From 4f305de9d8bd67510f011851f58c53cd7faba9f2 Mon Sep 17 00:00:00 2001
From: lochhh <changhuan.lo@ucl.ac.uk>
Date: Tue, 10 Sep 2024 23:31:40 +0100
Subject: [PATCH] Fix docstrings

---
 movement/analysis/kinematics.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/movement/analysis/kinematics.py b/movement/analysis/kinematics.py
index c770a0c21..88f8f460e 100644
--- a/movement/analysis/kinematics.py
+++ b/movement/analysis/kinematics.py
@@ -377,7 +377,7 @@ def compute_interkeypoint_distances(
     ----------
     data : xarray.DataArray
         The input data containing
-        ``('time', 'individuals', 'keypoints', 'space'`` as dimensions.
+        ``('time', 'individuals', 'keypoints', 'space')`` as dimensions.
     pairs : dict[str, str | list[str]], optional
         A dictionary containing the mapping between pairs of keypoints.
         The key is the keypoint and the value is a list of keypoints
@@ -431,16 +431,17 @@ def compute_interkeypoint_distances(
       * key1     (key1) <U4 32B 'ind1' 'ind2'
       * key2     (key2) <U4 32B 'ind1' 'ind2'
 
-
     The resulting ``dist_key1_key2`` is a DataArray containing the computed
     distances between ``key1`` and ``key2`` for all individuals
     at each time point.
 
     To obtain the distances between ``key1`` and ``key2`` within ``ind1``:
+
     >>> dist_key1_key2.sel(key1="ind1", key2="ind1")
 
     To obtain the distances between ``key1`` of ``ind1`` and
     ``key2`` of ``ind2``:
+
     >>> dist_key1_key2.sel(key1="ind1", key2="ind2")
 
     Compute the city block or Manhattan distance for multiple pairs of
@@ -536,7 +537,7 @@ def _compute_pairwise_distances(
     ----------
     data : xarray.DataArray
         The input data containing
-        ``('time', 'individuals', 'keypoints', 'space'`` as dimensions.
+        ``('time', 'individuals', 'keypoints', 'space')`` as dimensions.
     dim : str
         The dimension to compute the distances for. Must be either
         ``'individuals'`` or ``'keypoints'``.