examples/landau-zener-stuckelberg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QuTiP example: Landau-Zener-Stuckelberg inteferometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "J.R. Johansson and P.D. Nation\n",
    "\n",
    "For more information about QuTiP see [http://qutip.org](http://qutip.org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qutip import *\n",
    "from qutip.ui.progressbar import TextProgressBar as ProgressBar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Landau-Zener-Stuckelberg interferometry: Steady state of a strongly driven two-level system, using the one-period propagator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the parameters and start calculation\n",
    "delta  = 1.0  * 2 * np.pi  # qubit sigma_x coefficient\n",
    "w      = 2.0  * 2 * np.pi  # driving frequency\n",
    "T      = 2 * np.pi / w     # driving period \n",
    "gamma1 = 0.00001        # relaxation rate\n",
    "gamma2 = 0.005          # dephasing  rate\n",
    "\n",
    "eps_list = np.linspace(-20.0, 20.0, 101) * 2 * np.pi\n",
    "A_list   = np.linspace(  0.0, 20.0, 101) * 2 * np.pi\n",
    "\n",
    "# pre-calculate the necessary operators\n",
    "sx = sigmax(); sz = sigmaz(); sm = destroy(2); sn = num(2)\n",
    "\n",
    "# collapse operators\n",
    "c_op_list = [np.sqrt(gamma1) * sm, np.sqrt(gamma2) * sz]  # relaxation and dephasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ODE settings (for list-str format)\n",
    "options = Options()\n",
    "options.atol = 1e-6 # reduce accuracy to speed\n",
    "options.rtol = 1e-5 # up the calculation a bit\n",
    "options.rhs_reuse = True # Compile Hamiltonian only the first time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform the calculation for each combination of eps and A, store the result\n",
    "# in a matrix\n",
    "def calculate():\n",
    "\n",
    "    p_mat = np.zeros((len(eps_list), len(A_list)))\n",
    "    \n",
    "    H0 = - delta/2.0 * sx \n",
    "    \n",
    "    # Define H1 (first time-dependent term)\n",
    "    # String method:\n",
    "    H1 = [- sz / 2, 'eps']\n",
    "    # Function method:\n",
    "    # H1 = [- sz / 2, lambda t, args: args['eps'] ]\n",
    "    \n",
    "    # Define H2 (second time-dependent term)\n",
    "    # String method:\n",
    "    H2 = [sz / 2, 'A * sin(w * t)']\n",
    "    # Function method:\n",
    "    # H2 = [sz / 2, lambda t, args: args['A']*np.sin(args['w'] * t) ]\n",
    "    \n",
    "    H = [H0, H1, H2]\n",
    "    \n",
    "    pbar = ProgressBar(len(eps_list))\n",
    "    for m, eps in enumerate(eps_list):\n",
    "        pbar.update(m)\n",
    "        for n, A in enumerate(A_list):\n",
    "            args = {'w': w, 'A': A, 'eps': eps}\n",
    "\n",
    "            U = propagator(H, T, c_op_list, args, options=options)\n",
    "            rho_ss = propagator_steadystate(U)\n",
    "\n",
    "            p_mat[m,n] = np.real(expect(sn, rho_ss))\n",
    "\n",
    "    return p_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.9%. Run time:  10.91s. Est. time left: 00:00:01:29\n",
      "20.8%. Run time:  19.61s. Est. time left: 00:00:01:14\n",
      "30.7%. Run time:  27.71s. Est. time left: 00:00:01:02\n",
      "40.6%. Run time:  34.97s. Est. time left: 00:00:00:51\n",
      "50.5%. Run time:  41.99s. Est. time left: 00:00:00:41\n",
      "60.4%. Run time:  49.02s. Est. time left: 00:00:00:32\n",
      "70.3%. Run time:  55.84s. Est. time left: 00:00:00:23\n",
      "80.2%. Run time:  63.31s. Est. time left: 00:00:00:15\n",
      "90.1%. Run time:  71.95s. Est. time left: 00:00:00:07\n"
     ]
    }
   ],
   "source": [
    "p_mat = calculate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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BvBnAXyN5kf3yANt6LhKf9j0AICJnichxEW08nL7kXwHg3wD8XdntQzLm/QUSH/3DsP715xkA9qVtfZiIfH/6paiBRMPQDY6QzxVIXuxrHASwR0S+Q0ReCGAGiZthH5KvDa9T1S4SY2QHzPic3vPHAbiyQFsICaABQKrIeVj/VQbgmD/2gRjtFMA7AHy/qn4vkumJvfqEFwM4G8nLqPff85C4Mq4D8BokL51bATwAwEWq2uqp49MAXgLgF1S1PcC2/n8AmgDuEJHDAN4LYEpVr8lro4g8AcDPAPjF9GVYdvt+HMn0ut9U1bvX/gG4Gol//VFI/P+vBXA/ks/7JyMxSvrl7QAuSo0dIOlz70Zyr16CZBrn9yDRSnwOyVcGqOoSEgPlJhG5s6e+5wC4ak1vQMhWkXAWFCFk1IjI7wP4kqq+t6T6TkTyS/ujAJqq+mdl1JvWXUpbReRxAH4RwCUlGCi99ZZ6Lfs89h8BuFdVX1dCXZ8D8BJVvSG3MCER0AAgZMwQkQci+aX4veYXfNH6ZpHEDng5EnHepwFckP7S3GrdpbVVRO5C8mWgBeA6VX3ZOLWPkEmDBgAhY0QqOvsggP+pqnYO+Vgx7m0d9/YRMmqoASBkTBCRKSS/Vn9/3F9Y497WcW8fIeMADQBCxocXIlGv/590zruNAjdOjHtbx719hIwcugAIIYSQCsIvAIQQQkgFoQFACCGEVBAaAIQQQkgFoQFACCGEVBAaAIQQQkgFoQFACCGEVBAaAIQQQkgFoQFACCGEVBAaAIQQQkgFoQFACCGEVBAaAIQQQkgFoQFACCGEVBAaAIQQQkgFoQFACCGEVBAaAIQQQkgFoQFACCGEVBAaAIQQQkgFoQFACCGEVJChGQAicqaIfExEbhKRG0XkV9P1x4vIlSLytfT/4zbY/8Vpma+JyIuH1W5CCCFkEhFVHc6BRE4FcKqqfkFEdgO4FsAPAbgYwEFVfa2I/DaA41T15Wbf4wFcA+B8AJru+zhVPbTZMU88vq5nnzkNpDtZ1F1r2g0xy/kUqTcGr167Jqa9tp62U2/brOqamnZKWPOydjPLdefYdbNb3WmhPQfb3k7E9Y2pt+vUY+vumCLe9ZyVrB3dMNcBCO/TdAnXAQjb23IuTd69W3LGgLqpd8o5cdvmmPbavtbUsGLb3t3OsVfMNbZFirQXCO9TB+G97JrrJeZ6Tju/q1a1Y9aEx7bPRs2UsctAeI29Pm2pOe2zNcc8c/Y62PsGAHPm2bD3LWlPljLGiGTd5njXM64OzS2TV3dMe70y9t55R7n2+tX7VfWkTRuQMhVTqAxU9S4Ad6V/L4jIzQBOB/BcABekxd4G4CoALze7PxPAlap6EABE5EoAFwK4bLNjnn3mNK7+8FkAgK7zIK9qK7fdszKdWfYeHktL27llpqX/S+/Vax/4acm+dr322noOdBtBmQPd7H7L3ex1ePxsdhkArmtm69ldC9u7r5btsntkNihjr41t76I2g33sdYip17v/R7qrZjm7veY8cQ+e2pVZvqW1FJTpmEf1NGMd7ZKZYJ+86wAARzXb3m+3w/vd0Gw99t5d2wyv507JXptT7KiMsM0x7T1k+trt7fA+NTTbvqfMBUVwY2s5szxjnu8T6+F1yGsvEI4TR5xnY6GbfZnPGQPg5Hq2PwDAbe2FzLLtD0D4bNj22rEICK/xasTYM18L+5odJ2KeOXsdVh1j7pzp7LX4cnPFaU+2njLGCABoI1uvPUfvelq890bHGDGe0ZVXd8xY3gqMxvDeeeN7/dSv3b7pwTP7jwARORvAYwB8DsApqXEAAHcDOMXZ5XQAd/Qs35mu8+q+RESuEZFr7jsQXkBCCCGEjMAAEJFdAP4FwMtU9WjvNk38EVvySajqpap6vqqef9IJ3kdoQgghhAzVABCRaSQv/3eq6nvT1fek+oA1ncC9zq77AZzZs3xGui6amE/3hBBCSFUYmgZAEqXMmwHcrKp/0bPpcgAvBvDa9P8POLt/GMAf9cwQeAaAV/RzfN+Xk/3YUHeEbVXAitiAUARmsf7+URMj6Cljn5bj57Qc7oY+S+tT9/pjGUxLWO+0WP9t1j85J6E/0vqoPSFWEVqBuMzzhVs/8Y6gzG7JuvcciUIhrH/Xw5ZYjhBSNzT7jE1LjFgv/6Tq9tmN+H7qnWPNGQP65aiG2oJvtBfNmvCrrB1rivxYC64DQpFikec9hrLqHdSYsBnD/Fn83wD8JIDvF5Hr0n8XIXnx/4CIfA3A09NliMj5IvImAEjFf38A4PPpv1evCQIJIYQQ0j/DnAXwSWw8i+5pTvlrAPxsz/JbALxlMK0jhBBCqkVlHOPUABBCCCHrDO0LwKiJmZu/HSnD/+T5d60vedr4XM+bCSdn2zm+XccfaXUX3QhfqMWbd1uWHy7UgWSPtdAN/ZyWjobG5nRtMP49ez1tgCEAmM5xDD+gHm5f6mbb23HDOm2Od59sX/O0Jl78CIude2+vblmaBa8ee3dbEceyWoc5x987rB8pnr88D+9e2vOeQTjt2sbICDUBTiCgAfnqy8Jei0FpC4YBfxYTQgghFYQGACGEEFJBKmMAFAm9SwghhEwqlXkrxsRedl2lEe6dUczf3Aoxc4etb3anM1fcMieDCb0ck+BkUFjfbS1Cs2Bj2QNAy8TsD73WxQg0C86cdOsvt1gdARDGdd9Xkp/TJpWx2hIAmI+IxzFvDPrFAWl8YhJP2Tn+Hl0bP2KEbuOYOAD2mfP6iCXm+d/paVTMsbezT71MhjHuVeYLACGEEELWoQFACCGEVBAaAIQQQkgFqYwBQBEgIYQQsk5l3oqDDARkA3h0IgReVjg4ykiFnujGtmY2Qvxmy1ghGRAmgykioPQESVZIVCTYCRBei3qB9u2r22Q2nkBqMEInL7DOXI7xa68dAMzXBiPoDI/tJS/q/95NDyiRV8sRzIVBh/oXyHkBm6zoq0gf9hKaeedQBjbQV8wY4d2nPNFf0Wd5WHhivSLje4zQ0go4t5rEabyvLCGEEEIGAg0AQgghpIJUxgCgBoAQQghZpzJvRU8DYIN8eL6oGJ+L9feMMllEES2B5zecMetifKx2n9WS4liMU6ClPdJ01mYTBO2rhWVaRg9RVp+wfkPv7ucda07CRD+rapMBFUnalH/f5jwNQMSwNG3avNxtm+25VUThPRs22NJ0hO97vmb28RJwjdHvsXBMC6mZU8gLOAX459gytbvXfMJwdQ2mGx3uhu+sHfUwyNhWGJ8eRwghhJChQQOAEEIIqSCVMQCoASCEEELWqcxbcVVbwTo7P7YKvqdY5h2/cB5WJ+AlzumoXXb8p7J5mUEqAqx+w/p3YyzmaXde8ObLMcQkB6k7XbhIv7bn6c913rw93r2199+bQ1+kvUViB8QQlwQnpoyJFeLUOyitkNVvxPQj276mex2sTij/HnhlYq7xpOElZLJYTdUgqMwXAEIIIYSsQwOAEEIIqSA0AAghhJAKUhkDYFbKnT/ZDzE+N1JtJrGPTOI5ETJJVFoEaCkS7CSWINHHwI7UP17woFqBa2GDfNQRBrIIxXD5xxnmi8QKseyV2VnLt5n9gCjZc7BiOEix8wwT03jJVjZvs7fdCvFixJoeeefkBeyJCWY1KMFczD2wrbMCP49Z88Tf74xH8yWcknefvD6Rh70OnmTNil1jAhm5fW2MAn2NEnvNZ4eQBKkyXwAIIYQQsg4NAEIIIaSCVMYAGKUGgBBCCBk3KqMBWNHVYF0r8MtTtLSG9QEX8ct6e7RMGZsIxMOWGaRWw2ID68T4Ob1gJw3tbLktMcl1BmXRe0cuI0mTlxSnDMrqIzFnGNMn7PO0tzY8FZC9Fi2nL9ofSDHXzz4bMQGcPO1GGXoOX8e0vbQFCyZhXYy2ZKtU5gsAIYQQQtahAUAIIYRUkMoYADtkdtRNIIQQQsaGSmsAvLnYpDhFkri0IhKBxJQZFHYOdYy/0vMJ1yXrdw3jIRTzV1pfbREfq7c9JkGLTaa1w1Tj6TtizrKIT9jeJ//YMTEn8vUm1vddRnuTekcX28Bi723Hae+cWY7RCXkU0RtZytCjFMU/dv8aD5uAzd4DAGibcWR6i6/wynwBIIQQQsg6NAAIIYSQClIZA4AaAEIIIWSdymgAFruNYJ31sHh+z64Uic+erbmob2xQ2Pb482Vtmf79kzOOT3PVHKoZ4Z9sqPVzOwxIv2GPFeOn9crUNbuuWUDX4PVPq2OZq5VzIew5eP7IvLniMdqNsvzeti3+s5zvJ47RPlj/fcw5TBmfsKdRCI49oD69rGGOjl1m2T5znkd7UPkYthtF8mR4uoxVte+NwTNebyZCCCGEDAUaAIQQQkgFoQFACCGEVJDKGAC7anbWKiGEEFJdKiMCPNpdCdbZ4BaDCiVRVqCIcScUFzpljDhmQfOvw5Ips1u2nlhnI6zAq1soKVJMcpjscoyQqGhgnTIIgmYBWEb2PhxntlshGRD2iUEJybzrEiOyiwmaE55D/7+jBnfenlA0u867L5ZVI1qddsTQYQCfGIFseK26Oe0ZNxG1JSahWQwxQb3KFoqO95UlhBBCyECgAUAIIYRUkMoYAHtqO0bdBEIIIWRsqIwG4FB3OVjX0Kz9M1eSb9n6aQaV5GOQWP+T57vLwz1vc226mn9tbJmWU28YCCb0y9UiziHGD1cG1qfeighS4wXjsQzTp97KuXf2+QKAeXOens+6SF+LIca/H+PPLfI8B+c0xPxW1uffiugjVh8172gAvKRXZbDdAgx5iZ3KqMfriUUSO23G0AwAEXkLgGcDuFdVz03XvQfAw9Ii+wAcVtXznH1vA7AAoAOgrarnD6HJhBBCyMQyzC8AbwXwegBvX1uhqs9f+1tE/hzAkU32f6qq3j+w1hFCCCEVYmgaAFX9OICD3jYREQA/BuCyQR3/uNr8oKomhBBCth3jogF4CoB7VPVrG2xXAB8REQXwD6p66UYVicglAC4BgLNOXz+9A92loGzX+Ci9uc7+HP4snr+5igzKd1cTm5hkiA5UQ8w5emWmjd/YJgOK8e/HzN/2KMOnbn3CgO/j72VJp4N187KaWS7aZ4r41GP8+zaBkTcm1ArMf7d4OoKYREThPtlz8hI0FRmd7Lz/svzck4B9J+QlxSrKklPtbIHkdJsxLgbAC7H5r/8nq+p+ETkZwJUickv6RSEgNQ4uBYDzHz03ujcFIYQQMsaMfBqgiEwB+BEA79mojKruT/+/F8D7ADxhOK0jhBBCJpORGwAAng7gFlW909soIjtFZPfa3wCeAeCGIbaPEEIImTiGZgCIyGUAPgPgYSJyp4i8JN30ApjP/yJymohckS6eAuCTIvIlAFcD+DdV/fd+j39CbWfxxhNCCCETxtA0AKr6wg3WX+ys+zaAi9K/bwXw6K0e/+7OYrCuaRLydNHe6mHSeoz0wFMiVEBT44mj6kZAMx0RAGcmEN2E9ZYdIKNs8oRiRRK0eJR1HawYzqOh/Q8fNgnSoMRlnjArRmRn78MoP5FagV9MIKsYuhFdZE7sdQjvUxnBwjaqu/86wmO3TLKqYQYYyrt3nrjcCoVt0LRBMA4uAEIIIYQMGRoAhBBCSAWpjAHwgPquUTeBEEIIGRvGJQ7AwNnfWQjWtWwgoDHyEY0bno8tD8+/a2uxyWHcYwd+43xRRVm+8GEFQFnQem6ZozoTrNsprcxyWX7OGH+k1XNY5iRfUzOoICoNRy8RJC9yLrktM1/zznt8BTwx/d4G+fGYM+PRdkvQAwyuzfd1mpnlnbVyxu6lbvZ5su8noHyt03i/dQghhBAyEGgAEEIIIRWkMgbA6fXdo24CIYQQMjZURgNwW9vTAGRP35sfG8wddtxK1i8zKL9mDHZ+aRHffVFiErTMBIlU8tlprnmj4H2yeP40u876e8tIrAMALVPvcjdMnGPpOn7tjpNUpgxi5szvzPHxn1ALNQIdszxdsH/mJeByn+UiSXuG6Psu4t8Nxp6I+zYX0Wdi7ouX0GhUePPq7bUpognw+pld03DK7Cnh0ngagI7aJ2hrVOYLACGEEELWoQFACCGEVBAaAIQQQkgFqYwBcPYURYCEDJMglgAhZKyojAjwW+2FwNqxyUyaJdlDeUKy+Hryg+SMM34CkSxzkh8Ax75ImhFCp3G/djaY0bR44p7p3DJzQSClwdj0XvCluYhDhQGFsotlBTaJqccTVVlskJxBJsHJo5goMMS2bjaivdOmTCtHdBmLJ0peRTaY1ZQXoWmMsGLSvc71zLt3nljTCoOvXz09KPO0+W/FNDGaynwBIIQQQsg6NAAIIYSQClIZA+AsagAIIYSQY1RGA/CN9iJsdJiOsX+8QCtF/HDWv1OLSL4RQ0xbxtt7FgYPiQnOEQYlCf2RrUL+UidQjbl3swWEbJ5PuK6bB0CaczUAWY6vtYJ1MZ7ZIomnYpQFNmGMZdbpjR1kgwcNM8nMdETiqZggOeOE7cMxQchiNEnWV19z6i0ryNiwgpXZ8dN7/m1bvDF3ugTdilevTUR19vT9QZmlkmPMVeYLACGEEELWoQFACCGEVJDKGAAPnto16iYQQgghY0NlNABfay0G6xomAUun7vnG+p9zbj1LZc113m54yUJqaudQx/gj7T75x3aTokhEmRGx20mcY7GJlIC4mAhl4F1zz8ef3SfcqWjynzxi7uXuCJ2FjVvgJXoZFC0zckxHKDxiztveuxjdRfjMjXdSpGFSxvjuaTVsnIqW83xZncBWqcwXAEIIIYSsQwOAEEIIqSA0AAghhJAKUhkD4JxpigAnhaK5FcaZvDn125Fhze92jz15XWSkjPJeksFRGRHgl5sryAuT01IvcEkYfCUoY4Q4VuAx7bywxl3oMijsyztmYAkESBrWY8VwrYigL1Z0BcQF1ikDT1SXG1jH2d5BvrCtDDyjK07Aads8nPYCoREQE+THit/mI5JVDYpA4Oc0344jHWdYseLRooK+QRkB9pqXlyDKBMAq0P5hJhWzSYY6TvKqhvOO2go06wghhJAKQgOAEEIIqSCVMQC+a2bHqJtACCGEjA2V0QB8drUNIBv4p6EzmWXrgwGK+aNaZhc3eURFRUrW3xcXlMQmJsmnFRHAJabMoLA+9ZgAOV6ZehCEqkhSpHCfGB1LGT5h99glJC+qO/XOSf5wZ6/xoLQ6XgAfuy5Gx9LQfE2FvXpxuhubKC08Tvgsl5UcqJzBsYz2eGPE3lrWD+8n9sler1mZNtvDej+7ck5m+fTpQ0GZkxEGtNsKlfkCQAghhJB1aAAQQgghFaQyBsB3z1bG20EIIYTkUpm34lUNgdUALHezGoADnTBY0IOmGrl1Wx+QTdjQksHMawXGK0BH6Ksf3bz1RkSSFK9MTKKhMrBXZjrCz+0nVyqpQQWOnYfny7XaBy8pSgzB/HerWXCaW8S3XJY/Ogb7dEfpWCKuX0zfmkTy5/AXm1Nv74t3D4pc84fPfjuz/JGj3xWUOW3f4b7r3Yxq9gxCCCGk4tAAIIQQQipIZQyAC+aqGXqXEEII8aiMBuBfl+eDdUvd2cxyQ6eDMjEzxa3/sWnsqnnH711WjOmwntHFLo/B+pLtfGMP64f1/NHdIB9Dvu+25ZUx9XRL0m9Y7DnEaDm8MjG++by57DF90fOFD9M/bslrc9HcBdMm9r+dzx1bTxGCcSSi6wUxRyKaNs73bdyIuVb2HgBALaePeFfBxv5f7Yav55aTH2ArVOYLACGEEELWoQFACCGEVBAaAIQQQkgFqYwB8Jz55VE3gRBCCBkbKiMCfPficcG6I+2sMPCkqYWgTEygkiAwhE6Z5WLBb2xSFC+ByKAESeNMjDCnGyGg8srUB2QSFw14k4e9Fl7wmG5OUhk/MY1ZMaBu5on1YhLweG3uxbuNcULL4TxPZSUZsmJXL/GYd43HibYRSU8NSMhsn43Zki6LJzhu5YgdvSBkLXPengjwvs4es2ZrP2wr8wWAEEIIIevQACCEEEIqyNAMABF5i4jcKyI39Kx7lYjsF5Hr0n8XbbDvhSLyFRH5uoj8dpHjv2BXmFuZEEIIqSrD1AC8FcDrAbzdrP9LVf2zjXYSkTqAvwXwAwDuBPB5EblcVW/q5+DvXDg+WNdSG/Qj9D01NCbQyub1dtAO9gl8mOPtpiuNMpIXeT5NGwDFC85h8YMFbb6j1WUAQM0EM/LK5PlhY3zPRf3TeX2tLH+0xQ3yZA7laSNigsUEyYBMNXO1cgIXeddmWD71mJA5ddM+r21l6BrK0kYMKnmZ98wV2cc+y37Qsfy6856p+zpzwbqFzo7M8iPmvx2UKZuhfQFQ1Y8DOFhg1ycA+Lqq3qqqTQDvBvDcUhtHCCGEVIxx0AD8kohcn7oIQqk+cDqAO3qW70zXuYjIJSJyjYhcc9+B4aSeJYQQQrYbozYA/h7AgwGcB+AuAH++1QpV9VJVPV9Vzz/phPVP8T++u8jHB0IIIWQyGWkcAFW9Z+1vEXkjgA86xfYDOLNn+Yx0XV/8zeGzgnWLnWwyoMOdMGHQcjd/TmpTbRKP7D4xPqMYXL/SkOZrbzemI5L4zOXMjwfKm78/qDgAFu+M8vyReXOWNyImkVMeXtti9DF5bS7LZz1dwjkOkvkguVZ+EqS4BFxG1zLAJD6Dmvdv+1GRZ9CNkWHq6To6sbw4FfO1VrDu2qWzM8t3rIQfxF9w8tWb1tsvI+3dInJqz+IPA7jBKfZ5AOeIyHeIyAyAFwC4fBjtI4QQQiaVoX0BEJHLAFwA4EQRuRPAKwFcICLnIfkdexuAn0vLngbgTap6kaq2ReSXAHwYSa7bt6jqjcNqNyGEEDKJDM0AUNUXOqvfvEHZbwO4qGf5CgBXDKhphBBCSOUYbwdXifzyvm+NugmEEELI2DDRyYAU60FFXnnvo4Lth1vZwAvLMzNBmWaEQKVlE7KYfZqOnVUk+IonYrFiJxvcwga2GCaeEKvIeVvRUs0R3dgAKPUIDZgNHjRMygomE1NPnvipERFEZVDBb1qOWCpGwOntVwa2r7ULJvKyBIm9Ip6DuYhEX+MuUhwnbDKgbkQ/8+vJLnsBxfLGOU84aAPINTrTQZlAYL5FcSZ7DyGEEFJBaAAQQgghFaQyBsDvn3z9qJtACCGEjA0TrQHo5dfvenywrtnNnv6yCQwEAMvdUBdgaRl/TkOzvhs3UESEDzDGX17Ep75Vv9E4Yi3ZGN/onFMmxh8+CGKCs8Qk1/FYzTknL3HSsH4ZeM9Bw/jd9zj7NYwGwOo5PM1CGYGLAC/AzGB0N9MxGoBt9htulGNPkSN746sd7xecd8S8NDatt6Hhq/f+1V2Z5XN23RuUsQmDgCObHieP7dV7CCGEEFIKNAAIIYSQClIZA+DPT/38qJtACCGEjA2V0QD8wp3/LVi33M76blYcX46ddwmE84KXjM9/qZvVEjRq3mUOk0HkUcTfXxbWd2eThYwa6/OP8Y16ZVo20UfOsodXxq4bpu/W+sst1qcJAPO17D5lJdexeL76vPYCYZtt7IBBtbcoMf3IXouYPmIT/XhJaMbtWc2jrHHO1mOTtsXgaVTsvH8vDsCybn7N7+7sDdY9fNfdmeVD7Z1BmXvaVhFzd1CmH7ZXzyCEEEJIKdAAIIQQQioIDQBCCCGkglTGAPi7Mz416iYQQgghY0NlRIAvvv2pwbpDDZMMaEeYfGFZbXCg5aDMjBEGtkyQhyLJIpIyWeGdlwBlNiJYSP5x8pMMlUUZ9VrhExAGs/HKxLTFWsSBqCqi+THBTsoSOgXJoCJES+OE1948AZXHoJIVxVAosVeEIC3mWQkEfjKYQDteECWb4KgouYlznOfJnrdXx7IJKGVrKfqctkz/tKJvAJjOuQ93NI8P1i105jLLHUece6Q9v2m9/VKZLwCEEEIIWYcGACGEEFJBKmMAvO2BHxt1EwghhJCxoTIagJ+87ftzy6x0Qg1ALcJP1EQ2WJBNIGQ1AQDQ0c2TRSRlbNKR/AAzgwqiYcMhxRDjNywrSIn1AcfU6+oEci5fjN/Q8+/adWVoNzyK+MJnJT8ozTCJ0SyMMvDPoBJw2R5bRMcyeWm+4vDuf17gH1eHIfllbOC3ljM65iWRW3Z0A0vt7LrFTljHvukV076t3fHKfAEghBBCyDo0AAghhJAKUhkD4B1n/+eom0AIIYSMDROuAdBjPpJnf+W5wdaucfgst0O/zOGOnXcZ+u4b3SmznPURhQmF4nx11tdo57oDwLT1R47RlO+y5glbPP9+Df37hL16rN/Vxl6I8RuOMmmTR9BHDDOOr9nTmwyCjnOYuYi57HOmzfa+xfjPy6KILsTbY1qK6FjMfHg3GVD/9VpaGiZBG7eES3l4Sa/y8O6T1XRNuwnirP++mVk66CT6sT7/u1ds4h9gV70ZrNsKlfkCQAghhJB1aAAQQgghFYQGACGEEFJBKmMAfPBhHxjasabF5AYoNIs+ZFAx3WN8mCShFXGtYsoMiiIP9KwTr2FY1Evq0l6ejDIoK05FsWP3f3G2m1++LGJ0N01zL2Oe0xgtzO2tE3PLWOoljRFb1RtNuAhwnQtv/uFgXaMVBv6xeAEbgjKB4CPfCIjpWDGJXYokuPCw+41y4CvCMEVf280IsII5D2sEDFPIaI2AzUOoJNj2rmo3awSU2B3ss9Axoq9aRMKomP7gJ6eqnhEQjmFxP6AC0bQXHKgEI8ATdVsj4BGz+02J8FVbxAi4vxmKB7fyrG6vUZ4QQgghpUADgBBCCKkglTEA/v073zfqJhBCCCFjw0RrABTr/pHv+9ILg+2r7awvp9EJL0fL8fdYloxOoGGSRdjAQIAfAMUSBgLyfFoU8Hl4iYgscT5Xew8i/P8DEqTF4GkhpnPsfC/xz6A0ANaf6h07RpRoz2lQGpAYTU2UJiSi34wyAdMosdc4CLYVcVm8QEVdtcs2yFe4T1hvuG5Zs+O91Xsl+23+at07tRKs++riKZnlW+8/IShz7gPuzixvVcBdmS8AhBBCCFmHBgAhhBBSQSpjAPzXoy8bdRMIIYSQsWGiNQDAuo/kSde8ONjWbGVP/2gznPO/0J3LPYbVACx2svss1Hc47crH+sI8PUI3InFKWG+Ozw0AgnrLCWZUhDAmgZeYpBxbNs8P2yoYv2F6hP7d6Ryfelnzzy1eMih7ff1rFZMEJ1vPqvHnxtRRFjF6CdtvyvL3hzEKBpWAa3T91+tHtQidiI2Z0jRjWAvt3DpiEgh543ItuA/Z9i50wvfKwdXse+KUvQthmUa2jDt290FlvgAQQgghZB0aAIQQQkgFqYwB8Jnz3zbqJhBCCCFjw0RrABR6zEfymE/8XLBdJOs/WWmFUcgX21lfjTfvsmvjSxufkOfvaWi+7WX9htaHBYRxyYvMoSUbY33U3rxgi1emrKQ3efj+/Jw4AM4c+pjzLEJMHICYOf32nEbp84+J+2Djfkw7p2jvXUwsi0Gx3RKEeToMqwFoqe0z+f3MS8B2uDOfWV7ohBqvb7ePM+07lFn+5H0PCvZpdbPj+5HlsN6Z6XzdQj9U5gsAIYQQQtahAUAIIYRUEBoAhBBCSAWpjAHwxaf8w6ibQAghhIwNQxMBishbADwbwL2qem667k8BPAdAE8A3APy0qh529r0NwAKSCDBtVT0/9rhr4pDHXvXSYFunkxVdNDuhyG65GwoDLfe1d2f36WT3adW9JEMRIkCja2l0nXqklVkuksTF3ceuGiMxoSd0K3LensiqZgJ/WJFaTCCgUVIkwIwnEqwbEZgrsjIBWmKCs4THjmtPWGbzgEJh+q3B4QUzslgRqBecaVAJjQZFkWfOC+pTRr0eDZOQZ06yAroYmWPDCfJjk7t574i8AHLqBBhatYHp7t8ZlNlz4lJ2HycoWj8M8wvAWwFcaNZdCeBcVX0UgK8CeMUm+z9VVc/r5+VPCCGEEJ+hGQCq+nEAB826j6jqmln2WQBnDKs9hBBCSJUZJw3AzwD40AbbFMBHRORaEbmkSOVfuODvCzeMEEIImTTGIhCQiPwugDaAd25Q5Mmqul9ETgZwpYjckn5R8Oq6BMAlAHDG6XW0Uk/P+Ve8LCw7k/WfdB2/zEI7PxnQkXY2MIQNBOQFkzjaDRMPWWywoIaGns2WrubWY7E+Ns+HGfhYS/D3bkfsWXoBXCx+kJfRkZfIZVCJXjxfrl3n+b3LaE9ZyaG8ZCtFErCMMhnUoCir39jracejGE2AN4bZ8fy+7p7M8vG1A7n1Wh1Bss5oADqhBmCpvvn4/q17jw/Wze1oZpZn94Rj+9JStt5GhKZiM0Y+iovIxUjEgT+u6j9Zqro//f9eAO8D8ISN6lPVS1X1fFU9/4QTRn56hBBCyFgy0jekiFwI4LcA/KCqLm9QZqeI7F77G8AzANwwvFYSQgghk8fQDAARuQzAZwA8TETuFJGXAHg9gN1IPutfJyJvSMueJiJXpLueAuCTIvIlAFcD+DdV/fd+j3/NRa8r4zQIIYSQiWBoGgBVfaGz+s0blP02gIvSv28F8OhCx8R6oo7v+cCvBR4rNcl1Vpvh5Vjp5M8oXjHzQO9dzcYF2FUPfTl1sb6b0BazsQI8DYBNENRBOckiypqLOyysP7KoD9j6pIN59RGXxfP32rs9qOs7KH9+zFz3IhRtr43hUHf0O8Mixr8/J+F88nHGPj8dNwlaOX2ijHpaji+8ZpK92dgWrYj75o25iya522In9PfvNbECrIaqPpXvu283wz7TbWXXLXe31u/pJCeEEEIqCA0AQgghpILQACCEEEIqSGUMgE8/9y9G3QRCKkU3Kto6IWRUjEUgoEHRVWAp1YE8859/I7B27ADVXA0FH/eu7Mose8ksDjSzgYCW21lRoBUJAl6yiFC8Z4NQ+IGAsmcVI6ix5+3tUySpzCQO+Fak5iVxsXhlPJFSL16/ssGWYhKpeHhJj/K226RIbqCVAn0tOLbTz/La62GvuX/c/oV4MedYpE94wYTKCl5kKXI9i+Bd8zLOqei4MmMS5dTMvWxFCEeXnIBtV9798MzyA3cfCsrsqLeCdZljL4Vjuew2AZBWnddzy45HWxNQVuYLACGEEELWoQFACCGEVJDKGAAf/tE/G3UTCCGEkLFhsjUAWE+o89z3/hry3NodJ/DCYjM/ac9iK+vPX27bZBGhv+dod4dZsxCUaZqkQl31fMvZW9jRxmZNdXH9kVv0LQG+/9T61AflNywL66OO8vd6QZ2GpI/wkuuUgZ/YZ/Nz8nQPtq/NDqi9HjEJrWJ0FvYae/fbEvTpIBBYOZTl77f3tm386UCJCZdykv+4yZdMt/Gupg2SZjVUVj/lsexoAB59/P7M8v3NXUGZthm7g+tXC8+pfefObBFnDLayhbs79j3SH+Mz0hJCCCFkaNAAIIQQQipIZQyAD/wI4wAQQggha0y0BqCDGg6niRte8o+/GJxse7eZm9t0/DKd/Hn2B1ezfphON7vPIRMnAAh99x7Lmo0f4CYDsjqB3FpDn1rLjQOwuV9uUrF+TTt3OMbv6fnhrZagrOQ69r4Uid9QFOvj32EOPagEQsMk5hxidBdh0qMCuoERUlZbYsaRpW62X83W8uM3NB2dwMFOdtztmnOwGgGP65fPDNZdeyC77qQdS0GZq+8/K7NcO/nqbIHFcPyvNbPL7Z3hOU0tZts8J1tL/jY+PYwQQgghQ4MGACGEEFJBaAAQQgghFaQyBsCbf+JvR90EQjZkEjUWqwVzFxBChsNkiwBVsKCJCPBl73pJEDyivmKC0syGYp5mx4rswkGt1c2Wadl9nIAThztWGBgmlLACtIWOTSAEdKf6F5eFgTbCMkE9JWnLQtHa9iIUc3llnOQ6QQCk/pPrjNJI8IOx5L/grREwbQRzZQUuste8pWHgmqmIQ8VcY3sv44ShRkzstC+mb00iZfT7lnPt7PjZ6GZF1IfdIDodsxTeW5v8xyZ/81jVrFhv5uSVoEz7DhMIqOn0K3MpwqRy/VGZLwCEEEIIWYcGACGEEFJBtmQAiMi+ktoxcF73ojePugmEEELI2BClARCRnQAeCeDcnv/PBbATwL5BNW6rNHUKtzVPAgD89bt/MLB2utbN3Qr9SEvL2WQQni/0/oWs72aqnvV7Ls6FPqJFx59vWdbssb3EFEtd277cagM8/5ltcV7iF49xT/TjtyXrAxxUch1LzPUtcg8GSSNH5OcFZ7EagLKIu34x+piuWc5PaFVV331Z2DF1wQQ3211Q+2IDp9mgaS1HgdRFK7M8b6PzADi4mtVvrbTCAG07p7P7NYzmo91yjj2b7Xv1lXB8qpvmHO6GQeb6IdcAEJHbAEwDuAnALQBuBvBCAOep6r1bOjohhBBCRkLMz7F/BXAQwBtV9ZdV9e8ArPLlTwghhGxfcg0AVf1lAM8GcJGIfF5EnoVgMsL48ysvuHzUTSCEEELGhigNgKreDuBiEXkkgD8A8AAReaqqfmygrdsiXdSw3E282W9554WhtWPchtINfXntRvYSeT7Btpn3Pz2V9fcstUINwFq71usNfZhHjU5gWsK5wzapkOfPD/axfk4vDoBsOxuvFPKSttj53HF1hEl6wlgM3jx75Jax8RpCb2QxAu2D07xWgS4SnMOA3Od+nIV8nYB7HwxlJFwapm6gY7QatYg+bInTn+RH9fDqsWu6mr02XkwHy3I3PLbVAFgN1UzHq7eRWTrqxAr42t0nZZb37GoEZdom9sucZNs39c2w3tZuowFYdcYRc6j72ruDMv3QV09Q1RtV9UcAPBXA74rIf23p6IQQQggZCYUiAarq5wA8XUSeVnJ7CCGEEDIEtjQnS1U/WlZDBs3P/Pi/j7oJhBBCyNgw0bkAVrtTuH3lRADAVe88P/BQdfaaFY7fW9smxrjjw2qvZmtuSNb31JjOxoEGgOVOfvxo67OyugEgjAPQ0nzfovVzNh07cNb43WJ8t9bX6PlTa5wy7RI3R328dBl5vx5mhhRDIZaYPlwkF8A4YZ/BUR87Rm9g40U0YfOv5GsAas7YbWP/HzH5VzxNlWXRifN/4t6lbL3LYUyXdid73oe72XdA86TwnTBzX/Z17DWvvppdXnDzGcQT/QVAEn5CRH4vXT5LRJ6wpaMTQgghZCT04wL4OwBPQhIECAAWADDHLiGEELIN6ccF8ERVfayIfBEAVPWQiOR/xyaEEELI2NHPF4CWiNSRzggWkZMQTuEcWy748WtG3QRCCCFkbOjnC8BfA3gfgJNF5DUAfhTA/x5Iq0pitTOFry4kQRv2f/CBgblTMzqMWiO0h7ozWXGJlwCl28yKVppWiLdzJdjncCs/icNCNysuOdTeGZTZW1/OLLc036ZrGaGTt09LBmPbhYFA8oOHbDe8YEG1HHGmJy6NKTMsQZqXFCnv819M4p9hJocqeo3ziAsOlS3T2T6/nTbEiiqnIrqiJwy2wcvseNSICM7U0PBVZkXULZMM6Jur2YA+CXdnlhbaocDv7nv2ZZal7tzL+WzWnrq5NvO3h+1d3WeCei2GF9S+s25d8c4hnmgDQFXfKSLXAngaEv3sD6nqzVs6OiGEEEJGQl/TAFX1FiQZAQkhhBCyjYlJB/xrm21X1b8orzmD4/Rn3564AQghhBAS9QVgLdvAwwA8HsBaWr3nALh6EI0qi9X2FL55/wkAgB2f2BV4m63byAZZAIB2J+uHWXYSBqkpI6be5WaYouVIKxvAwQuicaiV9fkvd8J6bDKgJc1PB2OTuHj+szkTCKhIEBpvnzISqQwT62MflM+6FeHn9AMrje56zub4vqeda2WTFw2TVkSQHHuNPe2Dt64MYrQEwyImqFdZgakWuiahmU1wFhHcrNENxzCbDGj/6r7ceux533zglKDMnuOygYAWF0KdQHM1e+xgzD0xvHZTS2asaQZFgnVfOnRaWKgPcg0AVf19ABCRjwN4rKoupMuvAvBvWzo6IYQQQkZCPybnKQB67Y9muo4QQggh24x+RIBvB3C1iLwPySyA5wJ420BaNQBWnrKIHZ/YNepmEEIIIWNBP9MAXyMiHwLwFCTBgH5aVb84sJaVgHYEq0eSuaD7rg994xLmYwioNbN+mSXHXy5GF6BmebXlzFFt5/vqj7azOoF2N5wzf8Qkg7D+M49GMM/W0xa0Mstl+W6tL9GbDx2TQGS7EfiNzeWMub42fgMA1IfkU/fiDXg+/sx27z4OKFlNOM8+zKQSc2RbZnqbaVaGiY2ZMBvVh8O70DGvIatJ8pKVWZY1jEpxpL158p+7GzYbXKhr2DPbCMrcdWRPZtnr5p1WdqXVOcwecLRkVpPmaACmVrLtu38hjA3TD9EGwFoSoB6eIyLPUdVXb6kFhBBCCBk6/bgAeqWPcwCeDYCBgAghhJBtSPS3VlX9855/rwFwAYAH9XMwEXmLiNwrIjf0rDteRK4Uka+l/x+3wb4vTst8TURe3M9xCSGEEJJlK87WeQBn9LnPWwFcaNb9NoCPquo5AD6aLmcQkeMBvBLAEwE8AcArNzIUNuLwo1r5hQghhJCK0I8G4MtYly7VAZwE4A/6OZiqflxEzjarn4vkawKQzCq4CsDLTZlnArhSVQ+mbbkSiSFx2aYH7ArqC8kp7v1qvpin7YgCa63sfjZBDwDIataOslKYzlxoZy00s4kqvKAai52ssGWxFR5733Q2GdC9nd2mxEKwT8MkxfBEgE0TNqmLfAPKnoMnbBtW8ppxx16HGLGZV2ZoyYCc4wwqIM6giPm1YxMYeeddRjCocQr6E4M3PhXpe149XRPop2EEfTFC1yWT+AcAVo3w7obDp2aWH3P8/tx6u16/r2WFjKvN8F6qEQF+q539vdpyJqRNZYfyIPEPAHRms+2ZmQ7Frv3Qjwbg2T1/twHco6oROvpcTlHVu9K/74YfW+B0AHf0LN+ZrgsQkUsAXAIA9eP6+khACCGEVIZ+zNBfUNXb03/7VbUtIn9SZmNUVRH+gO63jktV9XxVPb++a2tTJAghhJBJpR8D4Aecdc8qoQ33iMipAJD+f69TZj+AM3uWz0jXRXPkoaOLQU4IIYSMGzHZAF8K4BcAPEhEru/ZtBvAp0pow+UAXgzgten/H3DKfBjAH/UI/54B4BV5FddawNw9ic9k7oBToB6WD4qYBEFHPQ1A2wQCqmWXu+3QzlpqZv1cXSdAxsHV7BeMRie8XYdb2WAXjW5+gKGl7oxZDv1n+2pZh5SXDCQP1983pMA13vUcVCKfGOyx67Z9ET5hz+c+rOvpkXc9p4L0W4NLBuTdb0uMZiEvuFFV8e6bTa7Ulf6TLQGhBsmOR1azBIT32xvDDplAQA0TfK3VDe+1rfdoIxzvV02iH3XG9+n7s2W6Ef3KBv7xAtXNHs227+Bq/ni/GTEagHcB+BCAP0ZWob+wJsqLRUQuQyL4O1FE7kSi7H8tgH8SkZcAuB3Aj6Vlzwfw86r6s6p6UET+AMDn06pe3e+xCSGEELJOTDbAIwCOAHjhVg+mqhvV8TSn7DUAfrZn+S0A3rLVNhBCCCEkQgMgIp9M/18QkaM9/xZE5Ojgm1gOjRNG3QJCCCFkfIj5AvDk9H87wXz80XW//o77Q9+TTmV9ghLhnryj5VgSOft126EPa7WdvfSub8z4/O0+AHCklU0GtKrW9xT65RY069fydAPWp9ZFmBTDYo/lnVPNXOQYH7atN2af8IqPNzG+Z6/MoHzqFm/O93aL6RB1jY0Wo4j2ZdwoohMZlLbES2jVtHFJzHjUcjQAloXujmDdPY1s0p6O8fl/a/n4sH1W19AN+7hN9iatsF+1TsoKyj6z+JDMsp3zD4R6M/d9ZJOIfWNrGW6peCGEEEIqSMwsgAUkdodn7quq7nHWE0IIIWSMiXEBbL9P/4QQQgjZlH5yAcwhiQfwZCRfBD4B4A2qmu8cHgNWThRXB0AIGQx1qaGj+XPDCSGjoZ9cAG9Hklnmb9LlFwF4B4Dnld2osqg3gT3fSgagzozjwTD2wNRKWMTq4450QrHJ1Eq27rYVEjVDw2O1lb30LUest2ICV1gRCxAKBRec9llsGSscTNqTFd40I8RQVjDlDf2DEhcNSpA2qKQttr0xQWrGTXQXc22CMub2+wF8BiPhtAI/jyC4kRPcxt6HUQaYigmAFLNP3jl4z60V9FkBHQDsMF225Tz+NhCQXb67s9dp0WJmadkENwOAoybh2u7Z7G9VK0gGgMPdbPSd5ZWw3u6q6Z9OPp6pxey4fPzUUmbZE/h1TbUzq46I2gQHmj24tTGhHwPgXFV9RM/yx0Tkpi0dnRBCCCEjoR/T9Qsi8t1rCyLyRADXlN8kQgghhAyafr4APA7Ap0XkW+nyWQC+IiJfRjIb4FGlt65E6k313QCEEEJIBenHALhwYK0YENJVTK0kfqnVvaFfsW58890pJ9iJSdBw+0oYCMj6buoNkwxoNqy3tWo0AI7/zCYMUg3rWZ7O+ssOtvNTIC+YhEaHO/NBmePrWR9bjKfR+gk93UDwyWnMbTIrYqsV8SMDqJkraH3+MX7kaQn78LJmA47UPK1GCdc4RqMwzHosVn9SdwMXxdw7u9/2CpUyKI1NwxmfOjYoTUQktZYzhtnxZ06yfbrj7GNZ7IRJexaNBmDfXFbkdc9yGETHnqWXyA3N7DrphO2zCeL2r+7LLBtJAIDQv+/dSulmV3q6tX6INgBU9fatHYoQQggh40I/0wDPB/C7AB6Y7ifYBp/+CSGEEBLSjwvgnQB+E8CXEfdFeKyYPdJx3QCEEEJIFenHALhPVS8fWEsGgQL11cRWaZwwhZpxWtlED4EPBuE04P3L+4IydaMTaO/IHsfzEVm8JBlLK1kf1vRUOOG01ckaNYud2aCMZdb42LxkGzYZUEPzfaE2lkHL8cPaWbVF5jF7WN9nWabeoOIAWIrO8fd83ZY8HYMXrCfwqQ/Id+8Ro7tggKF4wmcs/+mw+3j9zEaAi9EfLGn4yqkb7cCyEVV5iX6SkDTr3Nk4Lihh5/kvGE2AjcXi4WoAjH/f+vuBcJ7/bUtZ7ZgjWcDUoeyyfa8AgJh3mJdUqB/6MQBeKSJvAvBRAMfyFqnqe7fWBEIIIYQMm34MgJ8G8HAA01h3ASgAGgCEEELINqMfA+DxqvqwgbWEEEIIIUOjHwfnp0XkEfnFxpO5A46Df1gwB1GplKUbGCeGpTWoCuOWN4GMH0eW8vOmTDr9fAH4bgDXicg3kWgABEBXVR89kJaVzOreKYixAazoT5ykDlYEeJ8TPMKWCUV/GhgBasosd8MBq20EKOIE2uiagW6lYxIIOWKpIybwxrIjHFyuZ9e1IkSAQTIgx/CxwULsPgCiAtdYI2CUCVmK4LU3zwjwXmrDetGVdX1tPYM05sKkPfnXKkxeNHnGZgz2uVxyHlMr1tvtDaCGhiMCtIl8lo0AueP0PTuu2XEPAOamsmLn2+45MbNcn+oERsBCN9s+bTlBvczYbQO/AeG75UvfODOzvMcR79nLV2vn/3KcWdxa/ywaCVAAnAngFVs6OiGEEEJGQl+RAEXkMUjSAD8PwDcB/MugGkYIIYSQwZFrAIjIQwG8MP13P4D3ABBVfeqA21Yqs0faWN3bzwcPQgghZHKJeSPeAuATAJ6tql8HABH5XwNtVUlIWzF9KAlZsHTWfJBIASbJhJfLopZ1I+HgQpg4p261BcaXo63QR9RpZf1nDScYT7eT9T/pVH7CoJWODbUTYjUAHg2dNsv5XcUGM/L2mVYrvMitdqRBX4okAxolXjCWvAAtg0ogM0hsm+0yRYBbI7i+3thoBkwvmJml7pTpGH3RkU7WL3+iFW85LLZCHdPdR/dklufnVzPL7U74LN/bCTVeFvueiAkgd8LJRzPLTcnqEZJ6TB+OqLfe2NqzGzOa/QiAuwB8TETeKCJPw9jncCOEEELIZuQaAKr6flV9AZIgQB8D8DIAJ4vI34vIMwbcPkIIIYQMgOjvmaq6pKrvUtXnADgDwBcBvHxgLSuZnd/aYtBkQgghZIIopIpT1UMALk3/bQsap+5AvWX8WtPG52L8/QBQm856O5oLoa9p3k5/tS5rz4VtdAEHuqFfvmt0Au16WNHKatZX3zRzcz3/7rLRCXjJgOzcXKsJ8DCXF00n6UgH2YtVxP/sxw7oP+HJKCnio/biBNgkLV3v2gyIMmIDePfS+pY98uIHeMmLygq2NKigTWXoTbzrYq9xxymTd6ymc69tbJCO5scBONoNs+DYef+LJlNOq+voo8y4MVULz+mEXUuZ5UNL2TG242gA6mYckdWwjJix2wuRYmULh2/KJgPa7b1r7OXzno1mtn3tua0J28db0UQIIYSQgUADgBBCCKkglTEA5u5aGXUTCCGEkLFhsiPjCKAzif+oedxMGLPfLAc+GAC1plk+Gl4yO1/T1tN14vzDxJg+4Mw/1bbxNWlYj11370q2Hs8neKC1M7M8HfjPgV317JzZJbXxBcyFAdAwzjAvf0DLtLdTQAPg6QasL3y7MchcBnn+8kHF4/fPKd9PHNMeVwdSArbNnr98nPGuSxGdTcucd9d5lm2cj4WIeB2elsjqjSyH2qE+qmX0BodXQ23BkZXsutVWtr3NlbAtd7f3ZZanlpw+bOPHOKc9nZUfoPHgrNO/9rXwnGtGROVJYeqr2YPtuHc1LNQHlfkCQAghhJB1aAAQQgghFYQGACGEEFJBKmMAzBwKfdaEEEJIVZloEaCootZIxBeNU+aDjBZWdNGdDoVkYkQ1UwtOGaNrskEg7Pak3uyyl6BHjAiw03QC65gEQaud7C1tOcKchXZWHDPjZJ3Y0ckG51gywTq6aAT7LAUJhEKRTUuzYpgYwVeMiKmMYCeDxAaPGWaCI9sHdsjm24HRJtPJay+Q3yfKElUOKuhPWRTpR0WeJy+o14IJ6rPbKqYRPt+uCNAEJjvayta7ox7Wu2ySijXaYb31mklW1Mh/3d28clpmuTvrCI6Xsh2y7ujwrF7bJoTzhIOBSL3j3CdzX7ozWwt4Nt69mxBCCCEDgQYAIYQQUkEqYwDM3cNkQIQQQsgaE60BQFdRW0ocNI2z9qHW3jwZkO9zyfpuZo46xzG72aRCjos98O/f194dFupYR1LoDO20szbcSivrC/MC7TQ7+bd9xfjlbMIOj0Y3W6/n77PBQzqaH8jC+hG9cxqlz3qUhL5uJxlMjs93lNfTBpwBQg3AHmc/2+btHgiqLDz/frFgW/k01T7v+eOKp3VaNHojG0BsqRkGSWuZc2p1Ql94o5ltT61udEKt8Pfv4Va2fdJ0NF+Brz4ogikjkaovZdtn9WdevfVGeBe0nm3zzKFQi9UPlfkCQAghhJB1aAAQQgghFaQyBsDctw6PugmEEELI2DDZGoAeVk/bCzEaAJu0x/PV140/35vzaV1satxRNi4AANRXsr6lry6dEpQRowHoNkN7rSPZg3W62TKrzjzhZTNntuv4TzsmJsJCJ0y2YbE+fxs7AAB2SnZOb8uJJ5CH5+cskvDEi0Fg6xmUL3yY88uLXJth4fnui6Q32W4akFHGgeh6k9ANTTPf3EvsZXVBVgPk0eiGuqBVs59NPDTltLdh9FDtrtO+hZwxyxlP71zZl1kWJ5FbvYDbvb6cPZaNLwOEugCtOcdeNS+T9tb60ci/AIjIw0Tkup5/R0XkZabMBSJypKfM742ouYQQQshEMPIvAKr6FQDnAYCI1AHsB/A+p+gnVPXZQ2waIYQQMrGM/AuA4WkAvqGqt4+6IYQQQsgkM24GwAsAXLbBtieJyJdE5EMi8siNKhCRS0TkGhG5ptleD/4z++0jJTeVEEII2b6M3AWwhojMAPhBAK9wNn8BwANVdVFELgLwfgDnePWo6qUALgWAvfOnqc4mopP23jnUjGBCV02ChplQdGGD+tRXHcFcNmZOICaMSfxwcHVnbhkrCgQAGJFKp5O16RqO2MSKANuOwGfXVFbgYxN/eCImW8YLHtSorWSWnXgYAUGiH09AY7IrjbPwrSh+gpuOKeP04RIEcsMU2U1L/rGseLBu9vGvw7j93tk6tp97QX/sk+o9P/ZytcwKP6hXdt2yzgRlOppVzN3T2huUaRvV9MHVbDCeI81QzLdghINLjfDY03PZgbi1aMo4gdUONXZkll29pNnNviOSurOL9eyw5yeIM8nq7DIAdKdMP69v7bkcpyfiWQC+oKr32A2qelRVF9O/rwAwLSInDruBhBBCyKQwTgbAC7HB538ReYBIYuKLyBOQtPvAENtGCCGETBRj4QIQkZ0AfgDAz/Ws+3kAUNU3APhRAC8VkTaAFQAvUPW+Y23M1JEG2nvz57ITQgghVWAsDABVXQJwgln3hp6/Xw/g9QVqBrqJE6ezZ4ef7Ke3tOdOCfw9+X4ZGw8jRgNwaHVHWMhSC4+tRhew2jK+MRuVCECjHfrzgjKdbJkj7TCJh+VwJ6tj8PyGNoFIy73oWayf0yYCAYD6BPr8Y4jxzU/nfOiLSaQzTE1FXnuB7Rf4pwg2UFWMhsG7T0FQn4hAQBbvWa6Z9nmBvxAR6GvFjDVTtWy9u6fD0FD2efd6Q7tpxr5Vc/2cy3DP0WxSNi/wW+Dzdx6NetMsm8sw1XCSAenmy0l7jOZnqUjYrJ79t7Q3IYQQQrYlNAAIIYSQClIZA6B+dCW/ECGEEFIRxkIDMDC6gKTJEzrH74R0jE+tlV2utxx7yMwvnnU0AGrmYho3N7rOnE/rN7rv6K6wjE1E4SSvwGz2HDqtrN/rsJPEp21iBSx2Qt/dvtms0ypI2OH54Y1vcbUT+g1tbIBmhA1q5zZ7Uo5g/nNBF3GYIKiWs3285pfb+fAxTDuJidy54gOgqC9/WkJtyzjj9ZuwTPaax5xhXL2bH8ejYWKDtOygBmDV6AK8572Ng5nlw61QS2T1Rgca2TI1p723NB+QbV8zbJ+2Tb82GqraSniFa6ZMO6J72qRyQDiHf2bRvCO8es1p1ladis3gp1NbG3vGZ+QihBBCyNCgAUAIIYRUEBoAhBBCSAWpjAFQP7g06iZMFDG+R0IIIePLZIsAoUAnEVJ0j9sFMcmAYIROtaYX5McmaHCOYgQdNtFDTCCglSOhWK9u6qk55lrXBAKyywsa1rvczCbF2DcfzpA4vJrdb2E2rMcaAY2uSQ7SDRN02GAhDUdcZGmZxEM2UQkA2CNV1UApIkh09ykQLKYsYgR+Vjw4Ccmfwj7bv9DRE292zLWKEXi2jAjwcMcR75nn/YhTxh5r0WZOQ5icrNnOjgniRMSxgYm8U1Kj4JOuXQ73kU9lkxXVnOCxwfjeDsuoEeNOrZh3TdsJBGQEfl4gIOmW+1xW5gsAIYQQQtahAUAIIYRUkMoYALVDi6NuAiGEEDI2TLgGQIB64kfr7p4LHUU2aIrjSLJ+GZv4BwBqNjiDDeDj+HJqJlmEF+RHjD/fyesTOLJscqBvt44LdumYQEBLzdAvJ7PZRtuEHZ4fcaGbdZi1nAZbXYCnE7DY5D/WPwkAHXMdXD9nTFCPCbSJ84LteNtjPI15yWqK6jCqkeinHM2C7efLTr9vmOdwzjqxHaw2p+M8c/Z5rzlO9ZbpA8vtMOhY14iopoz46dBSqC24r51N2tO1QX8cpJU/Li88NOvQn789fEXa0/S0BPWmfW+YfdwEQv0/L9LMv5ebMXmjHSGEEEJyoQFACCGEVJDKGAC1hfy81IQQQkhVmHANwDq6YyaIAxAkB+o4vtAIt0ytZfYxOTG8ZBHWPV5bCv3lEa46IGhzdvlgO0wyZDUAdhkA2p1se442jX/f8e8uOomHLDapiBcHoIumKZM9J08DYGMFlJUMaBI0AXUn2c+4UCR5UbKfOSdz/8f5nAeJnfMPhM9LS8OBxfZ7G6/D0+ostrPP+7Qz0Nnnsu08u00z1tixx2oCAODLC2dkltXTUDVy4ih44S+sTMxN9JNfxt6GqVVzaCcOgB1Srf4MQBjLZotU8ykhhBBCKg4NAEIIIaSC0AAghBBCKkhlDABZsRPvh3jsCLdNlL+/APe3Qg1AGQQ+d69Mt/9Y5jE0C8RIJxtTVX/5JNKyiUlKYrkbzt8fFq32YJ73IC6AQ82J818GA7pNfTPZIkBVoJXcQd29w0n+k33rdmfzO1q9FQozgn4UkfzHTURhk0yYQ9WaTq+pmaQoTr3WCLCxQprOAzYznX0pNJ2XuTUCjrR3ZJbna83ACFiWrJiooV4goKyx5g1q1giwwiZPpGjpRBgx406eGK5oPd2IhDGDggZJgu2fNee6eAGF8oyAhiPEsyxr+MK3RoAN9NXqhGNEwwYqMol/AGCllR0DllbDMcEaAYtOQKE8as4L3xoBtcXwHKwRYIO4xYzlNee9ESSRM9fKC/KjdZPQaIvPKZ80QgghpILQACCEEEIqSGUMAFkIc94TQgghVWWyNQA96J4woYRNBiROcAa7Tp1kQIG/x+7j+J5Mbh03OUQQYMiRKATuPhMQ444VJxlQ0wTamAqdWGoqtgk7vKQjqzbjhcN8PetAO+oGD8pmbrTJTBrOcebqWUdd0WQrQRKhwMFHAWKZFA20ZPfrFEw8NCr8YDxGoxShhbBaFz9IVrbP1iOejZhAQPZ5d5N/mfbYoD8A0OluPta0HY3S/oW92RWO7iF4dM1p1x1duJh6YsTZMX74QAPmBQKyq7xARaumImoACCGEENIvNAAIIYSQClIZA0COLo+6CYQQQsjYUBkNAOZmIE0zoXPOnL7jT7G+eW+Kbb2ZLdSZyY/yYH1L9YazT1TsgOx+1nd/cDXUPkgt295uNzx22yQIWm7Z+fuh7bhk5uZ262G9jXpW/LCq4bxgO/95yfgfW44fPkx4kh/Bw9MJ2HV1k9XDix0Q46u1+w1zrvs4JTQaVLKlcY8dMKiYEy0zZnlBso52szqbTm01KGPb1zAZzZY7oQZgxazrRmTgWu2ErxyrC2g2s2W88WmllS3jBfUJdFV5yw6eNismYVB91czp726+nNRj+ojXZew7qtVyCsUz3k8NIYQQQgYCDQBCCCGkglTHAGiMLhcAIYQQMm5MuAZAgW7qoNkxF/pPjI/Fzt8HgG7H+G4cl4saN3uu78kpY+f8e3i+pmBdO+sLO7oazrPvGv9+10kg0Okan7rx0x12koM0THCDWSeTxorx5zc1vws2jE7A0wA0NHsBO8i/oNYfnexn5mKbPuOEgRgrBuULd6+V0ZtYLcQk5FrwiInRX6he0/e8a563j4f1zbfcZy77rB7pZAc1b46/jQPgxec/0MnmB7EaJQBoGn9+vZ4973Yz1AnNTpmBz9EJWMGWzQXgJfqJGrvte8OLFWDj/Af7RMQO8DRp7XKfqep8ASCEEELIMWgAEEIIIRWEBgAhhBBSQapjAKw0Rt0CQgghZGyYcBEg1hP+7AjFcDUTGKg9FwpdQiFGftIJK8zzA/iYtnjiQtMcNyhFTrCgAws7nYpN8o1OaAfa4BtW8HVfZ0+wT8ME+ZiqhcFDpoxi5ogRCQFhMJ5lzYqLbFsAoCU2MUlQJCBI/OMcu2hSoe2EF4ynZgRog5Lz1ZznaZwCF5VFTL+y68LEVGG9tp97yYAaaoN4hc9lG4cyy/a59BJ9WdGvTeIDAIe7WTFhox3WI2ZgC5L/OI/gPXdmk5zVvDHWiPzyxkrvWDHCay84XBh0aHMButseR5wNGyzILvfJ5D1phBBCCMmFBgAhhBBSQapjAFADQAghhBxjsjUACqDdEwiobZw3U1n7R5xAQDJlAgE5V0xssCDjP3f9SNbX5ASlCHxLnjvaHsu6iNqOjWfdRk69NhCQDeBxX3t3sE+oAQj9Uzvr2YiMy50weEhLsxdsyQk6FBxbV81yvm3r+WGt33Xa3IOyNAGDCiYzTPKuRRX0E0XxAvjYxD7TnujHYPu5DZrlcbQT6qFsv7fJf5qOBqDZzfrqPQ3AzY3TM8st699HONZYTZK2HG1WPSKQTp42yxmXbVAf7xbY04xJGFQzYg0/yE/24Pa9khzcZqfb2jO2/UYdQgghhGwZGgCEEEJIBRkbA0BEbhORL4vIdSJyjbNdROSvReTrInK9iDy2rwNQA0AIIYQcY9w0AE9V1fs32PYsAOek/54I4O/T/zdB1+dSzu/I9ZeIN+/STsb3fDfW12T9+11nn06ETsDcHTeegK3aagBWwlusnfw4AB0Tl2B1KlvPYZMsBAh9gJ5P0M4n9pKMWN9xo5v1a845QRNsUiFvPrRNrtJyJuO2zLGnC/ixvSQu9py8+e/bDavVmBUzL3xg0QOGxzATGlldgO2ffvIqm+gnfJ7ss+ppauyxamZgWe2E44h9vq0mIKkn//qp0TF12xHPRstqn5x9rD7Kus+dXYJVzuNvE/l4pxgklgvGaafimDgAlgppAJ4L4O2a8FkA+0Tk1FE3ihBCCNmOjJMBoAA+IiLXisglzvbTAdzRs3xnui6DiFwiIteIyDXN7sqAmkoIIYRsb8bJBfBkVd0vIicDuFJEblHVj/dbiapeCuBSANg7czLnIRFCCCEOY/MFQFX3p//fC+B9AJ5giuwHcGbP8hnpujiW+TWAkGHi5VoghIwPY/EFQER2Aqip6kL69zMAvNoUuxzAL4nIu5GI/46o6l2bVqxYF1LsnA8FEzbQgpscIiLgRE7iB0+gEggFvcMEYkLn2DkCRFfpYte5aphsRfYy7F/NJuMAgFYnKwJq1kJRUNsEX1rphIFLGkZcttwNk5dYpk0kpYbmd23vBRV0CXMdJkHYNkzCwEphn9huhIl9jIDO+V0VCFAdcaFN7BMjQLViVyuGBULR32o3fObsOdnn0gsEZEWA7W543p86+JBse51AQDbxmA065g3BYocsV4i3eZmoJG2eODuoJz9gjy3jCsNjDGYbzC5GKLgJY2EAADgFwPskuatTAN6lqv8uIj8PAKr6BgBXALgIwNcBLAP46RG1lRBCCNn2jIUBoKq3Ani0s/4NPX8rgF8cZrsIIYSQSWVsNAADZ2l51C0ghBBCxoax+AIwUNb8KjvmgI7j0OnBCwQUJHVwEjR0A//O5gEoknZtfpykHrOLFx8jJxAQVkMbT2o5QSoAdE0Soe50dvnuxp5gn5ZNDuIEu2magCI2MBAANIwvLEhw4ri9dtZsMiAvKYoNBOQkZAkCq9gb5VQ7gdRNcqKup5fA5s9TPXDU5h9nUrFaCM9zGwT1idAAWK2L1++tr97TxzSMJsE+l20nsJYN/GM1QAAA0xzr3wecQGRWM+UF+THBgrxAavZZDQO0ObtEJEoL1kUkAwp1A169ORo1ALDakS1qAKrx9BFCCCEkAw0AQgghpIJUxwBgMiBCCCHkGNXSAARzM7P+E3V8LtIyZWbz5zHH+XtMGTtnFWEyIG9Oqm7uhgU6EU5rz8dmixjf3eHmjqCM9QE22mH3mqtnT3SpHSYmWTb+xsXOXLaAcwus79Pzc3a0YcqE590y6zrm5rnBbWIucTBX3Oolwk7izScfJ2J8/NuNYcV5aDr9yEtg1YuXmKhl+vlCJ3wul00cgOWO88yZMaBtkgp5c/ztmOAlDNq/sDe7T8RYE46NThwVqwHwYq10cpYLagAC/35MrAD7bvHGkZz3k7ufF4OgD8Z7hCGEEELIQKABQAghhFQQGgCEEEJIBamOAUARYKkcWpkfdRPImDPliTUIIWPDZIsAVaFp8B+Znw8FE50IwY8NJuEJBfNEfxFBfmKCBXn6pFCQYsUxYcVWa+RoiwJhYNcRE1ojICqXhRETeQFGDhvRUseUaWn4YrHrvKQoloZzbCtssklbbHIgD0+sFRzH3Nzt+KqMESludyPA3qdknRWGZvtMzQluFCYQCrEiwE6BQEBe8C37bHjBt+7u7MosL7WzCbg8EaAV/XacMouNbD2eCFDtuiBZWbBLMM555I7DZSVg84R4XhCfPOyx3HqtcD1PBb451fkCQAghhJBj0AAghBBCKkhlDABdZjIgQgghZI3J1gAAx/woMr8jN3GCuMEZIsrkBnlwglQE/h2nTETCIOs3CgNQOL4y62JzswyZMsa/t9IKu85UPXtwm/gHAJr1rM+q6fgjD3ez2oKVbtaPOO1E3mh0bSAgJymKuZle4JUOrB82e6xWwUAxNoBQbZvF0KlVJQtSBPZeTkVcGttvbMApAGgavUTd6k8cp/XRbjbwz1I3DPJjnw1PQ7O/fVxmuW0S/XhaHbvOaiGAcGyp2URkANpGA2AlNF6Qn2Bc9lzhOeNnTAI2z79vdWARsqDgveHWa98bEcGCooRXm1CZLwCEEEIIWYcGACGEEFJBKmMA6PLKqJtACCGEjA2TrwHQHg1Anv/EmVMZ+mXCQ1gfkE3a484ThZ2v7xSJiAOQ1xbPN2ZdgK5OwPrlIlxNdh6wNy/YzidudkJ/5H3tPZnlltln1WZJQpgUxfo9kzLZi+ElDAr3McmBIi6E56sNy9ibuf3my9ed+e5522NiJIwTMcmBYu637TdN57eX1aS0TJkuwofZ9vtVp9/b2BbWvw+Ez4v173txANrm2fW0RMGY0Hb6eW4sk3CXIgnXYuIAxNQb+Pwj3gnhsSP8+1HJgLb2PFXmCwAhhBBC1qEBQAghhFSQyhgA1AAQQggh60y2BkD1mI/EjQMQkwvAzvn09gmC69s5n14cgPxDx+QLCOqJyEMQ4LmjOnZuromR7/jybBwA63v01tWck7qntTezvNLJxgGYrbWDfaw/fzUiDoA3Z3pGrE4ge6wWWk69RWL/W21BWIcXU35U5Pn7h13PKAnj+tv5+uG9bEXEoLD+/I7pIy0N+17MHH+7zjv2N1dPyixbn7+n57G0O45OoGna48Yy2TwXQFSelJixsUic/6h8Afn+/KCMm3/FrHRzARgdCOMAEEIIIaRfaAAQQgghFYQGACGEEFJBKmMAUARICCGErDPZIkAgJxBQvnjLTf4TlMlZjhCbeKKQPBFL3LHzBYjqilhsmWw9XUcUZOMJeclBOhEBRu5v7Qrb03tsx261AVC8QECNIBBQWMYSCqhCIVa4TxiwpWMuaK1AQplxo5bz+8HbHiOYHCVWjOkFfsq7l17epJba5VCs1zTrbFKsFhaDfWwfjhEBtp0ydzb2mTI2CJEX5McIWZ1neWraJP9aCZ85O7aE4543huULBfMCtLmCxCLBgqICAdkEQs5OwXnnPyvKQECEEEII6RcaAIQQQkgFqYwBQA0AIYQQss7EawDWfCS1nfO5QROkE27XIKCDd5A+l5Hvu/f2iwqIEbN9a7EjAABdT1sgNoFQfnIQ62sEgKPtucyyDVyy2gm77axYX31YZtncyxgNwJzx+dvkQB5ecpiYhDFk/Ii5l1HJgIwP3et7LZMQqmv6/ZLj77Xal5aT6Mcm//HK2OBaMYGArJ7HG147TnCggLzxssiYG1EmxndfNAhRbtAh52KFieciEgYxEBAhhBBC+oUGACGEEFJBKmMAdJeWR90EQgghZGyYcA2AHvOR1Obnw+QKNrFPjM/FwfpuCvn3nbn4Yv3NRfxcUfEFHL92zrE9316tli1k5wkDYTIgL2HQgtEA1MzBO3VHW2BsWW8+9EI32929WAEz9exkYeuXbTo2s5073nKclnY+edd0iqIaAXvsSUi2M0qi/PnBvH8bOyC8/w1H62KxuhUbF2DZqcMmwfLjAJg5/c4zd6C5c9MyMYm9PM1P3jjil8nX2cSMsblxVIrGAYh4VHP9+THvGi8+S5BUiBoAQgghhPQJDQBCCCGkgtAAIIQQQipIZQyA7jJFgIQQQsgaky0CVBwTScjOHVAjzgmkJq7oooQybtCHfKGLLRIVuCIiUUWMUDBMTGEEP47AL8x/kR8IqNVxkqKYQD8z9XZQxmITnHhiqMPdHaZM2P2tMHBOmtntzj5WONaKSCAzPeaBgWwin0El8clLKFQUT4hXKyCQtPcNAFpBpixTwHm0bb+xAj9v3aoJFnRfJyvUA4CWEbZ6ibJsQCEv+JYN/BMj8As1a/ll/MQ+ZrkMgZ+DPbI/nm4u6E6OFVEmp31e0LkYQZ99h8UkDNqMynwBIIQQQsg6NAAIIYSQClIZA0CXmAyIEEIIWWOyNQA9yM4d4coCQX7cMkFAoaxdFeNHivGNFQpcUTQZUF4ADy+ORbd/v6FHw2gAauYC2uQmANAxyUw8DcDRrk0y5NRjbOIwwJDjY0U2eFAz4iTDQED5vrxB+eFjGJSvfpjY6+edky3jBQay7lurE2ib/gCEfc0GmErKZPu9TbazpDP59TpJe7qwQX3yNQD22XUDAdl9HF1Q3HiUM9YUTQZUSiAgT79ljxOR2CeGmGBBWwz8Yxn5Uy0iZ4rIx0TkJhG5UUR+1SlzgYgcEZHr0n+/N4q2EkIIIZPCOHwBaAP4dVX9gojsBnCtiFypqjeZcp9Q1WePoH2EEELIxDHyLwCqepeqfiH9ewHAzQBOL/041AAQQgghxxiHLwDHEJGzATwGwOeczU8SkS8B+DaA31DVGzeo4xIAlwDAHOaPzZMULxlQjM/F1u+VETFlzPao5BFx6wJiEg/l1OvtE6yKmhcc4Te0/khn0rSdp2zr8fzwVhfgJfo52NmVWbb+fQDoBMeyyVbCR6ajRn/gnFPdJjQy/cibb+7NJ9/ujLuWwN4XL6aDdS13zI0KkgUhth/Zvpft03e39jn1Zst4/v2WeTa8OAB5z6VtW3KsiFgmEfFD8mqJimVS0rhXZMyN0RLkJgdyjxMRF2CLmoCxMQBEZBeAfwHwMlU9ajZ/AcADVXVRRC4C8H4A53j1qOqlAC4FgL21E8Y72gohhBAyIsbCHBeRaSQv/3eq6nvtdlU9qqqL6d9XAJgWkROH3ExCCCFkYhi5ASAiAuDNAG5W1b/YoMwD0nIQkScgafeB4bWSEEIImSzGwQXw3wD8JIAvi8h16brfAXAWAKjqGwD8KICXikgbwAqAF6j2N9FSl5cTHQApB8VE+qgJIaQqjNwAUNVPIudVoqqvB/D6vuvGukiitnM+X1ThbY8Rb+TUGyU2KUsoGJPoJy/wBhCckz1FcfaLu1QRQkFbxnQPNxBQPd8aWejM5ZaxyVSs+MlL4rJqAr+0PHGUWWUTyngBZ2II9ispCc52wyb/8a5neOdC7H5uMiDbJ2ywKCdyjRUKev0oCARk6l3ohv3XPiv2OLHYQEA2aVdMUC+vTFS3zktg5gVJixg/cwV9AxIX+sfa+nskukwfTP7IQAghhJAAGgCEEEJIBamMAdBdWh51EwghhJCxYeQagIGieswnWtu1y98eU0eR4+aWyS7GBLsonNinyD45x3b9fXaXCP9+TJk8TUDMPgCw3J3NLE9LRNIWG8DF8SQ3jP/ZC/JiHZ3T1tfsdACbmMb6uUl/BNfP6cLW528T/yRlNu9ryyYwFAA0NBuYygvYk5e0J8a/7/X7vMBaG63LI9DzOEF+guc7apyL2MdQePzMI0p3VdCfn7dP1Htka2NCZb4AEEIIIWQdGgCEEEJIBamMAdBdXBx1EwghhJCxYbI1AD3U5ueBrvGX2OUYX47jcsmfbxoWEMdfFu6Xs+wQNSc17zgxx4qY4x+jE4iKAxBVj5mL7cyztgmCavXwZjZz5mJ3HN/tsrmXDUcDUJNWtr1meyvCl+fNbbc6gZqjUQjLbH+7355T3PbseXtxAex98BI72WRUTVNTQ0Ntie03fiKqzfuw16dt3ApXW1DoeTLxBWL8+x6D0igVoUDsAD+OSkzSOLPC1lN0Pr99Z22R7T8SEEIIIaRvaAAQQgghFYQGACGEEFJBKmMAdJdHFwiokF9+7GEmoDLhDH+Sh6cBIMWZzHG5PyZeBLiWDKi+a2d+4W45PSIqaY+l4KFzk2IUTUQUBOOwL3wnHaAVAXrVRgQUyhMtecmAuhHJgGyglZgB1SYDsqIrAFgyoj8v0cu0EYbZhDFzEhoBYYKbYkLBKrw2iiRT8gIr2TUxQ4IVBS57/TMnyRTgCwPDY2XrDvtnOcLbqEBfwYoYUaCXMGjzwD9Fg/wUSuRTYByOO05EIdvZIgR/usV3VmW+ABBCCCFkHRoAhBBCSAWpjAHQWVwadRMIIYSQsWHiNQBr1HfOFwu+YPOHxAQLKpJsxyEISjGgZEAxmoWoU4rKrbT1oCQxePu0jG92ttYKyoSJh0zQF8e/v9Sd2XQfwAkoZHy1Hce/b/3anYgLXKM2E4B/rTrI6jCmJbyXTbOf55e3gX9s8qejTjAe22+8PhIE37GaFU9bUCBIlrdP3nMZl/zLW5vdr5A+KoaIsbGsIGlR9eQ9q0VVv0UDCG1AZb4AEEIIIWQdGgCEEEJIBamMAdBZGl0cAEIIIWTcmHwNQDrXt75rl7OtgD+l0D75RYrOdc3bp6xgF0Xmy3p+wzhf4uZlPB9mMB/a8cOG9UT46iPqaWg2yVDdySAS+HfFxiQI622Z2AExc929WAH22tRk+9v9eTESYq6Vvb7JfuY4zrz6PF990/SHZJ/8fmX7XrCPqxvIjy8QpRMoENhrUMmAYsaaQQXxifPv5yy7+wzpXdMn238kIIQQQkjf0AAghBBCKkhlDIDO4uKom0AIIYSMDZOvAUhxNQAWJzZ4DI7LN3+fstw7A5rrWob+IOrQzj7WHxkX3zzflo2JtZ7bFuc4VgOwU1adY28+z9pqAoAwX4Dn156ujg2/Zez1rDn9Ku8+xR0nP/tCEZ97kTn/RY8dk7Mj3Cm/SNGcJ6VQYEwbasKgIu+fgu+sNTh6EEIIIRWEBgAhhBBSQWgAEEIIIRWkMgYARYCEEELIOpURAdbm56HdrGAiRoZjk//EaELchEF5DEpsMsQgGjHHLoMYEVNUABdHMJUnonKDscQEDzLNCcVm4S6rakVrITVzM2tO37NlYrCBdWoD+q3gBS6KOVZeoqSO0/liytj74PWjIJGTud82OVCyT/+BqWy93n302hfUW0BwGEVZor8ccV5MkLRBJUqLwRvv84Thhd4RQPAO2yqV+QJACCGEkHVoABBCCCEVpDIGQHeZyYAIIYSQNSqlAQgYQrKFrRD6iQbkyytCwUtn/fBxQX5Gd94xyYBKOY5zHZpGAzDjBAuKSXoT7jMc/35ZeDqB/H0c/75ZbjrPf4yvvow+UaSOuEBAxcrk7TfKZ3BQFPXDDw0mAyKEEELIIKABQAghhFSQyhgA1AAQQggh61RbA1CEAn6ZsubdDzUxhSXCBxiVMCSCIv7GYfkoOwWPE/h8I6opMuPX933bmvKT1Yw79pxitBB23n+5M6pzjr3NfOhFYgfEJQzaXtfBo1jCtfHUG1TmCwAhhBBC1qEBQAghhFQQGgCEEEJIBamMAUARICGEELIORYBkIikqCoxJrhLsY4MbOQF7wuNY27uT2xYvec10ofYa+duY6bKswC9ob0nE3OuoJD5BcqDh9b1JDNAzKEYqoh5TKvMFgBBCCCHr0AAghBBCKshYGAAicqGIfEVEvi4iv+1snxWR96TbPyciZ/d7DGoACCGEkHVGrgEQkTqAvwXwAwDuBPB5EblcVW/qKfYSAIdU9SEi8gIAfwLg+f0cJ0oDMKbBGjaDHsDJwPP/TscEtzF9dtrpELZMzTpDHR97XUb328D6/L0gP/ac+t2+4X58oiaGbXcnR/D+GYcvAE8A8HVVvVVVmwDeDeC5psxzAbwt/fufATxNJEJpRQghhBCXcTAATgdwR8/ynek6t4yqtgEcAXCCV5mIXCIi14jINS2sDqC5hBBCyPZnHAyAUlHVS1X1fFU9fxqzx9ZTA0AIIYSsM3INAID9AM7sWT4jXeeVuVNEpgDsBXAgr+IFHLr/P/SfbwdwIoD7sWQK2OXtSXJuk8kknxsw2efHc9ueTPK5AZN9fmvn9sDYHcbBAPg8gHNE5DuQvOhfAOBFpszlAF4M4DMAfhTAf6rmKyZU9SQAEJFrVPX8Uls9JvDcti+TfH48t+3JJJ8bMNnnV+TcRm4AqGpbRH4JwIeR5Cl9i6reKCKvBnCNql4O4M0A3iEiXwdwEImRQAghhJCCjNwAAABVvQLAFWbd7/X83QDwvGG3ixBCCJlUJk4EuAGXjroBA4Tntn2Z5PPjuW1PJvncgMk+v77PTSJc6YQQQgiZMKryBYAQQgghPdAAIIQQQirIxBoAIvKnInKLiFwvIu8TkX09216RJhb6iog8c4TNLIyIPE9EbhSRroic37P+bBFZEZHr0n9vGGU7i7DRuaXbtv29W0NEXiUi+3vu1UWjbtNWyUvstd0RkdtE5Mvp/bpm1O3ZCiLyFhG5V0Ru6Fl3vIhcKSJfS/8/bpRtLMoG5zYRz5uInCkiHxORm9Jx8lfT9X3fu4k1AABcCeBcVX0UgK8CeAUAiMgjkEwjfCSACwH8XZqQaLtxA4AfAfBxZ9s3VPW89N/PD7ldZeCe2wTdu17+sudeXZFffHzpSez1LACPAPDC9J5NGk9N79d2n0/+ViTPUS+/DeCjqnoOgI+my9uRtyI8N2Aynrc2gF9X1UcA+G4Av5g+Z33fu4k1AFT1I2neAAD4LJIIg0CSWOjdqrqqqt8E8HUkCYm2Fap6s6p+ZdTtGASbnNtE3LsJJiaxFxkTVPXjSOKq9NKbeO1tAH5omG0qiw3ObSJQ1btU9Qvp3wsAbkaSL6fvezexBoDhZwB8KP07JvnQduc7ROSLIvJfIvKUUTemRCbx3v1S6qZ6y3b93NrDJN4fiwL4iIhcKyKXjLoxA+AUVb0r/ftuAKeMsjEDYJKeN4jI2QAeA+BzKHDvxiIQUFFE5D8APMDZ9Luq+oG0zO8i+WTyzmG2rQxizs/hLgBnqeoBEXkcgPeLyCNV9ejAGlqAgue27djsPAH8PYA/QPJS+QMAf47EWCXjy5NVdb+InAzgShG5Jf21OXGoqorIJM0Tn6jnTUR2AfgXAC9T1aMicmxb7L3b1gaAqj59s+0icjGAZwN4Wk/ugJjkQ2NB3vltsM8qkORBVtVrReQbAB4KYKwES0XODdvo3q0Re54i8kYAHxxwcwbNtrs//aKq+9P/7xWR9yFxe0ySAXCPiJyqqneJyKkA7h11g8pCVe9Z+3u7P28iMo3k5f9OVX1vurrvezexLgARuRDAbwH4QVXtzQV8OYAXiMhsmoDoHABXj6KNg0BETloTxonIg5Cc362jbVVpTNS9Sx/SNX4YifhxO3MssZeIzCARbF4+4jaVhojsFJHda38DeAa2/z2zrCVeQ/r/JH2Nm4jnTZKf+m8GcLOq/kXPpr7v3cRGAkwTB81iPW3wZ9cU8alb4GeQuAZepqof8msZX0TkhwH8DYCTABwGcJ2qPlNE/geAVwNoAegCeKWq/uvIGlqAjc4t3bbt790aIvIOAOch+SR5G4Cf6/HhbUvSqVWvw3pir9eMtkXlkRrU70sXpwC8azufn4hcBuACJGlk7wHwSgDvB/BPAM4CcDuAH1PVbSem2+DcLsAEPG8i8mQAnwDwZSRjPAD8DhIdQF/3bmINAEIIIYRszMS6AAghhBCyMTQACCGEkApCA4AQQgipIDQACCGEkApCA4AQQgipIDQACCGEkApCA4AQQgipIDQACKkAItJJc6B/SUS+ICLfk67/9Ijas+lxRWSfiPzCsNpDSBVhICBCKoCILKrqrvTvZwL4HVX9vhE3a0PSLGcfVNVzR90WQiYVfgEgpHrsAXAISAyDtZUi8v40ze2Na6lu0/j3/5Z+ObhBRJ5vKxORs0XkFhF5p4jcLCL/LCLz6bZfS/e7QURe1rPPYs++N4vIG9PjfkREdgB4LYAHp18t/tQ55oXptutE5HMiwrGMkD7hFwBCKoCIdJDEDp8DcCqA70+zRfZ+GTheVQ+mL+DPA/g+JPHTL1TV/5mW2auqR0zdZwP4JpJUuZ8SkbcAuAnAxwC8FcB3AxAkscp/QlW/uHbcdN+vAzhfVa8TkX9CktTkk9jkC4CIfA3A927HWO6EjAu0mgmpBiuqep6qPhzAhQDeLr0JxBN+RUS+BOCzSNL6noPEaPgBEfkTEXmKffn3cIeqfir9+x8BPDn99z5VXVLVRQDvBfAUZ99vqup16d/XAjg74nyuAHC9iLwuoiwhxGFq1A0ghAwXVf2MiJyIJNsiAEBELgDwdABPUtVlEbkKwJyqflVEHgvgIgB/KCIfVdVXe9XmLG/Gas/fHQA7NiucChgFwKmq2u7jOISQHvgFgJCKISIPR5Ku90DP6r0ADqUv/4cj+WwPETkNwLKq/iOAPwXw2A2qPUtEnpT+/SIkn/A/AeCHRGReRHYiycH+ichmLgDYvcG25wH4qqq2JWFPZJ2EkB74BYCQarBDRK5L/xYAL1bVTo8X4N8B/LyI3AzgK0jcAADwXQD+VES6AFoAXrpB/V8B8Is9/v+/T42JtwK4Oi3zJlX9YkxjVfWAiHxKRG4A8CFV/c2ezZcBeHMqVFwB8AtIXAeEkD6gCJAQsiU4ZY+Q7QldAIQQQkgF4RcAQgghpILwCwAhhBBSQWgAEEIIIRWEBgAhhBBSQWgAEEIIIRWEBgAhhBBSQWgAEEIIIRWEBgAhhBBSQf5/Aons30E3DRMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "A_mat, eps_mat = np.meshgrid(A_list/(2*np.pi), eps_list/(2*np.pi))\n",
    "\n",
    "ax.pcolor(eps_mat, A_mat, p_mat, shading='auto')\n",
    "ax.set_xlabel(r'Bias point $\\epsilon$')\n",
    "ax.set_ylabel(r'Amplitude $A$')\n",
    "ax.set_title(\"Steadystate excitation probability\\n\" +\n",
    "             r'$H = -\\frac{1}{2}\\Delta\\sigma_x -\\frac{1}{2}\\epsilon\\sigma_z - \\frac{1}{2}A\\sin(\\omega t)$' + \"\\n\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>QuTiP</td><td>4.6.2</td></tr><tr><td>Numpy</td><td>1.21.4</td></tr><tr><td>SciPy</td><td>1.7.2</td></tr><tr><td>matplotlib</td><td>3.4.3</td></tr><tr><td>Cython</td><td>0.29.24</td></tr><tr><td>Number of CPUs</td><td>4</td></tr><tr><td>BLAS Info</td><td>OPENBLAS</td></tr><tr><td>IPython</td><td>7.29.0</td></tr><tr><td>Python</td><td>3.9.5 (default, May 11 2021, 08:20:37) \n",
       "[GCC 10.3.0]</td></tr><tr><td>OS</td><td>posix [linux]</td></tr><tr><td colspan='2'>Mon Nov 15 19:09:52 2021 CET</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qutip.ipynbtools import version_table\n",
    "\n",
    "version_table()"
   ]
  }
 ],
 "metadata": {
  "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}