updated spacing in Fig 1 and section numbering in diff tex file

and old firing_characterization file to firing_characterization_old.py
 also renamed ramp_examples.py as ramp_firing.py; cleaned up comments
 and code for rheobase_correlation.py, AUC_correlation.py, diversity_in_firing.py,
 ramp_firing.py, simulation_model_comparison.py

.gitignore

@@ -0,0 +1,385 @@
+*~
+
+# ---> TeX
+## Core latex/pdflatex auxiliary files:
+*.aux
+*.lof
+*.log
+*.lot
+*.fls
+*.out
+*.toc
+*.fmt
+*.fot
+*.cb
+*.cb2
+.*.lb
+
+## Intermediate documents:
+*.dvi
+*.xdv
+*-converted-to.*
+# these rules might exclude image files for figures etc.
+# *.ps
+# *.eps
+
+
+## Generated if empty string is given at "Please type another file name for output:"
+.pdf
+
+## Bibliography auxiliary files (bibtex/biblatex/biber):
+*.bbl
+*.bcf
+*.blg
+*-blx.aux
+*-blx.bib
+*.run.xml
+
+## Build tool auxiliary files:
+*.fdb_latexmk
+*.synctex
+*.synctex(busy)
+*.synctex.gz
+*.synctex.gz(busy)
+*.pdfsync
+
+## Build tool directories for auxiliary files
+# latexrun
+latex.out/
+
+## Auxiliary and intermediate files from other packages:
+# algorithms
+*.alg
+*.loa
+
+# achemso
+acs-*.bib
+
+# amsthm
+*.thm
+
+# beamer
+*.nav
+*.pre
+*.snm
+*.vrb
+
+# changes
+*.soc
+
+# comment
+*.cut
+
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+*.cpt
+
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+*.spl
+
+# endnotes
+*.ent
+
+# fixme
+*.lox
+
+# feynmf/feynmp
+*.mf
+*.mp
+*.t[1-9]
+*.t[1-9][0-9]
+*.tfm
+
+#(r)(e)ledmac/(r)(e)ledpar
+*.end
+*.?end
+*.[1-9]
+*.[1-9][0-9]
+*.[1-9][0-9][0-9]
+*.[1-9]R
+*.[1-9][0-9]R
+*.[1-9][0-9][0-9]R
+*.eledsec[1-9]
+*.eledsec[1-9]R
+*.eledsec[1-9][0-9]
+*.eledsec[1-9][0-9]R
+*.eledsec[1-9][0-9][0-9]
+*.eledsec[1-9][0-9][0-9]R
+
+# glossaries
+*.acn
+*.acr
+*.glg
+*.glo
+*.gls
+*.glsdefs
+*.lzo
+*.lzs
+
+# uncomment this for glossaries-extra (will ignore makeindex's style files!)
+# *.ist
+
+# gnuplottex
+*-gnuplottex-*
+
+# gregoriotex
+*.gaux
+*.gtex
+
+# htlatex
+*.4ct
+*.4tc
+*.idv
+*.lg
+*.trc
+*.xref
+
+# hyperref
+*.brf
+
+# knitr
+*-concordance.tex
+# TODO Comment the next line if you want to keep your tikz graphics files
+*.tikz
+*-tikzDictionary
+
+# listings
+*.lol
+
+# luatexja-ruby
+*.ltjruby
+
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+*.idx
+*.ilg
+*.ind
+
+# minitoc
+*.maf
+*.mlf
+*.mlt
+*.mtc[0-9]*
+*.slf[0-9]*
+*.slt[0-9]*
+*.stc[0-9]*
+
+# minted
+_minted*
+*.pyg
+
+# morewrites
+*.mw
+
+# nomencl
+*.nlg
+*.nlo
+*.nls
+
+# pax
+*.pax
+
+# pdfpcnotes
+*.pdfpc
+
+# sagetex
+*.sagetex.sage
+*.sagetex.py
+*.sagetex.scmd
+
+# scrwfile
+*.wrt
+
+# sympy
+*.sout
+*.sympy
+sympy-plots-for-*.tex/
+
+# pdfcomment
+*.upa
+*.upb
+
+# python
+*.pyc
+__pycache__/
+
+# pythontex
+*.pytxcode
+pythontex-files-*/
+
+# tcolorbox
+*.listing
+
+# thmtools
+*.loe
+
+# TikZ & PGF
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+*.md5
+*.auxlock
+
+# todonotes
+*.tdo
+
+# vhistory
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+*.ver
+
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+*.lod
+
+# xcolor
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+
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+
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+*.xdy
+
+# xypic precompiled matrices and outlines
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+*.xyd
+
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+
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+TSWLatexianTemp*
+
+## Editors:
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+*.bak
+*.sav
+
+# Texpad
+.texpadtmp
+
+# LyX
+*.lyx~
+
+# Kile
+*.backup
+
+# gummi
+.*.swp
+
+# KBibTeX
+*~[0-9]*
+
+# TeXnicCenter
+*.tps
+
+# auto folder when using emacs and auctex
+./auto/*
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+
+# expex forward references with \gathertags
+*-tags.tex
+
+# standalone packages
+*.sta
+
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+*.lpz
+
+
+beat_results_smoothed.npy
+beat_results_smoothed.pkl
+data_baseline.npy
+data_baseline.pkl
+data_beat.npy
+data_beat.pkl
+exlude_beats.npy
+beat_results_smoothed.csv
+.pdf.png
+compare.png
+2019-09-10-ab-invivo-1/2019-09-10-ab-invivo-1.nix
+2019-09-10-ab-invivo-1/chirpallspikess1.dat
+2019-09-10-ab-invivo-1/chirprates1.dat
+2019-09-10-ab-invivo-1/chirps-events.dat
+2019-09-10-ab-invivo-1/chirpss.dat
+2019-09-10-ab-invivo-1/chirpspikess1.dat
+2019-09-10-ab-invivo-1/chirptracess.dat
+2019-09-10-ab-invivo-1/chirpeodampls.dat
+meansquare2.csv
+highbeats.pdf
+highbeats.pdf
+differentcells_trans.png
+MPFmodulation_tob.png
+singlecellexample5.png
+
+highbeats.pdf
+beats.npy
+*.png
+*.svg
+highbeats_backup/
+highbeats_Review/
+1. Introduction.pdf
+abstract (2).pdf
+abstract.pdf
+abstract_illustrator-3.pdf
+abstract_illustrator.pdf
+ActivationFunction_SAM.pdf
+amexamples.pdf
+ammethodspowerspectra.pdf
+beatmethodspowerspectra.pdf
+beatspectra.pdf
+beatspectra_orig.pdf
+beatspectra_power.pdf
+beatspectra_rectify.pdf
+dampinghopkins.pdf
+doc/1pseperateaug.pdf
+doc/1psingleauthorgroup.pdf
+doc/elsdoc.pdf
+doc/elstest-1p.pdf
+doc/elstest-1pdoubleblind.pdf
+doc/elstest-3p.pdf
+doc/elstest-3pd.pdf
+doc/elstest-5p.pdf
+doc/jfigs.pdf
+efishsketch.pdf
+EIF.pdf
+EIF_appendix.pdf
+EIF_original.pdf
+elsarticle-template-num-names.pdf
+elsarticle/doc/1pseperateaug.pdf
+elsarticle/doc/1psingleauthorgroup.pdf
+elsarticle/doc/elsdoc.pdf
+elsarticle/doc/elstest-1p.pdf
+elsarticle/doc/elstest-1pdoubleblind.pdf
+elsarticle/doc/elstest-3p.pdf
+elsarticle/doc/elstest-3pd.pdf
+elsarticle/doc/elstest-5p.pdf
+elsarticle/doc/jfigs.pdf
+EODwaveform.pdf
+EODwaveform_otherfish.pdf
+graphic_abstract-2.pdf
+highbeats_bef2_rev.pdf
+highbeats_elsarticle.pdf
+JAR.pdf
+lockingspectra_adapt.pdf
+lowpass.pdf
+modulation_div_fr.pdf
+MSE.pdf
+MSE_modulation_error.pdf
+numerical_compar_both.pdf
+nyquist.pdf
+Phase.pdf
+population.pdf
+population_orig.pdf
+power.pdf
+problem.pdf
+quit.pdf
+rebuttal1.pdf
+rebuttal2.pdf
+rebuttal3.pdf
+rectify.pdf
+resonance.pdf
+RezeptorSkizze.pdf
+SAM.pdf
+samspectra.pdf
+Skizze2.pdf
+Spikes.pdf
+toblerone.pdf
+toblerone_animation.pdf
+CurrentBiology_Guidlines.PDF

Code/Functions/Cb_stellate_fxns.py

@@ -0,0 +1,971 @@
+# -*- coding: utf-8 -*-
+"""
+Functions for Cb Stellate model
+
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+
+import h5py
+import gc
+import scipy
+import json
+import copy
+import os
+import numpy as np
+from numba import types, njit
+from numba.typed import Dict
+from scipy import signal
+from Code.Functions.Utility_fxns import stimulus_init, NumpyEncoder
+
+
+@njit
+def Cb_stellate(V, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+                gating, current, ind_dict):
+    """ Simulate current and Vm for  Cb stellate cell model from Alexander et al 2019 (https://dx.doi.org/10.1523%2FENEURO.0126-19.2019)
+        for I_in
+
+        Parameters
+        ----------
+        V : float
+            Initial membrane potentia
+        g : dict
+            maximal conductance for currents
+        E : dict
+            Reversal potentials for currents
+        I_in : array
+            Current stimulus input to model
+        dt : float
+            fixed time step
+        currents_included : dict
+            Boolean to include current in simulation or not
+        stim_time :
+            length of stimulus ()
+        stim_num : int
+            number of different stimulus currents
+        C : float
+            membrane capacitance
+        shift : dict
+            for each gating parameter
+        scale : dict
+            for each gating parameter
+        b_param : dict
+            Boltzmann parameter values for each gating parameter
+        slope_shift : dict
+            for each gating parameter
+        gating : array
+            array of gating values over time
+        current : array
+            array of current values over time
+        ind_dict: dict
+            Dictionary of indices in arrays for gating variables
+
+
+        Returns
+        -------
+        V_m : array
+            Simulated membrane potential in response to I_in
+        """
+
+    V_m = np.zeros((stim_num, stim_time))
+    # initialize gating
+
+    # Na activation
+    m_inf = (1 / (1 + np.exp((V - shift["m"] - slope_shift["m"] - b_param["m"][0]) / b_param["m"][1]))) ** \
+            b_param["m"][2]
+    gating[ind_dict['m'], :] = np.ones((stim_num)) * m_inf
+
+    # Na inactivation
+    h_inf = ((1 - b_param["h"][3]) / (1 + np.exp((V - shift["h"] - slope_shift["h"] - b_param["h"][0])
+                                                 / b_param["h"][1])) + b_param["h"][3]) ** b_param["h"][2]
+    gating[ind_dict['h'], :] = np.ones((stim_num)) * h_inf
+
+    # K activation
+    n_inf = (1 / (1 + np.exp((V - shift["n"] - slope_shift["n"] - b_param["n"][0]) / b_param["n"][1]))) ** \
+            b_param["n"][2]
+    gating[ind_dict['n'], :] = np.ones((stim_num)) * n_inf
+
+    # A-type K activation
+    n_A_inf = (1 / (1 + np.exp((V - shift["n_A"] - slope_shift["n_A"] - b_param["n_A"][0]) / b_param["n_A"][1]))) ** \
+              b_param["n_A"][2]
+    gating[ind_dict['n_A'], :] = np.ones((stim_num)) * n_A_inf
+
+    # A-type K inactivation
+    h_A_inf = (1 / (1 + np.exp((V - shift["h_A"] - slope_shift["h_A"] - b_param["h_A"][0]) / b_param["h_A"][1]))) ** \
+              b_param["h_A"][2]
+    gating[ind_dict['h_A'], :] = np.ones((stim_num)) * h_A_inf
+
+    # mutant A-type K activation
+    n_A_mut_inf = (1 / (1 + np.exp((V - shift["n_A_mut"] - slope_shift["n_A_mut"] - b_param["n_A_mut"][0]) / b_param["n_A_mut"][1]))) ** \
+              b_param["n_A_mut"][2]
+    gating[ind_dict['n_A_mut'], :] = np.ones((stim_num)) * n_A_mut_inf
+
+    # mutant A-type K inactivation
+    h_A_mut_inf = (1 / (1 + np.exp((V - shift["h_A_mut"] - slope_shift["h_A_mut"] - b_param["h_A_mut"][0]) / b_param["h_A_mut"][1]))) ** \
+              b_param["h_A_mut"][2]
+    gating[ind_dict['h_A_mut'], :] = np.ones((stim_num)) * h_A_mut_inf
+
+    # T-type Ca activation
+    m_T_inf = (1 / (1 + np.exp((V - shift["m_T"] - slope_shift["m_T"] - b_param["m_T"][0]) / b_param["m_T"][1]))) ** \
+              b_param["m_T"][2]
+    gating[ind_dict['m_T'], :] = np.ones((stim_num)) * m_T_inf
+
+    # T-type Ca inactivation
+    h_T_inf = (1 / (1 + np.exp((V - shift["h_T"] - slope_shift["h_T"] - b_param["h_T"][0]) / b_param["h_T"][1]))) ** \
+              b_param["h_T"][2]
+    gating[ind_dict['h_T'], :] = np.ones((stim_num)) * h_T_inf
+
+    # initialize currents and Vm
+    current[ind_dict['Na'], :] = np.zeros((stim_num))
+    current[ind_dict['Kd'], :] = np.zeros((stim_num))
+    current[ind_dict['Leak'], :] = np.zeros((stim_num))
+    current[ind_dict['A'], :] = np.zeros((stim_num))
+    current[ind_dict['A_mut'], :] = np.zeros((stim_num))
+    current[ind_dict['T'], :] = np.zeros((stim_num))
+    V_m[:, 0] = V
+    t = 1
+    while t < stim_time:
+        # Na activation
+        m_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["m"] - slope_shift["m"] - b_param["m"][0]) / b_param["m"][1]))) ** \
+                b_param["m"][2]
+        gating[ind_dict['m'], :] = m_inf
+        gating[ind_dict['m'], :][gating[ind_dict['m'], :] > 1.] = 1.
+        gating[ind_dict['m'], :][gating[ind_dict['m'], :] < 0.] = 0.
+
+        # Na inactivation
+        h_inf = ((1 - b_param["h"][3]) / (1 + np.exp((V_m[:, t - 1] - shift["h"] - slope_shift["h"] - b_param["h"][0])
+                                                     / b_param["h"][1])) + b_param["h"][3]) ** b_param["h"][2]
+        tau_h = 0.1 + (2 * 322 * 46) / (4 * np.pi * (V_m[:, t - 1] - shift["h"] - -74) ** 2 + (46 ** 2))
+        h_dot = (h_inf - gating[ind_dict['h'], :]) * (1 / (tau_h * scale["h"]))
+        gating[ind_dict['h'], :] = gating[ind_dict['h'], :] + h_dot * dt
+        gating[ind_dict['h'], :][gating[ind_dict['h'], :] > 1.] = 1.
+        gating[ind_dict['h'], :][gating[ind_dict['h'], :] < 0.] = 0.
+
+        # K activation
+        n_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["n"] - slope_shift["n"] - b_param["n"][0]) / b_param["n"][1]))) ** \
+                b_param["n"][2]
+        tau_n = 6 / (1 + np.exp((V_m[:, t - 1] - shift["n"] + 23) / 15))
+        n_dot = (n_inf - gating[ind_dict['n'], :]) * (1 / (tau_n * scale["n"]))
+        gating[ind_dict['n'], :] = gating[ind_dict['n'], :] + n_dot * dt
+        gating[ind_dict['n'], :][gating[ind_dict['n'], :] > 1.] = 1.
+        gating[ind_dict['n'], :][gating[ind_dict['n'], :] < 0.] = 0.
+
+        # A-type K activation
+        n_A_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["n_A"] - slope_shift["n_A"] - b_param["n_A"][0]) / b_param["n_A"][1]))) ** \
+                  b_param["n_A"][2]
+        tau_n_A = 5
+        n_A_dot = (n_A_inf - gating[ind_dict['n_A'], :]) * (1 / (tau_n_A * scale["n_A"]))
+        gating[ind_dict['n_A'], :] = gating[ind_dict['n_A'], :] + n_A_dot * dt
+        gating[ind_dict['n_A'], :][gating[ind_dict['n_A'], :] > 1.] = 1.
+        gating[ind_dict['n_A'], :][gating[ind_dict['n_A'], :] < 0.] = 0.
+
+        # A-type K inactivation
+        h_A_inf = (1 / (1 + np.exp(
+            (V_m[:, t - 1] - shift["h_A"] - slope_shift["h_A"] - b_param["h_A"][0]) / b_param["h_A"][1]))) ** \
+                  b_param["h_A"][2]
+        tau_h_A = 10
+        h_A_dot = (h_A_inf - gating[ind_dict['h_A'], :]) * (1 / (tau_h_A * scale["h_A"]))
+        gating[ind_dict['h_A'], :] = gating[ind_dict['h_A'], :] + h_A_dot * dt
+        gating[ind_dict['h_A'], :][gating[ind_dict['h_A'], :] > 1.] = 1.
+        gating[ind_dict['h_A'], :][gating[ind_dict['h_A'], :] < 0.] = 0.
+
+        # mutant A-type K activation
+        n_A_mut_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["n_A_mut"] - slope_shift["n_A_mut"] - b_param["n_A_mut"][0]) / b_param["n_A_mut"][1]))) ** \
+                  b_param["n_A_mut"][2]
+        tau_n_A_mut = 5
+        n_A_mut_dot = (n_A_mut_inf - gating[ind_dict['n_A_mut'], :]) * (1 / (tau_n_A_mut * scale["n_A_mut"]))
+        gating[ind_dict['n_A_mut'], :] = gating[ind_dict['n_A_mut'], :] + n_A_mut_dot * dt
+        gating[ind_dict['n_A_mut'], :][gating[ind_dict['n_A_mut'], :] > 1.] = 1.
+        gating[ind_dict['n_A_mut'], :][gating[ind_dict['n_A_mut'], :] < 0.] = 0.
+
+        # mutant A-type K inactivation
+        h_A_mut_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["h_A_mut"] - slope_shift["h_A_mut"] - b_param["h_A_mut"][0]) / b_param["h_A_mut"][1]))) ** \
+                  b_param["h_A_mut"][2]
+        tau_h_A_mut = 10
+        h_A_mut_dot = (h_A_mut_inf - gating[ind_dict['h_A_mut'], :]) * (1 / (tau_h_A_mut * scale["h_A_mut"]))
+        gating[ind_dict['h_A_mut'], :] = gating[ind_dict['h_A_mut'], :] + h_A_mut_dot * dt
+        gating[ind_dict['h_A_mut'], :][gating[ind_dict['h_A_mut'], :] > 1.] = 1.
+        gating[ind_dict['h_A_mut'], :][gating[ind_dict['h_A_mut'], :] < 0.] = 0.
+
+        # T-type Ca activation
+        m_T_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["m_T"] - slope_shift["m_T"] - b_param["m_T"][0]) / b_param["m_T"][1]))) ** \
+                  b_param["m_T"][2]
+        gating[ind_dict['m_T'], :] = m_T_inf
+        gating[ind_dict['m_T'], :][gating[ind_dict['m_T'], :] > 1.] = 1.
+        gating[ind_dict['m_T'], :][gating[ind_dict['m_T'], :] < 0.] = 0.
+
+        # T-type Ca inactivation
+        h_T_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["h_T"] - slope_shift["h_T"] - b_param["h_T"][0]) / b_param["h_T"][1]))) ** \
+                  b_param["h_T"][2]
+        tau_h_T = 15
+        h_T_dot = (h_T_inf - gating[ind_dict['h_T'], :]) * (1 / (tau_h_T * scale["h_T"]))
+        gating[ind_dict['h_T'], :] = gating[ind_dict['h_T'], :] + h_T_dot * dt
+        gating[ind_dict['h_T'], :][gating[ind_dict['h_T'], :] > 1.] = 1.
+        gating[ind_dict['h_T'], :][gating[ind_dict['h_T'], :] < 0.] = 0.
+
+        # update currents
+        current[ind_dict['Leak'], :] = g["Leak"] * (V_m[:, t - 1] - E["Leak"]) * currents_included["Leak"]
+        current[ind_dict['Na'], :] = g["Na"] * (gating[ind_dict['m'], :] ** 3) * gating[ind_dict['h'], :] * \
+                                     (V_m[:, t - 1] - E["Na"]) * currents_included["Na"]
+        current[ind_dict['Kd'], :] = g["Kd"] * (gating[ind_dict['n'], :] ** 4) * (V_m[:, t - 1] - E["K"]) * \
+                                     currents_included["Kd"]
+        current[ind_dict['A'], :] = g["A"] * gating[ind_dict['n_A'], :] * gating[ind_dict['h_A'], :] * (
+                V_m[:, t - 1] - E["K"]) * currents_included["A"]
+        current[ind_dict['A_mut'], :] = g["A_mut"] * gating[ind_dict['n_A_mut'], :] * gating[ind_dict['h_A_mut'], :] * (
+                V_m[:, t - 1] - E["K"]) * currents_included["A_mut"]
+        current[ind_dict['T'], :] = g["T"] * gating[ind_dict['m_T'], :] * gating[ind_dict['h_T'], :] * (
+                V_m[:, t - 1] - E["Ca"]) * currents_included["T"]
+
+        # update dV/dt
+        V_dot = (1 / C) * (I_in[t, :] - (current[ind_dict['Leak'], :] + current[ind_dict['Na'], :] +
+                                         current[ind_dict['Kd'], :] + current[ind_dict['A'], :] +
+                                         current[ind_dict['A_mut'], :] + current[ind_dict['T'], :]))
+        # update V_m
+        V_m[:, t] = V_m[:, t - 1] + V_dot * dt
+        t += 1
+
+    return V_m
+
+
+def Cb_stellate_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+                    gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+                    initial_period, sec):
+    """ Simulate mutations for Cb Stellate model
+
+            Parameters
+            ----------
+            V_init : float
+                Initial membrane potential
+            g : dict
+                maximal conductance for currents
+            E : dict
+                Reversal potentials for currents
+            I_in : array
+                Current stimulus input to model
+            dt : float
+                fixed time step
+            currents_included : dict
+                Boolean to include current in simulation or not
+            stim_time :
+                length of stimulus ()
+            stim_num : int
+                number of different stimulus currents
+            C : float
+                membrane capacitance
+            shift : dict
+                for each gating parameter
+            scale : dict
+                for each gating parameter
+            b_param : dict
+                Boltzmann parameter values for each gating parameter
+            slope_shift : dict
+                for each gating parameter
+            gating : array
+                array of gating values over time
+            current : array
+                array of current values over time
+            prominence : float
+                required spike prominence
+            desired_AUC_width : float
+                width of AUC from rheobase to rheobase+desired_AUC_width
+            mutations : dict
+                dictionary of mutation effects
+            mut : str
+                mutation
+            folder : str
+                folder to save hdf5 file in
+            high : float
+                upper current step magnitude
+            low : float
+                lowest current step magnitude
+            number_steps : int
+                number of current steps between low and high
+            initial_period : float
+                length  of  I = 0 before current step
+            sec:
+                length of current step in seconds
+
+            Returns
+            -------
+            saves mut.hdf5 file to folder
+            """
+    # setup for simulation
+    g_multi = copy.deepcopy(g)
+    E_multi = copy.deepcopy(E)
+    b_param_multi = copy.deepcopy(b_param)
+    shift_multi = copy.deepcopy(shift)
+    scale_multi = copy.deepcopy(scale)
+    slope_shift_multi = copy.deepcopy(slope_shift)
+    currents_included_multi = copy.deepcopy(currents_included)
+    gating2 = copy.deepcopy(gating)
+    current2 = copy.deepcopy(current)
+
+    g_effect = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in g_multi.items():
+        g_effect[k] = v
+    b_param_effect = Dict.empty(key_type=types.unicode_type, value_type=types.float64[:], )
+    for k, v in b_param_multi.items():
+        b_param_effect[k] = np.array(v)
+    shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in shift_multi.items():
+        shift2[k] = v
+    scale2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in scale_multi.items():
+        scale2[k] = v
+    slope_shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in slope_shift_multi.items():
+        slope_shift2[k] = v
+    E2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in E_multi.items():
+        E2[k] = v
+    currents_included2 = Dict.empty(key_type=types.unicode_type, value_type=types.boolean, )
+    for k, v in currents_included_multi.items():
+        currents_included2[k] = v
+    ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+    i = 0
+    for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T']):
+        ind_dict[var] = i
+        i += 1
+    i = 0
+    for var in np.array(['Na', 'Kd', 'A', 'A_mut', 'T', 'Leak']):
+        ind_dict[var] = i
+        i += 1
+
+    # effect of mutation
+    g_effect['A_mut'] = g['A_mut'] * mutations[mut]['g_ratio']
+    b_param_effect['n_A_mut'][0] = b_param_effect['n_A_mut'][0] + mutations[mut]['activation_Vhalf_diff']
+    b_param_effect['n_A_mut'][1] = b_param_effect['n_A_mut'][1] * mutations[mut]['activation_k_ratio']
+
+    # initial simulation for current range from low to high
+    V_m = Cb_stellate(V_init * np.ones((stim_num)), g_effect, E2, I_in, dt, currents_included2, stim_time, stim_num, C,
+                      shift2, scale2, b_param_effect, slope_shift2, gating2, current2, ind_dict)
+    stim_start = np.int(initial_period * 1 / dt)
+    min_spike_height = V_m[0, stim_start] + prominence  ##########################################################
+
+    # create hdf5 file for the mutation and save data and metadata
+    fname = os.path.join(folder, "{}.hdf5".format(mut.replace(" ", "_")))
+    with h5py.File(fname, "a") as f:
+        data = f.create_group("data")
+        data.create_dataset('V_m', data=V_m, dtype='float64', compression="gzip", compression_opts=9)
+        metadata = {'I_high': high, 'I_low': low, 'stim_num': number_steps, 'stim_time': stim_time, 'sec': sec, 'dt': dt,
+                    'initial_period': initial_period, 'g': json.dumps(dict(g_effect)), 'E': json.dumps(dict(E2)), 'C': C,
+                    'V_init': V_init, 'ind_dict': json.dumps(dict(ind_dict)),
+                    'b_param': json.dumps(dict(b_param_effect), cls=NumpyEncoder), 'currents': json.dumps(dict(currents_included2)),
+                    'shift': json.dumps(dict(shift2)), 'scale': json.dumps(dict(scale2)), 'slope_shift': json.dumps(dict(slope_shift2)),
+                    'prominence': prominence, 'desired_AUC_width': desired_AUC_width, 'mut': str(mut),
+                    'min_spike_height': min_spike_height, }
+        data.attrs.update(metadata)
+
+    # firing frequency analysis and rheobase
+    with h5py.File(fname, "r+") as f:
+        I_mag = np.arange(low, high, (high - low) / stim_num)
+        analysis = f.create_group("analysis")
+        dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
+        for i in np.array(['spike_times', 'spike_times_rheo', 'ISI', 'Freq', 'amplitude']):
+            analysis.create_dataset(i, (I_mag.shape[0],), dtype=dtyp, compression="gzip", compression_opts=9)
+        analysis.create_dataset('F_inf', (I_mag.shape[0],), dtype='float64')
+        analysis.create_dataset('F_null', (I_mag.shape[0],), dtype='float64')
+        analysis.create_dataset('AP', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
+        analysis.create_dataset('AP_rheo', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
+        analysis.create_dataset('rheobase', (1,), dtype='float64', compression="gzip", compression_opts=9)
+        for stim in range(I_mag.shape[0]):
+            # spike detection
+            ap_all, ap_prop = scipy.signal.find_peaks(V_m[stim, stim_start:], min_spike_height,
+                                                      prominence=prominence, distance=1 * 1 / dt)
+            analysis['spike_times'][stim] = ap_all * dt
+            if analysis['spike_times'][stim].size == 0:
+                analysis['AP'][stim] = False
+            else:
+                analysis['AP'][stim] = True
+            analysis['amplitude'][stim] = np.array(ap_prop["peak_heights"])
+            analysis['ISI'][stim] = np.array([x - analysis['spike_times'][stim][i - 1] if i else None for i, x in
+                                              enumerate(analysis['spike_times'][stim])][1:])  # msec
+            analysis['Freq'][stim] = 1 / (analysis['ISI'][stim] / 1000)
+
+            if analysis['Freq'][stim].size != 0:
+                analysis['F_null'][stim] = analysis['Freq'][stim][0]
+                last_second_spikes = analysis['spike_times'][stim] > np.int(
+                    f['data'].attrs['stim_time'] * dt - (500 + initial_period))
+                if np.any(last_second_spikes == True):
+                    last_second_spikes_1 = np.delete(last_second_spikes,0)  # remove first spike because it has no ISI/freq
+                    analysis['F_inf'][stim] = np.mean(analysis['Freq'][stim][last_second_spikes_1])
+            else: # if no ISI detected
+                analysis['F_null'][stim] = 0
+                analysis['F_inf'][stim] = 0
+
+
+        # rheobase
+        # find where the first AP occurs
+        if np.any(analysis['AP'][:] == True):
+            # setup current range to be between the current step before and with first AP
+            I_first_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0]]
+            I_before_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0] - 1]
+            analysis.create_dataset('I_first_spike', data=I_first_spike, dtype='float64')
+            analysis.create_dataset('I_before_spike', data=I_before_spike, dtype='float64')
+            number_steps_rheo = 100
+            stim_time, I_in_rheo, stim_num_rheo, V_m_rheo = stimulus_init(I_before_spike, I_first_spike,
+                                                                               number_steps_rheo,
+                                                                               initial_period, dt, sec)
+            # simulate response to this current range and save
+            V_m_rheo = Cb_stellate(V_init * np.ones((stim_num_rheo)), g_effect, E2, I_in_rheo, dt, currents_included2,
+                                   stim_time, stim_num_rheo, C, shift2, scale2, b_param_effect, slope_shift2,
+                                   np.zeros((len(b_param), stim_num_rheo)),
+                                   np.zeros((len(currents_included), stim_num_rheo)), ind_dict)
+            f['data'].create_dataset('V_m_rheo', data=V_m_rheo, dtype='float64', compression="gzip", compression_opts=9)
+
+            # run AP detection
+            stim_start = np.int(initial_period * 1 / dt)
+            for stim in range(I_in_rheo.shape[1]):
+                ap_all, ap_prop = scipy.signal.find_peaks(V_m_rheo[stim, stim_start:], min_spike_height,
+                                                          prominence=prominence, distance=1 * 1 / dt)
+                analysis['spike_times_rheo'][stim] = ap_all * dt
+                if analysis['spike_times_rheo'][stim].size == 0:
+                    analysis['AP_rheo'][stim] = False
+                else:
+                    analysis['AP_rheo'][stim] = True
+            # find rheobase as smallest current step to elicit AP and save
+            analysis['rheobase'][:] = I_in_rheo[-1, np.argwhere(analysis['AP_rheo'][:] == True)[0][0]] * 1000
+
+    # AUC
+    # find rheobase of steady state firing (F_inf)
+    with h5py.File(fname, "a") as f:
+        F_inf = f['analysis']['F_inf'][:]
+        try:
+            # setup current range to be in current step between no and start of steady state firing
+            F_inf_start_ind = np.argwhere(F_inf != 0)[0][0]
+            dI = (high - low) / stim_num
+            I_start = I_mag[F_inf_start_ind - 1]
+            I_end = I_mag[F_inf_start_ind]
+            I_rel = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
+            I_rel = np.reshape(I_rel, (1, I_rel.shape[0]))
+            I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel
+            I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel
+            stim_num = I_in.shape[1]
+
+            # simulate response to this current step range
+            V_m_inf_rheo = Cb_stellate(V_init * np.ones(stim_num), g_effect, E2, I_in, dt, currents_included2,
+                                       stim_time, stim_num, C, shift2, scale2, b_param_effect, slope_shift2,
+                                       np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
+                                       ind_dict)
+
+            # save response, analyse and save
+            for i in np.array(['spike_times_Finf', 'ISI_Finf', 'Freq_Finf']):
+                f['analysis'].require_dataset(i, shape=(I_rel.shape[1],), dtype=dtyp, compression="gzip",
+                                              compression_opts=9)
+            f['analysis'].require_dataset('F_inf_Finf', shape=(I_rel.shape[1],), dtype='float64')
+            f['analysis'].require_dataset('F_null_Finf', shape=(I_rel.shape[1],), dtype='float64')
+            for stim in range(I_rel.shape[1]):
+                # spike peak detection
+                ap_all, ap_prop = scipy.signal.find_peaks(V_m_inf_rheo[stim, stim_start:], min_spike_height,
+                                                          prominence=prominence, distance=1 * 1 / dt)
+                f['analysis']['spike_times_Finf'][stim] = ap_all * dt
+                f['analysis']['ISI_Finf'][stim] = np.array(
+                    [x - f['analysis']['spike_times_Finf'][stim][i - 1] if i else None for i, x in
+                     enumerate(f['analysis']['spike_times_Finf'][stim])][1:])  # msec
+                f['analysis']['Freq_Finf'][stim] = 1 / (f['analysis']['ISI_Finf'][stim] / 1000)
+                if f['analysis']['Freq_Finf'][stim].size != 0:
+                    last_second_spikes = f['analysis']['spike_times_Finf'][stim] > np.int(
+                        f['data'].attrs['stim_time'] * dt - (500 + f['data'].attrs['initial_period']))
+                    if np.any(last_second_spikes == True):
+                        last_second_spikes_1 = np.delete(last_second_spikes, 0)  # remove first spike because it has no ISI/freq
+                        f['analysis']['F_inf_Finf'][stim] = np.mean(
+                            f['analysis']['Freq_Finf'][stim][last_second_spikes_1])
+                    else:
+                        f['analysis']['F_null_Finf'][stim] = 0
+                        f['analysis']['F_inf_Finf'][stim] = 0
+
+            # find AUC relative to F_inf rheobase
+            # setup current inputs with current range around F_inf rheobase
+            I_first_inf = I_rel[0, np.argwhere(f['analysis']['F_inf_Finf'][:] != 0)[0][0]]
+            I_start = I_first_inf - 0.00005
+            I_end = I_first_inf + desired_AUC_width * high
+            I_rel2 = np.arange(I_start, I_end + (I_end - I_start) / 200, (I_end - I_start) / 200)
+            I_rel2 = np.reshape(I_rel2, (1, I_rel2.shape[0]))
+            I_in2 = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel2
+            I_in2[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel2
+            stim_num = I_in2.shape[1]
+
+            # simulate response to this current step range
+            V_m_AUC = Cb_stellate(V_init * np.ones(stim_num), g_effect, E2, I_in2, dt, currents_included2, stim_time,
+                                  stim_num, C, shift2, scale2, b_param_effect, slope_shift2,
+                                  np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
+                                  ind_dict)
+
+            # save data and analysis for this current step range including AUC
+            f['data'].require_dataset('V_m_AUC', shape=V_m_AUC.shape, dtype='float64')
+            f['data']['V_m_AUC'][:] = V_m_AUC
+            dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
+            for i in np.array(['spike_times_Finf_AUC', 'ISI_Finf_AUC', 'Freq_Finf_AUC']):  # analysis_names:
+                f['analysis'].require_dataset(i, shape=(I_rel2.shape[1],), dtype=dtyp, compression="gzip",
+                                              compression_opts=9)
+            f['analysis'].require_dataset('F_inf_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
+            f['analysis'].require_dataset('F_null_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
+            for stim in range(I_rel2.shape[1]):
+                # spike peak detection
+                ap_all, ap_prop = scipy.signal.find_peaks(V_m_AUC[stim, stim_start:], min_spike_height,
+                                                          prominence=prominence, distance=1 * 1 / dt)
+                f['analysis']['spike_times_Finf_AUC'][stim] = ap_all * dt
+                f['analysis']['ISI_Finf_AUC'][stim] = np.array(
+                    [x - f['analysis']['spike_times_Finf_AUC'][stim][i - 1] if i else None for i, x in
+                     enumerate(f['analysis']['spike_times_Finf_AUC'][stim])][1:])  # msec
+                f['analysis']['Freq_Finf_AUC'][stim] = 1 / (f['analysis']['ISI_Finf_AUC'][stim] / 1000)
+                if f['analysis']['Freq_Finf_AUC'][stim].size != 0:
+                    last_second_spikes = f['analysis']['spike_times_Finf_AUC'][stim] > np.int(
+                        f['data'].attrs['stim_time'] * dt - (500 + f['data'].attrs['initial_period']))
+                    if np.any(last_second_spikes == True):
+                        last_second_spikes_1 = np.delete(last_second_spikes, 0)  # remove first spike because it has no ISI/freq
+                        f['analysis']['F_inf_Finf_AUC'][stim] = np.mean(
+                            f['analysis']['Freq_Finf_AUC'][stim][last_second_spikes_1])
+                    else:
+                        f['analysis']['F_null_Finf_AUC'][stim] = 0
+                        f['analysis']['F_inf_Finf_AUC'][stim] = 0
+            f['analysis'].require_dataset('AUC_rel_acc', shape=(1,), dtype='float64', compression="gzip",
+                                          compression_opts=9)
+            f['analysis']['AUC_rel_acc'][:] = np.trapz(f['analysis']['F_inf_Finf_AUC'][:],
+                                                       I_rel2[:] * 1000, dI * 1000)  # AUC
+        except:
+            f['analysis'].require_dataset('AUC_rel_acc', shape=(1,), dtype='float64', compression="gzip",
+                                          compression_opts=9)
+            f['analysis']['AUC_rel_acc'][:] = 0  # AUC
+            print('exception', f['analysis']['AUC_rel_acc'][()])
+
+    # ramp protocol #####################
+    # setup ramp current
+    sec = 4
+    ramp_len = np.int(sec *1000 *1/dt)
+    stim_time = ramp_len *2
+    I_amp = np.array([0, -0.000001])
+    I_amp = np.reshape(I_amp, (1,I_amp.shape[0]))
+    I_step = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1))@ I_amp
+    I_step[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_amp
+    stim_num_step = I_step.shape[1]
+    start = np.int(initial_period * 1 / dt)
+    I_step[start:np.int(start+ramp_len),0] = np.linspace(0, high, ramp_len)
+    I_step[np.int(start + ramp_len):np.int(start + ramp_len*2),0] = np.linspace(high, 0, ramp_len)
+    stim_time_step = I_step.shape[0]
+
+    # simulate response to ramp
+    V_m_step = Cb_stellate(-70 * np.ones(stim_num_step), g_effect, E2, I_step, dt, currents_included2, stim_time_step,
+                           stim_num_step, C, shift2, scale2, b_param_effect, slope_shift2,
+                           np.zeros((len(b_param), stim_num_step)), np.zeros((len(currents_included), stim_num_step)),
+                           ind_dict)
+
+
+    # spike peak detection
+    ap_all, ap_prop = scipy.signal.find_peaks(V_m_step[0, stim_start:], min_spike_height,
+                                                          prominence=prominence, distance=1 * 1 / dt)
+    # save ramp firing properties
+    with h5py.File(fname, "a") as f:
+        f['data'].create_dataset('V_m_ramp', data=V_m_step, dtype='float64', compression="gzip", compression_opts=9)
+        if ap_all.shape[0] != 0:
+            if ap_all[0] < stim_start + ramp_len:
+                f['analysis'].create_dataset('ramp_I_up', data =I_step[stim_start+ap_all[0], 0] *1000,  dtype='float64')
+            else:
+                f['analysis'].create_dataset('ramp_I_up', data =np.NaN,  dtype='float64')
+            if ap_all[-1] > stim_start + ramp_len:
+                f['analysis'].create_dataset('ramp_I_down', data =I_step[stim_start+ap_all[-1], 0]*1000,  dtype='float64')
+            else:
+                f['analysis'].create_dataset('ramp_I_down', data =np.NaN,  dtype='float64')
+            f['analysis'].create_dataset('hysteresis', data =f['analysis']['ramp_I_down'][()] - f['analysis']['ramp_I_up'][()],  dtype='float64')
+        else: # if no spikes in response to ramp
+            f['analysis'].create_dataset('ramp_I_up', data =np.NaN,  dtype='float64')
+            f['analysis'].create_dataset('ramp_I_down', data =np.NaN,  dtype='float64')
+            f['analysis'].create_dataset('hysteresis', data =np.NaN,  dtype='float64')
+        
+    #########################################
+    
+    if f.__bool__():
+        f.close()
+    gc.collect()
+
+
+def SA_Cb_stellate(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+                   gating, current, prominence, desired_AUC_width, folder, high, low, number_steps, initial_period,
+                   sec, lin_array, log_array, alt_types, alt_ind, alt):
+    """ Sensitivity analysis for Cb Stellate model
+
+            Parameters
+            ----------
+            V_init : float
+                Initial membrane potential
+            g : dict
+                maximal conductance for currents
+            E : dict
+                Reversal potentials for currents
+            I_in : array
+                Current stimulus input to model
+            dt : float
+                fixed time step
+            currents_included : dict
+                Boolean to include current in simulation or not
+            stim_time :
+                length of stimulus ()
+            stim_num : int
+                number of different stimulus currents
+            C : float
+                membrane capacitance
+            shift : dict
+                for each gating parameter
+            scale : dict
+                for each gating parameter
+            b_param : dict
+                Boltzmann parameter values for each gating parameter
+            slope_shift : dict
+                for each gating parameter
+            gating : array
+                array of gating values over time
+            current : array
+                array of current values over time
+            prominence: float
+                required spike prominence
+            desired_AUC_width: float
+                width of AUC from rheobase to rheobase + desired_AUC_width
+            folder : str
+                folder to save hdf5 in
+            high : float
+                upper current step magnitude
+            low : float
+                lowest current step magnitude
+            number_steps : int
+                number of current steps between low and high
+            initial_period: float
+                length  of  I = 0 before current step
+            sec : float
+                length of current step in seconds
+            lin_array : array
+                array of linear shifts from -10 tp 10
+            log_array : array
+                array of log2 values from -1 to 1
+            alt_types : array
+                array of arrays of strings of [variable, alteration type]
+            alt_ind : int
+                index for alt_type
+            alt :
+                index for lin_array of log_array
+
+            Returns
+            -------
+            saves hdf5 file to folder
+            """
+
+    # setup for simulation
+    g_multi = copy.deepcopy(g)
+    E_multi = copy.deepcopy(E)
+    b_param_multi = copy.deepcopy(b_param)
+    shift_multi = copy.deepcopy(shift)
+    scale_multi = copy.deepcopy(scale)
+    slope_shift_multi = copy.deepcopy(slope_shift)
+    currents_included_multi = copy.deepcopy(currents_included)
+    gating2 = copy.deepcopy(gating)
+    current2 = copy.deepcopy(current)
+
+    g2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in g_multi.items():
+        g2[k] = v
+    b_param2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64[:], )
+    for k, v in b_param_multi.items():
+        b_param2[k] = np.array(v)
+    shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in shift_multi.items():
+        shift2[k] = v
+    scale2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in scale_multi.items():
+        scale2[k] = v
+    slope_shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in slope_shift_multi.items():
+        slope_shift2[k] = v
+    E2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
+    for k, v in E_multi.items():
+        E2[k] = v
+    currents_included2 = Dict.empty(key_type=types.unicode_type, value_type=types.boolean, )
+    for k, v in currents_included_multi.items():
+        currents_included2[k] = v
+    ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+    i = 0
+    for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T']):
+        ind_dict[var] = i
+        i += 1
+    i = 0
+    for var in np.array(['Na', 'Kd', 'A', 'A_mut', 'T', 'Leak']):
+        ind_dict[var] = i
+        i += 1
+
+    # alteration to gating ##########################################################################
+    if alt_types[alt_ind, 1] == 'shift':
+        shift2[alt_types[alt_ind, 0]] = lin_array[alt]
+        fname = os.path.join(folder,
+                             "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0], lin_array[alt]))
+
+    elif alt_types[alt_ind, 1] == 'slope':
+        b_param2[alt_types[alt_ind, 0]][1] = b_param2[alt_types[alt_ind, 0]][1] * log_array[alt]
+        fname = os.path.join(folder,
+                             "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0],
+                                                    np.int(np.around(log_array[alt], 2) * 100)))
+        if b_param2[alt_types[alt_ind, 0]][2] != 1:
+            # adjust slope_shift to account for slope not changing (rotating) around 0.5
+            V_test = np.arange(-150, 100, 0.001)
+            if b_param2[alt_types[alt_ind, 0]].shape[0] == 4:
+                steady_state = ((1 - b_param2[alt_types[alt_ind, 0]][3]) / (
+                        1 + np.exp(
+                    (V_test - b_param2[alt_types[alt_ind, 0]][0]) / b_param2[alt_types[alt_ind, 0]][1])) +
+                                b_param2[alt_types[alt_ind, 0]][3]) ** b_param2[alt_types[alt_ind, 0]][2]
+                orig = ((1 - b_param[alt_types[alt_ind, 0]][3]) / (
+                        1 + np.exp((V_test - b_param[alt_types[alt_ind, 0]][0]) / b_param[alt_types[alt_ind, 0]][1])) +
+                        b_param[alt_types[alt_ind, 0]][3]) ** b_param[alt_types[alt_ind, 0]][2]
+            else:
+                steady_state = (1 / (1 + np.exp(
+                    (V_test - b_param2[alt_types[alt_ind, 0]][0]) / b_param2[alt_types[alt_ind, 0]][1]))) ** \
+                               b_param2[alt_types[alt_ind, 0]][2]
+                orig = (1 / (1 + np.exp(
+                    (V_test - b_param[alt_types[alt_ind, 0]][0]) / b_param[alt_types[alt_ind, 0]][1]))) ** \
+                       b_param[alt_types[alt_ind, 0]][2]
+            orig_V_half = V_test[(np.abs(orig - 0.5)).argmin()]
+            V_half_new = V_test[(np.abs(steady_state - 0.5)).argmin()]
+            slope_shift2[alt_types[alt_ind, 0]] = orig_V_half - V_half_new
+
+    elif alt_types[alt_ind, 1] == 'g':
+        g2[alt_types[alt_ind, 0]] = g2[alt_types[alt_ind, 0]] * log_array[alt]
+        fname = os.path.join(folder, "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0],
+                                                            np.int(np.around(log_array[alt], 2) * 100)))
+    ###########################################################################
+
+    # initial simulation for current range from low to high
+    V_m = Cb_stellate(V_init * np.ones((stim_num)), g2, E2, I_in, dt, currents_included2, stim_time, stim_num, C,
+                      shift2, scale2, b_param2, slope_shift2, gating2, current2, ind_dict)
+    stim_start = np.int(initial_period * 1 /dt)
+    min_spike_height = V_m[0, stim_start] + prominence
+
+    # create hdf5 file and save data and metadata
+    with h5py.File(fname, "a") as f:
+        data = f.create_group("data")
+        data.create_dataset('V_m', data=V_m, dtype='float64', compression="gzip", compression_opts=9)
+        metadata = {'I_high': high, 'I_low': low,   'stim_num': number_steps, 'stim_time': stim_time, 'sec': sec, 'dt': dt,
+                    'initial_period': initial_period, 'g': json.dumps(dict(g2)), 'E': json.dumps(dict(E2)), 'C': C,
+                    'V_init': V_init, 'ind_dict': json.dumps(dict(ind_dict)),
+                    'b_param': json.dumps(dict(b_param2), cls=NumpyEncoder),
+                    'currents': json.dumps(dict(currents_included2)), 'shift': json.dumps(dict(shift2)),
+                    'var_change': str(alt_types[alt_ind, 1]), 'var': str(alt_types[alt_ind, 0]),
+                    'scale': json.dumps(dict(scale2)), 'slope_shift': json.dumps(dict(slope_shift2)),
+                    'prominence': prominence, 'desired_AUC_width': desired_AUC_width,
+                    'alteration_info': str(alt_types[alt_ind, :]), 'alt_index': str(alt),
+                    'min_spike_height': min_spike_height, }
+        data.attrs.update(metadata)
+        if alt_types[alt_ind, 1] == 'shift':
+            meta2 = {'alteration': lin_array[alt]}
+        else:
+            meta2 = {'alteration': log_array[alt]}
+        data.attrs.update(meta2)
+
+    # firing frequency analysis and rheobase
+    with h5py.File(fname, "r+") as f:
+        I_mag = np.arange(low, high, (high - low) / stim_num)
+        analysis = f.create_group("analysis")
+        dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
+        for i in np.array(['spike_times', 'spike_times_rheo', 'ISI', 'Freq']):
+            analysis.create_dataset(i, (I_mag.shape[0],), dtype=dtyp, compression="gzip", compression_opts=9)
+        analysis.create_dataset('F_inf', (I_mag.shape[0],), dtype='float64')
+        analysis.create_dataset('F_null', (I_mag.shape[0],), dtype='float64')
+        analysis.create_dataset('AP', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
+        analysis.create_dataset('AP_rheo', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
+        for i in np.array(['rheobase']):
+            analysis.create_dataset(i, (1,), dtype='float64', compression="gzip", compression_opts=9)
+        try:
+            for stim in range(I_mag.shape[0]):
+                # spike detection
+                ap_all, ap_prop = scipy.signal.find_peaks(V_m[stim, stim_start:], min_spike_height,
+                                                          prominence=prominence, distance=1 * 1 / dt)
+                analysis['spike_times'][stim] = ap_all * dt
+                if analysis['spike_times'][stim].size == 0:
+                    analysis['AP'][stim] = False
+                else:
+                    analysis['AP'][stim] = True
+                analysis['ISI'][stim] = np.array([x - analysis['spike_times'][stim][i - 1] if i else None for i, x in
+                                                  enumerate(analysis['spike_times'][stim])][1:])  # msec
+                analysis['Freq'][stim] = 1 / (analysis['ISI'][stim] / 1000)
+
+                if analysis['Freq'][stim].size != 0:
+                    #####################################################################################################################################################
+                    analysis['F_null'][stim] = analysis['Freq'][stim][0]
+                    if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0:  # if first spike is after 1 ms
+                        half_second_spikes = np.logical_and(analysis['spike_times'][stim] < np.int(
+                            ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
+                                                            analysis['spike_times'][stim] > np.int(
+                                                                ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][
+                                                                    0]] * dt))  # spikes in half second window
+                        if np.sum(half_second_spikes == True) > 1:
+                            half_second_spikes_1 = np.delete(half_second_spikes,
+                                                             np.argwhere(half_second_spikes == True)[0][0])
+                            analysis['F_inf'][stim] = np.mean(analysis['Freq'][stim][half_second_spikes_1])
+                        else:
+                            analysis['F_inf'][stim] = 0
+                    else:
+                        analysis['F_inf'][stim] = 0
+                else: # if no ISI detected
+                    analysis['F_null'][stim] = 0
+                    analysis['F_inf'][stim] = 0
+        except:
+            print(alt_types[alt_ind, 0], alt_types[alt_ind, 1], alt, 'Freq analysis unsuccessful')
+
+        # rheobase
+        # find where the first AP occurs
+        if np.any(analysis['AP'][:] == True):
+            # setup current range to be between the current step before and with first AP
+
+            I_first_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0]]
+            I_before_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0] - 1]
+            analysis.create_dataset('I_first_spike', data=I_first_spike, dtype='float64')
+            analysis.create_dataset('I_before_spike', data=I_before_spike, dtype='float64')
+            number_steps_rheo = 100
+            stim_time, I_in_rheo, stim_num_rheo, V_m_rheo = stimulus_init(I_before_spike, I_first_spike,
+                                                                               number_steps_rheo, initial_period,
+                                                                               dt, sec)
+
+            # simulate response to this current range and save
+            V_m_rheo = Cb_stellate(V_init * np.ones((stim_num_rheo)), g2, E2, I_in_rheo, dt, currents_included2,
+                                   stim_time, stim_num_rheo, C, shift2, scale2, b_param2, slope_shift2,
+                                   np.zeros((len(b_param), stim_num_rheo)),
+                                   np.zeros((len(currents_included), stim_num_rheo)), ind_dict)
+            # run AP detection
+            try:
+                # stim_start = np.int(initial_period * 1 / dt)
+                for stim in range(I_in_rheo.shape[1]):  # range(stim_num):
+                    ap_all, ap_prop = scipy.signal.find_peaks(V_m_rheo[stim, stim_start:], min_spike_height,
+                                                              prominence=prominence, distance=1 * 1 / dt)
+                    analysis['spike_times_rheo'][stim] = ap_all * dt
+                    if analysis['spike_times_rheo'][stim].size == 0:
+                        analysis['AP_rheo'][stim] = False
+                    else:
+                        analysis['AP_rheo'][stim] = True
+
+            except:
+                print(alt_types[alt_ind, 0], alt_types[alt_ind, 1], alt, 'RHEOBASE NO AP')
+            # find rheobase as smallest current step to elicit AP and save
+            analysis['rheobase'][:] = I_in_rheo[-1, np.argwhere(analysis['AP_rheo'][:] == True)[0][0]] * 1000
+
+    # AUC
+    # find rheobase of steady state firing (F_inf)
+    with h5py.File(fname, "a") as f:
+        F_inf = f['analysis']['F_inf'][:]
+        try:
+            # setup current range to be in current step between no and start of steady state firing
+            F_inf_start_ind = np.argwhere(F_inf != 0)[0][0]
+            dI = (high - low) / stim_num
+            I_start = I_mag[F_inf_start_ind - 1]
+            I_end = I_mag[F_inf_start_ind]
+            I_rel = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
+            I_rel = np.reshape(I_rel, (1, I_rel.shape[0]))
+            I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel
+            I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel
+            stim_num = I_in.shape[1]
+
+            # simulate response to this current step range
+            V_m_inf_rheo = Cb_stellate(V_init * np.ones(stim_num), g2, E2, I_in, dt, currents_included2, stim_time,
+                                       stim_num, C, shift2, scale2, b_param2, slope_shift2,
+                                       np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
+                                       ind_dict)
+
+            # save response, analyse and save
+            for i in np.array(['spike_times_Finf', 'ISI_Finf', 'Freq_Finf']):
+                f['analysis'].require_dataset(i, shape=(I_rel.shape[1],), dtype=dtyp, compression="gzip",
+                                              compression_opts=9)
+            f['analysis'].require_dataset('F_inf_Finf', shape=(I_rel.shape[1],), dtype='float64')
+            f['analysis'].require_dataset('F_null_Finf', shape=(I_rel.shape[1],), dtype='float64')
+            for stim in range(I_rel.shape[1]):
+                # spike peak detection
+                ap_all, ap_prop = scipy.signal.find_peaks(V_m_inf_rheo[stim, stim_start:], min_spike_height,
+                                                          prominence=prominence, distance=1 * 1 / dt)
+                f['analysis']['spike_times_Finf'][stim] = ap_all * dt
+                f['analysis']['ISI_Finf'][stim] = np.array(
+                    [x - f['analysis']['spike_times_Finf'][stim][i - 1] if i else None for i, x in
+                     enumerate(f['analysis']['spike_times_Finf'][stim])][1:])  # msec
+                f['analysis']['Freq_Finf'][stim] = 1 / (f['analysis']['ISI_Finf'][stim] / 1000)
+                if f['analysis']['Freq_Finf'][stim].size != 0:
+                    if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0:  # if first spike is after 1 ms
+                        half_second_spikes = np.logical_and(f['analysis']['spike_times_Finf'][stim] < np.int(
+                            ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
+                                                            f['analysis']['spike_times_Finf'][stim] > np.int(
+                                                                ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt))  # spikes in half second window
+                        if np.sum(half_second_spikes == True) > 1:
+                            half_second_spikes_1 = np.delete(half_second_spikes,
+                                                             np.argwhere(half_second_spikes == True)[0][0])  # remove first spike because it has no ISI/freq
+                            f['analysis']['F_inf_Finf'][stim] = np.mean(
+                                f['analysis']['Freq_Finf'][stim][half_second_spikes_1])
+                        else:
+                            f['analysis']['F_inf_Finf'][stim] = 0
+                    else:
+                        f['analysis']['F_null_Finf'][stim] = 0
+                        f['analysis']['F_inf_Finf'][stim] = 0
+                else:
+                    f['analysis']['F_null_Finf'][stim] = 0
+                    f['analysis']['F_inf_Finf'][stim] = 0
+
+            # find AUC relative to F_inf rheobase
+            # setup current inputs with current range around F_inf rheobase
+            I_first_inf = I_rel[0, np.argwhere(f['analysis']['F_inf_Finf'][:] != 0)[0][0]]
+            I_start = I_first_inf - 0.00005
+            I_end = I_first_inf + 0.2 * high
+            I_rel2 = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
+            I_rel2 = np.reshape(I_rel2, (1, I_rel2.shape[0]))
+            I_in2 = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel2
+            I_in2[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel2
+            stim_num = I_in2.shape[1]
+
+            # simulate response to this current step range
+            V_m_AUC = Cb_stellate(V_init * np.ones(stim_num), g2, E2, I_in2, dt, currents_included2, stim_time,
+                                  stim_num, C, shift2, scale2, b_param2, slope_shift2,
+                                  np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
+                                  ind_dict)
+
+            # save data and analysis for this current step range including AUC
+            f['data'].require_dataset('V_m_AUC', shape=V_m_AUC.shape, dtype='float64')
+            f['data']['V_m_AUC'][:] = V_m_AUC
+            dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
+            for i in np.array(['spike_times_Finf_AUC', 'ISI_Finf_AUC', 'Freq_Finf_AUC']):  # analysis_names:
+                f['analysis'].require_dataset(i, shape=(I_rel2.shape[1],), dtype=dtyp, compression="gzip",
+                                              compression_opts=9)
+            f['analysis'].require_dataset('F_inf_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
+            f['analysis'].require_dataset('F_null_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
+            for stim in range(I_rel2.shape[1]):
+                # spike peak detection
+                ap_all, ap_prop = scipy.signal.find_peaks(V_m_AUC[stim, stim_start:], min_spike_height,
+                                                          prominence=prominence, distance=1 * 1 / dt)
+                f['analysis']['spike_times_Finf_AUC'][stim] = ap_all * dt
+                f['analysis']['ISI_Finf_AUC'][stim] = np.array(
+                    [x - f['analysis']['spike_times_Finf_AUC'][stim][i - 1] if i else None for i, x in
+                     enumerate(f['analysis']['spike_times_Finf_AUC'][stim])][1:])  # msec
+                f['analysis']['Freq_Finf_AUC'][stim] = 1 / (f['analysis']['ISI_Finf_AUC'][stim] / 1000)
+                if f['analysis']['Freq_Finf_AUC'][stim].size != 0:
+                    if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0:  # if first spike is after 1 ms
+                        half_second_spikes = np.logical_and(f['analysis']['spike_times_Finf_AUC'][stim] < np.int(
+                            ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
+                                                            f['analysis']['spike_times_Finf_AUC'][stim] > np.int(
+                                                                ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][
+                                                                    0]] * dt))  # spikes in half second window
+                        if np.sum(half_second_spikes == True) > 1:
+                            half_second_spikes_1 = np.delete(half_second_spikes,
+                                                             np.argwhere(half_second_spikes == True)[0][0])  # remove first spike because it has no ISI/freq
+                            f['analysis']['F_inf_Finf_AUC'][stim] = np.mean(
+                                f['analysis']['Freq_Finf_AUC'][stim][half_second_spikes_1])
+                        else:
+                            f['analysis']['F_inf_Finf_AUC'][stim] = 0
+                    else:
+                        f['analysis']['F_null_Finf_AUC'][stim] = 0
+                        f['analysis']['F_inf_Finf_AUC'][stim] = 0
+                else:
+                    f['analysis']['F_null_Finf_AUC'][stim] = 0
+                    f['analysis']['F_inf_Finf_AUC'][stim] = 0
+            f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
+                                          compression_opts=9)
+            f['analysis']['AUCc'][:] = np.trapz(f['analysis']['F_inf_Finf_AUC'][:],
+                                                       I_rel2[:] * 1000, dI * 1000)
+        except:
+            f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
+                                          compression_opts=9)
+            f['analysis']['AUC'][:] = 0
+    if f.__bool__():
+        f.close()
+    gc.collect()
+

Code/Functions/Utility_fxns.py

@@ -0,0 +1,144 @@
+"""
+General functions used throughout simulation scripts
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+
+import json
+import numpy as np
+from numba import njit
+
+@njit
+def sqrt_log(x, *p):
+    """ Function of sum of square root and logarithmic function to fit to fI curves
+    Parameters
+    ----------
+    x : array
+        input x value
+    *p : array
+        parameters for function
+
+    Returns
+    -------
+    p[0] * np.sqrt(x + p[1]) + p[2] * np.log(x + (p[1] + 1)) + p[3]
+    """
+    return p[0] * np.sqrt(x + p[1]) + p[2] * np.log(x + (p[1] + 1)) + p[3]
+
+
+@njit
+def capacitance(diam, C_m):
+    """ calculate the capacitance of a cell's membrane give the diameter
+    Parameters
+    ----------
+    diam: float
+        diameter of spherical cell body
+    C_m: float
+        specific membrane capacitance of cell
+
+    Returns
+    -------
+    capacitance: float
+        capacitance of cell
+    surf_area: flaot
+        surface area of cell
+    """
+    r = diam * 1E-4 / 2  # cm
+    l = r
+    surf_area = 2 * np.pi * r ** 2 + 2 * np.pi * r * l  # cm^2
+    C = C_m * surf_area  # uF
+    return (C, surf_area)
+
+
+@njit
+def stimulus_init(low, high, number_steps, initial_period, dt, sec):
+    """ initiation of stimulus pulse from stimulus magnitudes I_in_mag
+    Parameters
+    ----------
+    low : float
+        lowest current step magnitude
+    high : float
+        upper current step magnitude
+    number_steps : int
+        number of current steps between low and high
+    initial_period : float
+        length of I=0 before current step
+    dt : float
+        time step
+    sec:
+        length of current step in seconds
+
+    Returns
+    -------
+    stim_time: float
+        length of I_in in samples
+    I_in: array
+        I input array
+    stim_num: int
+        number of current steps
+    V_m: array
+        membrane potential array initialization of shape:(stim_num, stim_time)
+    """
+    stim_time = np.int(1000 * 1 / dt * sec)  # 1000 msec/sec * 1/dt
+    I_in_mag = np.arange(low, high, (high - low) / number_steps)
+    I_in_mag = np.reshape(I_in_mag, (1, I_in_mag.shape[0]))
+    I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_in_mag
+    I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_in_mag
+    stim_num = I_in.shape[1]  # number of different currents injected
+    stim_time = stim_time + np.int(initial_period * 1 / dt)
+    V_m = np.zeros((stim_num, stim_time))
+
+    return stim_time, I_in, stim_num, V_m
+
+class NumpyEncoder(json.JSONEncoder):
+    """ Special json encoder for numpy types """
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        return json.JSONEncoder.default(self, obj)
+
+
+def init_dict(variable):
+    """ Initialize dictionaries for simulations
+    Parameters
+    ----------
+    variable : array
+        array of variable name strings to put in dicts
+
+    Returns
+    -------
+    shift : dict
+        with every variable name in variable = 0.
+    scale : dict
+        with every variable name in variable = 1.
+    slope_shift : dict
+        with every variable name in variable = 0.
+    E : dict
+        empty dictionary
+    currents_included : dict
+        empty dictionary
+    b_param : dict
+        with every variable name in variable = np.array of 2 zeros
+    g : dict
+        empty dictionary
+    """
+    shift = {}
+    scale = {}
+    slope_shift = {}
+    b_param ={}
+    g = {}
+    E ={}
+    currents_included = {}
+    for var in variable:
+        shift[var] = 0.
+        scale[var] = 1.0
+        slope_shift[var] = 0.
+        b_param[var] = np.zeros(3, dtype=np.dtype('float64'))
+    return shift, scale, slope_shift, E, currents_included, b_param, g
+

Code/KCNA1_mutations/mut_Cb_stellate.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in Cb Stellate model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+import numpy as np
+from joblib import Parallel, delayed
+import json
+import os
+from numba import types
+from numba.typed import Dict
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns import Cb_stellate_mut
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.0015
+number_steps = 200
+initial_period = 1000
+num_gating = 9
+num_current = 6
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut',  'm_T', 'h_T'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A','n_A_mut', 'h_A_mut', 'm_T', 'h_T']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['n_A_mut'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A_mut'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["A_mut"] = True
+currents_included["A"] = True
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 3.4 * surf_area
+g["Kd"] = 9.0556 * surf_area
+g["A_mut"] = 15.0159 * surf_area * 0.5
+g["A"] = 15.0159 * surf_area* 0.5
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+
+# save folder
+folder = './KCNA1_mutations/Cb_stellate'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+#
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+
+# %%
+prominence = 50
+desired_AUC_width = high/5
+Parallel(n_jobs=8, verbose=9)(
+    delayed(Cb_stellate_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+                    gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+                    initial_period, sec) for mut in list(mutations.keys()))
+

Code/KCNA1_mutations/mut_Cb_stellate_Delta_Kv.py

@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in Cb Stellate \Delta Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+
+import numpy as np
+from joblib import Parallel, delayed
+import json
+import os
+from numba import types
+from numba.typed import Dict
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns_Kv import Cb_stellate_Kv_mut
+
+
+
+#%%
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.0015
+number_steps = 200
+initial_period = 1000
+num_gating = 11
+num_current = 7
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["A"] = False
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 34 * surf_area
+g["Kd"] = 9.0556 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 1.50159  / 2 * surf_area
+else:
+    g["Kv"] = 1.50159 / 2 * surf_area * 2
+g["Kv_mut"] = 1.50159  / 2 * surf_area
+g["A"] = 0 * surf_area
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+# save folder
+folder = './KCNA1_mutations/Cb_stellate_Delta_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+#
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+# %%
+prominence = 50
+desired_AUC_width = high/5
+Parallel(n_jobs=8, verbose=9)(
+    delayed(Cb_stellate_Kv_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale,
+                                b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low,
+                                number_steps, initial_period, sec) for mut in list(mutations.keys()))

Code/KCNA1_mutations/mut_Cb_stellate_Kv.py

@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in Cb Stellate +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+
+import numpy as np
+from joblib import Parallel, delayed
+import json
+import os
+from numba import types
+from numba.typed import Dict
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns_Kv import Cb_stellate_Kv_mut
+
+
+#%%
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.0015
+number_steps = 200
+initial_period = 1000
+num_gating = 11
+num_current = 7
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["A"] = True
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 3.4 * surf_area
+g["Kd"] = 9.0556 * (1-Kv_ratio) * surf_area
+g["Kv"] = 6. * Kv_ratio/2 * surf_area * (2 * int(currents_included["Kv_mut"] == False))
+g["Kv_mut"] = 6. * Kv_ratio/2 * surf_area
+g["A"] = 15.0159 * surf_area
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+
+# save folder
+folder = './KCNA1_mutations/Cb_stellate_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+#
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+
+# %%
+prominence = 50
+desired_AUC_width = high/5
+Parallel(n_jobs=8, verbose=9)(
+    delayed(Cb_stellate_Kv_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+                    gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+                    initial_period, sec) for mut in list(mutations.keys()))

Code/KCNA1_mutations/mut_FS_Kv.py

@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in FS +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+
+import numpy as np
+import os
+import json
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict, NumpyEncoder
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(56.9, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 502
+V_init = -70
+V_T = -57.9
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param = {}
+b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.])
+b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'] = np.array([-57.37, 20.98, 1.])
+b_param['p'] = np.array([-45., -9.9998807337, 1.])
+b_param['s'] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.4
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 58. * surf_area
+g["Kd"] = 3.9 * (1 - Kv_ratio) * surf_area
+g["M"] = 0.075 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 3.9 * Kv_ratio / 2 * surf_area
+else:
+    g["Kv"] = 3.9 * Kv_ratio / 2 * surf_area * 2
+g["Kv_mut"] = 3.9 * Kv_ratio / 2 * surf_area
+g["L"] = 0. * surf_area
+g["Leak"] = 0.038 * surf_area
+
+# save folder
+folder = './KCNA1_mutations/FS_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(Pospischil_mut)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
+                   b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
+                   high, low, number_steps, initial_period, sec)
+    for mut in list(mutations.keys()))

Code/KCNA1_mutations/mut_RS_inhib_Kv.py

@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in RS Inhibitory +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+
+import numpy as np
+from joblib import Parallel, delayed
+import os
+import h5py
+import json
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+
+# model parameters
+tau_max_p = 608
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.8, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 934
+V_init = -70
+V_T = -67.9
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param = {}
+b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'] = np.array([-57.37, 20.98, 1.0])
+b_param['p'] = np.array([-45., -9.9998807337, 1.])
+b_param['s'] = np.array([-14.16, -10.15, 1.0])
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -56.2
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 10. * surf_area
+g["Kd"] = 2.1 * (1 - Kv_ratio) * surf_area
+g["M"] = 0.0098 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 2.1 * Kv_ratio / 2 * surf_area
+else:
+    g["Kv"] = 2.1 * Kv_ratio / 2 * surf_area * 2
+g["Kv_mut"] = 2.1 * Kv_ratio / 2 * surf_area
+g["L"] = 0. * surf_area
+g["Leak"] = 0.0205 * surf_area
+
+
+# save folder
+folder = './KCNA1_mutations/RS_inhib_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+
+prominence = 50
+desired_AUC_width = high/5
+
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(Pospischil_mut)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
+                            scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut,
+                            folder, high, low, number_steps, initial_period, sec)
+    for mut in list(mutations.keys()))

Code/KCNA1_mutations/mut_RS_pyramidal_Kv.py

@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in RS Pyramidal +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+
+import numpy as np
+import os
+import json
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.4, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 608
+V_init = -70
+V_T = -56.2
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param = {}
+b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'] = np.array([-57.37, 20.98, 1.0])
+b_param['p'] = np.array([-45., -9.9998807337, 1.])
+b_param['s'] = np.array([-14.16, -10.15, 1.0])
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.3
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 56. * surf_area
+g["Kd"] = 6. * (1 - Kv_ratio) * surf_area
+g["M"] = 0.075 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 6. * Kv_ratio / 2 * surf_area
+else:
+    g["Kv"] = 6. * Kv_ratio / 2 * surf_area * 2
+g["Kv_mut"] = 6. * Kv_ratio / 2 * surf_area
+g["L"] = 0. * surf_area
+g["Leak"] = 0.0205 * surf_area
+
+# save folder
+folder = './KCNA1_mutations/RS_pyramidal_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+
+# # %%
+prominence = 50
+desired_AUC_width = high/5
+Parallel(n_jobs=8, verbose=9)(
+    delayed(Pospischil_mut)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
+                            scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut,
+                            folder, high, low, number_steps, initial_period, sec)
+    for mut in list(mutations.keys()))

Code/KCNA1_mutations/mut_STN.py

@@ -0,0 +1,110 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in STN model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+import numpy as np
+from joblib import Parallel, delayed
+import json
+import os
+from numba import types
+from numba.typed import Dict
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns import STN_mut
+
+
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.005
+number_steps = 100
+initial_period = 1000
+num_gating = 15
+num_current = 8
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r', 'Ca_conc', 'E_Ca']))
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'] = np.array([-40., -1. ,   1.])
+b_param['h'] = np.zeros(4)
+b_param['h'] = np.array([-45.5,  1. ,   1., 0.05])
+b_param['n'] = np.array([-41., -1.,   1.])
+b_param['p'] = np.array([-56., -1., 1.])
+b_param['q'] = np.array([-85., 1., 1.])
+b_param['r'] = np.array([0.17, -0.08, 1.])
+b_param['a'] = np.array([-45.,   -14.7,   1.])
+b_param['b'] = np.array([-90., 7.5,   1.])
+b_param['a_mut'] = np.array([-45.,   -14.7,   1.])
+b_param['b_mut'] = np.array([-90., 7.5,   1.])
+b_param['c'] = np.array([-30.6,  -5.,   1.])
+b_param['d1'] = np.array([-60, 7.5, 1.])
+b_param['d2'] = np.array([0.1, 0.02, 1.])
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["A_mut"] =True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 49. * surf_area
+g["Kd"] = 27. * (1-Kv_ratio)* surf_area
+g["A"] = 5. * surf_area *0.5
+g["A_mut"] = 5. * surf_area *0.5
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+V_init = -70
+
+# save folder
+folder = './KCNA1_mutations/STN'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+#
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+prominence = 50
+desired_AUC_width = high/5
+#%%
+Parallel(n_jobs=8, verbose=9)(
+    delayed(STN_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+            gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+            initial_period, sec, vol) for mut in list(mutations.keys()))

Code/KCNA1_mutations/mut_STN_Delta_Kv.py

@@ -0,0 +1,132 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in STN \Delta Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+import numpy as np
+from joblib import Parallel, delayed
+import json
+import os
+from numba import types
+from numba.typed import Dict
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns_Kv import STN_Kv_mut
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.005
+number_steps = 100
+initial_period = 1000
+num_gating = 17
+num_current = 9
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'] = np.array([-40., -1. ,   1.])
+b_param['h'] = np.zeros(4)
+b_param['h'] = np.array([-45.5,  1. ,   1., 0.05])
+b_param['n'] = np.array([-41., -1.,   1.])
+b_param['p'] = np.array([-56., -1., 1.])
+b_param['q'] = np.array([-85., 1., 1.])
+b_param['r'] = np.array([0.17, -0.08, 1.])
+b_param['a'] = np.array([-45.,   -14.7,   1.])
+b_param['b'] = np.array([-90., 7.5,   1.])
+b_param['c'] = np.array([-30.6,  -5.,   1.])
+b_param['d1'] = np.array([-60, 7.5, 1.])
+b_param['d2'] = np.array([0.1, 0.02, 1.])
+b_param['s'] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 49. * surf_area
+g["Kd"] = 27. * (1-Kv_ratio)* surf_area
+if currents_included["Kv_mut"]==True:
+    g["Kv"] = 27. * Kv_ratio/2 * surf_area
+else:
+    g["Kv"] = 27. * Kv_ratio/2 * surf_area * 2
+g["Kv_mut"] = 27. * Kv_ratio/2 * surf_area
+g["A"] = 5. * surf_area
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+
+
+# save folder
+folder = './KCNA1_mutations/STN_Delta_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+#
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+prominence = 50
+desired_AUC_width = high/5
+Parallel(n_jobs=8, verbose=9)(
+    delayed(STN_Kv_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
+                        slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low,
+                        number_steps, initial_period, sec, vol) for mut in list(mutations.keys()))
+
+

Code/KCNA1_mutations/mut_STN_Kv.py

@@ -0,0 +1,132 @@
+# -*- coding: utf-8 -*-
+"""
+Script to simulate all KCNA1 mutations in STN +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+import numpy as np
+from joblib import Parallel, delayed
+import json
+import os
+from numba import types
+from numba.typed import Dict
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns_Kv import STN_Kv_mut
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.005
+number_steps = 100
+initial_period = 1000
+num_gating = 17
+num_current = 9
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
+
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+
+# gating parameters
+b_param['m'] = np.array([-40., -1. ,   1.])
+b_param['h'] = np.zeros(4)
+b_param['h'] = np.array([-45.5,  1. ,   1., 0.05])
+b_param['n'] = np.array([-41., -1.,   1.])
+b_param['p'] = np.array([-56., -1., 1.])
+b_param['q'] = np.array([-85., 1., 1.])
+b_param['r'] = np.array([0.17, -0.08, 1.])
+b_param['a'] = np.array([-45.,   -14.7,   1.])
+b_param['b'] = np.array([-90., 7.5,   1.])
+b_param['c'] = np.array([-30.6,  -5.,   1.])
+b_param['d1'] = np.array([-60, 7.5, 1.])
+b_param['d2'] = np.array([0.1, 0.02, 1.])
+b_param['s'] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 49. * surf_area
+g["Kd"] = 27. * (1-Kv_ratio)* surf_area
+if currents_included["Kv_mut"]==True:
+    g["Kv"] = 27. * Kv_ratio/2 * surf_area
+else:
+    g["Kv"] = 27. * Kv_ratio/2 * surf_area * 2
+g["Kv_mut"] = 27. * Kv_ratio/2 * surf_area
+g["A"] = 5. * surf_area
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+V_init = -70
+
+# save folder
+folder = './KCNA1_mutations/STN_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+#
+# mutation properties
+mutations = json.load(open("./KCNA1_mutations/mutations_effects_dict.json"))
+
+prominence = 50
+desired_AUC_width = high/5
+Parallel(n_jobs=8, verbose=9)(
+    delayed(STN_Kv_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
+                     slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+                     initial_period, sec, vol) for mut in list(mutations.keys()))
+

Code/KCNA1_mutations/mutations_effects_dict.json

@@ -0,0 +1 @@
+{"I407M": {"activation_Vhalf_diff": 44.2, "activation_k_ratio": 1.0, "g_ratio": 0.43523316100000004}, "G311D": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 0.333333333}, "V408A 1": {"activation_Vhalf_diff": 1.0, "activation_k_ratio": 1.0, "g_ratio": 0.658536585}, "V404I": {"activation_Vhalf_diff": 11.3, "activation_k_ratio": 1.25, "g_ratio": 1.0}, "V408A 3": {"activation_Vhalf_diff": 0.9, "activation_k_ratio": 1.228915663, "g_ratio": 1.0}, "F249C": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 0.001}, "F414S": {"activation_Vhalf_diff": 14.9, "activation_k_ratio": 1.0, "g_ratio": 0.079493088}, "F184C 2": {"activation_Vhalf_diff": 25.9, "activation_k_ratio": 0.87654321, "g_ratio": 0.151}, "E325D 1": {"activation_Vhalf_diff": 60.4, "activation_k_ratio": 2.160493827, "g_ratio": 0.038}, "G311S": {"activation_Vhalf_diff": 31.7, "activation_k_ratio": 1.654320988, "g_ratio": 0.22899999999999998}, "V408A 2": {"activation_Vhalf_diff": 0.4, "activation_k_ratio": 0.9259259259999999, "g_ratio": 0.498}, "E325D 2": {"activation_Vhalf_diff": 21.8, "activation_k_ratio": 1.518072289, "g_ratio": 1.0}, "C185W 1": {"activation_Vhalf_diff": 4.4, "activation_k_ratio": 1.0, "g_ratio": 0.298791019}, "A242P": {"activation_Vhalf_diff": -4.8, "activation_k_ratio": 0.947368421, "g_ratio": 0.1}, "T226R": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "R307C": {"activation_Vhalf_diff": 11.4, "activation_k_ratio": 1.0, "g_ratio": 0.254608295}, "T226M": {"activation_Vhalf_diff": 14.8, "activation_k_ratio": 1.0, "g_ratio": 0.039}, "V408L": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "R167M": {"activation_Vhalf_diff": 10.0, "activation_k_ratio": 1.0, "g_ratio": 0.283246978}, "wt": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "F249I": {"activation_Vhalf_diff": -0.6, "activation_k_ratio": 0.7654320990000001, "g_ratio": 0.01}, "N255D": {"activation_Vhalf_diff": 31.0, "activation_k_ratio": 1.0, "g_ratio": 0.180904523}, "E283K": {"activation_Vhalf_diff": 9.3, "activation_k_ratio": 1.054054054, "g_ratio": 0.666666667}, "F414C": {"activation_Vhalf_diff": 2.2, "activation_k_ratio": 1.216666667, "g_ratio": 1.0}, "R324T": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "R239S": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "F303V": {"activation_Vhalf_diff": 36.0, "activation_k_ratio": 1.333333333, "g_ratio": 0.035714286}, "C185W 2": {"activation_Vhalf_diff": 0.8, "activation_k_ratio": 0.739130435, "g_ratio": 1.0}, "F184C 1": {"activation_Vhalf_diff": 28.0, "activation_k_ratio": 1.0, "g_ratio": 0.39024390200000003}, "I262M": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 0.075268817}, "R417Stop": {"activation_Vhalf_diff": 8.5, "activation_k_ratio": 1.684210526, "g_ratio": 0.02}, "V174F": {"activation_Vhalf_diff": 35.1, "activation_k_ratio": 0.75308642, "g_ratio": 0.076}, "I262T": {"activation_Vhalf_diff": 33.6, "activation_k_ratio": 2.136363636, "g_ratio": 0.3}, "I177N": {"activation_Vhalf_diff": -2.8, "activation_k_ratio": 1.226666667, "g_ratio": 1.0}, "T226A": {"activation_Vhalf_diff": 14.3, "activation_k_ratio": 0.740740741, "g_ratio": 0.024}}

Code/Neuron_models/Cb_stellate_Delta_Kv_model.py

@@ -0,0 +1,90 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run Cb Stellate \Delta Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+
+import numpy as np
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns_Kv import Cb_stellate_Kv_mut
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.0015
+number_steps = 200
+initial_period = 1000
+num_gating = 11
+num_current = 7
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["A"] = False
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 34 * surf_area
+g["Kd"] = 9.0556 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 1.50159  / 2 * surf_area
+else:
+    g["Kv"] = 1.50159 / 2 * surf_area * 2
+g["Kv_mut"] = 1.50159  / 2 * surf_area
+g["A"] = 0 * surf_area
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+mut = 'Cb_stellate_Delta_Kv'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+Cb_stellate_Kv_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+                    gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+                    initial_period, sec)

Code/Neuron_models/Cb_stellate_Kv_model.py

@@ -0,0 +1,89 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run Cb Stellate +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns_Kv import Cb_stellate_Kv_mut
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.0015
+number_steps = 200
+initial_period = 1000
+num_gating = 11
+num_current = 7
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["A"] = True
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 3.4 * surf_area
+g["Kd"] = 9.0556 * (1-Kv_ratio) * surf_area
+g["Kv"] = 6. * Kv_ratio/2 * surf_area * (2 * int(currents_included["Kv_mut"] == False))
+g["Kv_mut"] = 6. * Kv_ratio/2 * surf_area
+g["A"] = 15.0159 * surf_area
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+mut = 'Cb_stellate_Kv'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+Cb_stellate_Kv_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+                    gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+                    initial_period, sec)

Code/Neuron_models/Cb_stellate_model.py

@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run Cb Stellate model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns import Cb_stellate_mut
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.0015
+number_steps = 200
+initial_period = 1000
+num_gating = 9
+num_current = 6
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut',  'm_T', 'h_T'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A','n_A_mut', 'h_A_mut', 'm_T', 'h_T']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['n_A_mut'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A_mut'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["A_mut"] = True
+currents_included["A"] = True
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 3.4 * surf_area
+g["Kd"] = 9.0556 * surf_area
+g["A_mut"] = 15.0159 * surf_area * 0.5
+g["A"] = 15.0159 * surf_area* 0.5
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+mut = 'Cb_stellate'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+
+Cb_stellate_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+                    gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+                    initial_period, sec)

Code/Neuron_models/FS_Kv_model.py

@@ -0,0 +1,102 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run FS +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(56.9, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.4
+
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+Kv_ratio = 0.10
+g["Na"] = 58. * surf_area
+g["Kd"] = 3.9 * (1 - Kv_ratio) * surf_area
+g["M"] = 0.075 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 3.9 * Kv_ratio / 2 * surf_area
+else:
+    g["Kv"] = 3.9 * Kv_ratio / 2 * surf_area * 2
+g["Kv_mut"] = 3.9 * Kv_ratio / 2 * surf_area
+g["L"] = 0. * surf_area
+g["Leak"] = 0.038 * surf_area
+tau_max_p = 502
+V_init = -70
+V_T = -57.9
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+mut = 'FS_Kv'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+Pospischil_mut(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
+                   b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
+                   high, low, number_steps, initial_period, sec)

Code/Neuron_models/FS_model.py

@@ -0,0 +1,100 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run FS model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(56.9, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.4
+
+# currents in model
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = False
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 58. * surf_area
+g["Kd"] = 3.9  * surf_area
+g["M"] = 0.075 * surf_area
+g["Kv"] = 0.
+g["Kv_mut"] = 0.
+g["L"] = 0.
+g["Leak"] = 0.038 * surf_area
+tau_max_p = 502
+V_init = -70
+V_T = -57.9
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+mut = 'FS'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+Pospischil_mut(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
+                   b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
+                   high, low, number_steps, initial_period, sec)

Code/Neuron_models/RS_inhib_Kv_model.py

@@ -0,0 +1,116 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run RS Inhibitory +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init,  init_dict
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.00035
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.8, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 934
+V_init = -70
+V_T = -67.9
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -56.2
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 10. * surf_area
+g["Kd"] = 2.1 * (1 - Kv_ratio) * surf_area
+g["M"] = 0.0098 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 2.1 * Kv_ratio / 2 * surf_area
+else:
+    g["Kv"] = 2.1 * Kv_ratio / 2 * surf_area * 2
+g["Kv_mut"] = 2.1 * Kv_ratio / 2 * surf_area
+g["L"] = 0. * surf_area
+g["Leak"] = 0.0205 * surf_area
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5 #0.0005
+
+folder = '../Neuron_models'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+mut = 'RS_inhib_Kv'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+Pospischil_mut(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
+                   b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
+                   high, low, number_steps, initial_period, sec)

Code/Neuron_models/RS_inhib_model.py

@@ -0,0 +1,102 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run RS Inhibitory model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.00035
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.8, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 934
+V_init = -70
+V_T = -67.9
+
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+# reversal potential
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -56.2
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = False
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+g["Na"] = 10. * surf_area
+g["Kd"] = 2.1 * surf_area
+g["M"] = 0.0098 * surf_area
+g["Kv"] = 0.
+g["Kv_mut"] = 0.
+g["L"] = 0.
+g["Leak"] = 0.0205 * surf_area
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+mut = 'RS_inhib'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+Pospischil_mut(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
+                   b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
+                   high, low, number_steps, initial_period, sec)
+

Code/Neuron_models/RS_pyramidal_Kv_model.py

@@ -0,0 +1,119 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run RS Pyramidal +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+# model parameters
+tau_max_p = 608
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.4, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+V_init = -70
+V_T = -56.2
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+
+mut_act_Vhalf_wt = -30.01851851851851
+mut_act_k_wt = -7.7333333333333325
+s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
+s_diff_k = mut_act_k_wt - b_param['s'][1]
+b_param['s'][1] = b_param['s'][1] + s_diff_k
+b_param['u'][1] = b_param['u'][1] + s_diff_k
+b_param['s'][0]  = b_param['s'][0] + s_diff_Vhalf
+b_param['u'][0]  = b_param['u'][0] + s_diff_Vhalf
+b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
+b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
+b_param['s_mut'][0]  = b_param['s_mut'][0] + s_diff_Vhalf
+b_param['u_mut'][0]  = b_param['u_mut'][0] + s_diff_Vhalf
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.3
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = False
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 56. * surf_area
+g["Kd"] = 6. * (1 - Kv_ratio) * surf_area
+g["M"] = 0.075 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 6. * Kv_ratio / 2 * surf_area
+else:
+    g["Kv"] = 6. * Kv_ratio / 2 * surf_area * 2
+g["Kv_mut"] = 6. * Kv_ratio / 2 * surf_area
+g["L"] = 0. * surf_area
+g["Leak"] = 0.0205 * surf_area
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+mut = 'RS_pyramidal_Kv'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+Pospischil_mut(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
+                   b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
+                   high, low, number_steps, initial_period, sec)
+

Code/Neuron_models/RS_pyramidal_model.py

@@ -0,0 +1,101 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run RS Pyramidal model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Pospischil_fxns import Pospischil_mut
+
+# model parameters
+tau_max_p = 608
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.4, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+V_init = -70
+V_T = -56.2
+
+# initialize parameters
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array(
+    [-45., -9.9998807337, 1.])  # -45 with 10 mV shift so that it contributes to resting potential
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.3
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = False
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 56. * surf_area
+g["Kd"] = 6.  * surf_area
+g["M"] = 0.075 * surf_area
+g["Kv"] = 0.
+g["Kv_mut"] = 0.
+g["L"] = 0.
+g["Leak"] = 0.0205 * surf_area
+
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high / 5
+
+folder = '../Neuron_models'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+mut = 'RS_pyramidal'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+Pospischil_mut(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
+                   b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
+                   high, low, number_steps, initial_period, sec)

Code/Neuron_models/STN_Delta_Kv_model.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run STN \Delta Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns_Kv import STN_Kv_mut
+
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.005
+number_steps = 100
+initial_period = 1000
+num_gating = 17
+num_current = 9
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = {}
+i = 0
+for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'] = np.array([-40., -1. ,   1.])
+b_param['h'] = np.zeros(4)
+b_param['h'] = np.array([-45.5,  1. ,   1., 0.05])
+b_param['n'] = np.array([-41., -1.,   1.])
+b_param['p'] = np.array([-56., -1., 1.])
+b_param['q'] = np.array([-85., 1., 1.])
+b_param['r'] = np.array([0.17, -0.08, 1.])
+b_param['a'] = np.array([-45.,   -14.7,   1.])
+b_param['b'] = np.array([-90., 7.5,   1.])
+b_param['c'] = np.array([-30.6,  -5.,   1.])
+b_param['d1'] = np.array([-60, 7.5, 1.])
+b_param['d2'] = np.array([0.1, 0.02, 1.])
+b_param['s'] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 49. * surf_area
+g["Kd"] = 27. * (1-Kv_ratio)* surf_area
+if currents_included["Kv_mut"]==True:
+    g["Kv"] = 27. * Kv_ratio/2 * surf_area
+else:
+    g["Kv"] = 27. * Kv_ratio/2 * surf_area * 2
+g["Kv_mut"] = 27. * Kv_ratio/2 * surf_area
+g["A"] = 5. * surf_area
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+mut = 'STN_Delta_Kv'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+STN_Kv_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+            gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+            initial_period, sec, vol)
+

Code/Neuron_models/STN_Kv_model.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run STN +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns_Kv import STN_Kv_mut
+
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.005
+number_steps = 100
+initial_period = 1000
+num_gating = 17
+num_current = 9
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = {}
+i = 0
+for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'] = np.array([-40., -1., 1.])
+b_param['h'] = np.zeros(4)
+b_param['h'] = np.array([-45.5,  1.,   1., 0.05])
+b_param['n'] = np.array([-41., -1.,   1.])
+b_param['p'] = np.array([-56., -1., 1.])
+b_param['q'] = np.array([-85., 1., 1.])
+b_param['r'] = np.array([0.17, -0.08, 1.])
+b_param['a'] = np.array([-45.,   -14.7,   1.])
+b_param['b'] = np.array([-90., 7.5,   1.])
+b_param['c'] = np.array([-30.6,  -5.,   1.])
+b_param['d1'] = np.array([-60, 7.5, 1.])
+b_param['d2'] = np.array([0.1, 0.02, 1.])
+
+b_param['s'] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = True
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 49. * surf_area
+g["Kd"] = 27. * (1-Kv_ratio)* surf_area
+if currents_included["Kv_mut"]==True:
+    g["Kv"] = 27. * Kv_ratio/2 * surf_area
+else:
+    g["Kv"] = 27. * Kv_ratio/2 * surf_area * 2
+g["Kv_mut"] = 27. * Kv_ratio/2 * surf_area
+g["A"] = 5. * surf_area
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+mut = 'STN_Kv'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+STN_Kv_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+            gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+            initial_period, sec, vol)
+

Code/Neuron_models/STN_model.py

@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run STN model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+import numpy as np
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns import STN_mut
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.005
+number_steps = 100
+initial_period = 1000
+num_gating = 15
+num_current = 8
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r', 'Ca_conc', 'E_Ca']))
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = {}
+i = 0
+for var in np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r', 'Ca_conc','E_Ca']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'L', 'A_mut', 'T', 'Leak', 'Ca_K']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'] = np.array([-40., -1. ,   1.])
+b_param['h'] = np.zeros(4)
+b_param['h'] = np.array([-45.5,  1. ,   1., 0.05])
+b_param['n'] = np.array([-41., -1.,   1.])
+b_param['p'] = np.array([-56., -1., 1.])
+b_param['q'] = np.array([-85., 1., 1.])
+b_param['r'] = np.array([0.17, -0.08, 1.])
+b_param['a'] = np.array([-45.,   -14.7,   1.])
+b_param['b'] = np.array([-90., 7.5,   1.])
+b_param['a_mut'] = np.array([-45.,   -14.7,   1.])
+b_param['b_mut'] = np.array([-90., 7.5,   1.])
+b_param['c'] = np.array([-30.6,  -5.,   1.])
+b_param['d1'] = np.array([-60, 7.5, 1.])
+b_param['d2'] = np.array([0.1, 0.02, 1.])
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["A_mut"] =True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 49. * surf_area
+g["Kd"] = 27. * (1-Kv_ratio)* surf_area
+g["A"] = 5. * surf_area *0.5
+g["A_mut"] = 5. * surf_area *0.5
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+V_init = -70
+
+
+prominence = 50
+min_spike_height = 0
+desired_AUC_width = high/5
+
+folder = '../Neuron_models'
+mut = 'STN'
+mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+STN_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+            gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
+            initial_period, sec, vol)

Code/Sensitivity_Analysis/SA_Cb_stellate.py

@@ -0,0 +1,112 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for Cb Stellate model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns import SA_Cb_stellate
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 9
+num_current = 6
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut',  'm_T', 'h_T'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut',  'm_T', 'h_T']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'A_mut', 'T', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['n_A_mut'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A_mut'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["A_mut"] = False
+currents_included["A"] = True
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 3.4 * surf_area
+g["Kd"] = 9.0556 * surf_area
+g["A_mut"] = 0.
+g["A"] = 15.0159 * surf_area
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+
+# save folder
+folder = '../Sensitivity_Analysis/Data/Cb_stellate'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n', 'n_A', 'h_A'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'A', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Cb_stellate)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
+                            slope_shift,gating, current, prominence, desired_AUC_width, folder, high, low, number_steps,
+                            initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+                    for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+
+
+

Code/Sensitivity_Analysis/SA_Cb_stellate_Delta_Kv.py

@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for Cb Stellate \Delta Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns_Kv import SA_Cb_stellate_Kv
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 11
+num_current = 7
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'Kv', 'Kv_mut', 'T', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = False
+currents_included["A"] = False
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 34 * surf_area
+g["Kd"] = 9.0556 * surf_area
+if currents_included["Kv_mut"] == True:
+    g["Kv"] = 1.50159  / 2 * surf_area
+else:
+    g["Kv"] = 1.50159 / 2 * surf_area * 2
+g["Kv_mut"] = 1.50159  / 2 * surf_area
+g["A"] = 0 * surf_area
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+# save folder
+folder = './Sensitivity_Analysis/Data/Cb_stellate_Delta_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n', 's', 'u'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Cb_stellate_Kv)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
+                            slope_shift,gating, current, prominence, desired_AUC_width, folder, high, low, number_steps,
+                            initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+                    for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+

Code/Sensitivity_Analysis/SA_Cb_stellate_Kv.py

@@ -0,0 +1,116 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for Cb Stellate +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.Cb_stellate_fxns_Kv import SA_Cb_stellate_Kv
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 11
+num_current = 7
+C, surf_area = capacitance(61.4, 1.50148)
+variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'Kv', 'Kv_mut', 'T', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-37.,  -3,   1])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-40.,   4.,   1., 0])
+b_param['n'][:] = np.array([-23, -5,   1])
+b_param['n_A'][:] = np.array([-27, -13.2,   1.])
+b_param['h_A'][:] = np.array([-80., 6.5,   1.])
+b_param['m_T'][:] = np.array([-50., -3,   1.])
+b_param['h_T'][:] = np.array([-68., 3.75, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 55.
+E["K"] = -80.
+E["Ca"] = 22.
+E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = False
+currents_included["A"] = True
+currents_included["T"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 3.4 * surf_area
+g["Kd"] = 9.0556 * (1-Kv_ratio) * surf_area
+g["Kv"] = 9.0556 * Kv_ratio * surf_area
+g["Kv_mut"] = 0.
+g["A"] = 15.0159 * surf_area
+g["T"] = 0.45045 * surf_area
+g["Leak"] = 0.07407 * surf_area
+
+
+# save folder
+folder = './Sensitivity_Analysis/Data/Cb_stellate_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n', 'n_A', 'h_A', 's', 'u'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'A', 'Kv', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Cb_stellate_Kv)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
+                            slope_shift,gating, current, prominence, desired_AUC_width, folder, high, low, number_steps,
+                            initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+                    for alt_ind in range(alt_types.shape[0]) for alt in range(21))

Code/Sensitivity_Analysis/SA_FS.py

@@ -0,0 +1,120 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for FS model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict, NumpyEncoder
+from Code.Functions.Pospischil_fxns import SA_Pospischil
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(56.9, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 502
+V_init = -70
+V_T = -57.9 # Threshold
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# create dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.4
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = False
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 58. * surf_area
+g["Kd"] = 3.9 * surf_area
+g["M"] = 0.075 * surf_area
+g["Kv"] = 0.
+g["Kv_mut"] = 0.
+g["L"] = 0.
+g["Leak"] = 0.038 * surf_area
+
+
+# folder to save to
+folder = '../Sensitivity_Analysis/Data/FS'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+
+
+
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
+                           b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low,
+                           number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+    for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+

Code/Sensitivity_Analysis/SA_FS_Kv.py

@@ -0,0 +1,117 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for FS +Kv model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict, NumpyEncoder
+from Code.Functions.Pospischil_fxns import SA_Pospischil
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(56.9, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 502
+V_init = -70
+V_T = -57.9
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.4
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.10
+g["Na"] = 58. * surf_area
+g["Kd"] = 3.9 * (1 - Kv_ratio) * surf_area
+g["M"] = 0.075 * surf_area
+g["Kv"] = 3.9 * Kv_ratio * surf_area
+g["Kv_mut"] = 0.
+g["L"] = 0. * surf_area
+g["Leak"] = 0.038 * surf_area
+
+# save folder
+folder = '../Sensitivity_Analysis/Data/FS_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n',  's', 'u'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,20, base=2)
+log_array = np.append(log_array,1)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
+                           scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high,
+                           low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+        for alt_ind in range(alt_types.shape[0]) for alt in range(21))

Code/Sensitivity_Analysis/SA_RS_inhib.py

@@ -0,0 +1,120 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for RS Inhibitory model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict, NumpyEncoder
+from Code.Functions.Pospischil_fxns import SA_Pospischil
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.00035
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.8, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 934
+V_init = -70
+V_T = -67.9
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -56.2
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = False
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 10. * surf_area
+g["Kd"] = 2.1 * surf_area
+g["M"] = 0.0098 * surf_area
+g["Kv"] = 0.
+g["Kv_mut"] = 0.
+g["L"] = 0.
+g["Leak"] = 0.0205 * surf_area
+
+# save folder
+folder = '../Sensitivity_Analysis/Data/RS_inhib'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+
+
+
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
+                           scale,
+                           b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low,
+                           number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+    for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+

Code/Sensitivity_Analysis/SA_RS_inhib_Kv.py

@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for RS Inhibitory +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict, NumpyEncoder
+from Code.Functions.Pospischil_fxns import SA_Pospischil
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.00035
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.8, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 934
+V_init = -70
+V_T = -67.9
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'][:] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'][:] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'][:] = np.array([-57.37, 20.98, 1.])
+b_param['p'][:] = np.array([-45., -9.9998807337, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -56.2
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 10. * surf_area
+g["Kd"] = 2.1 * (1 - Kv_ratio) * surf_area
+g["M"] = 0.0098 * surf_area
+g["Kv"] = 2.1 * Kv_ratio * surf_area
+g["Kv_mut"] = 0.
+g["L"] = 0. * surf_area
+g["Leak"] = 0.0205 * surf_area
+
+# save folder
+folder = '../Sensitivity_Analysis/Data/RS_inhib_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n', 's', 'u'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
+                           scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high,
+                           low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+    for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+
+

Code/Sensitivity_Analysis/SA_RS_pyramidal.py

@@ -0,0 +1,102 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for RS Pyramidal model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict, NumpyEncoder
+from Code.Functions.Pospischil_fxns import SA_Pospischil
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.4, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 608
+V_init = -70
+V_T = -56.2
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# gating parameters
+b_param = {}
+b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'] = np.array([-57.37, 20.98, 1.0])
+b_param['p'] = np.array([-45., -9.9998807337, 1.])
+b_param['s'] = np.array([-14.16, -10.15, 1.0])
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potential
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.3
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = False
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 56. * surf_area
+g["Kd"] = 6. * surf_area
+g["M"] = 0.075 * surf_area
+g["Kv"] = 0.
+g["Kv_mut"] = 0.
+g["L"] = 0.
+g["Leak"] = 0.0205 * surf_area
+
+folder = '../Sensitivity_Analysis/Data/RS_pyramidal'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
+                           scale,
+                           b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low,
+                           number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+    for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+

Code/Sensitivity_Analysis/SA_RS_pyramidal_Kv.py

@@ -0,0 +1,117 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for RS Pyramidal +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict, NumpyEncoder
+from Code.Functions.Pospischil_fxns import SA_Pospischil
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 10
+num_current = 7
+C, surf_area = capacitance(61.4, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
+    np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
+tau_max_p = 608
+V_init = -70
+V_T = -56.2
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# create dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param = {}
+b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
+b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.05])
+b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
+b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
+b_param['r'] = np.array([-57.37, 20.98, 1.0])
+b_param['p'] = np.array([-45., -9.9998807337, 1.])
+b_param['s'] = np.array([-14.16, -10.15, 1.0])
+b_param['u'] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 50.
+E["K"] = -90.
+E["Ca"] = 120.
+E["Leak"] = -70.3
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] = True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = False
+currents_included["L"] = False
+currents_included["M"] = True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.1
+g["Na"] = 56. * surf_area
+g["Kd"] = 6. * (1 - Kv_ratio) * surf_area
+g["M"] = 0.075 * surf_area
+g["Kv"] = 6. * Kv_ratio * surf_area
+g["Kv_mut"] = 0.
+g["L"] = 0. * surf_area
+g["Leak"] = 0.0205 * surf_area
+
+# save folder
+folder = '../Sensitivity_Analysis/Data/RS_pyramidal_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n', 's', 'u'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Pospischil)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
+                           scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high,
+                           low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
+    for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+
+

Code/Sensitivity_Analysis/SA_STN.py

@@ -0,0 +1,116 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for STN model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns import SA_STN
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 15
+num_current = 8
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r', 'Ca_conc', 'E_Ca']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r','Ca_conc','E_Ca']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'L', 'A_mut', 'T', 'Leak', 'Ca_K']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-40., -1., 1.])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-45.5,  1.,   1., 0.05])
+b_param['n'][:] = np.array([-41., -1.,   1.])
+b_param['p'][:] = np.array([-56., -1., 1.])
+b_param['q'][:] = np.array([-85., 1., 1.])
+b_param['r'][:] = np.array([0.17, -0.08, 1.])
+b_param['a'][:] = np.array([-45.,   -14.7,   1.])
+b_param['b'][:] = np.array([-90., 7.5,   1.])
+b_param['a_mut'][:] = np.array([-45.,   -14.7,   1.])
+b_param['b_mut'][:] = np.array([-90., 7.5,   1.])
+b_param['c'][:] = np.array([-30.6,  -5.,   1.])
+b_param['d1'][:] = np.array([-60, 7.5, 1.])
+b_param['d2'][:] = np.array([0.1, 0.02, 1.])
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["A_mut"] =False
+currents_included["Leak"] = True
+
+# model conductances
+g["Na"] = 49. * surf_area
+g["Kd"] = 57. * surf_area
+g["A"] = 5. * surf_area
+g["A_mut"] = 0.
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+
+# save folder
+folder = '../Sensitivity_Analysis/Data/STN'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n', 'a', 'b'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'A', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% run SA with multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_STN)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+           gating, current, prominence, desired_AUC_width, folder, high, low, number_steps, initial_period, sec, vol,
+           lin_array, log_array, alt_types, alt_ind, alt)
+                    for alt_ind in range(alt_types.shape[0]) for alt in range(21))

Code/Sensitivity_Analysis/SA_STN_Delta_Kv.py

@@ -0,0 +1,131 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for STN \Delta Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns_Kv import SA_Kv_STN
+
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 17
+num_current = 9
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
+    ind_dict[var] = i
+    i += 1
+
+
+# gating parameters
+b_param['m'][:] = np.array([-40., -1. ,   1.])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-45.5,  1. ,   1., 0.05])
+b_param['n'][:] = np.array([-41., -1.,   1.])
+b_param['p'][:] = np.array([-56., -1., 1.])
+b_param['q'][:] = np.array([-85., 1., 1.])
+b_param['r'][:] = np.array([0.17, -0.08, 1.])
+b_param['a'][:] = np.array([-45.,   -14.7,   1.])
+b_param['b'][:] = np.array([-90., 7.5,   1.])
+b_param['c'][:] = np.array([-30.6,  -5.,   1.])
+b_param['d1'][:] = np.array([-60, 7.5, 1.])
+b_param['d2'][:] = np.array([0.1, 0.02, 1.])
+
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = False
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.01
+g["Na"] = 49. * surf_area
+g["Kd"] = 57. * surf_area
+g["Kv"] = 0.5 * surf_area
+g["Kv_mut"] = 0
+g["A"] = 0. * surf_area
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+V_init = -70
+
+# save folder
+folder = '../Sensitivity_Analysis/Data/STN_Delta_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n', 's','u'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %%  run SA with multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Kv_STN)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
+                    slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low, number_steps, initial_period,
+                    sec, vol, lin_array, log_array, alt_types, alt_ind, alt)
+    for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+
+
+

Code/Sensitivity_Analysis/SA_STN_Kv.py

@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+Script to run sensitivity analysis for STN +Kv1.1 model
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
+import numpy as np
+from numba import types
+from numba.typed import Dict
+from joblib import Parallel, delayed
+import os
+from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
+from Code.Functions.STN_fxns_Kv import SA_Kv_STN
+
+# model parameters
+dt = 0.01
+sec = 2
+low = 0
+high = 0.001
+number_steps = 200
+initial_period = 1000
+num_gating = 17
+num_current = 9
+d = 61.4
+r = d/2 * 10**-6 # radius in meters
+vol = np.pi * r**3 # volume in liters
+C, surf_area = capacitance(d, 1)
+stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
+shift, scale, slope_shift,  E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
+V_init = -70
+
+# initialize arrays
+current = np.zeros((num_current, stim_num))
+gating = np.zeros((num_gating, stim_num))
+
+# initialize dictionary
+ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
+i = 0
+for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
+    ind_dict[var] = i
+    i += 1
+i = 0
+for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
+    ind_dict[var] = i
+    i += 1
+
+# gating parameters
+b_param['m'][:] = np.array([-40., -1. ,   1.])
+b_param['h'] = np.zeros(4)
+b_param['h'][:] = np.array([-45.5,  1. ,   1., 0.05])
+b_param['n'][:] = np.array([-41., -1.,   1.])
+b_param['p'][:] = np.array([-56., -1., 1.])
+b_param['q'][:] = np.array([-85., 1., 1.])
+b_param['r'][:] = np.array([0.17, -0.08, 1.])
+b_param['a'][:] = np.array([-45.,   -14.7,   1.])
+b_param['b'][:] = np.array([-90., 7.5,   1.])
+b_param['c'][:] = np.array([-30.6,  -5.,   1.])
+b_param['d1'][:] = np.array([-60, 7.5, 1.])
+b_param['d2'][:] = np.array([0.1, 0.02, 1.])
+b_param['s'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u'] = np.zeros(4)
+b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
+b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
+b_param['u_mut'] = np.zeros(4)
+b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
+
+
+# reversal potentials
+E["Na"] = 60.
+E["K"] = -90.
+E["Leak"] = -60.
+
+# model currents
+currents_included["Na"] = True
+currents_included["Kd"] =True
+currents_included["Kv"] = True
+currents_included["Kv_mut"] = False
+currents_included["L"] = True
+currents_included["T"] = True
+currents_included["Ca_K"] = True
+currents_included["A"] =True
+currents_included["Leak"] = True
+
+# model conductances
+Kv_ratio = 0.01
+g["Na"] = 49. * surf_area
+g["Kd"] = 57. * (1-Kv_ratio)* surf_area
+g["Kv"] = 57. * Kv_ratio * surf_area
+g["Kv_mut"] = 0
+g["A"] = 5. * surf_area
+g["L"] = 5. * surf_area
+g["T"] = 5. * surf_area
+g["Ca_K"] = 1 * surf_area
+g["Leak"] = 0.035 * surf_area
+
+# save folder
+folder = '../Sensitivity_Analysis/Data/STN_Kv'
+if not os.path.isdir(folder):
+    os.makedirs(folder)
+
+#%% setup for one-factor-at-a-time SA
+var = np.array(['m', 'h', 'n', 'a', 'b', 's','u'])
+type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
+                       np.array(['slope' for i in range(var.shape[0])]))
+cur = np.array(['Na', 'Kd', 'A','Kv', 'Leak'])
+type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
+var = np.append(var, var)
+var = np.append(var, cur)
+alt_types = np.c_[var, type_names]
+lin_array = np.arange(-10, 11, 1)
+log_array = np.logspace(-1,1,21, base=2)
+
+# %% run SA with multiprocessing
+prominence = 50
+desired_AUC_width = high/5
+
+Parallel(n_jobs=8, verbose=9)(
+    delayed(SA_Kv_STN)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
+               gating, current, prominence, desired_AUC_width, folder, high, low, number_steps, initial_period, sec,
+               vol, lin_array, log_array, alt_types, alt_ind, alt)
+        for alt_ind in range(alt_types.shape[0]) for alt in range(21))
+
+
+

Code/csv_generation/Plotting_data_collection.py

@@ -0,0 +1,333 @@
+"""
+Script to analyse and collect data from simulations
+
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+import pandas as pd
+import h5py
+import json
+import os
+import numpy as np
+from ast import literal_eval
+import string
+import matplotlib.cm as cm
+
+# %% rheo_{}_ex.csv, AUC_{}_ex.csv #####################################################################################
+# Collect examples of effects of a shift, a slope change and a change in g on AUC and rheobase
+## AUC ###################
+AUC_shift = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'IB',
+                                   '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$'])
+
+AUC_slope = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'IB',
+                                   'RS Pyramidal +$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$'])
+
+AUC_g = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'IB',
+                                   'RS Pyramidal +$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$'])
+
+script_dir = os.path.dirname(os.path.realpath("__file__"))
+fname = os.path.join(script_dir, )
+models = ['RS_pyramidal', 'RS_inhib', 'FS','RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv',  'Cb_stellate','Cb_stellate_Kv',
+          'Cb_stellate_Kv_only','STN','STN_Kv', 'STN_Kv_only']
+model_labels = ['RS Pyramidal','RS Inhibitory', 'FS','RS Pyramidal +$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$']
+
+shift_interest = 'n'
+for i in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df/{}_shift_AUC.json'.format(models[i])) as json_file:
+        data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        data.replace(0., np.NaN, inplace=True)
+        data = (data - data.loc['0', :])/ data.loc['0', :] # normalize AUC
+        data.sort_index(inplace=True)
+        AUC_shift[model_labels[i]] =data[shift_interest]
+AUC_shift['alteration'] = AUC_shift.index
+
+
+slope_interest = 's'
+for i in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df/{}_slope_AUC.json'.format(models[i])) as json_file:
+        data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        data.replace(0., np.NaN, inplace=True)
+        data = (data - data.loc['1.0', :])/ data.loc['1.0', :] # normalize AUC
+        data.sort_index(inplace=True)
+        try:
+            AUC_slope[model_labels[i]] = data[slope_interest]
+        except:
+            pass
+AUC_slope['alteration'] = AUC_slope.index
+
+
+g_interest = 'Kd'
+for i in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df/{}_g_AUC.json'.format(models[i])) as json_file:
+        data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        data.replace(0., np.NaN, inplace=True)
+        data = (data - data.loc['1.0', :])/ data.loc['1.0', :] # normalize AUC
+        data.sort_index(inplace=True)
+        AUC_g[model_labels[i]] =data[g_interest]
+AUC_g['alteration'] = AUC_g.index
+
+AUC_shift.to_csv('AUC_shift_ex.csv')
+AUC_slope.to_csv('AUC_slope_ex.csv')
+AUC_g.to_csv('AUC_g_ex.csv')
+
+
+## rheobase ###################
+rheo_shift = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'IB',
+                                   'RS Pyramidal +$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$'])
+
+rheo_slope = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'RS Pyramidal +$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$'])
+
+rheo_g = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'IB',
+                                   'RS Pyramidal +$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$'])
+
+script_dir = os.path.dirname(os.path.realpath("__file__"))
+fname = os.path.join(script_dir, )
+
+models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
+          'STN_Kv',
+          'STN_Kv_only']
+model_labels = ['RS Pyramidal','RS Inhibitory', 'FS','RS Pyramidal +$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$']
+
+shift_interest = 's'
+for i in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df/{}_shift_rheo.json'.format(models[i])) as json_file:
+        data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        data.replace(0., np.NaN, inplace=True)
+        data = (data - data.loc['0', :]) #/ data.loc['0', :]  # normalize AUC
+        data.sort_index(inplace=True)
+        try:
+            rheo_shift[model_labels[i]] = data[shift_interest]
+        except:
+            pass
+rheo_shift['alteration'] = rheo_shift.index
+
+slope_interest = 'u'
+for i in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df/{}_slope_rheo.json'.format(models[i])) as json_file:
+
+        data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        data.replace(0., np.NaN, inplace=True)
+        data = (data - data.loc['1.0', :]) #/ data.loc['1.0', :]  # normalize AUC
+        data.sort_index(inplace=True)
+        try:
+            rheo_slope[model_labels[i]] = data[slope_interest]
+        except:
+            pass
+rheo_slope['alteration'] = rheo_slope.index
+
+g_interest = 'Leak'
+for i in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df/{}_g_rheo.json'.format(models[i])) as json_file:
+        data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        data.replace(0., np.NaN, inplace=True)
+        data = (data - data.loc['1.0', :]) #/ data.loc['1.0', :]  # normalize AUC
+        data.sort_index(inplace=True)
+        rheo_g[model_labels[i]] = data[g_interest]
+rheo_g['alteration'] = rheo_g.index
+
+rheo_shift.to_csv('rheo_shift_ex.csv')
+rheo_slope.to_csv('rheo_slope_ex.csv')
+rheo_g.to_csv('rheo_g_ex.csv')
+
+
+# %% Model fI ##########################################################################################################
+# for each model generate summary csv of sensitivity analysis results on AUC and rheobase
+# | (index) | mag | alt   | type  | F     | I     |
+# | 0       | -10 | m     | shift | array | array |
+
+models = ['RS Pyramidal', 'RS Inhibitory', 'FS', '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}$']
+
+model_save_name = {'RS Pyramidal': 'RS_pyr_posp', 'RS Inhibitory': 'RS_inhib_posp', 'FS': 'FS_posp',
+                   'RS Pyramidal +$\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}$': 'STN_Kv_only'}
+top_file_dir = '../Sensitivity_Analysis/Data'
+data_dir = {'RS Pyramidal': os.path.join(top_file_dir, 'RS_pyramidal_{}_fI.json'),
+            'RS Inhibitory': os.path.join(top_file_dir, 'RS_inhib_{}_fI.json'),
+            'FS': os.path.join(top_file_dir, 'FS_{}_fI.json'),
+            'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'RS_pyramidal_{}_fI.json'),
+            'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'RS_inhib_{}_fI.json'),
+            'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'FS_{}_fI.json'),
+            'Cb stellate': os.path.join(top_file_dir, 'Cb_stellate_{}_fI.json'),
+            'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'Cb_stellate_Kv_{}_fI.json'),
+            'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'Cb_stellate_Kv_only_{}_fI.json'),
+            'STN': os.path.join(top_file_dir, 'STN_{}_fI.json'),
+            'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'STN_Kv_{}_fI.json'),
+            'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'STN_Kv_only_{}_fI.json')}
+
+I_dir = {'RS Pyramidal': os.path.join(top_file_dir, 'RS_pyramidal_{}_I_mag.json'),
+            'RS Inhibitory': os.path.join(top_file_dir, 'RS_pyramidal_{}_I_mag.json'),
+            'FS': os.path.join(top_file_dir, 'RS_pyramidal_{}_I_mag.json'),
+            'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'RS_pyramidal_{}_I_mag.json'),
+            'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'RS_inhib_{}_I_mag.json'),
+            'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'FS_{}_I_mag.json'),
+            'Cb stellate': os.path.join(top_file_dir, 'Cb_stellate_{}_I_mag.json'),
+            'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'Cb_stellate_Kv_{}_I_mag.json'),
+            'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'Cb_stellate_Kv_only_{}_I_mag.json'),
+            'STN': os.path.join(top_file_dir, 'STN_{}_I_mag.json'),
+            'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'STN_Kv_{}_I_mag.json'),
+            'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': os.path.join(top_file_dir, 'STN_Kv_only_{}_I_mag.json')}
+
+
+def concat_dfs(df, data_dir, I_dir, type='shift'):
+    with open(data_dir.format(type)) as json_file:
+        data = pd.read_json(json_file, convert_axes=False)
+    with open(I_dir.format(type)) as json_file:
+        I = pd.read_json(json_file, convert_axes=False)
+    for c in data.columns:
+        # print(c)
+        df = df.append(pd.DataFrame(data=(np.array([data.index,['{}'.format(c) for i in range(0, len(data[c]))],
+                                                      [type for i in range(0, len(data[c]))], data[c], I[c]]).T),
+                                      columns=['mag','alt', 'type', 'F', 'I']))
+    return df
+
+
+summary_df = pd.DataFrame(columns=['mag','alt', 'type', 'F', 'I'])
+for m in models:
+    print(m)
+    df = pd.DataFrame(columns=['mag','alt', 'type', 'F', 'I'])
+    df = concat_dfs(df, data_dir[m], I_dir[m], type='shift')
+    df = concat_dfs(df, data_dir[m], I_dir[m], type='slope')
+    df = concat_dfs(df, data_dir[m], I_dir[m], type='g')
+    folder = os.path.join(os.path.dirname(os.path.realpath("__file__")), 'Model_fI')
+    if not os.path.isdir(folder):
+        os.makedirs(folder)
+    df.to_csv(os.path.join(folder, '{}_fI.csv'.format(model_save_name[m])))
+
+
+
+# %% firing_values.csv, model_spiking.csv, model_F_inf.csv #############################################################
+# generate firing_values.csv with values for hysteresis from ramp current input
+# generate model_spiking.csv with example model response to step current
+# generate model_F_inf.csv with model fI curve data
+import numpy as np
+import pandas as pd
+
+models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
+          'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
+model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'RS pyramidal +Kv1.1', 'RS inhibitory +Kv1.1', 'FS +Kv1.1',
+               'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1', 'STN', 'STN +Kv1.1',
+               'STN $\Delta$Kv1.1']
+firing_values = pd.DataFrame(columns=models, index=['spike_ind', 'ramp_up', 'ramp_down'])
+models = ['RS_pyr', 'RS_pyr_Kv', 'RS_inhib', 'RS_inhib_Kv', 'FS', 'FS_Kv',
+          'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Delta_Kv',
+          'STN', 'STN_Kv', 'STN_Delta_Kv']
+col_names = ['I', 'I_inhib']
+for mod in models: col_names.append(mod)
+model_F_inf = pd.DataFrame(columns=col_names)
+col_names = ['t']
+for mod in models: col_names.append(mod)
+spiking = pd.DataFrame(columns=col_names)
+
+# index for example trace
+spike_ind = {'RS_pyramidal': 60, 'RS_inhib':25, 'FS':50, 'RS_pyramidal_Kv':60,
+            'RS_inhib_Kv':50, 'FS_Kv':50, 'Cb_stellate':60, 'Cb_stellate_Kv':130,
+            'Cb_stellate_Kv_only':75, 'STN': 25, 'STN_Kv':95, 'STN_Kv_only':80}
+
+for model_name in models:
+    folder = '../Neuron_models/{}'.format(model_name)
+    fname = os.path.join(folder, "{}.hdf5".format(model_name))  # RS_inhib
+    with h5py.File(fname, "r+") as f:
+        I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
+                          (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num']) * 1000
+        start = np.int(f['data'].attrs['initial_period'] * 1 / f['data'].attrs['dt'])
+        stim_len = np.int((f['data'].attrs['stim_time'] - start) * f['data'].attrs['dt'])
+        time = np.arange(0, stim_len, f['data'].attrs['dt'])
+        spiking[model_name] = f['data']['V_m'][spike_ind[model_name]][start:]
+        model_F_inf[model_name] = f['analysis']['F_inf'][:]
+        firing_values.loc['spike_ind', model_name] = I_mag[spike_ind[model_name]]
+        firing_values.loc['ramp_down', model_name] = f['analysis']['ramp_I_down'][()]
+        firing_values.loc['ramp_up', model_name] = f['analysis']['ramp_I_up'][()]
+firing_values.to_csv('firing_values.csv')
+spiking.to_csv('model_spiking.csv')
+model_F_inf.to_csv('model_F_inf.csv')
+# %% model_ramp.csv ####################################################################################################
+# generate model_ramp.csv with model responses to ramp input
+# | (index) | t | models ....
+import numpy as np
+import pandas as pd
+
+models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', '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 +Kv1.1', 'Cb stellate $\Delta$Kv1.1',
+               'STN', 'STN +Kv1.1', 'STN $\Delta$Kv1.1']
+col_names = ['t']
+for mod in models: col_names.append(mod)
+model_ramp = pd.DataFrame(columns=col_names)
+sec = 4
+dt = 0.01
+ramp_len = int(sec * 1000 * 1 / dt)
+t_ramp = np.arange(0, ramp_len) * dt
+model_ramp.loc[:, 't'] = t_ramp
+
+for model_name in models:
+    folder = '../Neuron_models/{}'.format(model_name)
+    fname = os.path.join(folder, "{}.hdf5".format(model_name))  # RS_inhib
+    with h5py.File(fname, "r+") as f:
+        model_ramp.loc[:, model_name] = f['analysis']['V_m_ramp'][()]
+
+model_ramp.to_csv('model_ramp.csv')
+
+#%% sim_mut_AUC.csv, sim_mut_rheo.csv ##################################################################################
+# generate mutation plot data in sim_mut_AUC.csv and sim_mut_rheo.csv for the AUC and rheobase effects respectively for
+# each mutation in each model
+mutations = json.load(open("../KCNA1_mutations/mutations_effects_dict.json"))
+keys_to_remove = ['V408L', 'T226R', 'R239S', 'R324T']
+for key in keys_to_remove:
+    del mutations[key]
+mutations_f = []
+mutations_n = []
+for mut in mutations:
+    mutations_n.append(mut)
+    mutations_f.append(mut.replace(" ", "_"))
+
+models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', '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 +Kv1.1', 'Cb stellate $\Delta$Kv1.1',
+               'STN', 'STN +Kv1.1', 'STN $\Delta$Kv1.1']
+AUC = pd.DataFrame(columns=mutations_n)
+rheobase = pd.DataFrame(columns=mutations_n)
+save_folder = '../KCNA1_mutations'
+if not os.path.isdir(save_folder):
+    os.makedirs(save_folder)
+for model_name in models:
+    folder = '../KCNA1_mutations/{}'.format(model_name)
+    for mut in list(mutations_n):
+        fname = os.path.join(folder, "{}.hdf5".format(mut.replace(" ", "_")))
+        with h5py.File(fname, "r+") as f:
+            rheobase.loc[mut.replace(" ", "_"), model_name] = f['analysis']['rheobase'][()]
+            AUC.loc[mut.replace(" ", "_"), model_name] = f['analysis']['AUC'][()]
+AUC.replace(0., np.NaN, inplace=True)
+rheobase.replace(0., np.NaN, inplace=True)
+rheobase = (rheobase - rheobase.loc['WT', :]) /rheobase.loc['WT', :]
+AUC = (AUC - AUC.loc['WT', :]) /AUC.loc['WT', :]
+AUC.to_csv(os.path.join(save_folder, 'sim_mut_AUC.csv'))
+rheobase.to_csv(os.path.join(save_folder, 'sim_mut_rheobase.csv'))

Code/csv_generation/SA_collection.py

@@ -0,0 +1,311 @@
+"""
+Script to analyse and collect data from sensitivity analysis simulations
+
+
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
+
+import numpy as np
+import h5py
+import pandas as pd
+import os
+from ast import literal_eval
+import json
+import scipy.stats as stats
+
+# folder
+top_dir = '../Sensitivity_Analysis/Data'
+
+#%% create JSON files for each alt type for each model in '../Sensitivity_Analysis/Data/SA_summary_df/file.json'
+for dir_name in next(os.walk(top_dir))[1]: # for each folder in top_file_dir (each model)
+    folder = os.path.join(top_dir, dir_name)
+    print(folder)
+    # for each alt_type create pandas dataframe
+    shift_AUC = pd.DataFrame()
+    shift_rheo = pd.DataFrame()
+    shift_fI = pd.DataFrame(dtype=object)
+    shift_I_mag = pd.DataFrame(dtype=object)
+
+    slope_AUC = pd.DataFrame()
+    slope_rheo = pd.DataFrame()
+    slope_fI = pd.DataFrame(dtype=object)
+    slope_I_mag = pd.DataFrame(dtype=object)
+
+    g_AUC = pd.DataFrame()
+    g_rheo = pd.DataFrame()
+    g_fI = pd.DataFrame(dtype=object)
+    g_I_mag = pd.DataFrame(dtype=object)
+
+
+    for root, dirs, files in os.walk(folder):
+        for file in files:
+            if file.endswith('.hdf5'):
+                with h5py.File(os.path.join(folder, file), "r+") as f:
+                    alt = f['data'].attrs['alteration']
+
+                    test = f['data'].attrs['alteration_info'].replace(' ', ',')
+                    alt_info = literal_eval(test)
+                    var = alt_info[0]
+                    alt_type = alt_info[1]
+                    if alt_type == 'shift':
+                        shift_AUC.loc[alt, var] = f['analysis']['AUC'][()]
+                        try:
+                            shift_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
+                        except:
+                            print('shift', var, alt)
+                        shift_fI.loc[alt, var] = 0
+                        shift_fI = shift_fI.astype(object)
+                        shift_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
+                        shift_I_mag.loc[alt, var] = 0
+                        shift_I_mag = shift_I_mag.astype(object)
+                        shift_I_mag.at[alt, var] = ((np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
+                                                               (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) /
+                                                               f['data'].attrs['stim_num'])) * 1000).tolist() #nA
+                    elif alt_type == 'slope':
+                        slope_AUC.loc[alt, var] = f['analysis']['AUC'][()]
+                        try:
+                            slope_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
+                        except:
+                            print('slope', var, alt)
+                        slope_fI.loc[alt, var] = 0
+                        slope_fI = slope_fI.astype(object)
+                        slope_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
+                        slope_I_mag.loc[alt, var] = 0
+                        slope_I_mag = slope_I_mag.astype(object)
+                        slope_I_mag.at[alt, var] = ((np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
+                                                               (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) /
+                                                               f['data'].attrs['stim_num'])) * 1000).tolist()
+                    elif alt_type == 'g':
+                        g_AUC.loc[alt, var] = f['analysis']['AUC'][()]
+                        try:
+                            g_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
+                        except:
+                            print('g', var, alt)
+                        g_fI.loc[alt, var] = 0
+                        g_fI = g_fI.astype(object)
+                        g_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
+                        g_I_mag.loc[alt, var] = 0
+                        g_I_mag = g_I_mag.astype(object)
+                        g_I_mag.at[alt, var] = ((np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
+                                                               (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) /
+                                                               f['data'].attrs['stim_num'])) * 1000).tolist()
+                    else:
+                        print(file, 'Unknown alteration type')
+
+    #save df with folder+alt_type
+    save_folder = os.path.join(top_dir, 'SA_summary_df')
+    if not os.path.isdir(save_folder):
+        os.makedirs(save_folder)
+    shift_AUC.to_json(os.path.join(save_folder, '{}_shift_AUC.json'.format(dir_name)))
+    shift_rheo.to_json(os.path.join(save_folder, '{}_shift_rheo.json'.format(dir_name)))
+    shift_fI.to_json(os.path.join(save_folder, '{}_shift_fI.json'.format(dir_name)))
+    shift_I_mag.to_json(os.path.join(save_folder, '{}_shift_I_mag.json'.format(dir_name)))
+
+    slope_AUC.to_json(os.path.join(save_folder, '{}_slope_AUC.json'.format(dir_name)))
+    slope_rheo.to_json(os.path.join(save_folder, '{}_slope_rheo.json'.format(dir_name)))
+    slope_fI.to_json(os.path.join(save_folder, '{}_slope_fI.json'.format(dir_name)))
+    slope_I_mag.to_json(os.path.join(save_folder, '{}_slope_I_mag.json'.format(dir_name)))
+
+    g_AUC.to_json(os.path.join(save_folder, '{}_g_AUC.json'.format(dir_name)))
+    g_rheo.to_json(os.path.join(save_folder, '{}_g_rheo.json'.format(dir_name)))
+    g_fI.to_json(os.path.join(save_folder, '{}_g_fI.json'.format(dir_name)))
+    g_I_mag.to_json(os.path.join(save_folder, '{}_g_I_mag.json'.format(dir_name)))
+
+
+#%% AUC Correlation analysis
+alt_dict = {}
+alt_dict['m'] = 'Na activation'
+alt_dict['h'] = 'Na inactivation'
+alt_dict['n'] = 'K activation'
+alt_dict['s'] = '$K_V1.1$ activation'
+alt_dict['u'] = '$K_V1.1$inactivation'
+alt_dict['a'] = 'A activation'
+alt_dict['b'] = 'A inactivation'
+alt_dict['n_A'] = 'A activation'
+alt_dict['h_A'] = 'A inactivation'
+alt_dict['Na'] = 'Na'
+alt_dict['Kd'] = 'K'
+alt_dict['Kv'] = '$K_V1.1$'
+alt_dict['A'] = 'A'
+alt_dict['Leak'] = 'Leak'
+
+# models = directory names in top_file_dir and what json files are named
+models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
+          'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
+# model_names = names of models for json files and ultimately csv files for plotting
+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$']
+
+shift_df = pd.DataFrame(columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', '$\Delta V_{1/2}$', 'color'])  # for boxplots
+for mod in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df/{}_shift_AUC.json'.format(models[mod])) as json_file:
+        df = pd.read_json(json_file)
+        df.sort_index(inplace=True)
+        df.replace(0., np.NaN, inplace=True)
+        df = (df - df.loc[0, :]) / df.loc[0, :]
+        zero_ind = np.argwhere(df.index == 0)[0][0]
+        ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
+        df2 = df.loc[ind, :]
+    for c in df.keys():
+        tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
+        tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
+        ratio_tau = tau_l / tau
+        shift_df = shift_df.append(pd.Series([model_names[mod], tau, p,tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=shift_df.columns), ignore_index=True)
+
+
+# models = directory names in top_file_dir and what json files are named
+models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
+          'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
+# model_names = names of models for json files and ultimately csv files for plotting
+model_names = ['RS Pyramidal','RS Inhibitory','FS', 'RS Pyramidal +$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$']
+
+slope_df = pd.DataFrame(columns=['model', 'corr', 'p_value','local corr', 'local p_value', 'ratio', 'Slope (k)', 'color'])  # for boxplots
+for mod in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df//{}_slope_AUC.json'.format(models[mod])) as json_file:
+        df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        df.index = df.index.map(float)
+        df.sort_index(inplace=True)
+        df.replace(0., np.NaN, inplace=True)
+        df = (df - df.loc[1.0, :]) / df.loc[1.0, :]
+        zero_ind = np.argwhere(df.index == 1)[0][0]
+        ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
+        df2 = df.loc[ind, :]
+    for c in df.keys():
+        tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
+        tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
+        ratio_tau = tau_l/tau
+        slope_df = slope_df.append(pd.Series([model_names[mod], tau, p, tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=slope_df.columns), ignore_index=True)
+
+# models = directory names in top_file_dir and what json files are named
+models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
+          'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
+# model_names = names of models for json files and ultimately csv files for plotting
+model_names = ['RS Pyramidal','RS Inhibitory','FS', 'RS Pyramidal +$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$']
+
+g_df = pd.DataFrame(columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', 'g', 'color'])  # for boxplots
+for mod in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df//{}_g_AUC.json'.format(models[mod])) as json_file:
+        df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        df.index = df.index.map(float)
+        df.sort_index(inplace=True)
+        df.replace(0., np.NaN, inplace=True)
+        df = (df - df.loc[1.0, :]) / df.loc[1.0, :]
+        zero_ind = np.argwhere(df.index == 1)[0][0]
+        ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
+        df2 = df.loc[ind, :]
+    for c in df.keys():
+        tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
+        tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
+        ratio_tau = tau_l / tau
+        g_df = g_df.append(pd.Series([model_names[mod], tau, p,tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=g_df.columns), ignore_index=True)
+
+shift_df.to_json('./Sensitivity_analysis/shift_box_kendall_corr.json')
+slope_df.to_json('./Sensitivity_analysis/slope_box_kendall_corr.json')
+g_df.to_json('./Sensitivity_analysis/g_kendall_corr.json')
+
+
+#%% rheobase correlation analysis
+
+# models = directory names in top_file_dir and what json files are named
+models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
+          'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
+# model_names = names of models for json files and ultimately csv files for plotting
+model_names = ['RS Pyramidal','RS Inhibitory','FS', 'RS Pyramidal +$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$']
+
+shift_df = pd.DataFrame(columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', '$\Delta V_{1/2}$', 'color'])  # for boxplots
+for mod in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df//{}_shift_rheo.json'.format(models[mod])) as json_file:
+        df = pd.read_json(json_file)
+        df.sort_index(inplace=True)
+        df.replace(0., np.NaN, inplace=True)
+        df = (df - df.loc[0, :]) / df.loc[0, :]
+        zero_ind = np.argwhere(df.index == 0)[0][0]
+        ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
+        df2 = df.loc[ind, :]
+    for c in df.keys():
+        tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
+        tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
+        ratio_tau = tau_l / tau
+        shift_df = shift_df.append(pd.Series([model_names[mod], tau, p,tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=shift_df.columns), ignore_index=True)
+
+
+# models = directory names in top_file_dir and what json files are named
+models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
+          'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
+# model_names = names of models for json files and ultimately csv files for plotting
+model_names = ['RS Pyramidal','RS Inhibitory','FS', 'RS Pyramidal +$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$']
+
+slope_df = pd.DataFrame(columns=['model', 'corr', 'p_value','local corr', 'local p_value', 'ratio', 'Slope (k)', 'color'])  # for boxplots
+for mod in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df//{}_slope_rheo.json'.format(models[mod])) as json_file:
+        df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        df.index = df.index.map(float)
+        df.sort_index(inplace=True)
+        df.replace(0., np.NaN, inplace=True)
+        df = (df - df.loc[1.0, :]) / df.loc[1.0, :]
+        zero_ind = np.argwhere(df.index == 1)[0][0]
+        ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
+        df2 = df.loc[ind, :]
+    for c in df.keys():
+        tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
+        tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
+        ratio_tau = tau_l/tau
+        slope_df = slope_df.append(pd.Series([model_names[mod], tau, p, tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=slope_df.columns), ignore_index=True)
+
+# models = directory names in top_file_dir and what json files are named
+models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
+          'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
+# model_names = names of models for json files and ultimately csv files for plotting
+model_names = ['RS Pyramidal','RS Inhibitory','FS', 'RS Pyramidal +$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$']
+
+g_df = pd.DataFrame(columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', 'g', 'color'])  # for boxplots
+for mod in range(len(models)):
+    with open('../Sensitivity_Analysis/Data/SA_summary_df//{}_g_rheo.json'.format(models[mod])) as json_file:
+        df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
+        df.index = df.index.map(float)
+        df.sort_index(inplace=True)
+        df.replace(0., np.NaN, inplace=True)
+        df = (df - df.loc[1.0, :]) / df.loc[1.0, :]
+        zero_ind = np.argwhere(df.index == 1)[0][0]
+        ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
+        df2 = df.loc[ind, :]
+    for c in df.keys():
+        tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
+        tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
+        ratio_tau = tau_l / tau
+        g_df = g_df.append(pd.Series([model_names[mod], tau, p,tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=g_df.columns), ignore_index=True)
+
+shift_df.to_json('./Sensitivity_analysis/rheo_shift_box_kendall_corr.json')
+slope_df.to_json('./Sensitivity_analysis/rheo_slope_box_kendall_corr.json')
+g_df.to_json('./Sensitivity_analysis/rheo_g_kendall_corr.json')
+
+#%% create csv files for plotting
+# | (index) | model | corr   | p_value  | g | color
+#% AUC
+AUC_shift_json = pd.read_json('./Sensitivity_analysis/shift_kendall_corr_rel.json', orient='records')
+AUC_slope_json = pd.read_json('./Sensitivity_analysis/slope_kendall_corr_rel.json', orient='records') #, lines=True)
+AUC_g_json = pd.read_json('./Sensitivity_analysis/g_kendall_corr_rel.json', orient='records')
+AUC_shift_df = AUC_shift_json[['model', 'corr', 'p_value', 'g', 'color']]
+AUC_slope_df = AUC_slope_json[['model', 'corr', 'p_value', 'g', 'color']]
+AUC_g_df = AUC_g_json[['model', 'corr', 'p_value', 'g', 'color']]
+AUC_shift_df.to_csv('AUC_shift_corr.csv')
+AUC_slope_df.to_csv('AUC_scale_corr.csv')
+AUC_g_df.to_csv('AUC_g_corr.csv')
+
+#% rheo
+rheo_shift_json = pd.read_json('./Sensitivity_analysis/rheo_shift_kendall_corr.json', orient='records')
+rheo_slope_json = pd.read_json('./Sensitivity_analysis/rheo_slope_kendall_corr.json', orient='records') #, lines=True)
+rheo_g_json = pd.read_json('./Sensitivity_analysis/rheo_g_kendall_corr.json', orient='records')
+rheo_shift_df = rheo_shift_json[['model', 'corr', 'p_value', 'g', 'color']]
+rheo_slope_df = rheo_slope_json[['model', 'corr', 'p_value', 'g', 'color']]
+rheo_g_df = rheo_g_json[['model', 'corr', 'p_value', 'g', 'color']]
+rheo_shift_df.to_csv('rheo_shift_corr.csv')
+rheo_slope_df.to_csv('rheo_scale_corr.csv')
+rheo_g_df.to_csv('rheo_g_corr.csv')

Code/run_simulations.sh

@@ -0,0 +1,47 @@
+# Bash shell script to run all simulations and analysis
+
+# Individual Neuronal models
+python ./Neuron_models/RS_pyramidal_model.py
+python ./Neuron_models/RS_pyramidal_Kv_model.py
+python ./Neuron_models/RS_inhib_model.py
+python ./Neuron_models/RS_inhib_Kv_model.py
+python ./Neuron_models/FS_model.py
+python ./Neuron_models/FS_Kv_model.py
+python ./Neuron_models/STN_model.py
+python ./Neuron_models/STN_Kv_model.py
+python ./Neuron_models/STN_Delta_Kv_model.py
+python ./Neuron_models/Cb_stellate_model.py
+python ./Neuron_models/Cb_stellate_Kv_model.py
+python ./Neuron_models/Cb_stellate_Delta_Kv_model.py
+
+
+# Sensitivity Analysis
+python ./Sensitivity_Analysis/SA_RS_pyramidal.py
+python ./Sensitivity_Analysis/SA_RS_pyramidal_Kv.py
+python ./Sensitivity_Analysis/SA_RS_inhib.py
+python ./Sensitivity_Analysis/SA_RS_inhib_Kv.py
+python ./Sensitivity_Analysis/SA_FS.py
+python ./Sensitivity_Analysis/SA_FS_Kv.py
+python ./Sensitivity_Analysis/SA_STN.py
+python ./Sensitivity_Analysis/SA_STN_Kv.py
+python ./Sensitivity_Analysis/SA_STN_Delta_Kv.py
+python ./Sensitivity_Analysis/SA_Cb_stellate.py
+python ./Sensitivity_Analysis/SA_Cb_stellate_Kv.py
+python ./Sensitivity_Analysis/SA_Cb_stellate_Delta_Kv.py
+
+
+# KCNA1 mutations in models
+python ./KCNA1_mutatoins/mut_RS_pyramidal_Kv.py
+python ./KCNA1_mutatoins/mut_RS_inhib_Kv.py
+python ./KCNA1_mutatoins/mut_FS_Kv.py
+python ./KCNA1_mutatoins/mut_STN.py
+python ./KCNA1_mutatoins/mut_STN_Kv.py
+python ./KCNA1_mutatoins/mut_STN_Delta_Kv.py
+python ./KCNA1_mutatoins/mut_Cb_stellate.py
+python ./KCNA1_mutatoins/mut_Cb_stellate_Kv.py
+python ./KCNA1_mutatoins/mut_Cb_stellate_Delta_Kv.py
+
+
+# Data collection and csv generation
+python SA_collection.py
+python Plotting_data_collection.py

Figure_org/Figure_update.org~

@@ -1,292 +0,0 @@
-# Instructions for org mode export
-# - to get export options: ctrl-c ctrl-e
-# - to export as latex pdf: ctrl-c ctrl-e l p
-
-
-#+OPTIONS: ':nil *:nil -:t ::t <:t H:3 \n:nil ^:t arch:headline
-#+OPTIONS: author:nil broken-links:nil c:nil creator:nil
-#+OPTIONS: d:(not "LOGBOOK") date:nil e:t email:nil f:t inline:t num:nil
-#+OPTIONS: p:nil pri:nil prop:nil stat:t tags:t tasks:t tex:t
-#+OPTIONS: timestamp:t title:nil toc:nil todo:nil |:t
-#+TITLE: Nils Koch
-#+DATE: <2021-08-16 Mon>
-#+EMAIL: nils.koch@mail.mcgill.ca
-#+TAGS: noexport(n)
-#+LATEX_CLASS: article
-#+LATEX_CLASS_OPTIONS: [letterpaper,10pt]
-#+LATEX_HEADER: \usepackage[margin=0.6in]{geometry}
-#+LATEX_HEADER: \usepackage{fontawesome}
-#+LATEX_HEADER: \usepackage{titling}
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-#+LATEX_HEADER: \usepackage{url}
-#+LATEX_HEADER: \usepackage{academicons}
-#+LATEX_HEADER: \usepackage{float}
-#+LATEX_HEADER: \usepackage{hyperref}
-#+LATEX_HEADER: \parindent=0in
-#+LATEX_HEADER: \def\refname{\large References}
-#+LATEX_HEADER: \newcommand{\eg}{e.g.\ }
-#+LATEX_HEADER: \newcommand{\ie}{i.e.,\xspace }
-#+LATEX_HEADER: \newcommand{\Aref}[1]{[A\ref{it:#1}]}
-#+LATEX_HEADER: \newcommand{\Cref}[1]{[C\ref{it:#1}]}
-#+LATEX_HEADER: \newcommand{\Pref}[1]{[P\ref{it:#1}]}
-#+LATEX_HEADER: \usepackage{academicons}
-#+LATEX_HEADER: \usepackage[breaklinks=true]{hyperref}
-#+LATEX_HEADER: \usepackage{graphicx}
-#+LATEX_HEADER: \usepackage{graphbox}
-#+LATEX_HEADER: \usepackage{matlab-prettifier}
-#+LATEX_HEADER: \usepackage[scaled]{helvet} \renewcommand\familydefault{\sfdefault}
-#+LATEX_HEADER: \graphicspath{{./Figures/}}
-#+LATEX_HEADER_EXTRA:
-# For citation colors
-#+LATEX_HEADER_EXTRA: \hypersetup{
-#+LATEX_HEADER_EXTRA:     colorlinks,
-#+LATEX_HEADER_EXTRA:     linkcolor={red!50!black},
-#+LATEX_HEADER_EXTRA:     citecolor={blue!50!black},
-#+LATEX_HEADER_EXTRA:     urlcolor={black!70}
-#+LATEX_HEADER_EXTRA: }
-#+DESCRIPTION:
-#+KEYWORDS:
-#+subtitle:
-
-#+LATEX_COMPILER: pdflatex
-#+BEGIN_EXPORT latex
-  \renewcommand{\maketitle}
-    \maketitle{
-      \begin{center}
-        \Huge \textbf{Update on Figures}}}\\ 
-      \end{center}
-   }
-   
-  %\thispagestyle{empty} %remove header on first page
-  \titleformat{\section}{\Large\bfseries}{\thesection}{0pt}{}
-  \titleformat{\subsection}{\large\bfseries}{\thesection}{0pt}{}
-  \titlespacing*{\section}{0pt}{0ex}{0ex}
-  \titlespacing*{\subsection}{0pt}{0ex}{0ex}
-  \titlespacing*{\itemsep}{0pt}{0ex}{0ex}
-  \pagestyle{fancy}
-  \fancyhf{}
-  \renewcommand{\headrulewidth}{0pt} % remove lines as well
-  \renewcommand{\footrulewidth}{0pt} % remove lines as well
-  %\lfoot{\textcolor{black!50}{\footnotesize \today}}
-  %\cfoot{\textcolor{black!50}{\footnotesize Nils Koch }}
-  \rfoot{\textcolor{black!50}{\thepage}}
-  %\lhead{}
-  \chead{\today}
-  %\rhead{}
-\newcommand{\squishlist}{
-\begin{list}{$\bullet$}
-{ \setlength{\itemsep}{0pt} \setlength{\parsep}{3pt} \setlength{\topsep}{3pt} \setlength{\partopsep}{0pt}
-\setlength{\leftmargin}{1.5em} \setlength{\labelwidth}{1em} \setlength{\labelsep}{0.5em} } }
-\newcommand{\squishend}{\end{list} }
-#+END_EXPORT
-
- 
-* Firing Characterization:
-** Question figure addresses:
-   Firing is a complicated phenomenon. How can it be simply characterized to compare the
-   effects of changes in current properties?
-
-** Method by which data is generated:
-   Schematic diagram that does not contain underlying data - contains different square
-   root functions.
-
-** Conclusion from Figure:
-   Firing can be characterized by the rheobase and the AUC (proprotional to the increase in
-   firing after the rheobase). The rheobase and firing in a small range above it (AUC) are
-   likely important for determining network excitability (I think this makes sense,
-   would need references to support this).
-  
-#+BEGIN_EXPORT latex
-\begin{figure}[H]
-\includegraphics[align=c,width=10cm]{firing_characterization.pdf}
-\caption{A. Demonstrates AUC in cyan. B. Demonstrates what combinations of increased and
-decreased rheobase and AUC look like in terms of fI curves.}
-\label{fig:firing_charact}
-\end{figure}
-#+END_EXPORT
-
-* Diversity in Model Firing:
-   We have used a number of neuronal models that do not burst to look at the effects
-   of changes in current properties in firing given different cell types/current
-   environments
-
-** Question figure addresses:
-   Which model is used?
-** Rationale:
-   The effect of a change in a current property cannot be assessed in only one cell
-   type to understand the general effects of this change and to assess whether differences
-   occur across cell types.
-** Method by which data is generated:
-   Models from different sources are used and an example spike train is shown for each model
-   along with a fI curve. The black dot on the fI curve indicates where the spike train is
-   taken from and the green and red dots indicate the current at which the first and last
-   spike occurs from an increasing and decreasing current ramp respectively. (These ramps
-   can be seen in the ramp figure at the end).
-** Conclusion from Figure:
-   The models use are diverse and display a variety of spike shapes, firing behaviours, and
-   fI curve shapes.
-
-#+BEGIN_EXPORT latex
-\begin{figure}[H]
-\includegraphics[align=c,width=18cm]{diversity_in_firing.pdf}
-\caption{Spike trains and corresponding fI curves from: A. Cb stellate, B. RS Inhibitory,
-C. FS, D. RS Pyramidal, E. RS Inhibitory +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-F. Cb stellate +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), 
-G. FS +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-H. RS Pyramidal +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), I. STN +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-J.Cb stellate \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) , K. STN \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), L. STN,
-where +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) indicates the addition of Kv1.1 to the model
-and \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) indicates the exchange of the A type K+ current for Kv1.1. The black
-dot on the fI curve indicates where the spike train is taken from and the green and red
-dots indicate the current at which the first and last spike occurs from an increasing and
-decreasing current ramp respectively.}
-\label{fig:div_firing}
-\end{figure}
-#+END_EXPORT
-
-
-* IB model issue:                                                  :noexport:
-** Issue Background:
-   IB (intrinsicially bursting) model from Pospischil et al. 2008 uses a leak conductance
-   of 0.1. This results in bursting like firing initially (see bottom right of Figure
-   \ref{fig:IB}) and requires large currents to be injected (> 0.75 nA). When the Kv1.1
-   current is added to this model (bottom right Figure \ref{fig:IB}) the bursting
-   behaviour is diminished but the currents needed to get firing are still high. As a
-   result I decreased the leak conductance by a factor of 10 to 0.01 as seen in the
-   upper right of Figure \ref{fig:IB}. This is the model that I used for the sensitivity
-   analysis and modelling of Kv1.1 mutations. However, when the original IB model has a
-   reduced leak current (of 0.01) it bursts and only for a small current range (see upper
-   left of Figure \ref{fig:IB}).
-** Issue:
-   Which model to use? To use the model with reduced leak that has nice properties with
-   Kv1.1 added, the bursting and weird fI curve of the model without Kv1.1 needs to be
-   addressed (bursting is not well captured by the analysis methods). To use the model with
-   the large leak current (the original model), the large input currents is concerning and
-   the sensitivity analysis and Kv1.1 mutation modelling would need to be re-done for this
-   model with and without Kv1.1 (with 20/24 cores on the Kraken this would take 2-3 days
-   I think). Alternatively, as neither option really sits well with me, we could remove this
-   model from all figure and discussion and focus on the other models that in their
-   original states (and with Kv1.1 added) have repetitive firing without bursting.
-#+BEGIN_EXPORT latex
-\begin{figure}[H]
-\includegraphics[align=c,width=15cm]{IB_issue_plot.pdf}
-\caption{The fI curves and exampling spike trains from the IB model with different
-Leak conductances without and with (+Kv1.1) Kv1.1 conductance.}
-\label{fig:IB}
-\end{figure}
-#+END_EXPORT
-
-* Rheobase Sensitivity Analysis:
-  I am not yet happy with this figure's layout
-** Question figure addresses:
-   How is rheobase affected by changes in current properties across models? Is the change
-   in rheobase always in the same direction across models?
-** Method by which data is generated:
-   A one factor at a time (OFAT) sensitivity analysis was performed on the currents common
-   to all or most models, where one current property was changed systematically at a time,
-   the firing responses simulated and the fI curves computed. From this fI curve the
-   largest injected current at which no firing occurs and the smallest injected
-   current at which firing occurs were obtained. This current interval was then simulated
-   to obtain the rheobase at greater resolution.
-** Conclusion from Figure:
-   Generally the effect on rheobase is similar across all models/current environments
-
-#+BEGIN_EXPORT latex
-\begin{figure}[H]
-\includegraphics[align=c,width=18cm]{rheobase_correlation.pdf}
-\caption{}
-\label{fig:rheo}
-\end{figure}
-#+END_EXPORT
-
-* AUC Sensitivity Analysis:
-  I prefer the first layout
-** Question figure addresses:
-   How is AUC affected by changes in current properties across models? Is the change
-   in AUC rheobase always in the same direction across models?
-
-** Method by which data is generated:
-   A one factor at a time (OFAT) sensitivity analysis was performed on the currents common
-   to all or most models, where one current property was changed systematically at a time,
-   the firing responses simulated and the steady-state fI curves computed. From this fI
-   curve the largest injected current at which no firing occurs was obtained and the
-   integral from this current using the composite trapezoidal rule for 1/5 of the current
-   range.
-
-** Conclusion from Figure:
-   A given current property change does not necessarily cause the same
-   change in rheobase and as such the outcome of a given change is dependent on the
-   current environment or cell type.
-  
-#+BEGIN_EXPORT latex
-\begin{figure}[H]
-\includegraphics[align=c,width=18cm]{AUC_correlation.pdf}
-\caption{}
-\label{fig:AUC}
-\end{figure}
-#+END_EXPORT
-
-
-* Kv1.1 mutation simulation:
-** Question figure addresses:
-   Do mutations of Kv1.1 cause similar effects on firing across cell types or is the effect
-   cell type (and thus neuronal network) dependent?
-** Method by which data is generated:
-   Published Kv1.1 mutations (Lauxmann et al 2021) are simulated in all models containing
-   Kv1.1 or an inactivating K^+ current by altering the current properties according to
-   those experimentally measured for each mutation. The firing of each model for each
-   mutation are then simulated and the rheobase and AUC are computed.
-** Conclusion from Figure:
-   The effects of Kv1.1 mutations on rheobase are highly correlated across models indicating
-   that these mutations affect the rheobase in a similar fashion. However, the effect of
-   Kv1.1 mutations vary across models as seen by the different correlation magnitudes
-   between models. Thus although these mutations affect rheobase in a similar manner, the
-   effect on AUC cannot easily be generalized and depends on cell type.
-
-   Furthermore, this Figure demonstrates why characterization of mutations in terms of
-   LOF or GOF in relation to firing overlooks potentially important characteristics of
-   the changes in firing seen in different cell types. Thus, the characterization LOF
-   and GOF is useful at a channel level to characterize the effects of a mutation on
-   the current, but cannot and should not be blindly extended to characterize the
-   effects of the mutation on firing as LOF and GOF, not only because the current
-   environment in which this mutation occurs is a key determinant of the firing outcome,
-   but also that firing is complex and not easily characterized as LOF or GOF.
-  
-#+BEGIN_EXPORT latex
-\begin{figure}[H]
-\includegraphics[align=c,width=18cm]{simulation_model_comparison.pdf}
-\caption{}
-\label{fig:kv11}
-\end{figure}
-#+END_EXPORT
-
-* Ramp Firing - For Supplements?:
-** Question figure addresses:
-   How does the firing of the models look like with a ramp protocol?
-** Method by which data is generated:
-   A 4 second ramp with the same current range as the step currents used to obtain fI
-   plots is used and the firing of all models is simulated. The resulting spike trains
-   are plotted.
-** Conclusion from Figure:
-   The diversity of firing seen with step currents is also seen with current ramps. The
-   ramps highlight the hysteresis in models.
-  
-#+BEGIN_EXPORT latex
-\begin{figure}[H]
-\includegraphics[align=c,width=20cm]{ramp_firing.pdf}
-\caption{A. Cb stellate, B. RS Inhibitory,
-C. FS, D. RS Pyramidal, E. RS Inhibitory +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-F. Cb stellate +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), 
-G. FS +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-H. RS Pyramidal +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), I. STN +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-J.Cb stellate \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) , K. STN \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), L. STN,
-where +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) indicates the addition of Kv1.1 to the model
-and \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) indicates the exchange of the A type K+
-current for Kv1.1.}
-\label{fig:ramp}
-\end{figure}
-#+END_EXPORT

Figure_org/Figure_update.tex~

@@ -1,278 +0,0 @@
-% Created 2022-02-16 Wed 15:41
-% Intended LaTeX compiler: pdflatex
-\documentclass[letterpaper,10pt]{article}
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-\usepackage[margin=0.6in]{geometry}
-\usepackage{fontawesome}
-\usepackage{titling}
-\usepackage{titlesec}
-\usepackage{fancyhdr}
-\usepackage{setspace}
-\usepackage{xcolor}
-\usepackage{tabularx,xspace}
-\usepackage{url}
-\usepackage{academicons}
-\usepackage{float}
-\usepackage{hyperref}
-\parindent=0in
-\def\refname{\large References}
-\newcommand{\eg}{e.g.\ }
-\newcommand{\ie}{i.e.,\xspace }
-\newcommand{\Aref}[1]{[A\ref{it:#1}]}
-\newcommand{\Cref}[1]{[C\ref{it:#1}]}
-\newcommand{\Pref}[1]{[P\ref{it:#1}]}
-\usepackage{academicons}
-\usepackage[breaklinks=true]{hyperref}
-\usepackage{graphicx}
-\usepackage{graphbox}
-\usepackage{matlab-prettifier}
-\usepackage[scaled]{helvet} \renewcommand\familydefault{\sfdefault}
-\graphicspath{{./Figures/}}
-
-\hypersetup{
-colorlinks,
-linkcolor={red!50!black},
-citecolor={blue!50!black},
-urlcolor={black!70}
-}
-\date{}
-\title{Nils Koch}
-\hypersetup{
- pdfauthor={},
- pdftitle={Nils Koch},
- pdfkeywords={},
- pdfsubject={},
- pdfcreator={Emacs 27.2 (Org mode 9.4.4)}, 
- pdflang={English}}
-\begin{document}
-
-  \renewcommand{\maketitle}
-    \maketitle{
-      \begin{center}
-        \Huge \textbf{Update on Figures}}}\\ 
-      \end{center}
-   }
-   
-  %\thispagestyle{empty} %remove header on first page
-  \titleformat{\section}{\Large\bfseries}{\thesection}{0pt}{}
-  \titleformat{\subsection}{\large\bfseries}{\thesection}{0pt}{}
-  \titlespacing*{\section}{0pt}{0ex}{0ex}
-  \titlespacing*{\subsection}{0pt}{0ex}{0ex}
-  \titlespacing*{\itemsep}{0pt}{0ex}{0ex}
-  \pagestyle{fancy}
-  \fancyhf{}
-  \renewcommand{\headrulewidth}{0pt} % remove lines as well
-  \renewcommand{\footrulewidth}{0pt} % remove lines as well
-  %\lfoot{\textcolor{black!50}{\footnotesize \today}}
-  %\cfoot{\textcolor{black!50}{\footnotesize Nils Koch }}
-  \rfoot{\textcolor{black!50}{\thepage}}
-  %\lhead{}
-  \chead{\today}
-  %\rhead{}
-\newcommand{\squishlist}{
-\begin{list}{$\bullet$}
-{ \setlength{\itemsep}{0pt} \setlength{\parsep}{3pt} \setlength{\topsep}{3pt} \setlength{\partopsep}{0pt}
-\setlength{\leftmargin}{1.5em} \setlength{\labelwidth}{1em} \setlength{\labelsep}{0.5em} } }
-\newcommand{\squishend}{\end{list} }
-
-
-\section*{Firing Characterization:}
-\label{sec:org2ff19f9}
-\subsection*{Question figure addresses:}
-\label{sec:org39e44a3}
-Firing is a complicated phenomenon. How can it be simply characterized to compare the
-effects of changes in current properties?
-
-\subsection*{Method by which data is generated:}
-\label{sec:org8f27edf}
-Schematic diagram that does not contain underlying data - contains different square
-root functions.
-
-\subsection*{Conclusion from Figure:}
-\label{sec:org219d22f}
-Firing can be characterized by the rheobase and the AUC (proprotional to the increase in
-firing after the rheobase). The rheobase and firing in a small range above it (AUC) are
-likely important for determining network excitability (I think this makes sense,
-would need references to support this).
-
-\begin{figure}[H]
-\includegraphics[align=c,width=10cm]{firing_characterization.pdf}
-\caption{A. Demonstrates AUC in cyan. B. Demonstrates what combinations of increased and
-decreased rheobase and AUC look like in terms of fI curves.}
-\label{fig:firing_charact}
-\end{figure}
-
-\section*{Diversity in Model Firing:}
-\label{sec:org2d2ab53}
-We have used a number of neuronal models that do not burst to look at the effects
-of changes in current properties in firing given different cell types/current
-environments
-
-\subsection*{Question figure addresses:}
-\label{sec:org2a68d8a}
-Which model is used?
-\subsection*{Rationale:}
-\label{sec:org574a416}
-The effect of a change in a current property cannot be assessed in only one cell
-type to understand the general effects of this change and to assess whether differences
-occur across cell types.
-\subsection*{Method by which data is generated:}
-\label{sec:orgf4eccc9}
-Models from different sources are used and an example spike train is shown for each model
-along with a fI curve. The black dot on the fI curve indicates where the spike train is
-taken from and the green and red dots indicate the current at which the first and last
-spike occurs from an increasing and decreasing current ramp respectively. (These ramps
-can be seen in the ramp figure at the end).
-\subsection*{Conclusion from Figure:}
-\label{sec:org2b1b0e7}
-The models use are diverse and display a variety of spike shapes, firing behaviours, and
-fI curve shapes.
-
-\begin{figure}[H]
-\includegraphics[align=c,width=18cm]{diversity_in_firing.pdf}
-\caption{Spike trains and corresponding fI curves from: A. Cb stellate, B. RS Inhibitory,
-C. FS, D. RS Pyramidal, E. RS Inhibitory +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-F. Cb stellate +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), 
-G. FS +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-H. RS Pyramidal +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), I. STN +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-J.Cb stellate \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) , K. STN \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), L. STN,
-where +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) indicates the addition of Kv1.1 to the model
-and \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) indicates the exchange of the A type K+ current for Kv1.1. The black
-dot on the fI curve indicates where the spike train is taken from and the green and red
-dots indicate the current at which the first and last spike occurs from an increasing and
-decreasing current ramp respectively.}
-\label{fig:div_firing}
-\end{figure}
-
-
-\section*{Rheobase Sensitivity Analysis:}
-\label{sec:org482a6cf}
-I am not yet happy with this figure's layout
-\subsection*{Question figure addresses:}
-\label{sec:org8d09846}
-How is rheobase affected by changes in current properties across models? Is the change
-in rheobase always in the same direction across models?
-\subsection*{Method by which data is generated:}
-\label{sec:org679728a}
-A one factor at a time (OFAT) sensitivity analysis was performed on the currents common
-to all or most models, where one current property was changed systematically at a time,
-the firing responses simulated and the fI curves computed. From this fI curve the
-largest injected current at which no firing occurs and the smallest injected
-current at which firing occurs were obtained. This current interval was then simulated
-to obtain the rheobase at greater resolution.
-\subsection*{Conclusion from Figure:}
-\label{sec:orgab5a050}
-Generally the effect on rheobase is similar across all models/current environments
-
-\begin{figure}[H]
-\includegraphics[align=c,width=18cm]{rheobase_correlation.pdf}
-\caption{}
-\label{fig:rheo}
-\end{figure}
-
-\section*{AUC Sensitivity Analysis:}
-\label{sec:org84023d3}
-I prefer the first layout
-\subsection*{Question figure addresses:}
-\label{sec:org2aedc23}
-How is AUC affected by changes in current properties across models? Is the change
-in AUC rheobase always in the same direction across models?
-
-\subsection*{Method by which data is generated:}
-\label{sec:org7f21d20}
-A one factor at a time (OFAT) sensitivity analysis was performed on the currents common
-to all or most models, where one current property was changed systematically at a time,
-the firing responses simulated and the steady-state fI curves computed. From this fI
-curve the largest injected current at which no firing occurs was obtained and the
-integral from this current using the composite trapezoidal rule for 1/5 of the current
-range.
-
-\subsection*{Conclusion from Figure:}
-\label{sec:org2c7ad14}
-A given current property change does not necessarily cause the same
-change in rheobase and as such the outcome of a given change is dependent on the
-current environment or cell type.
-
-\begin{figure}[H]
-\includegraphics[align=c,width=18cm]{AUC_correlation.pdf}
-\caption{}
-\label{fig:AUC}
-\end{figure}
-
-
-\section*{Kv1.1 mutation simulation:}
-\label{sec:org747834c}
-\subsection*{Question figure addresses:}
-\label{sec:orgcae797a}
-Do mutations of Kv1.1 cause similar effects on firing across cell types or is the effect
-cell type (and thus neuronal network) dependent?
-\subsection*{Method by which data is generated:}
-\label{sec:org58691da}
-Published Kv1.1 mutations (Lauxmann et al 2021) are simulated in all models containing
-Kv1.1 or an inactivating K\^{}+ current by altering the current properties according to
-those experimentally measured for each mutation. The firing of each model for each
-mutation are then simulated and the rheobase and AUC are computed.
-\subsection*{Conclusion from Figure:}
-\label{sec:org4808be0}
-The effects of Kv1.1 mutations on rheobase are highly correlated across models indicating
-that these mutations affect the rheobase in a similar fashion. However, the effect of
-Kv1.1 mutations vary across models as seen by the different correlation magnitudes
-between models. Thus although these mutations affect rheobase in a similar manner, the
-effect on AUC cannot easily be generalized and depends on cell type.
-
-Furthermore, this Figure demonstrates why characterization of mutations in terms of
-LOF or GOF in relation to firing overlooks potentially important characteristics of
-the changes in firing seen in different cell types. Thus, the characterization LOF
-and GOF is useful at a channel level to characterize the effects of a mutation on
-the current, but cannot and should not be blindly extended to characterize the
-effects of the mutation on firing as LOF and GOF, not only because the current
-environment in which this mutation occurs is a key determinant of the firing outcome,
-but also that firing is complex and not easily characterized as LOF or GOF.
-
-\begin{figure}[H]
-\includegraphics[align=c,width=18cm]{simulation_model_comparison.pdf}
-\caption{}
-\label{fig:kv11}
-\end{figure}
-
-\section*{Ramp Firing - For Supplements?:}
-\label{sec:orgde070fe}
-\subsection*{Question figure addresses:}
-\label{sec:org86a05b7}
-How does the firing of the models look like with a ramp protocol?
-\subsection*{Method by which data is generated:}
-\label{sec:org4560e37}
-A 4 second ramp with the same current range as the step currents used to obtain fI
-plots is used and the firing of all models is simulated. The resulting spike trains
-are plotted.
-\subsection*{Conclusion from Figure:}
-\label{sec:orgdda18ae}
-The diversity of firing seen with step currents is also seen with current ramps. The
-ramps highlight the hysteresis in models.
-
-\begin{figure}[H]
-\includegraphics[align=c,width=20cm]{ramp_firing.pdf}
-\caption{A. Cb stellate, B. RS Inhibitory,
-C. FS, D. RS Pyramidal, E. RS Inhibitory +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-F. Cb stellate +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), 
-G. FS +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-H. RS Pyramidal +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), I. STN +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\),
-J.Cb stellate \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) , K. STN \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\), L. STN,
-where +\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) indicates the addition of Kv1.1 to the model
-and \(\Delta\)\(\mathrm{K}_{\mathrm{V}}\mathrm{1.1}\) indicates the exchange of the A type K+
-current for Kv1.1.}
-\label{fig:ramp}
-\end{figure}
-\end{document}

Figure_org/Figure_update_Beamer.tex~

@@ -1,159 +0,0 @@
-% Created 2022-02-16 Wed 15:40
-% Intended LaTeX compiler: pdflatex
-\documentclass[presentation]{beamer}
-\usepackage[utf8]{inputenc}
-\usepackage[T1]{fontenc}
-\usepackage{graphicx}
-\usepackage{grffile}
-\usepackage{longtable}
-\usepackage{wrapfig}
-\usepackage{rotating}
-\usepackage[normalem]{ulem}
-\usepackage{amsmath}
-\usepackage{textcomp}
-\usepackage{amssymb}
-\usepackage{capt-of}
-\usepackage{hyperref}
-\usepackage[backend=bibtex,sorting=none, style=numeric-comp, citestyle=numeric-comp]{biblatex}
-\usepackage{endnotes}
-\bibliography{H2v01.bib} %% point at your bib file
-\usepackage{tikz,bm}
-\usepackage{epstopdf}
-\graphicspath{{./Figures/}}
-\definecolor{mcgillred}{RGB}{237, 27, 47}
-\definecolor{darkred}{RGB}{158,9,24}
-\definecolor{palegray}{RGB}{244,244,244}
-\definecolor{grayt}{RGB}{180,180,180}
-\definecolor{grayb}{RGB}{228,228,228}
-\definecolor{black}{RGB}{0,0,0}
-\setbeamercolor{palette primary}{bg=mcgillred,fg=white}
-\setbeamercolor{palette secondary}{bg=mcgillred,fg=white}
-\setbeamercolor{palette tertiary}{bg=mcgillred,fg=white}
-\setbeamercolor{palette tertiary}{bg=mcgillred,fg=white}
-\setbeamercolor{structure}{fg=mcgillred}
-\setbeamercolor{title}{fg=mcgillred}
-\setbeamercolor{frametitle}{fg=mcgillred}
-\setbeamercolor{block body}{bg=palegray,fg=black}
-\setbeamercolor{block title}{bg=mcgillred,fg=white}
-\setbeamerfont{title}{size=\Large, series=\bfseries}
-\setbeamercolor{alerted text}{fg=black}
-\setbeamerfont{alerted text}{series=\bfseries}
-\setbeamercolor{block title alerted}{bg=red,fg=black}
-\setbeamerfont{block title alerted}{series=\mdseries}
-\newcommand{\nox}{\(\mathrm{NO_{x}}\)}
-\newlength\figH
-\newlength\figW
-\setbeamertemplate{caption}{\raggedright\insertcaption\par}
-\setbeamerfont{caption}{size=\tiny}
-\setlength\abovecaptionskip{-5pt}
-\renewcommand{\footnote}{\endnote}
-\usepackage{animate}
-\usetheme{Boadilla}
-\author{Nils  Koch}
-\date{}
-\title{Figure update}
-\subtitle{}
-\hypersetup{
- pdfauthor={Nils  Koch},
- pdftitle={Figure update},
- pdfkeywords={},
- pdfsubject={},
- pdfcreator={Emacs 27.2 (Org mode 9.4.4)}, 
- pdflang={English}}
-\begin{document}
-
-\maketitle
-
-\section{Introduction}
-\label{sec:org79d6537}
-\begin{frame}[label={sec:orgb1bc930}]{Intro to org to Beamer}
-\begin{itemize}
-\item This is not that visually appealling, but I haven't taken the time to set the colors and style to my liking in the Latex header
-\item I have tried to include as many examples of things I find useful as possible
-\begin{itemize}
-\item sometimes this makes the slide not make much sense, but the example of how to do things is hopefully useful
-\end{itemize}
-\item The only slight pain with using org for both Beamer and normal Latex pdf is:
-\begin{itemize}
-\item you cannot have the pdf open in some pdf viewers and export to it (eg adobe)
-\item I use Sumatra (\url{https://www.sumatrapdfreader.org/free-pdf-reader})
-\begin{itemize}
-\item can have your pdf open in sumatra, export to it and it will update the pdf to the new exported file while open
-\end{itemize}
-\end{itemize}
-\end{itemize}
-\end{frame}
-
-\section{Figure update}
-\label{sec:org5e0c8fa}
-\begin{frame}[label={sec:org9c64cb6}]{Firing Characterization}
-\vspace{-0.5cm}
-\begin{columns}
-\begin{column}[t]{0.65\columnwidth}
-\begin{itemize}
-\item Firing is a complicated phenomenon
-\item How to best characterize it?
-\begin{itemize}
-\item rheobase
-\item AUC
-\end{itemize}
-\end{itemize}
-\end{column}
-\begin{column}[t]{0.35\columnwidth}
-\begin{center}
-\includegraphics[width=0.95\textwidth]{firing_characterization.pdf}
-\end{center}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}[label={sec:orgca6ec9d}]{Diversity in Firing Properties of the Models}
-\begin{center}
-\includegraphics[width=0.55\textwidth]{diversity_in_firing.pdf}
-\end{center}
-\end{frame}
-
-\begin{frame}[label={sec:org47d8006}]{Sensitivy analysis: OFAT}
-\vspace{-2cm}
-\begin{itemize}
-\item[{$\square$}] one factor at a time sensitivity analysis
-\item simulate firing responses
-\begin{itemize}
-\item compute fI curves
-\begin{itemize}
-\item get rheobase
-\item compute AUC
-\end{itemize}
-\end{itemize}
-\end{itemize}
-\end{frame}
-\begin{frame}[label={sec:org7ee95f9}]{Rheobase sensitivity Analysis}
-\begin{center}
-\includegraphics[width=0.85\textwidth]{rheobase_correlation.pdf}
-\end{center}
-\tiny Koch et al. 2022
-\end{frame}
-\begin{frame}[label={sec:org43a0268}]{AUC Sensitivity Analysis}
-\begin{itemize}
-\item AUC = area under the curve
-\end{itemize}
-\begin{center}
-\includegraphics[width=0.75\textwidth]{AUC_correlation.pdf}
-\end{center}
-\begin{itemize}
-\item AUC over the initial non-zero fI curve is a proxy for slope
-\end{itemize}
-\end{frame}
-
-\begin{frame}[label={sec:orgba8be96}]{\(K_V1.1\) Mutations}
-\begin{center}
-\includegraphics[width=0.95\textwidth]{simulation_model_comparison.pdf}
-\end{center}
-\end{frame}
-
-\begin{frame}[label={sec:orgbe7a060}]{Ramp Firing}
-\begin{center}
-\includegraphics[width=0.95\textwidth]{ramp_firing.pdf}
-\end{center}
-\end{frame}
-\end{document}

Figures/AUC_correlation.py

@@ -13,7 +13,14 @@ 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
 
@@ -44,7 +51,7 @@ def GetColor2Marker(markers):
     mkcolors = [(palette[i]) for i in range(len(markers))]
     return dict(zip(mkcolors,markers))
 
-def fixlegend(ax,markers,markersize=8,**kwargs):
+def fixlegend(ax,markers,markersize=3,**kwargs):
     # Fix Legend
     legtitle =  ax.get_legend().get_title().get_text()
     _,l = ax.get_legend_handles_labels()
@@ -86,13 +93,27 @@ def new_scatter(self, *args, **kwargs):
     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'):
-    # size =4
-    # ax.tick_params(axis='x', rotation=90)
+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 +$K_V1.1$',
@@ -113,23 +134,9 @@ def boxplot_with_markers(ax,max_width, alteration='shift'):
                   '#873770',  # magenta
                   ]
 
-    # 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] # sns.color_palette("hls", 14, desat=0.75)#[cm.tab20(i) for i in range(20)]
+    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]
@@ -139,72 +146,60 @@ def boxplot_with_markers(ax,max_width, alteration='shift'):
         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,
+                      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')
-        ax.plot([lim[0], lim[1]], [1, 1], ':k')
-        ax.plot([lim[0], lim[1]], [-1, -1], ':k')
-        # ax.get_legend().remove()
-        # sns.despine(trim=True, bottom=True, ax=ax)
-        # ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
+        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, #dodge=True,
-                      palette=clr_dict, linewidth=0, orient='v', ax=ax,
+        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], ':k')
-        ax.plot([lim[0], lim[1]], [0, 0], ':r')
-        ax.plot([lim[0], lim[1]], [1, 1], ':k')
-        ax.plot([lim[0], lim[1]], [-1, -1], ':k')
-        # Markers = ["o","s","v","^","<",">","p","h","P","X","D","*","8","o"]
-        # ax=fixlegend(ax, Markers)
-        # Tweak the visual presentation
-        # ax.get_legend().remove()
-        # ax.xaxis.grid(False)
-        # ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
-        # ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
+        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)
-        # sns.despine(trim=True, bottom=True, ax=ax)
+        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, #dodge=True,
-                      palette=clr_dict, linewidth=0, orient='v', ax=ax,
+        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')
-        ax.plot([lim[0], lim[1]], [1, 1], ':k')
-        ax.plot([lim[0], lim[1]], [-1, -1], ':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.xaxis.grid(False)
-        # sns.despine(trim=True, bottom=True, ax=ax)
-        # ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
         ax.set_title("Conductance (g)", y=1.05)
-        # ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
+        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 $\it{\tau}$')
-    ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
-    # fixlegend(ax, Markers)
+
 
 def model_legend(ax, marker_s_leg, pos, ncol):
     colorslist = [  '#40A787', # cyan'#
@@ -221,15 +216,13 @@ def model_legend(ax, marker_s_leg, pos, ncol):
         '#D03050'  # pink
     ]
     import matplotlib.colors
-    colors = [matplotlib.colors.to_rgb(c) for c in colorslist] #sns.color_palette("hls", 14, desat=0.75) #[cm.tab20(i) for i in range(20)]
-
+    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='RS pyramidal')
@@ -291,8 +284,7 @@ def plot_AUC_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='
                   ]
 
     import matplotlib.colors
-    colors = [matplotlib.colors.to_rgb(c) for c in
-              colorslist]  #sns.color_palette("hls", 14, desat=0.75)#[cm.tab20(i) for i in range(20)]
+    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]
@@ -306,24 +298,42 @@ def plot_AUC_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='
         ax.set_xticks([0.5, 1, 2])
         ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
         ax.xaxis.set_minor_formatter(NullFormatter())
-        ax.set_xlabel('Slope factor k')
+        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')
+        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=1, zorder=1, linewidth=1)
-    ax.set_ylabel('Normalized AUC')
+            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'):
@@ -356,7 +366,7 @@ def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color
         c += 1
     ax.set_ylabel('Frequency [Hz]')
     ax.set_xlabel('Current [nA]')
-    ax.set_title(model)
+    ax.set_title(model, x=0.2, y=1.0)
     ax.spines["right"].set_visible(False)
     ax.spines["top"].set_visible(False)
     L = ax.get_ylim()
@@ -365,6 +375,7 @@ def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color
 
 
 #%%
+boxplot_style()
 color_dict = {'Cb stellate': '#40A787', # cyan'#
               'RS Inhibitory': '#F0D730',  # yellow
               'FS': '#C02717',  # red
@@ -378,58 +389,67 @@ 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 = 8
-max_width = 10
-pad_x = 0.6
-pad_y= 0.3
-pad_w = 0.8
-pad_h = 0.5
-
-fig = plt.figure(figsize=(18,10))
-gs = fig.add_gridspec(3, 6, wspace=0.9)
-plt.subplots_adjust(hspace=0.8) #, wspace=0.6
-ax0 = fig.add_subplot(gs[0,2:6])
+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[1,2:6])
+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[2,2:6])
+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=2,transform=ax0_fI.transAxes,  color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+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=2,transform=ax1_fI.transAxes,  color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+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=2,transform=ax2_fI.transAxes,  color=color_dict['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+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)
 
-# label subplots with letters
-ax0_fI.text(-0.6, 1.28, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=16, weight='bold') #-0.09
-ax1_fI.text(-0.6, 1.28, string.ascii_uppercase[1], transform=ax1_fI.transAxes,size=16, weight='bold')
-ax2_fI.text(-0.6, 1.28, string.ascii_uppercase[2], transform=ax2_fI.transAxes,size=16, weight='bold')
-
 # 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 = (0.25, -0.6)
-ncol = 6
+pos = (0.225, -0.9)
+ncol = 5
 model_legend(ax2,  marker_s_leg, pos, ncol)
 
 # plot examples
@@ -437,76 +457,21 @@ plot_AUC_alt(ax0_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='l
 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.savefig('./Figures/AUC_correlation.pdf', bbox_inches='tight', dpi=fig.dpi)
+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()
-
-# #%% alternative layout
-# # boxplot_style()
-# marker_s_leg = 8
-# max_width = 10
-# pad_x = 0.2
-# pad_y= 0.4
-# pad_w = 0.22
-# pad_h = 0.6
-#
-# fig = plt.figure(figsize=(12,20))
-# gs = fig.add_gridspec(3, 1, hspace=0.4, wspace=0.4)
-# gs0 = gs[0].subgridspec(2, 4, wspace=0.6, hspace=1.)
-# gs1 = gs[1].subgridspec(2, 4, wspace=0.6, hspace=1.)
-# gs2 = gs[2].subgridspec(2, 4, wspace=0.6, hspace=1.)
-# ax0 = fig.add_subplot(gs0[1,:])
-# ax0_ex = fig.add_subplot(gs0[0,2:])
-# ax0_fI = fig.add_subplot(gs0[0,:2])
-# ax1 = fig.add_subplot(gs1[1,:])
-# ax1_ex = fig.add_subplot(gs1[0,2:])
-# ax1_fI = fig.add_subplot(gs1[0,:2])
-# ax2 = fig.add_subplot(gs2[1,:])
-# ax2_ex = fig.add_subplot(gs2[0,2:])
-# ax2_fI = fig.add_subplot(gs2[0,:2])
-#
-#
-# 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=2,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=2,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=2,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)
-#
-# # label subplots with letters
-# ax0_fI.text(-0.2, 1.38, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=16, weight='bold') #-0.09
-# ax1_fI.text(-0.2, 1.38, string.ascii_uppercase[1], transform=ax1_fI.transAxes,size=16, weight='bold')
-# ax2_fI.text(-0.2, 1.38, string.ascii_uppercase[2], transform=ax2_fI.transAxes,size=16, weight='bold')
-#
-# # 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 = (0.5, -1.)
-# 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='red', color2='maroon', alteration='shift')
-# # plot_rheo_alt(ax1_ex,model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='orchid', color2='purple',alteration='slope')
-# # plot_rheo_alt(ax2_ex, model='Cb stellate', color1='darkviolet', color2='indigo', alteration='g')
-# # 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')
-#
-# # plot examples
-# plot_AUC_alt(ax0_ex,model='FS', 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')
-# fig.savefig('AUC_correlation_2.pdf', bbox_inches='tight') #
-# # plt.saveas(fig, 'AUC_correlation_2.png')
-# plt.show()

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.jpg', dpi=fig.dpi) #pdf #eps
+plt.show()

Figures/diversity_in_firing.py

@@ -4,7 +4,16 @@ import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 from matplotlib.transforms import Bbox
 import string
-# from plotstyle import plot_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)
+plt.rcParams['xtick.labelsize'] = 6
+plt.rcParams['ytick.labelsize'] = 6
 
 #### from https://gist.github.com/dmeliza/3251476  #####################################################################
 from matplotlib.offsetbox import AnchoredOffsetbox
@@ -41,7 +50,7 @@ class AnchoredScaleBar(AnchoredOffsetbox):
         AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,
                                    child=bars, prop=prop, frameon=False, **kwargs)
 
-
+plt.rcParams.update({'font.size': 6})
 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
@@ -77,13 +86,21 @@ def add_scalebar(ax, matchx=True, matchy=True, hidex=True, hidey=True, **kwargs)
 
 def plot_spike_train(ax, model='RS Pyramidal', stop=750):
     model_spiking = pd.read_csv('./Figures/Data/model_spiking.csv')
-    stop_ind = np.int(np.argmin(np.abs(model_spiking['t'] - stop)))
-    ax.plot(model_spiking['t'][0:stop_ind], model_spiking[model][0:stop_ind], 'k', linewidth=1.5)
+    stop_ind = int(np.argmin(np.abs(model_spiking['t'] - stop)))
+    # ax.plot(model_spiking['t'][0:stop_ind], model_spiking[model][0:stop_ind], 'k', linewidth=0.5)
+    if model == 'STN':
+        ax.plot(model_spiking['t'][0:stop_ind], model_spiking[model][0:stop_ind], 'k', linewidth=0.25)
+    else:
+        ax.plot(model_spiking['t'][0:stop_ind], model_spiking[model][0:stop_ind], 'k', linewidth=0.5) #1.5
+    # if model == 'STN':
+    #     ax.plot(model_spiking['t'][0:stop_ind], model_spiking[model][0:stop_ind], 'k', linewidth=0.625)
+    # else:
+    #     ax.plot(model_spiking['t'][0:stop_ind], model_spiking[model][0:stop_ind], 'k', linewidth=1.) #1.5
     ax.set_ylabel('V')
     ax.set_xlabel('Time [s]')
-    ax.set_ylim(-80, 60)
+    ax.set_ylim(-85, 60)
     ax.axis('off')
-    ax.set_title(model)
+    ax.set_title(model, fontsize=7, y=1.1)
 
 
 def plot_fI(ax, model='RS Pyramidal'):
@@ -91,68 +108,115 @@ def plot_fI(ax, model='RS Pyramidal'):
     model_F_inf = pd.read_csv('./Figures/Data/model_F_inf.csv')
     if model=='RS Inhibitory':
         ax.plot(model_F_inf['I_inhib'], model_F_inf[model], color='grey')
-        ax.plot(firing_values.loc['spike_ind', model], model_F_inf[model][np.int(np.argmin(np.abs(model_F_inf['I_inhib'] - firing_values.loc['spike_ind', model])))],
+        ax.plot(firing_values.loc['spike_ind', model], model_F_inf[model][int(np.argmin(np.abs(model_F_inf['I_inhib'] - firing_values.loc['spike_ind', model])))],
                 '.', color='k', markersize=3)
         ax.plot(firing_values.loc['ramp_up', model],
-                model_F_inf[model][np.int(np.argmin(np.abs(model_F_inf['I_inhib'] - firing_values.loc['ramp_up', model])))],
+                model_F_inf[model][int(np.argmin(np.abs(model_F_inf['I_inhib'] - firing_values.loc['ramp_up', model])))],
                 '.', color='g', markersize=3)
         ax.plot(firing_values.loc['ramp_down', model],
-                model_F_inf[model][np.int(np.argmin(np.abs(model_F_inf['I_inhib'] - firing_values.loc['ramp_down', model])))],
+                model_F_inf[model][int(np.argmin(np.abs(model_F_inf['I_inhib'] - firing_values.loc['ramp_down', model])))],
                 '.', color='r', markersize=3)
     else:
         ax.plot(model_F_inf['I'], model_F_inf[model], color='grey')
         ax.plot(firing_values.loc['spike_ind', model],
-                model_F_inf[model][np.int(np.argmin(np.abs(model_F_inf['I'] - firing_values.loc['spike_ind', model])))],
+                model_F_inf[model][int(np.argmin(np.abs(model_F_inf['I'] - firing_values.loc['spike_ind', model])))],
                 '.', color='k', markersize=3)
         ax.plot(firing_values.loc['ramp_up', model],
-                model_F_inf[model][np.int(np.argmin(np.abs(model_F_inf['I'] - firing_values.loc['ramp_up', model])))],
+                model_F_inf[model][int(np.argmin(np.abs(model_F_inf['I'] - firing_values.loc['ramp_up', model])))],
                 '.', color='g', markersize=3)
         ax.plot(firing_values.loc['ramp_down', model],
-                model_F_inf[model][np.int(np.argmin(np.abs(model_F_inf['I'] - firing_values.loc['ramp_down', model])))],
+                model_F_inf[model][int(np.argmin(np.abs(model_F_inf['I'] - firing_values.loc['ramp_down', model])))],
                 '.', color='r', markersize=3)
-    f = 8
+    f = 6
     ax.set_ylabel('Frequency [Hz]', fontsize=f)
-    ax.set_xlabel('Current [pA]', fontsize=f)
+    ax.set_xlabel('Current [nA]', fontsize=f)
     ax.spines['right'].set_visible(False)
     ax.spines['top'].set_visible(False)
+    ax.tick_params(axis='y', labelrotation=90)
+
 
 #%%
 
 # plot layout
-fig = plt.figure(figsize=(10,10))
-gs0 = fig.add_gridspec(3, 2, wspace=0.3)
-gs00 = gs0[:,0].subgridspec(6, 3, wspace=0.7, hspace=1)
-gs01 = gs0[:,1].subgridspec(6, 3, wspace=0.7, hspace=1)
-
-ax1_spikes = fig.add_subplot(gs00[0,0:2])
-ax1_fI = fig.add_subplot(gs00[0, 2])
-ax2_spikes = fig.add_subplot(gs01[0,0:2])
-ax2_fI = fig.add_subplot(gs01[0, 2])
-ax3_spikes = fig.add_subplot(gs00[1,0:2])
-ax3_fI = fig.add_subplot(gs00[1, 2])
-ax4_spikes = fig.add_subplot(gs01[1,0:2])
-ax4_fI = fig.add_subplot(gs01[1, 2])
-ax5_spikes = fig.add_subplot(gs00[2, 0:2])
-ax5_fI = fig.add_subplot(gs00[2,  2])
-ax6_spikes = fig.add_subplot(gs01[2, 0:2])
-ax6_fI = fig.add_subplot(gs01[2,  2])
-ax7_spikes = fig.add_subplot(gs00[3,0:2])
-ax7_fI = fig.add_subplot(gs00[3, 2])
-ax8_spikes = fig.add_subplot(gs01[3,0:2])
-ax8_fI = fig.add_subplot(gs01[3, 2])
-ax9_spikes = fig.add_subplot(gs00[4,0:2])
-ax9_fI = fig.add_subplot(gs00[4, 2])
-ax10_spikes = fig.add_subplot(gs01[4,0:2])
-ax10_fI = fig.add_subplot(gs01[4, 2])
-ax11_spikes = fig.add_subplot(gs00[5,0:2])
-ax11_fI = fig.add_subplot(gs00[5, 2])
-ax12_spikes = fig.add_subplot(gs01[5,0:2])
-ax12_fI = fig.add_subplot(gs01[5, 2])
+fig = plt.figure()
+gs0 = fig.add_gridspec(3, 3, wspace=0.4, hspace=0.2)
+
+# gs00 = gs0[:,0].subgridspec(5, 3, wspace=1.8, hspace=1.5)
+# gs01 = gs0[:,1].subgridspec(5, 3, wspace=1.8, hspace=1.5)
+# gs02 = gs0[:,2].subgridspec(5, 3, wspace=1.8, hspace=1.5)
+gs00 = gs0[:,0].subgridspec(5, 2, wspace=0.8, hspace=1.5)
+gs01 = gs0[:,1].subgridspec(5, 2, wspace=0.8, hspace=1.5)
+gs02 = gs0[:,2].subgridspec(5, 2, wspace=0.8, hspace=1.5)
+
+ax_diag = fig.add_subplot(gs02[2:, :])
+import matplotlib.image as mpimg
+img = mpimg.imread('./Figures/model_diagram2.png')
+ax_diag.imshow(img)
+ax_diag.spines['top'].set_visible(False)
+ax_diag.spines['bottom'].set_visible(False)
+ax_diag.spines['left'].set_visible(False)
+ax_diag.spines['right'].set_visible(False)
+ax_diag.set_yticks([])
+ax_diag.set_xticks([])
+# ax_diag.text(-0.12, 1.075, string.ascii_uppercase[12], transform=ax_diag.transAxes, size=10, weight='bold')
+ax_diag.text(-0.22, 1.075, string.ascii_uppercase[12], transform=ax_diag.transAxes, size=10, weight='bold')
+
+
+
+ax1_spikes = fig.add_subplot(gs00[0,0])
+ax1_fI = fig.add_subplot(gs00[0, 1])
+ax2_spikes = fig.add_subplot(gs01[0,0])
+ax2_fI = fig.add_subplot(gs01[0, 1])
+ax3_spikes = fig.add_subplot(gs02[0,0])
+ax3_fI = fig.add_subplot(gs02[0, 1])
+ax4_spikes = fig.add_subplot(gs00[1,0])
+ax4_fI = fig.add_subplot(gs00[1, 1])
+ax5_spikes = fig.add_subplot(gs01[1, 0])
+ax5_fI = fig.add_subplot(gs01[1,  1])
+ax6_spikes = fig.add_subplot(gs02[1,0])
+ax6_fI = fig.add_subplot(gs02[1, 1])
+ax7_spikes = fig.add_subplot(gs00[2,0])
+ax7_fI = fig.add_subplot(gs00[2, 1])
+ax8_spikes = fig.add_subplot(gs01[2,0])
+ax8_fI = fig.add_subplot(gs01[2, 1])
+ax9_spikes = fig.add_subplot(gs00[3,0])
+ax9_fI = fig.add_subplot(gs00[3, 1])
+ax10_spikes = fig.add_subplot(gs01[3,0])
+ax10_fI = fig.add_subplot(gs01[3, 1])
+ax11_spikes = fig.add_subplot(gs00[4,0])
+ax11_fI = fig.add_subplot(gs00[4, 1])
+ax12_spikes = fig.add_subplot(gs01[4, 0])
+ax12_fI = fig.add_subplot(gs01[4,  1])
+
+# ax1_spikes = fig.add_subplot(gs00[0,0:2])
+# ax1_fI = fig.add_subplot(gs00[0, 2])
+# ax2_spikes = fig.add_subplot(gs01[0,0:2])
+# ax2_fI = fig.add_subplot(gs01[0, 2])
+# ax3_spikes = fig.add_subplot(gs02[0,0:2])
+# ax3_fI = fig.add_subplot(gs02[0, 2])
+# ax4_spikes = fig.add_subplot(gs00[1,0:2])
+# ax4_fI = fig.add_subplot(gs00[1, 2])
+# ax5_spikes = fig.add_subplot(gs01[1, 0:2])
+# ax5_fI = fig.add_subplot(gs01[1,  2])
+# ax6_spikes = fig.add_subplot(gs02[1,0:2])
+# ax6_fI = fig.add_subplot(gs02[1, 2])
+# ax7_spikes = fig.add_subplot(gs00[2,0:2])
+# ax7_fI = fig.add_subplot(gs00[2, 2])
+# ax8_spikes = fig.add_subplot(gs01[2,0:2])
+# ax8_fI = fig.add_subplot(gs01[2, 2])
+# ax9_spikes = fig.add_subplot(gs00[3,0:2])
+# ax9_fI = fig.add_subplot(gs00[3, 2])
+# ax10_spikes = fig.add_subplot(gs01[3,0:2])
+# ax10_fI = fig.add_subplot(gs01[3, 2])
+# ax11_spikes = fig.add_subplot(gs00[4,0:2])
+# ax11_fI = fig.add_subplot(gs00[4, 2])
+# ax12_spikes = fig.add_subplot(gs01[4, 0:2])
+# ax12_fI = fig.add_subplot(gs01[4,  2])
 
 spike_axs = [ax1_spikes, ax2_spikes, ax3_spikes, ax4_spikes, ax5_spikes,ax6_spikes, ax7_spikes, ax8_spikes,
-             ax9_spikes, ax10_spikes, ax11_spikes, ax12_spikes]#, ax13_spikes, ax14_spikes]
-fI_axs = [ax1_fI, ax2_fI, ax3_fI, ax4_fI, ax5_fI,ax6_fI, ax7_fI, ax8_fI, ax9_fI,  ax10_fI,
-             ax11_fI, ax12_fI] #, ax13_fI, ax14_fI]
+              ax11_spikes,ax9_spikes,ax10_spikes,   ax12_spikes]#, ax13_spikes, ax14_spikes]
+fI_axs = [ax1_fI, ax2_fI, ax3_fI, ax4_fI, ax5_fI,ax6_fI, ax7_fI,  ax8_fI, ax11_fI, ax9_fI,  ax10_fI,
+              ax12_fI] #, ax13_fI, ax14_fI]
 
 # model order
 models = ['Cb stellate','RS Inhibitory','FS', 'RS Pyramidal','RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
@@ -167,18 +231,102 @@ for i in range(len(models)):
     plot_fI(fI_axs[i], model=models[i])
 
 # add scalebars
-add_scalebar(ax11_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100 ms',
-                 labely='50 mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.2, -0.05, 1, 1),
+# add_scalebar(ax6_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100\u2009ms',
+#                  labely='50\u2009mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.275, -0.05, 1, 1),
+#                           bbox_transform=ax6_spikes.transAxes)
+# add_scalebar(ax11_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100\u2009ms',
+#                  labely='50\u2009mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.275, -0.05, 1, 1),
+#                           bbox_transform=ax11_spikes.transAxes)
+# add_scalebar(ax12_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100\u2009ms',
+#                  labely='50\u2009mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.275, -0.05, 1, 1),
+#                           bbox_transform=ax12_spikes.transAxes)
+add_scalebar(ax6_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100\u2009ms',
+                 labely='50\u2009mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.475, -0.05, 1, 1),
+                          bbox_transform=ax6_spikes.transAxes)
+add_scalebar(ax11_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100\u2009ms',
+                 labely='50\u2009mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.475, -0.05, 1, 1),
                           bbox_transform=ax11_spikes.transAxes)
-add_scalebar(ax12_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100 ms',
-                 labely='50 mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.2, -0.05, 1, 1),
+add_scalebar(ax12_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100\u2009ms',
+                 labely='50\u2009mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.475, -0.05, 1, 1),
                           bbox_transform=ax12_spikes.transAxes)
+
+
 # add subplot labels
 for i in range(0,len(models)):
-    spike_axs[i].text(-0.18, 1.08, string.ascii_uppercase[i], transform=spike_axs[i].transAxes, size=16, weight='bold')
-
+    # spike_axs[i].text(-0.18, 1.08, string.ascii_uppercase[i], transform=spike_axs[i].transAxes, size=10, weight='bold')
+    spike_axs[i].text(-0.572, 1.3, string.ascii_uppercase[i], transform=spike_axs[i].transAxes, size=10, weight='bold')
 # save
-fig.savefig('./Figures/diversity_in_firing.pdf', bbox_inches='tight')
+fig.set_size_inches(cm2inch(21,15))
+fig.savefig('./Figures/diversity_in_firing_diagram.jpg', dpi=300, bbox_inches='tight') #pdf # eps
 plt.show()
 
 
+
+# #%%
+#
+# # plot layout
+# fig = plt.figure()
+# gs0 = fig.add_gridspec(3, 2, wspace=0.3, hspace=0)
+# gs00 = gs0[:,0].subgridspec(6, 3, wspace=1.5, hspace=1.5)
+# gs01 = gs0[:,1].subgridspec(6, 3, wspace=1.2, hspace=1.3)
+#
+# ax1_spikes = fig.add_subplot(gs00[0,0:2])
+# ax1_fI = fig.add_subplot(gs00[0, 2])
+# ax2_spikes = fig.add_subplot(gs01[0,0:2])
+# ax2_fI = fig.add_subplot(gs01[0, 2])
+# ax3_spikes = fig.add_subplot(gs00[1,0:2])
+# ax3_fI = fig.add_subplot(gs00[1, 2])
+# ax4_spikes = fig.add_subplot(gs01[1,0:2])
+# ax4_fI = fig.add_subplot(gs01[1, 2])
+# ax5_spikes = fig.add_subplot(gs00[2, 0:2])
+# ax5_fI = fig.add_subplot(gs00[2,  2])
+# ax6_spikes = fig.add_subplot(gs01[2, 0:2])
+# ax6_fI = fig.add_subplot(gs01[2,  2])
+# ax7_spikes = fig.add_subplot(gs00[3,0:2])
+# ax7_fI = fig.add_subplot(gs00[3, 2])
+# ax8_spikes = fig.add_subplot(gs01[3,0:2])
+# ax8_fI = fig.add_subplot(gs01[3, 2])
+# ax9_spikes = fig.add_subplot(gs00[4,0:2])
+# ax9_fI = fig.add_subplot(gs00[4, 2])
+# ax10_spikes = fig.add_subplot(gs01[4,0:2])
+# ax10_fI = fig.add_subplot(gs01[4, 2])
+# ax11_spikes = fig.add_subplot(gs00[5,0:2])
+# ax11_fI = fig.add_subplot(gs00[5, 2])
+# ax12_spikes = fig.add_subplot(gs01[5,0:2])
+# ax12_fI = fig.add_subplot(gs01[5, 2])
+#
+# spike_axs = [ax1_spikes, ax2_spikes, ax3_spikes, ax4_spikes, ax5_spikes,ax6_spikes, ax7_spikes, ax8_spikes,
+#              ax9_spikes, ax10_spikes, ax11_spikes, ax12_spikes]#, ax13_spikes, ax14_spikes]
+# fI_axs = [ax1_fI, ax2_fI, ax3_fI, ax4_fI, ax5_fI,ax6_fI, ax7_fI, ax8_fI, ax9_fI,  ax10_fI,
+#              ax11_fI, ax12_fI] #, ax13_fI, ax14_fI]
+#
+# # model order
+# 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 spike train and fI for each model
+# for i in range(len(models)):
+#     plot_spike_train(spike_axs[i], model=models[i])
+#     plot_fI(fI_axs[i], model=models[i])
+#
+# # add scalebars
+# add_scalebar(ax11_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100\u2009ms',
+#                  labely='50\u2009mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.2, -0.05, 1, 1),
+#                           bbox_transform=ax11_spikes.transAxes)
+# add_scalebar(ax12_spikes, matchx=False, matchy=False, hidex=True, hidey=True, sizex=100, sizey=50, labelx='100\u2009ms',
+#                  labely='50\u2009mV', loc=3, pad=-0.5, borderpad=-1.0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.2, -0.05, 1, 1),
+#                           bbox_transform=ax12_spikes.transAxes)
+# # add subplot labels
+# for i in range(0,len(models)):
+#     # spike_axs[i].text(-0.18, 1.08, string.ascii_uppercase[i], transform=spike_axs[i].transAxes, size=10, weight='bold')
+#     spike_axs[i].text(-0.18, 1.2, string.ascii_uppercase[i], transform=spike_axs[i].transAxes, size=10, weight='bold')
+# # save
+# fig.set_size_inches(cm2inch(17.6,20))
+# fig.savefig('./Figures/diversity_in_firing.jpg', dpi=fig.dpi) #pdf # eps
+# # fig.savefig('./Figures/diversity_in_firing.png', dpi=fig.dpi) #pdf # eps
+# plt.show()
+
+

Figures/firing_characterization.py

@@ -1,8 +1,191 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
+import matplotlib.ticker as ticker
+from matplotlib.colors import colorConverter as cc
+from matplotlib.colors import to_hex
 import string
-from plotstyle import plot_style
+from plotstyle import scheme_style
+
+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
+              ]
+
+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 show_spines(ax, spines='lrtb'):
+    """ Show and hide spines.
+
+    From github.com/janscience/plottools.git  spines.py
+
+    Parameters
+    ----------
+    ax: matplotlib figure, matplotlib axis, or list of matplotlib axes
+        Axis on which spine and ticks visibility is manipulated.
+        If figure, then apply manipulations on all axes of the figure.
+        If list of axes, apply manipulations on each of the given axes.
+    spines: string
+        Specify which spines and ticks should be shown.
+        All other ones or hidden.
+        'l' is the left spine, 'r' the right spine,
+        't' the top one and 'b' the bottom one.
+        E.g. 'lb' shows the left and bottom spine, and hides the top
+        and and right spines, as well as their tick marks and labels.
+        '' shows no spines at all.
+        'lrtb' shows all spines and tick marks.
+
+    Examples
+    --------
+    ```py
+    import matplotlib.pyplot as plt
+    import plottools.spines
+
+    fig, (ax0, ax1, ax2) = plt.subplots(1, 3)
+    ax0.show_spines('lb')
+    ax1.show_spines('bt')
+    ax2.show_spines('tr')
+    ```
+    ![show](figures/spines-show.png)
+    """
+    # collect spine visibility:
+    xspines = []
+    if 't' in spines:
+        xspines.append('top')
+    if 'b' in spines:
+        xspines.append('bottom')
+    yspines = []
+    if 'l' in spines:
+        yspines.append('left')
+    if 'r' in spines:
+        yspines.append('right')
+    # collect axes:
+    if isinstance(ax, (list, tuple, np.ndarray)):
+        axs = ax
+    elif hasattr(ax, 'get_axes'):
+        # ax is figure:
+        axs = ax.get_axes()
+    else:
+        axs = [ax]
+    if not isinstance(axs, (list, tuple)):
+        axs = [axs]
+    for ax in axs:
+        # hide spines:
+        ax.spines['top'].set_visible('top' in xspines)
+        ax.spines['bottom'].set_visible('bottom' in xspines)
+        ax.spines['left'].set_visible('left' in yspines)
+        ax.spines['right'].set_visible('right' in yspines)
+        # ticks:
+        if len(xspines) == 0:
+            ax.xaxis.set_ticks_position('none')
+            ax.xaxis.label.set_visible(False)
+            ax.xaxis._orig_major_locator = ax.xaxis.get_major_locator()
+            ax.xaxis.set_major_locator(ticker.NullLocator())
+        else:
+            if hasattr(ax.xaxis, '_orig_major_locator'):
+                ax.xaxis.set_major_locator(ax.xaxis._orig_major_locator)
+                delattr(ax.xaxis, '_orig_major_locator')
+            elif isinstance(ax.xaxis.get_major_locator(), ticker.NullLocator):
+                ax.xaxis.set_major_locator(ticker.AutoLocator())
+            if len(xspines) == 1:
+                ax.xaxis.set_ticks_position(xspines[0])
+                ax.xaxis.set_label_position(xspines[0])
+            else:
+                ax.xaxis.set_ticks_position('both')
+                ax.xaxis.set_label_position('bottom')
+        if len(yspines) == 0:
+            ax.yaxis.set_ticks_position('none')
+            ax.yaxis.label.set_visible(False)
+            ax.yaxis._orig_major_locator = ax.yaxis.get_major_locator()
+            ax.yaxis.set_major_locator(ticker.NullLocator())
+        else:
+            if hasattr(ax.yaxis, '_orig_major_locator'):
+                ax.yaxis.set_major_locator(ax.yaxis._orig_major_locator)
+                delattr(ax.yaxis, '_orig_major_locator')
+            elif isinstance(ax.yaxis.get_major_locator(), ticker.NullLocator):
+                ax.yaxis.set_major_locator(ticker.AutoLocator())
+            if len(yspines) == 1:
+                ax.yaxis.set_ticks_position(yspines[0])
+                ax.yaxis.set_label_position(yspines[0])
+            else:
+                ax.yaxis.set_ticks_position('both')
+                ax.yaxis.set_label_position('left')
+
+
+
+def lighter(color, lightness):
+    """ Make a color lighter.
+
+    From github.com/janscience/plottools.git  colors.py
+
+    ![lighter](figures/colors-lighter.png)
+
+    Parameters
+    ----------
+    color: dict or matplotlib color spec
+        A matplotlib color (hex string, name color string, rgb tuple)
+        or a dictionary with an 'color' or 'facecolor' key.
+    lightness: float
+        The smaller the lightness, the lighter the returned color.
+        A lightness of 0 returns white.
+        A lightness of 1 leaves the color untouched.
+        A lightness of 2 returns black.
+
+    Returns
+    -------
+    color: string or dict
+        The lighter color as a hexadecimal RGB string (e.g. '#rrggbb').
+        If `color` is a dictionary, a copy of the dictionary is returned
+        with the value of 'color' or 'facecolor' set to the lighter color.
+
+    Examples
+    --------
+    For 40% lightness of blue do
+    ```py
+    import plottools.colors as c
+    colors = c.palettes['muted']
+    lightblue = c.lighter(colors['blue'], 0.4)
+    ```
+    """
+    try:
+        c = color['color']
+        cd = dict(**color)
+        cd['color'] = lighter(c, lightness)
+        return cd
+    except (KeyError, TypeError):
+        try:
+            c = color['facecolor']
+            cd = dict(**color)
+            cd['facecolor'] = lighter(c, lightness)
+            return cd
+        except (KeyError, TypeError):
+            if lightness > 2:
+                lightness = 2
+            if lightness > 1:
+                return darker(color, 2.0-lightness)
+            if lightness < 0:
+                lightness = 0
+            r, g, b = cc.to_rgb(color)
+            rl = r + (1.0-lightness)*(1.0 - r)
+            gl = g + (1.0-lightness)*(1.0 - g)
+            bl = b + (1.0-lightness)*(1.0 - b)
+            return to_hex((rl, gl, bl)).upper()
+                
 
 def plot_sqrt(ax, a=1, b=0.2, c=100, d=0):
     x = np.linspace(0, 1, 10000)
@@ -13,156 +196,174 @@ def plot_sqrt(ax, a=1, b=0.2, c=100, d=0):
     ax.set_xlim(0,1)
     ax.set_ylim(0, ax.get_ylim()[1])
 
-def plot_AUC(ax, a=1, b=0.2, c=100, d=0, width=0.2):
-    x = np.linspace(0, 1, 10000)
+def plot_AUC(ax, a=1, b=0.2, c=180, d=0, width=0.2):
+    x = np.linspace(0, 1, 1000)
     y = c*np.sqrt(a*(x - b)) + d
-    ax.plot(x, y, 'k')
+    ax.plot(x, y, colorslist[9])
     ax.set_xlabel('Current [nA]')
     ax.set_ylabel('Frequency [Hz]')
-    ax.fill_between(x,y, where=(x<=b+width), color='c')
-    ax.set_xlim(0,1)
-    ax.set_ylim(0, ax.get_ylim()[1])
+    ax.fill_between(x, y, where=(x<=b+width), color=lighter(colorslist[9], 0.3))
+    ax.text(0.3, 15, 'AUC', ha='center')
+    ax.annotate('', (0.2, 10), (0, 10), arrowprops=dict(arrowstyle="<->"))
+    ax.text(0.1, 20, 'rheobase', ha='center')
+    ax.set_xlim(0, 0.5)
+    ax.set_ylim(0, 100)
+    ax.yaxis.set_major_locator(ticker.MultipleLocator(50))
 
 def plot_diff_sqrt(ax, a=1, b=0.2, c=100, d=0, a2=1, b2=0.2, c2=100, d2=0):
+    show_spines(ax, 'lb')
     x = np.linspace(0, 1, 10000)
     y = c*np.sqrt(a*(x - b)) + d
     y2 = c2 * np.sqrt(a2 * (x - b2)) + d2
-    ax.plot(x, y, 'k')
-    ax.plot(x, y2, 'b')
+    ax.plot(x, y, colorslist[9])
+    ax.plot(x, y2, colorslist[2])
     ax.set_xlim(0,1)
     ax.set_ylim(0, ax.get_ylim()[1])
     ax.set_xticks([])
     ax.set_yticks([])
 
+
 def plot_quadrant(ax):
-    ax.plot(ax.get_xlim(),[0,0], '--k', linewidth=1.25)
-    ax.plot([0,0],ax.get_ylim(), '--k', linewidth=1.25)
-    ax.tick_params(axis=u'both', which=u'both', length=0)
-    ax.set_xticks([-0.55, 0, 0.55])
+    ax.spines['left'].set_position('zero')
+    ax.spines['bottom'].set_position('zero')
+    ax.text(1.2, -0.15, '$\\Delta$ rheobase', ha='right')
+    ax.text(-0.03, 0.7, '$\\Delta$ AUC', ha='right', rotation=90)
+    ax.tick_params(length=0)
+    ax.set_xlim(-1, 1)
+    ax.set_ylim(-1, 1)
+    ax.annotate('', (1, 0), (-1, 0), arrowprops=dict(arrowstyle="->"))
+    ax.annotate('', (0, 1), (0, -1), arrowprops=dict(arrowstyle="->"))
+    ax.set_xticks([-0.5, 0.5])
     a = ax.get_xticks().tolist()
     a[0] = '\u2212'
-    a[1] = 0
-    a[2] = '+'
+    a[1] = '+'
     ax.set_xticklabels(a)
-    ax.set_yticks([-0.55, 0, 0.55])
+    ax.set_yticks([-0.5, 0.5])
     b = ax.get_xticks().tolist()
     b[0] = '\u2212'
-    b[1] = 0
-    b[2] = '+'
+    b[1] = '+'
     ax.set_yticklabels(b)
 
-    ax.tick_params(labelsize=16)
-    # ax.set_yticks([0])
-    # ax.axis('off')
-
 
-plot_style()
 
-fig = plt.figure(figsize=(4, 6))
-gs = gridspec.GridSpec(3,2, top=0.95, bottom=0.1, left=0.15, right = 0.95, hspace=0.8, wspace=0.6)
-ax1 = fig.add_subplot(gs[0,:])
+# scheme_style()
+#%% with legend
+fig = plt.figure(figsize=cm2inch(7, 12))
+gs = gridspec.GridSpec(3,6, top=0.95, bottom=0.1, left=0.15, right = 0.95, hspace=0.8, wspace=0.6)
+ax1 = fig.add_subplot(gs[0,1:])
+show_spines(ax1, 'lb')
 plot_AUC(ax1, width=0.2)
 ax3 = fig.add_subplot(gs[1:, :])
 
 # add panel letter labels
-ax_list = fig.axes
-i =0
-for a in ax_list:
-    a.text(-0.18, 1.08, string.ascii_uppercase[i], transform=a.transAxes,size=16, weight='bold')
-    i += 1
-
-ax3.set_ylabel('$\Delta$ AUC')
-ax3.set_xlabel('$\Delta$ rheobase')
-ax3.set_xlim(-1, 1)
-ax3.set_ylim(-1, 1)
+ax1.text(-0.4, 1.2, string.ascii_uppercase[0], transform=ax1.transAxes, size=16, weight='bold')
+ax3.text(-0.15, 1.05, string.ascii_uppercase[1], transform=ax3.transAxes, size=16, weight='bold')
+
+show_spines(ax3, '')
 plot_quadrant(ax3) # plot delineation into quadrants
 
 
 inset_ylim = (0, 100)
 # top left
-ax3.text(x=-0.75, y=0.25, s=r'''$\uparrow$ AUC
-$\downarrow$ rheobase''')
-ax3_TL = ax3.inset_axes([0, 0.8, 0.3, 0.2])
+lfsize = 8
+ax3.text(x=-0.9, y=0.7, s='$\\uparrow$ AUC\n$\\downarrow$ rheobase', fontsize=lfsize)
+ax3.text(-0.95, 0.35, 'GOF', ha='right')
+ax3_TL = ax3.inset_axes([0.07, 0.6, 0.3, 0.2])
 plot_diff_sqrt(ax3_TL, b2=0.1, c2=200)
 ax3_TL.set_ylim(inset_ylim)
+
 # top right
-ax3.text(x=0.25, y=0.25, s=r'''$\uparrow$ AUC
-$\uparrow$ rheobase''')
-ax3_TR = ax3.inset_axes([0.7, 0.8, 0.3, 0.2])
+ax3.text(x=0.22, y=0.7, s='$\\uparrow$ AUC\n$\\uparrow$ rheobase', fontsize=lfsize)
+ax3.text(0.95, 0.35, 'LOF/\nGOF?')
+ax3_TR = ax3.inset_axes([0.63, 0.6, 0.3, 0.2])
 plot_diff_sqrt(ax3_TR, b2=0.4, c2=200)
 ax3_TR.set_ylim(inset_ylim)
+
 # bottom left
-ax3.text(x=-0.75, y=-0.35, s=r'''$\downarrow$ AUC
-$\downarrow$ rheobase''')
-ax3_BL = ax3.inset_axes([0, 0, 0.3, 0.2])
-plot_diff_sqrt(ax3_BL, b2=0.1, c2=75)
+ax3.text(x=-0.9, y=-0.95, s='$\\downarrow$ AUC\n$\downarrow$ rheobase', fontsize=lfsize)
+ax3.text(-0.95, -0.55, 'GOF/\nLOF?', ha='right')
+ax3_BL = ax3.inset_axes([0.07, 0.15, 0.3, 0.2])
+plot_diff_sqrt(ax3_BL, b2=0.07, c2=65)
 ax3_BL.set_ylim(inset_ylim)
+
 # bottom right
-ax3.text(x=0.25, y=-0.35, s=r'''$\downarrow$ AUC
-$\uparrow$ rheobase''')
-ax3_BR = ax3.inset_axes([0.7, 0, 0.3, 0.2])
-plot_diff_sqrt(ax3_BR, b2=0.4, c2=75)
+ax3.text(x=0.22, y=-0.95, s='$\\downarrow$ AUC\n$\\uparrow$ rheobase', fontsize=lfsize)
+ax3.text(0.95, -0.55, 'LOF')
+ax3_BR = ax3.inset_axes([0.63, 0.15, 0.3, 0.2])
+plot_diff_sqrt(ax3_BR, b2=0.4, c2=60)
 ax3_BR.set_ylim(inset_ylim)
-fig.savefig('./Figures/firing_characterization.pdf', bbox_inches='tight')
+
+
+
+import matplotlib.lines as mlines
+wt = mlines.Line2D([], [], color=colorslist[9],  linestyle='-',label='WT')
+mut = mlines.Line2D([], [], color=colorslist[2],  linestyle='-',label='Altered')
+pos = (0.575, 0.05)
+ncol = 2
+fig.legend(handles=[wt, mut], loc='center', bbox_to_anchor=pos, ncol=ncol, frameon=False, fontsize=8)
+
+fig.set_size_inches(cm2inch(8.5,12))
+fig.savefig('./Figures/firing_characterization.png', dpi=fig.dpi) #pdf #eps
 plt.show()
 
 
 
-#%% OLD
-# fig = plt.figure()
-# # fig, axs = plt.subplots(2, 3, figsize=(6, 4))
-# # fig.subplots_adjust(top=0.95, bottom=0.1, left=0.1, right=0.95, hspace=0.6, wspace=0.6)
-# gs = gridspec.GridSpec(2,3, top=0.95, bottom=0.1, left=0.1, right = 0.95, hspace=0.6, wspace=0.6)
-# ax1 = fig.add_subplot(gs[0,0])
-# plot_AUC(ax1, width=0.2)
-# ax2 = fig.add_subplot(gs[1, 0])
-# ax3 = fig.add_subplot(gs[:, 1:])
-# # ax_list = fig.axes
-# # import string
-# # i =0
-# # for a in ax_list:
-# #     a.text(-0.13, 1.05, string.ascii_uppercase[i], transform=a.transAxes,size=12, weight='bold')
-# #     i += 1
-# ax3.set_ylabel('$\Delta$ AUC')
-# ax3.set_xlabel('$\Delta$ rheobase')
-# ax3.set_xlim(-1,1)
-# ax3.set_ylim(-1,1)
-# plot_quadrant(ax3)
-#
-# # top left
-# ax3.text(x=-0.75, y=0.25, s=r'''$\uparrow$ AUC
-# $\downarrow$ rheobase''')
-# ax3_TL = ax3.inset_axes([0,0.8,0.3,0.2])
-# plot_diff_sqrt(ax3_TL, b2=0.1, c2=200)
-#
-# # top right
-# ax3.text(x=0.25, y=0.25, s=r'''$\uparrow$ AUC
-# $\uparrow$ rheobase''')
-# ax3_TR = ax3.inset_axes([0.7, 0.8, 0.3, 0.2])
-# plot_diff_sqrt(ax3_TR, b2=0.4, c2=200)
-#
-# # bottom left
-# ax3.text(x=-0.75, y=-0.35, s=r'''$\downarrow$ AUC
-# $\downarrow$ rheobase''')
-# ax3_BL = ax3.inset_axes([0, 0, 0.3, 0.2])
-# plot_diff_sqrt(ax3_BL, b2=0.1, c2=75)
-#
-# # bottom right
-# ax3.text(x=0.25, y=-0.35, s=r'''$\downarrow$ AUC
-# $\uparrow$ rheobase''')
-# ax3_BR = ax3.inset_axes([0.7, 0, 0.3, 0.2])
-# plot_diff_sqrt(ax3_BR, b2=0.4, c2=75)
-#
-# # ax3_TL.sharey(ax3_TR)
-#
-# # ax3_TL.get_shared_x_axes().join(ax3_TL, ax3_TR, ax3_BL, ax3_BR)
-# # ax3_TL.get_shared_y_axes().join(ax3_TL, ax3_TR, ax3_BL, ax3_BR)
-# ax_list = [ax3_TL, ax3_TR, ax3_BL, ax3_BR] #< your axes objects
-# ax_list[0].get_shared_x_axes().join(*ax_list)
-# plt.show()
-#
-#     # plot_sqrt(axs[0,0])
-#     # plot_temperature(axs[0,1])
-#     # plot_signal_n_power(axs[1,0], axs[1,1])
-#     # fig.savefig('coolresult.pdf')
-#
+#%% with arrows
+fig = plt.figure(figsize=cm2inch(7, 12))
+# gs = gridspec.GridSpec(3,6, top=0.95, bottom=0.1, left=0.15, right = 0.95, hspace=0.8, wspace=0.6)
+gs = gridspec.GridSpec(3,6, top=0.9, bottom=0.05, left=0.15, right = 0.9, hspace=0.8, wspace=0.6)
+ax1 = fig.add_subplot(gs[0,1:])
+show_spines(ax1, 'lb')
+plot_AUC(ax1, width=0.2)
+ax3 = fig.add_subplot(gs[1:, :])
+
+# add panel letter labels
+ax1.text(-0.4, 1.2, string.ascii_uppercase[0], transform=ax1.transAxes, size=16, weight='bold')
+ax3.text(-0.15, 1.05, string.ascii_uppercase[1], transform=ax3.transAxes, size=16, weight='bold')
+
+show_spines(ax3, '')
+plot_quadrant(ax3) # plot delineation into quadrants
+
+
+inset_ylim = (0, 100)
+# top left
+lfsize = 8
+ax3.text(x=-0.9, y=0.7, s='$\\uparrow$ AUC\n$\\downarrow$ rheobase', fontsize=lfsize)
+ax3.text(-0.95, 0.35, 'GOF', ha='right')
+ax3_TL = ax3.inset_axes([0.07, 0.6, 0.3, 0.2])
+plot_diff_sqrt(ax3_TL, b2=0.09, c2=200)
+ax3_TL.set_ylim(inset_ylim)
+ax3_TL.annotate('', (0.25, 7), (0.085, 7), arrowprops=dict(arrowstyle="<|-", color=colorslist[2],lw=0.5, mutation_scale=5), zorder=-10) #
+ax3_TL.annotate('', (0.55, 55), (0.3, 90), arrowprops=dict(arrowstyle="<|-", color=colorslist[2],lw=0.5, mutation_scale=5), zorder=-10) #
+# top right
+ax3.text(x=0.22, y=0.7, s='$\\uparrow$ AUC\n$\\uparrow$ rheobase', fontsize=lfsize)
+ax3.text(0.95, 0.35, 'LOF/\nGOF?')
+ax3_TR = ax3.inset_axes([0.63, 0.6, 0.3, 0.2])
+plot_diff_sqrt(ax3_TR, b2=0.4, c2=200)
+ax3_TR.set_ylim(inset_ylim)
+ax3_TR.annotate('', (0.17, 7), (0.41, 7), arrowprops=dict(arrowstyle="<|-", color=colorslist[2],lw=0.5, mutation_scale=5), zorder=-10) #
+ax3_TR.annotate('', (0.8, 70), (0.6, 90), arrowprops=dict(arrowstyle="<|-", color=colorslist[2],lw=0.5, mutation_scale=5), zorder=-10) #
+
+# bottom left
+ax3.text(x=-0.9, y=-0.95, s='$\\downarrow$ AUC\n$\downarrow$ rheobase', fontsize=lfsize)
+ax3.text(-0.95, -0.55, 'GOF/\nLOF?', ha='right')
+ax3_BL = ax3.inset_axes([0.07, 0.15, 0.3, 0.2])
+plot_diff_sqrt(ax3_BL, b2=0.06, c2=65)
+ax3_BL.set_ylim(inset_ylim)
+ax3_BL.annotate('', (0.25, 7), (0.07, 7), arrowprops=dict(arrowstyle="<|-", color=colorslist[2],lw=0.5, mutation_scale=5), zorder=-10) #
+ax3_BL.annotate('', (0.8, 80), (0.85, 55), arrowprops=dict(arrowstyle="<|-", color=colorslist[2],lw=0.5, mutation_scale=5), zorder=-10) #
+
+# bottom right
+ax3.text(x=0.22, y=-0.95, s='$\\downarrow$ AUC\n$\\uparrow$ rheobase', fontsize=lfsize)
+ax3.text(0.95, -0.55, 'LOF')
+ax3_BR = ax3.inset_axes([0.63, 0.15, 0.3, 0.2])
+plot_diff_sqrt(ax3_BR, b2=0.4, c2=60)
+ax3_BR.set_ylim(inset_ylim)
+ax3_BR.annotate('', (0.18, 7), (0.42, 7), arrowprops=dict(arrowstyle="<|-", color=colorslist[2],lw=0.5, mutation_scale=5), zorder=-10) #
+ax3_BR.annotate('', (0.7, 75), (0.8, 37.5), arrowprops=dict(arrowstyle="<|-", color=colorslist[2],lw=0.5, mutation_scale=5), zorder=-10) #
+
+
+
+fig.set_size_inches(cm2inch(8.3,12))
+fig.savefig('./Figures/firing_characterization_arrows.pdf', dpi=300, bbox_inches='tight') #, dpi=fig.dpi #pdf #eps
+plt.show()

Figures/firing_characterization_old.py

@@ -0,0 +1,182 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import string
+# from plotstyle import plot_style
+from plotstyle import scheme_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)
+
+def plot_sqrt(ax, a=1, b=0.2, c=100, d=0):
+    x = np.linspace(0, 1, 10000)
+    y = c*np.sqrt(a*(x - b)) + d
+    ax.plot(x, y)
+    ax.set_xlabel('Current [nA]')
+    ax.set_ylabel('Frequency [Hz]')
+    ax.set_xlim(0,1)
+    ax.set_ylim(0, ax.get_ylim()[1])
+
+def plot_AUC(ax, a=1, b=0.2, c=100, d=0, width=0.2):
+    x = np.linspace(0, 1, 10000)
+    y = c*np.sqrt(a*(x - b)) + d
+    ax.plot(x, y, 'k')
+    ax.set_xlabel('Current [nA]')
+    ax.set_ylabel('Frequency [Hz]')
+    ax.fill_between(x,y, where=(x<=b+width), color='c')
+    ax.set_xlim(0,1)
+    ax.set_ylim(0, 100)
+    ax.yaxis.set_major_locator(ticker.MultipleLocator(50))
+
+def plot_diff_sqrt(ax, a=1, b=0.2, c=100, d=0, a2=1, b2=0.2, c2=100, d2=0):
+    x = np.linspace(0, 1, 10000)
+    y = c*np.sqrt(a*(x - b)) + d
+    y2 = c2 * np.sqrt(a2 * (x - b2)) + d2
+    ax.plot(x, y, 'k')
+    ax.plot(x, y2, 'b')
+    ax.set_xlim(0,1)
+    ax.set_ylim(0, ax.get_ylim()[1])
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+def plot_quadrant(ax):
+    ax.plot(ax.get_xlim(),[0,0], '--k', linewidth=1.25)
+    ax.plot([0,0],ax.get_ylim(), '--k', linewidth=1.25)
+    ax.tick_params(axis=u'both', which=u'both', length=0)
+    ax.set_xticks([-0.55, 0, 0.55])
+    a = ax.get_xticks().tolist()
+    a[0] = '\u2212'
+    a[1] = 0
+    a[2] = '+'
+    ax.set_xticklabels(a)
+    ax.set_yticks([-0.55, 0, 0.55])
+    b = ax.get_xticks().tolist()
+    b[0] = '\u2212'
+    b[1] = 0
+    b[2] = '+'
+    ax.set_yticklabels(b)
+
+    # ax.tick_params(labelsize=16)
+    # ax.set_yticks([0])
+    # ax.axis('off')
+
+
+# plot_style()
+scheme_style()
+
+fig = plt.figure(figsize=cm2inch(7, 12))
+gs = gridspec.GridSpec(3,2, top=0.95, bottom=0.1, left=0.15, right = 0.95, hspace=0.8, wspace=0.6)
+ax1 = fig.add_subplot(gs[0,:])
+plot_AUC(ax1, width=0.2)
+ax3 = fig.add_subplot(gs[1:, :])
+
+# add panel letter labels
+ax_list = fig.axes
+i =0
+for a in ax_list:
+    a.text(-0.18, 1.08, string.ascii_uppercase[i], transform=a.transAxes,size=16, weight='bold')
+    i += 1
+
+ax3.set_ylabel('$\Delta$ AUC')
+ax3.set_xlabel('$\Delta$ rheobase')
+ax3.set_xlim(-1, 1)
+ax3.set_ylim(-1, 1)
+plot_quadrant(ax3) # plot delineation into quadrants
+
+
+inset_ylim = (0, 100)
+# top left
+lfsize = 8
+ax3.text(x=-0.75, y=0.25, s=r'''$\uparrow$ AUC
+$\downarrow$ rheobase''', fontsize=lfsize)
+ax3_TL = ax3.inset_axes([0, 0.8, 0.3, 0.2])
+plot_diff_sqrt(ax3_TL, b2=0.1, c2=200)
+ax3_TL.set_ylim(inset_ylim)
+# top right
+ax3.text(x=0.25, y=0.25, s=r'''$\uparrow$ AUC
+$\uparrow$ rheobase''', fontsize=lfsize)
+ax3_TR = ax3.inset_axes([0.7, 0.8, 0.3, 0.2])
+plot_diff_sqrt(ax3_TR, b2=0.4, c2=200)
+ax3_TR.set_ylim(inset_ylim)
+# bottom left
+ax3.text(x=-0.75, y=-0.35, s=r'''$\downarrow$ AUC
+$\downarrow$ rheobase''', fontsize=lfsize)
+ax3_BL = ax3.inset_axes([0, 0, 0.3, 0.2])
+plot_diff_sqrt(ax3_BL, b2=0.1, c2=75)
+ax3_BL.set_ylim(inset_ylim)
+# bottom right
+ax3.text(x=0.25, y=-0.35, s=r'''$\downarrow$ AUC
+$\uparrow$ rheobase''', fontsize=lfsize)
+ax3_BR = ax3.inset_axes([0.7, 0, 0.3, 0.2])
+plot_diff_sqrt(ax3_BR, b2=0.4, c2=75)
+ax3_BR.set_ylim(inset_ylim)
+# fig.savefig('./Figures/firing_characterization.pdf', bbox_inches='tight')
+print(fig.dpi)
+fig.set_size_inches(cm2inch(8.17,15))
+fig.savefig('./Figures/firing_characterization_old.pdf', dpi=fig.dpi) #bbox_inches='tight', dpi=fig.dpi
+plt.show()
+
+
+
+#%% OLD
+# fig = plt.figure()
+# # fig, axs = plt.subplots(2, 3, figsize=(6, 4))
+# # fig.subplots_adjust(top=0.95, bottom=0.1, left=0.1, right=0.95, hspace=0.6, wspace=0.6)
+# gs = gridspec.GridSpec(2,3, top=0.95, bottom=0.1, left=0.1, right = 0.95, hspace=0.6, wspace=0.6)
+# ax1 = fig.add_subplot(gs[0,0])
+# plot_AUC(ax1, width=0.2)
+# ax2 = fig.add_subplot(gs[1, 0])
+# ax3 = fig.add_subplot(gs[:, 1:])
+# # ax_list = fig.axes
+# # import string
+# # i =0
+# # for a in ax_list:
+# #     a.text(-0.13, 1.05, string.ascii_uppercase[i], transform=a.transAxes,size=12, weight='bold')
+# #     i += 1
+# ax3.set_ylabel('$\Delta$ AUC')
+# ax3.set_xlabel('$\Delta$ rheobase')
+# ax3.set_xlim(-1,1)
+# ax3.set_ylim(-1,1)
+# plot_quadrant(ax3)
+#
+# # top left
+# ax3.text(x=-0.75, y=0.25, s=r'''$\uparrow$ AUC
+# $\downarrow$ rheobase''')
+# ax3_TL = ax3.inset_axes([0,0.8,0.3,0.2])
+# plot_diff_sqrt(ax3_TL, b2=0.1, c2=200)
+#
+# # top right
+# ax3.text(x=0.25, y=0.25, s=r'''$\uparrow$ AUC
+# $\uparrow$ rheobase''')
+# ax3_TR = ax3.inset_axes([0.7, 0.8, 0.3, 0.2])
+# plot_diff_sqrt(ax3_TR, b2=0.4, c2=200)
+#
+# # bottom left
+# ax3.text(x=-0.75, y=-0.35, s=r'''$\downarrow$ AUC
+# $\downarrow$ rheobase''')
+# ax3_BL = ax3.inset_axes([0, 0, 0.3, 0.2])
+# plot_diff_sqrt(ax3_BL, b2=0.1, c2=75)
+#
+# # bottom right
+# ax3.text(x=0.25, y=-0.35, s=r'''$\downarrow$ AUC
+# $\uparrow$ rheobase''')
+# ax3_BR = ax3.inset_axes([0.7, 0, 0.3, 0.2])
+# plot_diff_sqrt(ax3_BR, b2=0.4, c2=75)
+#
+# # ax3_TL.sharey(ax3_TR)
+#
+# # ax3_TL.get_shared_x_axes().join(ax3_TL, ax3_TR, ax3_BL, ax3_BR)
+# # ax3_TL.get_shared_y_axes().join(ax3_TL, ax3_TR, ax3_BL, ax3_BR)
+# ax_list = [ax3_TL, ax3_TR, ax3_BL, ax3_BR] #< your axes objects
+# ax_list[0].get_shared_x_axes().join(*ax_list)
+# plt.show()
+#
+#     # plot_sqrt(axs[0,0])
+#     # plot_temperature(axs[0,1])
+#     # plot_signal_n_power(axs[1,0], axs[1,1])
+#     # fig.savefig('coolresult.pdf')
+#

Figures/model_g.py

@@ -0,0 +1,197 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import string
+from plotstyle import scheme_style
+import pandas as pd
+from matplotlib import cm
+
+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 show_spines(ax, spines='lrtb'):
+    """ Show and hide spines.
+
+    From github.com/janscience/plottools.git  spines.py
+
+    Parameters
+    ----------
+    ax: matplotlib figure, matplotlib axis, or list of matplotlib axes
+        Axis on which spine and ticks visibility is manipulated.
+        If figure, then apply manipulations on all axes of the figure.
+        If list of axes, apply manipulations on each of the given axes.
+    spines: string
+        Specify which spines and ticks should be shown.
+        All other ones or hidden.
+        'l' is the left spine, 'r' the right spine,
+        't' the top one and 'b' the bottom one.
+        E.g. 'lb' shows the left and bottom spine, and hides the top
+        and and right spines, as well as their tick marks and labels.
+        '' shows no spines at all.
+        'lrtb' shows all spines and tick marks.
+
+    Examples
+    --------
+    ```py
+    import matplotlib.pyplot as plt
+    import plottools.spines
+
+    fig, (ax0, ax1, ax2) = plt.subplots(1, 3)
+    ax0.show_spines('lb')
+    ax1.show_spines('bt')
+    ax2.show_spines('tr')
+    ```
+    ![show](figures/spines-show.png)
+    """
+    # collect spine visibility:
+    xspines = []
+    if 't' in spines:
+        xspines.append('top')
+    if 'b' in spines:
+        xspines.append('bottom')
+    yspines = []
+    if 'l' in spines:
+        yspines.append('left')
+    if 'r' in spines:
+        yspines.append('right')
+    # collect axes:
+    if isinstance(ax, (list, tuple, np.ndarray)):
+        axs = ax
+    elif hasattr(ax, 'get_axes'):
+        # ax is figure:
+        axs = ax.get_axes()
+    else:
+        axs = [ax]
+    if not isinstance(axs, (list, tuple)):
+        axs = [axs]
+    for ax in axs:
+        # hide spines:
+        ax.spines['top'].set_visible('top' in xspines)
+        ax.spines['bottom'].set_visible('bottom' in xspines)
+        ax.spines['left'].set_visible('left' in yspines)
+        ax.spines['right'].set_visible('right' in yspines)
+        # ticks:
+        if len(xspines) == 0:
+            ax.xaxis.set_ticks_position('none')
+            ax.xaxis.label.set_visible(False)
+            ax.xaxis._orig_major_locator = ax.xaxis.get_major_locator()
+            ax.xaxis.set_major_locator(ticker.NullLocator())
+        else:
+            if hasattr(ax.xaxis, '_orig_major_locator'):
+                ax.xaxis.set_major_locator(ax.xaxis._orig_major_locator)
+                delattr(ax.xaxis, '_orig_major_locator')
+            elif isinstance(ax.xaxis.get_major_locator(), ticker.NullLocator):
+                ax.xaxis.set_major_locator(ticker.AutoLocator())
+            if len(xspines) == 1:
+                ax.xaxis.set_ticks_position(xspines[0])
+                ax.xaxis.set_label_position(xspines[0])
+            else:
+                ax.xaxis.set_ticks_position('both')
+                ax.xaxis.set_label_position('bottom')
+        if len(yspines) == 0:
+            ax.yaxis.set_ticks_position('none')
+            ax.yaxis.label.set_visible(False)
+            ax.yaxis._orig_major_locator = ax.yaxis.get_major_locator()
+            ax.yaxis.set_major_locator(ticker.NullLocator())
+        else:
+            if hasattr(ax.yaxis, '_orig_major_locator'):
+                ax.yaxis.set_major_locator(ax.yaxis._orig_major_locator)
+                delattr(ax.yaxis, '_orig_major_locator')
+            elif isinstance(ax.yaxis.get_major_locator(), ticker.NullLocator):
+                ax.yaxis.set_major_locator(ticker.AutoLocator())
+            if len(yspines) == 1:
+                ax.yaxis.set_ticks_position(yspines[0])
+                ax.yaxis.set_label_position(yspines[0])
+            else:
+                ax.yaxis.set_ticks_position('both')
+                ax.yaxis.set_label_position('left')
+
+def plot_g(ax, df, models, i, let_x, let_y, titlesize=10, letsize=12):
+    # c = [cm.plasma(x) for x in np.linspace(0., 1., 9)]
+    # c = [cm.turbo(x) for x in np.linspace(0., 1., 9)]
+    c = [cm.gray(x) for x in np.linspace(0., 0.75, 9)]
+    myorder = [0, 4, 1, 6, 2,7, 3,8]
+    colors = [c[i] for i in myorder]
+    ax.set_ylim(0.01, 60)
+    df.plot.bar(y=models[i], rot=90, ax=ax, legend=False,
+                ylabel='$\mathrm{g}_{\mathrm{max}}$ [$\mathrm{mS}/ \mathrm{cm}^2$]',
+                color=colors)
+    ax.set_title(models[i], fontsize=titlesize)
+    show_spines(ax, spines='lb')
+    ax.text(let_x, let_y, string.ascii_uppercase[i], transform=ax.transAxes, size=letsize, weight='bold')
+    ax.set_yscale('log')
+    ax.set_xlim(-0.5, 9)
+    # ymin, ymax = ax.get_ylim()
+    # ax.set_ylim(0.001, 60)
+    from matplotlib.ticker import ScalarFormatter
+    for axis in [ax.yaxis]:
+        axis.set_major_formatter(ScalarFormatter())
+    # if i == 1 or i == 4:
+    #     print(i)
+    ax.set_yticks([0.1,1.0, 10])
+        # ax.yaxis.set_major_formatter(ScalarFormatter())
+
+        # ax.set_yticklabels([0.1,1.0, 10])
+        # ax.set_yscale('log')
+    import matplotlib.ticker
+    # # ax.yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())
+    locmin = matplotlib.ticker.LogLocator(base=10.0, subs=(0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10), numticks=100)
+    ax.yaxis.set_minor_locator(locmin)
+    ax.yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())
+    ax.yaxis.set_tick_params(labelleft= True)
+    return ax
+
+
+index = ['$\mathrm{g}_{\mathrm{Na}}$', '$\mathrm{g}_{\mathrm{Kd}}$', '$\mathrm{g}_{\mathrm{K_V1.1}}$',
+         '$\mathrm{g}_{\mathrm{A}}$', '$\mathrm{g}_{\mathrm{M}}$', '$\mathrm{g}_{\mathrm{L}}$',
+         '$\mathrm{g}_{\mathrm{T}}$', ' $\mathrm{g}_{\mathrm{Ca,K}}$', ' $\mathrm{g}_{\mathrm{Leak}}$']
+df = pd.DataFrame({'RS Pyramidal': [56, 6, 0, 0, 0.075, 0, 0, 0, 0.0205],
+                   'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [56, 5.4, 0.6, 0, 0.075, 0, 0, 0, 0.0205],
+                   'RS Inhibitory': [10, 2.1, 0, 0, 0.0098, 0, 0, 0, 0.0205],
+                   'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [10, 1.89, 0.21, 0, 0.0098, 0, 0, 0, 0.0205],
+                   'FS': [58, 3.9, 0, 0, 0.075, 0, 0, 0, 0.038],
+                   'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [58, 3.51, 0.39, 0, 0.075, 0, 0, 0, 0.038],
+                   'Cb stellate': [3.4, 9.0556, 0, 15.0159, 0, 0, 0.4545, 0, 0.07407],
+                   'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [3.4, 8.15, 0.90556, 15.0159, 0, 0, 0.4545, 0, 0.07407],
+                   'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [3.4, 9.0556, 1.50159, 0, 0, 0, 0.4545, 0, 0.07407],
+                   'STN': [49, 57, 0, 5, 0, 5, 5, 1, 0.035],
+                   'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [49, 56.43, 0.57, 5, 0, 5, 5, 1, 0.035],
+                   'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [49, 57, 0.5, 0, 0, 5, 5, 1, 0.035]},
+                  index=index)
+
+
+#% with legend
+scheme_style()
+models = ['Cb stellate', 'RS Inhibitory', 'FS', 'RS Pyramidal', 'RS Inhibitory +$\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']
+fig, axs = plt.subplots(4, 3, figsize=cm2inch(20, 20))#,  sharey=True)
+plt.subplots_adjust(hspace=1.5, wspace=1.0)
+let_x = -0.6
+let_y = 1.2
+titlesize = 9
+letsize = 10
+axs[0, 0] = plot_g(axs[0, 0], df, models, 0, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[0, 1] = plot_g(axs[0, 1], df, models, 1, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[0, 2] = plot_g(axs[0, 2], df, models, 2, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[1, 0] = plot_g(axs[1, 0], df, models, 3, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[1, 1] = plot_g(axs[1, 1], df, models, 4, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[1, 2] = plot_g(axs[1, 2], df, models, 5, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[2, 0] = plot_g(axs[2, 0], df, models, 6, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[2, 1] = plot_g(axs[2, 1], df, models, 7, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[2, 2] = plot_g(axs[2, 2], df, models, 8, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[3, 0] = plot_g(axs[3, 0], df, models, 9, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[3, 1] = plot_g(axs[3, 1], df, models, 10, let_x, let_y, titlesize=titlesize, letsize=letsize)
+axs[3, 2] = plot_g(axs[3, 2], df, models, 11, let_x, let_y, titlesize=titlesize, letsize=letsize)
+# save
+# fig.set_size_inches(cm2inch(21,20))
+fig.savefig('./Figures/model_g.jpg', dpi=300, bbox_inches='tight')  # pdf # eps
+plt.show()
+

Figures/plotstyle.py

@@ -0,0 +1,59 @@
+import matplotlib.pyplot as plt
+
+
+def scheme_style():
+    plt.rcParams['xtick.direction'] = 'out'
+    plt.rcParams['ytick.direction'] = 'out'
+    plt.rcParams['axes.titlesize'] = 20
+    plt.rcParams['axes.labelsize'] = 8
+    plt.rcParams['lines.linewidth'] = 2
+    plt.rcParams['lines.markersize'] = 10
+    plt.rcParams['xtick.labelsize'] = 10
+    plt.rcParams['ytick.labelsize'] = 10
+    plt.rcParams['savefig.bbox'] = 'tight'
+    # plt.rcParams['figure.figsize'] = [8.0, 8.0]
+    plt.rcParams['figure.dpi'] = 300
+
+
+def plot_style():
+    plt.rcParams['xtick.direction'] = 'out'
+    plt.rcParams['ytick.direction'] = 'out'
+    plt.rcParams['axes.titlesize'] = 20
+    plt.rcParams['axes.labelsize'] = 12
+    plt.rcParams['lines.linewidth'] = 2
+    plt.rcParams['lines.markersize'] = 10
+    plt.rcParams['xtick.labelsize'] = 10
+    plt.rcParams['ytick.labelsize'] = 10
+    plt.rcParams['savefig.bbox'] = 'tight'
+    plt.rcParams['figure.dpi'] = 300
+
+def boxplot_style():
+    plt.rcParams['xtick.direction'] = 'in'
+    plt.rcParams['ytick.direction'] = 'in'
+    plt.rcParams['lines.linewidth'] = 1
+    plt.rcParams['lines.markersize'] = 0.5
+    plt.rcParams['xtick.labelsize'] = 7
+    plt.rcParams['ytick.labelsize'] = 7
+    plt.rcParams['savefig.bbox'] = 'tight'
+    plt.rcParams['axes.titlesize'] = 7
+    plt.rcParams['axes.labelsize'] = 7
+    plt.rcParams['legend.fontsize']= 6
+    plt.rcParams['xtick.labelsize'] = 6
+    plt.rcParams['ytick.labelsize']= 6
+    plt.rcParams['mathtext.default'] = 'regular'
+    plt.rcParams['figure.dpi'] = 300
+
+
+
+def sim_style():
+    plt.rcParams['xtick.direction'] = 'out'
+    plt.rcParams['ytick.direction'] = 'out'
+    plt.rcParams['axes.titlesize'] = 6
+    plt.rcParams['axes.labelsize'] = 8
+    plt.rcParams['lines.linewidth'] = 1
+    plt.rcParams['lines.markersize'] = 4
+    plt.rcParams['xtick.labelsize'] = 6
+    plt.rcParams['ytick.labelsize'] = 6
+    plt.rcParams['savefig.bbox'] = 'tight'
+    plt.rcParams.update({'font.size': 6})
+    plt.rcParams['figure.dpi'] = 300

Figures/ramp_examples.py

@@ -1,135 +0,0 @@
-# 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
-
-#### 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=0.25)
-    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=(20,10))
-
-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
-add_scalebar(ax11_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=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=16, weight='bold')
-
-fig.savefig('./Figures/ramp_firing.pdf')
-plt.show()

Figures/ramp_firing.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()
+

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.jpg', dpi=fig.dpi)#pdf #eps
+plt.show()
+

Figures/rheobase_correlation.py

@@ -13,7 +13,15 @@ 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
 
@@ -44,7 +52,7 @@ def GetColor2Marker(markers):
     mkcolors = [(palette[i]) for i in range(len(markers))]
     return dict(zip(mkcolors,markers))
 
-def fixlegend(ax,markers,markersize=8,**kwargs):
+def fixlegend(ax,markers,markersize=3,**kwargs):
     # Fix Legend
     legtitle =  ax.get_legend().get_title().get_text()
     _,l = ax.get_legend_handles_labels()
@@ -86,13 +94,44 @@ def new_scatter(self, *args, **kwargs):
     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'):
-    # size =4
-    # ax.tick_params(axis='x', rotation=90)
+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 +$K_V1.1$',
@@ -114,81 +153,87 @@ def boxplot_with_markers(ax,max_width, alteration='shift'):
                   ]
 
     import matplotlib.colors
-    colors = [matplotlib.colors.to_rgb(c) for c in colorslist] # sns.color_palette("hls", 14, desat=0.75)#[cm.tab20(i) for i in range(20)]
+    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 = 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, #dodge=True,
-                      palette=clr_dict, linewidth=0, orient='v', ax=ax,
+        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')
-        ax.plot([lim[0], lim[1]], [1, 1], ':k')
-        ax.plot([lim[0], lim[1]], [-1, -1], ':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)
         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, #dodge=True,
-                      palette=clr_dict, linewidth=0, orient='v', ax=ax,
+        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')
-        ax.plot([lim[0], lim[1]], [1, 1], ':k')
-        ax.plot([lim[0], lim[1]], [-1, -1], ':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)
         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, #dodge=True,
-                      palette=clr_dict, linewidth=0, orient='v', ax=ax,
+        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')
-        ax.plot([lim[0], lim[1]], [1, 1], ':k')
-        ax.plot([lim[0], lim[1]], [-1, -1], ':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)
         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 $\it{\tau}$')
-    ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
+
 
 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] #sns.color_palette("hls", 14, desat=0.75) #[cm.tab20(i) for i in range(20)]
+    # 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,
@@ -196,8 +241,30 @@ def model_legend(ax, marker_s_leg, pos, ncol):
               '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')
@@ -223,7 +290,6 @@ def model_legend(ax, marker_s_leg, pos, ncol):
     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)
 
-
 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)
@@ -260,8 +326,7 @@ def plot_rheo_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration=
                   '#873770',  # magenta
                   ]
     import matplotlib.colors
-    colors = [matplotlib.colors.to_rgb(c) for c in
-              colorslist]  #sns.color_palette("hls", 14, desat=0.75)#[cm.tab20(i) for i in range(20)]
+    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]
@@ -275,25 +340,45 @@ def plot_rheo_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration=
         ax.set_xticks([0.5, 1, 2])
         ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
         ax.xaxis.set_minor_formatter(NullFormatter())
-        ax.set_xlabel('Slope factor k')
+        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')
+        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)
+                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=1, zorder=1, linewidth=1)
+            ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=0.5, zorder=1, linewidth=1)
 
-    ax.set_ylabel('$\Delta$ Rheobase (nA)')
+    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'):
@@ -324,9 +409,30 @@ def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color
     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]')
-    ax.set_title(model)
+    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()
@@ -335,6 +441,7 @@ def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color
 
 
 #%%
+boxplot_style()
 color_dict = {'Cb stellate': '#40A787', # cyan'#
               'RS Inhibitory': '#F0D730',  # yellow
               'FS': '#C02717',  # red
@@ -350,46 +457,42 @@ color_dict = {'Cb stellate': '#40A787', # cyan'#
               }
 
 # plot setup
-marker_s_leg = 8
-max_width = 10
-pad_x = 0.6
-pad_y= 0.3
-pad_w = 0.8
-pad_h = 0.5
-
-fig = plt.figure(figsize=(18,10))
-gs = fig.add_gridspec(3, 6, wspace=0.9)
-plt.subplots_adjust(hspace=0.8) #, wspace=0.6
-ax0 = fig.add_subplot(gs[0,2:6])
+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[1,2:6])
+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[2,2:6])
+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=2,transform=ax0_fI.transAxes,  color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+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=2,transform=ax1_fI.transAxes,  color=color_dict['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
+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=2,transform=ax2_fI.transAxes,  color=color_dict['Cb stellate'], alpha=1, zorder=-1)
+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)
 
-# label fI subplots with letters
-ax0_fI.text(-0.6, 1.28, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=16, weight='bold') #-0.09
-ax1_fI.text(-0.6, 1.28, string.ascii_uppercase[1], transform=ax1_fI.transAxes,size=16, weight='bold')
-ax2_fI.text(-0.6, 1.28, string.ascii_uppercase[2], transform=ax2_fI.transAxes,size=16, weight='bold')
 
 # plot boxplots
 boxplot_with_markers(ax0,max_width, alteration='shift')
@@ -397,8 +500,8 @@ boxplot_with_markers(ax1,max_width, alteration='slope')
 boxplot_with_markers(ax2,max_width, alteration='g')
 
 # plot legend
-pos = (0.25, -0.6)
-ncol = 6
+pos = (0.225, -0.9)
+ncol = 5
 model_legend(ax2,  marker_s_leg, pos, ncol)
 
 # plot rheo across model for example alteration
@@ -406,67 +509,56 @@ plot_rheo_alt(ax0_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='
 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.savefig('./Figures/rheobase_correlation.pdf', bbox_inches='tight')
+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()
 
-# #%% alternative layout
-# marker_s_leg = 8
-# max_width = 10
-# pad_x = 0.2
-# pad_y= 0.4
-# pad_w = 0.22
-# pad_h = 0.6
-#
-# fig = plt.figure(figsize=(12,20))
-# gs = fig.add_gridspec(3, 1, hspace=0.4, wspace=0.4)
-# gs0 = gs[0].subgridspec(2, 4, wspace=0.6, hspace=1.)
-# gs1 = gs[1].subgridspec(2, 4, wspace=0.6, hspace=1.)
-# gs2 = gs[2].subgridspec(2, 4, wspace=0.6, hspace=1.)
-# ax0 = fig.add_subplot(gs0[1,:])
-# ax0_ex = fig.add_subplot(gs0[0,2:])
-# ax0_fI = fig.add_subplot(gs0[0,:2])
-# ax1 = fig.add_subplot(gs1[1,:])
-# ax1_ex = fig.add_subplot(gs1[0,2:])
-# ax1_fI = fig.add_subplot(gs1[0,:2])
-# ax2 = fig.add_subplot(gs2[1,:])
-# ax2_ex = fig.add_subplot(gs2[0,2:])
-# ax2_fI = fig.add_subplot(gs2[0,:2])
-#
-#
-# 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=2,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)
+
+#%%
+# 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()
+#%%
 #
-# 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=2,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=2,transform=ax2_fI.transAxes,  color=color_dict['Cb stellate'], alpha=1, zorder=-1)
-# rec = ax2_fI.add_patch(rec)
-# rec.set_clip_on(False)
+# cvals = [-2., 2]
+# colors = ['lightgrey', 'k']
 #
-# # label subplots with letters
-# ax0_fI.text(-0.2, 1.38, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=16, weight='bold') #-0.09
-# ax1_fI.text(-0.2, 1.38, string.ascii_uppercase[1], transform=ax1_fI.transAxes,size=16, weight='bold')
-# ax2_fI.text(-0.2, 1.38, string.ascii_uppercase[2], transform=ax2_fI.transAxes,size=16, weight='bold')
+# 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))
 #
-# # 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 = (0.5, -1.)
-# ncol = 6
-# model_legend(ax2,  marker_s_leg, pos, ncol)
+# 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)
 #
-# # # 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')
+# 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.savefig('rheobase_correlation_2.pdf', bbox_inches='tight')
+# 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()

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}$', color1='lightgrey', color2='k',alteration='slope')
+plot_rheo_alt(ax2_ex, model='Cb stellate', color1='lightgrey', color2='k', alteration='g')
+
+# label subplots with letters
+ax0_fI.text(-0.875, 1.35, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=10, weight='bold')
+ax0_ex.text(-0.8, 1.35, string.ascii_uppercase[1], transform=ax0_ex.transAxes, size=10, weight='bold')
+ax0.text(-0.075, 1.35, string.ascii_uppercase[2], transform=ax0.transAxes, size=10, weight='bold')
+
+ax1_fI.text(-0.875, 1.35, string.ascii_uppercase[3], transform=ax1_fI.transAxes,size=10, weight='bold')
+ax1_ex.text(-0.8, 1.35, string.ascii_uppercase[4], transform=ax1_ex.transAxes, size=10, weight='bold')
+ax1.text(-0.075, 1.35, string.ascii_uppercase[5], transform=ax1.transAxes, size=10, weight='bold')
+
+ax2_fI.text(-0.875, 1.35, string.ascii_uppercase[6], transform=ax2_fI.transAxes,size=10, weight='bold')
+ax2_ex.text(-0.8, 1.35, string.ascii_uppercase[7], transform=ax2_ex.transAxes, size=10, weight='bold')
+ax2.text(-0.075, 1.35, string.ascii_uppercase[8], transform=ax2.transAxes, size=10, weight='bold')
+
+# save
+fig.set_size_inches(cm2inch(20.75,12))
+# fig.savefig('./Figures/rheobase_correlation.pdf', dpi=fig.dpi)
+fig.savefig('./Figures/rheobase_correlation.jpg', dpi=fig.dpi) #bbox_inches='tight', dpi=fig.dpi # eps # pdf
+plt.show()
+
+
+#%%
+# fig, axs = plt.subplots(1,2)
+# axs[0] = plot_fI(axs[0] , model='FS +$\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()

Figures/simulation_model_comparison.py

@@ -3,30 +3,34 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import os
 import string
-from plotstyle import plot_style
+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]):
-            # print(col, col2)
             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([0.94, .25, .03, .4])
+    cbar_ax = fig.add_axes([0.685, 0.44, .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.axis('off')
     cbar_ax.set_xticks([])
     cbar_ax.set_yticks([])
     if df == 'AUC':
@@ -54,7 +58,7 @@ def correlation_plot(ax, df='AUC', title='', cbar=False):
     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=np.bool)
+    mask = np.zeros_like(tau, dtype=bool)
     mask[np.triu_indices_from(mask)] = True
     np.fill_diagonal(mask, False)
 
@@ -81,14 +85,20 @@ def correlation_plot(ax, df='AUC', title='', cbar=False):
                           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_ax.set_title(r'Kendall $\tau$', y=1.05)
-    ax.set_title(title)
+                          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)
+
 
-def mutation_plot2(ax, model='RS_pramidal'):
+def mutation_plot(ax, model='RS_pramidal'):
     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',  #'RS pyramidal', 'RS inhibitory', 'FS', 'IB',
+    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',
@@ -103,7 +113,6 @@ def mutation_plot2(ax, model='RS_pramidal'):
     ax_dict['RS_pyramidal'] = (0, 0)
     ax_dict['RS_inhib'] = (0, 1)
     ax_dict['FS'] = (1, 0)
-    # ax_dict['IB'] = (1, 1)
     ax_dict['Cb_stellate'] = (2, 0)
     ax_dict['Cb_stellate_Kv'] = (2, 1)
     ax_dict['Cb_stellate_Kv_only'] = (3, 0)
@@ -115,7 +124,6 @@ def mutation_plot2(ax, model='RS_pramidal'):
     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['IB'] = (-0.2, 0.2)
     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)
@@ -136,26 +144,32 @@ def mutation_plot2(ax, model='RS_pramidal'):
 
     mod = models.index(model)
     mut_names = AUC.index
-    ax.plot(rheo.loc[mut_names, model_names[mod]], AUC.loc[mut_names, model_names[mod]], linestyle='',
+    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=4)  # markeredgecolor=clr_dict[model_names[mod]],markerfacecolor=clr_dict[model_names[mod]],
+            markersize=2)
 
-    ax.plot(rheo.loc['wt', model_names[mod]], AUC.loc['wt', model_names[mod]], 'sk')
+    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]], AUC.loc['V174F', model_names[mod]], linestyle='',
+    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]], AUC.loc['F414C', model_names[mod]], linestyle='',
+    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]], AUC.loc['E283K', model_names[mod]], linestyle='',
+    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]], AUC.loc['V404I', model_names[mod]], linestyle='',
+    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_display_names[mod], fontsize=14)#, color=clr_dict[models[mod]])
-    ax.set_xlabel('$\Delta$ Rheobase (nA)')
-    ax.set_ylabel('$AUC_{contrast}$')
+    ax.set_title(model_display_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):
@@ -169,19 +183,18 @@ def mutation_legend(ax, marker_s_leg, pos, ncol):
     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+5, linestyle='None', label='Wild type')
+    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)
 
 
 
-plot_style()
-
+sim_style()
 # plot setup
-fig = plt.figure(figsize=(18,12))
-gs0 = fig.add_gridspec(1, 5, wspace=3.5)
-gsl = gs0[0:3].subgridspec(3, 3, wspace=0.6, hspace=0.6)
-gsr = gs0[3:5].subgridspec(2, 1, wspace=0.6, hspace=0.8)
+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(6, 1, wspace=0.6, hspace=1)
 
 ax00 = fig.add_subplot(gsl[0,0])
 ax01 = fig.add_subplot(gsl[0,1])
@@ -193,38 +206,42 @@ 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[0,0])
-axr1 = fig.add_subplot(gsr[1,0])
+axr0 = fig.add_subplot(gsr[0:2,0])
+axr1 = fig.add_subplot(gsr[4:,0])
 
 # plot mutations in each model
-ax00 = mutation_plot2(ax00, model='RS_pyramidal')
-ax01 = mutation_plot2(ax01, model='RS_inhib')
-ax02 = mutation_plot2(ax02, model='FS')
-ax10 = mutation_plot2(ax10, model='Cb_stellate')
-ax11 = mutation_plot2(ax11, model='Cb_stellate_Kv')
-ax12 = mutation_plot2(ax12, model='Cb_stellate_Kv_only')
-ax20 = mutation_plot2(ax20, model='STN')
-ax21 = mutation_plot2(ax21, model='STN_Kv')
-ax22 = mutation_plot2(ax22, model='STN_Kv_only')
+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 = (0.25, -0.45)
+pos = (0.425, -0.7)
 ncol = 5
 mutation_legend(ax21, marker_s_leg, pos, ncol)
 
 # plot correlation matrices
-correlation_plot(axr1,df = 'AUC', title='$AUC_{contrast}$', cbar=False)
-correlation_plot(axr0,df = 'rheo', title='$\Delta rheobase$', cbar=True)
+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(-0.28, 1.08, string.ascii_uppercase[i], transform=axs[i].transAxes, size=16, weight='bold')
+    # 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(-0.38, 1.2, string.ascii_uppercase[j], transform=axr0.transAxes, size=16, weight='bold')
-axr1.text(-0.38, 1.2, string.ascii_uppercase[j+1], transform=axr1.transAxes, size=16, weight='bold')
+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.savefig('./Figures/simulation_model_comparison.pdf', bbox_inches='tight')
+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()
+

Figures/simulation_model_comparison_letters.py

@@ -0,0 +1,275 @@
+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}$']
+    # model_letter_names = ['H', 'E', 'G', 'A', 'F', 'J', 'L', 'I', 'K']
+
+    models =['Cb_stellate','RS_inhib','Cb_stellate_Kv','FS', 'RS_pyramidal','STN_Kv', 'Cb_stellate_Kv_only','STN_Kv_only', 'STN']
+    model_names = ['Cb stellate','RS inhibitory', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS',
+                   'RS pyramidal', 'STN +$\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']
+    model_letter_names = ['A', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L']
+    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$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
+    #                'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
+    # # model_letter_names = ['Model H', 'Model E', 'Model G', 'Model A', 'Model F', 'Model J', 'Model L', 'Model I', 'Model K']
+    # model_letter_names = ['H', 'E', 'G', 'A', 'F', 'J', 'L', 'I', 'K']
+    models = ['Cb_stellate', 'RS_inhib', 'Cb_stellate_Kv', 'FS', 'RS_pyramidal', 'STN_Kv', 'Cb_stellate_Kv_only',
+              'STN_Kv_only', 'STN']
+    model_names = ['Cb stellate', 'RS inhibitory', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS',
+                   'RS pyramidal', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN']
+    model_letter_names = ['A', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L']
+
+    col_dict = {}
+    for m in range(len(models)):
+        col_dict[model_names[m]] = model_letter_names[m]
+    tau.rename(columns=col_dict, index=col_dict, inplace=True)
+    tau = tau[model_letter_names]
+
+    # plotting with or without colorbar
+    if cbar==False:
+        res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
+                          cbar=False, cmap=cmap, cbar_ax=cbar_ax, cbar_kws={"shrink": .52})
+    else:
+        res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
+                          cbar=True, cmap=cmap, cbar_ax=cbar_ax,
+                          cbar_kws={"orientation": "horizontal",
+                                    "ticks": [-1,-0.5, 0, 0.5, 1]} )
+        cbar_ax.set_title(r'Kendall $\tau$', y=1.02, loc='center', fontsize=6)
+        cbar_ax.tick_params(length=3)
+        for tick in cbar_ax.xaxis.get_major_ticks():
+            tick.label.set_fontsize(6)
+    ax.set_title(title, fontsize=8)
+    ax.set_xlabel("Model")
+    ax.set_ylabel("Model")
+
+def mutation_plot(ax, model='RS_pyramidal'):
+    models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
+              'STN_Kv', 'STN_Kv_only']
+    model_names = ['RS pyramidal', 'RS inhibitory', 'FS',
+                   'Cb stellate', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
+                   'Cb stellate $\Delta$$\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=3.8)
+
+ax00 = fig.add_subplot(gsl[1,1]) #model H
+ax01 = fig.add_subplot(gsl[0,1]) # model E
+ax02 = fig.add_subplot(gsl[1,0]) # model G
+ax10 = fig.add_subplot(gsl[0,0]) # model A
+ax11 = fig.add_subplot(gsl[0,2]) # model F
+ax12 = fig.add_subplot(gsl[2,0]) # model J
+ax20 = fig.add_subplot(gsl[2,2]) # model L
+ax21 = fig.add_subplot(gsl[1,2]) # model I
+ax22 = fig.add_subplot(gsl[2,1]) # model K
+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(ax22, 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]
+
+axs = [ax10, ax01, ax11, ax02, ax00, ax21, ax12, ax22, ax20]
+
+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(-0.27, 1.075, string.ascii_uppercase[j], transform=axr0.transAxes, size=10, weight='bold')
+axr1.text(-0.27, 1.075, 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.jpg', dpi=fig.dpi) #eps
+plt.show()
+