From 88746c1ea37009a84a4ee9ba68ae8a18212dfc8e Mon Sep 17 00:00:00 2001
From: radhakrishnatg <radhakrishnatg@gmail.com>
Date: Mon, 30 Dec 2024 07:32:22 -0500
Subject: [PATCH] Modified the argument name to sort_by_priority

---
 primo/data_parser/well_data.py  | 6 +++---
 primo/utils/clustering_utils.py | 8 ++++++--
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/primo/data_parser/well_data.py b/primo/data_parser/well_data.py
index 2a3d669..7a87c1c 100644
--- a/primo/data_parser/well_data.py
+++ b/primo/data_parser/well_data.py
@@ -298,7 +298,7 @@ def get_fully_partitioned_data(self):
 
         return None
 
-    def get_high_priority_wells(self, num_wells: int, sort: bool = True):
+    def get_high_priority_wells(self, num_wells: int, sort_by_priority: bool = True):
         """
         Returns the top n wells by priority
 
@@ -307,7 +307,7 @@ def get_high_priority_wells(self, num_wells: int, sort: bool = True):
         num_wells : int
             Returns the top `num_wells` number of wells
 
-        sort : bool, default = True
+        sort_by_priority : bool, default = True
             If True, arranges the wells in descending order of priority.
             If False, the wells are arranged as per their index.
         """
@@ -321,7 +321,7 @@ def get_high_priority_wells(self, num_wells: int, sort: bool = True):
             self.data.sort_values(cn, ascending=False).head(num_wells).index.to_list()
         )
 
-        if sort:
+        if sort_by_priority:
             # Constructed sub-data is ordered as per index, not priority.
             # So, sorting the data again
             hp_wells.data = hp_wells.data.sort_values(cn, ascending=False)
diff --git a/primo/utils/clustering_utils.py b/primo/utils/clustering_utils.py
index e7fd0d3..57edcb5 100644
--- a/primo/utils/clustering_utils.py
+++ b/primo/utils/clustering_utils.py
@@ -51,7 +51,7 @@ def distance_matrix(
 
     Returns
     -------
-    np.ndarray
+    pd.DataFrame
         Distance matrix to be used for the agglomerative
         clustering method
 
@@ -110,6 +110,9 @@ def perform_clustering(wd: WellData, distance_threshold: float = 10.0):
 
     Parameters
     ----------
+    wd : WellData
+        Object containing the information on all wells
+
     distance_threshold : float, default = 10.0
         Threshold distance for breaking clusters
 
@@ -152,7 +155,8 @@ def perform_clustering(wd: WellData, distance_threshold: float = 10.0):
 
 def get_pairwise_metrics(wd: WellData, list_wells: list) -> pd.DataFrame:
     """
-    Returns pairwise metric values for all well pairs in each clusters
+    Returns pairwise metric values for all possible pairs of wells in
+    `list_wells`.
 
     Parameters
     ----------