From b005b04937da00d3de25a463490c55387920a156 Mon Sep 17 00:00:00 2001
From: nkoch1 <nils@ualberta.ca>
Date: Sat, 24 Sep 2022 16:35:13 +0200
Subject: [PATCH] added figure plotting scripts *_letters.py to plot using
 models named by letter

---
 Figures/AUC_correlation.pdf                   | Bin 61647 -> 61046 bytes
 Figures/AUC_correlation.py                    |  15 +
 Figures/AUC_correlation_letters.py            | 543 +++++++++++++++
 Figures/ramp_firing_letters.py                | 322 +++++++++
 Figures/rheobase_correlation.pdf              | Bin 58828 -> 59113 bytes
 Figures/rheobase_correlation.py               |  57 +-
 Figures/rheobase_correlation_letters.py       | 648 ++++++++++++++++++
 Figures/simulation_model_comparison.pdf       | Bin 43179 -> 41586 bytes
 .../simulation_model_comparison_letters.py    | 259 +++++++
 manuscript.tex                                |  14 +-
 10 files changed, 1837 insertions(+), 21 deletions(-)
 create mode 100644 Figures/AUC_correlation_letters.py
 create mode 100644 Figures/ramp_firing_letters.py
 create mode 100644 Figures/rheobase_correlation_letters.py
 create mode 100644 Figures/simulation_model_comparison_letters.py

diff --git a/Figures/AUC_correlation.pdf b/Figures/AUC_correlation.pdf
index 2f3b0b183b8bcb624518dad4268bbc51ab997cd2..78af3e339bf103ed5aa5fb23eb7b7e8e0f24703c 100644
GIT binary patch
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diff --git a/Figures/AUC_correlation.py b/Figures/AUC_correlation.py
index ea2f11c..de09aa9 100644
--- a/Figures/AUC_correlation.py
+++ b/Figures/AUC_correlation.py
@@ -389,6 +389,21 @@ color_dict = {'Cb stellate': '#40A787', # cyan'#
               'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770',  # magenta
               'STN':  '#D03050'  # pink
               }
+model_letter = {
+'Cb stellate': 'A',
+'RS Inhibitory': 'B',
+'FS': 'C',
+'RS Pyramidal': 'D',
+'RS Inhibitory': 'E',
+'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'F',
+'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'G',
+'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'H',
+'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'I',
+'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'J',
+'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'K',
+'STN':'L',
+}
+
 
 # plot setup
 marker_s_leg = 2
diff --git a/Figures/AUC_correlation_letters.py b/Figures/AUC_correlation_letters.py
new file mode 100644
index 0000000..cd49d06
--- /dev/null
+++ b/Figures/AUC_correlation_letters.py
@@ -0,0 +1,543 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul  3 19:52:04 2021
+
+@author: nils
+"""
+import pandas as pd
+import numpy as np
+import string
+import textwrap
+import json
+import matplotlib
+import matplotlib.lines as mlines
+from matplotlib import ticker
+from matplotlib.ticker import NullFormatter
+from Figures.plotstyle import boxplot_style
+
+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)
+#%% ##################### From https://stackoverflow.com/questions/52878845/swarmplot-with-hue-affecting-marker-beyond-color ##
+# to change marker types in seaborn swarmplot
+
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+############## Begin hack ##############
+from matplotlib.axes._axes import Axes
+from matplotlib.markers import MarkerStyle
+from numpy import ndarray
+
+def GetColor2Marker(markers):
+    colorslist = ['#40A787',  # cyan'#
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                  '#007030',  # dark green
+                  '#AAB71B',  # lightgreen
+                  '#008797',  # light blue
+                  '#F78017',  # orange
+                  '#478010',  # green
+                  '#53379B',  # purple
+                  '#2060A7',  # blue
+                  '#873770',  # magenta
+                  '#D03050'  # pink
+                  ]
+    import matplotlib.colors
+    palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    mkcolors = [(palette[i]) for i in range(len(markers))]
+    return dict(zip(mkcolors,markers))
+
+def fixlegend(ax,markers,markersize=3,**kwargs):
+    # Fix Legend
+    legtitle =  ax.get_legend().get_title().get_text()
+    _,l = ax.get_legend_handles_labels()
+    colorslist = ['#40A787',  # cyan'#
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                  '#007030',  # dark green
+                  '#AAB71B',  # lightgreen
+                  '#008797',  # light blue
+                  '#F78017',  # orange
+                  '#478010',  # green
+                  '#53379B',  # purple
+                  '#2060A7',  # blue
+                  '#873770',  # magenta
+                  '#D03050'  # pink
+                  ]
+    import matplotlib.colors
+    palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    mkcolors = [(palette[i]) for i in range(len(markers))]
+    newHandles = [plt.Line2D([0],[0], ls="none", marker=m, color=c, mec="none", markersize=markersize,**kwargs) \
+                for m,c in zip(markers, mkcolors)]
+    ax.legend(newHandles,l)
+    leg = ax.get_legend()
+    leg.set_title(legtitle)
+
+old_scatter = Axes.scatter
+def new_scatter(self, *args, **kwargs):
+    colors = kwargs.get("c", None)
+    co2mk = kwargs.pop("co2mk",None)
+    FinalCollection = old_scatter(self, *args, **kwargs)
+    if co2mk is not None and isinstance(colors, ndarray):
+        Color2Marker = GetColor2Marker(co2mk)
+        paths=[]
+        for col in colors:
+            mk=Color2Marker[tuple(col)]
+            marker_obj = MarkerStyle(mk)
+            paths.append(marker_obj.get_path().transformed(marker_obj.get_transform()))
+        FinalCollection.set_paths(paths)
+    return FinalCollection
+Axes.scatter = new_scatter
+############## End hack. ##############
+########################################################################################################################
+#%% add gradient arrows
+import matplotlib.pyplot as plt
+import matplotlib.transforms
+import matplotlib.path
+from matplotlib.collections import LineCollection
+
+def gradientaxis(ax, start, end, cmap, n=100,lw=1):
+    # Arrow shaft: LineCollection
+    x = np.linspace(start[0],end[0],n)
+    y = np.linspace(start[1],end[1],n)
+    points = np.array([x,y]).T.reshape(-1,1,2)
+    segments = np.concatenate([points[:-1],points[1:]], axis=1)
+    lc = LineCollection(segments, cmap=cmap, linewidth=lw,zorder=15)
+    lc.set_array(np.linspace(0,1,n))
+    ax.add_collection(lc)
+    return ax
+#%%
+#%%
+def boxplot_with_markers(ax,max_width, alteration='shift', msize=3):
+    hlinewidth = 0.5
+    model_names = ['RS pyramidal','RS inhibitory','FS',
+                                  'RS pyramidal +$K_V1.1$','RS inhibitory +$K_V1.1$',
+                'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
+          'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
+          'STN $\Delta$$K_V1.1$']
+
+    colorslist = ['#007030',  # dark green
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                    '#478010',  # green
+                  '#AAB71B',  # lightgreen
+                '#F78017',  # orange
+                  '#40A787',  # cyan'#
+                  '#008797',  # light blue
+                '#2060A7',  # blue
+                  '#D03050',  # pink
+                  '#53379B',  # purple
+                  '#873770',  # magenta
+                  ]
+
+
+    import matplotlib.colors
+    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    clr_dict = {}
+    for m in range(len(model_names)):
+        clr_dict[model_names[m]] = colors[m]
+    print(colors)
+    print(clr_dict)
+    Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
+    if alteration=='shift':
+        i = 2  # Kd act
+        ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor = 'k')
+        df = pd.read_csv('./Figures/Data/AUC_shift_corr.csv')
+        sns.swarmplot(y="corr", x="$\Delta V_{1/2}$", hue="model", data=df,
+                      palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
+                      order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
+                             '$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
+                      hue_order=model_names, co2mk=Markers)
+        lim = ax.get_xlim()
+        ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
+        ax.set_title("Shift ($\Delta V_{1/2}$)", y=1.05)
+        ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
+                            '$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
+    elif alteration=='slope':
+        i = 3  # Kv1.1 act
+        ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
+        df = pd.read_csv('./Figures/Data/AUC_scale_corr.csv')
+
+        # Add in points to show each observation
+        sns.swarmplot(y="corr", x="Slope (k)", hue="model", data=df,
+                      palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
+                      order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
+                             '$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
+                      hue_order=model_names, co2mk=Markers)
+        lim = ax.get_xlim()
+        ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
+        ax.set_title("Slope (k)", y=1.05)
+        ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
+                            '$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
+    elif alteration=='g':
+        i = 1  # Kd
+        ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
+        df = pd.read_csv('./Figures/Data/AUC_g_corr.csv')
+
+        # Add in points to show each observation
+        sns.swarmplot(y="corr", x="g", hue="model", data=df,
+                      palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
+                      order=['Na', 'K', '$K_V1.1$', 'A', 'Leak'],
+                      hue_order=model_names, co2mk=Markers)
+        lim = ax.get_xlim()
+        # ax.plot([lim[0], lim[1]], [0,0], ':k')
+        ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
+        # Tweak the visual presentation
+        ax.set_title("Conductance (g)", y=1.05)
+        ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
+    else:
+        print('Please chose "shift", "slope" or "g"')
+    ax.get_legend().remove()
+    ax.xaxis.grid(False)
+    sns.despine(trim=True, bottom=True, ax=ax)
+    ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
+
+
+def model_legend(ax, marker_s_leg, pos, ncol):
+    # colorslist = [  '#40A787', # cyan'#
+    #     '#F0D730',  # yellow
+    #     '#C02717',  # red
+    #     '#007030',  # dark green
+    #     '#AAB71B',  # lightgreen
+    #     '#008797',  # light blue
+    #     '#F78017',  # orange
+    #     '#478010',  # green
+    #     '#53379B',  # purple
+    #     '#2060A7',  # blue
+    #     '#873770',  # magenta
+    #     '#D03050'  # pink
+    # ]
+    colorslist = ['#007030',  # dark green
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                  '#478010',  # green
+                  '#AAB71B',  # lightgreen
+                  '#F78017',  # orange
+                  '#40A787',  # cyan'#
+                  '#008797',  # light blue
+                  '#2060A7',  # blue
+                  '#D03050',  # pink
+                  '#53379B',  # purple
+                  '#873770',  # magenta
+                  ]
+
+    import matplotlib.colors
+    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
+              'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
+              'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
+              'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
+              'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
+              'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
+    Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
+    # RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']], markersize=marker_s_leg, linestyle='None',
+    #                      label='Model D')
+    # RS_i = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory']], marker=Markers[model_pos['RS Inhibitory']], markersize=marker_s_leg, linestyle='None',
+    #                      label='Model B')
+    # FS = mlines.Line2D([], [], color=colors[model_pos['FS']], marker=Markers[model_pos['FS']], markersize=marker_s_leg, linestyle='None', label='Model C')
+    # RS_p_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
+    #                      label='Model H')
+    # RS_i_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
+    #                      label='Model E')
+    # FS_Kv = mlines.Line2D([], [], color=colors[model_pos['Cb stellate']], marker=Markers[model_pos['Cb stellate']], markersize=marker_s_leg, linestyle='None', label='Model G')
+    # Cb = mlines.Line2D([], [], color=colors[8], marker=Markers[8], markersize=marker_s_leg, linestyle='None',
+    #                    label='Model A')
+    # Cb_pl = mlines.Line2D([], [], color=colors[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
+    #                       label='Model F')
+    # Cb_sw = mlines.Line2D([], [], color=colors[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
+    #                       label='Model J')
+    # STN = mlines.Line2D([], [], color=colors[model_pos['STN']], marker=Markers[model_pos['STN']], markersize=marker_s_leg, linestyle='None', label='Model L')
+    # STN_pl = mlines.Line2D([], [], color=colors[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
+    #                        label='Model I')
+    # STN_sw = mlines.Line2D([], [], color=colors[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
+    #                        label='Model K')
+
+
+
+    RS_p = mlines.Line2D([], [], color='#007030', marker="^",
+                         markersize=marker_s_leg, linestyle='None',
+                         label='Model D')
+    RS_i = mlines.Line2D([], [], color='#F0D730', marker="o",
+                         markersize=marker_s_leg, linestyle='None',
+                         label='Model B')
+    FS = mlines.Line2D([], [], color='#C02717', marker="o", markersize=marker_s_leg,
+                       linestyle='None', label='Model C')
+    RS_p_Kv = mlines.Line2D([], [], color='#478010',
+                            marker="D",
+                            markersize=marker_s_leg, linestyle='None',
+                         label='Model H')
+    RS_i_Kv = mlines.Line2D([], [], color='#AAB71B',
+                            marker="^",
+                            markersize=marker_s_leg, linestyle='None',
+                         label='Model E')
+    FS_Kv = mlines.Line2D([], [], color='#F78017',
+                          marker="D", markersize=marker_s_leg,
+                          linestyle='None', label='Model G')
+    Cb = mlines.Line2D([], [], color='#40A787', marker="o",
+                       markersize=marker_s_leg, linestyle='None',
+                       label='Model A')
+    Cb_pl = mlines.Line2D([], [], color='#008797',
+                          marker="^",
+                          markersize=marker_s_leg, linestyle='None',
+                          label='Model F')
+    Cb_sw = mlines.Line2D([], [], color='#2060A7',
+                          marker="s",
+                          markersize=marker_s_leg, linestyle='None',
+                          label='Model J')
+    STN = mlines.Line2D([], [], color='#D03050', marker="s", markersize=marker_s_leg,
+                        linestyle='None', label='Model L')
+    STN_pl = mlines.Line2D([], [], color='#53379B',
+                           marker="D",
+                           markersize=marker_s_leg, linestyle='None',
+                           label='Model I')
+    STN_sw = mlines.Line2D([], [], color='#873770',
+                           marker="s",
+                           markersize=marker_s_leg, linestyle='None',
+                           label='Model K')
+    # ax.legend(handles=[RS_p, RS_i, FS, RS_p_Kv, RS_i_Kv, FS_Kv, Cb, Cb_pl, Cb_sw, STN, STN_pl, STN_sw], loc='center',
+    #               bbox_to_anchor=pos, ncol=ncol, frameon=False)
+    ax.legend(handles=[Cb, RS_i, FS, RS_p, RS_i_Kv, Cb_pl, FS_Kv, RS_p_Kv, STN_pl, Cb_sw, STN_sw, STN], loc='center',
+              bbox_to_anchor=pos, ncol=ncol, frameon=False)
+
+def plot_AUC_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='shift'):
+    ax.spines["right"].set_visible(False)
+    ax.spines["top"].set_visible(False)
+    model_names = ['RS Pyramidal','RS Inhibitory','FS',
+                                  'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
+                'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
+          'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
+          'STN $\Delta$$K_V1.1$']
+
+    model_name_dict = {'RS Pyramidal': 'RS Pyramidal',
+                       'RS Inhibitory': 'RS Inhibitory',
+                       'FS': 'FS',
+                       'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Pyramidal +$K_V1.1$',
+                       'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Inhibitory +$K_V1.1$',
+                       'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS +$K_V1.1$',
+                       'Cb stellate': 'Cb stellate',
+                       'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate +$K_V1.1$',
+                       'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate $\Delta$$K_V1.1$',
+                       'STN': 'STN',
+                       'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN +$K_V1.1$',
+                       'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN $\Delta$$K_V1.1$'}
+    colorslist = ['#007030',  # dark green
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                  '#478010',  # green
+                  '#AAB71B',  # lightgreen
+                  '#F78017',  # orange
+                  '#40A787',  # cyan'#
+                  '#008797',  # light blue
+                  '#2060A7',  # blue
+                  '#D03050',  # pink
+                  '#53379B',  # purple
+                  '#873770',  # magenta
+                  ]
+
+    import matplotlib.colors
+    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    clr_dict = {}
+    for m in range(len(model_names)):
+        clr_dict[model_names[m]] = colors[m]
+    if alteration=='shift':
+        df = pd.read_csv('./Figures/Data/AUC_shift_ex.csv')
+        df = df.sort_values('alteration')
+        ax.set_xlabel('$\Delta$$V_{1/2}$')
+    elif alteration=='slope':
+        df = pd.read_csv('./Figures/Data/AUC_slope_ex.csv')
+        ax.set_xscale("log")
+        ax.set_xticks([0.5, 1, 2])
+        ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
+        ax.xaxis.set_minor_formatter(NullFormatter())
+        ax.set_xlabel('$k$/$k_{WT}$')
+    elif alteration=='g':
+        df = pd.read_csv('./Figures/Data/AUC_g_ex.csv')
+        ax.set_xscale("log")
+        ax.set_xticks([0.5, 1, 2])
+        ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
+        ax.xaxis.set_minor_formatter(NullFormatter())
+        ax.set_xlabel('$g$/$g_{WT}$')
+    for mod in model_names:
+        if mod == model_name_dict[model]:
+            ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=1, zorder=10, linewidth=2)
+        else:
+            ax.plot(df['alteration'], df[mod], color=clr_dict[mod],alpha=0.5, zorder=1, linewidth=1)
+
+    if alteration=='shift':
+        ax.set_ylabel('Normalized $\Delta$AUC', labelpad=4)
+    else:
+        ax.set_ylabel('Normalized $\Delta$AUC', labelpad=0)
+    x = df['alteration']
+    y = df[model_name_dict[model]]
+    ax.set_xlim(x.min(), x.max())
+    ax.set_ylim(df[model_names].min().min(), df[model_names].max().max())
+
+    # x axis color gradient
+    cvals = [-2., 2]
+    colors = ['lightgrey', 'k']
+    norm = plt.Normalize(min(cvals), max(cvals))
+    tuples = list(zip(map(norm, cvals), colors))
+    cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
+    (xstart, xend) = ax.get_xlim()
+    (ystart, yend) = ax.get_ylim()
+    print(ystart, yend)
+    start = (xstart, ystart * 1.0)
+    end = (xend, ystart * 1.0)
+    ax = gradientaxis(ax, start, end, cmap, n=200, lw=4)
+    ax.spines['bottom'].set_visible(False)
+    return ax
+
+def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color2='dodgerblue'):
+    model_save_name = {'RS Pyramidal': 'RS_pyr_posp',
+                       'RS Inhibitory': 'RS_inhib_posp',
+                       'FS': 'FS_posp',
+                       'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_pyr_Kv',
+                       'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_inhib_Kv',
+                       'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS_Kv',
+                       'Cb stellate': 'Cb_stellate',
+                       'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv',
+                       'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv_only',
+                       'STN': 'STN',
+                       'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv',
+                       'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv_only'}
+    cvals = [-2., 2]
+    colors = [color1, color2]
+    norm = plt.Normalize(min(cvals), max(cvals))
+    tuples = list(zip(map(norm, cvals), colors))
+    cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
+    colors = cmap(np.linspace(0, 1, 22))
+    df = pd.read_csv('./Figures/Data/Model_fI/{}_fI.csv'.format(model_save_name[model]))
+    df.drop(['Unnamed: 0'], axis=1)
+    newdf = df.loc[df.index[(df['alt'] == alt) & (df['type'] == type)], :]
+    newdf['mag'] = newdf['mag'].astype('float')
+    newdf = newdf.sort_values('mag').reset_index()
+    c = 0
+    for i in newdf.index:
+        ax.plot(json.loads(newdf.loc[i, 'I']), json.loads(newdf.loc[i, 'F']), color=colors[c])
+        c += 1
+    ax.set_ylabel('Frequency [Hz]')
+    ax.set_xlabel('Current [nA]')
+    if model == 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
+        ax.set_title("Model G", x=0.2, y=1.0)
+    elif model == 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
+        ax.set_title("Model I", x=0.2, y=1.0)
+    else:
+        ax.set_title("", x=0.2, y=1.0)
+    ax.spines["right"].set_visible(False)
+    ax.spines["top"].set_visible(False)
+    L = ax.get_ylim()
+    ax.set_ylim([0, L[1]])
+    return ax
+
+
+#%%
+boxplot_style()
+color_dict = {'Cb stellate': '#40A787', # cyan'#
+              'RS Inhibitory': '#F0D730',  # yellow
+              'FS': '#C02717',  # red
+              'RS Pyramidal': '#007030',  # dark green
+              'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#AAB71B',  # lightgreen
+              'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':  '#008797',  # light blue
+              'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#F78017',  # orange
+              'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#478010',  # green
+              'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#53379B',  # purple
+              'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#2060A7',  # blue
+              'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770',  # magenta
+              'STN':  '#D03050'  # pink
+              }
+model_letter = {
+'Cb stellate': 'A',
+'RS Inhibitory': 'B',
+'FS': 'C',
+'RS Pyramidal': 'D',
+'RS Inhibitory': 'E',
+'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'F',
+'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'G',
+'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'H',
+'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'I',
+'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'J',
+'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'K',
+'STN':'L',
+}
+
+
+# plot setup
+marker_s_leg = 2
+max_width = 20
+pad_x = 0.85
+pad_y= 0.4
+pad_w = 1.1
+pad_h = 0.7
+
+fig = plt.figure()
+gs = fig.add_gridspec(3, 7, wspace=1.2, hspace=1.)
+ax0 = fig.add_subplot(gs[0,2:7])
+ax0_ex = fig.add_subplot(gs[0,1])
+ax0_fI = fig.add_subplot(gs[0,0])
+ax1 = fig.add_subplot(gs[1,2:7])
+ax1_ex = fig.add_subplot(gs[1,1])
+ax1_fI = fig.add_subplot(gs[1,0])
+ax2 = fig.add_subplot(gs[2,2:7])
+ax2_ex = fig.add_subplot(gs[2,1])
+ax2_fI = fig.add_subplot(gs[2,0])
+
+line_width = 1
+# plot fI examples
+ax0_fI = plot_fI(ax0_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s',  color1='lightgrey', color2='k')
+rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1  + pad_h, fill=False, lw=line_width,transform=ax0_fI.transAxes,  color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+rec = ax0_fI.add_patch(rec)
+rec.set_clip_on(False)
+
+ax1_fI = plot_fI(ax1_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='slope', alt='u',  color1='lightgrey', color2='k')
+rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1  + pad_h, fill=False, lw=line_width,transform=ax1_fI.transAxes,  color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+rec = ax1_fI.add_patch(rec)
+rec.set_clip_on(False)
+
+ax2_fI = plot_fI(ax2_fI, model='STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='g', alt='Leak',  color1='lightgrey', color2='k')
+rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1  + pad_h, fill=False, lw=line_width,transform=ax2_fI.transAxes,  color=color_dict['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+rec = ax2_fI.add_patch(rec)
+rec.set_clip_on(False)
+
+# plot boxplots
+boxplot_with_markers(ax0,max_width, alteration='shift')
+boxplot_with_markers(ax1,max_width, alteration='slope')
+boxplot_with_markers(ax2,max_width, alteration='g')
+
+# plot legend
+pos = (0.225, -0.9)
+ncol = 6
+model_legend(ax2,  marker_s_leg, pos, ncol)
+
+# plot examples
+plot_AUC_alt(ax0_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='shift')
+plot_AUC_alt(ax1_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',alteration='slope')
+plot_AUC_alt(ax2_ex, model='STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='g')
+
+# label subplots with letters
+ax0_fI.text(-0.875, 1.35, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=10, weight='bold')
+ax0_ex.text(-0.8, 1.35, string.ascii_uppercase[1], transform=ax0_ex.transAxes, size=10, weight='bold')
+ax0.text(-0.075, 1.35, string.ascii_uppercase[2], transform=ax0.transAxes, size=10, weight='bold')
+
+ax1_fI.text(-0.875, 1.35, string.ascii_uppercase[3], transform=ax1_fI.transAxes,size=10, weight='bold')
+ax1_ex.text(-0.8, 1.35, string.ascii_uppercase[4], transform=ax1_ex.transAxes, size=10, weight='bold')
+ax1.text(-0.075, 1.35, string.ascii_uppercase[5], transform=ax1.transAxes, size=10, weight='bold')
+
+ax2_fI.text(-0.875, 1.35, string.ascii_uppercase[6], transform=ax2_fI.transAxes,size=10, weight='bold')
+ax2_ex.text(-0.8, 1.35, string.ascii_uppercase[7], transform=ax2_ex.transAxes, size=10, weight='bold')
+ax2.text(-0.075, 1.35, string.ascii_uppercase[8], transform=ax2.transAxes, size=10, weight='bold')
+
+#save
+fig.set_size_inches(cm2inch(20.75,12))
+fig.savefig('./Figures/AUC_correlation.pdf', dpi=fig.dpi) #pdf #eps
+# fig.savefig('./Figures/AUC_correlation.png', dpi=fig.dpi) #pdf #eps
+plt.show()
diff --git a/Figures/ramp_firing_letters.py b/Figures/ramp_firing_letters.py
new file mode 100644
index 0000000..9888ec9
--- /dev/null
+++ b/Figures/ramp_firing_letters.py
@@ -0,0 +1,322 @@
+# # plot ramp protocol and responses of each model to ramp
+# import numpy as np
+# import pandas as pd
+# import matplotlib.pyplot as plt
+# import matplotlib.gridspec as gridspec
+# from matplotlib.transforms import Bbox
+# import string
+#
+# 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)
+#
+# #### from https://gist.github.com/dmeliza/3251476  #####################################################################
+# from matplotlib.offsetbox import AnchoredOffsetbox
+# class AnchoredScaleBar(AnchoredOffsetbox):
+#     def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
+#                  pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None,
+#                  **kwargs):
+#         """
+#         Draw a horizontal and/or vertical  bar with the size in data coordinate
+#         of the give axes. A label will be drawn underneath (center-aligned).
+#         - transform : the coordinate frame (typically axes.transData)
+#         - sizex,sizey : width of x,y bar, in data units. 0 to omit
+#         - labelx,labely : labels for x,y bars; None to omit
+#         - loc : position in containing axes
+#         - pad, borderpad : padding, in fraction of the legend font size (or prop)
+#         - sep : separation between labels and bars in points.
+#         - **kwargs : additional arguments passed to base class constructor
+#         """
+#         from matplotlib.patches import Rectangle
+#         from matplotlib.offsetbox import AuxTransformBox, VPacker, HPacker, TextArea, DrawingArea
+#         bars = AuxTransformBox(transform)
+#         if sizex:
+#             bars.add_artist(Rectangle((0, 0), sizex, 0, ec=barcolor, lw=barwidth, fc="none"))
+#         if sizey:
+#             bars.add_artist(Rectangle((0, 0), 0, sizey, ec=barcolor, lw=barwidth, fc="none"))
+#
+#         if sizex and labelx:
+#             self.xlabel = TextArea(labelx)
+#             bars = VPacker(children=[bars, self.xlabel], align="center", pad=0, sep=sep)
+#         if sizey and labely:
+#             self.ylabel = TextArea(labely)
+#             bars = HPacker(children=[self.ylabel, bars], align="center", pad=0, sep=sep)
+#
+#         AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,
+#                                    child=bars, prop=prop, frameon=False, **kwargs)
+#
+#
+# def add_scalebar(ax, matchx=True, matchy=True, hidex=True, hidey=True, **kwargs):
+#     """ Add scalebars to axes
+#     Adds a set of scale bars to *ax*, matching the size to the ticks of the plot
+#     and optionally hiding the x and y axes
+#     - ax : the axis to attach ticks to
+#     - matchx,matchy : if True, set size of scale bars to spacing between ticks
+#                     if False, size should be set using sizex and sizey params
+#     - hidex,hidey : if True, hide x-axis and y-axis of parent
+#     - **kwargs : additional arguments passed to AnchoredScaleBars
+#     Returns created scalebar object
+#     """
+#
+#     def f(axis):
+#         l = axis.get_majorticklocs()
+#         return len(l) > 1 and (l[1] - l[0])
+#
+#     if matchx:
+#         kwargs['sizex'] = f(ax.xaxis)
+#         kwargs['labelx'] = str(kwargs['sizex'])
+#     if matchy:
+#         kwargs['sizey'] = f(ax.yaxis)
+#         kwargs['labely'] = str(kwargs['sizey'])
+#
+#     sb = AnchoredScaleBar(ax.transData, **kwargs)
+#     ax.add_artist(sb)
+#
+#     if hidex: ax.xaxis.set_visible(False)
+#     if hidey: ax.yaxis.set_visible(False)
+#     if hidex and hidey: ax.set_frame_on(False)
+#
+#     return sb
+# ########################################################################################################################
+#
+#
+# def plot_ramp_V(ax, model='RS Pyramidal'):  # , stop=750
+#     model_ramp = pd.read_csv('./Figures/Data/model_ramp.csv')
+#     ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.025)
+#     ax.set_ylabel('V')
+#     ax.set_xlabel('Time [s]')
+#     ax.set_ylim(-80, 60)
+#     ax.axis('off')
+#     ax.set_title(model)
+#
+# #% plot setup
+# fig = plt.figure(figsize=cm2inch(17.6,17.6))
+#
+# gs0 = fig.add_gridspec(3, 2, wspace=0.1)
+# gs00 = gs0[:,0].subgridspec(7, 2, wspace=0.6, hspace=1)
+# gs01 = gs0[:,1].subgridspec(7, 2, wspace=0.6, hspace=1)
+#
+# ax1_ramp = fig.add_subplot(gs00[0,0:2])
+# ax2_ramp = fig.add_subplot(gs01[0,0:2])
+# ax3_ramp = fig.add_subplot(gs00[1,0:2])
+# ax4_ramp = fig.add_subplot(gs01[1,0:2])
+# ax5_ramp = fig.add_subplot(gs00[2, 0:2])
+# ax6_ramp = fig.add_subplot(gs01[2, 0:2])
+# ax7_ramp = fig.add_subplot(gs00[3,0:2])
+# ax8_ramp = fig.add_subplot(gs01[3,0:2])
+# ax9_ramp = fig.add_subplot(gs00[4,0:2])
+# ax10_ramp = fig.add_subplot(gs01[4,0:2])
+# ax11_ramp = fig.add_subplot(gs00[5,0:2])
+# ax12_ramp = fig.add_subplot(gs01[5,0:2])
+#
+# ramp_axs = [ax1_ramp, ax2_ramp, ax3_ramp, ax4_ramp, ax5_ramp,ax6_ramp, ax7_ramp, ax8_ramp,
+#              ax9_ramp, ax10_ramp, ax11_ramp, ax12_ramp]
+#
+# # order of models
+# models = ['Cb stellate','RS Inhibitory','FS', 'RS Pyramidal','RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+#           'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+#           'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+#           'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+#           'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN']
+#
+# # plot ramps
+# for i in range(len(models)):
+#     plot_ramp_V(ramp_axs[i], model=models[i])
+#
+# # add scalebar
+# plt.rcParams.update({'font.size': 6})
+#
+# add_scalebar(ax11_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=50, labelx='1 s',
+#                  labely='50 mV', loc=3, pad=-2, borderpad=0, barwidth=1, bbox_to_anchor=Bbox.from_bounds(-0.05, 0.1, 1, 1),
+#                           bbox_transform=ax11_ramp.transAxes)
+# # add_scalebar(ax12_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=25, labelx='1 s',
+# #                  labely='25 mV', loc=3, pad=-2, borderpad=0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.05, 0.1, 1, 1),
+# #                           bbox_transform=ax12_ramp.transAxes)
+#
+# # add subplot labels
+# for i in range(0,len(models)):
+#     ramp_axs[i].text(-0.05, 1.08, string.ascii_uppercase[i], transform=ramp_axs[i].transAxes, size=10, weight='bold')
+#
+# fig.savefig('./Figures/ramp_firing.pdf', dpi=3000)
+# plt.show()
+
+
+
+# plot ramp protocol and responses of each model to ramp
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from matplotlib.transforms import Bbox
+import string
+
+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)
+
+#### from https://gist.github.com/dmeliza/3251476  #####################################################################
+from matplotlib.offsetbox import AnchoredOffsetbox
+class AnchoredScaleBar(AnchoredOffsetbox):
+    def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
+                 pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None,
+                 **kwargs):
+        """
+        Draw a horizontal and/or vertical  bar with the size in data coordinate
+        of the give axes. A label will be drawn underneath (center-aligned).
+        - transform : the coordinate frame (typically axes.transData)
+        - sizex,sizey : width of x,y bar, in data units. 0 to omit
+        - labelx,labely : labels for x,y bars; None to omit
+        - loc : position in containing axes
+        - pad, borderpad : padding, in fraction of the legend font size (or prop)
+        - sep : separation between labels and bars in points.
+        - **kwargs : additional arguments passed to base class constructor
+        """
+        from matplotlib.patches import Rectangle
+        from matplotlib.offsetbox import AuxTransformBox, VPacker, HPacker, TextArea, DrawingArea
+        bars = AuxTransformBox(transform)
+        if sizex:
+            bars.add_artist(Rectangle((0, 0), sizex, 0, ec=barcolor, lw=barwidth, fc="none"))
+        if sizey:
+            bars.add_artist(Rectangle((0, 0), 0, sizey, ec=barcolor, lw=barwidth, fc="none"))
+
+        if sizex and labelx:
+            self.xlabel = TextArea(labelx)
+            bars = VPacker(children=[bars, self.xlabel], align="center", pad=0, sep=sep)
+        if sizey and labely:
+            self.ylabel = TextArea(labely)
+            bars = HPacker(children=[self.ylabel, bars], align="center", pad=0, sep=sep)
+
+        AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,
+                                   child=bars, prop=prop, frameon=False, **kwargs)
+
+
+def add_scalebar(ax, matchx=True, matchy=True, hidex=True, hidey=True, **kwargs):
+    """ Add scalebars to axes
+    Adds a set of scale bars to *ax*, matching the size to the ticks of the plot
+    and optionally hiding the x and y axes
+    - ax : the axis to attach ticks to
+    - matchx,matchy : if True, set size of scale bars to spacing between ticks
+                    if False, size should be set using sizex and sizey params
+    - hidex,hidey : if True, hide x-axis and y-axis of parent
+    - **kwargs : additional arguments passed to AnchoredScaleBars
+    Returns created scalebar object
+    """
+
+    def f(axis):
+        l = axis.get_majorticklocs()
+        return len(l) > 1 and (l[1] - l[0])
+
+    if matchx:
+        kwargs['sizex'] = f(ax.xaxis)
+        kwargs['labelx'] = str(kwargs['sizex'])
+    if matchy:
+        kwargs['sizey'] = f(ax.yaxis)
+        kwargs['labely'] = str(kwargs['sizey'])
+
+    sb = AnchoredScaleBar(ax.transData, **kwargs)
+    ax.add_artist(sb)
+
+    if hidex: ax.xaxis.set_visible(False)
+    if hidey: ax.yaxis.set_visible(False)
+    if hidex and hidey: ax.set_frame_on(False)
+
+    return sb
+########################################################################################################################
+
+
+def plot_ramp_V(ax, model='RS Pyramidal'):  # , stop=750
+    model_ramp = pd.read_csv('./Figures/Data/model_ramp.csv')
+    # ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.0025)
+    ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.1)
+    ax.set_ylabel('V')
+    ax.set_xlabel('Time [s]')
+    ax.set_ylim(-80, 60)
+    ax.axis('off')
+    ax.set_title(model, fontsize=8)
+
+def plot_I_ramp(ax):
+    dt = 0.01
+    I_low = 0
+    I_high = 0.001
+    initial_period = 1000
+
+    sec = 4
+    ramp_len = int(4 * 1000 * 1 / dt)
+    stim_time = ramp_len * 2
+    I_amp = np.array([0])
+    I_amp = np.reshape(I_amp, (1, I_amp.shape[0]))
+    I_ramp = np.zeros((stim_time, 1)) @ I_amp
+    I_ramp[:, :] = np.ones((stim_time, 1)) @ I_amp
+    stim_num_step = I_ramp.shape[1]
+    start=0
+    I_ramp[start:int(start + ramp_len), 0] = np.linspace(0, I_high, ramp_len)
+    I_ramp[int(start + ramp_len):int(start + ramp_len * 2), 0] = np.linspace(I_high, 0, ramp_len)
+
+    t = np.arange(0, 4000 * 2, dt)
+    ax.plot(t, I_ramp)
+    ax.set_ylabel('I')
+    ax.set_xlabel('Time [s]')
+    ax.axis('off')
+    ax.set_title('Ramp Current', fontsize=8, x=0.5, y=-0.5)
+    return ax
+
+#% plot setup
+fig = plt.figure(figsize=cm2inch(17.6,25))
+
+gs0 = fig.add_gridspec(2, 1, wspace=0.)
+gs00 = gs0[:].subgridspec(13, 1, wspace=0.7, hspace=1.0)
+
+ax1_ramp = fig.add_subplot(gs00[0])
+ax2_ramp = fig.add_subplot(gs00[1])
+ax3_ramp = fig.add_subplot(gs00[2])
+ax4_ramp = fig.add_subplot(gs00[3])
+ax5_ramp = fig.add_subplot(gs00[4])
+ax6_ramp = fig.add_subplot(gs00[5])
+ax7_ramp = fig.add_subplot(gs00[6])
+ax8_ramp = fig.add_subplot(gs00[7])
+ax9_ramp = fig.add_subplot(gs00[8])
+ax10_ramp = fig.add_subplot(gs00[9])
+ax11_ramp = fig.add_subplot(gs00[10])
+ax12_ramp = fig.add_subplot(gs00[11])
+ax13_I = fig.add_subplot(gs00[12])
+ramp_axs = [ax1_ramp, ax2_ramp, ax3_ramp, ax4_ramp, ax5_ramp,ax6_ramp, ax7_ramp, ax8_ramp,
+             ax9_ramp, ax10_ramp, ax11_ramp, ax12_ramp]
+
+# order of models
+models = ['Cb stellate','RS Inhibitory','FS', 'RS Pyramidal','RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+          'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+          'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+          'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+          'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN']
+
+# plot ramps
+for i in range(len(models)):
+    plot_ramp_V(ramp_axs[i], model=models[i])
+
+# add scalebar
+plt.rcParams.update({'font.size': 6})
+
+add_scalebar(ax12_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=50, labelx='1 s',
+                 labely='50 mV', loc=3, pad=-2, borderpad=0, barwidth=1, bbox_to_anchor=Bbox.from_bounds(0.01, 0.05, 1, 1),
+                          bbox_transform=ax12_ramp.transAxes)
+
+ax13_I = plot_I_ramp(ax13_I)
+add_scalebar(ax13_I, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=0.0005, labelx='1 s',
+             labely='0.5 $I_{max}$', loc=3, pad=-2, borderpad=0, barwidth=1,
+             bbox_to_anchor=Bbox.from_bounds(0.0, -0.01, 1, 1),  bbox_transform=ax13_I.transAxes)
+
+# add subplot labels
+for i in range(0,len(models)):
+    ramp_axs[i].text(-0.01, 1.1, string.ascii_uppercase[i], transform=ramp_axs[i].transAxes, size=10, weight='bold')
+
+#save
+fig.set_size_inches(cm2inch(17.6,22))
+fig.savefig('./Figures/ramp_firing.png', dpi=fig.dpi)#pdf #eps
+plt.show()
+
diff --git a/Figures/rheobase_correlation.pdf b/Figures/rheobase_correlation.pdf
index a77443ef3ab23ca4545cb1d714602dd7ca9d14ad..173de7e96f44a3ab4281193d6c9591ac2550e68b 100644
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diff --git a/Figures/rheobase_correlation.py b/Figures/rheobase_correlation.py
index f97e172..0f69e6d 100644
--- a/Figures/rheobase_correlation.py
+++ b/Figures/rheobase_correlation.py
@@ -157,6 +157,8 @@ def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
     clr_dict = {}
     for m in range(len(model_names)):
         clr_dict[model_names[m]] = colors[m]
+    print(colors)
+    print(clr_dict)
     Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
     if alteration=='shift':
         i = 3  # Kv1.1 act
@@ -217,27 +219,52 @@ def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
 
 
 def model_legend(ax, marker_s_leg, pos, ncol):
-    colorslist = [  '#40A787', # cyan'#
-        '#F0D730',  # yellow
-        '#C02717',  # red
-        '#007030',  # dark green
-        '#AAB71B',  # lightgreen
-        '#008797',  # light blue
-        '#F78017',  # orange
-        '#478010',  # green
-        '#53379B',  # purple
-        '#2060A7',  # blue
-        '#873770',  # magenta
-        '#D03050'  # pink
-    ]
-    import matplotlib.colors
-    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    # colorslist = [  '#40A787', # cyan'#
+    #     '#F0D730',  # yellow
+    #     '#C02717',  # red
+    #     '#007030',  # dark green
+    #     '#AAB71B',  # lightgreen
+    #     '#008797',  # light blue
+    #     '#F78017',  # orange
+    #     '#478010',  # green
+    #     '#53379B',  # purple
+    #     '#2060A7',  # blue
+    #     '#873770',  # magenta
+    #     '#D03050'  # pink
+    # ]
+    # import matplotlib.colors
+    # colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
     model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
               'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
               'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
               'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
               'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
               'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
+
+    # model_pos = {'Cb stellate': 0, 'RS Inhibitory': 1, 'FS': 2, 'RS Pyramidal': 3,
+    #              'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 4,
+    #              'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 5,
+    #              'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 6,
+    #              'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 7,
+    #              'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 8,
+    #              'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 9,
+    #              'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 10, 'STN': 11}
+    colorslist = ['#007030',  # dark green
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                  '#478010',  # green
+                  '#AAB71B',  # lightgreen
+                  '#F78017',  # orange
+                  '#40A787',  # cyan'#
+                  '#008797',  # light blue
+                  '#2060A7',  # blue
+                  '#D03050',  # pink
+                  '#53379B',  # purple
+                  '#873770',  # magenta
+                  ]
+
+    import matplotlib.colors
+    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
     Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
     RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']], markersize=marker_s_leg, linestyle='None',
                          label='RS pyramidal')
diff --git a/Figures/rheobase_correlation_letters.py b/Figures/rheobase_correlation_letters.py
new file mode 100644
index 0000000..cf02cf0
--- /dev/null
+++ b/Figures/rheobase_correlation_letters.py
@@ -0,0 +1,648 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul  3 19:52:04 2021
+
+@author: nils
+"""
+import pandas as pd
+import numpy as np
+import string
+import textwrap
+import json
+import matplotlib
+import matplotlib.lines as mlines
+from matplotlib import ticker
+from matplotlib.ticker import NullFormatter
+from Figures.plotstyle import boxplot_style
+
+
+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)
+#%% ##################### From https://stackoverflow.com/questions/52878845/swarmplot-with-hue-affecting-marker-beyond-color ##
+# to change marker types in seaborn swarmplot
+
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+############## Begin hack ##############
+from matplotlib.axes._axes import Axes
+from matplotlib.markers import MarkerStyle
+from numpy import ndarray
+
+def GetColor2Marker(markers):
+    colorslist = ['#40A787',  # cyan'#
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                  '#007030',  # dark green
+                  '#AAB71B',  # lightgreen
+                  '#008797',  # light blue
+                  '#F78017',  # orange
+                  '#478010',  # green
+                  '#53379B',  # purple
+                  '#2060A7',  # blue
+                  '#873770',  # magenta
+                  '#D03050'  # pink
+                  ]
+    import matplotlib.colors
+    palette = [matplotlib.colors.to_rgb(c) for c in colorslist]  #
+    mkcolors = [(palette[i]) for i in range(len(markers))]
+    return dict(zip(mkcolors,markers))
+
+def fixlegend(ax,markers,markersize=3,**kwargs):
+    # Fix Legend
+    legtitle =  ax.get_legend().get_title().get_text()
+    _,l = ax.get_legend_handles_labels()
+    colorslist = ['#40A787',  # cyan'#
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                  '#007030',  # dark green
+                  '#AAB71B',  # lightgreen
+                  '#008797',  # light blue
+                  '#F78017',  # orange
+                  '#478010',  # green
+                  '#53379B',  # purple
+                  '#2060A7',  # blue
+                  '#873770',  # magenta
+                  '#D03050'  # pink
+                  ]
+    import matplotlib.colors
+    palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    mkcolors = [(palette[i]) for i in range(len(markers))]
+    newHandles = [plt.Line2D([0],[0], ls="none", marker=m, color=c, mec="none", markersize=markersize,**kwargs) \
+                for m,c in zip(markers, mkcolors)]
+    ax.legend(newHandles,l)
+    leg = ax.get_legend()
+    leg.set_title(legtitle)
+
+old_scatter = Axes.scatter
+def new_scatter(self, *args, **kwargs):
+    colors = kwargs.get("c", None)
+    co2mk = kwargs.pop("co2mk",None)
+    FinalCollection = old_scatter(self, *args, **kwargs)
+    if co2mk is not None and isinstance(colors, ndarray):
+        Color2Marker = GetColor2Marker(co2mk)
+        paths=[]
+        for col in colors:
+            mk=Color2Marker[tuple(col)]
+            marker_obj = MarkerStyle(mk)
+            paths.append(marker_obj.get_path().transformed(marker_obj.get_transform()))
+        FinalCollection.set_paths(paths)
+    return FinalCollection
+Axes.scatter = new_scatter
+############## End hack. ##############
+########################################################################################################################
+#%% add gradient arrows
+import matplotlib.pyplot as plt
+import matplotlib.transforms
+import matplotlib.path
+from matplotlib.collections import LineCollection
+def rainbowarrow(ax, start, end, cmap, n=50,lw=3):
+    # Arrow shaft: LineCollection
+    x = np.linspace(start[0],end[0],n)
+    y = np.linspace(start[1],end[1],n)
+    points = np.array([x,y]).T.reshape(-1,1,2)
+    segments = np.concatenate([points[:-1],points[1:]], axis=1)
+    lc = LineCollection(segments, cmap=cmap, linewidth=lw)
+    lc.set_array(np.linspace(0,1,n))
+    ax.add_collection(lc)
+    # Arrow head: Triangle
+    tricoords = [(0,-0.02),(0.025,0),(0,0.02),(0,-0.02)]
+    angle = np.arctan2(end[1]-start[1],end[0]-start[0])
+    rot = matplotlib.transforms.Affine2D().rotate(angle)
+    tricoords2 = rot.transform(tricoords)
+    tri = matplotlib.path.Path(tricoords2, closed=True)
+    ax.scatter(end[0],end[1], c=1, s=(4*lw)**2, marker=tri, cmap=cmap,vmin=0)
+    ax.autoscale_view()
+    return ax
+
+def gradientaxis(ax, start, end, cmap, n=100,lw=1):
+    # Arrow shaft: LineCollection
+    x = np.linspace(start[0],end[0],n)
+    y = np.linspace(start[1],end[1],n)
+    points = np.array([x,y]).T.reshape(-1,1,2)
+    segments = np.concatenate([points[:-1],points[1:]], axis=1)
+    lc = LineCollection(segments, cmap=cmap, linewidth=lw,zorder=15)
+    lc.set_array(np.linspace(0,1,n))
+    ax.add_collection(lc)
+    return ax
+#%%
+def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
+    hlinewidth = 0.5
+    model_names = ['RS pyramidal','RS inhibitory','FS',
+                                  'RS pyramidal +$K_V1.1$','RS inhibitory +$K_V1.1$',
+                'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
+          'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
+          'STN $\Delta$$K_V1.1$']
+
+    colorslist = ['#007030',  # dark green
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                    '#478010',  # green
+                  '#AAB71B',  # lightgreen
+                '#F78017',  # orange
+                  '#40A787',  # cyan'#
+                  '#008797',  # light blue
+                '#2060A7',  # blue
+                  '#D03050',  # pink
+                  '#53379B',  # purple
+                  '#873770',  # magenta
+                  ]
+
+    import matplotlib.colors
+    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    clr_dict = {}
+    for m in range(len(model_names)):
+        clr_dict[model_names[m]] = colors[m]
+    Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
+    if alteration=='shift':
+        i = 3  # Kv1.1 act
+        ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
+        df = pd.read_csv('./Figures/Data/rheo_shift_corr.csv')
+        sns.swarmplot(y="corr", x="$\Delta V_{1/2}$", hue="model", data=df,
+                      palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
+                      order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
+                             '$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
+                      hue_order=model_names, co2mk=Markers)
+        lim = ax.get_xlim()
+        ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
+        ax.set_title("Shift ($\Delta V_{1/2}$)", y=1.05)
+        ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
+                            '$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
+    elif alteration=='slope':
+        i = 4  # Kv1.1 inact
+        ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
+        df = pd.read_csv('./Figures/Data/rheo_scale_corr.csv')
+
+        # Add in points to show each observation
+        sns.swarmplot(y="corr", x="Slope (k)", hue="model", data=df,
+                      palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
+                      order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
+                             '$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
+                      hue_order=model_names, co2mk=Markers)
+        lim = ax.get_xlim()
+        ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
+        ax.set_title("Slope (k)", y=1.05)
+        ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
+                            '$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
+    elif alteration=='g':
+        i = 4  # Leak
+        ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
+        df = pd.read_csv('./Figures/Data/rheo_g_corr.csv')
+
+        # Add in points to show each observation
+        sns.swarmplot(y="corr", x="g", hue="model", data=df,
+                      palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
+                      order=['Na', 'K', '$K_V1.1$', 'A', 'Leak'],
+                      hue_order=model_names, co2mk=Markers)
+        lim = ax.get_xlim()
+        ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
+        ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
+        ax.set_title("Conductance (g)", y=1.05)
+        ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
+    else:
+        print('Please chose "shift", "slope" or "g"')
+    ax.get_legend().remove()
+    ax.xaxis.grid(False)
+    sns.despine(trim=True, bottom=True, ax=ax)
+    ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
+
+
+def model_legend(ax, marker_s_leg, pos, ncol):
+    colorslist = [  '#40A787', # cyan'#
+        '#F0D730',  # yellow
+        '#C02717',  # red
+        '#007030',  # dark green
+        '#AAB71B',  # lightgreen
+        '#008797',  # light blue
+        '#F78017',  # orange
+        '#478010',  # green
+        '#53379B',  # purple
+        '#2060A7',  # blue
+        '#873770',  # magenta
+        '#D03050'  # pink
+    ]
+    model_names = ['RS pyramidal', 'RS inhibitory', 'FS',
+                   'RS pyramidal +$K_V1.1$', 'RS inhibitory +$K_V1.1$',
+                   'FS +$K_V1.1$', 'Cb stellate', 'Cb stellate +$K_V1.1$',
+                   'Cb stellate $\Delta$$K_V1.1$', 'STN', 'STN +$K_V1.1$',
+                   'STN $\Delta$$K_V1.1$']
+
+    # colorslist = ['#007030',  # dark green
+    #               '#F0D730',  # yellow
+    #               '#C02717',  # red
+    #               '#478010',  # green
+    #               '#AAB71B',  # lightgreen
+    #               '#F78017',  # orange
+    #               '#40A787',  # cyan'#
+    #               '#008797',  # light blue
+    #               '#2060A7',  # blue
+    #               '#D03050',  # pink
+    #               '#53379B',  # purple
+    #               '#873770',  # magenta
+    #               ]
+
+    import matplotlib.colors
+    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    clr_dict = {}
+    for m in range(len(model_names)):
+        clr_dict[model_names[m]] = colors[m]
+
+    import matplotlib.colors
+    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    # model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
+    #           'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
+    #           'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
+    #           'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
+    #           'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
+    #           'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
+    Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
+
+    model_pos = {'RS Pyramidal':0, 'RS Inhibitory':1, 'FS':2,
+                   'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':3, 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
+                   'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'Cb stellate':6, 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7,
+                   'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8, 'STN':9, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10,
+                   'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':11}
+
+
+
+    RS_p = mlines.Line2D([], [], color='#007030', marker="^",
+                         markersize=marker_s_leg, linestyle='None',
+                         label='Model D')
+    RS_i = mlines.Line2D([], [], color='#F0D730', marker="o",
+                         markersize=marker_s_leg, linestyle='None',
+                         label='Model B')
+    FS = mlines.Line2D([], [], color='#C02717', marker="o", markersize=marker_s_leg,
+                       linestyle='None', label='Model C')
+    RS_p_Kv = mlines.Line2D([], [], color='#478010',
+                            marker="D",
+                            markersize=marker_s_leg, linestyle='None',
+                         label='Model H')
+    RS_i_Kv = mlines.Line2D([], [], color='#AAB71B',
+                            marker="^",
+                            markersize=marker_s_leg, linestyle='None',
+                         label='Model E')
+    FS_Kv = mlines.Line2D([], [], color='#F78017',
+                          marker="D", markersize=marker_s_leg,
+                          linestyle='None', label='Model G')
+    Cb = mlines.Line2D([], [], color='#40A787', marker="o",
+                       markersize=marker_s_leg, linestyle='None',
+                       label='Model A')
+    Cb_pl = mlines.Line2D([], [], color='#008797',
+                          marker="^",
+                          markersize=marker_s_leg, linestyle='None',
+                          label='Model F')
+    Cb_sw = mlines.Line2D([], [], color='#2060A7',
+                          marker="s",
+                          markersize=marker_s_leg, linestyle='None',
+                          label='Model J')
+    STN = mlines.Line2D([], [], color='#D03050', marker="s", markersize=marker_s_leg,
+                        linestyle='None', label='Model L')
+    STN_pl = mlines.Line2D([], [], color='#53379B',
+                           marker="D",
+                           markersize=marker_s_leg, linestyle='None',
+                           label='Model I')
+    STN_sw = mlines.Line2D([], [], color='#873770',
+                           marker="s",
+                           markersize=marker_s_leg, linestyle='None',
+                           label='Model K')
+    #
+    # RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']],
+    #                      markersize=marker_s_leg, linestyle='None',
+    #                      label='Model D')
+    # RS_i = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory']], marker=Markers[model_pos['RS Inhibitory']],
+    #                      markersize=marker_s_leg, linestyle='None',
+    #                      label='Model B')
+    # FS = mlines.Line2D([], [], color=colors[model_pos['FS']], marker=Markers[model_pos['FS']], markersize=marker_s_leg,
+    #                    linestyle='None', label='Model C')
+    # RS_p_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                         marker=Markers[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                         markersize=marker_s_leg, linestyle='None',
+    #                      label='Model H')
+    # RS_i_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                         marker=Markers[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                         markersize=marker_s_leg, linestyle='None',
+    #                      label='Model E')
+    # FS_Kv = mlines.Line2D([], [], color=colors[model_pos['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                       marker=Markers[model_pos['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg,
+    #                       linestyle='None', label='Model G')
+    # Cb = mlines.Line2D([], [], color=colors[model_pos['Cb stellate']], marker=Markers[model_pos['Cb stellate']],
+    #                    markersize=marker_s_leg, linestyle='None',
+    #                    label='Model A')
+    # Cb_pl = mlines.Line2D([], [], color=colors[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                       marker=Markers[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                       markersize=marker_s_leg, linestyle='None',
+    #                       label='Model F')
+    # Cb_sw = mlines.Line2D([], [], color=colors[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                       marker=Markers[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                       markersize=marker_s_leg, linestyle='None',
+    #                       label='Model J')
+    # STN = mlines.Line2D([], [], color=colors[model_pos['STN']], marker=Markers[model_pos['STN']], markersize=marker_s_leg,
+    #                     linestyle='None', label='Model L')
+    # STN_pl = mlines.Line2D([], [], color=colors[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                        marker=Markers[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                        markersize=marker_s_leg, linestyle='None',
+    #                        label='Model I')
+    # STN_sw = mlines.Line2D([], [], color=colors[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                        marker=Markers[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
+    #                        markersize=marker_s_leg, linestyle='None',
+    #                        label='Model K')
+    # ax.legend(handles=[RS_p, RS_i, FS, RS_p_Kv, RS_i_Kv, FS_Kv, Cb, Cb_pl, Cb_sw, STN, STN_pl, STN_sw], loc='center',
+    #               bbox_to_anchor=pos, ncol=ncol, frameon=False)
+    ax.legend(handles=[Cb, RS_i, FS, RS_p, RS_i_Kv, Cb_pl, FS_Kv, RS_p_Kv, STN_pl, Cb_sw, STN_sw, STN], loc='center',
+                  bbox_to_anchor=pos, ncol=ncol, frameon=False)
+
+def plot_rheo_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='shift'):
+    ax.spines["right"].set_visible(False)
+    ax.spines["top"].set_visible(False)
+    model_names = ['RS Pyramidal','RS Inhibitory','FS',
+                                  'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
+                'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
+          'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
+          'STN $\Delta$$K_V1.1$']
+
+    model_name_dict = {'RS Pyramidal': 'RS Pyramidal',
+                       'RS Inhibitory': 'RS Inhibitory',
+                       'FS': 'FS',
+                       'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Pyramidal +$K_V1.1$',
+                       'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Inhibitory +$K_V1.1$',
+                       'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS +$K_V1.1$',
+                       'Cb stellate': 'Cb stellate',
+                       'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate +$K_V1.1$',
+                       'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate $\Delta$$K_V1.1$',
+                       'STN': 'STN',
+                       'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN +$K_V1.1$',
+                       'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN $\Delta$$K_V1.1$'}
+
+    colorslist = ['#007030',  # dark green
+                  '#F0D730',  # yellow
+                  '#C02717',  # red
+                  '#478010',  # green
+                  '#AAB71B',  # lightgreen
+                  '#F78017',  # orange
+                  '#40A787',  # cyan'#
+                  '#008797',  # light blue
+                  '#2060A7',  # blue
+                  '#D03050',  # pink
+                  '#53379B',  # purple
+                  '#873770',  # magenta
+                  ]
+    import matplotlib.colors
+    colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
+    clr_dict = {}
+    for m in range(len(model_names)):
+        clr_dict[model_names[m]] = colors[m]
+    if alteration=='shift':
+        df = pd.read_csv('./Figures/Data/rheo_shift_ex.csv')
+        df = df.sort_values('alteration')
+        ax.set_xlabel('$\Delta$$V_{1/2}$')
+    elif alteration=='slope':
+        df = pd.read_csv('./Figures/Data/rheo_slope_ex.csv')
+        ax.set_xscale("log")
+        ax.set_xticks([0.5, 1, 2])
+        ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
+        ax.xaxis.set_minor_formatter(NullFormatter())
+        ax.set_xlabel('$k$/$k_{WT}$')
+    elif alteration=='g':
+        df = pd.read_csv('./Figures/Data/rheo_g_ex.csv')
+        ax.set_xscale("log")
+        ax.set_xticks([0.5, 1, 2])
+        ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
+        ax.xaxis.set_minor_formatter(NullFormatter())
+        ax.set_xlabel('$g$/$g_{WT}$')
+    for mod in model_names:
+        if mod == model_name_dict[model]:
+                ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=1, zorder=10, linewidth=2)
+        else:
+            ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=0.5, zorder=1, linewidth=1)
+
+    ax.set_ylabel('$\Delta$ Rheobase (nA)', labelpad=0)
+    x = df['alteration']
+    y = df[model_name_dict[model]]
+    ax.set_xlim(x.min(), x.max())
+    ax.set_ylim(df[model_names].min().min(), df[model_names].max().max())
+
+    # x axis color gradient
+    cvals = [-2., 2]
+    colors = ['lightgrey', 'k']
+    norm = plt.Normalize(min(cvals), max(cvals))
+    tuples = list(zip(map(norm, cvals), colors))
+    cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
+    (xstart, xend) = ax.get_xlim()
+    (ystart, yend) = ax.get_ylim()
+    print(ystart, yend)
+    start = (xstart, ystart*1.0)
+    end = (xend, ystart*1.0)
+    ax = gradientaxis(ax, start, end, cmap, n=200,lw=4)
+    ax.spines['bottom'].set_visible(False)
+    # ax.set_ylim(ystart, yend)
+
+    #xlabel tick colors
+    # my_colors = ['lightgrey', 'grey',  'k']
+    # for ticklabel, tickcolor in zip(ax.get_xticklabels(), my_colors):
+    #     ticklabel.set_color(tickcolor)
+    return ax
+
+def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color2='dodgerblue'):
+    model_save_name = {'RS Pyramidal': 'RS_pyr_posp',
+                       'RS Inhibitory': 'RS_inhib_posp',
+                       'FS': 'FS_posp',
+                       'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_pyr_Kv',
+                       'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_inhib_Kv',
+                       'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS_Kv',
+                       'Cb stellate': 'Cb_stellate',
+                       'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv',
+                       'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv_only',
+                       'STN': 'STN',
+                       'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv',
+                       'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv_only'}
+    cvals = [-2., 2]
+    colors = [color1, color2]
+    norm = plt.Normalize(min(cvals), max(cvals))
+    tuples = list(zip(map(norm, cvals), colors))
+    cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
+    colors = cmap(np.linspace(0, 1, 22))
+    df = pd.read_csv('./Figures/Data/Model_fI/{}_fI.csv'.format(model_save_name[model]))
+    df.drop(['Unnamed: 0'], axis=1)
+    newdf = df.loc[df.index[(df['alt'] == alt) & (df['type'] == type)], :]
+    newdf['mag'] = newdf['mag'].astype('float')
+    newdf = newdf.sort_values('mag').reset_index()
+    c = 0
+    for i in newdf.index:
+        ax.plot(json.loads(newdf.loc[i, 'I']), json.loads(newdf.loc[i, 'F']), color=colors[c])
+        c += 1
+
+    # colors2 = [colors[10, :], 'k']
+    # norm2 = plt.Normalize(min(cvals), max(cvals))
+    # tuples2 = list(zip(map(norm2, cvals), colors2))
+    # cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples2)
+    #
+    # colors3 = [colors[11, :], 'lightgrey']
+    # norm3 = plt.Normalize(min(cvals), max(cvals))
+    # tuples3 = list(zip(map(norm3, cvals), colors3))
+    # cmap3 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples3)
+    #
+    # start = (1.1, json.loads(newdf.loc[10, 'F'])[-1])
+    # end = (1.1, json.loads(newdf.loc[20, 'F'])[-1])#-json.loads(newdf.loc[20, 'F'])[-1]*0.1)
+    # ax = rainbowarrow(ax, start, end, cmap2, n=50, lw=1)
+    # ax.text(1.15, json.loads(newdf.loc[20, 'F'])[-1], '$+ \Delta V$', fontsize=4, color='k')
+    #
+    # start = (1.1, json.loads(newdf.loc[10, 'F'])[-1])
+    # end = (1.1, json.loads(newdf.loc[0, 'F'])[-1])#-json.loads(newdf.loc[0, 'F'])[-1]*0.1)
+    # ax = rainbowarrow(ax, start, end, cmap3, n=50, lw=1)
+    # ax.text(1.15, json.loads(newdf.loc[0, 'F'])[-1], '$- \Delta V$', fontsize=4, color='lightgrey')
+
+    ax.set_ylabel('Frequency [Hz]')
+    ax.set_xlabel('Current [nA]')
+    if model == 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
+        ax.set_title("Model G", x=0.2, y=1.0)
+    elif model == 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
+        ax.set_title("Model F", x=0.2, y=1.0)
+    elif model == 'Cb stellate':
+        ax.set_title("Model A", x=0.2, y=1.0)
+    else:
+        ax.set_title("", x=0.2, y=1.0)
+    # plot_rheo_alt(ax0_ex, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',
+    #               alteration='shift')
+    # plot_rheo_alt(ax1_ex, model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',
+    #               alteration='slope')
+    # plot_rheo_alt(ax2_ex, model='Cb stellate', color1='lightgrey', color2='k', alteration='g')
+
+    # ax.set_title(model, x=0.2, y=1.025)
+    ax.spines["right"].set_visible(False)
+    ax.spines["top"].set_visible(False)
+    L = ax.get_ylim()
+    ax.set_ylim([0, L[1]])
+    return ax
+
+
+#%%
+boxplot_style()
+color_dict = {'Cb stellate': '#40A787', # cyan'#
+              'RS Inhibitory': '#F0D730',  # yellow
+              'FS': '#C02717',  # red
+              'RS Pyramidal': '#007030',  # dark green
+              'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#AAB71B',  # lightgreen
+              'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':  '#008797',  # light blue
+              'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#F78017',  # orange
+              'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#478010',  # green
+              'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#53379B',  # purple
+              'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#2060A7',  # blue
+              'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770',  # magenta
+              'STN':  '#D03050'  # pink
+              }
+
+# plot setup
+marker_s_leg = 2
+max_width = 20
+pad_x = 0.85
+pad_y= 0.4
+pad_w = 1.1
+pad_h = 0.7
+
+fig = plt.figure()
+gs = fig.add_gridspec(3, 7, wspace=1.2, hspace=1.)
+ax0 = fig.add_subplot(gs[0,2:7])
+ax0_ex = fig.add_subplot(gs[0,1])
+ax0_fI = fig.add_subplot(gs[0,0])
+ax1 = fig.add_subplot(gs[1,2:7])
+ax1_ex = fig.add_subplot(gs[1,1])
+ax1_fI = fig.add_subplot(gs[1,0])
+ax2 = fig.add_subplot(gs[2,2:7])
+ax2_ex = fig.add_subplot(gs[2,1])
+ax2_fI = fig.add_subplot(gs[2,0])
+
+line_width = 1
+# plot fI curves
+ax0_fI = plot_fI(ax0_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s',  color1='lightgrey', color2='k')
+rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1  + pad_h, fill=False, lw=line_width,transform=ax0_fI.transAxes,  color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+rec = ax0_fI.add_patch(rec)
+rec.set_clip_on(False)
+
+ax1_fI = plot_fI(ax1_fI, model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='slope', alt='u',  color1='lightgrey', color2='k')
+rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1  + pad_h, fill=False, lw=line_width,transform=ax1_fI.transAxes,  color=color_dict['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+rec = ax1_fI.add_patch(rec)
+rec.set_clip_on(False)
+
+ax2_fI = plot_fI(ax2_fI, model='Cb stellate', type='g', alt='Leak',  color1='lightgrey', color2='k')
+rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1  + pad_h, fill=False, lw=line_width,transform=ax2_fI.transAxes,  color=color_dict['Cb stellate'], alpha=1, zorder=-1)
+rec = ax2_fI.add_patch(rec)
+rec.set_clip_on(False)
+
+
+# plot boxplots
+boxplot_with_markers(ax0,max_width, alteration='shift')
+boxplot_with_markers(ax1,max_width, alteration='slope')
+boxplot_with_markers(ax2,max_width, alteration='g')
+
+# plot legend
+pos = (0.225, -0.9)
+ncol = 6
+model_legend(ax2,  marker_s_leg, pos, ncol)
+
+# plot rheo across model for example alteration
+plot_rheo_alt(ax0_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='shift')
+plot_rheo_alt(ax1_ex,model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',alteration='slope')
+plot_rheo_alt(ax2_ex, model='Cb stellate', color1='lightgrey', color2='k', alteration='g')
+
+# label subplots with letters
+ax0_fI.text(-0.875, 1.35, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=10, weight='bold')
+ax0_ex.text(-0.8, 1.35, string.ascii_uppercase[1], transform=ax0_ex.transAxes, size=10, weight='bold')
+ax0.text(-0.075, 1.35, string.ascii_uppercase[2], transform=ax0.transAxes, size=10, weight='bold')
+
+ax1_fI.text(-0.875, 1.35, string.ascii_uppercase[3], transform=ax1_fI.transAxes,size=10, weight='bold')
+ax1_ex.text(-0.8, 1.35, string.ascii_uppercase[4], transform=ax1_ex.transAxes, size=10, weight='bold')
+ax1.text(-0.075, 1.35, string.ascii_uppercase[5], transform=ax1.transAxes, size=10, weight='bold')
+
+ax2_fI.text(-0.875, 1.35, string.ascii_uppercase[6], transform=ax2_fI.transAxes,size=10, weight='bold')
+ax2_ex.text(-0.8, 1.35, string.ascii_uppercase[7], transform=ax2_ex.transAxes, size=10, weight='bold')
+ax2.text(-0.075, 1.35, string.ascii_uppercase[8], transform=ax2.transAxes, size=10, weight='bold')
+
+# save
+fig.set_size_inches(cm2inch(20.75,12))
+fig.savefig('./Figures/rheobase_correlation.pdf', dpi=fig.dpi)
+# fig.savefig('./Figures/rheobase_correlation.png', dpi=fig.dpi) #bbox_inches='tight', dpi=fig.dpi # eps # pdf
+plt.show()
+
+
+#%%
+# fig, axs = plt.subplots(1,2)
+# axs[0] = plot_fI(axs[0] , model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s',  color1='lightgrey', color2='k')
+# plt.show()
+#%%
+#
+#
+# cvals = [-2., 2]
+# colors = ['lightgrey', 'k']
+#
+# norm = plt.Normalize(min(cvals), max(cvals))
+# tuples = list(zip(map(norm, cvals), colors))
+# cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
+# colors = cmap(np.linspace(0, 1, 22))
+#
+# colors2 = [colors[10,:], 'k']
+# norm2 = plt.Normalize(min(cvals), max(cvals))
+# tuples2 = list(zip(map(norm2, cvals), colors2))
+# cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples2)
+#
+# colors3 = [colors[11,:], 'lightgrey']
+# norm3 = plt.Normalize(min(cvals), max(cvals))
+# tuples3 = list(zip(map(norm3, cvals), colors3))
+# cmap3 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples3)
+#
+# fig, axs = plt.subplots(1,2)
+# start = (0,0)
+# end = (1,1)
+# axs[0] = rainbowarrow(axs[0], start, end, cmap2, n=50,lw=3)
+# start = (0,0)
+# end = (-1,-1)
+# axs[0] = rainbowarrow(axs[0], start, end, cmap3, n=50,lw=3)
+# plt.show()
diff --git a/Figures/simulation_model_comparison.pdf b/Figures/simulation_model_comparison.pdf
index 051120debfda928d0f97d6bdf42b0fb9b2d2116c..9e037eba71caf3449fe460eaed977baaa53ea9bd 100644
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diff --git a/Figures/simulation_model_comparison_letters.py b/Figures/simulation_model_comparison_letters.py
new file mode 100644
index 0000000..21d0434
--- /dev/null
+++ b/Figures/simulation_model_comparison_letters.py
@@ -0,0 +1,259 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import os
+import string
+from Figures.plotstyle import sim_style
+import seaborn as sns
+import scipy.stats as stats
+import matplotlib.lines as mlines
+
+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 Kendall_tau(df):
+    tau = df.corr(method='kendall')
+    p = pd.DataFrame(columns=df.columns, index=df.columns)
+    for col in range((df.columns).shape[0]):
+        for col2 in range((df.columns).shape[0]):
+            if col != col2:
+                _, p.loc[df.columns[col], df.columns[col2]] = stats.kendalltau(
+                    df[df.columns[col]], df[df.columns[col2]], nan_policy='omit')
+    return tau, p
+
+def correlation_plot(ax, df='AUC', title='', cbar=False):
+    # cbar_ax = fig.add_axes([0.685, 0.44, .15, .01])
+    cbar_ax = fig.add_axes([0.685, 0.48, .15, .01])
+    cbar_ax.spines['left'].set_visible(False)
+    cbar_ax.spines['bottom'].set_visible(False)
+    cbar_ax.spines['right'].set_visible(False)
+    cbar_ax.spines['top'].set_visible(False)
+    cbar_ax.set_xticks([])
+    cbar_ax.set_yticks([])
+    if df == 'AUC':
+        df = pd.read_csv(os.path.join('./Figures/Data/sim_mut_AUC.csv'), index_col='Unnamed: 0')
+    elif df == 'rheo':
+        df = pd.read_csv(os.path.join('./Figures/Data/sim_mut_rheo.csv'), index_col='Unnamed: 0')
+
+    # array for names
+    cmap = sns.diverging_palette(220, 10, as_cmap=True)
+    models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
+              'STN_Kv', 'STN_Kv_only']
+    model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate',
+                   'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
+                   'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
+    # model_letter_names = ['Model H', 'Model E', 'Model G', 'Model A', 'Model F', 'Model J', 'Model L', 'Model I', 'Model K']
+    model_letter_names = ['H', 'E', 'G', 'A', 'F', 'J', 'L', 'I', 'K']
+    col_dict = {}
+    for m in range(len(models)):
+        col_dict[model_names[m]] = model_letter_names[m]
+    df.rename(columns=col_dict, inplace=True)
+    df = df[model_letter_names]
+
+    # calculate correlation matrix
+    tau, p = Kendall_tau(df)
+    # tau = tau.drop(columns='STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', index='RS pyramidal')
+
+    # mask to hide upper triangle of matrix
+    mask = np.zeros_like(tau, dtype=bool)
+    mask[np.triu_indices_from(mask)] = True
+    np.fill_diagonal(mask, False)
+
+    # models and renaming of tau
+    models = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate',
+       'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+       'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
+       'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
+    model_names = ['RS pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'RS inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'Cb stellate',  'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
+                   'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
+    # model_letter_names = ['Model H', 'Model E', 'Model G', 'Model A', 'Model F', 'Model J', 'Model L', 'Model I', 'Model K']
+    model_letter_names = ['H', 'E', 'G', 'A', 'F', 'J', 'L', 'I', 'K']
+    col_dict = {}
+    for m in range(len(models)):
+        col_dict[model_names[m]] = model_letter_names[m]
+    tau.rename(columns=col_dict, index=col_dict, inplace=True)
+    tau = tau[model_letter_names]
+
+    # plotting with or without colorbar
+    if cbar==False:
+        res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
+                          cbar=False, cmap=cmap, cbar_ax=cbar_ax, cbar_kws={"shrink": .52})
+    else:
+        res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
+                          cbar=True, cmap=cmap, cbar_ax=cbar_ax,
+                          cbar_kws={"orientation": "horizontal",
+                                    "ticks": [-1,-0.5, 0, 0.5, 1]} )
+        cbar_ax.set_title(r'Kendall $\tau$', y=1.02, loc='center', fontsize=6)
+        cbar_ax.tick_params(length=3)
+        for tick in cbar_ax.xaxis.get_major_ticks():
+            tick.label.set_fontsize(6)
+    ax.set_title(title, fontsize=8)
+    ax.set_xlabel("Model")
+    ax.set_ylabel("Model")
+
+def mutation_plot(ax, model='RS_pyramidal'):
+    models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
+              'STN_Kv', 'STN_Kv_only']
+    model_names = ['RS pyramidal', 'RS inhibitory', 'FS',
+                   'Cb stellate', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
+    model_display_names = ['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'Cb stellate',
+                   'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
+                   'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
+    model_letter_names = ['Model H',
+                           'Model E',
+                           'Model G', 'Model A',
+                           'Model F',
+                           'Model J', 'Model L',
+                           'Model I',
+                           'Model K']
+    col_dict = {}
+    for m in range(len(models)):
+        col_dict[models[m]] = model_display_names[m]
+
+    ax_dict = {}
+    ax_dict['RS_pyramidal'] = (0, 0)
+    ax_dict['RS_inhib'] = (0, 1)
+    ax_dict['FS'] = (1, 0)
+    ax_dict['Cb_stellate'] = (2, 0)
+    ax_dict['Cb_stellate_Kv'] = (2, 1)
+    ax_dict['Cb_stellate_Kv_only'] = (3, 0)
+    ax_dict['STN'] = (3, 1)
+    ax_dict['STN_Kv'] = (4, 0)
+    ax_dict['STN_Kv_only'] = (4, 1)
+
+    ylim_dict = {}
+    ylim_dict['RS_pyramidal'] = (-0.1, 0.3)
+    ylim_dict['RS_inhib'] = (-0.6, 0.6)
+    ylim_dict['FS'] = (-0.06, 0.08)
+    ylim_dict['Cb_stellate'] = (-0.1, 0.4)
+    ylim_dict['Cb_stellate_Kv'] = (-0.1, 0.5)
+    ylim_dict['Cb_stellate_Kv_only'] = (-1, 0.8)
+    ylim_dict['STN'] = (-0.01, 0.015)
+    ylim_dict['STN_Kv'] = (-0.4, 0.6)
+    ylim_dict['STN_Kv_only'] = (-0.03, 0.3)
+    Marker_dict = {'Cb stellate': 'o', 'RS Inhibitory': 'o', 'FS': 'o', 'RS Pyramidal': "^",
+                   'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "^",
+                   'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "^",
+                   'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
+                   'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
+                   'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
+                   'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "s",
+                   'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "s", 'STN': "s"}
+
+    AUC = pd.read_csv(os.path.join('./Figures/Data/sim_mut_AUC.csv'), index_col='Unnamed: 0')
+    rheo = pd.read_csv(os.path.join('./Figures/Data/sim_mut_rheo.csv'), index_col='Unnamed: 0')
+
+    mod = models.index(model)
+    mut_names = AUC.index
+    ax.plot(rheo.loc[mut_names, model_names[mod]]*1000, AUC.loc[mut_names, model_names[mod]]*100, linestyle='',
+            markeredgecolor='grey', markerfacecolor='grey', marker=Marker_dict[model_display_names[mod]],
+            markersize=2)
+
+    ax.plot(rheo.loc['wt', model_names[mod]], AUC.loc['wt', model_names[mod]]*100, 'sk')
+
+    mut_col = sns.color_palette("pastel")
+    ax.plot(rheo.loc['V174F', model_names[mod]]*1000, AUC.loc['V174F', model_names[mod]]*100, linestyle='',
+            markeredgecolor=mut_col[0], markerfacecolor=mut_col[0], marker=Marker_dict[model_display_names[mod]],markersize=4)
+    ax.plot(rheo.loc['F414C', model_names[mod]]*1000, AUC.loc['F414C', model_names[mod]]*100, linestyle='',
+            markeredgecolor=mut_col[1], markerfacecolor=mut_col[1], marker=Marker_dict[model_display_names[mod]],markersize=4)
+    ax.plot(rheo.loc['E283K', model_names[mod]]*1000, AUC.loc['E283K', model_names[mod]]*100, linestyle='',
+            markeredgecolor=mut_col[2], markerfacecolor=mut_col[2], marker=Marker_dict[model_display_names[mod]],markersize=4)
+    ax.plot(rheo.loc['V404I', model_names[mod]]*1000, AUC.loc['V404I', model_names[mod]]*100, linestyle='',
+            markeredgecolor=mut_col[3], markerfacecolor=mut_col[5], marker=Marker_dict[model_display_names[mod]],markersize=4)
+    ax.set_title(model_letter_names[mod], pad=14)
+    ax.set_xlabel('$\Delta$Rheobase [pA]')
+    ax.set_ylabel('Normalized $\Delta$AUC (%)')
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+    # ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0),useMathText=True)
+
+    xmin, xmax = ax.get_xlim()
+    ymin, ymax = ax.get_ylim()
+    ax.hlines(0, xmin, xmax, colors='lightgrey', linestyles='--')
+    ax.vlines(0, ymin,ymax, colors='lightgrey', linestyles='--')
+    return ax
+
+def mutation_legend(ax, marker_s_leg, pos, ncol):
+    colors = sns.color_palette("pastel")
+
+    Markers = ["o", "o", "o", "o"]
+    V174F = mlines.Line2D([], [], color=colors[0], marker=Markers[0], markersize=marker_s_leg, linestyle='None',
+                         label='V174F')
+    F414C = mlines.Line2D([], [], color=colors[1], marker=Markers[1], markersize=marker_s_leg, linestyle='None',
+                         label='F414C')
+    E283K = mlines.Line2D([], [], color=colors[2], marker=Markers[2], markersize=marker_s_leg, linestyle='None', label='E283K')
+    V404I = mlines.Line2D([], [], color=colors[5], marker=Markers[3], markersize=marker_s_leg, linestyle='None',
+                         label='V404I')
+    WT = mlines.Line2D([], [], color='k', marker='s', markersize=marker_s_leg+2, linestyle='None', label='Wild type')
+
+    ax.legend(handles=[WT, V174F, F414C, E283K, V404I], loc='center', bbox_to_anchor=pos, ncol=ncol, frameon=False)
+
+
+
+sim_style()
+# plot setup
+fig = plt.figure()
+gs0 = fig.add_gridspec(1, 6, wspace=-0.2)
+gsl = gs0[0:3].subgridspec(3, 3, wspace=0.9, hspace=0.8)
+gsr = gs0[4:6].subgridspec(7, 1, wspace=0.6, hspace=0.8)
+
+ax00 = fig.add_subplot(gsl[0,0])
+ax01 = fig.add_subplot(gsl[0,1])
+ax02 = fig.add_subplot(gsl[0,2])
+ax10 = fig.add_subplot(gsl[1,0])
+ax11 = fig.add_subplot(gsl[1,1])
+ax12 = fig.add_subplot(gsl[1,2])
+ax20 = fig.add_subplot(gsl[2,0])
+ax21 = fig.add_subplot(gsl[2,1])
+ax22 = fig.add_subplot(gsl[2,2])
+
+axr0 = fig.add_subplot(gsr[0:3,0])
+axr1 = fig.add_subplot(gsr[4:,0])
+
+# plot mutations in each model
+ax00 = mutation_plot(ax00, model='RS_pyramidal')
+ax01 = mutation_plot(ax01, model='RS_inhib')
+ax02 = mutation_plot(ax02, model='FS')
+ax10 = mutation_plot(ax10, model='Cb_stellate')
+ax11 = mutation_plot(ax11, model='Cb_stellate_Kv')
+ax12 = mutation_plot(ax12, model='Cb_stellate_Kv_only')
+ax20 = mutation_plot(ax20, model='STN')
+ax21 = mutation_plot(ax21, model='STN_Kv')
+ax22 = mutation_plot(ax22, model='STN_Kv_only')
+
+marker_s_leg = 4
+pos = (0.425, -0.7)
+ncol = 5
+mutation_legend(ax21, marker_s_leg, pos, ncol)
+
+# plot correlation matrices
+correlation_plot(axr1,df = 'AUC', title='Normalized $\Delta$AUC', cbar=False)
+correlation_plot(axr0,df = 'rheo', title='$\Delta$Rheobase', cbar=True)
+
+# add subplot labels
+axs = [ax00, ax01,ax02, ax10, ax11, ax12, ax20, ax21, ax22]
+j=0
+for i in range(0,9):
+    # axs[i].text(-0.48, 1.175, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
+    axs[i].text(-0.625, 1.25, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
+    j +=1
+axr0.text(-0.77, 1.1, string.ascii_uppercase[j], transform=axr0.transAxes, size=10, weight='bold')
+axr1.text(-0.77, 1.1, string.ascii_uppercase[j+1], transform=axr1.transAxes, size=10, weight='bold')
+
+# save
+fig.set_size_inches(cm2inch(22.2,15))
+fig.savefig('./Figures/simulation_model_comparison.pdf', dpi=fig.dpi) #eps
+# fig.savefig('./Figures/simulation_model_comparison.png', dpi=fig.dpi) #eps
+plt.show()
+
diff --git a/manuscript.tex b/manuscript.tex
index 9f584fd..886e83b 100644
--- a/manuscript.tex
+++ b/manuscript.tex
@@ -190,8 +190,7 @@ However, the effect of a given channelopathy on the firing behavior of different
 
 For instance, altering relative amplitudes of ionic currents can dramatically influence the firing behavior and dynamics of neurons \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012, Noebels2017, Layer2021}, however other properties of ionic currents impact neuronal firing as well. Cell-type specific effects one firing can occur for instance increases inhibitory interneuron but not pyramidal neuron firing with R1648H mutation in \textit{SCN1A} \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the R1627H  \textit{SCN8A} mutation is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{makinson_scn1a_2016}.
 
-\textcolor{red}{Despite this evidence of cell-type specific effects of ion channel mutations on firing, the dependence of firing outcomes of ion channel mutations is generally not known. Cell-type specificity is likely vital for successful precision medicine treatment approaches. For example, Dravet syndrome was identified as the consquence of LOF mutations in \textit{SCN1A} \citep{Claes2001,Fujiwara2003,Ohmori2002}, however limited succes in treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001}. Once it became evident that only inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations alternative approaches, based on this understanding such as gene therapy, began to show promise \citep{Colasante2020, Yu2006}. Due to the high clinical relevance of understanding cell-type dependent effects of channelopathies, we use computationaly modelling approaches to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}.
-} 
+Despite this evidence of cell-type specific effects of ion channel mutations on firing, the dependence of firing outcomes of ion channel mutations is generally not known. Cell-type specificity is likely vital for successful precision medicine treatment approaches. For example, Dravet syndrome was identified as the consquence of LOF mutations in \textit{SCN1A} \citep{Claes2001,Fujiwara2003,Ohmori2002}, however limited succes in treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001}. Once it became evident that only inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations alternative approaches, based on this understanding such as gene therapy, began to show promise \citep{Colasante2020, Yu2006}. Due to the high clinical relevance of understanding cell-type dependent effects of channelopathies, we use computationaly modelling approaches to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}.
 
 %Computational modelling approaches can be used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}. 
 
@@ -311,7 +310,8 @@ Qualitative differences can be found, for example, when increasing the maximal c
         \centering
         \includegraphics[width=\linewidth]{Figures/AUC_correlation.pdf}
         \linespread{1.}\selectfont
-        \caption[]{Effects of altered channel kinetics on AUC in various neuron models. The fI curves corresponding to shifts in FS \(+\)\Kv model delayed rectifier K half activation \(V_{1/2}\) (A), changes \Kv activation slope factor \(k\) in the FS \(+\)\Kv model (D), and changes in maximal conductance of delayed rectifier K current in the STN \(+\)\Kv model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); D), \Kv activation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the delayed rectifier K current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \ndAUC (C), slope factor k and \ndAUC (F) as well as maximal current conductances and \ndAUC (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\)  and \ndAUC for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively. }
+%        \caption[]{Effects of altered channel kinetics on AUC in various neuron models. The fI curves corresponding to shifts in FS \(+\)\Kv model delayed rectifier K half activation \(V_{1/2}\) (A), changes \Kv activation slope factor \(k\) in the FS \(+\)\Kv model (D), and changes in maximal conductance of delayed rectifier K current in the STN \(+\)\Kv model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); D), \Kv activation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the delayed rectifier K current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \ndAUC (C), slope factor k and \ndAUC (F) as well as maximal current conductances and \ndAUC (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\)  and \ndAUC for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively. }
+	\caption[]{Effects of altered channel kinetics on AUC in various neuron models. The fI curves corresponding to shifts in model G delayed rectifier K half activation \(V_{1/2}\) (A), changes \Kv activation slope factor \(k\) in model G (D), and changes in maximal conductance of delayed rectifier K current in the  model I (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); D), \Kv activation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the delayed rectifier K current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \ndAUC (C), slope factor k and \ndAUC (F) as well as maximal current conductances and \ndAUC (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\)  and \ndAUC for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively. }
         \label{fig:AUC_correlation}
 \end{figure}
 
@@ -321,14 +321,15 @@ Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) a
 
 %Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlated with rheobase similarly across model there were some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affected the rheobase both with positive and negative correlations in different models \textcolor{red}{\noteuh{Würde diese hier noch mal benennen, damit es klar wird. }}\notenk{Ich mache das ungern, weil ich für jedes (Na-current inactivation, \Kv-current inactivation, and A-current activation) 2 Liste habe (+ und - rheobase Aenderungen} (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occurred in some models as a result of K-current activation  \(V_{1/2}\) and slope factor \(k\), \Kv-current inactivation slope factor \(k\), and A-current activation slope factor \(k\) in some models \textcolor{red}{\noteuh{Auch hier die unterschiedlcihen betroffenen cell type models benennen, einfach in Klammer dahinter.}}\notenk{Hier mache ich das auch ungern, für ähnlichen Gründen}. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. 
 
-Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlated with rheobase similarly across model there were some exceptions. Rheobase was affected with both with positive and negative correlations in different models as a result of changing slope factor of \textcolor{red}{Na-current inactivation (positive: models A-H and J; negative: models I, K and L), \Kv-current inactivation (positive: models I and K; negative: models E-G, J, H), and A-current activation (positive: models A,F and L; negative: model I)}  (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occurred in some models as a result of \textcolor{red}{K-current activation  \(V_{1/2}\) (e.g. model J) and slope factor \(k\) (models F and G), \Kv-current inactivation slope factor \(k\) (model K), and A-current activation slope factor \(k\) (model L) }. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. 
+Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlated with rheobase similarly across model there were some exceptions. Rheobase was affected with both with positive and negative correlations in different models as a result of changing slope factor of Na-current inactivation (positive: models A-H and J; negative: models I, K and L), \Kv-current inactivation (positive: models I and K; negative: models E-G, J, H), and A-current activation (positive: models A,F and L; negative: model I; \Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occurred in some models as a result of K-current activation  \(V_{1/2}\) (e.g. model J) and slope factor \(k\) (models F and G), \Kv-current inactivation slope factor \(k\) (model K), and A-current activation slope factor \(k\) (model L). Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. 
 
 
 \begin{figure}[tp]
         \centering
         \includegraphics[width=\linewidth]{Figures/rheobase_correlation.pdf}
         \linespread{1.}\selectfont
-        \caption[]{Effects of altered channel kinetics on rheobase.  The fI curves corresponding to shifts in FS \(+\)\Kv model \Kv activation \(V_{1/2}\) (A), changes \Kv inactivation slope factor \(k\) in the Cb stellate \(+\)\Kv model (D), and changes in maximal conductance of the leak current in the Cb stellate model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \drheo of fI curves is plotted against \Kv half activation potential (\(\Delta V_{1/2}\); B), \Kv inactivation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the leak current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \drheo (C), slope factor k and \drheo (F) as well as maximal current conductances and \drheo (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\)  and \drheo for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively.}
+%        \caption[]{Effects of altered channel kinetics on rheobase.  The fI curves corresponding to shifts in FS \(+\)\Kv model \Kv activation \(V_{1/2}\) (A), changes \Kv inactivation slope factor \(k\) in the Cb stellate \(+\)\Kv model (D), and changes in maximal conductance of the leak current in the Cb stellate model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \drheo of fI curves is plotted against \Kv half activation potential (\(\Delta V_{1/2}\); B), \Kv inactivation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the leak current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \drheo (C), slope factor k and \drheo (F) as well as maximal current conductances and \drheo (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\)  and \drheo for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively.}
+        \caption[]{Effects of altered channel kinetics on rheobase.  The fI curves corresponding to shifts in model G \Kv activation \(V_{1/2}\) (A), changes \Kv inactivation slope factor \(k\) in model F (D), and changes in maximal conductance of the leak current in model A (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \drheo of fI curves is plotted against \Kv half activation potential (\(\Delta V_{1/2}\); B), \Kv inactivation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the leak current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \drheo (C), slope factor k and \drheo (F) as well as maximal current conductances and \drheo (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\)  and \drheo for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively.}
         \label{fig:rheobase_correlation}
 \end{figure}
 
@@ -339,7 +340,8 @@ Mutations in \textit{KCNA1} are associated with episodic ataxia type~1 (EA1) and
         \centering
         \includegraphics[width=\linewidth]{Figures/simulation_model_comparison.pdf}
         \linespread{1.}\selectfont
-        \caption[]{Effects of episodic ataxia type~1 associated \textit{KCNA1} mutations on firing.  Effects of \textit{KCNA1} mutations on AUC (percent change in normalized \(\Delta\)AUC) and rheobase (\(\Delta\)Rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models. All \textit{KCNA1} Mutations are marked in grey with the V174F, F414C, E283K, and V404I \textit{KCNA1} mutations highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \textit{KCNA1} mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type, and grey dashed lines denote the quadrants of firing characterization (see \Cref{fig:firing_characterization}).}
+%        \caption[]{Effects of episodic ataxia type~1 associated \textit{KCNA1} mutations on firing.  Effects of \textit{KCNA1} mutations on AUC (percent change in normalized \(\Delta\)AUC) and rheobase (\(\Delta\)Rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models. All \textit{KCNA1} Mutations are marked in grey with the V174F, F414C, E283K, and V404I \textit{KCNA1} mutations highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \textit{KCNA1} mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type, and grey dashed lines denote the quadrants of firing characterization (see \Cref{fig:firing_characterization}).}
+	\caption[]{Effects of episodic ataxia type~1 associated \textit{KCNA1} mutations on firing.  Effects of \textit{KCNA1} mutations on AUC (percent change in normalized \(\Delta\)AUC) and rheobase (\(\Delta\)Rheobase) compared to wild type for model H (A), model E (B), model G (C), model A (D), model F (E), model J (F), model L (G), model I (H) and model K (I). All \textit{KCNA1} Mutations are marked in grey with the V174F, F414C, E283K, and V404I \textit{KCNA1} mutations highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \textit{KCNA1} mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type, and grey dashed lines denote the quadrants of firing characterization (see \Cref{fig:firing_characterization}).}
         \label{fig:simulation_model_comparision}
 \end{figure}