Improve docstrings

src/corrgi/corrgi.py

@@ -11,14 +11,16 @@ def compute_autocorrelation(
     """Calculates the auto-correlation for a catalog.
 
     Args:
-        catalog (Catalog): The catalog.
-        random (Catalog): A random samples catalog.
+        catalog (Catalog): The galaxies catalog (D).
+        random (Catalog): A random samples catalog (R).
         corr_type (type[Correlation]): The corrgi class corresponding to the type of
             correlation (AngularCorrelation, RedshiftCorrelation, or ProjectedCorrelation).
         **kwargs (dict): The arguments for the creation of the correlation instance.
 
     Returns:
-        A numpy array with the result of the auto-correlation, using the natural estimator.
+        A numpy array with the result of the auto-correlation, according to the estimator
+        provided in the correlation kwargs. More information on how to set up the input parameters
+        in https://gundam.readthedocs.io/en/latest/introduction.html#set-up-input-parameters.
     """
     correlation = corr_type(**kwargs)
     correlation.validate([catalog, random])
@@ -32,15 +34,17 @@ def compute_crosscorrelation(
     """Computes the cross-correlation between two catalogs.
 
     Args:
-        left (Catalog): Left catalog for the cross-correlation.
-        right (Catalog): Right catalog for the cross-correlation.
-        random (Catalog): A random samples catalog.
+        left (Catalog): Left catalog for the cross-correlation (D).
+        right (Catalog): Right catalog for the cross-correlation (C).
+        random (Catalog): A random samples catalog (R).
         corr_type (type[Correlation]): The corrgi class corresponding to the type of
             correlation (AngularCorrelation, RedshiftCorrelation, or ProjectedCorrelation).
         **kwargs (dict): The arguments for the creation of the correlation instance.
 
     Returns:
-        A numpy array with the result of the cross-correlation, using the natural estimator.
+        A numpy array with the result of the cross-correlation, according to the estimator
+        provided in the correlation kwargs. More information on how to set up the input parameters
+        in https://gundam.readthedocs.io/en/latest/introduction.html#set-up-input-parameters.
     """
     correlation = corr_type(**kwargs)
     correlation.validate([left, right, random])

src/corrgi/estimators/davis_peebles_estimator.py

@@ -14,18 +14,18 @@ class DavisPeeblesEstimator(Estimator):
     def compute_autocorrelation_counts(
         self, catalog: Catalog, random: Catalog
     ) -> list[np.ndarray, np.ndarray, np.ndarray | int]:
-        """Computes the auto-correlation counts for the provided catalog (`DD/RR - 1`)"""
+        """Computes the auto-correlation counts for the provided catalog"""
         raise NotImplementedError()
 
     def compute_crosscorrelation_counts(
         self, left: Catalog, right: Catalog, random: Catalog
-    ) -> list[np.ndarray, np.ndarray, np.ndarray | int]:
+    ) -> list[np.ndarray, np.ndarray]:
         """Computes the cross-correlation counts for the provided catalog.
 
         Args:
-            left (Catalog): A left galaxy samples catalog.
-            right (Catalog): A right galaxy samples catalog.
-            random (Catalog): A random samples catalog.
+            left (Catalog): A left galaxy samples catalog (D).
+            right (Catalog): A right galaxy samples catalog (C).
+            random (Catalog): A random samples catalog (R).
 
         Returns:
             The CD and CR counts for the DP estimator.

src/corrgi/estimators/estimator.py

@@ -22,8 +22,8 @@ def compute_auto_estimate(self, catalog: Catalog, random: Catalog) -> np.ndarray
         """Computes the auto-correlation for this estimator.
 
         Args:
-            catalog (Catalog): The catalog of galaxy samples.
-            random (Catalog): The catalog of random samples.
+            catalog (Catalog): The catalog of galaxy samples (D).
+            random (Catalog): The catalog of random samples (R).
 
         Returns:
             The statistical estimate of the auto-correlation function, as a numpy array.
@@ -39,9 +39,9 @@ def compute_cross_estimate(self, left: Catalog, right: Catalog, random: Catalog)
         """Computes the cross-correlation for this estimator.
 
         Args:
-            left (Catalog): The left catalog of galaxy samples.
-            right (Catalog): The right catalog of galaxy samples.
-            random (Catalog): The catalog of random samples.
+            left (Catalog): The left catalog of galaxy samples (D).
+            right (Catalog): The right catalog of galaxy samples (C).
+            random (Catalog): The catalog of random samples (R).
 
         Returns:
             The statistical estimate of the cross-correlation function, as a numpy array.
@@ -65,7 +65,7 @@ def compute_autocorrelation_counts(
     @abstractmethod
     def compute_crosscorrelation_counts(
         self, left: Catalog, right: Catalog, random: Catalog
-    ) -> list[np.ndarray, np.ndarray, np.ndarray]:
+    ) -> list[np.ndarray, np.ndarray]:
         """Computes the cross-correlation counts (CD, CR)."""
         raise NotImplementedError()
 
@@ -102,7 +102,7 @@ def _get_cross_args(
         counts_cr: np.ndarray,
     ) -> list:
         """Returns the args for the cross-correlation estimator routine"""
-        counts_bdd = self.correlation.get_bdd_counts()  # TODO: Change this
+        counts_bdd = self.correlation.get_bdd_counts()
         args = [num_galaxies, num_random, counts_cd, counts_bdd, counts_cr]
         if isinstance(self.correlation, ProjectedCorrelation):
             # The projected routines require an additional parameter

src/corrgi/estimators/natural_estimator.py

@@ -17,8 +17,8 @@ def compute_autocorrelation_counts(
         """Computes the auto-correlation counts for the provided catalog (`DD/RR - 1`).
 
         Args:
-            catalog (Catalog): A galaxy samples catalog.
-            random (Catalog): A random samples catalog.
+            catalog (Catalog): A galaxy samples catalog (D).
+            random (Catalog): A random samples catalog (R).
 
         Returns:
             The DD, RR and DR counts for the natural estimator.

tests/corrgi/test_projected.py

@@ -83,7 +83,7 @@ def test_pccf_counts_with_weights_are_correct(
     pcf_gals1_weight_catalog,
     pcf_rans_weight_catalog,
     pccf_cd_counts_with_weights,
-    pccf_cr_counts_with_weight,
+    pccf_cr_counts_with_weights,
     pcf_params,
 ):
     estimator = DavisPeeblesEstimator(
@@ -93,4 +93,4 @@ def test_pccf_counts_with_weights_are_correct(
         pcf_gals_weight_catalog, pcf_gals1_weight_catalog, pcf_rans_weight_catalog
     )
     npt.assert_allclose(counts_cd, pccf_cd_counts_with_weights, rtol=1e-3)
-    npt.assert_allclose(counts_cr, pccf_cr_counts_with_weight, rtol=2e-3)
+    npt.assert_allclose(counts_cr, pccf_cr_counts_with_weights, rtol=2e-3)