diff --git a/doc/gs_single_cell.rst b/doc/gs_single_cell.rst
index 08d34b057f8267f7ece108a77f0cb425ec57fa8b..1565dd1ab1344746b9bb2303b425415d2b4f8fea 100644
--- a/doc/gs_single_cell.rst
+++ b/doc/gs_single_cell.rst
@@ -150,7 +150,7 @@ The other measurement we have is that of the potential, which we plot in step **
 Arbor stores sampled quantities under :meth:`arbor.single_cell_model.traces<arbor._arbor.
 single_cell_model.traces>`. You should be seeing something like this:
 
-.. figure:: gen-images/single_cell_model_result.svg
+.. figure:: images/single_cell_model_result.svg
     :width: 400
     :align: center
 
diff --git a/doc/scripts/make_images.py b/doc/scripts/make_images.py
index 956971a9b35daf1296108339fa519c47b013373d..0df83945c9063a7b80ce3fd821eae19b240c6b1b 100644
--- a/doc/scripts/make_images.py
+++ b/doc/scripts/make_images.py
@@ -2,8 +2,6 @@ import copy
 import svgwrite
 import math
 import inputs
-import seaborn
-import pandas
 
 tag_colors = ['white', '#ffc2c2', 'gray', '#c2caff']
 
@@ -11,16 +9,6 @@ tag_colors = ['white', '#ffc2c2', 'gray', '#c2caff']
 # ############################################
 #
 
-def dataframe_line_plot(input_csv, output_svg):
-    print('generating:', output_svg)
-    dataframe = pandas.read_csv(input_csv,index_col=0)
-    axes = dict(zip(['x','y','hue','col','style'],dataframe.columns.values)) # 5D seems enough for now.
-    seaborn.relplot(data=dataframe, kind="line", **axes).savefig(output_svg)
-
-#
-# ############################################
-#
-
 def translate(x, f, xshift):
     return (f*x[0]+xshift, -f*x[1])
 
@@ -292,8 +280,6 @@ def generate(path=''):
     label_image(inputs.label_morph, [inputs.reg_radgt5],  path+'/radiusgt_label.svg')
     label_image(inputs.label_morph, [inputs.reg_radge5],  path+'/radiusge_label.svg')
 
-    # dataframe_line_plot(path+'/../images/single_cell_model_result.csv', path+'/single_cell_model_result.svg')
-
 
 if __name__ == '__main__':
     generate('.')