minor fixes from tempered branch; update changelog

CHANGELOG.md

@@ -1,8 +1,11 @@
 ## 3.X.Y – YYYY-MM-DD
 
+### General
+
 - Documented uses of `Model` class in general contexts (previously only cosmo)
 - `Model` methods to compute log-probabilities and derived parameters now have an `as_dict` keyword (default `False`), for more informative return value.
 - Added ``--minimize`` flag to ``cobaya-run`` for quick minimization (replaces sampler, uses previous output).
+- Add COBAYA_USE_FILE_LOCKING environment variable to allow disabling of file locks. Warning not to use --test with MPI.
 
 ### Cosmological likelihoods and theory codes
 
@@ -13,6 +16,9 @@
 - Removed problematic `zrei: zre` alias (fixes #199, thanks @pcampeti)
 - Added `Omega_b|cdm|nu_massive(z)` and `angular_diameter_distance_2`
 - Returned values for `get_sigma_R` changed from `R, z, sigma(z, R)` to `z, R, sigma(z, R)`.
+- Support setting individual Accuracy parameters, e.g. Accuracy.AccurateBB
+- Calculate accurate BB when tensors are requested
+- Fix for using derived parameters with post-processing
 
 #### CLASS
 
@@ -21,7 +27,7 @@
 
 #### BAO
 
-- Added Boss DR16 likelihood (#185, by @Pablo-Lemos)
+- Added Boss DR16 likelihoods (#185, by @Pablo-Lemos)
 
 #### BICEP-Keck
 

cobaya/collection.py

@@ -95,9 +95,9 @@ class SampleCollection(BaseCollection):
     (returns a copy, not a view).
 
     Note for developers: when expanding this class or inheriting from it, always access
-    the underlying DataFrame as `self.data` and not `self._data`, to ensure the cache has
-    been dumped. If you really need to access the actual attribute `self._data` in a
-    method, make sure to decorate it with `@ensure_cache_dumped`.
+    the underlying DataFrame as ``self.data`` and not ``self._data``, to ensure the cache
+    has been dumped. If you really need to access the actual attribute ``self._data`` in a
+    method, make sure to decorate it with ``@ensure_cache_dumped``.
     """
 
     def __init__(self, model, output=None, cache_size=_default_cache_size, name=None,
@@ -233,7 +233,7 @@ def _check_before_adding(self, values: Union[Sequence[float], np.ndarray],
             if derived is not None:
                 # A simple np.allclose is not enough, because np.allclose([1], []) = True!
                 if len(derived) != len(logpost.derived) or \
-                   not np.allclose(derived, logpost.derived):
+                        not np.allclose(derived, logpost.derived):
                     raise LoggedError(
                         self.log,
                         "derived params not consistent with those of LogPosterior object "
@@ -285,7 +285,7 @@ def _cache_add_row(self, pos: int, values: Union[Sequence[float], np.ndarray],
             for name, value in zip(self.chi2_names, logposterior.loglikes):
                 self._cache[pos, self._icol[name]] = -2 * value
             self._cache[pos, self._icol[OutPar.chi2]] = -2 * logposterior.loglike
-        if logposterior.derived != []:
+        if len(logposterior.derived):
             for name, value in zip(self.derived_params, logposterior.derived):
                 self._cache[pos, self._icol[name]] = value
 

cobaya/conventions.py

@@ -40,9 +40,9 @@ def get_version():
 # Names for the samples' fields internally and in the output
 class OutPar:
     weight = "weight"  # sample weight
-    # log-posterior, or in general the total log-probability
+    # minus log-posterior, or in general the total minus log-probability
     minuslogpost = "minuslogpost"
-    minuslogprior = "minuslogprior"  # log-prior
+    minuslogprior = "minuslogprior"  # minus log-prior
     chi2 = "chi2"  # chi^2 = -2 * loglike (not always normalized to be useful)
 
 

cobaya/model.py

@@ -119,7 +119,7 @@ def make_finite(self):
             object.__setattr__(self, 'loglikes', np.nan_to_num(self.loglikes))
             object.__setattr__(self, 'loglike', np.nan_to_num(self.loglike))
 
-    def as_dict(self, model: "Model") -> Dict[str, float]:
+    def as_dict(self, model: "Model") -> Dict[str, Union[float, Dict[str, float]]]:
         """
         Given a :class:`~model.Model`, returns a more informative version of itself,
         containing the names of priors, likelihoods and derived parameters.

cobaya/theories/classy/classy.py

@@ -576,8 +576,8 @@ def get_can_provide_params(self):
         return names
 
     def get_can_support_params(self):
-        # non-exhaustive list of supported input parameters that will be assigne do classy
-        # if they are varied
+        # non-exhaustive list of supported input parameters that will be assigned to
+        # classy if they are varied
         return ['H0']
 
     def get_version(self):

docs/theories_and_dependencies.rst

@@ -31,7 +31,7 @@ The theory code also needs to tell other theory codes and likelihoods the things
 
 Use a ``get_X`` method when you need to add optional arguments to provide different outputs from the computed quantity.
 Quantities returned by  :meth:`~.theory.Theory.get_can_provide` should be stored in the state dictionary by the calculate function
-or returned by the ``get_results(X)`` for each quantity ``X`` (which by default just returns the value stored in the current state dictionary).
+or returned by the ``get_result(X)`` for each quantity ``X`` (which by default just returns the value stored in the current state dictionary).
 The results stored by calculate for a given set of input parameters are cached, and ``self.current_state`` is set to the current state
 whenever ``get_X``, ``get_param`` etc are called.
 
@@ -136,4 +136,3 @@ sampling settings for 'Xin' so that configuring it is transparent to the user, e
 If multiple theory codes can provide the same quantity, it may be ambiguous which to use to compute which.
 When this happens use the ``provides`` input .yaml keyword to specify that a specific theory computes a
 specific quantity.
-