propagate n_eff properly; have test output produce files from which m…

…odels can be read.

prospect/fitting/fitting.py

@@ -430,7 +430,7 @@ def run_nested(observations, model, sps,
                lnprobfn=lnprobfn,
                nested_sampler="dynesty",
                nested_nlive=1000,
-               nested_neff=1000,
+               nested_target_n_effective=1000,
                verbose=False,
                **kwargs):
     """Thin wrapper on :py:class:`prospect.fitting.nested.run_nested_sampler`
@@ -454,6 +454,13 @@ def run_nested(observations, model, sps,
         ``model``, and ``sps`` as keywords. By default use the
         :py:func:`lnprobfn` defined above.
 
+    nested_target_n_effective : int
+        Target number of effective samples
+
+    nested_nlive : int
+        Number of live points for the nested sampler.  Meaning somewhat
+        dependent on the chosen sampler
+
     Returns
     --------
     result: Dictionary
@@ -476,7 +483,7 @@ def run_nested(observations, model, sps,
                                 nested_sampler=nested_sampler,
                                 verbose=verbose,
                                 nested_nlive=nested_nlive,
-                                nested_neff=nested_neff,
+                                nested_neff=nested_target_n_effective,
                                 nested_sampler_kwargs=ns_kwargs,
                                 nested_run_kwargs=nr_kwargs)
     info, result_obj = output

prospect/fitting/nested.py

@@ -53,6 +53,8 @@ def run_nested_sampler(model,
     obj : Object
         The sampling object.  This will depend on the nested sampler being used.
     """
+    if verbose:
+        print(f"running {nested_sampler} for {nested_neff} effective samples")
 
     go = time.time()
 

tests/tests_samplers.py

@@ -13,15 +13,15 @@
 from prospect.fitting import fit_model
 from prospect.fitting.nested import parse_nested_kwargs
 from prospect.io.write_results import write_hdf5
-
+from prospect.io.read_results import results_from
 
 #@pytest.fixture
-def get_sps():
+def get_sps(**kwargs):
     sps = CSPSpecBasis(zcontinuous=1)
     return sps
 
 
-def build_model(add_neb=False, add_outlier=False):
+def build_model(add_neb=False, add_outlier=False, **kwargs):
     model_params = templates.TemplateLibrary["parametric_sfh"]
     model_params["logzsol"]["isfree"] = False  # built for speed
     if add_neb: # skip for speed
@@ -63,10 +63,11 @@ def build_obs(**kwargs):
 
     parser = get_parser()
     parser.set_defaults(nested_target_n_effective=256,
+                        nested_nlive=512,
                         verbose=0)
     args = parser.parse_args()
     run_params = vars(args)
-    run_params["parameter_file"] = __file__
+    run_params["param_file"] = __file__
 
     # test the parsing
     run_params["nested_sampler"] = "dynesty"
@@ -99,11 +100,14 @@ def build_obs(**kwargs):
                    results[sampler],
                    None,
                    sps=sps)
+        ires, iobs, im = results_from(hfile)
+        assert (im is not None)
 
+    # compare runtime
     for sampler in samplers:
         print(sampler, results[sampler]["duration"])
 
-
+    # compare posteriors
     colors = ["royalblue", "darkorange", "firebrick"]
     import matplotlib.pyplot as pl
     from prospect.plotting import corner