More testing

firecrown/connector/cobaya/ccl.py

@@ -38,13 +38,6 @@ def initialize(self):
         self.z_Pk = np.arange(0.0, 6.0, 1)
         self.Pk_kmax = 1.0
 
-    def get_param(self, p: str) -> None:
-        """Return the current value of the parameter named 'p'.
-
-        This implementation always returns None.
-        """
-        return None
-
     def initialize_with_params(self):
         """Required by Cobaya.
 

firecrown/connector/cobaya/likelihood.py

@@ -29,16 +29,19 @@ def initialize(self):
         if not hasattr(self, "build_parameters"):
             build_parameters = NamedParameters()
         else:
-            build_parameters = self.build_parameters
+            if isinstance(self.build_parameters, dict):
+                build_parameters = NamedParameters(self.build_parameters)
+            else:
+                if not isinstance(self.build_parameters, NamedParameters):
+                    raise TypeError(
+                        "build_parameters must be a NamedParameters or dict"
+                    )
+                build_parameters = self.build_parameters
 
         self.likelihood, self.tools = load_likelihood(
             self.firecrownIni, build_parameters
         )
 
-    def get_param(self, p: str):
-        """Return the current value of the parameter named 'p'."""
-        return self._current_state["derived"][p]
-
     def initialize_with_params(self) -> None:
         """Required by Cobaya.
 

firecrown/likelihood/gauss_family/gauss_family.py

@@ -10,7 +10,6 @@
 from __future__ import annotations
 from typing import List, Optional, Tuple, Sequence
 from typing import final
-from abc import abstractmethod
 import warnings
 
 import numpy as np
@@ -23,7 +22,6 @@
 from ...modeling_tools import ModelingTools
 from ...updatable import UpdatableCollection
 from .statistic.statistic import Statistic
-from ...parameters import RequiredParameters, DerivedParameterCollection
 
 
 class GaussFamily(Likelihood):
@@ -128,48 +126,3 @@ def compute_chisq(self, tools: ModelingTools) -> float:
         chisq = np.dot(x, x)
 
         return chisq
-
-    @final
-    def _reset(self) -> None:
-        """Implementation of Likelihood interface method _reset.
-
-        This resets all statistics and calls the abstract method
-        _reset_gaussian_family."""
-        self._reset_gaussian_family()
-        self.statistics.reset()
-
-    @final
-    def _get_derived_parameters(self) -> DerivedParameterCollection:
-        derived_parameters = (
-            self._get_derived_parameters_gaussian_family()
-            + self.statistics.get_derived_parameters()
-        )
-
-        return derived_parameters
-
-    @abstractmethod
-    def _reset_gaussian_family(self) -> None:
-        """Abstract method to reset GaussianFamily state. Must be implemented by all
-        subclasses."""
-
-    @final
-    def _required_parameters(self) -> RequiredParameters:
-        """Return a RequiredParameters object containing the information for
-        this Updatable.
-
-        This includes the required parameters for all statistics, as well as those
-        for the derived class.
-
-        Derived classes must implement required_parameters_gaussian_family."""
-        stats_rp = self.statistics.required_parameters()
-        stats_rp = self._required_parameters_gaussian_family() + stats_rp
-
-        return stats_rp
-
-    @abstractmethod
-    def _required_parameters_gaussian_family(self):
-        """Required parameters for GaussFamily subclasses."""
-
-    @abstractmethod
-    def _get_derived_parameters_gaussian_family(self) -> DerivedParameterCollection:
-        """Get derived parameters for GaussFamily subclasses."""

firecrown/likelihood/gauss_family/gaussian.py

@@ -3,10 +3,8 @@
 """
 
 from __future__ import annotations
-from typing import final
 
 from .gauss_family import GaussFamily
-from ...parameters import RequiredParameters, DerivedParameterCollection
 from ...modeling_tools import ModelingTools
 
 
@@ -17,15 +15,3 @@ def compute_loglike(self, tools: ModelingTools):
         """Compute the log-likelihood."""
 
         return -0.5 * self.compute_chisq(tools)
-
-    @final
-    def _reset_gaussian_family(self):
-        pass
-
-    @final
-    def _required_parameters_gaussian_family(self):
-        return RequiredParameters([])
-
-    @final
-    def _get_derived_parameters_gaussian_family(self) -> DerivedParameterCollection:
-        return DerivedParameterCollection([])

firecrown/likelihood/gauss_family/statistic/cluster_number_counts.py

@@ -1,11 +1,11 @@
 """Cluster Number Count statistic support.
 This module reads the necessary data from a SACC file to compute the
 theoretical prediction of cluster number counts inside bins of redshift
-and a mass proxy. For further information, check README.md.
+and a mass proxy.
 """
 
 from __future__ import annotations
-from typing import List, Dict, Tuple, Optional, final
+from typing import List, Dict, Tuple, Optional
 
 import numpy as np
 
@@ -14,10 +14,6 @@
 
 from .statistic import Statistic, DataVector, TheoryVector
 from .source.source import SourceSystematic
-from ....parameters import (
-    RequiredParameters,
-    DerivedParameterCollection,
-)
 from ....models.cluster_abundance import ClusterAbundance
 from ....models.cluster_mass import ClusterMass, ClusterMassArgument
 from ....models.cluster_redshift import ClusterRedshift, ClusterRedshiftArgument
@@ -29,14 +25,12 @@ class ClusterNumberCounts(Statistic):
     multiplicity functions, volume element,  etc.).
     This subclass implements the read and computes method for
     the Statistic class. It is used to compute the theoretical prediction of
-    cluster number counts given a SACC file and a cosmology. For
-    further information on how the SACC file shall be created,
-    check README.md.
+    cluster number counts.
     """
 
     def __init__(
         self,
-        sacc_tracer: str,
+        survey_tracer: str,
         cluster_abundance: ClusterAbundance,
         cluster_mass: ClusterMass,
         cluster_redshift: ClusterRedshift,
@@ -47,18 +41,13 @@ def __init__(
         """Initialize the ClusterNumberCounts object.
         Parameters
 
-        :param sacc_tracer: The SACC tracer. There must be only one tracer for all
-            the number Counts data points. Following the SACC file
-            documentation README.md, this string should be
-            'cluster_counts_true_mass'.
+        :param survey_tracer: name of the survey tracer in the SACC data.
         :param cluster_abundance: The cluster abundance model to use.
         :param systematics: A list of the statistics-level systematics to apply to
             the statistic. The default of `None` implies no systematics.
-
         """
         super().__init__()
-
-        self.sacc_tracer = sacc_tracer
+        self.sacc_tracer = survey_tracer
         self.systematics = systematics or []
         self.data_vector: Optional[DataVector] = None
         self.theory_vector: Optional[TheoryVector] = None
@@ -74,32 +63,6 @@ def __init__(
                 "At least one of use_cluster_counts and use_mean_log_mass must be True."
             )
 
-    @final
-    def _reset(self) -> None:
-        """Reset all contained Updatable objects."""
-        self.cluster_abundance.reset()
-        self.cluster_mass.reset()
-        self.cluster_redshift.reset()
-
-    @final
-    def _required_parameters(self) -> RequiredParameters:
-        """Return an empty RequiredParameters."""
-        return (
-            self.cluster_abundance.required_parameters()
-            + self.cluster_mass.required_parameters()
-            + self.cluster_redshift.required_parameters()
-        )
-
-    @final
-    def _get_derived_parameters(self) -> DerivedParameterCollection:
-        """Return an empty DerivedParameterCollection."""
-        derived_parameters = DerivedParameterCollection([])
-        derived_parameters += self.cluster_abundance.get_derived_parameters()
-        derived_parameters += self.cluster_mass.get_derived_parameters()
-        derived_parameters += self.cluster_redshift.get_derived_parameters()
-
-        return derived_parameters
-
     def _read_data_type(self, sacc_data, data_type):
         """Internal function to read the data from the SACC file."""
         tracers_combinations = np.array(
@@ -163,21 +126,19 @@ def _read_data_type(self, sacc_data, data_type):
 
         return data_vector_list, sacc_indices_list
 
-    def read(self, sacc_data):
+    def read(self, sacc_data: sacc.Sacc):
         """Read the data for this statistic from the SACC file.
-        This function takes the SACC file and extract the necessary
-        parameters needed to compute the number counts likelihood.
-        Check README.MD for a complete description of the method.
 
-        :param sacc_data: The data in the sacc format.
+        :param sacc_data: The data in the SACC format.
         """
 
-        survey_tracer: SurveyTracer = sacc_data.get_tracer(self.sacc_tracer)
-        if survey_tracer is None:
+        try:
+            survey_tracer: SurveyTracer = sacc_data.get_tracer(self.sacc_tracer)
+        except KeyError as exc:
             raise ValueError(
                 f"The SACC file does not contain the SurveyTracer "
                 f"{self.sacc_tracer}."
-            )
+            ) from exc
         if not isinstance(survey_tracer, SurveyTracer):
             raise ValueError(
                 f"The SACC tracer {self.sacc_tracer} is not a SurveyTracer."
@@ -220,10 +181,9 @@ def get_data_vector(self) -> DataVector:
     def compute_theory_vector(self, tools: ModelingTools) -> TheoryVector:
         """Compute a Number Count statistic using the data from the
         Read method, the cosmology object, and the Bocquet16 halo mass function.
-        Check README.MD for a complete description of the method.
 
         :param tools: ModelingTools firecrown object
-            Firecrown object used to load the required cosmology.
+            used to load the required cosmology.
 
         :return: Numpy Array of floats
             An array with the theoretical prediction of the number of clusters