plots updatead for asymmetry.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cacc8f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.constants import e, k\n",
    "from scipy.interpolate import interp1d\n",
    "import itertools\n",
    "k=1/1160"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b618d5c",
   "metadata": {},
   "source": [
    "## $$ \\gamma  \\to \\Gamma \\frac{ e^{\\frac{\\epsilon _o}{\\text{kT}}}+\\text{N}}{e^{\\frac{\\epsilon _o}{\\text{kT}}}-1} \\ \\ \\ \\ \\ \\ \\ \\ \\          \\eta    \\to \\frac{\\gamma N}{4 \\text{kT} \\cosh ^2\\left(\\frac{\\epsilon _o}{2 \\text{kT}}\\right)} \\ \\ \\ \\ \\ \\ \\                          \\lambda \\to \\frac{e^{\\frac{\\epsilon _o}{\\text{kT}}}+1}{e^{\\frac{\\epsilon _o}{\\text{kT}}}+\\text{N}} $$   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c48af0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gamma(g0, v0, T, N):\n",
    "    numerator = np.exp(v0/(k*T)) + N\n",
    "    denominator = np.exp(v0/(k*T)) - 1\n",
    "    return g0*numerator/denominator\n",
    "\n",
    "def etta(g0, v0, T, N):\n",
    "    numerator = N*gamma(g0, v0, T, N)\n",
    "    denominator = 4*k*T*((np.cosh(v0/(2*k*T)))**2)\n",
    "    return numerator/denominator\n",
    "\n",
    "def llambda(v0, T, N):\n",
    "    numerator = np.exp(v0/(k*T)) + 1\n",
    "    denominator = np.exp(v0/(k*T)) + N\n",
    "    return numerator/denominator\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c992be76",
   "metadata": {},
   "source": [
    "## $$ C = e^2 \\left(p_o \\left(\\text{dn}_g-\\text{dn}_e\\right)+\\text{dn}_e\\right)+\\frac{ e^2 \\eta  \\lambda ^2 \\text{ds}_{\\text{mg}} \\left(n_g-n_e\\right)}{\\gamma ^2+w^2} \\gamma $$\n",
    "\n",
    "$$ p_o = \\frac{e^{\\frac{\\epsilon _o}{\\text{kT}}}}{e^{\\frac{\\epsilon _o}{\\text{kT}}}+\\text{nN}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef1295c",
   "metadata": {},
   "source": [
    "## $$\\frac{1}{R} = \\frac{e^2 \\eta  \\lambda ^2 \\text{ds}_{\\text{mg}} \\left(n_g-n_e\\right)}{\\gamma ^2+w^2} w^2$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36ef44bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def p0(smg, T, N):\n",
    "    numerator = np.exp(smg/(k*T))\n",
    "    denominator = np.exp(smg/(k*T)) + N\n",
    "    return numerator/denominator\n",
    "\n",
    "def q_capacitance(smg, T, N, dne, dng):\n",
    "    return (e**2) * (p0(smg, T, N)*(dng - dne) + dne)\n",
    "\n",
    "def tunnel_capacitance(g0, smg, dsmg, T, N, ne, ng, w):\n",
    "    numerator = (e**2)*etta(g0, smg, T, N)*(llambda(smg, T, N)**2)*dsmg*(ng - ne)*gamma(g0, smg, T, N)\n",
    "    denominator = gamma(g0, smg, T, N)**2 + w**2\n",
    "    return numerator/denominator\n",
    "\n",
    "def total_capacitance(g0, smg, dsmg, T, N, ne, ng, dne, dng, w):\n",
    "    return tunnel_capacitance(g0, smg, dsmg, T, N, ne, ng, w) + q_capacitance(smg, T, N, dne, dng)\n",
    "\n",
    "def I_resistance(g0, smg, dsmg, T, N, ne, ng, w):\n",
    "    numerator = (e**2)*etta(g0, smg, T, N)*(llambda(smg, T, N)**2)*dsmg*(ng - ne)*(w**2)\n",
    "    denominator = gamma(g0, smg, T, N)**2 + w**2\n",
    "    return numerator/denominator\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27adff3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# p0(0.1,100,10)\n",
    "# q_capacitance(0.1,100,10,0.1,0.1)\n",
    "# tunnel_capacitance(1e4,0.1,0.01,100,10,0.1,0.12,1e6)\n",
    "# total_capacitance(1e4,0.1,0.01,100,10,0.1,0.12,0.01,0.02,1e6)\n",
    "# I_resistance(1e4,0.1,0.01,100,10,0.1,0.12,1e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce0d376c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def capacitance_data(gammaa,v0,g0,T,N,w,U=0.333):\n",
    "\n",
    "    op1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\optical1.dat\") # smg\n",
    "\n",
    "    chi1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\chi1.dat\") # dng\n",
    "    n1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\n1.dat\") # ng\n",
    "\n",
    "    chi2 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\chi2.dat\") # dne\n",
    "    n2 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\n2.dat\") # ne\n",
    "\n",
    "\n",
    "    s_mg_f = interp1d(op1[:,0],op1[:,1])\n",
    "    ds_mg_f = interp1d(op1[:,0],np.gradient(op1[:,1],-0.01*U))\n",
    "\n",
    "    n_g_f = interp1d(n1[:,0],n1[:,1])\n",
    "    chi_g_f = interp1d(chi1[:,0],chi1[:,1])\n",
    "    n_e_f = interp1d(n2[:,0],n2[:,1])\n",
    "    chi_e_f = interp1d(chi2[:,0],chi2[:,1])\n",
    "    \n",
    "\n",
    "    s_mg = s_mg_f(v0)\n",
    "    ds_mg = ds_mg_f(v0)\n",
    "\n",
    "    n_g = n_g_f(v0)\n",
    "    dn_g = chi_g_f(v0)\n",
    "    n_e = n_e_f(v0)\n",
    "    dn_e = chi_e_f(v0)\n",
    "    \n",
    "    return total_capacitance(g0, s_mg, ds_mg, T, N, n_e, n_g, dn_e, dn_g, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4884b9b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def capacitance_data0(gammaa,v0,g0,T,N,w,U=0.333):\n",
    "\n",
    "    op1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\optical1.dat\") # smg\n",
    "\n",
    "    chi1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\chi1.dat\") # dng\n",
    "    n1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\n1.dat\") # ng\n",
    "\n",
    "    chi2 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\chi2.dat\") # dne\n",
    "    n2 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\n2.dat\") # ne\n",
    "\n",
    "\n",
    "    s_mg_f = interp1d(op1[:,0],op1[:,1])\n",
    "    ds_mg_f = interp1d(op1[:,0],np.gradient(op1[:,1],-0.01*U))\n",
    "\n",
    "    n_g_f = interp1d(n1[:,0],n1[:,1])\n",
    "    chi_g_f = interp1d(chi1[:,0],chi1[:,1])\n",
    "    n_e_f = interp1d(n2[:,0],n2[:,1])\n",
    "    chi_e_f = interp1d(chi2[:,0],chi2[:,1])\n",
    "    \n",
    "\n",
    "    s_mg = s_mg_f(v0)\n",
    "    ds_mg = ds_mg_f(v0)\n",
    "\n",
    "    n_g = n_g_f(v0)\n",
    "    dn_g = chi_g_f(v0)\n",
    "    n_e = n_e_f(v0)\n",
    "    dn_e = 0 #chi_e_f(v0)\n",
    "    \n",
    "    return total_capacitance(g0, s_mg, ds_mg, T, N, n_e, n_g, dn_e, dn_g, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "308e9bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6413120499424118e-38"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capacitance_data(0.035,0.3,1e6,40,10,1e7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "71a4a6c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def res_data(gammaa,v0,g0,T,N,w,U=0.333):\n",
    "\n",
    "    op1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\optical1.dat\") # smg\n",
    "\n",
    "    # chi1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\chi1.dat\") # dng\n",
    "    n1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\n1.dat\") # ng\n",
    "\n",
    "    # chi2 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\chi2.dat\") # dne\n",
    "    n2 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U={U}\\\\U={U}_Gamma={gammaa}\\\\n2.dat\") # ne\n",
    "\n",
    "\n",
    "    s_mg_f = interp1d(op1[:,0],op1[:,1])\n",
    "    ds_mg_f = interp1d(op1[:,0],np.gradient(op1[:,1],0.01*U))\n",
    "\n",
    "    n_g_f = interp1d(n1[:,0],n1[:,1])\n",
    "    # chi_g_f = interp1d(chi1[:,0],chi1[:,1])\n",
    "    n_e_f = interp1d(n2[:,0],n2[:,1])\n",
    "    # chi_e_f = interp1d(chi2[:,0],chi2[:,1])\n",
    "    \n",
    "\n",
    "    s_mg = s_mg_f(v0)\n",
    "    ds_mg = ds_mg_f(v0)\n",
    "\n",
    "    n_g = n_g_f(v0)\n",
    "    # dn_g = chi_g_f(v0)\n",
    "    n_e = n_e_f(v0)\n",
    "    # dn_e = chi_e_f(v0)\n",
    "    \n",
    "    return I_resistance(g0, s_mg, ds_mg, T, N, n_e, n_g, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ba76fd2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4268525224800994e-46"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_data(0.035,0.3,1e6,40,10,1e7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d70fb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def caps_exp(dset):\n",
    "    text = f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\experimental_data\\\\dataset_{dset}.csv\"\n",
    "    dataset = np.loadtxt(text, skiprows=1,delimiter=',')[:,0:2]\n",
    "    exp_caps12 = dataset[:,1]\n",
    "    vex = dataset[:,0]*1e3\n",
    "    filter_arry = (vex<0.6)*(-0.6<vex)\n",
    "    exp_cap1 = exp_caps12[filter_arry]\n",
    "    vex1 = vex[filter_arry]\n",
    "    return vex1, exp_cap1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8410c879",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gammaa = '0.035'\n",
    "\n",
    "def capplot(gammaa,g0vals,Tvals,Nvals,save=False,format='png'):\n",
    "    params = list(itertools.product(g0vals,Tvals,Nvals))\n",
    "\n",
    "    fig, axes = plt.subplots(int(np.ceil(len(params)/4)),4,figsize=(20,4*int(np.ceil(len(params)/4))))\n",
    "    axes = axes.flatten()\n",
    "    idx = 0\n",
    "    v0 = np.arange(-0.6,0.6,.01)\n",
    "    w = 268*1e6\n",
    "    for parameter in params:\n",
    "        g0, T, N = parameter\n",
    "\n",
    "        # cvalues = capacitance_data(gammaa,1+2*v0,g0,T,N,w)*1e15\n",
    "        cvalues0 = capacitance_data0(gammaa,1+2*v0,g0,T,N,w)/(e*e)\n",
    "\n",
    "        label = f'$\\Gamma^0 = 10^{({int(np.log10(g0))})}$ \\n T = {T} \\n N = $10^{({int(np.log10(N))})}$'\n",
    "        # axes[idx].plot(v0,cvalues,label=label)\n",
    "        axes[idx].plot(v0,cvalues0,label=label)\n",
    "        axes[idx].set_ylabel(f'$ C (fF) $')\n",
    "        axes[idx].set_xlabel(f'$ c_1 \\epsilon_o + c_2 $')\n",
    "        # axes[idx].set_title(label)\n",
    "        axes[idx].legend(fontsize=12)\n",
    "        idx = idx + 1\n",
    "\n",
    "    parameter_string = f\"\\\n",
    "            $U          = 0.333 $\\\n",
    "            $\\\\Gamma_t  = {gammaa} $\\\n",
    "            $ dn_e      = 0 $\\\n",
    "            $\\\\omega    = 268 MHz $\"\n",
    "\n",
    "    fig.text(\n",
    "        0.5, 0.92, parameter_string, ha='center', va='center', wrap=False,\n",
    "        bbox=dict(ec='black', alpha=1, color='azure'), fontsize=12)\n",
    "    if save:\n",
    "        plt.savefig(f\"plots2{gammaa}.{format}\", format=format)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c3091074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rok_fit = [10.67, 1.088, 35.67, 0.01932]\n",
    "#     Ne, scalex, scaley, T = rok_fit\n",
    "#     T = T/k\n",
    "#     N = 10**Ne\n",
    "# capplot(gammaa,g0vals,Tvals,Nvals,save=False,format='png'):\n",
    "capplot('0.005',[1e10,1e9,1e8,1e7],[0.01932/k],[10**10.67],save=False,format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "64d11d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gammaa = '0.035'\n",
    "%matplotlib widget\n",
    "def ressplot(gammaa,g0vals,Tvals,Nvals,save=False,format='png'):\n",
    "    params = list(itertools.product(gammaa,g0vals,Tvals,Nvals))\n",
    "\n",
    "    fig, axes = plt.subplots(int(np.ceil(len(params)/4)),4,figsize=(20,4*int(np.ceil(len(params)/4))))\n",
    "    axes = axes.flatten()\n",
    "    idx = 0\n",
    "    v0 = np.arange(-0.6,0.6,.01)\n",
    "    w = 268*1e6\n",
    "    for parameter in params:\n",
    "        gammat, g0, T, N = parameter\n",
    "\n",
    "        # cvalues = capacitance_data(gammaa,1+2*v0,g0,T,N,w)*1e15\n",
    "        \n",
    "        cvalues0 = res_data(gammat,1+2*v0,g0,T,N,w)/(e*e)\n",
    "\n",
    "        label = f'$\\Gamma^0 = 10^{({int(np.log10(g0))})}$' # \\n N = $10^{({int(np.log10(N))})}$'\n",
    "        # axes[idx].plot(v0,cvalues,label=label)\n",
    "        axes[idx].plot(v0,cvalues0,label=label)\n",
    "        axes[idx].set_ylabel(f'$ 1/R $')\n",
    "        axes[idx].set_xlabel(f'$ \\epsilon_o $')\n",
    "        # axes[idx].set_title(label)\n",
    "        axes[idx].legend(fontsize=12)\n",
    "        idx = idx + 1\n",
    "\n",
    "    parameter_string = f\"\\\n",
    "            $U          = 0.333 $\\\n",
    "            $\\\\Gamma_t  = {gammat} $\\\n",
    "            $ dn_e      = 0 $\\\n",
    "            $ T =       {T} $\\\n",
    "            $ log_{({10})}(N) =    {np.log10(N)}$\\\n",
    "            $\\\\omega    = 268 MHz $\"\n",
    "\n",
    "    fig.text(\n",
    "        0.5, 0.97, parameter_string, ha='center', va='center', wrap=False,\n",
    "        bbox=dict(ec='black', alpha=1, color='azure'), fontsize=12)\n",
    "    if save:\n",
    "        plt.savefig(f\"plots2{gammaa}.{format}\", format=format)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "feaa8888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fef2bdd0b0e4d3ca16aece6232148e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ressplot(['0.005','0.080','0.160'],[1e10,1e9,1e8,1e7],[0.01932/k],[10**10.67],save=False,format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9e506103",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gammaa = '0.035'\n",
    "\n",
    "def resplot(gammaa,g0vals,Tvals,Nvals,save=False,format='png'):\n",
    "    params = list(itertools.product(g0vals,Tvals,Nvals))\n",
    "\n",
    "    fig, axes = plt.subplots(int(np.ceil(len(params)/4)),4,figsize=(20,4*int(np.ceil(len(params)/4))))\n",
    "    axes = axes.flatten()\n",
    "    idx = 0\n",
    "    v0 = np.arange(-0.6,0.6,.01)\n",
    "    w = 268*1e6\n",
    "    for parameter in params:\n",
    "        g0, T, N = parameter\n",
    "\n",
    "        cvalues = res_data(gammaa,1+2*v0,g0,T,N,w)\n",
    "\n",
    "        label = f'$\\Gamma^0 = 10^{(int(np.log10(g0)))}$ \\n T = {T} \\n N = $10^{(int(np.log10(N)))}$'\n",
    "        axes[idx].plot(v0,cvalues,label=label)\n",
    "        axes[idx].set_ylabel(f'$ \\\\frac{{1}}{{R}} \\Omega $')\n",
    "        axes[idx].set_xlabel(f'$ \\epsilon_o $')\n",
    "        # axes[idx].set_title(label)\n",
    "        axes[idx].legend(fontsize=12)\n",
    "        idx = idx + 1\n",
    "\n",
    "    parameter_string = f\"\\\n",
    "            $U          = 0.333 $\\\n",
    "            $\\\\Gamma_t      = {gammaa} $\\\n",
    "            $\\\\omega      = 268 MHz $\"\n",
    "\n",
    "    fig.text(\n",
    "        0.5, 0.92, parameter_string, ha='center', va='center', wrap=False,\n",
    "        bbox=dict(ec='black', alpha=1, color='azure'), fontsize=12)\n",
    "    if save:\n",
    "        plt.savefig(f\"plotsres{gammaa}.{format}\", format=format)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "906e86d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resplot('0.005',[1e10,1e9,1e8,1e7],[0.01932/k],[10**10.67],save=False,format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3a959a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def symplot(g0): \n",
    "    sets = [\"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\", \"13\"]\n",
    "    gammas = [\"0.0425\", \"0.050\", \"0.03125\", \"0.02875\", \"0.02375\", \"0.020\",\"0.015\", \"0.01125\", \"0.00875\"]\n",
    "    rok_fit = [10.67, 1.088, 35.67, 0.01932]\n",
    "    Ne, scalex, scaley, T = rok_fit\n",
    "    T = T/k\n",
    "    N = 10**Ne\n",
    "    g0 = 1e8\n",
    "    vx = np.arange(-0.6,0.6,.01)\n",
    "    v0 = 1 + (scalex*np.arange(-0.6,0.6,.01))\n",
    "    w = 268*1e6\n",
    "\n",
    "    fig = plt.figure(constrained_layout=True,figsize=(20,45))\n",
    "    # fig.suptitle(f'U = 0.333, $\\Gamma_0$ = {g0:.0e}',fontsize=14)\n",
    "\n",
    "    fig.suptitle(f'U = 0.333, $\\\\Gamma_0$ = {g0:.0e} Hz, Sym, N=10^{Ne}, Scalex = {scalex}, Scaley = {scaley}, T = {T} mK')\n",
    "\n",
    "    # create 3x1 subfigs\n",
    "    subfigs = fig.subfigures(nrows=9, ncols=1)\n",
    "\n",
    "    for dset, gamma_t, subfig in zip(sets, gammas,subfigs):\n",
    "        subfig.suptitle(f'$\\\\Gamma_t$ = {gamma_t}  dataset = {dset}')\n",
    "        axis = subfig.subplots(nrows=1, ncols=4)\n",
    "        op1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U=0.333\\\\U=0.333_Gamma={gamma_t}\\\\optical1.dat\")\n",
    "        chi1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U=0.333\\\\U=0.333_Gamma={gamma_t}\\\\chi1.dat\")\n",
    "        n1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U=0.333\\\\U=0.333_Gamma={gamma_t}\\\\n1.dat\")\n",
    "        n2 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U=0.333\\\\U=0.333_Gamma={gamma_t}\\\\n1.dat\")\n",
    "        vex, exp_caps = caps_exp(dset)\n",
    "\n",
    "        gap = interp1d(op1[:,0],op1[:,1])\n",
    "        dgap = interp1d(op1[:,0],np.gradient(op1[:,1],0.01))\n",
    "        chi = interp1d(chi1[:,0],chi1[:,1])\n",
    "        nng = interp1d(n1[:,0],n1[:,1])\n",
    "\n",
    "\n",
    "\n",
    "        ng = nng(v0)\n",
    "        smg = gap(v0)\n",
    "        dsmg = dgap(v0)\n",
    "        dng = chi(v0)\n",
    "        model_cap = scaley*extd_capacitance(g0, smg, T, N, w, ng, dsmg, dng)/(e*e)\n",
    "        rok_cap = scaley*rok_capacitance(g0, smg, T, N, w, ng, dsmg, dng)/(e*e)\n",
    "        exp_caps = 1e18*(exp_caps + exp_caps[::-1])/2\n",
    "\n",
    "        axis[0].plot(vex,exp_caps,label='experiment')\n",
    "        axis[1].plot(vex,exp_caps,label='experiment')\n",
    "        axis[0].plot(vx,rok_cap,label='Rok')\n",
    "        axis[1].plot(vx,model_cap,label='esterli N States')\n",
    "        axis[2].plot(vx,smg, label = f'$\\\\delta / \\\\Delta $')\n",
    "        axis[3].plot(vx,ng, label = f'$n_g$')\n",
    "\n",
    "        axis[0].set_ylim(-5,350)\n",
    "        axis[1].set_ylim(-5,350)\n",
    "        axis[2].set_ylim(0.0,1.4)\n",
    "        axis[3].set_ylim(0,2.1)\n",
    "\n",
    "        axis[0].set_ylabel(f'$S_y$ Capacitance, (aF)')\n",
    "        # axis[1].set_ylabel(f'$S_y$ Capacitance, (F/e)')\n",
    "\n",
    "\n",
    "        for haxis in axis:\n",
    "            haxis.legend()\n",
    "            haxis.set_xlim(-0.63,0.63)\n",
    "            haxis.set_xlabel(f'$\\epsilon_0 (mV) $')\n",
    "\n",
    "\n",
    "    # ptext = f'U = 0.333, $\\\\Gamma_0$ = {g0:.0e}, Sym, N={Ne}, Scalex = {scalex}, Scaley = {scaley}'\n",
    "    # fig.text(0.24, 0.999, ptext,ha='center', va='center', wrap=False, \n",
    "    #     bbox=dict(ec='black', alpha=1, color='azure'), fontsize=15)\n",
    "\n",
    "    g0log = int(np.log10(g0))\n",
    "    plt.savefig(f\"Gamma0 = 10^{g0log}.{'pdf'}\", format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "734afdff",
   "metadata": {},
   "outputs": [],
   "source": [
    "for g00 in [1e5,1e6,1e7,1e8]:\n",
    "    symplot(g00)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a000e51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29303548f70>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gammaa = '0.035'\n",
    "\n",
    "n1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U=0.333\\\\U=0.333_Gamma={gammaa}\\\\n1.dat\") # ng\n",
    "chi1 = np.genfromtxt(f\"f1web.ijs.si\\\\~zitko\\\\data\\\\chi\\\\U=0.333\\\\U=0.333_Gamma={gammaa}\\\\chi1.dat\") # chi1\n",
    "dn1 = np.gradient(n1[:,1],-0.01*0.333)\n",
    "\n",
    "\n",
    "plt.plot(chi1[:,0],chi1[:,1],label='chi1')\n",
    "plt.plot(n1[:,0],dn1,label='dn1')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9a95097",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4b7906d0e6d9ab4a3c84b3fb6b823b0d60fdeeb04660fd42eec21a83604d63ff"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}