upDn_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48e46c95",
   "metadata": {},
   "source": [
    "# The Up-Dn System \n",
    "\n",
    "### Marco Arieli Herrera-Valdez\n",
    "#### Laboratorio de Dinámica, Biofísica, y Fisiología de Sistemas, Facultad de Ciencias, UNAM\n",
    "Last modified: 20230101\n",
    "\n",
    "$\\nu$ is the membrane potential\n",
    "\n",
    "$\\nu \\mapsto v = \\nu/\\nu_T$ to simplify notation\n",
    "\n",
    "\\begin{eqnarray}\n",
    "\\partial_t v &=& J_F - J_{U}(v,w) - J_D(v,w) - J_{UD}(v)\\\\\n",
    "\\partial_t w &=& w^\\kappa \\left[ \\alpha_w(v)(1-w) -  \\beta_w(v) w \\right]\n",
    "\\end{eqnarray}\n",
    "\n",
    "\n",
    "### Auxiliary functions\n",
    "\n",
    "Auxiliary functions\n",
    "\\begin{eqnarray}\n",
    "\\alpha_i (v; v_i, b_i, z_i) &=& r_i \\exp \\left[b_i z_i (v-v_i)  \\right], \\quad \\textrm{forward rate}\n",
    "\\\\\n",
    "\\beta_i (v; v_i, b_i, z_i) &=& r_i \\exp \\left[(b_i-1) z_i (v-v_i)  \\right], \\quad \\textrm{backward rate}\n",
    "\\\\\n",
    "\\sigma_i (v; v_i) &=& \\frac{\\alpha_i(v)}{\\alpha_i(v)+ \\beta_i(v)} = \\frac{1}{1+ \\exp \\left[z_i (v_i-v)  \\right]}, \\quad \\textrm{steady state} \n",
    "\\\\\n",
    "J_U(v,w) &=& N_U r_U m_{\\infty}(v) (1-w) \\left[ \\alpha_U(v) - \\beta_U(v) \\right], \\quad \\textrm{Up flow} \n",
    "\\\\\n",
    "J_D(v,w) &=& N_D r_D w \\left[ \\alpha_D(v) - \\beta_D(v) \\right], \\quad \\textrm{Dn flow} \n",
    "\\\\\n",
    "J_{UD}(v,w) &=& N_{UD} r_{UD} w \\left[ \\alpha_{UD}(v) - \\beta_{UD}(v) \\right], \\quad \\textrm{UpDn pump flow} \n",
    "\\end{eqnarray}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e073db29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
    "\n",
    "import numpy as np\n",
    "import numpy.linalg as la\n",
    "from numpy.random import uniform\n",
    "import matplotlib.pylab as pl\n",
    "import sympy as sy\n",
    "from sympy.plotting import plot, PlotGrid, plot3d\n",
    "from sympy.utilities.iterables import flatten\n",
    "import time\n",
    "%matplotlib notebook\n",
    "#%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6af1792f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def delDictItem(d,key):\n",
    "    r = d.copy()\n",
    "    del(r[key])\n",
    "    return r\n",
    "\n",
    "def dictMerge(dict1, dict2):\n",
    "    return(dict2.update(dict1))\n",
    "\n",
    "def biasedExp(x,a,b=1):\n",
    "    return sy.exp(b * (x-a))\n",
    "\n",
    "def sigmoid(x,a,n=1):\n",
    "    xn = x**n\n",
    "    return xn /(xn + a**n)\n",
    "\n",
    "def secant_method(f, x0, x1, tol=1e-5, n=0):\n",
    "    n += 1 # increment counter\n",
    "    y0, y1 = f(x0), f(x1) # calculate function values at endpoints\n",
    "    xn = x1 - y1 * ((x1 - x0) / (y1 - y0)) # calculate next root approximation\n",
    "    if -tol < y1 < tol: # check tolerance condition\n",
    "        return xn, n\n",
    "    # recursive call with updated interval\n",
    "    return secant_method(f, x1, xn, n=n)\n",
    "# ----------------------\n",
    "# Numerics\n",
    "# ----------------------\n",
    "\n",
    "def RK2_autonomousStep(f, U, stepSize):\n",
    "    k = stepSize * f(U) / 2\n",
    "    return U + stepSize * f(U + k)\n",
    "\n",
    "def RK2_nonautonomousStep(f, U, p, stepSize):\n",
    "    k = stepSize * f(U,p) / 2\n",
    "    return U + stepSize * f( U + k, p)\n",
    "\n",
    "\n",
    "# ----------------------\n",
    "# Bifurcation\n",
    "# ----------------------\n",
    "def fixedPoint2D_typeMarker(eigenValues,ms=5,color_nodo='orange',color_foco='blue'):\n",
    "    pf=dict()\n",
    "    l1,l2 = eigenValues\n",
    "    r1,r2 = np.real(eigenValues)\n",
    "    i1,i2 = np.imag(eigenValues)\n",
    "    pf['eigVal_1'] = l1; pf['eigVal_2']=l2; pf['r1']=r1; pf['r2']=r2; pf['i1']=i1; pf['i2']=i2;\n",
    "    pf['transp'] = 1; pf['mfc']= 'white'; pf['ms']= ms\n",
    "    if (i1*i2<0): \n",
    "        pf['type']='focus';pf['mec']=color_foco;\n",
    "        if r1>0: \n",
    "            pf['localDyn']='repeller'; pf['marker']='o'; pf['mfc'] ='white'; \n",
    "        elif r1<0:\n",
    "            pf['localDyn']='attractor'; pf['marker']='o'; pf['mfc'] =color_foco\n",
    "        else:\n",
    "            pf['localDyn']='centre'; pf['marker']='o'; pf['mfc']= 'gray'; pf['mec']= 'cyan'; pf['ms']=1.5*ms\n",
    "    else: \n",
    "        pf['type']='node'; pf['mec']=color_nodo; \n",
    "        if (r1*r2) < 0:\n",
    "            pf['localDyn'] ='saddle'; pf['marker']='x'; pf['mfc']=color_nodo; pf['ms'] = 2*ms\n",
    "        elif (r1*r2>0):\n",
    "            if r1>0: pf['localDyn']='repeller'; pf['marker']='o'; pf['mfc'] ='white' \n",
    "            if r1<0: pf['localDyn']='attractor'; pf['marker']='o'; pf['mfc'] =color_nodo \n",
    "        elif (r1*r2 == 0) | (r1*r2<1e-15): \n",
    "            pf['localDyn']='degen'; pf['marker']='o'; pf['mfc']= 'lightgray'; pf['mec']= 'cyan'; pf['ms']=1.5*ms\n",
    "    return pf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e055b7",
   "metadata": {},
   "source": [
    "Transformation of the slow variable for comparison with $v$\n",
    "    \\begin{eqnarray}\n",
    "    w_{\\infty} &=& \\left\\{ 1 + \\exp \\left[ g_w (v_w - v\\right] \\right]^{-1} \\\\\n",
    "    v_{w\\infty} &=& v_w - \\frac{1}{g_w} \\log \\left( \\frac{1}{w_{\\infty}} - 1 \\right)  \n",
    "    \\end{eqnarray}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8868bfc6",
   "metadata": {},
   "source": [
    "### Up-Dn class\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11f70ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "class system2D: \n",
    "    \n",
    "    def __init__(self, params, variables=('x','y')):\n",
    "        self.vars = variables\n",
    "        self.pars = params.copy();\n",
    "        self.pars_orig = params.copy()\n",
    "        self.dict2symbols(params)\n",
    "        self.createStateVars(variables)\n",
    "        self.pars['stepSize'] = self.pars['timeStep']\n",
    "        #self.defineFunctions()\n",
    "        #self.updateFunctions()\n",
    "        return\n",
    "    \n",
    "    def createStateVars(self,variables):\n",
    "        print(\"Setting %s and %s as state variables\"%(variables[0],variables[1]))\n",
    "        str1 = \"self.%s, self.%s = sy.symbols(%s)\"%(variables[0],variables[1],variables)\n",
    "        print(str1)\n",
    "        return exec(str1)\n",
    "    \n",
    "    def resetParameters(self):\n",
    "        print('Resetting dictionary to the original', self.pars_orig); self.pars = self.pars_orig.copy(); \n",
    "        return \n",
    "        \n",
    "    def dict2symbols(self,di):\n",
    "        for k in di.keys():\n",
    "            exec(\"self.%s = sy.Symbol('%s')\"%(k,k))\n",
    "        return\n",
    "    \n",
    "    def nuFu_expr(self, expression, variables):\n",
    "        str1 = \"self.%s_ = sy.lambdify(%s, self.%s.subs(self.pars), 'numpy')\"%(expression, variables, expression)\n",
    "        #print(str1)\n",
    "        return exec(str1)\n",
    "    \n",
    "    def trayectory_Autonomous(self,f):\n",
    "        self.timeSamples = np.arange(self.pars['timeMin'], self.pars['timeMax'], self.pars['timeStep'])\n",
    "        self.nSteps = len(self.timeSamples)            \n",
    "        U = np.zeros((self.nSteps, np.prod(np.shape(self.pars['ic']))),\"float64\")\n",
    "        U[0] = self.pars['ic']\n",
    "        for i in range(self.nSteps-1):\n",
    "            U[i+1] = RK2_autonomousStep(f, U = U[i], stepSize = self.pars['timeStep'])\n",
    "        return U.transpose()\n",
    "    \n",
    "    def subsJacobian(self):\n",
    "        self.sysJacobian =  self.evolution.subs(self.pars).jacobian(self.vars)\n",
    "        return \n",
    "        \n",
    "    def eigvaluesFromFP(self,fp):\n",
    "        ws,vs = fp\n",
    "        self.subsJacobian()\n",
    "        self.nuFu_expr(expression= 'sysJacobian', variables=self.vars)\n",
    "        return la.eigvals(self.sysJacobian_(ws,vs))\n",
    "    \n",
    "    def parameterFromFP(self, fp, parName):\n",
    "        wStar,vStar = fp; #print('Fixed point: ',fp)\n",
    "        pp = self.pars.copy(); pp.pop(parName); \n",
    "        #print(self.J_inf.subs(pp).subs({'w':wStar, 'v':vStar}))\n",
    "        fpEq = sy.Eq( self.ssEquation.subs(pp).subs( [(self.vars[0],wStar),(self.vars[1],vStar)], 0))\n",
    "        return np.float64(sy.solve(fpEq, parName)[0])\n",
    "    \n",
    "    def fpType(self,eigenValues, ms=5, nodeColor='orange',focusColor='blue'):\n",
    "        return fixedPoint2D_typeMarker(eigenValues, ms, nodeColor, focusColor)\n",
    "\n",
    "    def cod1FPs(self, fps, parName, ms=5, nodeColor='blue', focusColor='gray'):\n",
    "        \"\"\"Inputs:\n",
    "        fps ~ list of fixed points (each entry is an ordered pair, tuple, or array)\n",
    "        \"\"\"\n",
    "        tStart = time.process_time()\n",
    "        pp = dict(p); \n",
    "        pp.pop(parName)\n",
    "        cod1 = list()\n",
    "        nfp = len(fps)\n",
    "        fps = list(); parValues = list(); evs = list(); fpTypes=list()\n",
    "        for n in range(nfp): \n",
    "            parValues.append(self.parameterFromFP(fps[n],parName))\n",
    "            self.pars[parName] = parValues[n]\n",
    "            self.nuFu_Jacobian_subsPars()\n",
    "            evs.append(self.eigvaluesFromFP(fps[n]))\n",
    "            #print('(parameter, ev)=(%g,%g)'%(pv,ev))\n",
    "            fpTypes.append(self.fpType(evs[n], ms, nodeColor, focusColor))\n",
    "        cod1={ 'fps':fps, 'parName': parValues, 'evs':evs, 'fpTypes':fpTypes, 'nFPs':nfp}\n",
    "        print('Took %d seconds to calculate the fixed point list for %s'%(time.process_time()-tStart, parName))\n",
    "        return cod1\n",
    "    \n",
    "    def bifurcationDiagram_Cod1(self, ax, fps, coordinate, parName, nodeColor='gray', focusColor='gray', yLabel=''):\n",
    "        cod1 = self.cod1FPs(fps, parName = parName, ms=3, color_nodo=nodeColor,color_foco=focusColor)\n",
    "        for n in range(cod1['nFPs']):\n",
    "              fpt= cod1['fpTypes'][n]\n",
    "              ax.plot(cod1['parName'][n], cod1['fps'][n][coordinate],fpt['marker'], \\\n",
    "                    markerfacecolor=fpt['mfc'], markeredgecolor=fpt['mec'])\n",
    "        ax.set_xlabel(parName); \n",
    "        ax.set_ylabel(yLabel)\n",
    "        self.pars = self.pars_orig\n",
    "        return cod1  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "50fe4289",
   "metadata": {},
   "outputs": [],
   "source": [
    "class UD(system2D):\n",
    "    def __init__(self, params,variables):\n",
    "        super().__init__(params,variables)\n",
    "        self.defineFunctions()\n",
    "        #self.updateFunctions()\n",
    "        return\n",
    "    \n",
    "    def defineFunctions(self):\n",
    "        print(\"Defining all functions. Notice v is normalized by v_T\")\n",
    "        self.e_m = biasedExp( x=self.v, a=self.v_m, b=self.g_m)\n",
    "        self.e_w = biasedExp( x=self.v, a=self.v_w, b=self.g_w)\n",
    "        self.e_U = biasedExp( x=self.v, a=self.v_U, b=1)\n",
    "        self.e_D = biasedExp( x=self.v, a=self.v_D, b=1)\n",
    "        self.e_UD = biasedExp( x=self.v, a=self.v_UD, b=1)\n",
    "        self.alpha_w = self.r_w * (self.e_w ** self.b_w)\n",
    "        self.beta_w = self.r_w * (self.e_w ** (self.b_w-1))\n",
    "        self.alpha_U = self.r_U * (self.e_U ** self.b_U)\n",
    "        self.beta_U = self.r_U * (self.e_U ** (self.b_U-1))\n",
    "        self.alpha_D = self.r_D * (self.e_D ** self.b_D)\n",
    "        self.beta_D = self.r_D * (self.e_D ** (self.b_D-1))\n",
    "        self.alpha_UD = self.r_UD * (self.e_UD ** self.b_UD)\n",
    "        self.beta_UD = self.r_UD * (self.e_UD ** (self.b_UD-1))\n",
    "        self.tau_w = 1/ (self.alpha_w + self.beta_w)\n",
    "        self.m_inf = self.e_m / ( 1 + self.e_m)\n",
    "        self.w_inf =  self.e_w / (1 + self.e_w)\n",
    "        self.v_w_inf = (self.v_w - sy.log(1/self.w - 1))/self.g_w\n",
    "        self.J_U = self.a_U * self.m_inf * (1-self.w) * (self.alpha_U - self.beta_U)\n",
    "        self.J_D = self.a_D * self.w * (self.alpha_D - self.beta_D)\n",
    "        self.J_UD = self.a_UD * (self.alpha_UD - self.beta_UD)\n",
    "        self.dv = self.a_F - self.J_U - self.J_D - self.J_UD\n",
    "        self.dw = self.w**self.kappa_w * (self.alpha_w - (self.alpha_w + self.beta_w)*self.w )\n",
    "        self.w_vNull = (self.a_F - self.J_UD - self.a_U * self.m_inf * (self.alpha_U - self.beta_U)) \\\n",
    "        / (self.a_D * (self.alpha_D - self.beta_D) - self.a_U * self.m_inf * (self.alpha_U - self.beta_U))\n",
    "        self.evolution = sy.Matrix([self.dw,self.dv])\n",
    "        self.ssEquation = self.dv.subs({'w': self.w_inf})\n",
    "        return\n",
    "    \n",
    "    def updateFunctions(self):\n",
    "        self.nuFu_expr(expression='dw',variables=['w','v'])\n",
    "        self.nuFu_expr(expression='dv',variables=['w','v'])\n",
    "        self.nuFu_expr(expression='m_inf',variables=['v'])\n",
    "        self.nuFu_expr(expression='w_inf',variables=['v'])\n",
    "        self.nuFu_expr(expression='w_vNull',variables=['v'])\n",
    "        self.nuFu_expr(expression='J_UD',variables=['v'])\n",
    "        self.nuFu_expr(expression='J_U',variables=['w','v'])\n",
    "        self.nuFu_expr(expression='J_D',variables=['w','v'])\n",
    "        self.nuFu_expr(expression='ssEquation',variables=['v'])\n",
    "        return \n",
    "    \n",
    "    def phasePlane(self, ax, W = np.linspace(0,1,50), V = np.linspace(-60,30,200)/26.64, wNullLabel='', vNullLabel='',plotNullClines=1):\n",
    "        self.nuFu_expr(expression='dw',variables=['w','v'])\n",
    "        self.nuFu_expr(expression='dv',variables=['w','v'])\n",
    "        self.nuFu_expr(expression='w_inf',variables=['v'])\n",
    "        self.nuFu_expr(expression='w_vNull',variables=['v'])\n",
    "        Wgrid,Vgrid = np.meshgrid(W, V)\n",
    "        dV = self.dv_(Wgrid,Vgrid); dW = self.dw_(Wgrid,Vgrid); \n",
    "        ax.streamplot(W,V*self.pars['v_T'],dW,dV*self.pars['v_T'], color = 'gray',linewidth=1);\n",
    "        if plotNullClines>0:\n",
    "            w_wNull = self.w_inf_(V); w_vNull = self.w_vNull_(V)\n",
    "            ax.plot(w_vNull, V*self.pars['v_T'], lw=2,   color = 'green', alpha=0.75);\n",
    "            ax.plot(w_wNull, V*self.pars['v_T'], lw=2, color = 'orange', alpha=0.75)\n",
    "        return ax\n",
    "    \n",
    "    def iClampSquareStims(self, iLevels, timeStimStart, timeStimStop):\n",
    "        nLevels = len(iLevels)\n",
    "        iAmps = list()\n",
    "        self.timeSamples = np.arange(self.pars['timeMin'],self.pars['timeMax'],self.pars['timeStep'])\n",
    "        self.nSteps = len(self.timeSamples)\n",
    "        a = np.int64(np.ceil( (timeStimStart-self.pars['timeMin'])/self.pars['timeStep']))\n",
    "        b = np.int64(np.floor( (timeStimStop-self.pars['timeMin'])/self.pars['timeStep']))\n",
    "        for n in range(nLevels):\n",
    "            iAmps.append(np.zeros(self.nSteps))\n",
    "            iAmps[n][a:b] = iLevels[n]\n",
    "        return iAmps\n",
    "    \n",
    "    def dvw_(self,U):\n",
    "        return np.array([self.dw_(*U),self.dv_(*U)])\n",
    "\n",
    "    def trayectory_nonAutonomous(self, parNames=[], parVals=[]):\n",
    "        '''\n",
    "        parNames and parVals must be lists of the same length. \n",
    "        Each element in parVals must have the same length as the timeSample vector\n",
    "        '''     \n",
    "        self.timeSamples = np.arange(self.pars['timeMin'],self.pars['timeMax'],self.pars['timeStep'])\n",
    "        self.nSteps = len(self.timeSamples)            \n",
    "        nPars = len(parNames)\n",
    "\n",
    "        diNP = self.pars.copy()\n",
    "        for n in range(nPars):\n",
    "            diNP = delDictItem(diNP,parNames[n])\n",
    "    \n",
    "        self.dwPars_ = sy.lambdify(self.vars+parNames, self.dw.subs(diNP), 'numpy')\n",
    "        self.dvPars_ = sy.lambdify(self.vars+parNames, self.dv.subs(diNP), 'numpy')\n",
    "        fPars = lambda Upars : np.array([self.dwPars_(*Upars),self.dvPars_(*Upars)])\n",
    "        U = np.zeros((self.nSteps, np.prod(np.shape(self.pars['ic']))),\"float64\")\n",
    "        U[0]=self.pars['ic'] \n",
    "        for nn in range(self.nSteps-1):\n",
    "            pars = np.array([parVals[n][nn] for n in range(nPars)])\n",
    "            UPars = np.hstack( [U[nn], pars])\n",
    "            k = self.pars['timeStep'] * fPars(UPars) / 2\n",
    "            kPars = np.hstack( [k, pars])\n",
    "            U[nn+1] = U[nn] + self.pars['timeStep'] * fPars( UPars + kPars)\n",
    "        self.updateFunctions()\n",
    "        return U.transpose()            \n",
    "            \n",
    "    def getDynamics(self, parNames=[], parVals=[]):\n",
    "        upDn.updateFunctions()\n",
    "        if len(parNames)==0: \n",
    "            self.wOrbit, self.vOrbit = self.trayectory_Autonomous(self.dvw_)\n",
    "        else: \n",
    "            self.wOrbit, self.vOrbit = self.trayectory_nonAutonomous(parNames, parVals)\n",
    "        self.uOrbit = self.vOrbit * self.pars['v_T']\n",
    "        self.dudt = np.zeros(len(self.uOrbit))\n",
    "        self.dudt[1:] = (self.uOrbit[1:]-self.uOrbit[:-1])/upDn.pars['timeStep']\n",
    "        print('Max dv/dt = %g V/s'% self.dudt.max())\n",
    "        self.v_w_inf = self.pars['v_w'] - np.log(1/self.wOrbit -1)/self.pars['g_w']\n",
    "        self.I_U = self.pars['vTCm'] * self.J_U_(self.wOrbit,self.vOrbit)  \n",
    "        self.I_D = self.pars['vTCm'] * self.J_D_(self.wOrbit,self.vOrbit) \n",
    "        self.I_UD = self.pars['vTCm'] * self.J_UD_(self.vOrbit)\n",
    "        return self.wOrbit, self.vOrbit\n",
    "    \n",
    "    def plotDynamicProfile(self,ax, vMin = -80, vMax = 40, iMin=-20, iMax=70, wMin=0, wMax=1): \n",
    "        #\n",
    "        print(type(self.v),type(self.w))\n",
    "        ax[0].plot(self.timeSamples, self.uOrbit, color='black', label=r'$(t,v)$')\n",
    "        ax[0].plot(self.timeSamples, self.pars['v_T']*self.v_w_inf, '--', color='black', label=r'$(t,w_{\\inf}^{-1}(w))$')\n",
    "        ax[0].set_xlabel(r'time (ms)'); ax[0].set_ylabel(r'mV')\n",
    "        ax[0].set_ylim(vMin,vMax); ax[0].legend()\n",
    "        ax[1].plot(self.dudt, self.uOrbit, color='black', label=r'$(\\partial_t v, v)$')\n",
    "        ax[1].plot(self.dudt, self.pars['v_T']*self.v_w_inf, '--', color='black', label=r'$(\\partial_t v, w_{\\inf}^{-1}(w))$')\n",
    "        ax[2].plot(self.wOrbit, self.uOrbit, color='black', label=r'$(w, v)$')\n",
    "        #ax[2].plot(self.wOrbit, self.pars['v_T']*self.v_w_inf, '--', color='black', label=r'$(w, w_{\\inf}^{-1}(w))$')\n",
    "        self.phasePlane(ax[2], V=np.linspace(vMin,vMax,300)/v_T, W=np.linspace(wMin,wMax,200))\n",
    "        for nn in range(3): ax[nn].set_ylim(vMin,vMax);\n",
    "        ax[3].plot(self.timeSamples, self.I_U, color='green', label=r'$I_{U}$')\n",
    "        ax[3].plot(self.timeSamples, self.I_D, color='orange', label=r'$I_{D}$')\n",
    "        ax[3].set_xlabel(r'time (ms)'); ax[3].set_ylabel(r'pA');\n",
    "        ax[6].plot(self.timeSamples, self.I_UD, color='blue', label=r'$I_{UD}$')\n",
    "        ax[6].set_xlabel(r'time (ms)'); #ax[6].set_ylabel(r'pA'); \n",
    "        ax[6].set_ylim(iMin,iMax); #ax[6].set_ylabel(r'mV'); \n",
    "        ax[7].plot(self.dudt, self.I_UD, color='blue', label=r'$I_{UD}$')\n",
    "        ax[4].plot(self.dudt, self.I_U, color='green', label=r'$I_{U}$')\n",
    "        ax[4].plot(self.dudt, self.I_D, color='orange', label=r'$I_{D}$')\n",
    "        ax[8].plot(self.wOrbit, self.I_UD, color='blue', label=r'$(w,I_{UD})$')\n",
    "        ax[5].plot(self.wOrbit, self.I_U, color='green', label=r'$(w,I_{U})$')\n",
    "        ax[5].plot(self.wOrbit, self.I_D, color='orange', label=r'$(w,I_{UD})$')\n",
    "        ax[7].set_xlabel(r'$\\partial_t v(t)$ (V/s)'); \n",
    "        ax[7].set_ylabel(r'pA'); ax[7].set_ylim(iMin,iMax);\n",
    "        for nn in range(6,9): ax[nn].set_ylim(iMin,iMax);\n",
    "        #pl.ion(); pl.draw()\n",
    "        for nn in range(9):  ax[nn].legend()\n",
    "        return ax\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ad4ce1",
   "metadata": {},
   "source": [
    "### UpDn class instance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20193e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v_T=26.6407, C_m=20, v_T C_m= 532.813\n",
      "Setting w and v as state variables\n",
      "self.w, self.v = sy.symbols(['w', 'v'])\n",
      "Defining all functions. Notice v is normalized by v_T\n"
     ]
    }
   ],
   "source": [
    "eCharge=1.60217733e-19 # Coulombs; \n",
    "kBoltzmann=1.38065812e-20 #mJ/K\n",
    "zeroT=273.15 #deg Kelvin\n",
    "TCelcius = 36\n",
    "v_T = kBoltzmann * (zeroT + TCelcius)/ eCharge\n",
    "C_m = 20.0; vTCm= v_T * C_m\n",
    "print(r'v_T=%g, C_m=%g, v_T C_m= %g'%(v_T,C_m, vTCm))\n",
    "\n",
    "voltages = {'v_w':-10.0/v_T, 'v_m': -20.0/v_T,'v_U':60.0/v_T, 'v_D':-90.0/v_T, 'v_UD':-80.0/v_T}\n",
    "biases = {'b_w':0.35, 'b_U':0.5, 'b_D':0.3, 'b_UD':0.1, 'g_m':4, 'g_w':2.15}\n",
    "rates = {'r_w':0.25, 'a_F': 0*100 / vTCm, 'r_U':1, 'r_D':1, 'r_UD':1e-4, 'a_U': 4, 'a_D': 10, 'a_UD':1000}\n",
    "numerics = {'timeMin': -1.0, 'timeMax':300.0, 'timeStep':1/40.0, 'ic': np.array([0.0001, -60.0/v_T]),\\\n",
    "            'vMin':-100/v_T,'vMax':40/v_T, 'wMin':0,'wMax':1, 'vStep':0.1,'wStep':0.01}\n",
    "p = {'v_T': v_T, 'C_m':C_m, 'vTCm': v_T * C_m, 'kappa_w':0.35}\n",
    "p= {**p, **voltages, **biases, **rates, **numerics}\n",
    "upDn = UD(params= p, variables=['w','v'])\n",
    "upDn.vRange = np.arange(upDn.pars['vMin'],upDn.pars['vMax'],upDn.pars['vStep'])\n",
    "upDn.wRange = np.arange(upDn.pars['wMin'],upDn.pars['wMax'],upDn.pars['wStep'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c6110c",
   "metadata": {},
   "source": [
    "## Stream vectors and the effects of activation parameters on the nullclines\n",
    "\n",
    "The nullclines of the system are given by the curves\n",
    "\\begin{eqnarray}\n",
    "    w_{vNull} &=& \\frac{J_F - a_{UD}  \\left[ \\alpha_{UD}(v) - \\beta_{UD}(v) \\right] - a_U m_{\\infty}(v) \\left[ \\alpha_U(v) - \\beta_U(v) \\right] }\n",
    "    { a_{D}  \\left[ \\alpha_{D}(v) - \\beta_{D}(v) \\right] - a_U m_{\\infty}(v) \\left[ \\alpha_U(v) - \\beta_U(v) \\right] }\n",
    "    \\\\\n",
    "    w_{wNull} &=& \\frac{ \\alpha_w(v)}{\\alpha_w(v)+\\beta_w(v)} = \\frac{1}{1 + \\exp \\left[ (v_w - w) g_w\\right]}\n",
    "\\end{eqnarray}\n",
    "\n",
    "The system may have 1, 2 (seldom), or 3 fixed points. The system is more likely to have 3 fixed points if the slope of the $w$-nullcline is smaller than the slope of the $v$-nullcline, and both curves are very close to each other.\n",
    "\n",
    "One important combination of parameters is the pair $(v_w, g_w)$. Larger values of $v_w$ shift the $w$-nullcline to the right, while larger values of $g_w$ bend the $w$-nullcline so that the curve is at higher points for smaller values of $v$. This means that the intersection between the $w$- and $v$-nullclines can shift toward larger $v$ values for smaller values of $g_w$, or for larger values of $v_w$. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baac0bad",
   "metadata": {},
   "source": [
    "## Dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56a9e963",
   "metadata": {},
   "outputs": [],
   "source": [
    "upDn.pars['v_m']= -20.0/v_T; upDn.pars['g_m']= 4;  \n",
    "upDn.pars['v_w']= -10.0/v_T; upDn.pars['g_w']= 2.4; upDn.pars['b_w'] = 0.65; upDn.pars['kappa_w']=0.35; upDn.pars['r_w'] = 0.25\n",
    "upDn.pars['b_D']= 0.3; #upDn.pars['b_UD']= 0.8\n",
    "upDn.pars['a_F'] = 0 / vTCm; # 70 pA en rheobase \n",
    "upDn.pars['a_U'] = 4.; upDn.pars['a_D'] = 6; upDn.pars['a_UD'] = 800;\n",
    "upDn.pars['timeMax'] = 30.0; upDn.pars['timeStep']=1/50.0; upDn.pars['ic'] = np.array([0.001, -50.0/v_T])\n",
    "#\n",
    "upDn.getDynamics()\n",
    "f = pl.figure(figsize=(11,9)); #pl.ioff(); \n",
    "ax = list(); rows = 1; cols= 3;\n",
    "for n in range(rows*cols):\n",
    "    ax.append(f.add_subplot(rows,cols,n+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e7f03a30",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max dv/dt = 292.002 V/s\n"
     ]
    },
    {
     "data": {
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       "};\n",
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       "\n",
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       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.canvas_div.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1100\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sympy.core.symbol.Symbol'> <class 'sympy.core.symbol.Symbol'>\n",
      "a_F = 0\n"
     ]
    }
   ],
   "source": [
    "upDn.pars['v_m']= -20.0/v_T; upDn.pars['g_m']= 4;  \n",
    "upDn.pars['v_w']= -10.0/v_T; upDn.pars['g_w']= 2.4; upDn.pars['b_w'] = 0.65; upDn.pars['kappa_w']=0.35; upDn.pars['r_w'] = 0.25\n",
    "upDn.pars['b_D']= 0.3; #upDn.pars['b_UD']= 0.8\n",
    "upDn.pars['a_F'] = 0 / vTCm; # 70 pA en rheobase \n",
    "upDn.pars['a_U'] = 4.; upDn.pars['a_D'] = 6; upDn.pars['a_UD'] = 800;\n",
    "upDn.pars['timeMax'] = 30.0; upDn.pars['timeStep']=1/50.0; upDn.pars['ic'] = np.array([0.001, -50.0/v_T])\n",
    "#\n",
    "upDn.getDynamics()\n",
    "f = pl.figure(figsize=(11,9)); #pl.ioff(); \n",
    "ax = list(); rows = 3; cols= 3;\n",
    "for n in range(rows*cols):\n",
    "    ax.append(f.add_subplot(rows,cols,n+1))\n",
    "ax=upDn.plotDynamicProfile(ax)\n",
    "for nn in [2,5,8]:\n",
    "    ax[nn].set_xlim(0,0.5)\n",
    "for nn in [0,1,2]:\n",
    "    ax[nn].set_ylim(-90,40)\n",
    "    ax[nn+6].set_ylim(-20,100)\n",
    "#\n",
    "print('a_F = %g'%upDn.pars['a_F'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e91db46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "54b145a0",
   "metadata": {},
   "source": [
    "### I-Clamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "83a615e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute('tabindex', '0');\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
       "    );\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'left: 0;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: absolute;' +\n",
       "            'top: 0;' +\n",
       "            'z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            /* This rescales the canvas back to display pixels, so that it\n",
       "             * appears correct on HiDPI screens. */\n",
       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
       "        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.canvas_div.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='39fba433-aa25-42ff-b7ae-911316ab0bb6'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resetting dictionary to the original {'v_T': 26.640650183085537, 'C_m': 20.0, 'vTCm': 532.8130036617107, 'kappa_w': 0.35, 'v_w': -0.37536621408546245, 'v_m': -0.7507324281709249, 'v_U': 2.252197284512775, 'v_D': -3.378295926769162, 'v_UD': -3.0029297126836996, 'b_w': 0.35, 'b_U': 0.5, 'b_D': 0.3, 'b_UD': 0.1, 'g_m': 4, 'g_w': 2.15, 'r_w': 0.25, 'a_F': 0.0, 'r_U': 1, 'r_D': 1, 'r_UD': 0.0001, 'a_U': 4, 'a_D': 10, 'a_UD': 1000, 'timeMin': -1.0, 'timeMax': 300.0, 'timeStep': 0.025, 'ic': array([ 1.00000000e-04, -2.25219728e+00]), 'vMin': -3.7536621408546247, 'vMax': 1.5014648563418498, 'wMin': 0, 'wMax': 1, 'vStep': 0.1, 'wStep': 0.01}\n",
      "<built-in method sort of numpy.ndarray object at 0x117644c30>\n",
      "Fixed point at (0.00320716,-81.1139)\n",
      "attractor node\n",
      "Fixed point at (0.266107,-22.5702)\n",
      "repeller node\n",
      "Fixed point at (0.266107,-22.5702)\n",
      "repeller node\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute('tabindex', '0');\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
       "    );\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'left: 0;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: absolute;' +\n",
       "            'top: 0;' +\n",
       "            'z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            /* This rescales the canvas back to display pixels, so that it\n",
       "             * appears correct on HiDPI screens. */\n",
       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
       "        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.canvas_div.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='58e7b027-5ee4-4dc0-b785-883b99216944'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(-0.01, 0.75)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upDn.pars['timeMax'] = 300.0; upDn.pars['ic'] = np.array([0.001,-80.0/v_T])\n",
    "\n",
    "iAmps = upDn.iClampSquareStims(iLevels=np.arange(-10,150,25)/upDn.pars['vTCm'], timeStimStart=100, timeStimStop=200)\n",
    "nCommands = len(iAmps)\n",
    "\n",
    "wOrbits = list(); vOrbits = list()  \n",
    "for n in range(nCommands):\n",
    "    wOrbit, vOrbit = upDn.trayectory_nonAutonomous(parNames=['a_F'], parVals=[iAmps[n]])\n",
    "    wOrbits.append(wOrbit);     vOrbits.append(vOrbit)\n",
    "    \n",
    "f = pl.figure(figsize=(9,4))\n",
    "ax = f.add_subplot(212); bx = f.add_subplot(211)\n",
    "hShift = 10; vShift = 20; tMin = -50; tMax = upDn.pars['timeMax']\n",
    "for n in range(nCommands):\n",
    "    bx.plot(upDn.timeSamples - n*hShift, vOrbits[n]*upDn.pars['v_T'] + n*vShift)\n",
    "    ax.plot(upDn.timeSamples, iAmps[n]*upDn.pars['vTCm'], '.', ms = 1)\n",
    "    ax.set_xlim(tMin,tMax); bx.set_xlim(tMin,tMax)\n",
    "\n",
    "upDn.resetParameters()\n",
    "#upDn.pars['v_w'] =10/upDn.pars['v_T']\n",
    "upDn.nuFu_expr(expression='ssEquation',variables=['v'])\n",
    "upDn.nuFu_expr(expression='w_inf',variables=['v'])\n",
    "J_inf = upDn.ssEquation_(upDn.vRange)\n",
    "print(J_inf.sort)\n",
    "x0s = [-3.1,-1.1,-0.46 ]; x1s = [-2.,-0.7,-0.25]\n",
    "nfps = len(x0s)\n",
    "fps = np.zeros((nfps,2),'float64')\n",
    "evs = list(); fpTypes =list()\n",
    "for nn in range(nfps):\n",
    "    v_star, nIter = secant_method(f = upDn.ssEquation_, x0=x0s[nn], x1=x1s[nn], tol=1e-4, n=0)\n",
    "    fps[nn] = np.array([upDn.w_inf_(v_star),v_star])\n",
    "    print(\"Fixed point at (%g,%g)\"%(fps[nn][0],fps[nn][1]*v_T))\n",
    "    evs.append(upDn.eigvaluesFromFP(fp = fps[nn]))\n",
    "    fpTypes.append(upDn.fpType(evs[nn]))\n",
    "    print(fpTypes[nn]['localDyn'] +  ' ' + fpTypes[nn]['type'])\n",
    "#\n",
    "f = pl.figure(figsize=(7,7)); ax = f.add_subplot(111)\n",
    "ax=upDn.phasePlane(ax, V=np.linspace(-90,20,300)/v_T, W=np.linspace(0,1,200))\n",
    "for nn in range(nfps):\n",
    "    ax.plot(fps[nn][0],fps[nn][1]* upDn.pars['v_T'], marker= fpTypes[nn]['marker'], mfc= fpTypes[nn]['mfc'], mec=fpTypes[nn]['mec'])\n",
    "ax.set_xlim(-0.01,0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06852ed6",
   "metadata": {},
   "source": [
    "### Variation on $(v_w,g_w)$\n",
    "\n",
    "Larger values of $v_w$ push the fixed point with the highest $w$-value further toward the third branch of the $v$-nullcline. This increases the chance of depolarization block. A similar effect can be caused by smaller values of $g_w$. In contrast, if $v_w$ is pushed toward smaller values, the fixed point with the largest $w$ value is pushed toward the middle (second) or first branches of the $v$-nullcline, shifting the vector field and splitting the phase plane into four regions between the nullclines. If $g_w$ is larger, the steepness of the $w$-nullcline increases, \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4f1c6c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute('tabindex', '0');\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
       "    );\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'left: 0;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: absolute;' +\n",
       "            'top: 0;' +\n",
       "            'z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            /* This rescales the canvas back to display pixels, so that it\n",
       "             * appears correct on HiDPI screens. */\n",
       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
       "        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.canvas_div.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "text": [
      "Resetting dictionary to the original {'v_T': 26.640650183085537, 'C_m': 20.0, 'vTCm': 532.8130036617107, 'kappa_w': 0.35, 'v_w': -0.37536621408546245, 'v_m': -0.7507324281709249, 'v_U': 2.252197284512775, 'v_D': -3.378295926769162, 'v_UD': -3.0029297126836996, 'b_w': 0.35, 'b_U': 0.5, 'b_D': 0.3, 'b_UD': 0.1, 'g_m': 4, 'g_w': 2.15, 'r_w': 0.25, 'a_F': 0.0, 'r_U': 1, 'r_D': 1, 'r_UD': 0.0001, 'a_U': 4, 'a_D': 10, 'a_UD': 1000, 'timeMin': -1.0, 'timeMax': 300.0, 'timeStep': 0.025, 'ic': array([ 1.00000000e-04, -2.25219728e+00]), 'vMin': -3.7536621408546247, 'vMax': 1.5014648563418498, 'wMin': 0, 'wMax': 1, 'vStep': 0.1, 'wStep': 0.01}\n"
     ]
    }
   ],
   "source": [
    "# Variation on (v_w,g_w)\n",
    "upDn.nuFu_expr(expression='dw',variables=['w','v'])\n",
    "upDn.nuFu_expr(expression='dv',variables=['w','v'])\n",
    "upDn.nuFu_expr(expression='w_inf',variables=['v'])\n",
    "upDn.nuFu_expr(expression='w_vNull',variables=['v'])\n",
    "W = np.arange(0,1,0.05); V = np.arange(-60,40,1)/v_T\n",
    "Wgrid,Vgrid = np.meshgrid(W, V)\n",
    "dV = upDn.dv_(Wgrid,Vgrid); \n",
    "w_wNull = upDn.w_inf_(V); w_vNull = upDn.w_vNull_(V)\n",
    "#\n",
    "fig = pl.figure(figsize=(13,7)); #pl.ioff()\n",
    "ax = list(); bx = list()\n",
    "gws = [2, 2.5, 3]; vws = np.array([-10,-5,0])/v_T\n",
    "ngws = len(gws); nvws = len(vws)\n",
    "\n",
    "for n in range(ngws*nvws):\n",
    "    ax.append(fig.add_subplot(ngws, nvws, n+1))\n",
    "    \n",
    "for m in range(ngws):\n",
    "    upDn.pars['g_w']= gws[m]; \n",
    "    for n in range(nvws):\n",
    "        upDn.pars['v_w']= vws[n];\n",
    "        ind = m*(nvws) + n\n",
    "        wNullLabel = r'$(v_w,g_w)=(%g,%g)$'%(upDn.pars['v_w']*v_T, upDn.pars['g_w'])\n",
    "        upDn.phasePlane(ax[ind])\n",
    "        ax[ind].set_xlim(0,0.5)\n",
    "        ax[ind].text(0.2,-2,wNullLabel)\n",
    "    \n",
    "upDn.resetParameters()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a55609a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Variation on $(v_m,g_m)$\n",
    "\n",
    "# Variation on (v_w,g_w)\n",
    "upDn.resetParameters()\n",
    "V = np.arange(-60,40,1)/upDn.pars['v_T']\n",
    "#\n",
    "f = pl.figure(figsize=(13,13)); ax = list(); \n",
    "gms = [3,4,5,6,7]; vms = np.array([-25,-20, -15])/upDn.pars['v_T']\n",
    "ngms = len(gms); nvms = len(vms)\n",
    "\n",
    "for n in range(ngms*nvms):\n",
    "    ax.append(f.add_subplot(ngms, nvms, n+1))\n",
    "    \n",
    "for m in range(ngms):\n",
    "    upDn.pars['g_m']= gms[m]; \n",
    "    for n in range(nvms):\n",
    "        upDn.pars['v_m']= vms[n];\n",
    "        upDn.nuFu_expr( expression='m_inf', variables=['v'])\n",
    "        mInf = upDn.m_inf_(V)\n",
    "        print(mInf)\n",
    "        ind = m *(nvms) + n\n",
    "        mInfLabel = r'$(v_m,g_m)$=(%g,%g)'%( upDn.pars['v_m']*upDn.pars['v_T'], upDn.pars['g_m'])\n",
    "        upDn.phasePlane(ax[ind],plotNullClines=1)\n",
    "        ax[ind].plot(mInf, V, lw=1,   color = 'blue', alpha=0.5,  label=mInfLabel);\n",
    "        #ax[ind].set_xlim(0,0.5)\n",
    "        ax[ind].legend()\n",
    "    \n",
    "upDn.resetParameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d750c48",
   "metadata": {},
   "source": [
    "### Channel gating: Activation, deactivation, and time constant functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a690394a",
   "metadata": {},
   "source": [
    "### Characteristic rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bab17a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "upDn.subsJacobian()\n",
    "#Ws = uniform(0,1,256); Vs = uniform(-60,30,256)/v_T\n",
    "vMin = -60; vMax=30; wMin=0; wMax=1\n",
    "vRange = np.linspace(vMin,vMax,100)/v_T; wRange=np.linspace(wMin,wMax,100)\n",
    "Ws,Vs = np.meshgrid(wRange,vRange)\n",
    "J = upDn.sysJacobian_(Ws,Vs)\n",
    "rows = 2; cols=2\n",
    "\n",
    "fig = pl.figure(figsize=(7,7)); ax = list(); \n",
    "wTicks = np.linspace(wMin,wMax,10)\n",
    "vTicks = np.linspace(vMin,vMax,10)/v_T\n",
    "for i in [0,1]:\n",
    "    for j in [0,1]:\n",
    "        ij = i* rows + j \n",
    "        ax.append(fig.add_subplot(rows,cols, ij +1))\n",
    "        z = J[i,j]\n",
    "        ax[ij].imshow(z, origin='lower')\n",
    "        ax[ij].set_xticks(ticks= wTicks, labels = wTicks)\n",
    "        ax[ij].set_yticks(ticks= vTicks, labels = vTicks*v_T)\n",
    "pl.show()        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c33ea3",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Steady state currents and K-current rectification\n",
    "\n",
    "\n",
    "### Steady state flows  ($I_{\\infty}$)\n",
    "\n",
    "### Bifurcation structures\n",
    "#### Characteristic time constants\n",
    "#### Bifurcations in codimension 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d704057",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fp = np.array([0.001,2])\n",
    "upDn.eigvaluesFromFP(fp)\n",
    "\n",
    "f=pl.figure(figsize=(9,4)); pl.ioff()\n",
    "ax = f.add_subplot(111)\n",
    "fpList= upDn.bifurcationDiagram_Cod1(ax, parName='a_F', nodeColor='gray', focusColor='blue')\n",
    "pl.ion(); pl.draw();pl.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19174fe8",
   "metadata": {},
   "source": [
    "#### Multiple bifurcation diagrams\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81d20938",
   "metadata": {},
   "outputs": [],
   "source": [
    "parNames = ['a_F','a_UD','a_U','a_D']\n",
    "xMin=[-5,0,0,0]\n",
    "xMax=[20,40,50,50]\n",
    "nPars = len(parNames);\n",
    "nCols = 2\n",
    "nRows = np.int32(np.ceil(nPars/nCols))\n",
    "f=pl.figure(figsize=(9,7)); ax = list()\n",
    "fpLists = list()\n",
    "for n in range(nPars):\n",
    "    ax.append(f.add_subplot(nRows,nCols,n+1));pl.ioff(); \n",
    "    fpLists.append(upDn.bifurcationDiagram_Cod1(ax[n], parName=parNames[n], nodeColor='gray', focusColor='blue'))\n",
    "    #ax[n].set_xlim(xMin[n],fpLists[n][parName].max())\n",
    "    pl.ion(); pl.draw();\n",
    "pl.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2d9dafb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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