')
     
     ax = None
     for model in ['floor', 'dpl', 'steep']:
-        pars = dpl + ares.util.ParameterBundle('mirocha2016:%s' % model)
+        pars = dpl + ares.util.ParameterBundle('mirocha2017:%s' % model)
         pars.update()
         sim = ares.simulations.Global21cm(**pars)
         sim.run()

doc/history.rst

@@ -4,6 +4,11 @@ Development History
 
 Here's an attempt to keep track of major changes to the code over time, which will be tagged in the bitbucket repository with version numbers. I haven't followed conventions for version numbering so far. Instead, I've simply tagged commits with a version number when a paper is submitted using that version of the code (e.g., v0.1 and v0.2), or when a series of noteworthy improvements or bug fixes have been made (v0.3).
 
+v0.5
+----
+- This is the version of the code used in `Mirocha & Furlanetto (2019) <http://adsabs.harvard.edu/abs/2018arXiv180303272M>`_. 
+- Note that several ``litdata'' modules have been updated so that the year is reflective of the year the paper was *published*, not *submitted*!
+
 v0.4
 ----
 - This is the version of the code used in `Mirocha et al. (2018) <http://adsabs.harvard.edu/abs/2018MNRAS.478.5591M>`_. The main addition is global Lyman-Werner feedback, which raises the minimum mass of star-forming halos self-consistently using an iterative technique. 

doc/index.rst

@@ -17,6 +17,12 @@ A few papers on how it works:
 - Parameter inference: `Mirocha, Harker, & Burns (2015) <http://adsabs.harvard.edu/abs/2015ApJ...813...11M>`_.
 - Galaxy luminosity functions: `Mirocha, Furlanetto, & Sun (2017) <http://adsabs.harvard.edu/abs/2017MNRAS.464.1365M>`_.
 
+And some applications:
+
+- `Mirocha & Furlanetto (2019) <http://adsabs.harvard.edu/abs/2018arXiv180303272M>`_
+- `Schneider (2018) <http://adsabs.harvard.edu/abs/2018PhRvD..98f3021S>`_
+- `Mirocha et al. (2018) <http://adsabs.harvard.edu/abs/2018MNRAS.478.5591M>`_
+
 Be warned: this code is still under active development -- use at your own
 risk! Correctness of results is not guaranteed. This documentation is as much of a work in progress as the code itself, so if you encounter gaps or errors please do let me know.