{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import os\n",
    "\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.rcParams['font.sans-serif'] = \"Arial\"\n",
    "plt.rcParams['font.family'] = \"sans-serif\"\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "tick_major = 6\n",
    "tick_minor = 4\n",
    "plt.rcParams[\"xtick.major.size\"] = tick_major\n",
    "plt.rcParams[\"xtick.minor.size\"] = tick_minor\n",
    "plt.rcParams[\"ytick.major.size\"] = tick_major\n",
    "plt.rcParams[\"ytick.minor.size\"] = tick_minor\n",
    "\n",
    "font_small = 12\n",
    "font_medium = 13\n",
    "font_large = 14\n",
    "plt.rc('font', size=font_small)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=font_medium)    # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=font_medium)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=font_small)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=font_small)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=font_small)    # legend fontsize\n",
    "plt.rc('figure', titlesize=font_large)   # fontsize of the figure title\n",
    "\n",
    "import matplotlib.colors as clr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Controls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:40:38.619248Z",
     "start_time": "2021-05-02T02:40:30.161499Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n",
      "\t1 \n",
      "Setting temperature to 34.000000 C\n",
      "Setting simulation time step to 0.100000 ms\n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "**********************\n",
      "cADpyr232_L5_TTPC1_0fb1ca4724[0].soma[0]\n",
      "1 \n",
      "1 \n",
      "1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuron import h\n",
    "h.load_file(\"runModel.hoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Model Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T03:22:53.581851Z",
     "start_time": "2021-05-02T03:22:53.571750Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def init_settings(nav12=1,\n",
    "                  nav16=1,\n",
    "                  dend_nav12=1, \n",
    "                  soma_nav12=1, \n",
    "                  ais_nav12=1, \n",
    "                  dend_nav16=1, \n",
    "                  soma_nav16=1,\n",
    "                  ais_nav16=1, \n",
    "                  axon_Kp=1,\n",
    "                  axon_Kt =1,\n",
    "                  axon_K=1,\n",
    "                  soma_K=1,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1,\n",
    "                  hcn=1):\n",
    "    \n",
    "    # create default model parameters to avoid loading the model\n",
    "    \n",
    "    h.dend_na12 = 0.026145/2 \n",
    "    h.dend_na16 = h.dend_na12 \n",
    "    h.dend_k = 0.004226 * soma_K\n",
    "\n",
    "\n",
    "    h.soma_na12 = 0.983955/10 \n",
    "    h.soma_na16 = h.soma_na12 \n",
    "    h.soma_K = 0.303472 * soma_K\n",
    "\n",
    "    h.ais_na16 = 4 \n",
    "    h.ais_na12 = 4 \n",
    "    h.ais_ca = 0.000990\n",
    "    h.ais_KCa = 0.007104\n",
    "\n",
    "    h.node_na = 2\n",
    "\n",
    "    h.axon_KP = 0.973538 * axon_Kp\n",
    "    h.axon_KT = 0.089259 * axon_Kt\n",
    "    h.axon_K = 1.021945 * axon_K\n",
    "\n",
    "    h.cell.axon[0].gCa_LVAstbar_Ca_LVAst = 0.001376286159287454\n",
    "    \n",
    "    #h.soma_na12 = h.soma_na12/2\n",
    "    h.naked_axon_na = h.soma_na16/5\n",
    "    h.navshift = -10\n",
    "    h.myelin_na = h.naked_axon_na\n",
    "    h.myelin_K = 0.303472\n",
    "    h.myelin_scale = 10\n",
    "    h.gpas_all = 3e-5 * gpas_all\n",
    "    h.cm_all = 1\n",
    "    \n",
    "    \n",
    "    h.dend_na12 = h.dend_na12 * nav12 * dend_nav12\n",
    "    h.soma_na12 = h.soma_na12 * nav12 * soma_nav12\n",
    "    h.ais_na12 = h.ais_na12 * nav12 * ais_nav12\n",
    "    \n",
    "    h.dend_na16 = h.dend_na16 * nav16 * dend_nav16\n",
    "    h.soma_na16 = h.soma_na16 * nav16 * soma_nav16\n",
    "    h.ais_na16 = h.ais_na16 * nav16 * ais_nav16\n",
    "    \n",
    "    h.hcn = hcn\n",
    "    \n",
    "#     h.km = km\n",
    "    \n",
    "    h.working()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Stimulation Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:48:59.026404Z",
     "start_time": "2021-05-02T02:48:59.022102Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def init_stim(sweep_len = 800, stim_start = 100, stim_dur = 500, amp = 0.5, dt = 0.01):\n",
    "    # updates the stimulation params used by the model\n",
    "    # time values are in ms\n",
    "    # amp values are in nA\n",
    "    \n",
    "    h(\"st.del = \" + str(stim_start))\n",
    "    h(\"st.dur = \" + str(stim_dur))\n",
    "    h(\"st.amp = \" + str(amp))\n",
    "    h.tstop = sweep_len\n",
    "    h.dt = dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:00.722086Z",
     "start_time": "2021-05-02T02:49:00.715276Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def run_model(start_Vm = -72):\n",
    "\n",
    "    h.finitialize(start_Vm)\n",
    "    timesteps = int(h.tstop/h.dt)\n",
    "    \n",
    "    Vm = np.zeros(timesteps)\n",
    "    I = {}\n",
    "    I['Na'] = np.zeros(timesteps)\n",
    "    I['Ca'] = np.zeros(timesteps)\n",
    "    I['K'] = np.zeros(timesteps)\n",
    "    t = np.zeros(timesteps)\n",
    "    \n",
    "    for i in range(timesteps):\n",
    "        Vm[i] = h.cell.soma[0].v\n",
    "        I['Na'][i] = h.cell.soma[0](0.5).ina\n",
    "        I['Ca'][i] = h.cell.soma[0](0.5).ica\n",
    "        I['K'][i] = h.cell.soma[0](0.5).ik\n",
    "        t[i] = i*h.dt / 1000\n",
    "        h.fadvance()\n",
    "        \n",
    "    return Vm, I, t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AP analysis code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:02.498832Z",
     "start_time": "2021-05-02T02:49:02.478323Z"
    },
    "code_folding": [
     81,
     90,
     94,
     99,
     113
    ]
   },
   "outputs": [],
   "source": [
    "def detect_spikes(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0002, properties=True):\n",
    "    '''\n",
    "    Method for idenifying spikes based on rates of change in the membrane potential\n",
    "    INPUTS:\n",
    "    Vm: array-like - membrane potential (mV)\n",
    "    time: array-like - time corresponding to Vm (sec)\n",
    "    dVdt_thresh: float - Threshold for determining spike initiation (V/s)\n",
    "    min_spike_len: float - Minimum length of time dVdt must be above dVdt_thresh to be considered a spike (sec)\n",
    "    properties: Bool - If true, returns spike_times and spike_properties. Otherwise returns only spike_properties\n",
    "\n",
    "    Output:\n",
    "    array of spike times\n",
    "\n",
    "    Identification of spike start times:\n",
    "    dVdt is first quanitified from Vm and time\n",
    "    Continuous tretches (runs) of dVdt above dVdt_thresh are identified, and then esured to last longer than min_spike_len\n",
    "\n",
    "    Spike Property measurement:\n",
    "    spike_end is determined by finding the second zero crossing of dVdt after spike dVdt_threshold\n",
    "    First zero crossing is AP peak, second should be end of repolarization phase\n",
    "    Persistent zero crossing (must stay above zero for 3 continous points) is used to make sure that noise in slowly repolarizing spikes doesn't trigger spike end\n",
    "    '''\n",
    "    if len(Vm) != len(time):\n",
    "        raise Exception(\"Vm and time must be the same length\")\n",
    "\n",
    "    #determine stretches where dVdt exceeds dVdt_thresh\n",
    "    dVdt = np.gradient(Vm,time)/1e3\n",
    "    runs = group_consecutives(np.argwhere((dVdt>=dVdt_thresh)).flatten())\n",
    "\n",
    "    #If runs are longer than min_spike_len count as a spike\n",
    "    dt = time[1]-time[0] #sample rate\n",
    "    min_run_len = np.ceil(min_spike_len/dt)\n",
    "    spike_times = []\n",
    "    for run in runs:\n",
    "        if len(run) > min_run_len:\n",
    "            spike_times.append(time[run[0]])\n",
    "    spike_times = np.asarray(spike_times)\n",
    "\n",
    "    if not properties: #just return spike_times\n",
    "        return spike_times\n",
    "\n",
    "    #get spike properties\n",
    "    spike_properties=[]\n",
    "    for spike_time in spike_times:\n",
    "        #find index of spike_time in time\n",
    "        spike_start_idx = np.argwhere(spike_time == time)[0][0]\n",
    "\n",
    "        #find zero crossings of dVdt after spike dVdt_threshold\n",
    "        zero_crosses = find_zero_crossing(dVdt[spike_start_idx:-1])\n",
    "        #make sure zero cross is persistent to account for noise\n",
    "        if len(zero_crosses) > 1:\n",
    "            spike_end_idx = spike_start_idx\\\n",
    "                            + zero_crosses[np.argwhere(np.diff(zero_crosses)>3)[0] + 1][0]\n",
    "        else: #Vm ends before spike can repolarize, therefore assigned Vm[-1] as spike end\n",
    "            spike_end_idx = len(Vm)-1\n",
    "\n",
    "        spike_Vm = Vm[spike_start_idx:spike_end_idx]\n",
    "        spike_time = time[spike_start_idx:spike_end_idx] - time[spike_start_idx]\n",
    "        spike_dVdt = dVdt[spike_start_idx:spike_end_idx]\n",
    "        spike = {}\n",
    "        spike['start_idx'] = spike_start_idx\n",
    "        spike['start_time'] = time[spike_start_idx]\n",
    "        spike['end_idx'] = spike_end_idx\n",
    "        spike['end_time'] = time[spike_end_idx]\n",
    "        spike['Vm'] = spike_Vm\n",
    "        spike['time'] = spike_time\n",
    "        spike['thresh'] = spike_Vm[0]\n",
    "        spike['peak_Vm'] = spike_Vm.max()\n",
    "        spike['height'] = np.max(spike_Vm)-spike_Vm[0]\n",
    "        spike['AHP'] = spike_Vm[0]-spike_Vm[-1]\n",
    "        spike['peak_dVdt'] = spike_dVdt.max()\n",
    "        spike['min_dVdt'] = spike_dVdt.min()\n",
    "        try:\n",
    "            half_pnts = find_zero_crossing(spike_Vm - (spike_Vm[0]+(np.max(spike_Vm)-spike_Vm[0])/2))\n",
    "            spike['half_width'] = (half_pnts[1]-half_pnts[0])*dt*1000\n",
    "        except: #For slowly repolarizing spikes this can sometimes fail\n",
    "            spike['half_width'] = np.nan\n",
    "\n",
    "        spike_properties.append(spike)\n",
    "    return spike_times,spike_properties\n",
    "\n",
    "def detect_spike_times(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0002):\n",
    "    '''\n",
    "    Wrapper of detect_spikes to only get spike times\n",
    "    '''\n",
    "    return detect_spikes(Vm, time,\n",
    "                          dVdt_thresh = dVdt_thresh,\n",
    "                          min_spike_len = min_spike_len,\n",
    "                          properties=False)\n",
    "\n",
    "def detect_spike_properties(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0001):\n",
    "    '''\n",
    "    Wrapper of detect_spikes to only get spike properties\n",
    "    '''\n",
    "    return detect_spikes(Vm, time,\n",
    "                          dVdt_thresh = dVdt_thresh,\n",
    "                          min_spike_len = min_spike_len,\n",
    "                          properties=True)[1]\n",
    "\n",
    "def group_consecutives(vals, step=1):\n",
    "    \"\"\"Return list of consecutive lists of numbers from vals (number list).\"\"\"\n",
    "    run = []\n",
    "    result = [run]\n",
    "    expect = None\n",
    "    for v in vals:\n",
    "        if (v == expect) or (expect is None):\n",
    "            run.append(v)\n",
    "        else:\n",
    "            run = [v]\n",
    "            result.append(run)\n",
    "        expect = v + step\n",
    "    return result\n",
    "\n",
    "def find_zero_crossing(x):\n",
    "    '''\n",
    "    returns array of indicies before a zero crossing occur\n",
    "    If your input array starts and stops with zeros, it will find a zero crossing at the beginning, but not at the end\n",
    "    '''\n",
    "    zero_crossings = np.where(np.diff(np.signbit(x)))[0]\n",
    "    return zero_crossings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FI Curve support functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T03:14:42.207959Z",
     "start_time": "2021-05-02T03:14:42.200132Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def FI_curve(stims, iclamp, orig, iclamp_amp, stim_start = 50, stim_dur = 300, sweep_len = 350, dt = 0.1):\n",
    "             \n",
    "    f = []\n",
    "    i = []\n",
    "    for amp in stims:\n",
    "        i.append(amp)\n",
    "        init_stim(stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt, amp=amp)\n",
    "        iclamp.amp = iclamp_amp if orig is False else 0\n",
    "        \n",
    "        Vm, I, t = run_model()\n",
    "        spike_times = detect_spike_times(Vm, t)\n",
    "        f.append(len(spike_times))\n",
    "    \n",
    "    return f, i\n",
    "\n",
    "def FI_curve_plot(stims, iclamp, orig, iclamp_amp, ax, label='', stim_start = 50, stim_dur = 300, sweep_len = 350, dt = 0.5):\n",
    "    f, i = FI_curve(stims=stims, iclamp=iclamp, orig=orig, iclamp_amp=iclamp_amp, stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt)\n",
    "    \n",
    "    ax.set_ylim(0,12)\n",
    "    ax.set_ylabel('Spikes per Epoch ({}ms)'.format(stim_dur))\n",
    "    ax.set_xlabel('Injected Current (nA)')\n",
    "    ax.plot(i, f, linewidth = 1, label=label) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 25% Ri Increase (Gpas = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_path = './ri increase_figures/'\n",
    "# if os.path.isdir(plot_path) is False:\n",
    "#     os.mkdir(plot_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpas_reduct = 0\n",
    "hcn_reduct = 0.52\n",
    "iclamp_amp = 0.0234"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. FI Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79pEnTx7mzZtHu3btqFGjBpGRkZw6dQqATz75BIDAwEA6derEtm3bqFy5MvPnz2f+/PlP3falS5fYsWMH4eHhlC9fnp07d1KiRAm2bNnCjh07cHFxYcmSJfz444+0b98+2Y5bCCGEEEmTIYqxxBRKqWHDhg28++675MmTBzAXXX369KFGjRrUq1fvifV37dpFhQoVqFy5MgAff/zxM/uJtW7dGjs7O7Jnz06pUqUICgqiYcOGLFiwgMWLF3Px4kX+++8/wsPDU+4AhRBCCPHS5DRlKoqLi3tiDLSHo4pnzZr1ifUdHBzQWj+y7FljLLm4uMT/rJRCa42Pjw+1a9cmLCyMpk2bMmDAgCe2J4QQQghjSTGWit544w2WLFmCv78/AHPnzsXV1RUHh6c3UNatW5fz589z/PhxAJYtW0ZISEiiB7XdtWsX1atX56uvvuK1115j5cqVxMXFJc/BCCGEECJZSDGWipo0acKXX35Jo0aNqFixIvPnz+eff/55ZmtX7ty5+eOPP/joo4+oWrUqGzduxMHBgcyZMydqf++99x4BAQGUL1+eChUqkDVrVoKCgrh3715yHpYQQgghkkDZ6mmr6tWr60OHDj2x/MyZM5QvX96ARMkvLCyMYcOGMXjwYDJnzoyPjw8tW7bk9u3bMuWTyNDS0+dcCJFhPPOLO0N04LdV2bNnx8nJiRo1auDo6IijoyNLly6VQkwIIYRIR6QYS+OGDRvGsGHDjI4hhBBCiBQifcaEEEIIIQwkxZgQQgghhIGkGBNCCCGEMJAUY0IIIYQQBpJiTAghhBDCQFKMpSKlFB4eHnh5eVGlShXKli1LjRo1eDhe2rRp0xg5cuQTj3vWcltz69Yt2rRpk2amZPL396d58+ZUqFCBSpUqsXfv3hc+Zt68ebRq1SoV0sH+/fupUaMG5cuXp3Hjxty5c+ep602cOJH8+fPj5eWFl5cXr776KgAmk4n+/ftTsWJFPDw8ePPNN+Nnf/j666/ZsWNHqhyHEEKIF9Ba2+S/atWq6ac5ffr0U5enBYD29/d/ZNmoUaP0K6+8YlCi1NW8eXN94MABo2PE69Chgx4+fLjWWusjR47oggUL6oiIiOc+Zu7cubply5Ypnu3Bgwe6cOHCes+ePVprradMmaKbN2/+1HU7duyoFy9e/MTymTNn6kaNGumoqCittdbffPON7tSpk9Za65CQEF2pUiUdGRmZQkeQstLy51wIIZ7hmTWNtIwZKDY2luvXr5M7d24ABg8eTM+ePZ9YL+Hy4sWLM3jwYF599VWKFSvGDz/8EL/enDlzqFixIp6enjRq1IgbN26wY8cOKleuTJ06dfD09OTBgwesWbOGWrVqUaVKFerWrcu+ffsA8PX1pV27dtSuXRt3d3caNGiAn58fAFOnTqVy5crUqFGDV199ldOnTwPm1q727dtTrVo1PD09GTFixFOPdf/+/fj5+VGjRg0ALl68SP369alUqRJNmjTh9ddfZ968eVy9epVixYrRtWtXvLy8qFy5Mrt377Y6X506deJbjB7+69GjB7Gxsfzzzz98/vnnAHh5eVG6dGk2bNjwwtftzp07tGzZEg8PD6pUqcKZM2cAuHv3Lu3ataNcuXJUqFCBCRMmANCgQQMGDRpE/fr1KV68OJ999hkmk4nTp08/kc3Ly4u5c+dy8OBBsmfPTt26dQHo3LkzW7duJTAw8Ik8e/fuZfHixXh6etKsWTNOnDgBQMWKFRk1ahSZMmUCoHr16ly7dg2AHDlyULduXWbMmPHC4xVCCJGyMsagr4NzpMI+QhO1WsOGDVFKERAQgLOzM61atWLu3Lkvtavw8HB2797NrVu3KFWqFJ9++ilhYWEMGDAAHx8fihQpgre3N8OHD6djx46cPHmSy5cvU6xYMS5cuMC3337Ljh07cHV15dSpU7z++utcvHiRJUuWULt2bQYMGIDWmpYtW7Jw4UL69u1L3759uXr1KgUKFGDhwoXs2bOHChUq0KlTJ7788ktat25NVFQULVq0oFSpUrzzzjuPZP7rr78eOb3XqVMnPvroI7744gvOnDlD9erV+fDDDwG4fv06r732GtOnT2f9+vW8++67XLt2zap8zzr1ePfuXUwmE3ny5IlfVrhwYW7evPnC5//y5cv8+eeflCpVij59+jB69Ghmz55N9+7dKVOmDCtXriQ0NJS6devSokULAC5dusSOHTsIDw+nfPny7Ny5k4YNG3L06NGn7mPJkiUUKVIk/raTkxN58uTh1q1buLq6xi+PiIigXLlyDBgwgPr167N06VKaN2/O2bNnqV27dvx6wcHBDBkyhG7dusUva9q0KePHj6dPnz4vPGYhhBApJ4MUY4krlFLD9u3bcXNzw8fHhxYtWtCwYUPy5s37Utto27YtAIUKFSJv3rwEBQWxc+dOmjVrFv8F3rdvXwB27NhBkSJFKFasGACbN2/mzp07NG7cOH57dnZ2XLx4kT59+rB7927Gjh3LhQsXOHnyJLVq1cLe3p4OHTpQp04dWrZsSbNmzXj//feJiIhg586dBAUFxbfQhYeHc/To0SeKsbNnz9KxY0fAXBgcOHCAXbt2AcT3iXooV65cvP/++wA0b94ce3t7jh8//tL5wNwyFhkZ+UiWunXr8t133z0xrZTWGnt7+xc+/zVr1qRUqVKAuUVt+fLlAGzZsoXffvsNMLc8nTx5Mv4xrVu3xs7OjuzZs1OqVCmCgoI4ffp0fM6E+vTpQ6ZMmRKVL0uWLGzcuDH+9jvvvMPQoUM5ePAgDRs2BMyFYLt27ahXrx49evSIX9fd3Z1z58698HiFEEKkrIxRjKVBVatWZdy4cXzyySdUqVKF4sWLJ/qxLi4u8T8rpdBa4+Dg8MiX9/379+NPSWXNmjV+eVxcHI0bN+bPP/+MX3bjxg0KFizIgAEDOHDgAJ9++ikNGzYkJiYmvrP9okWLOHnyJFu2bGHkyJEsXLiQWbNmobVm7969ZM6cGSC+xe9xSilMJhMADg7mt93DbQOPFBkP73/IZDJhb2//0vmWLl36zJax2NhYtNYEBQXFnya+ffs2hQsXfvqTnoCjo+Mjx/Uww+OvweXLl3FzcwOe/ppVqFDhmS1je/bs4fbt2/G3Y2JiCAwMpFChQo+sd+3aNVavXk2vXr3il2mt4zNu376dd999l/79+9OvX78njiMxxacQQoiUJX3GDPTee+9Rs2ZNvvzyyyRvq2HDhmzZsiX+irvp06fTv3//J9Zr3LgxmzZt4uzZswCsW7cOT09P7t+/z8aNG+nbty+dOnUib968bN68mbi4OAICAihSpAiurq707duXYcOGxfdpeuWVVxg7diwAISEh1K1bl1WrVj2x37Jly3Lp0iUAsmXLRt26deNPz165coWtW7fGFzL+/v7xfbfWrFmDo6MjHh4eL53veRwcHGjZsmV8n6njx49z+vRpGjRo8LJPfbzXX389/phCQ0Np3LgxFy5csGpbtWrVIjAwML6YnDNnDrVr1yZnzpyPrJclSxa+//57Dhw4AJhfz8jISGrWrImPjw/t27dnwYIFTxRiYH7ey5UrZ1U+IYQQyUdaxgw2adIkPD09HznVZA0PDw9GjRrFG2+8AUCBAgWYM2cO58+ff2S9ChUqMGPGDDp27BjforZ69WqyZs3Kjz/+SL9+/fjhhx9wdHSkXr16XLx4ETc3N77//nsaN26Mi4sLDg4OzJw5E4Dff/+dnj174uHhQXR0NO+99x4ffPDBE/nefvtt+vTpw88//wzAggUL6Ny5M1OmTKFQoUK4u7vHt645OzuzcOFCBgwYgIuLCytXrsTe3t6qfM8zZcoUPvvsMypVqoRSioULF5Ijh7l/YYsWLejWrRtt2rRJ9GswadIkvvjiCzw9PTGZTAwaNIhq1aol+vEJOTo6snz5cnr27ElERASurq4sWLAAMLfgtWjRgnXr1lGwYEGWLl1K165diY6OJnv27KxYsQInJycGDRqE1pqBAwcycOBAwHxqcsWKFQBs2LCBDh06WJVPCCFE8lEJTxXZkurVq+uH43MldObMGcqXL29AIvEizZo1Y+jQodSsWZPhw4fz1ltvUa5cOUJDQ/H09GT9+vVkzpyZSpUqER4ebmjWmTNnUqRIkfjiNr0JCwujTp06HDp06KmnldM6+ZwLIWyQetYd0jImUs306dPp2bMna9asoUyZMrz77rvY2dkRGxvLwIEDqVChAlevXjU6JmA+jZnwooL0ZvDgwXh7e9tkISaEEOmNtIwJIWyOfM6FEDbomS1j0oFfCCGEEMJAUowJIYQQQhhIijEhhBBCCANJMSaEEEIIYSApxoQQQgghDCTFmBBCCCGEgaQYS0VKKQICAh5ZNm/ePFq1amVQIiGEEEIYTYoxIYQQQggDSTGWhoSGhvLhhx9SqVIlPDw86N+/P7GxsYB5vsZvv/2WmjVrUqFCBZYuXUqHDh0oV64cjRo1IiIiwuD0QgghhLBGhpgOyWO+R4rv48THJxK1XsOGDbG3t4+/HRQUhKenJwC9e/fG1dWVEydOEB0dTZs2bRg9ejQDBw7kwYMH5M+fnwMHDvDrr7/y2WefcebMGQoUKECNGjVYtWoV77//foocmxBCCCFSToYoxhJbKKWG7du34+bmFn973rx5/P333wCsX7+ef//9F6UUmTJlolu3bnh7ezNw4EAA3nrrLQBKliyJh4cHhQoVAsDd3Z2goKBUPhIhhBBCJAc5TZmGmEwmlFKP3I6JiYm/nSlTpvifHR0dUzWbEEIIIVJGqhRjymy+Uqqf5ba9UspbKXVWKXVRKdUtNXKkdc2aNWPSpElorXnw4AEzZsygSZMmRscSQgghRApK8WJMKVUe2Aq8nWBxV6AMUAmoAfRVStVM6Sxp3YQJE/Dz88PDwwMPDw/Kli3Ld999Z3QsIYQQQqQgpbVO2R0oNQnYCzQFTmqtRyulNgMztNZ/WdYZDOTWWvdO7HarV6+uDx069MTyM2fOUL58+WTJLoRIm+RzLoSxIg4cIPraNaNjJFmcjuNUwEnq/u9bHFwyp/Tu1LPuSPEO/FrrngBKqaYJFhcBbiS4fRPwfNrjlVJdgC6PLy9atGgyphRCCCHEi9w/cRK/sWOIvX0HlxrVjY5jPQ03w29yOvAUWRyzEvZOMLlTvhh7JqOuprQDEjbJKSDuaStqrWcAMx5fXr169ZRt0hNCCCEEAA+uXMF//ATu+/jg1r07Od96E2WDF5Jprdl3ex/ePt7YK3u+rDaZmgWM7yVlVDF2HSiY4HZBzK1jQgghhEgjYvz8CJg8hXsbN5L7k08oOGI4dpmNa0FKipMBJ/E+7I1vpC+9qvSiSbEmj4xgYCSjirFVwKdKqTVAVqAjIFdUCiGEEGlA3L17BM6aTciSJeR4801KrF+HQ65cRseyytXQq0w8MpGjfkfp5tWNdqXa4WiXtlr1jCrGpgIlgWOAEzBda73ToCxCCCGEAEwPHhC8+HcCZ80ia4MGuK9YjmPBgi9+YBrkH+nP1GNT2XJtCx9V/Ihh9Ybh4uBidKynSrViTGv9SYKfY4G+qbVvIYQQQjybjosjdOUq/CdNwrl8eYrNn0em0qWNjmWVsOgw5p6cy1/n/6J9qfasab+GHJlyGB3ruTLEdEhCCCGEeJLWmvDt2/EfNw677DkoNGY0matWNTqWVR7EPeCPM38w99RcXiv8Gn+3/pv8WfIbHStRZDqkVKSUIiAg4JFl8+bNo1WrVgYl+n9KKTw8PPDy8qJKlSqULVuWGjVq8HAst2nTpjFy5MgnHves5bbm1q1btGnThpQedy+x/P39ad68ORUqVKBSpUrs3bv3hY9JzffS/v37qVGjBuXLl6dx48bcuXPnqetNnDiR/Pnz4+XlhZeXF6+++ipgnuqrf//+VKxYEQ8PD9588038/f0B+Prrr9mxY0eqHIcQGVnk4cNc++BD/Md5k+erryi2aKFNFmJxpjhWXFhBqxWt8PHzYU6zOQypO8RmCjHAXBXb4r9q1arppzl9+vRTl6cFgPb3939k2dy5c3XLli0NSvT/npZt1KhR+pVXXjEoUepq3ry5PnDggNEx4nXo0EEPHz5ca631kSNHdMGCBXVERMRzH5Na76UHDx7owoUL6z179mittZ4yZYpu3rz5U9ft2LGjXrx48RPLZ86cqRs1aqSjoqK01lp/8803ulOnTlprrUNCQnSlSpV0ZGTkMzOk5c+5EGnd/XPn9PWu3fT5hg118IoV2hQba3Qkq5hMJr312lbddkVb/dG6j/QR3yNGR3qRZ9Y00jKWhoSGhvLhhx9SqVIlPDw86N+/P7GxsQA4Ozvz7bffUrNmTSpUqMDSpUvp0KED5cqVo1GjRkRERDxzu/PmzeOTTz55qSyxsbFcv36d3LlzAzB48GB69uz5xHoJlxcvXpzBgwfz6quvUqxYMX744Yf49ebMmUPFihXx9PSkUaNG3Lhxgx07dlC5cmXq1KmDp6cnDx48YM2aNdSqVYsqVapQt25d9u3bB4Cvry/t2rWjdu3auLu706BBA/z8/ACYOnUqlStXpkaNGrz66qucPn0aMLd2tW/fnmrVquHp6cmIESOeeqz79+/Hz8+PGjVqAHDx4kXq169PpUqVaNKkCa+//jrz5s3j6tWrFCtWjK5du+Ll5UXlypXZvXu31fnq1KkT32L08F+PHj2IjY3ln3/+4fPPPwfAy8uL0qVLs2HDhhe+bnfu3KFly5Z4eHhQpUoVzpw5A8Ddu3dp164d5cqVo0KFCkyYMAGABg0aMGjQIOrXr0/x4sX57LPPMJlMnD59+olsXl5ezJ07l4MHD5I9e3bq1q0LQOfOndm6dSuBgYFP5Nm7dy+LFy/G09OTZs2aceLECQAqVqzIqFGjyJQpEwDVq1fnmmU07xw5clC3bl1mzHhieEEhRBLE3LrF7QEDuf6/T8n8Si1Krl9PznbtUPb2Rkd7aT6+Pny0/iMmHZ3EV9W/Yt4b8/DK62V0LKtliD5jZ8ql/LQp5c+eSdR6DRs2xD7BGz8oKAhPT/PkA71798bV1ZUTJ04QHR1NmzZtGD16NAMHDuTBgwfkz5+fAwcO8Ouvv/LZZ59x5swZChQoQI0aNVi1ahXvv/9+ko6hYcOG8adSnZ2dadWqFXPnzn2pbYSHh7N7925u3bpFqVKl+PTTTwkLC2PAgAH4+PhQpEgRvL29GT58OB07duTkyZNcvnyZYsWKceHCBb799lt27NiBq6srp06d4vXXX+fixYssWbKE2rVrM2DAALTWtGzZkoULF9K3b1/69u3L1atXKVCgAAsXLmTPnj1UqFCBTp068eWXX9K6dWuioqJo0aIFpUqV4p133nkk819//fXI6b1OnTrx0Ucf8cUXX3DmzBmqV6/Ohx9+CMD169d57bXXmD59OuvXr+fdd9/l2rVrVuV71qnHu3fvYjKZyJMnT/yywoULc/Pmi4fiu3z5Mn/++SelSpWiT58+jB49mtmzZ9O9e3fKlCnDypUrCQ0NpW7durRo0QKAS5cusWPHDsLDwylfvjw7d+6kYcOGHD169Kn7WLJkCUWKFIm/7eTkRJ48ebh16xaurq7xyyMiIihXrhwDBgygfv36LF26lObNm3P27Flq164dv15wcDBDhgyhW7f/H92madOmjB8/nj59+rzwmIUQzxcbHEzgtOmErlxJzvffo+SG9dhny2Z0LKucDz7PBJ8JXAi+QM8qPWnh3gJ7O9srJh+XIYqxxBZKqWH79u24ubnF3543bx5///03AOvXr+fff/9FKUWmTJno1q0b3t7eDBw4EIC33noLgJIlS+Lh4UGhQoUAcHd3Jygo6Il99ejRg3///ZegoCDCw8Px8vIiU6ZM7N+//7nZfHx8aNGiBQ0bNiRv3rwvdXxt27YFoFChQuTNm5egoCB27txJs2bN4r/A+/btC8COHTsoUqQIxYoVA2Dz5s3cuXOHxo0bx2/Pzs6Oixcv0qdPH3bv3s3YsWO5cOECJ0+epFatWtjb29OhQwfq1KlDy5YtadasGe+//z4RERHs3LmToKCg+Ba68PBwjh49+kQxdvbsWTp27AiYC4MDBw6wa9cugPg+UQ/lypUrvuht3rw59vb2HD9+/KXzgbllLDIy8pEsdevW5bvvvntiIEKt9SNF/LPUrFmTUqVKAeYWteXLlwOwZcsWfvvtN8Dc8nTy5Mn4x7Ru3Ro7OzuyZ89OqVKlCAoK4vTp008t7vv06UOmTJkSlS9Llixs3Lgx/vY777zD0KFDOXjwIA0bNgTMhWC7du2oV68ePXr0iF/X3d2dc+fOvfB4hRDPZoqMJGj+fILmLyB7i+aU+GcNDgn+yLMlt8NvM/noZPbc2sNnHp8xtsFYnOydjI6VbDJEMWYrTCbTI19yJpOJmJiY+NsPT+kAOCZiGorJkycD5oJvx44dzJs3L1E5qlatyrhx4/jkk0+oUqUKxYsXT9wBAC4u/z+Gi1IKrTUODg6PHNf9+/fjT0llzZo1fnlcXByNGzfmzz//jF9248YNChYsyIABAzhw4ACffvopDRs2JCYmJr6z/aJFizh58iRbtmxh5MiRLFy4kFmzZqG1Zu/evWS2jBb9sMXvcUopTCYTAA4O5o/Ew20DjxQZD+9/yGQyYW9v/9L5li5d+syWsdjYWLTWBAUFxZ8mvn37NoULF376k55AwvfFw+f/Ye6Er8Hly5fj/yh42mtWoUKFZ7aM7dmzh9u3b8ffjomJITAwMP6Pg4euXbvG6tWr6dWrV/wyrXV8xu3bt/Puu+/Sv39/+vXr98RxJKb4FEI8ScfEEPL33wRMmUrmGtUp/ucSnCx/9Nqa4KhgZp6YyepLq+lYtiNr268lq1PWFz/QxkifsTSkWbNmTJo0Ca01Dx48YMaMGTRp0sSQLO+99x41a9bkyy+/TPK2GjZsyJYtW+KvuJs+fTr9+/d/Yr3GjRuzadMmzp49C8C6devw9PTk/v37bNy4kb59+9KpUyfy5s3L5s2biYuLIyAggCJFiuDq6krfvn0ZNmxYfJ+mV155hbFjxwIQEhJC3bp1WbVq1RP7LVu2LJcuXQIgW7Zs1K1bN/707JUrV9i6dWt8IePv7x/fd2vNmjU4Ojri4eHx0vmex8HBgZYtW8b3mTp+/DinT5+mQYMGL/vUx3v99dfjjyk0NJTGjRtz4cIFq7ZVq1YtAgMD44vJOXPmULt2bXLmzPnIelmyZOH777/nwIEDgPn1jIyMpGbNmvj4+NC+fXsWLFjwRCEG5ue9XLlyVuUTIqPSJhNh69ZxqVUr7m3eQuGpUyk0dqxNFmKRMZFMPzadNivbEBMXw8q2K+lZpWe6LMRAWsbSlAkTJtCrVy88PDyIjo7mjTfe4Lvvvkvydj/55JOX7sAPMGnSJDw9PR851WQNDw8PRo0axRtvvAFAgQIFmDNnDufPn39kvQoVKjBjxgw6duwY36K2evVqsmbNyo8//ki/fv344YcfcHR0pF69ely8eBE3Nze+//57GjdujIuLCw4ODsycOROA33//nZ49e8Y/n++99x4ffPDBE/nefvtt+vTpw88//wzAggUL6Ny5M1OmTKFQoUK4u7vHt645OzuzcOFCBgwYgIuLCytXrsTe3t6qfM8zZcoUPvvsMypVqoRSioULF5Ijh3nQwhYtWtCtWzfatGmT6Ndg0qRJfPHFF3h6emIymRg0aBDVqlVL9OMTcnR0ZPny5fTs2ZOIiAhcXV1ZsGABYG7Ba9GiBevWraNgwYIsXbqUrl27Eh0dTfbs2VmxYgVOTk4MGjQIrTUDBw6MPw3v7u7OihUrANiwYQMdOnSwKp8QGVHE3r34jR4DSlHgp5/IUqeO0ZGsEmOKYdn5Zcw4PoPq+aqzuMViimYvanSsFKcSno6xJdWrV9cPx8BK6MyZM5Qvn/Id9kX60qxZM4YOHUrNmjUZPnw4b731FuXKlSM0NBRPT0/Wr19P5syZqVSpEuHh4YZmnTlzJkWKFIkvbtObsLAw6tSpw6FDh556Whnkcy7EQ/dPnsJ/7Biib90ib9++ZGvWDGVneye9TNrExqsbmXhkIkWyFaFP1T5UcK1gdKzk9sxZyaVlTAjMp0579uzJmjVrKFOmDO+++y52dnbExsYycOBAKlSowNWrV42OCZhPYya8qCC9GTx4MN7e3s8sxIQQEH31Kn7jx3P/0GHcenQn51tvoRLRlzgt2nt7L96HvbFTdvxU+ydqFahldKRUJy1jQgibI59zkVHF+PkRMGUK9zZsJPcnn5D7o07YWbpR2JpTAacY5zOOuxF36VWlF02LNX3iSu10RlrGhBBCCFsVd+8egbNnE/LHEnK0b0+J9etwyJXL6FhWuRZ2jQk+Ezjqd5SulbvSvnR7HO1ss1UvuaTLYkxrnd6rayEyrIfDkAiREZgePCD49z8InDWLrPXr475iOY4FCxodyyr+kf5MOzaNTdc28VGFjxhadyiZHW2zVS+5pbtizNnZmcDAQFxdXaUgEyId0VoTExODr68vWbJkMTqOEClKx8URumo1/pMm4lyuPMXmzSVT6dJGx7LKveh7zD05l6Xnl9KuZDvWtFtDTuecRsdKU9JdMfZw2hh/f3+jowghkpmDgwM5cuR4ZBYLIdITrTXh27fjP24cdtlzUGj0aDJXrWp0LKs8iHvAkrNLmHNyDvUL1+evVn9RIGsBo2OlSemuGHN0dMTd3d3oGEIIIcRLifTxwW/0GEz3wsjz5VdkbdjAJs/wxJniWHN5DVOOTqFc7nLMbjqbUrlKGR0rTUt3xZgQQghhS6LOn8d/nDdR586Sp1dvcrRpjbLB6cC01uy4sYMJRyaQ3Sk7v9X/Da+8XkbHsglSjAkhhBAGiLl9G/+JkwjfuRPXLp9TyHscdgnmILYlPr4+ePt4cy/6Hn2r9qV+4fo22apnFCnGhBBCiFQUGxxM4PQZhK5YQc73OlJy4wbss2UzOpZVLgRfYILPBM4Hn6dHlR60dG+JvZ3tteoZTYoxIYQQaZbWmgdnz6JjYoyOkmRRsVHc2r4eu6Vr0Q1rY5r3G75uufCNugpRRqd7ObE6lr/P/82eW3v4zOMzxjQYg5O9k9GxbJYUY0IIIdKkiH378BszlrjQUOxz5jQ6jtU0muCoIPwi/QnNm5nd3QoTlMcPLk2FS0ans16dQnX4p/0/ZHOyzVa9tESKMSGEEGnK/VOn8B8zluibN8nbtw/Z3njDZie/3nR1ExOPTKRg1hL0rTaBiq4V6Wh0MJHmSDEmhBAiTYi+dg3/8eOJOHiQPN27k/Ptt2128ut9t/fh7eMNwPevfE/tgrWNDSTSNCnGhBBCGCrW35+AqVMJW7ee3B9/RIGhQ7Gz0VkWTgWewvuwN7fDb9Orqnnyaztle616InVJMSaEEMIQceHh5smvf/+DHO3a2fzk1xOPTMTH14dulbvJ5NfipUgxJoQQIlWZoqMJ/v13AmfOImu9ergvX4ZjoUJGx7JKwP0Aph2bxsarG+lUoRND6gyRya/FS5NiTAghRKrQcXGErl5DwMSJZCpThqJz5uBctozRsayScPLrtiXbsrrdanI522arnjDeC4sxpZQd0Ah4DSgMxAE3gS3Av1prnaIJhRBC2DStNeE7duA/dhx2WbNScNRvZK5WzehYVkk4+XW9QvVY2mopBbMWNDqWsHHPLcaUUp2B74Bw4DBwx/KYksD7gL1SapjWem5KBxVCCGF7In2O4DdmDHGhIeT96iuyNmxok9PkxJni+OfyP0w+Opmyucoyq+ksSucqbXQskU48sxhTSq0DzgDNtdbnnrFOJeALpVRHrXWzFMoohBDCxjy4cAG/cd5EnTlDnl69yNG2jc1Ofr3z5k7G+4wnm1M2Rr46kqr5qhodS6Qzz2sZ66G1vvK8B2utTwI9lFIlkjeWEEIIWxRz54558usdO3D97DMKjRtrs5NfH/E7wrjD47gXfY/eVXrToEgDm2zVE2nfM4uxhIWYUiqr1jpcKWUPvAUEaq23Jlj3csrGFEIIkZbFBgcTOGMmocuXk7NjR0puWI999uxGx7LKw8mvzwWfo7tXd1qXaC2TX4sUlZgO/B8AU4AcwK/Ah4BJKTVea/1rCucTQgiRhpkiIwlasJCgefPI1qwZ7qtX45gvr9GxrHIn/A6Tj05m963dfFrpU0Y3GE0me9ts1RO2JTFDW/QH2imlHIHPgabAXeBfzMWZEEKIDEbHxBCybDkBU6bgUrUqxf74nUzu7kbHskpIVAgzT8xk1aVVvFPmHZn8WqS6xBRjRbTW25VSDYH7Wuv9AEop22x/FkIIYTWtNfc2bsR/nDcOBQpQePIkXDw8jI5llciYSBadWcTC0wtpWqwpK9qsIE/mPEbHEhlQYoqxG0qp9piHstgE8UNeXEjJYEIIIdKWiH378BszFm2KI98PP5Clbh2b7NAeY4phxYUVTD82nSr5qrCoxSKKZS9mdCyRgSWmGPsamAOEAG2UUq9jPj35ZgrmEkIIkUbcP3UK/zFjib55kzx9epO9eXOUne1Nfm3SJjZd28SkI5MokKUAExpNoKJbRaNjCfHiYkxrvQUo+vC2UuoWUEBrHZOSwYQQQhgr+vp1/L3HE3HwAG5ffEGut99GOTkZHcsq/935j3GHx6G15tta31KnYB2jIwkRLzFXU2YFOgHFALsEy9Fa90/BbEIIIQwQ6+9PwNSphK1bT+6PP6LA0CHYZclidCyrnA48jfdhb26G36R3ld40Ld4UO2V7rXoifUvMacq/geLAfsCUommEEEIYJi48nMDZswn5/Q9ytGtLiXVrccid2+hYVrkedp2JRyZyyPcQ3Ty78WaZN3G0czQ6lhBPlZhirC5QWGsdmtJhhBBCpD5TdDQhf/xBwIyZZK1Xj+LLluFUuJDRsawScD+AacemsfHqRj4s/yE/1/mZzI6ZjY4lxHMlphg7DuQFpBgTQoh0RMfFEbpmDQETJpKpTBmKzpmDc9kyRseyyr3oe8w9OZel55fSpmQbVrdbTS7nXEbHEiJRElOM9QV2KKXWYr6iMp70GRNCCNujtSZ85078x4zFLksWCv72K5mrVzc6llWi46JZcnYJs0/Opl6heixttZSCWQsaHUuIl5KYYuw3IAxwBmQ0PCGEsGGRR47gN2YMcSEh5P3yS7I2amSTY4XFmeL45/I/TDk6hdK5SjOz6UzK5LLNVj0hElOM1QDyaq0jk3vnlsFkf8Z8YUAQ8LnW+lJy70cIITK6Bxcv4jfOm6jTp8nTsyc52rVF2dve5Ndaa3bd3IW3jzdZHbPyy6u/UDVfVaNjCZEkiSnGjgJFgHPJuWOllAuwCKistb6olPoSmAC0TM79CCFERhZz5w7+EycRvmMHrp07U2jMaOycnY2OZZWjfkcZd3gcoQ9C6VO1Dw2KNLDJVj0hHpeYYuxfYJdS6h/MrVf64R1J7DNmDyggh+V2ViAqCdsTQghhERcSQsCMmYQuW0bOd9+l5Ib12Ge3zSmFLwZfZPyR8ZwNOksPrx60LtEaezvba9UT4lkSU4zlBdZhHvDVLbl2rLUOV0p1A/YqpQIxF2d1H19PKdUF6PL48qJFiz6+SAghMjzT/fsELVhI0Ny5ZGvWDPfVq3HMl9foWFa5G3GXyUcns+vmLj6t9CmjXxtNJvtMRscSItkprfWL10qJHSvlAawAmmmtLymlegOdAS+diFDVq1fXhw4dSumYQghhE3RMDCHLlhMwZQouVaqQp08fMpVwNzqWVUKiQph1YhYrL63knTLv8EmlT8juZJutekIk8Mxz6s9tGVNKVQI+ByoCmYFw4DSwSGud1EqoGfBvgg77k4FxgCsQkMRtCyFEhqC15t7GTfh7e+OQPz+FJ0/CxcPD6FhWiYyJZPGZxSw4vYAmxZqwvM1y8ma2zVY9IV7GM4sxpdSnmIe1+B1zC1Yk5oKsLLBeKdVPaz0/Cfv2AXoqpfJprX2BdsAVrbUUYkIIkQgR//2H35ix6LhY8n3/PVnq1rHJDu0xphhWXFjB9GPT8crrxcLmCymeo7jRsYRINc9rGfsRaK61Pvj4HUqphcBfgNXFmNZ6m1JqFOYBZaMxXxzQ1trtCSFERhF1+jR+Y8YSfeMGefr0Jnvz5ig725v8WmvNpmubmHhkIvmz5GdCowlUdKtodCwhUt3zirGcmIe1eJqTQLak7lxrPRnz6UkhhBAvEH39Ov7jJxBxYD9u3bqRq0MHlJOT0bGs8t+d//A+7I1Jm/i21rfUKVjH6EhCGOZ5xdg2YJZS6ket9bWHC5VShYFfgC0pHU4IIQTEBgQQMGUqYevWkeujThQY8jN2WbIYHcsqpwNP433Ym5vhN+lVpRfNijfDTtleq54Qyel5xdinwCzgouU04n3ABXAEVvGU4SaEEEIkn7jwcILmzCF48e/kaNeWEuvW4pA7t9GxrHI97DoTj0zkkO8hunp25a3Sb+Fo72h0LCHShGcWY1rrEOBtpVQWoDSQBXMn/gta6/DUiSeEEBmPKTqakCVLCJg+g6z16lJ82TKcChcyOpZVAu4HMO3YNDZe3ciH5T/k5zo/k9kxs9GxhEhTXjS0hQPQlEeHtiiqlNqktb6fCvmEECLD0HFxhP3zD/4TJpKpVCmKzpmNc9myRseySnh0OHNPzeXPc3/SukRrVrdbTS7nXEbHEiJNet7QFrWA5UAg5nkpHw5t0RGYrpRq/bQrLYUQQrwcrTXhO3fiP3Ycdi4uFBz5C5lr1DA6llWi46L589yfzDoxi3qF6vFnqz8plNU2W/WESC3PaxmbCnyvtZ77+B2WMcimAdVSKpgQQmQEkUeO4DdmDHHBIeT9si9ZGze2ybHC4kxxrL2ylslHJlM6V2lmNp1JmVxljI4lhE14XjFWBljwjPsWYB4tXwghhBUeXLyI3zhvok6dIk+vnuRo2xblkJjpgtMWrTW7bu7C28ebLI5ZGPHqCKrlk7/ThXgZz/vkn8I8V+SMp9zXFfNYY0IIIV5CzJ07+E+aRPj2Hbh27kyhMaOxc3Y2OpZVjvodZdzhcYQ+CKV31d40LNLQJlv1hDDa84qxbsBqpdS3mOejjMQ8tEU5wAlonvLxhBAifYgLCSFgxkxCly0j5zvvUHLDeuyz2+bk15dCLjHeZzxngs7QvXJ32pRsg72dvdGxhLBZzxva4ohSqhTQCPN8lA+HtpgIbNNaR6dORCGEsF2m+/cJWriIoLlzydakCe6rV+OYzzYnv74bcZfJRyez6+Yu/lfxf/xW/zecHWyzVU+ItOS5HRS01g+A9ZgnBs8JNACiUj6WEELYNh0bS8iy5QRMmYKLlxfFFi8mUwl3o2NZJSQqhFknZrHy0ko6lOnAmvZryO5km616QqRFzxvaogKwEPADvgc2AWGYT1GGKqXe0FpfT5WUQghhI7TW3Nu4CX9vbxzy5aPwxAm4eHoaHcsq92Pvs/jMYuafmk+TYk1Y3mY5eTPbZqueEGnZ81rGJgL/YJ4wfDMwXGs9Rpl7Z/4ETALapHhCIYSwERH/7cdvzBh0bCz5vvuOLPXq2mSH9hhTDCsurGD6sel45fViYfOFFM9R3OhYQqRbSmv99DuUCtVa51BK5QNuA5m01rGW+xyBO1prt9SL+qjq1avrQ4cOGbV7IYSIF3X6NH5jxxF97Rp5+vQhe4vmKDvbm/xaa83ma5uZeGQi+TLno2+1vlRyq2R0LCHSi2f+Zfa8lrFwpVR+rfVdyyCvCTdSDghNrnRCCGGLoq9fx3/8BCIO7MetazdyvdMB5eRkdCyr7L+zH+/D3sTpOAbVHETtgrVtslVPCFv0vGJsNrBFKVVDaz3/4UKl1DDMw14MSulwQgiRFsUGBBAwZSpha9eS66NOFBjyM3ZZshgdyypnAs/g7ePNjXs36FWlF82KN8NO2V6rnhC27HlDW/yolLr2lAnBY4EPtdYbUjaaEEKkLXHh4QTNmUvw4sVkb9uGEuvX4ZA7t9GxrHIj7AYTj0zkoO9Bunh24e3Sb+No72h0LCEypOddTZlHaz378eVa68FPWTev1tovmbMJIUSaYIqOJmTJEgJmzCRr3ToUX7YMp8K2Ofl1wP0Aph+bzoarG/ig/AcMrjOYzI6ZjY4lRIb2vNOUfyulNgMztda+T1tBKVUA+ALz+GP1kz+eEEIYR8fFEfbPP/hPmIhTqZIUnT0L57JljY5llfDocOadmseSc0toXaI1q9qtIrezbbbqCZHePK8Yawz0A44rpc4AB4A7gB1QAKgDFAe8LesKIUS6oLUmYtcu/MaOw87ZmYIjfyFzjRpGx7JKdFw0f577k1knZlG3YF3+bPUnhbLaZqueEOnV8/qMxQIjlVITgHaYW74qABq4DowG1j+lT5kQQtis+0eP4jd6DLHBweT9si9ZGze2yasK40xxrL2ylslHJlMqVylmNJlB2dy22aonRHr3zHHG0joZZ0wI48WFhhI4cyb3tmwFG/1d8lCMKYbQ+8GYYqLZ9HpuDlfJhsne9oqwhyJjIimYtSBfVvuSavmqGR1HCGHlOGNCCPFUpqgoghYuJGjOXLK9/jqFvMehMmUyOpZVAu4H8MfZP9h/Zz9vlv6AWl6t+DhTJj42OlgS2St7CmcrbJOtekJkNFKMCSESTcfGErJ8OQGTp+BSuTLFFi8iU4kSRseySuiDUGafmM3yi8t5u/TbzG41WCa/FkIYQooxIcQLaa25t3kz/uO8cciTh8ITxuNSubLRsazycPLrBacW0LhYY5n8WghhOCnGhBDPFbH/AH5jx6AfRJPv20FkqVfPJk99xZpiWXFxBdOOTaNynsrMbz4f9xzuRscSQogXF2NKqcbAFMAdsH+4GNBaa/tnPlAIYdOizp7Fb8xYoq9cMU9+3bKFzU5+veX6Fib4TCBv5rx4N/DGI4+H0bGEECJeYlrGpgJ/A38B0SkbRwhhtOibN82TX+/bh1vXruSaPMlmJ78+cOcA3j7exJhiGFhzIHUK1rHJVj0hRPqWmGLMVWv9bYonEUIYKjYwkICp0whbs4ZcnTqR/6efsM9qm5Nfnw06i/dhb66FXaNXlV684f6GTH4thEizElOMbVNKNddar0/xNEKIVBcXHkHQ3LkEL1pE9jZtKLFuLQ6urkbHssqNezeYdGQSB+4e4HOPz+lQpoNMfi2ESPOeN1H4X5hH23cFViqldgHBCdfRWr+TsvGEECnFPPn1nwTMmEGWOrUpvuxvnAoXNjqWVQLvBzL9+HTWX1nP++Xf56faP8nk10IIm/G8lrGTCX7emdJBhBCpQ5tMhK1di//4CTiVcKforJk4lytndCyrRMREMP/UfH4/+7tMfi2EsFnPm5vy54c/K6XKALe11uFKqZpAqNb6XGoEFEIkD601Ebt34zd2HCqTEwVGDCdLzZpGx7JKdFw0f53/i5nHZ1K7YG2WtFxC4Wy22aonhBCJGdrifWAaUA84DlQDhimlPtVar0rhfEKIZHD/2DHz5NeBgeT5si/ZXn/dJq8qNGkTay+vZfLRyZTIUYLpTabL5NdCCJuXmA78Q4BGWuvjAFrrqUqpw8ACQIoxIdKwB5cv4z/Om/vHj+PWswc527dHOdjeWM9aa3bf2s14n/E4OzgzrO4wquevbnQsIYRIFon5rZwXOPrYssNAvmRPI4RIFjG+vgRMmsS9LVtx7fwpBUf9hp2zs9GxrHLM/xjeh70Jigqid9XeNCrSyCZb9YQQ4lkSU4z5AAOA4QmW9cNckAkh0pC40FACZ84k5K+/yflOB0puWI99jhxGx7LK5dDLTPCZwMmAk3T36k6bkm1wsLO9Vj0hhHiRxPxm6wGsVUr1Ae4ABYEgoHVKBhNCJJ4pKoqghQsJmjOXbK83xn3VShzz5zc6llXuRtxl6rGpbL++nf9V+h8jXx2Js4NttuoJIURivLAY01qfUkqVBupiPjV5C9ivtY5J6XBCiOfTsbGErFhBwKTJuHh6UmzxIjKVKGF0LKuEPghl9onZLL+4nLdLv82a9mvIkck2W/WEEOJlJLbNvwzQFCgM+AKRmE9fCiEMoLXm3ubN+HuPx8HNjcITxuNSubLRsawSFRvF4jOLmX9qPo2KNmJZ62XkyyJdUoUQGUdihrZojnmS8NXANcAd2K2Uek9rvTqF8wkhHhOx/wB+Y8egH0STb9BAstSrZ5Md2mNNsay8uJKpx6ZSOU9l5jefj3sOd6NjCSFEqktMy9hwoEPCuSktBdqvmAs0IUQqiDp7Fr8xY4m+coU8fXqTvWVLlJ3tTX6ttWbr9a2M9xlPnsx5GNdgHJ55PI2OJYQQhklMMVYS2PjYso3AkuSPI4R4XPTNm/iPn0DEvn24de1KrsmTUE5ORseyysG7B/E+7M2DuAcMqDmAugXr2mSrnhBCJKfEFGPnMV85mXCA1zbAxRRJJIQAIDYwkICp0whbs4ZcnTqR/6efsM+axehYVjkbdBZvH2+uhl6lV5VeNHdvjp2yvVY9IYRICYkpxr4FVimlNvP/fcYaAO1SLpYQGVdceARB8+YRvHAh2Vu3psS6tTi4uhodyyo3791k0tFJ7L+zn889Pmdiw4k42jsaHUsIIdKUxAxtsVkpVQN4F/No/PuAvlrrSykdToiMREdHE/znUgKmTydL7doU//svnIoUMTqWVQLvBzLj+AzWXlnLB+U/4IdXfiCLo2226gkhREpL7NAWd4FLQARwAwhIjp0rpTyAiUAOIA7oqrWWkf1FhqJNJsLWrsV//ASc3N0pOnMGzuXLGx3LKhExEcw/NZ/fz/5OqxKtWNV2Fa4uttmqJ4QQqSUxQ1s0AlZgLsZuAcUAb6VU86QUTkqpzMAmoLPWep1Sqi2wGChn7TaFsCVaayJ278Zv7DiUkxMFhg8nS62aRseySnRcNH+d/4uZx2dSu2BtlrRcQuFshY2OJYQQNiExLWPjgd5a6/kPFyilPgOmALWSsO+mwCWt9TrL7dXAlSRsTwibcf/YMfzGjCXW3588X/YlW5MmNnlVoUmbWHdlHZOOTMI9hzvTm0ynbO6yRscSQgiborTWz19BqXAgh9Y6LsEyeyBAa53L6h0r1R+oCYQClYEQoL/W2uex9boAXR5/fNGiRatdu3bN2t0LYYgHly/jP86b+8eP49ajOznffBPlYHuTX2ut2XNrD+N9xpPJPhN9q/WlRv4aRscSQoi07Jl/cSfmW2A90BsYl2DZezw59tjLcgRaAA211vstpynXKaWKaa0fPFxJaz0DmPH4g6tXr/78KlKINCTG15eASZO4t2UruT/9HwV/+xU7FxejY1nluP9xxh0eR2BUIH2q9KFR0UY22aonhBBpRWKKsZzAGKVUV8z9xgpibsk6o5Q68HAlrfXLdna5DZzRWu+3PH6VUmoWUAI485LbEiJNigsNJXDWLEKW/kXODm9TcsN67HPY5uTXl0MvM9FnIscDjtO9cnfalmqLg53tteoJIURak5jfpIss/5LbesxFXjWt9WGlVH1AI/3GRDpgiooieNEiAmfPIdvrjXFftRLH/PmNjmUV3whfph6byrbr2/ik0if88uovODs4Gx1LCCHSjcSMMzb/acuVUkq/qMPZ87d7VynVDpiilMoCPADe1FpHWbtNIYymY2MJXbkS/0mTcfHwoNjiRWQqUcLoWFYJfRDK7JOzWX5hOW+Vfos17deQI5NttuoJIURa9sxiTCm1Q2vdIMHt7lrrKQlWCQWyJ2XnWutdJO2KTCHSBK0197ZswX+cNw6urhT2HoeLl5fRsawSFRvF72d/Z97JeTQq2ohlrZeRL0s+o2MJIUS69byWsaqP3R6GeTiLh6THrhBAxIED+I8ZiykqinwDB5Dl1VdtskN7rCmWVRdXMfXYVDzcPJjXfB4lcthmq54QQtiSl+l9+/i3i1zNKDK0qHPn8Bs7luiLl8jTpzfZW7VC2dne5Ndaa7Ze38qEIxNwc3FjbIOxeObxNDqWEEJkGC9TjEnxJQQQffMm/hMmELF3H25dupBz4kTsnJyMjmWVg3cP4n3YmwdxD+hfoz91C9a1yVY9IYSwZXJduhCJFBsURMDUaYStXk2uDz8k/4afsM9qm5Nfnws6h7ePN1dCr9CzSk9auLfATtleq54QQqQHzyvGHJRSzfn/05OP37ZP0WRCpBGmiAgC580jeMFCsrdqRYm1/+Dg5mZ0LKvcvHeTSUcn8d/t//jc83MmNJyAo72j0bGEECJDe14x5sejHfYDH7vtlyKJhEgjdHQ0wUv/ImD6NLLUeoXif/+FU5EiRseySuD9QGYcn8HaK2v5oNwH/PDmD2RxtM1WPSGESG+eWYxprYunYg4h0gxtMhG2dh3+EybgVLw4RWfMwLl8eaNjWSUiJoIFpxaw+OxiWrq3ZFXbVbi6uBodSwghRALSZ0wIC601EXv+xW/sWJSjIwWGDSNLrZed5SttiImLYen5pcw6MYtaBWrxR8s/KJLNNlv1hBAivZNiTAjg/vHj+I0ZS6yvL3m++pJsTZrY5FWFJm1i/ZX1TDoyieI5ijPt9WmUzV3W6FhCCCGeQ4oxkaE9uHwFf29v7h87hluP7uR8802Ug+19LLTW/Hv7X8b7jMfRzpEhdYdQI38No2MJIYRIhBd+6yilBgCTtNYRqZBHiFQR4+tLwKTJ3Nuyhdyf/o+Cv47EzsXF6FhWOeF/gnE+4/CP9KdP1T40LtrYJlv1hBAio0pME8AAYExKBxEiNcSFhRE4cxYhS5eS4+23KLl+HfY5cxodyypXQq8w8chEjvkfo3vl7rQt1RYHO9tr1RNCiIwuMb+5fwemKqX+AHxJMBK/1vp0SgUTIjmZoqIIXryYwNlzyNqoIe6rVuKYP7/RsaziG+HL1GNT2XZ9G59U+oQR9Ubg7OBsdCwhhBBWSkwx1t3yf+fHlmtk4FeRxunYWEJXrcJ/4iScK1Wk2MIFZCpZ0uhYVgl9EMqck3NYdmEZb5Z+kzXt15AjUw6jYwkhhEiiFxZjWmuZI0XYHK014Vu34jfOG4dcuSg0biyZq1QxOpZVomKj+OPsH8w9OZdGRRvxd+u/yZ/FNlv1hBBCPClRHUyUUjmBt4HCwGightZ6ewrmEsJqkQcP4jdmLKb798nX/xuy1K9vkx3aY02xrL60milHp+Dh5sG85vMokaOE0bGEEEIks8RcTVkDWAecAGoA84BVSqm+Wus5KRtPiMSLOnce/7FjeXDxInn69CZ7q1YoO9tr2NVas+36NiYcmUBu59yMaTCGynkqGx1LCCFECklMy9gEoJvWeplSKlhrfdUyYfg8QIoxYbjom7cImDiB8D3/4ta1C4UmTsDOycnoWFY5ePcg3j7eRMVG0a96P+oVqmeTrXpCCCESLzHFWDlgheVnDaC1/lcplTfFUgmRCLFBQQRMm0bYqtXk+uADSm7cgH3WrEbHssq5oHOM9xnP5dDL9PDqQcsSLbFTtteqJ4QQ4uUlphi7ALQE1jxcoJRqBJxPqVBCPI8pIoLA+fMJXrCQ7C1bUmLtPzi4uRkdyyq3wm8x6cgk9t3ex+een+Pd0Bsne9ts1RNCCGGdxBRj/YA1SqltQGal1DygNfBOSgYT4mlCV63Cd/RostR6heJ/LcWpiG1Ofh1jisH7sDerL63mvXLv8f2b35PFMYvRsYQQ4sVMcXBqBQReMjpJkkXFxnH8Zihe73yPU+ZshuVIzNAWu5RSHsB7wG3gDlBLa30xpcMJkVDIsmX4T55MkWnTcKlY0eg4VjNpE9/v+Z7Q6FBWtF2Bm4tttuoJITIYreH8Rtj6M2TKDu71jU5ktViTieM3Qzl8LZjiblm4HxOLkeckEjt3ym3gP+AqcAOw/XJY2JSwDRvx9x5P0QXzyeTubnQcq2mtGbF/BL6Rvkx7fZqMnC+EsA3X98OWn+B+CLz+E5R5A2zw4qI4k2a5z028t1ygfIHs9O9WljL5jGsReygxQ1tUBlYBWYC7QEHAXynVUmstRZlIceH//svdIUMoOmumTRdiABOPTOS4/3HmNJsjhZgQIu3zOwNbh8DdE9BgEFTuCHa2N/mO1potZ/wYtfEs2Z0d8e7oRY3iuY2OFS8xLWPTgNnAcK21SSllD/wEzAAap2Q4ISKPHOF2v28oPGkizhUqGB0nSeafms+W61uY98Y8sjrZ5lWfQogMIvQmbP8Fzm+Ael/C23PB0Tb/gDx0NYiR688SFhVD/2blaFw+b5obMigxxZgHUE9rbQLQWscppYYAgSmaTGR4UefOcbNnLwr+OpLM1aoZHSdJll9Yzu9nfmd+8/nkdk47f40JIcQjIoNg9xg4uhiqd4bePuBsm3Pgnrt7j1Ebz3Lmzj2+bFKG9lUKYW+XtoqwhxJTjO0H2gN/J1hWH3MfMiFSRPS1a9z4vAv5v/uWrPVtt5MowKarm5h0ZBJz35grc0oKIdKm6Aj4byrsmwwV20P3/yCbbf6+uhVyn7GbzrPjnB9fNCjJpPer4uyYtk+tJqYY8wOWKKW2Ahcx9xlrDuxXSi19uJLWWoa6EMkixteX6592xq1nD7K3aGF0nCT599a/DN8/nBlNZlAsezGj4wghxKPiYsBnAewaBUVfgc+2gGtJo1NZJTgimsnbL/K3z00+rFWM7d80ILuzo9GxEiUxxdhZYGiC237A0RRJIzK82OBgrn/amVzvdSTXO7Zd3x/1O8q3e77Fu6E3ZXOXNTqOEEL8P63h9ErYOhRyFoGOv0OhqkanskpkdCxz9lxh9p4rtPQswKa+9cmb3bb6tyVmnLGfUyOIEHHh4dz4vAvZGjXC9bPPjI6TJOeCztFnex9G1BtBlbxVjI4jhBD/7/IO2DIYtAlajoaSjYxOZJWYOBNLDt5g4tYL1HDPzfLudXF3s83BsxM7zpgQKcoUFcXN7j1wrliRPF99aXScJLkWdo0vtnzBoFqDqFuortFxhBDC7PZRcxEWcg0afQ8V2oOd7c2BazJp1p28w+iN5yicKzOzP66BR2HbvMjgISnGhOF0TAy3vvwKBzdX8v/4Q5q75Phl3I24S5dNXeju1Z03ir9hdBwhhDBPW7RtGFzbC699A1U/Bnvb6Ev1uD0XAvh1w1kAhrXzoF7p9DGDiRRjwlDaZOL2d9+h42IpOHIkyj5tX/HyPEFRQXTZ3IWO5Trydpm3jY4jhMjo7vnCrt/g5HJ4pTu0mQiZbHOMwxM3Q/l1w1luBkfSr1lZWlQqgF0aHabCGokqxpRSpbTWF5VSmYGemMcYm6O11imaTqRrWmt8h48g5vZtis6ciXIycmawpAmPDueLLV/QuGhj/lfpf0bHEUJkZFFhsHcCHJwFld+Hnocgi6vRqaxyJSCC0ZvOcfBKEL0bl+bdGkVwtLe9U6svkpjpkL4CfgByAZOBmoAJqAB8naLpRLoWMHEikUd8KDZ/PnYuLkbHsVpUbBS9tvXCw82D3lV6Gx1HCJFRxT6Ag7Nhz1go1QS67oKcRY1OZRW/e1FM2HqBtcfv0LmeO6Pe9iSzU/o9mZeYI/sMqKuUcgE6AjUwz1F5CinGhJUC580jbN16ii1ehH024ydptVaMKYZ+O/uRJ3Mevq31rU33dxNC2ChTHBxfCttHQL6K8NFqyGeb08eFRcUwY+dlFu2/xttVC7P16wbkzmK7Z00SKzHFWH6t9WmlVEvAT2t90jI/ZaYUzibSqZBlywhasIDiixbh4GqbTecAJm3i+z3fo9EMrzccO5X+ms6FEGmY1nB+I2z9GTJlgzdnQLHaRqeySlRMHIv+u8a0nZd4rUxe/ulVj8K5MhsdK9Ukphg7p5T6EmgFrFNKOQMDgeMpmkykS2EbN+Hn7U2x+QtwLFjQ6DhW01ozYv8IfCN9mfb6NBztbPPKJCGEjbq+H7b8BPeDofFPULY52GDLfJxJs+LILcZtPk/5AtlY/NkrlM1vu2dLrJWYYuwLYAIQCnwPvAK8hfmUpRCJFv7vv9z9+WeKzppJphLuRsdJkolHJnLc/zizm83G2cG2RnoWQtgwv7OwdQjcPQ4NBkHljmBne1eha63ZesaPURvPkc3ZAe+OXtQontvoWIZJzAj8RzFPDP7QDsAjhfKIdCryyBFu9/uGwhMn4FzBNvsyPDT/1Hw2X9vM/ObzyeaU8f6CE0IYIPQmbP8Fzm+Aen3h7TngaJt/CB66GsTI9WcJi4qhf7NyNC6fN8P3t03s0BYfA58DBYDXgOFAD611eApmE+lE1Llz3OzZi4IjfyFz9epGx0mS5ReW8/uZ35nffD65nTPuX3FCiFQSGQS7x8DRxVD9U+jtA862Odr8ed97/LbhHGfuhNH39dK8WbUw9ulorLCkSMzQFgOAD4DRmE9X3gMKA5OAT1IynLB90deucePzLuT/7luyvvaa0XGSZNPVTUw6Mok5zeaQP0t+o+MIIdKz6EjYPxX2ToKK7aD7f5DNNn/v3Aq5z7jN59l+1o8vGpRk0vtVcHa0vVOrKSkxLWNdgQZa6+tKKW+tdahSqgNwLoWzCRsX4+vL9U8749ajB9lbtDA6TpLsvbWX4fuHM73JdIrnKG50HCFEehUXA0cWws7foEgt6LwZ3EoZncoqwRHRTN5+kb99bvJhrWJs/6YB2Z3lYqenSUwxlgXws/z8sD0xEohLkUQiXYgNDuZ6587k7Pguud59x+g4SXLU7yiD9gzCu6E35XKXMzqOECI90hpOr4StQyFHYej4OxSqanQqq0RGxzJnzxVm77lCS88CbOpbn7zZbbN/W2pJTDG2CZhqGYlfK6UcgF+AbSmaTNisuPAIbnTpSraGDXH7/HOj4yTJuaBz9Nneh+H1hlMlbxWj4wgh0qPLO83DVGgTtBwNJRsZncgqMXEm/jx4g4nbLlC9eG6Wd6+Lu1sWo2PZhMQUY72BRUAA5paxCGA38H5yhVBKtQMWaq3l0jQbZ3rwgJs9euBcoQJ5vvrK6DhJci3sGt23dGdQrUHUK1TP6DhCiPTmzjHYMhiCrkDjH6BCe7CzvcGjtdasPXGHMZvOUyinC7M+qoFHYdu8yMAoiRnaIhhoqZTKBxQF7mitb1omDU8ypVRpzBcHyCUVNk7HxHDry69wcM1N/h9/sOlLle9G3KXr5q584fUFbxR/w+g4Qoj0JOgybBsGV/dA/W+g6sfgYJtT/vx7MYBfN5zFpDVD2lbk1dJ5jI5kkxJzNeW3WusRWmtfwNeyrAkwHSiRlJ1bCrpFwFfA70nZljCWNpm4/d136NgYCo4ch7K33StlgqOC6bq5K++UfYe3y7xtdBwhXp7WcOMARPgbnSTJYkwmzt65R6xJGx0lWbj67iX/jbVcK/0x15qsJ84hC5wLMjrWS4uN0yw5eJ0bQZF83bQsLT0KYCfDVFgtMacpOyullNZ6uFIqF+ANvI15rLGkmm7598yplZRSXYAujy8vWtQ2Z6JPj7TW+A4fQcyt2xSdNRPlZJt/4QGER4fTbUs3GhZpyKeVPjU6jhAv78ZB86mv8LvgVtboNFbTaO6ERnHRLxxnR3sc7dPHF/1px8KMKzCH8Ps54FgwEGx0JKs1rZifjjWK4Ghve6dW05rEFGP1gc2W04lvAEcBD6315aTsWCnVHYjVWs9RShV/1npa6xnAjMeXV69ePX38mZQOBEycSKSPD8Xmz8POxcXoOFaLio2i17ZeVHKtRJ+qfYyOI8TL8T9vnjD69hHLNDnvgX2ixvVOU7TW7Djnz68bzpLZyZ6BncpT0z19DbAsHR/E4xLTZ+yWUqo+5qsqj2qtk+t99AmQWSl1FHACXCw/t9Ba306mfYgUFjhvHmHr1lNs8SLss2c3Oo7VYkwx9NvZjzwuefi21rc23d9NZDBht2HHL3B2LdTtA2/NAkfb/KPI53owI9efJSgimv7NytKkQj75LIoM4ZnFmFLqIJCw9Skz0NRSMEUDaK1rWrvjhI+1tIyd1Fp7Wbs9kfpCli0naMECii9ahIOrq9FxrGbSJn749wdM2sTwV4djb4OT7ooM6H4w7BkHPgug2ifQ6zC45DI6lVUu+t1j1MZznLgZSt/Xy/Bm1UI4yKkvkYE8r2VsUqqlEDYnbNMm/LzHUWz+AhwLFjQ6jtW01vyy/xfuhN9hWpNpONrJ6NAijYu5D/unwd6JUK4VfLEXstvmZ/BO6H28N19gyxlfur5WgvEdZZockTE9sxjTWs9PrRBa66tA1tTan0iaiL17uTv4Z4rOmkmmEu5Gx0mSSUcnccz/GLObzcbFwTZP7YgMIi4Wji6CHb9C4erw6UZwK210KquEREYzdccl/jx0g/drFmVbvwbkcJE/hETG9bzTlJe11iWUUv48eroyntY6b4olE2nS/aNHufV1PwpPnIBzhQpGx0mS+afms+nqJuY3n082JxlvWKRRWsOZNbB1iHmi6HcXQeFqRqeyyv3oOObtvcrM3ZdpVjE/G/vWJ59MkyPEc09TfmT5XwZaEgBEnTvPjR49KTjyFzJXr250nCRZcWEFi88sZv4b88ntnL6u1BLpyJXd5mEqYh9A85FQsjHYYIf22DgTfx2+yfgtF6haLCd/datNyTxyMkSIh553mnKP5f+dlvko6wL5gRvAPq21DC2RgURfu8aNzz8n37eDyPraa0bHSZJNVzcx8chE5jSbQ4GsBYyOI8ST7p4wF2EBF6DRD1DpLZudJmfDybuM2nSOfNmcmdapGl5FchodS4g0JzEj8FcGVmHu03UHKAj4K6Vaaq0vpXA+kQbE+PpyvfNnuHXvTo6WLY2OkyR7b+1l+P7hTHt9GsVzFDc6jhCPCr4K24bDlZ3waj/o+IfNTpOz91IAv244R0ysiZ9aV6R+aTcZpkKIZ0jMiIDTgNnAcK21SSllD/yEeSDWxikZThgvNjiY6507k/Odd8jV8V2j4yTJUb+jDNw9EO+G3pR3LW90HCH+X7g/7BoFJ/6CWt2g1VjIZJv9GE/dDuXXDee4GhDB103L0NqzoEyTI8QLJKYY8wDqaa1NAFrrOKXUECAwRZMJw8WFR3CjS1eyNWiAW5fPjY6TJOeCztFnex+G1xtO1XxVjY4jhNmDe7BvsnmoCs93occByGqbEy1fD4xkzOZz7L0USK9GpehYoyhODrZ3alUIIySmGNsPtAf+TrCsPvBfiiQSaYLpwQNu9uiBc/ny5Pn6a6PjJMn1sOt039KdQTUH8WrhV42OIwTERsPhubB7DJRoAF12QK7iBoeyTkD4AyZuvcDqY7f5pI47I9p7kCWT7U3DJISREvOJ8QOWKKW2Ahcx9xlrDuxXSi19uJLW+p2UiShSm46N5dZXX+Pgmpv8P/1o0/08fCN86bK5C194fcEb7jIjnDCYyQQn/4Ztw8CtDHy4DPJ7GJ3KKuEPYpm56zLz912lfZVCbPnqNVyzZjI6lhA2KTHF2FlgaILbfpgnCxfpkDaZuPPdd+iYaAqOG4uyt93RsIOjgumyuQvvlH2Ht8vICC3CQFrDxS2w5WdwyATtpkDxekanssqD2Dh+33+dydsvUb+0G2t61qNI7sxGxxLCpiVmovCfUyOIMJ7WGt8RvxB94yZFZ89COdnmVVwA4dHhfLHlCxoWacinlT41Oo7IyG4eMg9TEe4LjX80T2Fkg63NJpNm1bFbjNl0njL5srGwc03KF8hudCwh0oXnFmNKqfaAk9b6T6VUbsxXVXoBq4GvtdaxKR9RpJaAiZOIPHyYYvPnYediu1MDRcVG0Xt7byq4VqBP1T5GxxEZlf952DYEbvlAg4FQ+X2wt72+VFprdpz357cN53B2tGNMh8rUKuFqdCwh0pXnTYf0KTAK+MayaBJQBPgK6Ab8gHmIC5EOBM2fT9jatRRbvAj77Lb7126MKYZvdn6Dq7Mr39X6zqb7uwkbFXYbdvwCZ9dCnd7w5kxwtM0/bnyuB/Pr+rMERkTzTbOyNK2QTz5TQqSA5/2Z1gtor7XepZTKDLwJtNBab1NKnQA2IcVYuhCybDmB8+dTfNEiHNzcjI5jNZM28eO/PxKrYxlRbwT2drbb303YoPvBsMcbfOZD1Y+h12FwyWV0Kqtc9Atn1MazHL8Zypevl+HNqoVwsJdhKoRIKc8rxkporXdZfq6JebLwh1MkXVRKySTh6UDYpk34eY+j2Pz5OBYsaHQcq2mt+WX/L9wOv820JtNwtHc0OpLIKGLuw/7psHcilGsJX+yF7Lb5WboTeh/vzRfYcsaXLvVLML5jFZwd5Y8aIVLa84qxOKWUk9Y6GmgAHLD8jFIqDxCRCvlECorYu5e7g3+myMwZZCpRwug4STLp6CSO+R9jdrPZuDjY5ikhYWPiYuHoYtj5KxSqCv9bD3nKGJ3KKqGRMUzZeZE/D97gvZpF2davATlc5A8aIVLL84qxXUA/pdTvwIfAhAT3fQvsTMlgImXdP3qUW1/3o/CE8bhUrGh0nCSZf2o+m65uYt4b88jmZJtTyAgbojWc/Qe2DoGs+eCdBVC4utGprBIVE8fcf68yc/dlmlXMx4Y+9cmfw9noWEJkOM8rxr4BNmAeY2wH5jkqUUpdBrIAtjlIjiDq3Hlu9OhJwZG/kLlGDaPjJMmKCytYfGYx89+Yj6uLXOElUtjVPeZhKmKioNkvUKqxTQ5TERtn4q/DNxm/5QJViubkr261KZknq9GxhMiwnlmMaa0vKKVKAW5aa/8Edw0Etmitg1I8nUh20devc+Pzz8k3aBBZX3vN6DhJsvnaZiYcmcCcZnMokLWA0XFEenb3hHnA1oDz0OgHqPQW2Nleh3atNRtP3eW3jefIl82ZaZ2q4VUkp9GxhMjwnjvojdZaA/6PLVv6jNVFGhfj68f1Tzvj1v0LcrRqaXScJNl7ey/D/hvGtNen4Z7D3eg4Ir0KvgrbhsPlHVC/H3T8HRxsczDkfZcC+XXDWaJjTfzUuiL1S7vJMBVCpBG2NwKhsEpscDDXO39Kzg4dyNWxo9FxkuSo31EG7hrIuIbjKO9a3ug4Ij2KCIBdo+D4n1CrG7QaC5lssz/i6dth/LrhLJcDwunXtCytPQtiZydFmBBpiRRjGUBceAQ3unQl62uv4drlc6PjJMm5oHP02d6HYfWGUS1fNaPjCDBPfh16A/PoN7btQXQMUT5LyHpsNpFl3yTsg12YMruZrx2PiDQ63ku5FxXLjF2X+PdSID0blmJmzeo4OdjeqVUhMgJlPhNpe6pXr64PHTpkdAybcHvAQLCzo8CI4TZ9WuJ+7H3ar2pPryq9aFnCtk+zpgtaw6Wt5r5U4b7mCbBtlNYQ/iCWsKhYTtiVZ45jR27b5Tc6VpLY2ynerFKYzq+6kzWT/N0tRBrwzC9g+YSmcxH//UfEgQOU/GeNTRdiANOPTcfDzUMKsbTg5mHY8hPcu2Oe/Lp8G5u8qtBk0qw5fpsxm87jnjcL/d8oyxsFc/CG0cGEEBmKFGPpmCk6mruDfyb/D99jlyWL0XGS5ELwBZZfWM6yNsuMjpKxBVwwj6918xA0GABeH9rs5Nc7LZNfOzrY8etbntQuKUOjCCGMYXu/RUWiBc6YiVOpkmRr1MjoKEli0iaG/jeUHl49yJM5j9FxMqawO7BzJJxZA3V6Qfvp4JTZ6FRWOXojhJHrz+B37wH9m5WlWcX8Nt9qLISwbVKMpVMPLl8heNEi3FcsNzpKki2/sJw4UxwdynYwOkrGcz8E/vWGw/OgSifoeQgy5zY4lHUu+YczeuM5jlwPoe/rpXm7WmGZ/FoIkSZIMZYOaa25O2QIrt264ljAtgdDDbwfyMQjE5nRZAZ2Sr44U03MfTgwA/6dAGWbQ7d/IUcho1NZ5W5oFOO3nmfjKfPk1+Pe9ZLJr4UQaYoUY+lQ2OrVxIWFkvvDD42OkmSjD42mTck2lM1d1ugoGUNcLBz7A3b8AgWrwP/WQR7bfO5DI2OYuvMSSw5e590aRdj+dQNyZJbJr4UQaY8UY+lMbHAwvqNGU2TqFJSDbb+8+27vw8fXhxVtVxgdJf3TGs6uNXfOz+IGHeZBkZpGp7JKVEwc8/deZfquyzStkI/1fV6lQA4Xo2MJIcQz2fa3tXiC35gxZG/WDBcPD6OjJMmDuAcM3z+cQbUGkdnRNjuK24yr/1omv46EpsOgdBObHKYiNs7EMp+beG+5QOXCOVnatTal8srk10KItE+KsXQk8tAhInbvocQ/a4yOkmSzTsyidM7SNCjSwOgo6ZfvKfOArf5noOH34NHBhie/9mX0pnO4ZnFi8gdVqVo0l9GxhBAi0aQYSyd0dDR3Bg8m36BB2GezzTn0HrocepklZ5fwV+u/jI6SPgVfg+0j4NI2ePVreHehzY6e/99l8+TXUTEmvmtZngZl8sgwFUIImyPFWDoROGcujoUKka1ZU6OjJInWmqH7htLVsyv5s9j2dDRpTkQA7BoNx5dAzS7Q6zA4Zzc6lVXO3Anjtw1nuegfztdNytKmskx+LYSwXVKMpQPR168TNG8exf/+2+ZbBVZfWk1ETATvlXvP6Cjpx4Nw2DcZ9k8Dj7ehxwHImtfoVFa5ERTJ2M3n2X0hgB4NSzKtUzUyOcgwFUII2ybFmI0zjyk2FNfPOuNU2DbHgXooJCqEcYfHMfn1ydjbyRdsksVGmwdr3T0a3OvD59sgt7vRqawSGP6ASdsvsuLILT6uXZwd3zSQya+FEOmG/DazcffWryfWz4/cH39sdJQkG3t4LG+4v0FF14pGR7FtJhOcWg7bhkLukvDBX1CgstGprBLxIJZZu68wb+8V2noVYstXr+GW1Tb7twkhxLNIMWbD4sLC8P1lJIXGj0c52vZglofuHmLv7b2sbLvS6Ci2S2u4tNV8haS9I7SZaG4Rs0HRsSb+OHCdSdsvUrekK6t61KOoqwxxIoRIn6QYs2F+48aRtWFDMletYnSUJImJi2Hof0MZUHMAWZ1kXCir3DpsHiss7DY0/hHKt7HJscJMJs2a47cZs+k87m5ZmPe/GlQsmMPoWEIIkaKkGLNR948dI3zL1nQxptjcU3Mpkq0Irxd93egotifgImwbAjcOQoMB4PUh2Nvex1prza4LAfy24SwO9nb8+pYntUu6Gh1LCCFShe391hbomBju/PgTefv3xz6HbbcaXA+7zsLTC/mz1Z82fyVoqgq7AztHwpk1UKcXtJsGTrZ5Gu/ojRB+XX8W33tR9G9WlmYV88t7QQiRoUgxZoOCFizEwTU32Vu1NDpKkmitGfbfMD6t9CkFsxY0Oo5tuB8C/46Hw3OhSifoeQgy5zY6lVUu+YczeuM5jlwPoc/rpelQrTAO9rY3A4AQQiSVFGM2JubWLQJnzqT4n0tsvvVg/ZX1BEQF8GGFD42OkvbFRMGBGeZCrGxz6PYv5LDNoUzuhkYxfut5Np7y5fNXSzD2HS9cnGQoEyFExiXFmA3RWnN36DByf/wRTsWKGR0nSUIfhDL60GjGNhiLo51tXwmaouJi4dgfsGMkFPSC/62DPGWNTmWV0PsxTNt5iT8OXOfdGkXY/nUDcmSW114IIaQYsyH3Nm8m+vp1Ck0Yb3SUJBvvM56GRRrildfL6Chpk9Zwdi1sHQJZ3KDDXChS0+hUVomKiWP+3qvM2HWZJhXysb7PqxTI4WJ0LCGESDOkGLMRceER+I74hYK//Yqdk5PRcZLkqN9RdtzYwcp2K42OkjZd/dc8TEVMJDQdBqWb2OQwFbFxJpb73GLclvNULpyTP7vWplReGbpECCEeZ2gxppT6EPgG0EAk0FtrfcjITGmV/4TxZKldmyw1bbN15KEYUwxD/htCv+r9yO5km5NUpxjfU+YBW/3PQMPvwaMD2Nleh3atNZtO+zJq4zlcszgx+YOqVC2ay+hYQgiRZhlWjCmlygKjgKpa6ztKqRbAcqCoUZnSqvsnTxG2dl26GFNs0elF5HHJQ3P35kZHSTuCr8H2EebR81/tB+8uBAfbnPJn/+VAft1wlsjoOL5rWZ4GZfLY/IUmQgiR0oxsGXsAfKa1vmO5fQjIr5Ry0lpHG5grTdFxcdz96Sfyfv01Drlsu3XhVvgt5pycw+8tfpcvaICIQPMk3sf+gJpdoJcPONtma+HZu2H8tuEc533v8XXTMrStXAg7O3mNhRAiMQwrxrTWV4GrAMr8zTwWWC2F2KOCF/+OXebM5GjfzugoSaK1ZsT+EXSq0Iki2YsYHcdQEfdCOP73L5S/tpi1ug7T9UgCt+eE7XuNjmY1Fyd7ejQsxdQPq5LJQYapEEKIl2F4B36lVBZgHlAEeOMp93cBujy+vGjR9H82M8bXl4ApUyj2+2Kbb0nacn0LN+/dxLuBt9FRDBP9IIojK70peWYqDtmqE/LBRtoWLUtbo4Mlg0wOdjjKgK1CCGEVozvwFwXWAGeAhlrr+4+vo7WeAcx4fHn16tV1yic0lu/wEeR6/z0ylShhdJQkCY8OZ+SBkfxW/zcc7TPeuFKmuDh81s8h/+HRuGQqTEj736lRua7RsYQQQqQRRnbgzwbsAOZrrX82KkdadW/7dqLOnaXgqN+MjpJkE49MpG7BulTLV83oKKlKm0yc3L0Sl13DyK7sCWk8Gs96rY2OJYQQIo0xsmWsJ1AMaK+Uap9geWOtdaBBmdIEU2QkvkOHUWDYUOwy2eZVdQ+dCjjFxqsbWdl2pdFRUtV5n51Eb/yRnNF++NcaQJWmH6FscJgKIYQQKc/IDvy/AL8Ytf+0zH/yZFyqVSNLnTpGR0mSWFMsP+/7ma+qf0VO55xGx0kVNy4cw3/V9xQOP8mVij0p264XRRxte5BeIYQQKcvwDvziUVHnzhG6YiUlVq8yOkqSLTm7hGxO2WhdIv2fmvO/fY3Lf/9AmaBt3HT/mGxf/E6tLNmMjiWEEMIGSDGWhmiTibs//kSePn1wcHMzOk6S3I24y/Tj01nQfIHNXwn6PKHBAZz+awjlby9D52+L6nmY2m75jI4lhBDChkgxloaELF0Kdnbk7PC20VGSbOSBkXQs1xH3HO5GR0kRUfcjOLrsN8pcnIN9znpEdd7JK0VKGR1LCCGEDZJiLI2I9ffHf/wEis6bZ/Mdvbdf387FkIv8Wv9Xo6Mku7jYWA6vmULRY+PJlLks995dSc3yGesqUSGEEMlLirE0wveXkeR8+y2cy5YxOkqSRMZE8suBXxhadyiZ7G37StCEtMnE0S2/k+u/kWS1z0FIy+lUqfG60bGEEEKkA1KMpQHhe/7l/rFjFBg+zOgoSTbl6BSq5atGrQK1jI6SbE7/txG7rYPJERdJaL3v8Wzwjs23XgohhEg7pBgzmCkqirtDhpD/xx+wc3ExOk6SnAs6x5rLa1jeZrnRUZLFlVP7Cf3nR/Ldv8RNr6+o2qoL9g7ykRFCCJG85JvFYAHTpuFcoQJZX3vN6ChJEmeKY8i+IfSu0htXF1ej4yTJnWvnuLn8B0qE/odvmS7kfnMFBZwzGx1LCCFEOiXFmIEeXLxIyJ9LcV+50ugoSfbX+b+wt7Onfen2L145jQr2v8O5vwZTzu8fYgu/i+OnR3klR26jYwkhhEjnpBgziDaZuDN4MG49euCYL6/RcZLEP9KfKUenMKfZHOyU7fWlirgXyvG/R1Du2mJwa0JMt33Uzl/U6FhCCCEyCCnGDBK6YgU66gG53utodJQk++3gb7xZ+k1K5bKtcbZioh/gs8KbEmem4pStChEfbuCVUpWMjiWEECKDkWLMALFBQfiNHUfRmTNQ9vZGx0mSPbf2cCLgBEPqDjE6SqKZ4uLw2TCH/IdG45KpEKHtF1Gtcj2jYwkhhMigpBgzgN+vv5GjVSucK1QwOkqS3I+9z7D/hvH9K9/j4mAbV4Ke2LUCl51DyaHsCGk8Cs96bYyOJIQQIoOTYiyVRfy3n4gDByj5zxqjoyTZjOMzqORWiXqF0n6r0oUju3iw4QdyRvvhV6M/Vd/4WMYKE0IIkSZIMZaKTNHR3B08mPzff4ddlixGx0mSi8EXWXZ+GcvaLDM6ynNdv3CcgFXfUzj8BFcq9qBs214UcUo/MwMIIYSwfVKMpaLAmTNxKlmSbI0bGx0lSUzaxND/htLdqzt5MucxOs5TBdy+xqVlP1ImcCu3i39E1m4LqZU1h9GxhBBCiCdIMZZKHly5QvDCRbivsP3R6VdcWEGMKYYOZToYHeUJYSGBnPprCOVv/Y3O1xrV8xCvuOU3OpYQQgjxTFKMpQKtNXd/HoJr1644FihgdJwkCbwfyIQjE5jeZDr2dmnnStCo+xEcXTaaMhdnYZ+zHlGf7uCVoqWNjiWEEEK8kBRjqSBszRriQkPJ3elDo6Mk2ZhDY2hdojXlcpczOgoAcbGxHF4zlaLHvMmUuQxh766gZvnqRscSQgghEk2KsRQWFxKC72+jKDJlMsrGJ5nef2c/h3wPsbLtSqOjoE0mjm79g5z7RpLNPhshzadSpVZTo2MJIYQQL822qwMb4DdmDNmbNsXF09PoKEnyIO4BQ/8byqCag8jsaOyk2Wf2b4Qtg8kZF0Fo3e+o3PAdGaZCCCGEzZJiLAVFHj5M+M5dlFj7j4EhgmD3GLh5MEmbmW13j5LE0HDzL8AvyZPtJd2PjsMvOIRc0aHc8PqSMq26Ym/jrY1CCCGEfJOlEG0ZUyzft4Owz5Yt9QNER8B/U2DfFKjYDl4fDFZO4n0l8i5/+PzKX9WHgHPuZI2ZGH73olhy8AbHAkJo61WIpq83I7+LbY/TJoQQQjwkxVgKCZw7D4cCBcjWrFnq7jguBnzmw85RUKw2fLYFXEtavTmtNUM3daaLV3fyl2mRjEFfLCgimsnbL7LMJ4xOr9RkXMcSZHd2TNUMQgghREqTYiwFRN+4QdDcuRT/+y+UUqmzU5MJTq+AbcMgZ1F4fwkUrJLkza65vIbw6HDeK/deMoRMnMjoWGbvvsKcf6/QyrMgm76sT95szqm2fyGEECI1STGWzLTW3B0ylNyffopT4cKps9NL22HLYPPPLcdCyYbJstmQqBDGHhrLpMaTcLBL+bdKTJyJJQdvMHHrBWq652ZF97oUd5PTkUIIIdI3KcaS2b0NG4i9exfX/32S8ju7fcRchIVch0Y/QIV2kIxXFY7zGUez4s2o5FYp2bb5NCaTZu2JO4zZdI4iuTMz++MaeBSWqYuEEEJkDFKMJaO4sDB8fxlJIW9vlGMK9m0KvATbhsK1ffDaN1D1Y7BP3v0d9j3Mnlt7WNV2VbJu93G7L/jz64azKBTD2nlQr7Rbiu5PCCGESGukGEtG/t7eZH3tNTJXTXpfrae65ws7f4VTK6B2d2g7GZyS/zReTFwMQ/YNYUCNAWR1yprs2wc4fjOEXzec5Vbwffo1K0uLSgWws0ul/nVCCCFEGiLFWDK5f/w4YZs3U3LNmuTfeFQo/DsBDs0Grw+g5yHI4pr8+7GYd2oehbIWokmxJsm+7SsBEYzedI6DV4Lo3bg079YogqO9DNgqhBAi45JiLBno2Fju/PgT+fr3xz5nzuTbcEwUHJwFe8ZB6abQdZf5SskUdCPsBgtOL2BJqyXJeiWoX1gU47deYN2JO3z2aglGve1JZid5+wkhhBDybZgMghYsxCF3LrK3apU8GzTFwfE/YfsIyFcJPl4D+Sokz7afQ2vNsP3D+F+l/1Eoa6Fk2WZYVAzTd15i8f7rdKhWmG1fNyBXFqdk2bYQQgiRHkgxlkQxt28TOGMGxZf8kfSWJK3h/AbYOgQyZYc3Z5oHbk0lG65uwP++P50qdErytqJi4lj03zWm7rhEw3J5Wdv7VQrldEmGlEIIIUT6IsVYEmituTt0GLk+6oRT8eJJ29j1/2DzT+b+YY1/hLLNIbUGjAXCosMYdXAUYxuMxdHO+isz40ya5T438d5ygfIFsvNHl1cok8+A6aCEEEIIGyHFWBLc27KF6KtXKTTe2/qN+J42t4T5noQGg6ByR7CzT7aMiTXBZwINijTAK6+XVY/XWrPljB+jNp4lh4sj4zt6Ub146s9jKYQQQtgaKcasFBcege/wERT89VfsnKzoAxVyA3b8Auc3Qr0vocM8cDRmyp9j/sfYdn0bK9qusOrxh64GMXL9WcKiYujfrByNy+dNvWmghBBCCBsnxZiVAiZOIMsrr5ClVs2Xe2BkEOweA0cXQ/XO0NsHnI0bbT7GZB5T7OvqX5Mj08vlOHf3HqM2nuXMnXt82aQM7asUwl7GChNCCCFeihRjVrh/6hSh/6ylxD8vMaZYdAT8NwX2TYGK7aD7f5Atf4plTKzFpxfj6uxKC/cWiX7MzeBIxm2+wM7zfnR7rSST3q+Ks2Pqn1oVQggh0gMpxl6Sjovj7k+DyfvVVzjkyvXiB8TFgM8C2DUKir4Cn20B15IpHzQRboffZvbJ2SxusThRpxWDI6KZvP0if/vc5MNaxdjWrwHZnVNw2ichhBAiA5Bi7CUF//4Hds7O5Hiz/fNXNJng9ErYNgxyFoGOv0OhqqmSMTG01ozYP4IPy39I0ezPH0g2MjqWOXuuMHvPFVp6FmBT3/rkzW5M/zYhhBAivZFi7CXE+PoSMHkyxRYven5L0qXtsGUwoKHlaCjZKLUiJtrW61u5fu86YxuMfeY6MXEmlhy8wcStF6jpnpsV3etS3C3558IUQgghMjIpxl6C74hfyPleRzKVfMZpxttHzEVYyHVo9D1UaA92aW/exfDocEYeGMnIV0fiZP/klaAmk2btiTuM2XSOIrkzM/vjGngUNu4iAyGEECI9k2Iske7t2EHUmTMU/HXkk3cGXjKfjry2F177Bqp+DPZpty/VpKOTqF2wNtXzV3/ivj0XAvh1w1kAhrXzoF5pt9SOJ4QQQmQoUowlgikyEt+hw8g/5GfsnBP0lbrnCzt/hVMroHZ3aDsJnNL2abxTgadYf2U9K9uufGT5iZuh/LrhLDeDI+nXrCwtKhXAToapEEIIIVKcFGOJEDBlCi5eXmStW9e8ICoU/p0Ah2ZD5feh5yHI4mpsyESIM8UxZN8Qvqz2JbmczVeCXgmIYPSmcxy8EkTvxqV5t0YRHO3T3qlVIYQQIr2SYuwFos6dJ2T5CkqsWgkxUeYCbM84KNUEuu6CnM+/EjEtWXJuCZkdMtO2ZFv87kUxYesF1h6/Q+d67ox625PMTvJ2EEIIIVKb0lobt3OlWgK/AJmA40BnrXVYYh5bvXp1fejQoZSMhzaZuPbe++Ro15Zc5TVsHwH5Kpkn8s5XIUX3ndzuRtylw5oOTGk0m41HNIv2X+PtqoXp0bAUubJYMZ2TEEIIIV7GM/v+GNYUopTKA8wF6mqtLyilfgVGAt2NyvS4kKVLISqUnEHj4XB2eHMmFKttdCyrjNg/kjIuTfnfjGs0KJuXtb1fpVBOF6NjCSGEEBmeYS1jSqkPgPe11i0tt4sDx4CcOhGhUrpl7MT+9cR2+4pN7e05WbQSwU7GT11krVgecOf+Wao5jGDgG5Uoky+b0ZGEEEKIjOaZLWNGFmMDgeJa626W2w5ADJAj4alKpVQXoMtTNlEWOJfCMd2AgBTeR1qWkY8/Ix87ZOzjl2PPuDLy8WfkY4fUOf4ArfUbT7vDyB7bdsDTKsG4hDe01jOAGamS6DFKqUNa6ycH48ogMvLxZ+Rjh4x9/HLsGfPYIWMff0Y+djD++I0cw+A6UDDB7UJAsNY6wqA8QgghhBCpzshibBPwilKqtOV2N2CVgXmEEEIIIVKdYacptdZ+Sqn/AX8rpZyAS8BHRuURQgghhDCCoaN8aq3XAeuMzCCEEEIIYSSZ9+b5DLlwIA3JyMefkY8dMvbxy7FnXBn5+DPysYPBx2/oCPxCCCGEEBmdtIwJIYQQQhhIijEhhBBCCANlmGJMKdVSKXVcKXVOKfWXUir7M9ZTSqn5Sql+CZbZK6W8lVJnlVIXlVLdEtxXWim1Syl1Wil1QClVLjWO52Uk8dhdlFJzlFInlVKnLD+7WO5rrZQKUkodTfAvzc21lJTjtywPeOwYP7AsT++v/d+PHXeoUmq15b5089orpT5USh2zHMNepVR1y/J0/7l/zrHb9Oc+Kcduuc9mP/OQ5Nfepj/3iTz2npb39Uml1CqlVF7LcuM+81rrdP8PyAP4AaUtt38FpjxlvfLANiAC6JdgeXfMV306ALmAs0BNy30HMM+xCdAcOImlL15a+JcMxz4MWIC5cLcH/gCGWO77BfjW6GNM4eMvC5x/xrbT9Wv/2Do1gGtAkfT02lte3ztAAcvtFsB1y8/p+nP/gmO32c99Mhy7zX7mk+P4H1vPpj73iTz2asBVzFMvAowGplt+Nuwzb/iTl0ov0AfA2gS3iwOhjz+RwCTgfWAej34hbwY6JLg9GJiAedaAMMAuwX1XgapGH3MyHntToEyC2/2B+ZafdwJbME/wvhuob/TxpsDx/w84Yzm+48CPmL+c0v1rn+B+J+A00D7BsnTx2luWtUxwOy8QbTnmdP25f8Gx2+znPhmO3WY/88lx/AmW2dznPjHHblnuaPnfGfMfGiMstw37zGeU05RFgBsJbt8EsgOPNK9qrXtqrX9P5OMLW5bf1lqbnnJfWpGkY9dab9JanwdQShUD+gJ/We4OBKYBXsAgYIVSKi0dOyT9tXfA/MvnDaA+0AzoRQZ47RPojPlYVyRYli5ee631Va31WjCfqgXGAqu11tHPeHy6+dw/79ht/HOf1Nfdlj/zkPTjf8gWP/eJ/Z0Xo5RqZ7m/PjD3OY9Plc+8oYO+pqJETUr+Eo9Xlsc+bbsP70srknrsACilqgErgEla638AtNZvJlhlj1JqL9CE/39jpwVJOn6t9cyEt5VSY4HemJusM8RrD3wJdEm4IL299kqpLJhbBotg/hJ+2uPT5ef+Gcf+8D5b/Nwn6dht/DMPyfTaY5uf+0Qfu9Z6JbBSKfU5/F979x9kZVXHcfz9sYUk/EGsGBSDWgSEZEP5o4IMskHKMIaYIH+QZVkg/ZhmDDP7nQzNMBNTGemMiiUzQMlQg4QVBdloDhSUoKXmgCIQuLKAMyAG3/44Z+nhmWX3LvfSZXc/r5k7cM9znvOcc589d773POd5Dg9KGtzK/v+3Pt9dRsaqXZS8vP/rSVHxs8CA/MuivO1kUfWC7JKmkoZvb46I2Tmtj6RbSm0X8EoN6lxLVbVf0rWSLigmkdrYXc79SNKPtjWFtC517iUNAh4mfbGOjYjmY+zf5fp9G23vzP2+qrZ38j4PtTn3nbXft9t2SYMljS7kuRs4hzRHrG59vrsEY9UuSv5L4JOSGiT1AaYCyyJiK/A0MAVA0uXAYeCxWlW8Bqpqu6QJpGvm40qXsvYBNwKTcr6RwMXAylpUuoaqPfcjgG/nu2x6ATOBxd3h3GfvBX4feZJE1mXOfb4TbDWwNCKmRsT+wuYu3e/bansn7/fVnvfO3Oeh+vZD5+33lXznDQAWSTorv78a2BgRTdSzz9dq8tnJ/iLdLfI30sTM5UBf4EJgQyt5F3D0JO4GYB6wCXiqtO3NpD/qjcA6TrLJnDVo+z+BXcCGwuv2vO1C4JHc9g2kX1d1b2+N2/8a0i+nx/O5n83/Vq7o0uc+p90O3NpK3i5x7knzXg6V/r43AI1dvd+30/ZO3e+rbHun7vPVtj9v77T9vr225zzTC21YAZyX0+vW570ckpmZmVkddZfLlGZmZmYnJQdjZmZmZnXkYMzMzMysjhyMmZmZmdWRgzEzMzOzOnIwZmYnFUnn1bsO9dBd221mDsbMrB2SVkuaWUG+90jaXOWxRpKeCn48+46QdMxn9Uh6taSvSfqHpH2SnpM0T9Jpx13hGpF0JbC4nTw3SZrRgTIvkxSSbiql95H0R0mnHmd1zazGHIyZWU1ExEMRcW6VxZwJ9KhBdY4iqQF4ELgEuDIiTgdGA8OBX9X6eMehL218H0t6I3ANcEcHyrwBuAuYIelI2ZGWvbkfuPW4ampmNedgzMwqJumbku6TtFzSS5IelzQubxsj6YVC3kslrZXULOlRSRcXto2QtCaPUD0j6WpJZwO/Bhpz2Y2Sekn6gaTnJW2TNFdSz1zGKZJmS2qStI20dMmxXAUMBiZHxJMAEbGFtBRKs6T+ks7NI0lHRsokrZN0Xf7/Zkl35uPNz5/F8vwZbJV0hqS35pHEZkmPSfpgoazNkm6W9KSkPXnf10q6CPgJMFLSjmPU/8vAwog4lMsKSZ+XtEXSi/mc9Cwcqx9wBXALcBD4UKm8BcBn8pIvZlZnDsbMrKM+CnyfNJqzAvhhOYPSIsTLgduAs4C5wApJfXPQ8ACwirT8zBTSiE9f4ANAU0ScFmmtuLnAMOAC4G2kZU2+mg8zHZgMvB04H3hXG3UeD6yIiAPFxIjYFRGTIuJYQVDZIGAgMCu/f1/+PM4HgrQ23pLc5s8BCyUNKew/kTQiN5S0vMpnI2ItaQ299RHRv3zA/HldA/yitOky0jqK7wQuBz5S2HYd8JuI2AncmetSbPce4NFcdzOrMwdjZtZRj0TEqog4CCwkBRVlVwF/iIhlEfGfiPg5aVHdycAooDfw3Yg4mIOR0cC2YgGSBHwCmBURTRGxC/gG8OmcZQrwo4jYEhG7ga+3UedG0lqL1bo/IvZHxN78fn1EbMzBzRXAzoj4cW7zamAZKTBqcUdE7MzB30pa/+zK3gEciIhnSunzImJfHul7uFTWp0hBGMA9wChJw0r7ryMtCG1mddZQ7wqYWadTDGpeAdRKnkHAeEnNhbQewJ+AvcD2iDjcsiEiNgCk+OuIfkAvYHVhYr6AnnnyeX/g+UL+zW3UeQfwutY2SDo7jyBVojyCVnw/CBheanMDsLTwvvzZVfKDeCCwvZX0VsuSNAYYAtxb+Nx6ADPzq8V2YGwFxzezE8zBmJmdCNuBxRExrSUhP7qhiXS5cYCkU1oCsnyX4F9KZTSR5juNbBkVktQb6B8RB/I8sXMK+d/QRn1WAnMl9YqI/YU69QOekzQeeDon9yzs11gqp3y3ZvH9dtKo4aWF8gcC+6lO0LGrGDeQLh3PLqS9G1gg6SsRsS+nNQCHqqybmdWAL1Oa2YmwCJiQH68gSaOAvwMXkeYq7QZmSWrIE/tvI42YvQycKqlnnqy+EJiTH8fQmzS3bEE+xs+AL0oaKukM4Ftt1GcJsAVYIulNAPmy3VLSaN1q4N/AHuBaSa+S9HGODvba8wAwTNLH8v5vyW2dWMG+LwOnqzQ0mD0LDKikApIagUnAPRGxo+VFuly6l6MvmQ4AtlZSrpmdWA7GzKzmIuIp0uTw75ECnJ8CXyrMNZsAvB94AbgPuD4iniAFbJuAJkmDgS/kPJtIgcOZpLliAHeT5kU9BPwLWNtGfQ4B40ijX6skvUQaLfsz8OFIDgIzSJfydgNjSAFWpW1+kXSjwHTSqN5vgfkRcVcFu6/J/+5u5flffwWQNLSCcqYBmyNifaluh0nB642FgO8S4HcVlGlmJ5gijvmMRDOzikkaCyyKiFbnZtnxkzQf2BIRc2pUXiPwBDAkP3fMzOrII2NmVrU8mjMcqHQivHXMHGCa0sNra+F60p2dzTUqz8yq4GDMzGphDvAd0nPBrMbyA2rvJT2PrCr5Qa8TOXqCv5nVkS9TmpmZmdWRR8bMzMzM6sjBmJmZmVkdORgzMzMzqyMHY2ZmZmZ15GDMzMzMrI7+C+8HUi8RNclKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "stims = np.arange(0.1,.31,.02)\n",
    "\n",
    "iclamp = h.IClamp(h.cell.soma[0](0.5))\n",
    "iclamp.delay = 300\n",
    "iclamp.dur = 900\n",
    "\n",
    "stim_start = 600\n",
    "stim_dur = 300\n",
    "sweep_len = 950\n",
    "dt = 0.2\n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=True, iclamp_amp=iclamp_amp, ax=ax, label='original', stim_start = stim_start, stim_dur = stim_dur, sweep_len = sweep_len, dt = dt)\n",
    "\n",
    "init_settings(gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=False, iclamp_amp=iclamp_amp, ax=ax, label='Ri increase (gpas=0, hcn=%.2f)'%(hcn_reduct), stim_start = stim_start, stim_dur = stim_dur, sweep_len = sweep_len, dt = dt)\n",
    "\n",
    "init_settings(nav12=0)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=True, iclamp_amp=iclamp_amp, ax=ax, label='Hom', stim_start = stim_start, stim_dur = stim_dur, sweep_len = sweep_len, dt = dt)\n",
    "\n",
    "init_settings(nav12=0, gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=False, iclamp_amp=iclamp_amp, ax=ax, label='Hom + Ri increase (gpas=0, hcn=%.2f)'%(hcn_reduct), stim_start = stim_start, stim_dur = stim_dur, sweep_len = sweep_len, dt = dt)\n",
    "\n",
    "plt.legend()\n",
    "plt.title('1. FI Curves (WT/Hom x Gpas,HCN Reduction)')\n",
    "# plt.savefig(plot_path+'fig_1_fi_curves.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Example Traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original: 82 MOhm\n",
      "Ri increase: 104 MOhm\n",
      "82.87821389708057 104.22053166903765 25.751420992807954\n",
      "diff: 21 MOhm (25%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_amp = -0.05\n",
    "init_stim(stim_start=600, stim_dur=300, sweep_len=1500, dt=0.2, amp=step_amp)\n",
    "\n",
    "iclamp = h.IClamp(h.cell.soma[0](0.5))\n",
    "iclamp.delay = 300\n",
    "iclamp.dur = 900\n",
    "\n",
    "iclamp.amp = 0\n",
    "init_settings()\n",
    "Vm, I, t = run_model()\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(t, Vm, lw=0.7, label='original')\n",
    "start = int(0.6/0.2e-3)\n",
    "end = int(0.9/0.2e-3)\n",
    "original_ri = (Vm[end]-Vm[start])/step_amp\n",
    "original_title = 'original: %d MOhm' %(np.round(original_ri, 2))\n",
    "print(original_title)\n",
    "\n",
    "iclamp.amp = 0\n",
    "init_settings(nav12=0)\n",
    "Vm, I, t = run_model()\n",
    "plt.plot(t, Vm, lw=0.7, label='nav12=0')\n",
    "start = int(0.6/0.2e-3)\n",
    "end = int(0.9/0.2e-3)\n",
    "# original_ri = (Vm[end]-Vm[start])/step_amp\n",
    "# original_title = 'original: %d MOhm' %(np.round(original_ri, 2))\n",
    "# print(original_title)\n",
    "\n",
    "\n",
    "iclamp.amp = iclamp_amp\n",
    "init_settings(gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "Vm, I, t = run_model()\n",
    "plt.plot(t, Vm, lw=0.7, label='gpas=0, hcn=%.2f)'%(hcn_reduct))\n",
    "gpas0_ri = (Vm[end]-Vm[start])/step_amp\n",
    "gpas0_title = 'Ri increase: %d MOhm' %(np.round(gpas0_ri, 2))\n",
    "print(gpas0_title)\n",
    "diff_ri = gpas0_ri-original_ri\n",
    "print(original_ri, gpas0_ri, diff_ri/original_ri*100)\n",
    "diff_title = 'diff: %d MOhm (%d%%)' % (np.round(diff_ri, 2), np.round(diff_ri/original_ri*100, 2))\n",
    "print(diff_title)\n",
    "\n",
    "iclamp.amp = iclamp_amp\n",
    "init_settings(nav12=0, gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "Vm, I, t = run_model()\n",
    "plt.plot(t, Vm, lw=0.7, label='nav12=0, gpas=0, hcn=%.2f)'%(hcn_reduct))\n",
    "\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Voltage (mV)')\n",
    "plt.legend()\n",
    "n50pa_step_title = '2. -50 pA for 300 ms (WT/Hom x Gpas,HCN Reduction): %.2f MOhm -> %.2f MOhm (%.2f%%)' %(original_ri, gpas0_ri, np.round(diff_ri/original_ri*100, 2))\n",
    "plt.title(n50pa_step_title)\n",
    "# plt.savefig(plot_path+'fig_2_n50pa_step.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 25% Ri Increase (Gpas = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "hcn_reduct = 0.5\n",
    "iclamp_amp = 0.0294"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. FI Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "stims = np.arange(0.1,.31,.02)\n",
    "\n",
    "iclamp = h.IClamp(h.cell.soma[0](0.5))\n",
    "iclamp.delay = 300\n",
    "iclamp.dur = 900\n",
    "\n",
    "stim_start = 600\n",
    "stim_dur = 300\n",
    "sweep_len = 950\n",
    "dt = 0.2\n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=True, iclamp_amp=iclamp_amp, ax=ax, label='original', stim_start = stim_start, stim_dur = stim_dur, sweep_len = sweep_len, dt = dt)\n",
    "\n",
    "init_settings(hcn=hcn_reduct)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=False, iclamp_amp=iclamp_amp, ax=ax, label='Ri increase (hcn=%.2f)'%(hcn_reduct), stim_start = stim_start, stim_dur = stim_dur, sweep_len = sweep_len, dt = dt)\n",
    "\n",
    "init_settings(nav12=0)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=True, iclamp_amp=iclamp_amp, ax=ax, label='Hom', stim_start = stim_start, stim_dur = stim_dur, sweep_len = sweep_len, dt = dt)\n",
    "\n",
    "init_settings(nav12=0, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=False, iclamp_amp=iclamp_amp, ax=ax, label='Hom + Ri increase (hcn=%.2f)'%(hcn_reduct), stim_start = stim_start, stim_dur = stim_dur, sweep_len = sweep_len, dt = dt)\n",
    "\n",
    "plt.legend()\n",
    "plt.title('3. FI Curves (WT/Hom x HCN Reduction)')\n",
    "# plt.savefig(plot_path+'fig_3_fi_curves.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Example Traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original: 82 MOhm\n",
      "Ri increase: 103 MOhm\n",
      "82.87821389708057 103.74363062720676 25.17599710345734\n",
      "diff: 20 MOhm (25%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_amp = -0.05\n",
    "init_stim(stim_start=600, stim_dur=300, sweep_len=1500, dt=0.2, amp=step_amp)\n",
    "\n",
    "iclamp = h.IClamp(h.cell.soma[0](0.5))\n",
    "iclamp.delay = 300\n",
    "iclamp.dur = 900\n",
    "\n",
    "iclamp.amp = 0\n",
    "init_settings()\n",
    "Vm, I, t = run_model()\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(t, Vm, lw=0.7, label='original')\n",
    "start = int(0.6/0.2e-3)\n",
    "end = int(0.9/0.2e-3)\n",
    "original_ri = (Vm[end]-Vm[start])/step_amp\n",
    "original_title = 'original: %d MOhm' %(np.round(original_ri, 2))\n",
    "print(original_title)\n",
    "\n",
    "iclamp.amp = 0\n",
    "init_settings(nav12=0)\n",
    "Vm, I, t = run_model()\n",
    "plt.plot(t, Vm, lw=0.7, label='nav12=0')\n",
    "\n",
    "\n",
    "iclamp.amp = iclamp_amp\n",
    "init_settings(hcn=hcn_reduct)\n",
    "Vm, I, t = run_model()\n",
    "plt.plot(t, Vm, lw=0.7, label='hcn=%.2f'%(hcn_reduct))\n",
    "gpas0_ri = (Vm[end]-Vm[start])/step_amp\n",
    "gpas0_title = 'Ri increase: %d MOhm' %(np.round(gpas0_ri, 2))\n",
    "print(gpas0_title)\n",
    "diff_ri = gpas0_ri-original_ri\n",
    "print(original_ri, gpas0_ri, diff_ri/original_ri*100)\n",
    "diff_title = 'diff: %d MOhm (%d%%)' % (np.round(diff_ri, 2), np.round(diff_ri/original_ri*100, 2))\n",
    "print(diff_title)\n",
    "\n",
    "iclamp.amp = iclamp_amp\n",
    "init_settings(nav12=0, hcn=hcn_reduct)\n",
    "Vm, I, t = run_model()\n",
    "plt.plot(t, Vm, lw=0.7, label='nav12=0, hcn=%.2f'%(hcn_reduct))\n",
    "\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Voltage (mV)')\n",
    "plt.legend()\n",
    "n50pa_step_title = '4. -50 pA for 300 ms (WT/Hom x HCN Reduction): %.2f MOhm -> %.2f MOhm (%.2f%%)' %(original_ri, gpas0_ri, np.round(diff_ri/original_ri*100, 2))\n",
    "plt.title(n50pa_step_title)\n",
    "# plt.savefig(plot_path+'fig_4_n50pa_step.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}