Test assign (#408)

* maintain order of existing defined input_params

* fix

* bump version

Author: cmbant

CHANGELOG.md

@@ -1,3 +1,16 @@
+
+## 3.5.7 - 2025-03-31
+
+### General
+
+- Fixed issue with changing parameter order using gaussian_mixture with input_params
+- cobaya-doc can be used to list components of a given type, e.g. `cobaya-doc bao`
+- Small tidies for float output with numpy2
+
+### Cosmology
+
+- BAO, DESI, and SN likelihood docs now automatically list all available internal likelihoods
+
 ## 3.5.6 - 2025-03-21
 
 ### Cosmology

cobaya/__init__.py

@@ -1,5 +1,5 @@
 __author__ = "Jesus Torrado and Antony Lewis"
-__version__ = "3.5.6"
+__version__ = "3.5.7"
 __obsolete__ = False
 __year__ = "2025"
 __url__ = "https://cobaya.readthedocs.io"

cobaya/likelihoods/bao/desi_dr2/desi_bao_all.py

@@ -3,6 +3,6 @@
 
 class desi_bao_all(BAO):
     r"""
-    DESI BAO likelihood for all tracers.
+    DESI BAO likelihood for all tracers. (bao.desi_dr2 is same as desi_bao_all)
     """
     bibtex_file = 'desi_dr2.bibtex'

cobaya/likelihoods/gaussian_mixture/gaussian_mixture.py

@@ -41,17 +41,11 @@ def d(self):
         """
         return len(self.input_params)
 
-    def initialize(self):
-        super().initialize()
-        self._input_params_order = self.input_params
-
-
     def initialize_with_params(self):
         """
         Initializes the gaussian distributions.
         """
         self.log.debug("Initializing")
-        self._input_params_order = self._input_params_order or self.input_params
         # Load mean and cov, and check consistency of n_modes and dimensionality
         if self.means is not None and self.covs is not None:
             # Wrap them in the right arrays (n_mode, param) and check consistency
@@ -126,7 +120,7 @@ def logp(self, **params_values):
         """
         self.wait()
         # Prepare the vector of sampled parameter values
-        x = np.array([params_values[p] for p in self._input_params_order])
+        x = np.array([params_values[p] for p in self.input_params])
         # Fill the derived parameters
         derived = params_values.get("_derived")
         if derived is not None:

cobaya/model.py

@@ -1079,11 +1079,18 @@ def _assign_params(self, info_likelihood, info_theory=None,
                 "Output params can only be computed by one likelihood/theory, "
                 "but some were claimed by more than one: %r.", multi_assigned_output)
         # Finished! Assign and update infos
-        for assign, option in ((input_assign, "input_params"),
-                               (output_assign, "output_params")):
+        for assign, option, output in ((input_assign, "input_params", False),
+                                       (output_assign, "output_params", True)):
             for component in self.components:
-                setattr(component, option,
-                        [p for p, assign in assign.items() if component in assign])
+                assign_params = [p for p, assign in assign.items() if component in assign]
+                current_assign = getattr(component, option)
+                if output or current_assign is unset_params:
+                    setattr(component, option, assign_params)
+                elif set(assign_params) != set(current_assign):
+                    raise LoggedError(
+                        self.log, "exising %s %r do not match assigned parameters %r",
+                        option, assign_params, current_assign)
+
                 # Update infos! (helper theory parameters stored in yaml with host)
                 inf = (info_likelihood if component in self.likelihood.values() else
                        info_theory)