diff --git a/src/wela/butterfilter.py b/src/wela/butterworth_filter.py
similarity index 96%
rename from src/wela/butterfilter.py
rename to src/wela/butterworth_filter.py
index 7322a9dd10945a14cc76c67f292cfd63826351a8..a9e100b98343e76032f2acbce78479fa718209fe 100644
--- a/src/wela/butterfilter.py
+++ b/src/wela/butterworth_filter.py
@@ -2,7 +2,7 @@ import numpy as np
 from scipy import signal
 
 
-def butterfilter(timeseries, params=None):
+def butterworth_filter(timeseries, params=None):
     """Apply Butterworth filter to time series arranged in rows."""
     # second-order-sections output
     # by default, using a digital filter
diff --git a/src/wela/plotting.py b/src/wela/plotting.py
index 0d9de10e01f8a56eea3f5d00d9e4395246429198..99189d942c026a50ad8f4c4597c221fce4f7628f 100644
--- a/src/wela/plotting.py
+++ b/src/wela/plotting.py
@@ -4,7 +4,7 @@ import matplotlib.cm
 import matplotlib.pylab as plt
 import numpy as np
 import numpy.matlib
-from wela.butterfilter import butterfilter
+from wela.butterworth_filter import butterworth_filter
 
 
 def kymograph(
@@ -571,7 +571,7 @@ def bud_to_bud_plot(
     title: str, optional
         Title for plot.
     filter: boolean
-        If True, apply a butter filter to each time series.
+        If True, apply a butterworth filter to each time series.
 
     Example
     -------
@@ -585,7 +585,7 @@ def bud_to_bud_plot(
         t, signal_data = dl.get_time_series(signal, group=group, df=df)
         t, buddings = dl.get_time_series("buddings", group=group, df=df)
     if filter:
-        signal_data = butterfilter(signal_data)
+        signal_data = butterworth_filter(signal_data)
     if np.max(t) > 48:
         # convert to hours
         t = t / 60