diff --git a/PySONIC/core/astim_titrations.log b/PySONIC/core/astim_titrations.log
index 953a3b6..c8e70d3 100644
--- a/PySONIC/core/astim_titrations.log
+++ b/PySONIC/core/astim_titrations.log
@@ -1,42 +1,1095 @@
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.06, 0.05, 100.0, 1.0, 'sonic')	46270.75195312497
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.4, 'sonic')	55572.50976562497
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6, 'sonic')	38813.78173828122
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8, 'sonic')	33174.133300781235
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10400000000000001, 0.0, 100.0, 1.0, 'sonic')	40338.13476562497
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.105, 0.0, 100.0, 1.0, 'sonic')	40205.38330078122
 titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.8, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.6, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 1.0, 'sonic')	19830.322265625
 titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.2, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.2, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 1.0, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 0.7000000000000001, 0.0, 100.0, 1.0, 'sonic')	19830.322265624985
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 'sonic')	38635.25390624997
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 0.9, 'sonic')	46211.24267578122
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 0.5, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 1.0, 'sonic')	38800.04882812497
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 501000.0, 0.1, 0.05, 100.0, 1.0, 1.0, 'sonic')	38800.04882812497
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.13, 1.0, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.11, 1.0, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.34, 1.0, 'sonic')	126544.18945312494
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	nan
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10300000000000001, 0.05, 100.0, 1.0, 1.0, 'sonic')	38562.01171874997
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10400000000000001, 0.05, 100.0, 1.0, 1.0, 'sonic')	38488.76953124997
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.105, 0.05, 100.0, 1.0, 1.0, 'sonic')	38374.32861328122
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.095, 0.05, 100.0, 1.0, 1.0, 'sonic')	39294.43359374997
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.065, 0.05, 100.0, 1.0, 1.0, 'sonic')	44567.87109374997
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.061, 0.05, 100.0, 1.0, 1.0, 'sonic')	45904.54101562497
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.101, 0.05, 100.0, 1.0, 1.0, 'sonic')	38708.49609374997
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 0.15, 0.1)	35430.908203124985
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 100000.0, 0.15, 0.1)	35559.082031249985
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.15, 0.1)	36099.243164062485
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 1000000.0, 0.15, 0.1)	37023.925781249985
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 2000000.0, 0.15, 0.1)	39166.25976562497
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 3000000.0, 0.15, 0.1)	41491.69921874997
 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 0.15, 0.1)	44201.66015624997
 titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 0.15, 0.1)	75988.76953124994
 titrate(NeuronalBilayerSonophore(22.6 nm, CorticalRS), 500000.0, 0.15, 0.1)	45254.51660156247
 titrate(NeuronalBilayerSonophore(45.3 nm, CorticalRS), 500000.0, 0.15, 0.1)	34826.660156249985
 titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 0.15, 0.1)	33755.493164062485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 0.15, 0.1)	22888.183593749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 10.0, 0.01)	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 10.0, 0.02)	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 10.0, 0.03)	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 10.0, 0.04)	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 10.0, 0.05)	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 10.0, 0.06)	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 10.0, 0.07)	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 10.0, 0.08)	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.06, 0.05, 100.0, 1.0, 'sonic')	46270.75195312497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.4, 'sonic')	55572.50976562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6, 'sonic')	38813.78173828122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8, 'sonic')	33174.133300781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10400000000000001, 0.0, 100.0, 1.0, 'sonic')	40338.13476562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.105, 0.0, 100.0, 1.0, 'sonic')	40205.38330078122
+titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.8, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.6, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 1.0, 'sonic')	19830.322265625
+titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.2, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.2, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 0.7000000000000001, 0.0, 100.0, 1.0, 'sonic')	19830.322265624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 'sonic')	38635.25390624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 0.9, 'sonic')	46211.24267578122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 0.5, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 1.0, 'sonic')	38800.04882812497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 501000.0, 0.1, 0.05, 100.0, 1.0, 1.0, 'sonic')	38800.04882812497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.13, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.11, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.34, 1.0, 'sonic')	126544.18945312494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10300000000000001, 0.05, 100.0, 1.0, 1.0, 'sonic')	38562.01171874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10400000000000001, 0.05, 100.0, 1.0, 1.0, 'sonic')	38488.76953124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.105, 0.05, 100.0, 1.0, 1.0, 'sonic')	38374.32861328122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.095, 0.05, 100.0, 1.0, 1.0, 'sonic')	39294.43359374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.065, 0.05, 100.0, 1.0, 1.0, 'sonic')	44567.87109374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.061, 0.05, 100.0, 1.0, 1.0, 'sonic')	45904.54101562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.101, 0.05, 100.0, 1.0, 1.0, 'sonic')	38708.49609374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 0.15, 0.1)	35430.908203124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 100000.0, 0.15, 0.1)	35559.082031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.15, 0.1)	36099.243164062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 1000000.0, 0.15, 0.1)	37023.925781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 2000000.0, 0.15, 0.1)	39166.25976562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 3000000.0, 0.15, 0.1)	41491.69921874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 0.15, 0.1)	44201.66015624997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 0.15, 0.1)	75988.76953124994
+titrate(NeuronalBilayerSonophore(22.6 nm, CorticalRS), 500000.0, 0.15, 0.1)	45254.51660156247
+titrate(NeuronalBilayerSonophore(45.3 nm, CorticalRS), 500000.0, 0.15, 0.1)	34826.660156249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 0.15, 0.1)	33755.493164062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.05, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.07, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.08, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.1, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	548419.1894531248
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	275592.0410156249
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	378652.95410156227
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	321130.37109374977
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	452453.61328124977
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	209362.79296874988
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	238970.94726562488
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	149450.68359374994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	165948.48632812488
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	124035.64453124994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	135855.10253906244
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	105871.58203124994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	114367.67578124994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	185815.14358520496
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	98565.67382812494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	74157.71484374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	86773.68164062494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	82086.18164062494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	70715.33203124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	92427.06298828119
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	77984.61914062494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	67932.12890624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	65313.72070312497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	62878.41796874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	52331.54296874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	58630.37109374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	60562.13378906247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	55288.69628906247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	53750.61035156247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	56900.02441406247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	50939.94140624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	49694.82421874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	48669.43359374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	46765.13671874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	45007.32421874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	47676.08642578122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	45840.45410156247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.51, 1.0, 'sonic')	43395.99609374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	41931.15234374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.54, 1.0, 'sonic')	41345.21484374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.52, 1.0, 'sonic')	42663.57421874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.5700000000000001, 1.0, 'sonic')	39752.19726562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.56, 1.0, 'sonic')	40264.89257812497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	44162.750244140596
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.55, 1.0, 'sonic')	40786.74316406247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.58, 1.0, 'sonic')	39294.43359374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.62, 1.0, 'sonic')	37481.689453124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.63, 1.0, 'sonic')	37097.167968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.61, 1.0, 'sonic')	37902.83203124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.6, 1.0, 'sonic')	38342.28515624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	38800.04882812497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.64, 1.0, 'sonic')	36676.025390624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.65, 1.0, 'sonic')	36291.503906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.66, 1.0, 'sonic')	35925.292968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.67, 1.0, 'sonic')	35577.392578124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.68, 1.0, 'sonic')	35266.113281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.73, 1.0, 'sonic')	33947.753906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.72, 1.0, 'sonic')	34185.791015624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.6900000000000001, 1.0, 'sonic')	34931.945800781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.71, 1.0, 'sonic')	34419.250488281235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.76, 1.0, 'sonic')	33288.574218749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.74, 1.0, 'sonic')	33728.027343749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.7000000000000001, 1.0, 'sonic')	34680.175781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.75, 1.0, 'sonic')	33508.300781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.77, 1.0, 'sonic')	33068.847656249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	30889.892578124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.78, 1.0, 'sonic')	32867.431640624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.79, 1.0, 'sonic')	32702.636718749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.8, 1.0, 'sonic')	32482.910156249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.05, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.07, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.08, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.1, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.81, 1.0, 'sonic')	32336.425781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.8200000000000001, 1.0, 'sonic')	32135.009765624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.8300000000000001, 1.0, 'sonic')	31988.525390624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.84, 1.0, 'sonic')	31842.041015624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.85, 1.0, 'sonic')	31695.556640624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.86, 1.0, 'sonic')	31604.003906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	453479.00390624977
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	378845.21484374977
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.87, 1.0, 'sonic')	31475.830078124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	551065.0634765623
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	275015.2587890624
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	320798.4924316404
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	238311.76757812488
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	185192.87109374988
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	208648.68164062488
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.88, 1.0, 'sonic')	31402.587890624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	165417.48046874988
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	148937.98828124994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	135479.73632812494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	123687.74414062494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	114147.94921874994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	98474.12109374994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	105670.16601562494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	31329.345703124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	92395.01953124994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	78112.79296874994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	86789.70336914057
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	82168.57910156244
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	70935.05859374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	74336.24267578122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	31269.836425781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	68151.85546874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	65588.37890624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	31237.792968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	31182.861328124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	55627.44140624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	60859.68017578122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	54089.35546874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	63132.476806640596
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	58882.14111328122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	57197.57080078122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.93, 1.0, 'sonic')	31127.643585205064
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	51324.46289062497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	50061.03515624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	49035.64453124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	52660.56060791013
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	46252.44140624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	47131.34765624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.9400000000000001, 1.0, 'sonic')	31091.308593749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	48044.586181640596
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.9500000000000001, 1.0, 'sonic')	31052.39868164061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	31484.985351562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.98, 1.0, 'sonic')	30963.134765624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.97, 1.0, 'sonic')	31018.066406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	45405.57861328122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.96, 1.0, 'sonic')	31018.066406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.51, 1.0, 'sonic')	43835.44921874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	44599.91455078122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.52, 1.0, 'sonic')	43103.02734374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 20000.0, 1.0, 0.0, 100.0, 0.99, 1.0, 'sonic')	30944.824218749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.55, 1.0, 'sonic')	41217.04101562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.56, 1.0, 'sonic')	40704.34570312497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	42398.07128906247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.54, 1.0, 'sonic')	41743.46923828122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.5700000000000001, 1.0, 'sonic')	40205.38330078122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.58, 1.0, 'sonic')	39733.88671874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	39294.43359374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6, 1.0, 'sonic')	38854.98046874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.61, 1.0, 'sonic')	38415.52734374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.64, 1.0, 'sonic')	37243.652343749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.65, 1.0, 'sonic')	36877.441406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.62, 1.0, 'sonic')	38003.54003906247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.66, 1.0, 'sonic')	36511.230468749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.63, 1.0, 'sonic')	37609.86328124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.75, 1.0, 'sonic')	34094.238281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.67, 1.0, 'sonic')	36163.330078124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.68, 1.0, 'sonic')	35852.050781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.72, 1.0, 'sonic')	34753.417968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.7000000000000001, 1.0, 'sonic')	35211.181640624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.74, 1.0, 'sonic')	34313.964843749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6900000000000001, 1.0, 'sonic')	35504.150390624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.71, 1.0, 'sonic')	34973.144531249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.73, 1.0, 'sonic')	34533.691406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.76, 1.0, 'sonic')	33892.822265624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.77, 1.0, 'sonic')	33728.027343749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.78, 1.0, 'sonic')	33526.611328124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.05, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.79, 1.0, 'sonic')	33361.816406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.07, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.08, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.1, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8, 1.0, 'sonic')	33215.332031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.81, 1.0, 'sonic')	33027.648925781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8200000000000001, 1.0, 'sonic')	32883.45336914061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8300000000000001, 1.0, 'sonic')	32775.878906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.84, 1.0, 'sonic')	32629.394531249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.86, 1.0, 'sonic')	32409.667968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.85, 1.0, 'sonic')	32510.375976562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	583190.9179687498
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	494219.97070312477
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.87, 1.0, 'sonic')	32336.425781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	414033.508300781
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	289270.0195312499
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	339321.89941406227
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.88, 1.0, 'sonic')	32217.407226562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	261895.75195312488
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	229531.86035156238
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	196691.89453124988
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	174133.30078124988
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	161682.12890624988
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	32135.009765624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	122515.86914062494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	137837.2192382812
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	110778.80859374994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	150731.2774658202
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	116070.55664062494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	105944.82421874994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	94885.25390624994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	101495.36132812494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	32061.767578124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	88897.70507812494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	84191.89453124994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	31988.525390624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	31915.283203124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	81262.20703124994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.93, 1.0, 'sonic')	31851.196289062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	75988.76953124994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	73645.01953124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	71447.75390624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	78511.04736328119
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	69689.94140624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	68005.37109374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	64947.50976562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	66421.50878906247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	63555.90820312497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	61047.36328124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	59875.48828124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	62246.70410156247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	58850.09765624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	57824.70703124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9400000000000001, 1.0, 'sonic')	31786.53717041014
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9500000000000001, 1.0, 'sonic')	31750.488281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	39953.61328124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.97, 1.0, 'sonic')	31622.314453124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.96, 1.0, 'sonic')	31677.246093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.99, 1.0, 'sonic')	31530.761718749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.98, 1.0, 'sonic')	31604.003906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	56890.86914062497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	55993.65234374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	55133.05664062497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.52, 1.0, 'sonic')	53576.66015624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.54, 1.0, 'sonic')	52111.81640624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.51, 1.0, 'sonic')	54327.39257812497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	52803.03955078122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.55, 1.0, 'sonic')	51452.63671874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.56, 1.0, 'sonic')	50793.45703124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.58, 1.0, 'sonic')	49694.82421874997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	49255.37109374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.5700000000000001, 1.0, 'sonic')	50161.74316406247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.61, 1.0, 'sonic')	48449.70703124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.6, 1.0, 'sonic')	48834.22851562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.66, 1.0, 'sonic')	46618.65234374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.63, 1.0, 'sonic')	47662.35351562497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.62, 1.0, 'sonic')	48037.71972656247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.64, 1.0, 'sonic')	47296.14257812497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.65, 1.0, 'sonic')	46945.953369140596
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.67, 1.0, 'sonic')	46284.48486328122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.6900000000000001, 1.0, 'sonic')	45666.50390624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.68, 1.0, 'sonic')	45977.78320312497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.7000000000000001, 1.0, 'sonic')	45373.53515624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.71, 1.0, 'sonic')	45098.87695312497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.72, 1.0, 'sonic')	44815.06347656247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.73, 1.0, 'sonic')	44567.87109374997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.74, 1.0, 'sonic')	44302.36816406247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.75, 1.0, 'sonic')	44055.17578124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.77, 1.0, 'sonic')	43615.72265624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.76, 1.0, 'sonic')	43835.44921874997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.78, 1.0, 'sonic')	43395.99609374997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.8, 1.0, 'sonic')	43048.09570312497
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.05, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.79, 1.0, 'sonic')	43208.31298828122
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.07, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.08, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.1, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.81, 1.0, 'sonic')	42883.30078124997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.8200000000000001, 1.0, 'sonic')	42736.81640624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.8300000000000001, 1.0, 'sonic')	42590.33203124997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.84, 1.0, 'sonic')	42443.84765624997
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.85, 1.0, 'sonic')	42315.67382812497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.86, 1.0, 'sonic')	42182.92236328122
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	495007.32421874977
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	574420.1660156248
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	532608.0322265623
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	406484.98535156227
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	462668.6096191404
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	432785.7971191404
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.87, 1.0, 'sonic')	42077.63671874997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	382946.77734374977
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.88, 1.0, 'sonic')	41949.46289062497
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	361047.36328124977
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	341564.94140624977
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	323986.81640624977
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	65075.68359374997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	292272.9492187499
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	278869.6289062499
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	307507.32421874977
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	266198.7304687499
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	254214.47753906238
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	41802.97851562497
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	233404.54101562488
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	243235.0158691405
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	224038.69628906238
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	215167.23632812488
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	206726.07421874988
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	41638.18359374997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	192297.36328124988
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	199346.92382812488
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	185577.39257812488
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	173034.66796874988
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	179141.23535156238
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	41271.97265624997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	167642.21191406238
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.51, 1.0, 'sonic')	162506.10351562488
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.52, 1.0, 'sonic')	157580.56640624988
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.93, 1.0, 'sonic')	41125.48828124997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.56, 1.0, 'sonic')	140148.92578124994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	152819.82421874988
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.55, 1.0, 'sonic')	143975.83007812494
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.54, 1.0, 'sonic')	148237.6098632812
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	41445.92285156247
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	129455.56640624994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.58, 1.0, 'sonic')	132897.94921874994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.5700000000000001, 1.0, 'sonic')	136441.04003906244
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.9400000000000001, 1.0, 'sonic')	40937.80517578122
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6, 1.0, 'sonic')	126159.66796874994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.62, 1.0, 'sonic')	120007.32421874994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.61, 1.0, 'sonic')	122937.01171874994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.9500000000000001, 1.0, 'sonic')	40777.58789062497
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.63, 1.0, 'sonic')	117315.67382812494
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.65, 1.0, 'sonic')	112188.72070312494
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.66, 1.0, 'sonic')	109753.41796874994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.68, 1.0, 'sonic')	105084.22851562494
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6900000000000001, 1.0, 'sonic')	102868.65234374994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.67, 1.0, 'sonic')	107370.75805664057
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.64, 1.0, 'sonic')	114697.19409942621
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.74, 1.0, 'sonic')	93640.13671874994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.96, 1.0, 'sonic')	40631.10351562497
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.7000000000000001, 1.0, 'sonic')	100817.87109374994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.71, 1.0, 'sonic')	98931.88476562494
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.72, 1.0, 'sonic')	97114.56298828119
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.97, 1.0, 'sonic')	40466.30859374997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.76, 1.0, 'sonic')	90344.23828124994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.73, 1.0, 'sonic')	95352.17285156244
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.75, 1.0, 'sonic')	91973.87695312494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.98, 1.0, 'sonic')	40319.82421874997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.77, 1.0, 'sonic')	88764.95361328119
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 4000000.0, 1.0, 0.0, 100.0, 0.99, 1.0, 'sonic')	40134.429931640596
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.78, 1.0, 'sonic')	87229.15649414057
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.79, 1.0, 'sonic')	85748.29101562494
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.05, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8200000000000001, 1.0, 'sonic')	81921.38671874994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.81, 1.0, 'sonic')	83120.72753906244
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8, 1.0, 'sonic')	84372.71118164057
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8300000000000001, 1.0, 'sonic')	80694.58007812494
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.84, 1.0, 'sonic')	79531.86035156244
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.85, 1.0, 'sonic')	78405.76171874994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.86, 1.0, 'sonic')	77307.12890624994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.87, 1.0, 'sonic')	76208.49609374994
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	515176.39160156227
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	74116.51611328122
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	73132.32421874997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.88, 1.0, 'sonic')	75145.33996582025
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	72134.39941406247
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.07, 1.0, 'sonic')	328985.59570312477
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	112316.89453124994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	71186.82861328122
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.08, 1.0, 'sonic')	234283.44726562488
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.1, 1.0, 'sonic')	137750.24414062494
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	94519.04296874994
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.93, 1.0, 'sonic')	70367.43164062497
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	175259.39941406238
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9400000000000001, 1.0, 'sonic')	69561.76757812497
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	71887.20703124997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.96, 1.0, 'sonic')	68005.37109374997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9500000000000001, 1.0, 'sonic')	68769.83642578122
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	81573.48632812494
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.97, 1.0, 'sonic')	67272.94921874997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.98, 1.0, 'sonic')	66540.52734374997
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	54766.84570312497
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.99, 1.0, 'sonic')	65808.10546874997
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	64888.00048828122
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	59028.62548828122
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	50958.25195312497
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	48101.80664062497
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	45739.74609374997
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	43615.72265624997
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	41711.42578124997
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	35119.628906249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	36163.330078124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	35147.094726562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	35073.852539062485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	35440.063476562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	37059.4024658203
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	39166.25976562497
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	38067.62695312497
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	35064.697265624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	40393.06640624997
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	35064.697265624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	35064.697265624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	35064.697265624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	34753.417968749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.51, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	35046.386718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.52, 1.0, 'sonic')	34918.212890624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.55, 1.0, 'sonic')	34419.250488281235
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.54, 1.0, 'sonic')	34606.933593749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.56, 1.0, 'sonic')	34268.188476562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.58, 1.0, 'sonic')	33975.219726562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.5700000000000001, 1.0, 'sonic')	34121.704101562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6, 1.0, 'sonic')	33728.027343749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	33837.31842041014
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.61, 1.0, 'sonic')	33581.542968749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.63, 1.0, 'sonic')	33361.816406249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.62, 1.0, 'sonic')	33462.524414062485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.64, 1.0, 'sonic')	33233.642578124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.67, 1.0, 'sonic')	32922.363281249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.68, 1.0, 'sonic')	32807.922363281235
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.65, 1.0, 'sonic')	33142.089843749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.66, 1.0, 'sonic')	33013.916015624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6900000000000001, 1.0, 'sonic')	32720.947265624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.7000000000000001, 1.0, 'sonic')	32629.394531249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.71, 1.0, 'sonic')	32556.152343749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.72, 1.0, 'sonic')	32441.711425781235
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.73, 1.0, 'sonic')	32363.891601562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.74, 1.0, 'sonic')	32281.494140624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.75, 1.0, 'sonic')	32208.251953124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.76, 1.0, 'sonic')	32135.009765624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.77, 1.0, 'sonic')	32061.767578124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.79, 1.0, 'sonic')	31915.283203124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.78, 1.0, 'sonic')	31988.525390624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8, 1.0, 'sonic')	31842.041015624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.81, 1.0, 'sonic')	31777.954101562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8300000000000001, 1.0, 'sonic')	31677.246093749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.84, 1.0, 'sonic')	31604.003906249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8200000000000001, 1.0, 'sonic')	31711.57836914061
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.99, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	101577.75878906244
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.87, 1.0, 'sonic')	31420.612335205064
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.88, 1.0, 'sonic')	31384.277343749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.85, 1.0, 'sonic')	31549.072265624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	62750.24414062497
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.86, 1.0, 'sonic')	31475.830078124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	46797.18017578122
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	38369.75097656247
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.05, 1.0, 'sonic')	33508.300781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	30450.439453124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	31329.345703124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.07, 1.0, 'sonic')	28527.832031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.08, 1.0, 'sonic')	27282.714843749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	26348.876953124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	25158.691406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.1, 1.0, 'sonic')	25633.621215820298
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	31265.258789062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	24810.791015624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	24973.29711914061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	24884.033203124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	24751.281738281235
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	31182.861328124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	24719.238281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	31237.792968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	24604.797363281235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	24572.753906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	24279.785156249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	23693.847656249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	23945.617675781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	23428.344726562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9500000000000001, 1.0, 'sonic')	31036.376953124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	23199.462890624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	22988.891601562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	22814.941406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	22622.680664062485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.96, 1.0, 'sonic')	31018.066406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	22467.041015624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	22302.246093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	22174.072265624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	21954.345703124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	22045.32623291014
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	21862.792968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	21890.258789062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	21789.550781249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.93, 1.0, 'sonic')	31123.352050781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	21734.619140624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	21716.308593749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.98, 1.0, 'sonic')	30905.91430664061
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.97, 1.0, 'sonic')	30963.134765624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	21661.376953124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	21643.066406249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.9400000000000001, 1.0, 'sonic')	31091.308593749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	21569.824218749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	21588.134765624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	21524.047851562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	21496.582031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	21459.38873291014
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	21441.650390624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	21423.339843749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	21350.097656249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	21368.408203124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	20837.402343749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	21384.42993164061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	21350.097656249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.52, 1.0, 'sonic')	21276.855468749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.51, 1.0, 'sonic')	21304.321289062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	21276.855468749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.55, 1.0, 'sonic')	21221.923828124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.56, 1.0, 'sonic')	21221.923828124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.5700000000000001, 1.0, 'sonic')	21203.613281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.54, 1.0, 'sonic')	21239.089965820298
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.58, 1.0, 'sonic')	21203.613281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.61, 1.0, 'sonic')	21130.371093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.6, 1.0, 'sonic')	21148.681640624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	21162.414550781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.62, 1.0, 'sonic')	21130.371093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.63, 1.0, 'sonic')	21130.371093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.65, 1.0, 'sonic')	21084.594726562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.64, 1.0, 'sonic')	21091.46118164061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.66, 1.0, 'sonic')	21075.439453124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.67, 1.0, 'sonic')	21057.128906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.68, 1.0, 'sonic')	21057.128906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.6900000000000001, 1.0, 'sonic')	21057.128906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.7000000000000001, 1.0, 'sonic')	21057.128906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.71, 1.0, 'sonic')	21015.930175781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.72, 1.0, 'sonic')	21011.352539062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.73, 1.0, 'sonic')	21002.197265624985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.75, 1.0, 'sonic')	20983.886718749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.76, 1.0, 'sonic')	20983.886718749985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.79, 1.0, 'sonic')	20942.687988281235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	102447.50976562494
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.81, 1.0, 'sonic')	20928.955078124985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	63629.15039062497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.84, 1.0, 'sonic')	20910.644531249985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	39093.01757812497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.85, 1.0, 'sonic')	20910.644531249985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	31125.64086914061
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.86, 1.0, 'sonic')	20910.644531249985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	27502.441406249985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	26330.566406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	26110.839843749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	25891.113281249985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	25744.628906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	25598.144531249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	20871.73461914061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	25469.970703124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	25378.417968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	20864.868164062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	20869.445800781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	25266.26586914061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	25186.157226562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 20000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	20855.712890624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	25112.915039062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	25044.250488281235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	25012.207031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	24938.964843749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	24884.033203124985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	24279.785156249985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	23712.158203124985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	23400.878906249985
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	22183.227539062485
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+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	33526.611328124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.87, 1.0, 'sonic')	21221.923828124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	32940.673828124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	32427.978515624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	31988.525390624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.88, 1.0, 'sonic')	21203.613281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	31604.003906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	31311.035156249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	21203.613281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	31018.066406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	21157.836914062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	30743.408203124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	30505.371093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	21148.681640624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	21130.371093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	30303.955078124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	30139.160156249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	29946.899414062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	29791.259765624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.93, 1.0, 'sonic')	21130.371093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	29644.775390624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	24279.785156249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.9400000000000001, 1.0, 'sonic')	21089.172363281235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	29515.457153320298
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	29406.738281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.96, 1.0, 'sonic')	21075.439453124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	29260.253906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.9500000000000001, 1.0, 'sonic')	21075.439453124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	28994.750976562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	28601.074218749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	28775.024414062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.98, 1.0, 'sonic')	21057.128906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.97, 1.0, 'sonic')	21057.128906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	28381.347656249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.99, 1.0, 'sonic')	21015.930175781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	28234.863281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	28042.602539062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	27886.962890624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	27593.994140624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	27740.478515624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	27461.242675781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	27227.783203124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	27355.957031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	27008.056640624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	27099.323272705064
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	26916.503906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	26715.087890624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	26797.485351562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	26623.535156249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	26477.050781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	26550.292968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	26364.89868164061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	26289.367675781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.52, 1.0, 'sonic')	26110.839843749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.51, 1.0, 'sonic')	26184.082031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.54, 1.0, 'sonic')	25964.355468749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	26218.41430664061
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	26037.597656249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.55, 1.0, 'sonic')	25909.423828124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.56, 1.0, 'sonic')	25845.336914062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.5700000000000001, 1.0, 'sonic')	25817.871093749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.58, 1.0, 'sonic')	25744.628906249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	25689.697265624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.6, 1.0, 'sonic')	25630.187988281235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.61, 1.0, 'sonic')	25598.144531249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.62, 1.0, 'sonic')	25543.212890624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.63, 1.0, 'sonic')	25524.902343749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.64, 1.0, 'sonic')	25451.660156249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.66, 1.0, 'sonic')	25378.417968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.68, 1.0, 'sonic')	25305.175781249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.65, 1.0, 'sonic')	25405.883789062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.67, 1.0, 'sonic')	25332.641601562485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.6900000000000001, 1.0, 'sonic')	25250.244140624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.72, 1.0, 'sonic')	25158.691406249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.7000000000000001, 1.0, 'sonic')	25231.933593749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.71, 1.0, 'sonic')	25177.001953124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.74, 1.0, 'sonic')	25085.449218749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.76, 1.0, 'sonic')	25030.517578124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.73, 1.0, 'sonic')	25112.915039062485
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.77, 1.0, 'sonic')	25012.207031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.75, 1.0, 'sonic')	25046.53930664061
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.78, 1.0, 'sonic')	24957.275390624985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	533679.1992187498
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.79, 1.0, 'sonic')	24938.964843749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.8, 1.0, 'sonic')	24901.77154541014
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	279089.3554687499
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	149482.7270507812
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.05, 1.0, 'sonic')	93200.68359374994
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.81, 1.0, 'sonic')	24884.033203124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.8200000000000001, 1.0, 'sonic')	24865.722656249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.07, 1.0, 'sonic')	54968.26171874997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	67817.68798828122
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.08, 1.0, 'sonic')	47497.55859374997
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	42617.79785156247
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.1, 1.0, 'sonic')	39605.71289062497
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.85, 1.0, 'sonic')	24792.480468749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.8300000000000001, 1.0, 'sonic')	24824.523925781235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.84, 1.0, 'sonic')	24810.791015624985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	37316.894531249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.86, 1.0, 'sonic')	24753.57055664061
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	35412.597656249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	34167.480468749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	33215.332031249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.87, 1.0, 'sonic')	24737.548828124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	32363.891601562485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	31018.066406249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	31631.469726562485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	30432.128906249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	29919.433593749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	22082.519531249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	29443.216323852525
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	29067.993164062485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	28747.558593749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.88, 1.0, 'sonic')	24719.238281249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	28454.589843749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	24719.238281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	24664.306640624985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	28161.621093749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	27886.962890624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	27648.925781249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	27429.199218749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	27227.783203124985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	27062.988281249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	26861.572265624985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	26696.777343749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.93, 1.0, 'sonic')	24604.797363281235
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	26403.808593749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.9500000000000001, 1.0, 'sonic')	24572.753906249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	26550.292968749985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.9400000000000001, 1.0, 'sonic')	24591.064453124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	26257.324218749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	26129.150390624985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	25891.113281249985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.96, 1.0, 'sonic')	24517.822265624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.99, 1.0, 'sonic')	24353.027343749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	25999.832153320298
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	25776.672363281235
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.97, 1.0, 'sonic')	24453.735351562485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	25689.697265624985
+titrate(NeuronalBilayerSonophore(32.0 nm, CorticalLTS), 4000000.0, 1.0, 0.0, 100.0, 0.98, 1.0, 'sonic')	24385.070800781235
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	25598.144531249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	25396.728515624985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	25231.933593749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	25483.703613281235
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	25323.486328124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	25158.691406249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	25085.449218749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	25012.207031249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	24938.964843749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	24884.033203124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	24819.946289062485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.52, 1.0, 'sonic')	24719.238281249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.54, 1.0, 'sonic')	24591.064453124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.51, 1.0, 'sonic')	24754.714965820298
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.55, 1.0, 'sonic')	24517.822265624985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.56, 1.0, 'sonic')	24426.269531249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.5700000000000001, 1.0, 'sonic')	24353.027343749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.58, 1.0, 'sonic')	24234.008789062485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.59, 1.0, 'sonic')	24151.611328124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.6, 1.0, 'sonic')	24060.058593749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.61, 1.0, 'sonic')	23986.816406249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.62, 1.0, 'sonic')	23913.574218749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.63, 1.0, 'sonic')	23840.332031249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.64, 1.0, 'sonic')	23767.089843749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.65, 1.0, 'sonic')	23693.847656249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.66, 1.0, 'sonic')	23620.605468749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.67, 1.0, 'sonic')	23547.363281249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.68, 1.0, 'sonic')	23474.121093749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.6900000000000001, 1.0, 'sonic')	23419.189453124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.7000000000000001, 1.0, 'sonic')	23345.947265624985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.71, 1.0, 'sonic')	23286.437988281235
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.72, 1.0, 'sonic')	23254.394531249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.73, 1.0, 'sonic')	23181.152343749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.74, 1.0, 'sonic')	23126.220703124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.76, 1.0, 'sonic')	23034.667968749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.75, 1.0, 'sonic')	23062.133789062485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.77, 1.0, 'sonic')	22961.425781249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.79, 1.0, 'sonic')	22888.183593749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.78, 1.0, 'sonic')	22915.649414062485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.8, 1.0, 'sonic')	22814.941406249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.81, 1.0, 'sonic')	22769.165039062485
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.8200000000000001, 1.0, 'sonic')	22741.699218749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.8300000000000001, 1.0, 'sonic')	22686.767578124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.84, 1.0, 'sonic')	22668.457031249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.85, 1.0, 'sonic')	22595.214843749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.87, 1.0, 'sonic')	22521.972656249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.86, 1.0, 'sonic')	22556.30493164061
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.88, 1.0, 'sonic')	22480.773925781235
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.89, 1.0, 'sonic')	22448.730468749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 1.0, 1.0, 'sonic')	nan
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.9, 1.0, 'sonic')	22407.531738281235
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.91, 1.0, 'sonic')	22375.488281249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.92, 1.0, 'sonic')	22334.289550781235
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.9400000000000001, 1.0, 'sonic')	22302.246093749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.93, 1.0, 'sonic')	22302.246093749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.9500000000000001, 1.0, 'sonic')	22247.314453124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic')	29772.949218749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.96, 1.0, 'sonic')	22229.003906249985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.97, 1.0, 'sonic')	22174.072265624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic')	27429.199218749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.04, 1.0, 'sonic')	25817.871093749985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.98, 1.0, 'sonic')	22155.761718749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic')	26348.876953124985
+titrate(NeuronalBilayerSonophore(16.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.99, 1.0, 'sonic')	22114.562988281235
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.05, 1.0, 'sonic')	25524.902343749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.06, 1.0, 'sonic')	25323.486328124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.07, 1.0, 'sonic')	25186.157226562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.11, 1.0, 'sonic')	24893.188476562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.08, 1.0, 'sonic')	25085.449218749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.09, 1.0, 'sonic')	25012.207031249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.1, 1.0, 'sonic')	24938.964843749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.12, 1.0, 'sonic')	24865.722656249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.13, 1.0, 'sonic')	24819.946289062485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.14, 1.0, 'sonic')	24792.480468749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.15, 1.0, 'sonic')	24792.480468749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.16, 1.0, 'sonic')	24737.548828124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.17, 1.0, 'sonic')	24719.238281249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.2, 1.0, 'sonic')	24673.461914062485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.18, 1.0, 'sonic')	24719.238281249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.19, 1.0, 'sonic')	24719.238281249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.21, 1.0, 'sonic')	24664.306640624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.22, 1.0, 'sonic')	24664.306640624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.28, 1.0, 'sonic')	24604.797363281235
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.23, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.25, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.24, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.26, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.27, 1.0, 'sonic')	24645.996093749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.29, 1.0, 'sonic')	24600.219726562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.31, 1.0, 'sonic')	24591.064453124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.3, 1.0, 'sonic')	24600.219726562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.32, 1.0, 'sonic')	24591.064453124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.33, 1.0, 'sonic')	24591.064453124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.34, 1.0, 'sonic')	24572.753906249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.36, 1.0, 'sonic')	24517.822265624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.35000000000000003, 1.0, 'sonic')	24572.753906249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.41000000000000003, 1.0, 'sonic')	23840.332031249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.37, 1.0, 'sonic')	24353.027343749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.38, 1.0, 'sonic')	24206.542968749985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.39, 1.0, 'sonic')	24078.369140624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.4, 1.0, 'sonic')	23941.040039062485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.45, 1.0, 'sonic')	23400.878906249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.42, 1.0, 'sonic')	23712.158203124985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.44, 1.0, 'sonic')	23492.431640624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.43, 1.0, 'sonic')	23583.41217041014
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.46, 1.0, 'sonic')	23281.860351562485
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.48, 1.0, 'sonic')	23107.910156249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.47000000000000003, 1.0, 'sonic')	23199.462890624985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.53, 1.0, 'sonic')	22700.500488281235
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.5, 1.0, 'sonic')	22961.425781249985
+titrate(NeuronalBilayerSonophore(64.0 nm, CorticalLTS), 500000.0, 1.0, 0.0, 100.0, 0.49, 1.0, 'sonic')	23034.667968749985
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+
diff --git a/PySONIC/core/bls_lookups.json b/PySONIC/core/bls_lookups.json
index 32f6a32..9d86ca1 100644
--- a/PySONIC/core/bls_lookups.json
+++ b/PySONIC/core/bls_lookups.json
@@ -1,696 +1,705 @@
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 }
\ No newline at end of file
diff --git a/PySONIC/plt/actmap.py b/PySONIC/plt/actmap.py
index b8d4731..5e041e9 100644
--- a/PySONIC/plt/actmap.py
+++ b/PySONIC/plt/actmap.py
@@ -1,284 +1,271 @@
 # -*- coding: utf-8 -*-
 # @Author: Theo Lemaire
 # @Email: theo.lemaire@epfl.ch
 # @Date:   2019-06-04 18:24:29
 # @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2019-08-22 15:44:09
+# @Last Modified time: 2019-09-30 19:19:23
 
 import os
 import pickle
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from matplotlib.ticker import FormatStrFormatter
 
 from ..core import NeuronalBilayerSonophore
 from ..utils import logger, si_format, loadData
 from ..constants import *
 from .pltutils import cm2inch, setNormalizer
 from ..postpro import detectSpikes
 
 
 class Map:
 
     @staticmethod
     def computeMeshEdges(x, scale):
         ''' Compute the appropriate edges of a mesh that quads a linear or logarihtmic distribution.
 
             :param x: the input vector
             :param scale: the type of distribution ('lin' for linear, 'log' for logarihtmic)
             :return: the edges vector
         '''
         if scale == 'log':
             x = np.log10(x)
         dx = x[1] - x[0]
         n = x.size + 1
         return {'lin': np.linspace, 'log': np.logspace}[scale](x[0] - dx / 2, x[-1] + dx / 2, n)
 
 
 class ActivationMap(Map):
 
     def __init__(self, root, pneuron, a, Fdrive, tstim, PRF, amps, DCs):
         self.root = root
         self.pneuron = pneuron
         self.a = a
         self.nbls = NeuronalBilayerSonophore(self.a, self.pneuron)
         self.Fdrive = Fdrive
         self.tstim = tstim
         self.PRF = PRF
         self.amps = amps
         self.DCs = DCs
 
         self.Ascale = self.getAmpScale()
         self.title = '{} neuron @ {}Hz, {}Hz PRF ({}m sonophore)'.format(
             self.pneuron.name, *si_format([self.Fdrive, self.PRF, self.a]))
         out_fname = 'actmap {} {}Hz PRF{}Hz {}s.csv'.format(
             self.pneuron.name, *si_format([self.Fdrive, self.PRF, self.tstim], space=''))
         out_fpath = os.path.join(self.root, out_fname)
 
         if os.path.isfile(out_fpath):
             self.data = np.loadtxt(out_fpath, delimiter=',')
         else:
             self.data = self.compute()
             np.savetxt(out_fpath, self.data, delimiter=',')
 
     def getAmpScale(self):
         Amin, Amax, nA = self.amps.min(), self.amps.max(), self.amps.size
         if np.all(np.isclose(self.amps, np.logspace(np.log10(Amin), np.log10(Amax), nA))):
             return 'log'
         elif np.all(np.isclose(self.amps, np.linspace(Amin, Amax, nA))):
             return 'lin'
         else:
             raise ValueError('Unknown distribution type')
 
     def classify(self, data):
         ''' Classify based on charge temporal profile. '''
 
         # Detect spikes in data
         ispikes, _ = detectSpikes(data)
 
         # Compute firing metrics
         if ispikes.size == 0:  # if no spike, assign -1
             return -1
         elif ispikes.size == 1:  # if only 1 spike, assign 0
             return 0
         else:  # if more than 1 spike, assign firing rate
             t = data['t'].values
             sr = 1 / np.diff(t[ispikes])
             return np.mean(sr)
 
-    @staticmethod
-    def correctAmp(A):
-        return np.round(A * 1e-3, 1) * 1e3
-
     def compute(self):
         logger.info('Generating activation map for %s neuron', self.pneuron.name)
         actmap = np.empty((self.amps.size, self.DCs.size))
         nfiles = self.DCs.size * self.amps.size
         for i, A in enumerate(self.amps):
             for j, DC in enumerate(self.DCs):
                 fname = '{}.pkl'.format(self.nbls.filecode(
-                    self.Fdrive, self.correctAmp(A), self.tstim, 0., self.PRF, DC, 1., 'sonic'))
+                    self.Fdrive, A, self.tstim, 0., self.PRF, DC, 1., 'sonic'))
                 fpath = os.path.join(self.root, fname)
                 if not os.path.isfile(fpath):
                     print(fpath)
                     logger.error('"{}" file not found'.format(fname))
                     actmap[i, j] = np.nan
                 else:
                     # Load data
                     logger.debug('Loading file {}/{}: "{}"'.format(
                         i * self.amps.size + j + 1, nfiles, fname))
                     df, _ = loadData(fpath)
                     actmap[i, j] = self.classify(df)
         return actmap
 
     @staticmethod
     def adjustFRbounds(actmap):
         ''' Check firing rate bounding. '''
         minFR, maxFR = (actmap[actmap > 0].min(), actmap.max())
         logger.info('FR range: %.0f - %.0f Hz', minFR, maxFR)
         if FRbounds is None:
             FRbounds = (minFR, maxFR)
         else:
             if minFR < FRbounds[0]:
                 logger.warning(
                     'Minimal firing rate (%.0f Hz) is below defined lower bound (%.0f Hz)',
                     minFR, FRbounds[0])
             if maxFR > FRbounds[1]:
                 logger.warning(
                     'Maximal firing rate (%.0f Hz) is above defined upper bound (%.0f Hz)',
                     maxFR, FRbounds[1])
 
     @staticmethod
     def fit2Colormap(actmap, cmap):
         actmap[actmap == -1] = np.nan
         actmap[actmap == 0] = 1e-3
         cmap.set_bad('silver')
         cmap.set_under('k')
         return actmap, cmap
 
     def addThresholdCurve(self, ax):
-        Athrs_fname = 'Athrs_{}_{:.0f}nm_{}Hz_PRF{}Hz_{}s.xlsx'.format(
-            self.pneuron.name, self.a * 1e9,
-            *si_format([self.Fdrive, self.PRF, self.tstim], 0, space=''))
-        fpath = os.path.join(self.root, Athrs_fname)
-        if os.path.isfile(fpath):
-            df = pd.read_excel(fpath, sheet_name='Data')
-            DCs = df['Duty factor'].values
-            Athrs = df['Adrive (kPa)'].values
-            iDCs = np.argsort(DCs)
-            DCs = DCs[iDCs]
-            Athrs = Athrs[iDCs]
-            ax.plot(DCs * 1e2, Athrs, '-', color='#F26522', linewidth=2,
-                    label='threshold amplitudes')
-            ax.legend(loc='lower center', frameon=False, fontsize=8)
-        else:
-            logger.warning('%s file not found -> cannot draw threshold curve', fpath)
+        Athrs = np.array([
+            self.nbls.titrate(self.Fdrive, self.tstim, 0., self.PRF, DC) for DC in self.DCs])
+        ax.plot(DCs * 1e2, Athrs, '-', color='#F26522', linewidth=2,
+                label='threshold amplitudes')
+        ax.legend(loc='lower center', frameon=False, fontsize=8)
 
     def render(self, Ascale='log', FRscale='log', FRbounds=None, fs=8, cmap='viridis',
-               interactive=False, Vbounds=None, trange=None, thresholds=False):
+               interactive=False, Vbounds=None, trange=None, thresholds=False,
+               figsize=cm2inch(8, 5.8)):
 
         # Compute FR normalizer
         mymap = plt.get_cmap(cmap)
         norm, sm = setNormalizer(mymap, FRbounds, FRscale)
 
         # Compute mesh edges
         xedges = self.computeMeshEdges(self.DCs, 'lin')
         yedges = self.computeMeshEdges(self.amps, self.Ascale)
 
         # Create figure
-        fig, ax = plt.subplots(figsize=cm2inch(8, 5.8))
+        fig, ax = plt.subplots(figsize=figsize)
         fig.subplots_adjust(left=0.15, bottom=0.15, right=0.8, top=0.92)
         ax.set_title(self.title, fontsize=fs)
         ax.set_xlabel('Duty cycle (%)', fontsize=fs, labelpad=-0.5)
         ax.set_ylabel('Amplitude (kPa)', fontsize=fs)
         ax.set_xlim(np.array([self.DCs.min(), self.DCs.max()]) * 1e2)
         for item in ax.get_xticklabels() + ax.get_yticklabels():
             item.set_fontsize(fs)
 
         # Plot activation map with specific color code
         actmap, cmap = self.fit2Colormap(self.data, plt.get_cmap(cmap))
         if Ascale == 'log':
             ax.set_yscale('log')
         ax.pcolormesh(xedges * 1e2, yedges * 1e-3, actmap, cmap=mymap, norm=norm)
         if thresholds:
             self.addThresholdCurve(ax)
 
         # Plot firing rate colorbar
         pos1 = ax.get_position()  # get the map axis position
         cbarax = fig.add_axes([pos1.x1 + 0.02, pos1.y0, 0.03, pos1.height])
         fig.colorbar(sm, cax=cbarax)
         cbarax.set_ylabel('Firing rate (Hz)', fontsize=fs)
         for item in cbarax.get_yticklabels():
             item.set_fontsize(fs)
 
         if interactive:
             fig.canvas.mpl_connect(
                 'button_press_event',
                 lambda event: self.onClick(event, (xedges, yedges), trange, Vbounds))
 
         return fig
 
     def onClick(self, event, meshedges, trange, Vbounds):
         ''' Retrieve the specific input parameters of the x and y dimensions
             when the user clicks on a cell in the 2D map, and define filename from it.
         '''
 
         # Get DC and A from x and y coordinates
         x, y = event.xdata, event.ydata
         DC = self.DCs[np.searchsorted(meshedges[0], x * 1e-2) - 1]
         Adrive = self.amps[np.searchsorted(meshedges[1], y * 1e3) - 1]
 
         # Define filepath
         fname = '{}.pkl'.format(self.nbls.filecode(
-            self.Fdrive, self.correctAmp(Adrive), self.tstim, 0., self.PRF, DC, 1. , 'sonic'))
+            self.Fdrive, Adrive, self.tstim, 0., self.PRF, DC, 1. , 'sonic'))
         fpath = os.path.join(self.root, fname)
 
         # Plot Q-trace
         try:
             self.plotQVeff(fpath, trange=trange, ybounds=Vbounds)
             plt.show()
         except FileNotFoundError as err:
             logger.error(err)
 
-    def plotQVeff(self, filepath, tonset=10e-3, trange=None, ybounds=None, fs=8, lw=1):
+    def plotQVeff(self, filepath, tonset=10e-3, trange=None, ybounds=None,
+                  fs=8, lw=1, figsize=cm2inch(7, 3)):
         ''' Plot superimposed profiles of membrane charge density and
             effective membrane potential.
 
             :param filepath: full path to the data file
             :param tonset: pre-stimulus onset to add to profiles (s)
             :param trange: time lower and upper bounds on graph (s)
             :param ybounds: y-axis bounds (mV / nC/cm2)
             :return: handle to the generated figure
         '''
         # Check file existence
         fname = os.path.basename(filepath)
         if not os.path.isfile(filepath):
             raise FileNotFoundError('Error: "{}" file does not exist'.format(fname))
 
         # Load data (with optional time restriction)
         logger.debug('Loading data from "%s"', fname)
         with open(filepath, 'rb') as fh:
             frame = pickle.load(fh)
             df = frame['data']
         if trange is not None:
             tmin, tmax = trange
             df = df.loc[(df['t'] >= tmin) & (df['t'] <= tmax)]
 
         # Load variables, add onset and rescale
         t, Qm, Vm = [df[k].values for k in ['t', 'Qm', 'Vm']]
         t = np.hstack((np.array([-tonset, t[0]]), t))  # s
         Vm = np.hstack((np.array([self.pneuron.Vm0] * 2), Vm))  # mV
         Qm = np.hstack((np.array([self.pneuron.Qm0()] * 2), Qm))  # C/m2
         t *= 1e3  # ms
         Qm *= 1e5  # nC/cm2
 
         # Determine axes bounds
         if ybounds is None:
             ybounds = (min(Vm.min(), Qm.min()), max(Vm.max(), Qm.max()))
 
         # Create figure
-        fig, ax = plt.subplots(figsize=cm2inch(7, 3))
+        fig, ax = plt.subplots(figsize=figsize)
         fig.canvas.set_window_title(fname)
         plt.subplots_adjust(left=0.2, bottom=0.2, right=0.95, top=0.95)
         for key in ['top', 'right']:
             ax.spines[key].set_visible(False)
         for key in ['bottom', 'left']:
             ax.spines[key].set_position(('axes', -0.03))
             ax.spines[key].set_linewidth(2)
         ax.yaxis.set_tick_params(width=2)
         ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))
         ax.set_xticks([])
         ax.set_xlabel('{}s'.format(si_format(np.ptp(t), space=' ')), fontsize=fs)
         ax.set_ylabel('mV - $\\rm nC/cm^2$', fontsize=fs, labelpad=-15)
         ax.set_ylim(ybounds)
         ax.set_yticks(ybounds)
         for item in ax.get_yticklabels():
             item.set_fontsize(fs)
 
         # Plot Qm and Vmeff profiles
         ax.plot(t, Vm, color='darkgrey', linewidth=lw)
         ax.plot(t, Qm, color='k', linewidth=lw)
 
         # fig.tight_layout()
         return fig
diff --git a/PySONIC/plt/pltutils.py b/PySONIC/plt/pltutils.py
index d003ff7..61a2822 100644
--- a/PySONIC/plt/pltutils.py
+++ b/PySONIC/plt/pltutils.py
@@ -1,407 +1,407 @@
 # -*- coding: utf-8 -*-
 # @Author: Theo Lemaire
 # @Email: theo.lemaire@epfl.ch
 # @Date:   2017-08-21 14:33:36
 # @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2019-08-24 22:02:53
+# @Last Modified time: 2019-09-27 19:25:34
 
 ''' Useful functions to generate plots. '''
 
 import re
 import numpy as np
 import pandas as pd
 from scipy.signal import peak_widths
 import matplotlib
 from matplotlib.patches import Rectangle
 from matplotlib import cm, colors
 import matplotlib.pyplot as plt
 
 from ..core import getModel
 from ..utils import logger, isIterable, loadData, rescale, swapFirstLetterCase
 from ..constants import SPIKE_MIN_DT, SPIKE_MIN_QAMP, SPIKE_MIN_QPROM
 
 # Matplotlib parameters
 matplotlib.rcParams['pdf.fonttype'] = 42
 matplotlib.rcParams['ps.fonttype'] = 42
 matplotlib.rcParams['font.family'] = 'arial'
 
 
 def figtitle(meta):
     ''' Return appropriate title based on simulation metadata. '''
     if 'Cm0' in meta:
         return '{:.0f}nm radius BLS structure: MECH-STIM {:.0f}kHz, {:.2f}kPa, {:.1f}nC/cm2'.format(
             meta['a'] * 1e9, meta['Fdrive'] * 1e-3, meta['Adrive'] * 1e-3, meta['Qm'] * 1e5)
     else:
         if 'DC' in meta:
             if meta['DC'] < 1:
                 wavetype = 'PW'
                 suffix = ', {:.2f}Hz PRF, {:.0f}% DC'.format(meta['PRF'], meta['DC'] * 1e2)
             else:
                 wavetype = 'CW'
                 suffix = ''
             if 'Astim' in meta:
                 return '{} neuron: {} E-STIM {:.2f}mA/m2, {:.0f}ms{}'.format(
                     meta['neuron'], wavetype, meta['Astim'], meta['tstim'] * 1e3, suffix)
             else:
                 return '{} neuron ({:.1f}nm): {} A-STIM {:.0f}kHz {:.2f}kPa, {:.0f}ms{} - {} model'.format(
                     meta['neuron'], meta['a'] * 1e9, wavetype, meta['Fdrive'] * 1e-3,
                     meta['Adrive'] * 1e-3, meta['tstim'] * 1e3, suffix, meta['method'])
         else:
             return '{} neuron: V-CLAMP {:.1f}-{:.1f}mV, {:.0f}ms'.format(
                     meta['neuron'], meta['Vhold'], meta['Vstep'], meta['tstim'] * 1e3)
 
 
 
 def cm2inch(*tupl):
     inch = 2.54
     if isinstance(tupl[0], tuple):
         return tuple(i / inch for i in tupl[0])
     else:
         return tuple(i / inch for i in tupl)
 
 
 def extractPltVar(model, pltvar, df, meta=None, nsamples=0, name=''):
     if 'func' in pltvar:
         s = 'model.{}'.format(pltvar['func'])
         try:
             var = eval(s)
         except AttributeError:
             var = eval(s.replace('model', 'model.pneuron'))
     elif 'key' in pltvar:
         var = df[pltvar['key']]
     elif 'constant' in pltvar:
         var = eval(pltvar['constant']) * np.ones(nsamples)
     else:
         var = df[name]
     if isinstance(var, pd.Series):
         var = var.values
     var = var.copy()
 
     if var.size == nsamples - 1:
         var = np.insert(var, 0, var[0])
     var *= pltvar.get('factor', 1)
 
     return var
 
 
 def setGrid(n, ncolmax=3):
     ''' Determine number of rows and columns in figure grid, based on number of
         variables to plot. '''
     if n <= ncolmax:
         return (1, n)
     else:
         return ((n - 1) // ncolmax + 1, ncolmax)
 
 
 def setNormalizer(cmap, bounds, scale='lin'):
     norm = {
         'lin': colors.Normalize,
         'log': colors.LogNorm
     }[scale](*bounds)
     sm = cm.ScalarMappable(norm=norm, cmap=cmap)
     sm._A = []
     return norm, sm
 
 
 class GenericPlot:
     def __init__(self, filepaths):
         ''' Constructor.
 
             :param filepaths: list of full paths to output data files to be compared
         '''
         if not isIterable(filepaths):
             filepaths = [filepaths]
         self.filepaths = filepaths
 
     def __call__(self, *args, **kwargs):
         return self.render(*args, **kwargs)
 
     @staticmethod
     def getData(entry, frequency=1, trange=None):
         if entry is None:
             raise ValueError('non-existing data')
         if isinstance(entry, str):
             data, meta = loadData(entry, frequency)
         else:
             data, meta = entry
         data = data.iloc[::frequency]
         if trange is not None:
             tmin, tmax = trange
             data = data.loc[(data['t'] >= tmin) & (data['t'] <= tmax)]
         return data, meta
 
     def render(self, *args, **kwargs):
         return NotImplementedError
 
     @staticmethod
     def getSimType(fname):
         ''' Get sim type from filename. '''
         mo = re.search('(^[A-Z]*)_(.*).pkl', fname)
         if not mo:
             raise ValueError('Could not find sim-key in filename: "{}"'.format(fname))
         return mo.group(1)
 
     @staticmethod
     def getModel(*args, **kwargs):
         return getModel(*args, **kwargs)
 
     @staticmethod
     def figtitle(*args, **kwargs):
         return figtitle(*args, **kwargs)
 
     @staticmethod
     def getTimePltVar(tscale):
         ''' Return time plot variable for a given temporal scale. '''
         return {
             'desc': 'time',
             'label': 'time',
             'unit': tscale,
             'factor': {'ms': 1e3, 'us': 1e6}[tscale],
             'onset': {'ms': 1e-3, 'us': 1e-6}[tscale]
         }
 
     @staticmethod
     def createBackBone(*args, **kwargs):
         return NotImplementedError
 
     @staticmethod
     def prettify(ax, xticks=None, yticks=None, xfmt='{:.0f}', yfmt='{:+.0f}'):
         try:
             ticks = ax.get_ticks()
             ticks = (min(ticks), max(ticks))
             ax.set_ticks(ticks)
             ax.set_ticklabels([xfmt.format(x) for x in ticks])
         except AttributeError:
             if xticks is None:
                 xticks = ax.get_xticks()
                 xticks = (min(xticks), max(xticks))
             if yticks is None:
                 yticks = ax.get_yticks()
                 yticks = (min(yticks), max(yticks))
             ax.set_xticks(xticks)
             ax.set_yticks(yticks)
             if xfmt is not None:
                 ax.set_xticklabels([xfmt.format(x) for x in xticks])
             if yfmt is not None:
                 ax.set_yticklabels([yfmt.format(y) for y in yticks])
 
     @staticmethod
     def addInset(fig, ax, inset):
         ''' Create inset axis. '''
         inset_ax = fig.add_axes(ax.get_position())
         inset_ax.set_zorder(1)
         inset_ax.set_xlim(inset['xlims'][0], inset['xlims'][1])
         inset_ax.set_ylim(inset['ylims'][0], inset['ylims'][1])
         inset_ax.set_xticks([])
         inset_ax.set_yticks([])
         inset_ax.add_patch(Rectangle((inset['xlims'][0], inset['ylims'][0]),
                                      inset['xlims'][1] - inset['xlims'][0],
                                      inset['ylims'][1] - inset['ylims'][0],
                                      color='w'))
         return inset_ax
 
     @staticmethod
     def materializeInset(ax, inset_ax, inset):
         ''' Materialize inset with zoom boox. '''
         # Re-position inset axis
         axpos = ax.get_position()
         left, right, = rescale(inset['xcoords'], ax.get_xlim()[0], ax.get_xlim()[1],
                                axpos.x0, axpos.x0 + axpos.width)
         bottom, top, = rescale(inset['ycoords'], ax.get_ylim()[0], ax.get_ylim()[1],
                                axpos.y0, axpos.y0 + axpos.height)
         inset_ax.set_position([left, bottom, right - left, top - bottom])
         for i in inset_ax.spines.values():
             i.set_linewidth(2)
 
         # Materialize inset target region with contour frame
         ax.plot(inset['xlims'], [inset['ylims'][0]] * 2, linestyle='-', color='k')
         ax.plot(inset['xlims'], [inset['ylims'][1]] * 2, linestyle='-', color='k')
         ax.plot([inset['xlims'][0]] * 2, inset['ylims'], linestyle='-', color='k')
         ax.plot([inset['xlims'][1]] * 2, inset['ylims'], linestyle='-', color='k')
 
         # Link target and inset with dashed lines if possible
         if inset['xcoords'][1] < inset['xlims'][0]:
             ax.plot([inset['xcoords'][1], inset['xlims'][0]],
                     [inset['ycoords'][0], inset['ylims'][0]],
                     linestyle='--', color='k')
             ax.plot([inset['xcoords'][1], inset['xlims'][0]],
                     [inset['ycoords'][1], inset['ylims'][1]],
                     linestyle='--', color='k')
         elif inset['xcoords'][0] > inset['xlims'][1]:
             ax.plot([inset['xcoords'][0], inset['xlims'][1]],
                     [inset['ycoords'][0], inset['ylims'][0]],
                     linestyle='--', color='k')
             ax.plot([inset['xcoords'][0], inset['xlims'][1]],
                     [inset['ycoords'][1], inset['ylims'][1]],
                     linestyle='--', color='k')
         else:
             logger.warning('Inset x-coordinates intersect with those of target region')
 
     def postProcess(self, *args, **kwargs):
         return NotImplementedError
 
     @staticmethod
     def removeSpines(ax):
         for item in ['top', 'right']:
             ax.spines[item].set_visible(False)
 
     @staticmethod
     def setXTicks(ax, xticks=None):
         if xticks is not None:
             ax.set_xticks(xticks)
 
     @staticmethod
     def setYTicks(ax, yticks=None):
         if yticks is not None:
             ax.set_yticks(yticks)
 
     @staticmethod
     def setTickLabelsFontSize(ax, fs):
         for tick in ax.xaxis.get_major_ticks() + ax.yaxis.get_major_ticks():
             tick.label.set_fontsize(fs)
 
     @staticmethod
     def setXLabel(ax, xplt, fs):
         ax.set_xlabel('$\\rm {}\ ({})$'.format(xplt['label'], xplt['unit']), fontsize=fs)
 
     @staticmethod
     def setYLabel(ax, yplt, fs):
         ax.set_ylabel('$\\rm {}\ ({})$'.format(yplt['label'], yplt.get('unit', '')), fontsize=fs)
 
     @classmethod
     def addCmap(cls, fig, cmap, handles, comp_values, comp_info, fs, prettify, zscale='lin'):
         # Create colormap and normalizer
         try:
             mymap = plt.get_cmap(cmap)
         except ValueError:
             mymap = plt.get_cmap(swapFirstLetterCase(cmap))
         norm, sm = setNormalizer(mymap, (comp_values.min(), comp_values.max()), zscale)
 
         # Adjust line colors
         for lh, z in zip(handles, comp_values):
             if isIterable(lh):
                 for item in lh:
                     item.set_color(sm.to_rgba(z))
             else:
                 lh.set_color(sm.to_rgba(z))
 
         # Add colorbar
         fig.subplots_adjust(left=0.1, right=0.8, bottom=0.15, top=0.95, hspace=0.5)
         cbarax = fig.add_axes([0.85, 0.15, 0.03, 0.8])
         cbar = fig.colorbar(sm, cax=cbarax, orientation='vertical')
         cbarax.set_ylabel('$\\rm {}\ ({})$'.format(
             comp_info['desc'].replace(' ', '\ '), comp_info['unit']), fontsize=fs)
         if prettify:
             cls.prettify(cbar)
         for item in cbarax.get_yticklabels():
             item.set_fontsize(fs)
 
 
 class ComparativePlot(GenericPlot):
 
     def __init__(self, filepaths, varname):
         ''' Constructor.
 
             :param filepaths: list of full paths to output data files to be compared
             :param varname: name of variable to extract and compare
         '''
         super().__init__(filepaths)
         self.varname = varname
         self.comp_ref_key = None
         self.meta_ref = None
         self.comp_info = None
         self.is_unique_comp = False
 
     def checkColors(self, colors):
         if colors is None:
             colors = ['C{}'.format(j) for j in range(len(self.filepaths))]
         return colors
 
     def checkLines(self, lines):
         if lines is None:
             lines = ['-'] * len(self.filepaths)
         return lines
 
     def checkLabels(self, labels):
         if labels is not None:
             if len(labels) != len(self.filepaths):
                 raise ValueError(
                     'Invalid labels ({}): not matching number of compared files ({})'.format(
                         len(labels), len(self.filepaths)))
             if not all(isinstance(x, str) for x in labels):
                 raise TypeError('Invalid labels: must be string typed')
 
     def checkSimType(self, meta):
         ''' Check consistency of sim types across files. '''
         if meta['simkey'] != self.meta_ref['simkey']:
             raise ValueError('Invalid comparison: different simulation types')
 
     def checkCompValues(self, meta, comp_values):
         ''' Check consistency of differing values across files. '''
         differing = {k: meta[k] != self.meta_ref[k] for k in meta.keys()}
         if sum(differing.values()) > 1:
             logger.warning('More than one differing inputs')
             self.comp_ref_key = None
             return []
         zkey = (list(differing.keys())[list(differing.values()).index(True)])
         if self.comp_ref_key is None:
             self.comp_ref_key = zkey
             self.is_unique_comp = True
             comp_values.append(self.meta_ref[self.comp_ref_key])
             comp_values.append(meta[self.comp_ref_key])
         else:
             if zkey != self.comp_ref_key:
-                logger.warning('inconsitent differing inputs')
+                logger.warning('inconsistent differing inputs')
                 self.comp_ref_key = None
                 return []
             else:
                 comp_values.append(meta[self.comp_ref_key])
         return comp_values
 
     def checkConsistency(self, meta, comp_values):
         ''' Check consistency of sim types and check differing inputs. '''
         if self.meta_ref is None:
             self.meta_ref = meta
         else:
             self.checkSimType(meta)
             comp_values = self.checkCompValues(meta, comp_values)
             if self.comp_ref_key is None:
                 self.is_unique_comp = False
         return comp_values
 
     def getCompLabels(self, comp_values):
         if self.comp_info is not None:
             comp_values = np.array(comp_values) * self.comp_info.get('factor', 1)
             comp_labels = [
                 '$\\rm{} = {}\ {}$'.format(self.comp_info['label'], x, self.comp_info['unit'])
                 for x in comp_values]
         else:
             comp_labels = comp_values
         return comp_values, comp_labels
 
     def chooseLabels(self, labels, comp_labels, full_labels):
         if labels is not None:
             return labels
         else:
             if self.is_unique_comp:
                 return comp_labels
             else:
                 return full_labels
 
     @staticmethod
     def getCommonLabel(lbls, sep=' ', merge_keys=None):
         if merge_keys is not None:
             for k in merge_keys:
                 lbls = [lbl.replace(f'{sep}{k}', f'-{k}') for lbl in lbls]
         splt_lbls = [lbl.split(sep) for lbl in lbls]
         ncomps = len(splt_lbls[0])
         splt_lbls = np.array(splt_lbls).T
         all_indentical = [np.all(x == x[0]) for x in splt_lbls]
         if np.sum(all_indentical) < ncomps - 1:
             logger.warning('More than one differing inputs')
             return ''
         common_lbl = sep.join(splt_lbls[all_indentical, 0])
         if merge_keys is not None:
             for k in merge_keys:
                 common_lbl = common_lbl.replace(f'-{k}', f'{sep}{k}')
         return common_lbl
diff --git a/PySONIC/plt/timeseries.py b/PySONIC/plt/timeseries.py
index 420dac1..bc41072 100644
--- a/PySONIC/plt/timeseries.py
+++ b/PySONIC/plt/timeseries.py
@@ -1,493 +1,492 @@
 # -*- coding: utf-8 -*-
 # @Author: Theo Lemaire
 # @Email: theo.lemaire@epfl.ch
 # @Date:   2018-09-25 16:18:45
 # @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2019-09-27 13:32:58
+# @Last Modified time: 2019-09-28 12:46:19
 
 import numpy as np
 import matplotlib.pyplot as plt
 
 from ..postpro import detectSpikes, convertPeaksProperties
 from ..utils import *
 from .pltutils import *
 
 
 class TimeSeriesPlot(GenericPlot):
     ''' Generic interface to build a plot displaying temporal profiles of model simulations. '''
 
     @classmethod
     def setTimeLabel(cls, ax, tplt, fs):
         return super().setXLabel(ax, tplt, fs)
 
     @classmethod
     def setYLabel(cls, ax, yplt, fs, grouplabel=None):
         if grouplabel is not None:
             yplt['label'] = grouplabel
         return super().setYLabel(ax, yplt, fs)
 
     def checkInputs(self, *args, **kwargs):
         return NotImplementedError
 
     @staticmethod
     def getStimStates(df):
         try:
             stimstate = df['stimstate']
         except KeyError:
             stimstate = df['states']
         return stimstate.values
 
     @classmethod
     def getStimPulses(cls, t, states):
         ''' Determine the onset and offset times of pulses from a stimulation vector.
 
             :param t: time vector (s).
             :param states: a vector of stimulation state (ON/OFF) at each instant in time.
             :return: 3-tuple with number of patches, timing of STIM-ON an STIM-OFF instants.
         '''
         # Compute states derivatives and identify bounds indexes of pulses
         dstates = np.diff(states)
         ipulse_on = np.where(dstates > 0.0)[0] + 1
         ipulse_off = np.where(dstates < 0.0)[0] + 1
         if ipulse_off.size < ipulse_on.size:
             ioff = t.size - 1
             if ipulse_off.size == 0:
                 ipulse_off = np.array([ioff])
             else:
                 ipulse_off = np.insert(ipulse_off, ipulse_off.size - 1, ioff)
 
         # Get time instants for pulses ON and OFF
         tpulse_on = t[ipulse_on]
         tpulse_off = t[ipulse_off]
         return tpulse_on, tpulse_off
 
     def addLegend(self, fig, ax, handles, labels, fs, color=None, ls=None):
         lh = ax.legend(handles, labels, loc=1, fontsize=fs, frameon=False)
         if color is not None:
             for l in lh.get_lines():
                 l.set_color(color)
         if ls:
             for l in lh.get_lines():
                 l.set_linestyle(ls)
 
     @classmethod
     def materializeSpikes(cls, ax, data, tplt, yplt, color, mode, add_to_legend=False):
         ispikes, properties = detectSpikes(data)
         t = data['t'].values
         Qm = data['Qm'].values
         if ispikes is not None:
             yoffset = 5
             ax.plot(t[ispikes] * tplt['factor'], Qm[ispikes] * yplt['factor'] + yoffset,
                     'v', color=color, label='spikes' if add_to_legend else None)
             if mode == 'details':
                 ileft = properties['left_bases']
                 iright = properties['right_bases']
                 properties = convertPeaksProperties(t, properties)
                 ax.plot(t[ileft] * tplt['factor'], Qm[ileft] * yplt['factor'] - 5,
                         '<', color=color, label='left-bases' if add_to_legend else None)
                 ax.plot(t[iright] * tplt['factor'], Qm[iright] * yplt['factor'] - 10,
                         '>', color=color, label='right-bases' if add_to_legend else None)
                 ax.vlines(
                     x=t[ispikes] * tplt['factor'],
                     ymin=(Qm[ispikes] - properties['prominences']) * yplt['factor'],
                     ymax=Qm[ispikes] * yplt['factor'],
                     color=color, linestyles='dashed',
                     label='prominences' if add_to_legend else '')
                 ax.hlines(
                     y=properties['width_heights'] * yplt['factor'],
                     xmin=properties['left_ips'] * tplt['factor'],
                     xmax=properties['right_ips'] * tplt['factor'],
                     color=color, linestyles='dotted', label='half-widths' if add_to_legend else '')
         return add_to_legend
 
     @staticmethod
     def prepareTime(t, tplt):
         if tplt['onset'] > 0.0:
             tonset = t.min() - 0.05 * np.ptp(t)
             t = np.insert(t, 0, tonset)
         return t * tplt['factor']
 
     @staticmethod
     def addPatches(ax, tpatch_on, tpatch_off, tplt, color='#8A8A8A'):
         for i in range(tpatch_on.size):
             ax.axvspan(tpatch_on[i] * tplt['factor'], tpatch_off[i] * tplt['factor'],
                        edgecolor='none', facecolor=color, alpha=0.2)
 
     @staticmethod
-    def plotInset(inset_ax, t, y, tplt, yplt, line, color, lw):
-        inset_window = np.logical_and(t > (inset['xlims'][0] / tplt['factor']),
-                                      t < (inset['xlims'][1] / tplt['factor']))
-        inset_ax.plot(t[inset_window] * tplt['factor'], y[inset_window] * yplt['factor'],
-                      linewidth=lw, linestyle=line, color=color)
+    def plotInset(inset_ax, inset, t, y, tplt, yplt, line, color, lw):
+        inset_ax.plot(t, y, linewidth=lw, linestyle=line, color=color)
         return inset_ax
 
     @staticmethod
     def addInsetPatches(ax, inset_ax, inset, tpatch_on, tpatch_off, tplt, color):
+        return
+        tfactor = tplt['factor']
         ybottom, ytop = ax.get_ylim()
         cond_on = np.logical_and(tpatch_on > (inset['xlims'][0] / tfactor),
                                  tpatch_on < (inset['xlims'][1] / tfactor))
         cond_off = np.logical_and(tpatch_off > (inset['xlims'][0] / tfactor),
                                   tpatch_off < (inset['xlims'][1] / tfactor))
         cond_glob = np.logical_and(tpatch_on < (inset['xlims'][0] / tfactor),
                                    tpatch_off > (inset['xlims'][1] / tfactor))
         cond_onoff = np.logical_or(cond_on, cond_off)
         cond = np.logical_or(cond_onoff, cond_glob)
         npatches_inset = np.sum(cond)
         for i in range(npatches_inset):
             inset_ax.add_patch(Rectangle((tpatch_on[cond][i] * tfactor, ybottom),
                                          (tpatch_off[cond][i] - tpatch_on[cond][i]) *
                                          tfactor, ytop - ybottom, color=color,
                                          alpha=0.1))
 
 
 class CompTimeSeries(ComparativePlot, TimeSeriesPlot):
     ''' Interface to build a comparative plot displaying profiles of a specific output variable
         across different model simulations. '''
 
     def __init__(self, filepaths, varname):
         ''' Constructor.
 
             :param filepaths: list of full paths to output data files to be compared
             :param varname: name of variable to extract and compare
         '''
         ComparativePlot.__init__(self, filepaths, varname)
 
     def checkPatches(self, patches):
         greypatch = False
         if patches == 'none':
             patches = [False] * len(self.filepaths)
         elif patches == 'all':
             patches = [True] * len(self.filepaths)
         elif patches == 'one':
             patches = [True] + [False] * (len(self.filepaths) - 1)
             greypatch = True
         elif isinstance(patches, list):
             if len(patches) != len(self.filepaths):
                 raise ValueError(
                     'Invalid patches ({}): not matching number of compared files ({})'.format(
                         len(patches), len(self.filepaths)))
             if not all(isinstance(p, bool) for p in patches):
                 raise TypeError('Invalid patch sequence: all list items must be boolean typed')
         else:
             raise ValueError(
                 'Invalid patches: must be either "none", all", "one", or a boolean list')
         return patches, greypatch
 
     def checkInputs(self, lines, labels, colors, patches):
         self.checkLabels(labels)
         lines = self.checkLines(lines)
         colors = self.checkColors(colors)
         patches, greypatch = self.checkPatches(patches)
         return lines, labels, colors, patches, greypatch
 
     @staticmethod
     def createBackBone(figsize):
         fig, ax = plt.subplots(figsize=figsize)
         ax.set_zorder(0)
         return fig, ax
 
     @classmethod
     def postProcess(cls, ax, tplt, yplt, fs, meta, prettify):
         cls.removeSpines(ax)
         if 'bounds' in yplt:
             ax.set_ylim(*yplt['bounds'])
         cls.setTimeLabel(ax, tplt, fs)
         cls.setYLabel(ax, yplt, fs)
         if prettify:
             cls.prettify(ax, xticks=(0, meta['tstim'] * tplt['factor']))
         cls.setTickLabelsFontSize(ax, fs)
 
     def render(self, figsize=(11, 4), fs=10, lw=2, labels=None, colors=None, lines=None,
                patches='one', inset=None, frequency=1, spikes='none', cmap=None,
                cscale='lin', trange=None, prettify=False):
         ''' Render plot.
 
             :param figsize: figure size (x, y)
             :param fs: labels fontsize
             :param lw: linewidth
             :param labels: list of labels to use in the legend
             :param colors: list of colors to use for each curve
             :param lines: list of linestyles
             :param patches: string indicating whether/how to mark stimulation periods
                 with rectangular patches
             :param inset: string indicating whether/how to mark an inset zooming on
                 a particular region of the graph
             :param frequency: frequency at which to plot samples
             :param spikes: string indicating how to show spikes ("none", "marks" or "details")
             :param cmap: color map to use for colobar-based comparison (if not None)
             :param cscale: color scale to use for colobar-based comparison
             :param trange: optional lower and upper bounds to time axis
             :return: figure handle
         '''
         lines, labels, colors, patches, greypatch = self.checkInputs(
             lines, labels, colors, patches)
 
         fcodes = []
 
         fig, ax = self.createBackBone(figsize)
         if inset is not None:
             inset_ax = self.addInset(fig, ax, inset)
 
         # Loop through data files
         handles, comp_values, full_labels = [], [], []
         tmin, tmax = np.inf, -np.inf
         for j, filepath in enumerate(self.filepaths):
 
             # Load data
             try:
                 data, meta = self.getData(filepath, frequency, trange)
             except ValueError as err:
                 continue
             if 'tcomp' in meta:
                 meta.pop('tcomp')
             full_labels.append(self.figtitle(meta))
 
             # Extract model
             model = self.getModel(meta)
             fcodes.append(model.filecode(meta))
 
             # Check consistency of sim types and check differing inputs
             comp_values = self.checkConsistency(meta, comp_values)
 
             # Extract time and stim pulses
             t = data['t'].values
             stimstate = self.getStimStates(data)
             tpatch_on, tpatch_off = self.getStimPulses(t, stimstate)
             tplt = self.getTimePltVar(model.tscale)
             t = self.prepareTime(t, tplt)
 
             # Extract y-variable
             pltvars = model.getPltVars()
             if self.varname not in pltvars:
                 raise KeyError(
                     'Unknown plot variable: "{}". Possible plot variables are: {}'.format(
                         self.varname, ', '.join(['"{}"'.format(p) for p in pltvars.keys()])))
             yplt = pltvars[self.varname]
             y = extractPltVar(model, yplt, data, meta, t.size, self.varname)
 
             #  Plot time series
             handles.append(ax.plot(t, y, linewidth=lw, linestyle=lines[j], color=colors[j])[0])
 
             # Optional: add spikes
             if self.varname == 'Qm' and spikes != 'none':
                 self.materializeSpikes(ax, data, tplt, yplt, colors[j], spikes)
 
             # Plot optional inset
             if inset is not None:
-                inset_ax = self.plotInset(inset_ax, t, y, tplt, yplt, lines[j], colors[j], lw)
+                inset_ax = self.plotInset(inset_ax, inset, t, y, tplt, yplt, lines[j], colors[j], lw)
 
             # Add optional STIM-ON patches
             if patches[j]:
                 ybottom, ytop = ax.get_ylim()
                 color = '#8A8A8A' if greypatch else handles[j].get_color()
                 self.addPatches(ax, tpatch_on, tpatch_off, tplt, color)
                 if inset is not None:
                     self.addInsetPatches(ax, inset_ax, inset, tpatch_on, tpatch_off, tplt, color)
 
             tmin, tmax = min(tmin, t.min()), max(tmax, t.max())
 
         # Determine labels
         if self.comp_ref_key is not None:
             self.comp_info = model.inputs().get(self.comp_ref_key, None)
         comp_values, comp_labels = self.getCompLabels(comp_values)
         labels = self.chooseLabels(labels, comp_labels, full_labels)
 
         # - split labels by space
         merge_keys = ['neuron']
         common_label = self.getCommonLabel(full_labels.copy(), sep=' ', merge_keys=merge_keys)
         ax.set_title(common_label, fontsize=fs)
 
         # Post-process figure
         self.postProcess(ax, tplt, yplt, fs, meta, prettify)
         ax.set_xlim(tmin, tmax)
         fig.tight_layout()
 
         if inset is not None:
             self.materializeInset(ax, inset_ax, inset)
 
         # Add labels or colorbar legend
         if cmap is not None:
             if not self.is_unique_comp:
                 raise ValueError('Colormap mode unavailable for multiple differing parameters')
             if self.comp_info is None:
                 raise ValueError('Colormap mode unavailable for qualitative comparisons')
             self.addCmap(
                 fig, cmap, handles, comp_values, self.comp_info, fs, prettify, zscale=cscale)
         else:
             self.addLegend(fig, ax, handles, labels, fs)
 
         # Add window title based on common pattern
         common_fcode = self.getCommonLabel(fcodes.copy(), sep='_')
         fig.canvas.set_window_title(common_fcode)
 
         return fig
 
 
 class GroupedTimeSeries(TimeSeriesPlot):
     ''' Interface to build a plot displaying profiles of several output variables
         arranged into specific schemes. '''
 
     def __init__(self, filepaths, pltscheme=None):
         ''' Constructor.
 
             :param filepaths: list of full paths to output data files to be compared
             :param varname: name of variable to extract and compare
         '''
         super().__init__(filepaths)
         self.pltscheme = pltscheme
 
     @staticmethod
     def createBackBone(pltscheme):
         naxes = len(pltscheme)
         if naxes == 1:
             fig, ax = plt.subplots(figsize=(11, 4))
             axes = [ax]
         else:
             fig, axes = plt.subplots(naxes, 1, figsize=(11, min(3 * naxes, 9)))
         return fig, axes
 
     @classmethod
     def postProcess(cls, axes, tplt, fs, meta, prettify):
         for ax in axes:
             cls.removeSpines(ax)
             # if prettify:
             #     cls.prettify(ax, xticks=(0, meta['tstim'] * tplt['factor']), yfmt=None)
             cls.setTickLabelsFontSize(ax, fs)
         for ax in axes[:-1]:
             ax.set_xticklabels([])
         cls.setTimeLabel(axes[-1], tplt, fs)
 
     def render(self, fs=10, lw=2, labels=None, colors=None, lines=None, patches='one', save=False,
                outputdir=None, fig_ext='png', frequency=1, spikes='none', trange=None,
                prettify=False):
         ''' Render plot.
 
             :param fs: labels fontsize
             :param lw: linewidth
             :param labels: list of labels to use in the legend
             :param colors: list of colors to use for each curve
             :param lines: list of linestyles
             :param patches: boolean indicating whether to mark stimulation periods
                 with rectangular patches
             :param save: boolean indicating whether or not to save the figure(s)
             :param outputdir: path to output directory in which to save figure(s)
             :param fig_ext: string indcating figure extension ("png", "pdf", ...)
             :param frequency: frequency at which to plot samples
             :param spikes: string indicating how to show spikes ("none", "marks" or "details")
             :param trange: optional lower and upper bounds to time axis
             :return: figure handle(s)
         '''
 
         figs = []
         for filepath in self.filepaths:
 
             # Load data and extract model
             try:
                 data, meta = self.getData(filepath, frequency, trange)
             except ValueError as err:
                 continue
             model = self.getModel(meta)
 
             # Extract time and stim pulses
             t = data['t'].values
             stimstate = self.getStimStates(data)
 
             tpatch_on, tpatch_off = self.getStimPulses(t, stimstate)
             tplt = self.getTimePltVar(model.tscale)
             t = self.prepareTime(t, tplt)
 
             # Check plot scheme if provided, otherwise generate it
             pltvars = model.getPltVars()
             if self.pltscheme is not None:
                 for key in list(sum(list(self.pltscheme.values()), [])):
                     if key not in pltvars:
                         raise KeyError('Unknown plot variable: "{}"'.format(key))
                 pltscheme = self.pltscheme
             else:
                 pltscheme = model.getPltScheme()
 
             # Create figure
             fig, axes = self.createBackBone(pltscheme)
 
             # Loop through each subgraph
             for ax, (grouplabel, keys) in zip(axes, pltscheme.items()):
                 ax_legend_spikes = False
 
                 # Extract variables to plot
                 nvars = len(keys)
                 ax_pltvars = [pltvars[k] for k in keys]
                 if nvars == 1:
                     ax_pltvars[0]['color'] = 'k'
                     ax_pltvars[0]['ls'] = '-'
 
                 # Set y-axis unit and bounds
                 self.setYLabel(ax, ax_pltvars[0].copy(), fs, grouplabel=grouplabel)
                 if 'bounds' in ax_pltvars[0]:
                     ax_min = min([ap['bounds'][0] for ap in ax_pltvars])
                     ax_max = max([ap['bounds'][1] for ap in ax_pltvars])
                     ax.set_ylim(ax_min, ax_max)
 
                 # Plot time series
                 icolor = 0
                 for yplt, name in zip(ax_pltvars, pltscheme[grouplabel]):
                     color = yplt.get('color', 'C{}'.format(icolor))
                     y = extractPltVar(model, yplt, data, meta, t.size, name)
                     ax.plot(t, y, yplt.get('ls', '-'), c=color, lw=lw,
                             label='$\\rm {}$'.format(yplt['label']))
                     if 'color' not in yplt:
                         icolor += 1
 
                     # Optional: add spikes
                     if name == 'Qm' and spikes != 'none':
                         ax_legend_spikes = self.materializeSpikes(
                             ax, data, tplt, yplt, color, spikes, add_to_legend=True)
 
                 # Add legend
                 if nvars > 1 or 'gate' in ax_pltvars[0]['desc'] or ax_legend_spikes:
                     ax.legend(fontsize=fs, loc=7, ncol=nvars // 4 + 1, frameon=False)
 
             # Set x-limits and add optional patches
             for ax in axes:
                 ax.set_xlim(t.min(), t.max())
                 if patches != 'none':
                     self.addPatches(ax, tpatch_on, tpatch_off, tplt)
 
             # Post-process figure
             self.postProcess(axes, tplt, fs, meta, prettify)
             axes[0].set_title(self.figtitle(meta), fontsize=fs)
             fig.tight_layout()
 
             fig.canvas.set_window_title(model.filecode(meta))
 
             # Save figure if needed (automatic or checked)
             if save:
                 filecode = model.filecode(meta)
                 if outputdir is None:
                     outputdir = os.path.split(filepath)[0]
                 plt_filename = '{}/{}.{}'.format(outputdir, filecode, fig_ext)
                 plt.savefig(plt_filename)
                 logger.info('Saving figure as "{}"'.format(plt_filename))
                 plt.close()
 
             figs.append(fig)
         return figs
 
 
 if __name__ == '__main__':
     # example of use
     filepaths = OpenFilesDialog('pkl')[0]
     comp_plot = CompTimeSeries(filepaths, 'Qm')
     fig = comp_plot.render(
         lines=['-', '--'],
         labels=['60 kPa', '80 kPa'],
         patches='one',
         colors=['r', 'g'],
         xticks=[0, 100],
         yticks=[-80, +50],
         inset={'xcoords': [5, 40], 'ycoords': [-35, 45], 'xlims': [57.5, 60.5], 'ylims': [10, 35]}
     )
 
     scheme_plot = GroupedTimeSeries(filepaths)
     figs = scheme_plot.render()
 
     plt.show()
diff --git a/PySONIC/postpro.py b/PySONIC/postpro.py
index 3619615..cd73f49 100644
--- a/PySONIC/postpro.py
+++ b/PySONIC/postpro.py
@@ -1,414 +1,416 @@
 # -*- coding: utf-8 -*-
 # @Author: Theo Lemaire
 # @Email: theo.lemaire@epfl.ch
 # @Date:   2017-08-22 14:33:04
 # @Last Modified by:   Theo Lemaire
-# @Last Modified time: 2019-09-06 14:37:30
+# @Last Modified time: 2019-09-30 18:01:49
 
 ''' Utility functions to detect spikes on signals and compute spiking metrics. '''
 
 import pickle
 import numpy as np
 import pandas as pd
 from scipy.interpolate import interp1d
 from scipy.optimize import brentq
 from scipy.signal import find_peaks, peak_prominences
 
 from .constants import *
 from .utils import logger, debug, isIterable, loadData, plural
 
 
 def detectCrossings(x, thr=0.0, edge='both'):
     '''
         Detect crossings of a threshold value in a 1D signal.
 
         :param x: 1D array_like data.
         :param edge: 'rising', 'falling', or 'both'
         :return: 1D array with the indices preceding the crossings
     '''
     ine, ire, ife = np.array([[], [], []], dtype=int)
     x_padright = np.hstack((x, x[-1]))
     x_padleft = np.hstack((x[0], x))
     if edge.lower() in ['falling', 'both']:
         ire = np.where((x_padright <= thr) & (x_padleft > thr))[0]
     if edge.lower() in ['rising', 'both']:
         ife = np.where((x_padright >= thr) & (x_padleft < thr))[0]
     ind = np.unique(np.hstack((ine, ire, ife))) - 1
     return ind
 
 
 def getFixedPoints(x, dx, filter='stable', der_func=None):
     ''' Find fixed points in a 1D plane phase profile.
 
         :param x: variable (1D array)
         :param dx: derivative (1D array)
         :param filter: string indicating whether to consider only stable/unstable
             fixed points or both
         :param: der_func: derivative function
         :return: array of fixed points values (or None if none is found)
     '''
     fps = []
     edge = {'stable': 'falling', 'unstable': 'rising', 'both': 'both'}[filter]
     izc = detectCrossings(dx, edge=edge)
     if izc.size > 0:
         for i in izc:
             # If derivative function is provided, find root using iterative Brent method
             if der_func is not None:
                 fps.append(brentq(der_func, x[i], x[i + 1], xtol=1e-16))
 
             # Otherwise, approximate root by linear interpolation
             else:
                 fps.append(x[i] - dx[i] * (x[i + 1] - x[i]) / (dx[i + 1] - dx[i]))
         return np.array(fps)
     else:
         return np.array([])
 
 
 def getEqPoint1D(x, dx, x0):
     ''' Determine equilibrium point in a 1D plane phase profile, for a given starting point.
 
         :param x: variable (1D array)
         :param dx: derivative (1D array)
         :param x0: abscissa of starting point (float)
         :return: abscissa of equilibrium point (or np.nan if none is found)
     '''
 
     # Find stable fixed points in 1D plane phase profile
     x_SFPs = getFixedPoints(x, dx, filter='stable')
     if x_SFPs.size == 0:
         return np.nan
 
     # Determine relevant stable fixed point from y0 sign
     y0 = np.interp(x0, x, dx, left=np.nan, right=np.nan)
     inds_subset = x_SFPs >= x0
     ind_SFP = 0
     if y0 < 0:
         inds_subset = ~inds_subset
         ind_SFP = -1
     x_SFPs = x_SFPs[inds_subset]
 
     if len(x_SFPs) == 0:
         return np.nan
 
     return x_SFPs[ind_SFP]
 
 
 def convertTime2SampleCriterion(x, dt, nsamples):
     if isIterable(x) and len(x) == 2:
         return (convertTime2Sample(x[0], dt, nsamples), convertTime2Sample(x[1], dt, nsamples))
     else:
         if isIterable(x) and len(x) == nsamples:
             return np.array([convertTime2Sample(item, dt, nsamples) for item in x])
         elif x is None:
             return None
         else:
             return int(np.ceil(x / dt))
 
 def computeTimeStep(t):
     ''' Compute time step based on time vector.
 
         :param t: time vector (s)
         :return: average time step (s)
     '''
 
     # Compute time step vector
     dt = np.diff(t)  # s
 
     # Raise error if time step vector is not uniform
     is_uniform_dt = np.allclose(np.diff(dt), np.zeros(dt.size - 1), atol=1e-5)
     if not is_uniform_dt:
         raise ValueError(f'non-uniform time step: from {dt.min():.2e} s (index {dt.argmin()}) to {dt.max():.2e} s (index {dt.argmax()})')
 
     # Return average dt value
     return np.mean(dt)  # s
 
 
 def resample(t, y, dt):
     ''' Resample a dataframe at regular time step. '''
     n = int(np.ptp(t) / dt) + 1
     ts = np.linspace(t.min(), t.max(), n)
     ys = np.interp(ts, t, y)
     return ts, ys
 
 
 def resolveIndexes(indexes, y, choice='max'):
     if indexes.size == 0:
         return indexes
     icomp = np.array([np.floor(indexes), np.ceil(indexes)]).astype(int).T
     ycomp = np.array([y[i] for i in icomp])
     method = {'min': np.argmin, 'max': np.argmax}[choice]
     ichoice = method(ycomp, axis=1)
     return np.array([x[ichoice[i]] for i, x in enumerate(icomp)])
 
 
 def resampleDataFrame(data, dt):
     ''' Resample a dataframe at regular time step. '''
     t = data['t'].values
     n = int(np.ptp(t) / dt) + 1
     tnew = np.linspace(t.min(), t.max(), n)
     new_data = {}
     for key in data:
         kind = 'nearest' if key == 'stimstate' else 'linear'
         new_data[key] = interp1d(t, data[key].values, kind=kind)(tnew)
     return pd.DataFrame(new_data)
 
 
 def prependDataFrame(data):
     ''' Resample dataframe at regular time step. '''
     tnew = np.insert(data['t'].values, 0, 0)
     new_data = {'t': tnew}
     for key in data:
         if key == 't':
             x0 = 0.
         elif key == 'stimstate':
             x0 = 0
         else:
             x0 = data[key].values[0]
         new_data[key] = np.insert(data[key].values, 0, x0)
     return pd.DataFrame(new_data)
 
 def find_tpeaks(t, y, **kwargs):
     ''' Wrapper around the scipy.signal.find_peaks function that provides a time vector
         associated to the signal, and translates time-based selection criteria into
         index-based criteria before calling the function.
 
         :param t: time vector
         :param y: signal vector
         :return: 2-tuple with peaks timings and properties dictionary
     '''
 
     # Remove initial samples from vectors if time values are redundant
     ipad = 0
     while t[ipad + 1] == t[ipad]:
         ipad += 1
     if ipad > 0:
         ss = 'from vectors (redundant time values)'
         if ipad == 1:
             logger.debug(f'Removing index 0 {ss}')
         else:
             logger.debug(f'Removing indexes 0-{ipad - 1} {ss}')
         t = t[ipad:]
         y = y[ipad:]
 
     # If time step is irregular, resample vectors at a uniform time step
     try:
         dt = computeTimeStep(t)  # s
         t_raw, y_raw = None, None
         indexes_raw = None
     except ValueError as err:
         new_dt = max(np.diff(t).min(), 1e-7)
         logger.debug(f'Resampling vector at regular time step (dt = {new_dt:.2e}s)')
         t_raw, y_raw = t.copy(), y.copy()
         indexes_raw = np.arange(t_raw.size)
         t, y = resample(t, y, new_dt)
         dt = computeTimeStep(t)  # s
         ipad = 0
 
     # Compute index vector
     nsamples = t.size
     indexes = np.arange(nsamples)
 
     # Convert provided time-based input criteria into samples-based criteria
     time_based_inputs = ['distance', 'width', 'wlen', 'plateau_size']
     for key in time_based_inputs:
         if key in kwargs:
             kwargs[key] = convertTime2SampleCriterion(kwargs[key], dt, nsamples)
     if 'width' not in kwargs:
         kwargs['width'] = 1
 
     # Find peaks in the regularly sampled signal
     ipeaks, properties = find_peaks(y, **kwargs)
     # print(t[ipeaks] * 1e3)
 
     # Adjust peak prominences and bases with restricted analysis window length
     # based on smallest peak width
     if len(ipeaks) > 0:
         wlen = 5 * min(properties['widths'])
         properties['prominences'], properties['left_bases'], properties['right_bases'] = peak_prominences(
             y, ipeaks, wlen=wlen)
 
     # If needed, re-project index-based outputs onto original sampling
     if t_raw is not None:
         logger.debug(f're-projecting index-based outputs onto original sampling')
 
         # Interpolate peak indexes and round to neighbor integer with max y value
         ipeaks_raw = np.interp(t[ipeaks], t_raw, indexes_raw, left=np.nan, right=np.nan)
         ipeaks = resolveIndexes(ipeaks_raw, y_raw, choice='max')
         # print(t_raw[ipeaks] * 1e3)
 
         # Interpolate peak base indexes and round to neighbor integer with min y value
         for key in ['left_bases', 'right_bases']:
             if key in properties:
                 # print(key)
                 # print(t[properties[key]] * 1e3)
                 ibase_raw = np.interp(t[properties[key]], t_raw, indexes_raw, left=np.nan, right=np.nan)
                 properties[key] = resolveIndexes(ibase_raw, y_raw, choice='min')
                 # print(t_raw[properties[key]] * 1e3)
 
         # Interpolate peak half-width interpolated positions
         for key in ['left_ips', 'right_ips']:
             if key in properties:
                 # print(key)
                 # print(properties[key])
                 # print(dt * properties[key] * 1e3)
                 properties[key] = np.interp(dt * properties[key], t_raw, indexes_raw, left=np.nan, right=np.nan)
                 # print(properties[key])
 
     # If original vectors were cropped, correct offset in index-based outputs
     if ipad > 0:
         logger.debug(f'offseting index-based outputs by {ipad} to compensate initial cropping')
         ipeaks += ipad
         for key in ['left_bases', 'right_bases', 'left_ips', 'right_ips']:
             if key in properties:
                 properties[key] += ipad
 
     # Convert index-based peak widths into time-based widths
     if 'widths' in properties:
         properties['widths'] = np.array(properties['widths']) * dt
 
     # Return updated properties
     return ipeaks, properties
 
 
 def detectSpikes(data, key='Qm', mpt=SPIKE_MIN_DT, mph=SPIKE_MIN_QAMP, mpp=SPIKE_MIN_QPROM):
     ''' Detect spikes in simulation output data, by detecting peaks with specific height, prominence
         and distance properties on a given signal.
 
         :param data: simulation output dataframe
         :param key: key of signal on which to detect peaks
         :param mpt: minimal time interval between two peaks (s)
         :param mph: minimal peak height (in signal units)
         :param mpp: minimal peak prominence (in signal units)
         :return: indexes and properties of detected spikes
     '''
     if key not in data:
         raise ValueError(f'{key} vector not available in dataframe')
 
     # Detect peaks
     return find_tpeaks(
         data['t'].values,
         data[key].values,
         height=mph,
         distance=mpt,
         prominence=mpp
     )
 
 
 def convertPeaksProperties(t, properties):
     ''' Convert index-based peaks properties into time-based properties.
 
         :param t: time vector (s)
         :param properties: properties dictionary (with index-based information)
         :return: properties dictionary (with time-based information)
     '''
     indexes = np.arange(t.size)
     for key in ['left_bases', 'right_bases','left_ips', 'right_ips']:
         if key in properties:
             properties[key] = np.interp(properties[key], indexes, t, left=np.nan, right=np.nan)
     return properties
 
 
 def computeFRProfile(data):
     ''' Compute temporal profile of firing rate from simulaton output.
 
         :param data: simulation output dataframe
         :return: firing rate profile interpolated along time vector
     '''
     # Detect spikes in data
     ispikes, _ = detectSpikes(data)
 
     # Compute firing rate as function of spike time
     t = data['t'].values
     tspikes = t[ispikes][:-1]
     sr = 1 / np.diff(t[ispikes])
+    if len(sr) == 0:
+        return np.ones(t.size) * np.nan
 
     # Interpolate firing rate vector along time vector
     return np.interp(t, tspikes, sr, left=np.nan, right=np.nan)
 
 
 def computeSpikingMetrics(filenames):
     ''' Analyze the charge density profile from a list of files and compute for each one of them
         the following spiking metrics:
         - latency (ms)
         - firing rate mean and standard deviation (Hz)
         - spike amplitude mean and standard deviation (nC/cm2)
         - spike width mean and standard deviation (ms)
 
         :param filenames: list of files to analyze
         :return: a dataframe with the computed metrics
     '''
 
     # Initialize metrics dictionaries
     keys = [
         'latencies (ms)',
         'mean firing rates (Hz)',
         'std firing rates (Hz)',
         'mean spike amplitudes (nC/cm2)',
         'std spike amplitudes (nC/cm2)',
         'mean spike widths (ms)',
         'std spike widths (ms)'
     ]
     metrics = {k: [] for k in keys}
 
     # Compute spiking metrics
     for fname in filenames:
 
         # Load data from file
         data, meta = loadData(fname)
         tstim = meta['tstim']
         t = data['t'].values
 
         # Detect spikes in data and extract features
         ispikes, properties = detectSpikes(data)
         widths = properties['widths']
         prominences = properties['prominences']
 
         if ispikes.size > 0:
             # Compute latency
             latency = t[ispikes[0]]
 
             # Select prior-offset spikes
             ispikes_prior = ispikes[t[ispikes] < tstim]
         else:
             latency = np.nan
             ispikes_prior = np.array([])
 
         # Compute spikes widths and amplitude
         if ispikes_prior.size > 0:
             widths_prior = widths[:ispikes_prior.size]
             prominences_prior = prominences[:ispikes_prior.size]
         else:
             widths_prior = np.array([np.nan])
             prominences_prior = np.array([np.nan])
 
         # Compute inter-spike intervals and firing rates
         if ispikes_prior.size > 1:
             ISIs_prior = np.diff(t[ispikes_prior])
             FRs_prior = 1 / ISIs_prior
         else:
             ISIs_prior = np.array([np.nan])
             FRs_prior = np.array([np.nan])
 
         # Log spiking metrics
         logger.debug('%u spikes detected (%u prior to offset)', ispikes.size, ispikes_prior.size)
         logger.debug('latency: %.2f ms', latency * 1e3)
         logger.debug('average spike width within stimulus: %.2f +/- %.2f ms',
                      np.nanmean(widths_prior) * 1e3, np.nanstd(widths_prior) * 1e3)
         logger.debug('average spike amplitude within stimulus: %.2f +/- %.2f nC/cm2',
                      np.nanmean(prominences_prior) * 1e5, np.nanstd(prominences_prior) * 1e5)
         logger.debug('average ISI within stimulus: %.2f +/- %.2f ms',
                      np.nanmean(ISIs_prior) * 1e3, np.nanstd(ISIs_prior) * 1e3)
         logger.debug('average FR within stimulus: %.2f +/- %.2f Hz',
                      np.nanmean(FRs_prior), np.nanstd(FRs_prior))
 
         # Complete metrics dictionaries
         metrics['latencies (ms)'].append(latency * 1e3)
         metrics['mean firing rates (Hz)'].append(np.mean(FRs_prior))
         metrics['std firing rates (Hz)'].append(np.std(FRs_prior))
         metrics['mean spike amplitudes (nC/cm2)'].append(np.mean(prominences_prior) * 1e5)
         metrics['std spike amplitudes (nC/cm2)'].append(np.std(prominences_prior) * 1e5)
         metrics['mean spike widths (ms)'].append(np.mean(widths_prior) * 1e3)
         metrics['std spike widths (ms)'].append(np.std(widths_prior) * 1e3)
 
     # Return dataframe with metrics
     return pd.DataFrame(metrics, columns=metrics.keys())