Speed up plotting (#1210)

Speed up plotting in seaborn.relplot by disabling calculation of confidence intervals

doc/tutorial/single_cell_model.rst

@@ -142,7 +142,7 @@ results! Let's take a look at what the spike detector and a voltage probes from
     import pandas, seaborn # You may have to pip install these.
     seaborn.set_theme() # Apply some styling to the plot
     df = pandas.DataFrame({'t/ms': m.traces[0].time, 'U/mV': m.traces[0].value})
-    seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV").savefig('single_cell_model_result.svg')
+    seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",ci=None).savefig('single_cell_model_result.svg')
 
 Step **(7)** accesses :meth:`arbor.single_cell_model.spikes<arbor.single_cell_model.spikes>`
 to print the spike times. A single spike should be generated at around the same time the stimulus

python/example/network_ring.py

@@ -152,4 +152,4 @@ for gid in range(ncells):
     df_list.append(pandas.DataFrame({'t/ms': times, 'U/mV': volts, 'Cell': f"cell {gid}"}))
 
 df = pandas.concat(df_list)
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Cell").savefig('network_ring_result.svg')
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Cell",ci=None).savefig('network_ring_result.svg')

python/example/single_cell_model.py

@@ -37,7 +37,7 @@ else:
 print("Plotting results ...")
 seaborn.set_theme() # Apply some styling to the plot
 df = pandas.DataFrame({'t/ms': m.traces[0].time, 'U/mV': m.traces[0].value})
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV").savefig('single_cell_model_result.svg')
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",ci=None).savefig('single_cell_model_result.svg')
 
 # (9) Optionally, you can store your results for later processing.
 df.to_csv('single_cell_model_result.csv', float_format='%g')

python/example/single_cell_multi_branch.py

@@ -107,4 +107,4 @@ df = pandas.DataFrame()
 for t in m.traces:
     df=df.append(pandas.DataFrame({'t/ms': t.time, 'U/mV': t.value, 'Location': t.location, "Variable": t.variable}) )
 
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable").savefig('single_cell_multi_branch_result.svg')
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable",ci=None).savefig('single_cell_multi_branch_result.svg')

python/example/single_cell_swc.py

@@ -87,4 +87,4 @@ for t in m.traces:
 
 df = pandas.concat(df_list)
 
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable").savefig('single_cell_swc.svg')
+seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable",ci=None).savefig('single_cell_swc.svg')