diff --git a/bw_timex/timex_lca.py b/bw_timex/timex_lca.py
index bd464e2..b76092a 100644
--- a/bw_timex/timex_lca.py
+++ b/bw_timex/timex_lca.py
@@ -1174,7 +1174,7 @@ def create_labelled_dynamic_inventory_dataframe(self) -> pd.DataFrame:
 
         df = self.dynamic_inventory_df.copy()
         df["flow"] = df["flow"].apply(lambda x: bd.get_node(id=x)["name"])
-        
+
         activity_name_cache = {}
 
         for activity in df["activity"].unique():
@@ -1184,7 +1184,7 @@ def create_labelled_dynamic_inventory_dataframe(self) -> pd.DataFrame:
                 )
 
         df["activity"] = df["activity"].map(activity_name_cache)
-        
+
         return df
 
     def plot_dynamic_inventory(self, bio_flows, cumulative=False) -> None:
diff --git a/docs/content/installation.md b/docs/content/installation.md
index 8346245..836ccd3 100644
--- a/docs/content/installation.md
+++ b/docs/content/installation.md
@@ -4,7 +4,7 @@
 
 ```{note}
 1) We recommend installation via `conda` or `mamba`.
-2) bw_timex depends on Brightway25, and will install bw25-compatible versions of the bw packages. This means that it cannot be added to existing virtual environments that are based on Brightway2, e.g., environments containing [Activity Browser](https://github.com/LCA-ActivityBrowser/activity-browser). Please install bw_timex in a separate environment following the instructions below. 
+2) bw_timex depends on Brightway25, and will install bw25-compatible versions of the bw packages. This means that it cannot be added to existing virtual environments that are based on Brightway2, e.g., environments containing [Activity Browser](https://github.com/LCA-ActivityBrowser/activity-browser). Please install bw_timex in a separate environment following the instructions below.
 ```
 
 ## Installing `bw_timex` using `conda` or `mamba`
diff --git a/docs/content/theory.md b/docs/content/theory.md
index fcee16b..09a5730 100644
--- a/docs/content/theory.md
+++ b/docs/content/theory.md
@@ -50,7 +50,7 @@ import numpy as np
 from bw_temporalis import TemporalDistribution
 
 two_and_four_years_ahead = TemporalDistribution(
-    date=np.array([2, 4], dtype="timedelta64[Y]"), 
+    date=np.array([2, 4], dtype="timedelta64[Y]"),
     amount=np.array([0.3, 0.7])
 )
 
@@ -65,7 +65,7 @@ two_and_four_years_ahead.graph(resolution="Y")
 The other distribution spreads an amount over the following 4 months, with decreasing shares:
 ```python
 spread_over_four_months = TemporalDistribution(
-    date=np.array([0, 1, 2, 3], dtype="timedelta64[M]"), 
+    date=np.array([0, 1, 2, 3], dtype="timedelta64[M]"),
     amount=np.array([0.4, 0.3, 0.2, 0.1])
 )