Yet even more docstrings (#445)

* Improve docstrings and type annotations
* Fix bug in NumberCounts property computation

Author: marcpaterno

firecrown/likelihood/__init__.py

@@ -1,4 +1,4 @@
-"""Classes used to represent likelihoods and functions to support them.
+"""Classes used to represent likelihoods, and functions to support them.
 
 Subpackages contain specific likelihood implementations, e.g., Gaussian and Student-t.
 The submodule :mod:`firecrown.likelihood.likelihood` contain the abstract base class for

firecrown/likelihood/binned_cluster_number_counts.py

@@ -1,8 +1,4 @@
-"""This module holds classes needed to predict the binned cluster number counts.
-
-The binned cluster number counts statistic predicts the number of galaxy
-clusters within a single redshift and mass bin.
-"""
+"""Binned cluster number counts statistic support."""
 
 from __future__ import annotations
 
@@ -26,11 +22,7 @@
 
 
 class BinnedClusterNumberCounts(Statistic):
-    """The Binned Cluster Number Counts statistic.
-
-    This class will make a prediction for the number of clusters in a z, mass bin
-    and compare that prediction to the data provided in the sacc file.
-    """
+    """A statistic representing the number of clusters in a z, mass bin."""
 
     def __init__(
         self,
@@ -39,6 +31,13 @@ def __init__(
         cluster_recipe: ClusterRecipe,
         systematics: None | list[SourceSystematic] = None,
     ):
+        """Initialize this statistic.
+
+        :param cluster_properties: The cluster observables to use.
+        :param survey_name: The name of the survey to use.
+        :param cluster_recipe: The cluster recipe to use.
+        :param systematics: The systematics to apply to this statistic.
+        """
         super().__init__()
         self.systematics = systematics or []
         self.theory_vector: None | TheoryVector = None
@@ -50,7 +49,10 @@ def __init__(
         self.bins: list[SaccBin] = []
 
     def read(self, sacc_data: sacc.Sacc) -> None:
-        """Read the data for this statistic and mark it as ready for use."""
+        """Read the data for this statistic and mark it as ready for use.
+
+        :param sacc_data: The data in the sacc format.
+        """
         # Build the data vector and indices needed for the likelihood
         if self.cluster_properties == ClusterProperty.NONE:
             raise ValueError("You must specify at least one cluster property.")
@@ -77,12 +79,19 @@ def read(self, sacc_data: sacc.Sacc) -> None:
         super().read(sacc_data)
 
     def get_data_vector(self) -> DataVector:
-        """Gets the statistic data vector."""
+        """Gets the statistic data vector.
+
+        :return: The statistic data vector.
+        """
         assert self.data_vector is not None
         return self.data_vector
 
     def _compute_theory_vector(self, tools: ModelingTools) -> TheoryVector:
-        """Compute a statistic from sources, concrete implementation."""
+        """Compute a statistic from sources, concrete implementation.
+
+        :param tools: The modeling tools used to compute the statistic.
+        :return: The computed statistic.
+        """
         assert tools.cluster_abundance is not None
 
         theory_vector_list: list[float] = []
@@ -116,6 +125,9 @@ def get_binned_cluster_property(
         Using the data from the sacc file, this function evaluates the likelihood for
         a single point of the parameter space, and returns the predicted mean mass of
         the clusters in each bin.
+
+        :param cluster_counts: The number of clusters in each bin.
+        :param cluster_properties: The cluster observables to use.
         """
         assert tools.cluster_abundance is not None
 
@@ -124,8 +136,6 @@ def get_binned_cluster_property(
             total_observable = self.cluster_recipe.evaluate_theory_prediction(
                 tools.cluster_abundance, this_bin, self.sky_area, cluster_properties
             )
-            cluster_counts.append(counts)
-
             mean_observable = total_observable / counts
             mean_values.append(mean_observable)
 
@@ -137,6 +147,9 @@ def get_binned_cluster_counts(self, tools: ModelingTools) -> list[float]:
         Using the data from the sacc file, this function evaluates the likelihood for
         a single point of the parameter space, and returns the predicted number of
         clusters in each bin.
+
+        :param tools: The modeling tools used to compute the statistic.
+        :return: The number of clusters in each bin.
         """
         assert tools.cluster_abundance is not None
 

firecrown/likelihood/gauss_family/__init__.py

@@ -1 +1,3 @@
+"""Backward compatibility support for deprecated directory structure."""
+
 # flake8: noqa

firecrown/likelihood/gauss_family/statistic/source/__init__.py

@@ -1 +1,3 @@
+"""Backward compatibility support for deprecated directory structure."""
+
 # flake8: noqa

firecrown/likelihood/gaussfamily.py

@@ -1,4 +1,4 @@
-"""Support for the family of Gaussian likelihood."""
+"""Support for the family of Gaussian likelihoods."""
 
 from __future__ import annotations
 
@@ -30,7 +30,12 @@
 
 
 class State(Enum):
-    """The states used in GaussFamily."""
+    """The states used in GaussFamily.
+
+    GaussFamily and all subclasses enforce a statemachine behavior based on
+    these states to ensure that the necessary initialization and setup is done
+    in the correct order.
+    """
 
     INITIALIZED = 1
     READY = 2
@@ -62,6 +67,12 @@ def enforce_states(
     If terminal is None the state of the object is not modified.
     If terminal is not None and the call to the wrapped method returns
     normally the state of the object is set to terminal.
+
+    :param initial: The initial states allowable for the wrapped method
+    :param terminal: The terminal state ensured for the wrapped method. None
+        indicates no state change happens.
+    :param failure_message: The failure message for the AssertionError raised
+    :return: The wrapped method
     """
     initials: list[State]
     if isinstance(initial, list):
@@ -74,6 +85,9 @@ def decorator_enforce_states(func: Callable[P, T]) -> Callable[P, T]:
 
         This closure is what actually contains the values of initials, terminal, and
         failure_message.
+
+        :param func: The method to be wrapped
+        :return: The wrapped method
         """
 
         @wraps(func)
@@ -132,8 +146,11 @@ class GaussFamily(Likelihood):
     def __init__(
         self,
         statistics: Sequence[Statistic],
-    ):
-        """Initialize the base class parts of a GaussFamily object."""
+    ) -> None:
+        """Initialize the base class parts of a GaussFamily object.
+
+        :param statistics: A list of statistics to be include in chisquared calculations
+        """
         super().__init__()
         self.state: State = State.INITIALIZED
         if len(statistics) == 0:
@@ -160,7 +177,12 @@ def __init__(
     def create_ready(
         cls, statistics: Sequence[Statistic], covariance: npt.NDArray[np.float64]
     ) -> GaussFamily:
-        """Create a GaussFamily object in the READY state."""
+        """Create a GaussFamily object in the READY state.
+
+        :param statistics: A list of statistics to be include in chisquared calculations
+        :param covariance: The covariance matrix of the statistics
+        :return: A ready GaussFamily object
+        """
         obj = cls(statistics)
         obj._set_covariance(covariance)
         obj.state = State.READY
@@ -178,6 +200,8 @@ def _update(self, _: ParamsMap) -> None:
         for its own reasons must be sure to do what this does: check the state
         at the start of the method, and change the state at the end of the
         method.
+
+        :param _: a ParamsMap object, not used
         """
 
     @enforce_states(
@@ -201,7 +225,10 @@ def _reset(self) -> None:
         failure_message="read() must only be called once",
     )
     def read(self, sacc_data: sacc.Sacc) -> None:
-        """Read the covariance matrix for this likelihood from the SACC file."""
+        """Read the covariance matrix for this likelihood from the SACC file.
+
+        :param sacc_data: The SACC data object to be read
+        """
         if sacc_data.covariance is None:
             msg = (
                 f"The {type(self).__name__} likelihood requires a covariance, "
@@ -216,11 +243,13 @@ def read(self, sacc_data: sacc.Sacc) -> None:
 
         self._set_covariance(covariance)
 
-    def _set_covariance(self, covariance):
+    def _set_covariance(self, covariance: npt.NDArray[np.float64]) -> None:
         """Set the covariance matrix.
 
         This method is used to set the covariance matrix and perform the
         necessary calculations to prepare the likelihood for computation.
+
+        :param covariance: The covariance matrix for this likelihood
         """
         indices_list = []
         data_vector_list = []
@@ -276,6 +305,7 @@ def get_cov(
         :param statistic: The statistic for which the sub-covariance matrix
             should be returned. If not specified, return the covariance of all
             statistics.
+        :return: The covariance matrix (or portion thereof)
         """
         assert self.cov is not None
         if statistic is None:
@@ -301,7 +331,10 @@ def get_cov(
         failure_message="read() must be called before get_data_vector()",
     )
     def get_data_vector(self) -> npt.NDArray[np.float64]:
-        """Get the data vector from all statistics in the right order."""
+        """Get the data vector from all statistics in the right order.
+
+        :return: The data vector
+        """
         assert self.data_vector is not None
         return self.data_vector
 
@@ -315,6 +348,7 @@ def compute_theory_vector(self, tools: ModelingTools) -> npt.NDArray[np.float64]
         """Computes the theory vector using the current instance of pyccl.Cosmology.
 
         :param tools: Current ModelingTools object
+        :return: The computed theory vector
         """
         theory_vector_list: list[npt.NDArray[np.float64]] = [
             stat.compute_theory_vector(tools) for stat in self.statistics
@@ -329,7 +363,10 @@ def compute_theory_vector(self, tools: ModelingTools) -> npt.NDArray[np.float64]
         "get_theory_vector()",
     )
     def get_theory_vector(self) -> npt.NDArray[np.float64]:
-        """Get the theory vector from all statistics in the right order."""
+        """Get the already-computed theory vector from all statistics.
+
+        :return: The theory vector, with all statistics in the right order
+        """
         assert (
             self.theory_vector is not None
         ), "theory_vector is None after compute_theory_vector() has been called"
@@ -343,7 +380,14 @@ def get_theory_vector(self) -> npt.NDArray[np.float64]:
     def compute(
         self, tools: ModelingTools
     ) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:
-        """Calculate and return both the data and theory vectors."""
+        """Calculate and return both the data and theory vectors.
+
+        This method is dprecated and will be removed in a future version of Firecrown.
+
+        :param tools: the ModelingTools to be used in the calculation of the
+            theory vector
+        :return: a tuple containing the data vector and the theory vector
+        """
         warnings.warn(
             "The use of the `compute` method on Statistic is deprecated."
             "The Statistic objects should implement `get_data` and "
@@ -359,7 +403,12 @@ def compute(
         failure_message="update() must be called before compute_chisq()",
     )
     def compute_chisq(self, tools: ModelingTools) -> float:
-        """Calculate and return the chi-squared for the given cosmology."""
+        """Calculate and return the chi-squared for the given cosmology.
+
+        :param tools: the ModelingTools to be used in the calculation of the
+            theory vector
+        :return: the chi-squared
+        """
         theory_vector: npt.NDArray[np.float64]
         data_vector: npt.NDArray[np.float64]
         residuals: npt.NDArray[np.float64]
@@ -386,6 +435,10 @@ def get_sacc_indices(
         """Get the SACC indices of the statistic or list of statistics.
 
         If no statistic is given, get the indices of all statistics of the likelihood.
+
+        :param statistics: The statistic or list of statistics for which the
+            SACC indices are desired
+        :return: The SACC indices
         """
         if statistic is None:
             statistic = [stat.statistic for stat in self.statistics]
@@ -409,7 +462,15 @@ def get_sacc_indices(
     def make_realization(
         self, sacc_data: sacc.Sacc, add_noise: bool = True, strict: bool = True
     ) -> sacc.Sacc:
-        """Create a new realization of the model."""
+        """Create a new realization of the model.
+
+        :param sacc_data: The SACC data object containing the covariance matrix
+            to be read
+        :param add_noise: If True, add noise to the realization.
+        :param strict: If True, check that the indices of the realization cover
+            all the indices of the SACC data object.
+        :return: The SACC data object containing the new realization
+        """
         sacc_indices = self.get_sacc_indices()
 
         if add_noise:

firecrown/likelihood/gaussian.py

@@ -13,12 +13,19 @@
 class ConstGaussian(GaussFamily):
     """A Gaussian log-likelihood with a constant covariance matrix."""
 
-    def compute_loglike(self, tools: ModelingTools):
-        """Compute the log-likelihood."""
+    def compute_loglike(self, tools: ModelingTools) -> float:
+        """Compute the log-likelihood.
+
+        :params tools: The modeling tools used to compute the likelihood.
+        :return: The log-likelihood.
+        """
         return -0.5 * self.compute_chisq(tools)
 
     def make_realization_vector(self) -> np.ndarray:
-        """Create a new realization of the model."""
+        """Create a new (randomized) realization of the model.
+
+        :return: A new realization of the model
+        """
         theory_vector = self.get_theory_vector()
         assert self.cholesky is not None
         new_data_vector = theory_vector + np.dot(