MSS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Основные предположения: \n",
    "* Газ совершенный ($\\gamma = const$)\n",
    "* Газ полностью диссоциирует при переходе через фронт"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "from sympy import *\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from ipywidgets import interact_manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Основные уравнения"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\rho_1 v_1 = \\rho_2 v_2 \\;\\; - \\;\\; \\text{баланс массы}$$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$p_1 + \\rho_1v_1^2 = p_2 + \\rho_2 v_2^2 \\;\\;-\\;\\; \\text{сохранение импульса}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\frac{v_1^2}{2} + (\\frac{\\gamma_1}{\\gamma_1 - 1})\\frac{p_1}{\\rho_1} = \\frac{v_2^2}{2} + (\\frac{\\gamma_2}{\\gamma_2 - 1})\\frac{p_2}{\\rho_2} \\;\\; - \\;\\; \\text{Интеграл Бернулли}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ (\\varepsilon_1 - \\varepsilon_2) + \\frac{1}{2}(p_2+p_1)(\\frac{1}{\\rho_1} - \\frac{1}{\\rho_2}) = 0 \\;\\; - \\;\\; \\text{Адиабата Гюгонио}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$M_i = \\frac{v_i}{a_i} \\;\\; - \\;\\; \\text{Число маха}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ a = \\sqrt{\\frac{\\gamma p}{\\rho}} \\;\\; - \\;\\; \\text{Скорость звука}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# начальные данные\n",
    "M1 = 5\n",
    "p1 = 1*10**5\n",
    "rho1 = 1.12\n",
    "gamma1 = 7/5\n",
    "v1 = M1 * sqrt(gamma1*p1/rho1)\n",
    "mol = 0.028"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "p2, rho2, v2 = symbols('p2 rho2 v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_eq(M1, gamma2):\n",
    "    hi1 = gamma1/(gamma1 - 1)\n",
    "    hi2 = gamma2/(gamma2 - 1)\n",
    "    v1 = M1 * sqrt(gamma1*p1/rho1)\n",
    "    ans = nonlinsolve([Eq(rho1*v1, rho2*v2), Eq(p1 + rho1*v1**2, p2 + rho2*v2**2), Eq(1/(gamma1 - 1)*p1/rho1 - 1/(gamma2 - 1)*p2/rho2 + 0.5*(p1 + p2)*(1/rho1 - 1/rho2),)], (p2, rho2, v2))\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Постороим графики для модели, не учитывающей диссоциацию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb1d78ccc9454b37815a318a914a84fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=11.200000000000001, description='M1', max=20.0, min=2.5), Button(descr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact_manual(M1=(2.5, 20, 0.1))\n",
    "def plot_m(M1):\n",
    "    x = np.linspace(-1, 1, 500)\n",
    "\n",
    "    gamm2 = 5/3\n",
    "    ans = solve_eq(M1, gamm2)\n",
    "    ans2 = solve_eq(M1, 7/5)\n",
    "\n",
    "    plt.figure(figsize=(15, 15))\n",
    "    ax = plt.subplot(321)\n",
    "    plt.plot(x, [p1] * (len(x)//2) + [ans2.args[1][0]]*(len(x)//2))\n",
    "    plt.title('давление без распада Гюгонио')\n",
    "    plt.ylabel('Давление, Па')\n",
    "    plt.text(-1, 600000, \"$p_0 = ${} $*10^5$\".format(p1/10**5))\n",
    "    plt.text(0.1, 500000, \"$p_1 = ${} $*10^5$\".format(round(ans2.args[1][0] /10**5, 2)))\n",
    "\n",
    "\n",
    "\n",
    "    plt.subplot(322)\n",
    "    plt.plot(x, [rho1] * (len(x)//2) + [ans2.args[1][1]]*(len(x)//2))\n",
    "    plt.title('Плотность без распада Гюгонио')\n",
    "    plt.ylabel('Плотность, $кг/м^3$')\n",
    "    plt.text(-1, 3, \"$rho _0 = ${}\".format(rho1))\n",
    "    plt.text(0.1, 2, \"$rho _1 = ${} \".format(round(ans2.args[1][1], 2)))\n",
    "\n",
    "    plt.subplot(3, 2, 3)\n",
    "    plt.plot(x, [v1] * (len(x)//2) + [ans2.args[1][2]]*(len(x)//2), label='Скорость потока')\n",
    "    plt.plot(x, [sqrt(gamma1*p1/rho1)] * (len(x)//2) + [sqrt(gamma1*ans2.args[1][0]/ans2.args[1][1])] * (len(x)//2), label='Скорость звука')\n",
    "    plt.title('Скорость без распада Гюгонио')\n",
    "    plt.ylabel('Скорость, м/c')\n",
    "    plt.text(-1, 600, \"$v _0 = ${}\".format(round(v1, 2)))\n",
    "    plt.legend()\n",
    "    plt.text(0.1, 1000, \"$v _1 = ${} \".format(round(ans2.args[1][2], 2)))\n",
    "   \n",
    "\n",
    "    plt.subplot(3, 2, 4)\n",
    "    plt.plot(x, [p1*mol/(8.31*rho1)] * (len(x)//2) + [ans2.args[1][0]*mol/(8.31*ans2.args[1][1])]*(len(x)//2))\n",
    "    plt.title('Температура без распада Гюгонио')\n",
    "    plt.ylabel('Температура, К')\n",
    "    plt.text(-1, 1000, \"$T _0 = ${}\".format(round(p1*mol/(8.31*rho1), 2)))\n",
    "    plt.text(0.1, 600, \"$T _1 = ${} \".format(round(ans2.args[1][0]*mol/(8.31*ans2.args[1][1]), 2)))\n",
    "\n",
    "\n",
    "    plt.subplot(3, 2, 5)\n",
    "    plt.plot(x, [M1] * (len(x)//2) + [ans2.args[1][2] / sqrt(gamma1*ans2.args[1][0]/ans2.args[1][1])]*(len(x)//2))\n",
    "    plt.title('числа маха без распада Гюгонио')\n",
    "    plt.text(-1, 2.5, \"$M _0 = ${}\".format(round(M1, 2)))\n",
    "    plt.text(0.1, 1, \"$M _1 = ${} \".format(round(ans2.args[1][2] / sqrt(gamma1*ans2.args[1][0]/ans2.args[1][1]), 2)))\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Построим зависимости параметров от маха для этой модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_p = []\n",
    "r_t1 = []\n",
    "r_rho = []\n",
    "r_v = []\n",
    "for x in np.arange(2.5, 13, 0.1):\n",
    "    ans = solve_eq(x, 7/5)\n",
    "    r_p.append(ans.args[1][0])\n",
    "    r_t1.append(ans.args[1][0]*mol/(8.31*ans.args[1][1]))\n",
    "    r_rho.append(ans.args[1][1])\n",
    "    r_v.append(ans.args[1][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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RMzMzuf3225k+fTrNmjXjscceK9U9ai6++GJuuukmnHO88cYb/O1vfyur+CIiUsEFvX8E7+bbVapUKXJe7R9FRI6OjsyVsX79+rF27doD2vJ3TPXr1ycjI4Px48eXal1VqlShb9++nHjiicXuDEVEArVuNjG5uomyFE/7RxGpbHLzHMu255bra0T9kblIkpeXx7Jly6hbt+4B7bVr1+aWW26hc+fONG/enN69e5d6nX/6058A2LJlS5lmFREpExmbYNQVtKvWBvoVf+6wVF7aP4pIZfT6dyt4fGomPXpup8exdcrlNVTMHaGMjIxD2hYuXMgVV1xBUlLSIdMef/xxHn/88ULX1bx5c+bPn39A28qVKw8Yr1+//iFtIiKBcg4+vBP2ZbCy4zU0CDqPRATtH0VEYPXWPTz52RK6HhNL92a1y+11VMyVoU6dOvH0008HHUNEJDymvwZLP4Xz/sGevccGnUYimPaPIlKZOOd46L25xMfEMLhjPGZWbq9VqnPmzOw+M1tgZvPN7C0zSzSzumb2uZkt9Z/rhMz/sJktM7MlZnZuSHtPM5vnT/uXlec7ExGR8rNlKXz6B2h1JvS+Jeg0IiIiEePtn9bw/S9befj89tRNLN9LlJS4djNrAtwN9HLOdQJigYHAQ8Bk51xrYLI/jpl18Kd3BPoDL5pZrL+6l4ChQGv/oRMsRESiTW42vHcLxCfCJS9CjK6lJSIiArBhZybDPl7EiS3rcc0Jzcr99Uq7B44DkswsDqgKrAMuAUb400cAl/rDlwBjnXNZzrkVwDLgBDNrBNR0zv3gnHPAyJBlREQkWqQ+AetmwUXPQc1GQacRERGJCM45/vjBfLLz8njiis7l2r0yX4nFnHNuLfAksBpYD+x0zn0GJDvn1vvzrIeCc9+bAGtCVpHmtzXxhw9uFxGRaLHqB5jyNHS7HjpcEnSaiGVmr5nZJjObH9J22KcniIhI9JgwZx1fLNrI/We35bh61cLymiVeAMXf2VwCtAB2AO+Y2fXFLVJImyumvbDXHIrXHZPk5GRSU1MPmF6rVi3S09NLih4Wubm5EZPlcJSUOzMz85DtHgkyMjIiMldJojU3RG925S57sTm76f3TvbiEBkyvfgG5ITkjOXdA3gBewOuFki//9IQnzOwhf/zBg05PaAx8YWZtnHPle3MiEREpM1sysnhswgK6NavNr05pEbbXLc3VLM8CVjjnNgOY2XvAScBGM2vknFvvd6Hc5M+fBoR2EG2K1y0zzR8+uP0QzrnhwHCAXr16uZSUlAOmL1q0iBo1apQievnYunUr/fr1A2D9+vXExcVxzDHHADBt2rRCb2CakpLCk08+Sa9evcjJyeHiiy+mc+fO/P3vfw9r9nzp6enFbsPExES6d+8exkSlk5qaysGfh2gQrbkherMrdzl4byjs2wq/+pRTmx14P7CIzh0A59w3Ztb8oOZLgBR/eASQCjxIyOkJwAozWwacAPwQlrAiInLUHpuwgN1Zufzzyi7ExoTvGo+lKeZWA33NrCqwF+gHTAd2A4OBJ/znD/35JwBjzOxpvG8YWwPTnHO5ZpZuZn2BqcAg4PmyfDPhUq9ePWbPng3Aww8/TL169XjggQdKvfytt95K3bp1eeKJJ8opoYhIGZs3Hua+DSkPQ7PS39hZDnDA6QlmFnp6wo8h8xV5GkI09VwB9V4Jt2g9Qq7c4aXcZW/Gxhw+mpvF5a3jWbtoBmsX7Z9W3rlLLOacc1PNbDwwE8gBZuEdNasOjDOzIXgF3wB//gVmNg5Y6M9/R0hXkdvwup4kARP9R6XyyCOPsHr1aj755JOwnBQpInLUdqyBj34DTXvDqaX/4kpKrdSnIUR6z5WDldQLJNIsWrSI5557jg0bNnDuuedy2223FTqfeq+ULeUOL+UuWzv27OO3z3xDh0Y1+fuNJxMfe+AlSco7d6luGu6cexR49KDmLLyjdIXNPwwYVkj7dKDTYWYs3sSHYMO8Ml0lDTvDeWV/1OyVV17hlVde4cUXXyQ+Pr7M1y8iUubycr3ulS4XLn8ZYku125DCHe7pCRXGF198wYgRI3jzzTfD/tq33norgwYN4uSTTy5x3vbt2/Of//yHnTt38pvf/CYM6UQk2v35o4Vs272P12/sfUghFw66OVAYTZo0iW+//ZZhw4axbdu2oOOIiJRsyjOw+ns4/0moG74TuiuoCXinJcChpycMNLMEM2uBf3pCAPnKRW5uLnPmzAnsSNbUqVPp27dvqeefMGEC55xzTsG58SIiRfly8Ubem7mW21Na0alJrUAyRP9XrOVwBK28vPrqq5x44on8+te/5sEHH+Tll18OOpKISNHSZkDq36Dj5dB1YNBpooqZvYV3sZP6ZpaG17vlCQ7/9ISoNGDAAJo1a8b06dM555xz+Pnnn2nYsCGnnnoqS5cuZdSoUZx11lksXryYoUOHsnXrVho1asTYsWOpX79+oetcuXIlF154IfPnz2fRokUMHDiQjz76iNzcXNq3b0/btm0B6NKlCyNHehcRXbRoEW3atCE2NpYBAwaQnJzM7NmzWbNmDaNHj2b48OH8+OOPnHrqqbz66qsAXHzxxZxxxhkMHDiQa6+9NjwbTESizq7MbH7/3nzaJFfnzjOPDyxH9BdzEaZfv36MHDmSJk0OPXe9Vi2vYn/ggQfo3r0733//PSeddFK4I4qIlCwrA94dAtUbwoVPg87xPSzOuWuKmHRYpydEq3nz5tG+fXs+/vhjatSoQdeuXenUqRPffvst7733HqNHj+bUU0/liiuuYNSoUXTv3p2///3vPPPMMwwbVvxmWLt2LQMHDmTMmDE0a9aMlStX0qpVq4ILk4WaOHEi/fv3L8h04okn8sILL/DII48wZMgQUlNTqVevHsnJybz44ov88MMPvPfee2RkZHD++eeXx6YRkQpi2EeL2JSeyX9vOJmEuNjAcqiYK0N5eXksW7aMunXrFjtffHw8//73v7n99tuZPn06cXH6bxCRCDPxQdi+Em78CJLqlDi7RJ57J93L7A2zy3Sd3Rp249n+zxY7T2ZmJtu2beORRx5h7969ZGdns23btoKrPufk5FC7dm0++OADTjnllILulx06dGDChAnFrjsjI4P+/ftz5pln0rFjxxLzfvrpp7z++utkZmayY8cO7r33XgCSkpIYMmQIjRo1AqBq1apUqVKFlJQUUlJSou7CLSISXl//vJm3p6/h16e3omuz2oFmURVxlH7/+98X/MJfuHAhV1xxBUlJSYfMd/AlSU8//fRCv0UUEQnc/Pdg9ijvypXNTwk6jUSZBQsW0KdPn4IvKhcuXEjXrl2JifFO0587dy6dOnVi4cKFdO7cuWC5efPm0aFDh2LXvWbNGt58802eeOIJFi1aRPv27Yucd8+ePezYsYPGjRszY8YMevToUZBhzpw5BVeqTEtLo3HjxrrCtIiUSnpmNg+/O5dWx1Tj3rNaBx1HxVxZ6tSpE08//XTQMUREjtyO1fC/e6FJL0h5KOg0chRKOoJWXubNm0eXLl0KxufMmUPXrl0LxufOncsll1xCbm5uwZeay5cv580332TKlClA0acstG/fnmuvvZYmTZpw66238vXXXxeZ46uvvuKMM84oyHRwhvyMc+bMOSCviEhx/vrJYjbsymT8bSeRGB9c98p8upqliIh4Cm5DkAdXvAKxuoWKHL7CirnQ8fnz59OpUyduuOEG1q1bR+fOnRk4cCCvvfYa9erVK9UpC6effjrt2rXjpZdeKnKeg8+X69atG+B1A927dy916njdh0MLOxGR4kxZuoW3pq3mllNb0uPYyDgFQUfmRETE8+1TsPoHuOy/ug2BHLGnnnqq2PHly5cXDH/wwQeHLF/UKQvNmzdn/vz5BePDhw8vGA5tz/f999/zzDPPHJIhMTGRFStWFIw//PDDxb0dERHA61754LtzaXlMNe47u03QcQqomBMREVg9FVKfgM4DoMvVQaeRSqysTlmYOXNmGaQREfH8beJi1u/cGzHdK/Opm6WISGW3dwe8ezPUagoX6DYEIiIioaYs3cKYqau5OYK6V+bTkTkRkcrMOfjoPti1Fn71KSTWDDqRiIhIxAjtXvmbCOpemU9H5kREKrNZo2DBe3DG76FZ76DTiIiIRJS/frKI9Tv38uSArhHVvTKfijkRkcpqy1KY+Dtofiqccl/QaaSMOOeCjlChafuKVB7f/LyZt6at4ZbTIq97ZT4Vc5XQokWLuPfee7nyyiuLvayziFRgOVkw/iaIS4TLh0NM5H3bKIcvMTGRrVu3quAoJ845tm7dSmJiYtBRRKSc7fK7Vx7foDr3nRV53Svz6Zy5MvTFF18wYsQI3nzzzbC/9q233sqgQYM4+eSTS5y3ffv2PPvss1SrVo1bbrklDOlEJOJ8/ihsmAfXjIWajYNOI2WkadOmpKWlsXnz5qCjAN493aKx8Ckud2JiIk2bNg1zIhEJt8c/WsjGXZm8d/vJEdm9Mp+KuTKSm5vLnDlz6N69eyCvP3XqVF588cVSz//JJ5/w3HPPceedd5ZjKhGJSEsmwdSX4IRboe15QaeRMhQfH0+LFpFzj8DU1NTA9otHI1pzi0jZ+HLxRsZNT+O2lFZ0a1Y76DjFUjfLozBgwAAefvhhzjjjDP72t78xZ84cNmzYwKmnnkrDhg354osvAFi8eDGnnXYaHTt25KyzzmLLli1FrnPlypV06tQJ8LpDdu3alTVr1rBy5UqSkpLo1q0b3bp1Y9CgQQXLLFq0iDZt2hAbG8uAAQO48847OeWUUzjuuOOYMmUKgwYNok2bNgwZMqRgmfPPP5/vv/+e0aNHl9PWEZGItGs9fHAbJHeGs/8cdBoREZGIsmPPPh56dx5tk2tw71mtg45TIh2ZOwrz5s2jZcuWfPXVVwB07dqVTp068e233/Lee+8xevRoTj31VK644gpGjRpF9+7d+fvf/84zzzzDsGHDil332rVrGThwIGPGjKFZs2asXLmSVq1aMXv27EPmnThxIv379y/IdOKJJ/LCCy/wyCOPMGTIEFJTU6lXrx7Jycm8+OKL/PDDD4wdOxbnHOeff36ZbxcRiVB5ufDeLZCTCVe+BvHR1/1NRESkPD02YQHbdu/jtRt7kxAXud0r80V9MXfvpHuZvWF2ma6zW8NuPNv/2WLnyczMZNu2bTz00EMAZGdns23bNh544AEAcnJyqF27Nh988AGnnHJKQXeNDh06MGHChGLXnZGRQf/+/TnzzDPp2LFjiXk//fRTXn/9dTIzM9mxYwf33nsvAElJSQwZMoRGjRoBULVqVapUqUJKSgo9e/akRo0aJa5bRCqQb5+Gld/CxS/AMZF7MreIiEgQJs1fzwez13HvWa3p1KRW0HFKRd0sj9CCBQvo06cPcXFePbxw4UK6du1KTIy3SefOnUunTp1YuHAhnTt3Llhu3rx5dOjQodh1r1mzhocffpivvvqKRYsWFTvvnj172LFjB40bN2bBggX06NGjIMOcOXPo06cPAGlpaTRu3BgzO+L3LCJRbNX3kPpX6DwAul8fdBoREZGIsjUjiz+8P59OTWpyxxnHBx2n1KL+yFxJR9DKy7x58+jSpUvB+Jw5c+jatWvB+Ny5c7nkkkvIzc0t6Bq5fPly3nzzTaZMmQJAv379GDlyJE2aNDlg3e3bt+faa6+lSZMm3HrrrXz99ddF5vjqq68444wzCjIdnCE/45w5cw7IKyKVyJ5t8O7NUKc5XPgM6EsdERGRAs45/vD+fNIzcxgzoBvxsdFzvCt6kkaYwoq50PH58+fTqVMnbrjhBtatW0fnzp0ZOHAgr732GvXq1SMvL49ly5ZRt27dIl/j9NNPp127dsXeC+7g8+W6desGeN1A9+7dS5063g0OQws7EalEnIMP74CMTd55cgnqXi0iIhLqw9nrmLRgA/ed3Ya2DaNrPxn1R+aC8tRTTwGQnp5+wHi+5cuXFwx/8MEHhyy/cOFCrrjiCpKSkg5ob968OfPnzy8YHz58eMFwaHu+77//nmeeeeaQDImJiaxYsaJg/OGHHy7xPYlIBTT1v7DkEzj3b9BYl1oXEREJtWFnJo98OJ8ex9Zm6Gktg45z2FTMBaRTp048/fTTR72emTNnlkEaEamQ1s2Cz/8P2vSHvrcFnUZERCSiOOd48N25ZOc6nrqqG7Ex0XcagrpZiohURJm74J2boNoxcOlLOk9ORETkIG9NW8PXP2/m4fPb0aJ+taDjHBEdmRMRqWicg4/uhR2r4caPoWrR5+aKiIhURqu37uHxjxdy8vH1uL7PcUHHOWI6MiciUtHMHAnz34UzHobjTgw6jYiISETJzXPc/85sYmOMf17ZlZgo7F6ZT0fmREQqko0LYOLvoMXpcMpvgk4jIiIScV75djk/rdzO01d1pXHtpJIXiGA6MiciUlFkZcA7N0JCTbjiFYiJDTqRiIhIRFm8YRdPffYz/Ts25LLuTUpeIMLpyJyISEXxyQOwZSkM+gCqNwg6jYiISETZl5PHfW/PoWZSHMMu64RVgIuDqZgTEakIZo2GOW/B6Q9By5Sg04iIiEScZ7/4mUXrd/HyoF7Uq54QdJwyoW6WESI7O5uePXsGHUNEotGmxd5Rueanwum/CzqNiIhIxJmxahv/+foXrurVlLM7JAcdp8yomIsQU6ZM4aSTTgo6hohEm3274Z3BUKWazpMTEREpxO6sHO57ew5N6iTxyEUdg45TplTMHYEHH3yQF198sWD8scce46mnnip2GTPjoYceKhjv06cPKSkpBeOTJk3ivPPOA2DkyJF06dKFrl27csMNN5RteBGpWD5+ADYv8Qq5Gg2DTiMiIhJxHv94EWu27+GpAd2onlCxzjIr8d2YWVvg7ZCmlsAjwEi/vTmwErjKObfdX+ZhYAiQC9ztnPvUb+8JvAEkAZ8A9zjn3FG9g3vvhdmzj2oVh+jWDZ59tsjJAwcO5N577+X2228HYNy4cUyaNKnYVVarVo0ZM2aQm5vLkiVLDjnh8quvvuLRRx9lwYIFDBs2jO+++4769euzbdu2o303IlJRzRoFc8boPDkREZEiTF60kbemrebW01tyQou6QccpcyUemXPOLXHOdXPOdQN6AnuA94GHgMnOudbAZH8cM+sADAQ6Av2BF80sv9/PS8BQoLX/6F+m7yZMunfvzqZNm1i3bh3z5s2jTp06HHvssSUud+655zJp0iRee+01brrppoL2devWUbduXapWrcqXX37JlVdeSf369QGoW7fifehEpAxsXOgdlWtxms6TExERKcTWjCwefHce7RrW4Ddntwk6Trk43OOM/YBfnHOrzOwSIMVvHwGkAg8ClwBjnXNZwAozWwacYGYrgZrOuR8AzGwkcCkw8ajeQTFH0MrTlVdeyfjx41m9ejUDBw4s1TI33HADt99+O7t27WLgwIG89dZbAEycOJFzzz0XAOdchbhMqoiUo6wM7zy5hBpwuc6TExEROZhzjoffm8euvdmMuvkEEuIq5r7ycIu5gcBb/nCyc249gHNuvZnl39SoCfBjyDJpflu2P3xw+yHMbCjeETySk5NJTU09YHqtWrVIT08/zOhl66KLLuKuu+5i69atTJw4sSBPz549mTFjRqHLVK1alerVq3P66aeze/ducnNzSU9P56OPPuKPf/wj6enp9O3bl2uvvZabb76ZevXqsW3btnI5Opf/2kXJzMw8ZLtHgoyMjIjMVZJozQ3Rm73C5naO9ouepsGWZczp+id2zFgELApXvCJF6/YWEZGK6Z3paXy2cCN/OL897RrWDDpOuSl1MWdmVYCLgYdLmrWQNldM+6GNzg0HhgP06tXLhV4oBGDRokXUqFGjhBjl64QTTmDPnj00btyY1q1bA7BlyxbMrMhsNWrUYMSIEQBMnz6d2NhYqlatyooVKwpuS3DCCSfwf//3f1x44YXExsbSvXt33njjjTLPn56eXuw2TExMpHv37mX+ukcrNTWVgz8P0SBac0P0Zq+wuae/Bpu+gTP/SLfT7g5brpJE6/YWEZGKZ/XWPfzpfwvo27IuQ05pEXSccnU4R+bOA2Y65zb64xvNrJF/VK4RsMlvTwOahSzXFFjntzctpD1qzZs374CjWz/++CN33HFHofNmZGQcMN6rVy9SU1OZMmUKffv2PWDa4MGDGTx4cNkHFpHotm42THwQWvWDU+4POo2IiEjEycnN475xs4mJMZ66qhsxMRX79KXDKeauYX8XS4AJwGDgCf/5w5D2MWb2NNAY70In05xzuWaWbmZ9ganAIOD5o8wfUS688MLDXuaUU07hlFNOKYc0IlKh7N3hnSdX7Ri4/GWI0Z1lREREDvafr39hxqrtPHN1V5rUTgo6TrkrVTFnZlWBs4FbQ5qfAMaZ2RBgNTAAwDm3wMzGAQuBHOAO51yuv8xt7L81wUSO9uInIiKVgXPw4R2wMw1u/ASq1Qs6kYiISMSZs2YHz36xlAu7NOLSboVemqPCKVUx55zbA9Q7qG0r3tUtC5t/GDCskPbpQKfDj1noa+iqj+XkaG/9JyJl7IcXYPFHcO7f4Ng+QacRERGJOHv25XDf27M5pkYCwy7tXGnqhKjsp5OYmMjWrVtVdJQD5xxbt24lMTEx6CgiArDqB/j8UWh/MfS9Leg0IiIiEWnYx4tYsXU3T13VlVpV44OOEzaHe2uCiNC0aVPS0tLYvHlz0FHIzMyMysKnuNyJiYk0bdq00GkiEkYZm+CdG6FOc7jk31BJvmUUERE5HJMXbWT01NUMPa0lJ7WqH3ScsIrKYi4+Pp4WLSLjMqOpqakReQn/kkRrbpFKIy8X3h0CmTvg+nchseLeI0dERORIbU7P4nfj59KuYQ3uP6dN0HHCLiqLORGRCu+rYbDiG++IXMMyOdVYRESkQnHO8bvxc8jIyuGtoX1JiIsNOlLYReU5cyIiFdqSSfDtU9BjEHS/Pug0IiIiEenNH1fx1ZLN/P789rRJrhF0nEComBMRiSCJezfA+0OhUVc4759BxxEREYlIP29MZ9jHizij7TEMOvG4oOMERsWciEikyM6k44K/e8NXjYT46Lu4koiISHnLysnl7rdmUT0hjn9c2bXS3IagMDpnTkQkUnzyADUylsM1b3tXsBQREZFD/GPSEhZvSOfVwb04pkZC0HECpSNzIiKRYMYImPUmK4+7Ctr2DzqNiIhIRPrm5828OmUFg048jn7tk4OOEzgVcyIiQVs3Cz75LbQ6k5XNBwadRkREJCJtzcji/nfm0Ca5Or8/v33QcSKCijkRkSDt2QZvD4LqDeDyV8Aq32WVRURESuLdhmAuO/dm89zA7iTGa38JKuZERIKTlwvv3QIZG+CqEVCtXtCJREREItKXa3KYvHgTD/VvR/tGNYOOEzF0ARQRkaCkPgHLvoALn4EmPYNOIyIiEpGWbEhn7OJ9nN7mGG46uXnQcSKKjsyJiARhyUT45h/Q7XroeVPQaURERCJSZrZ3G4KkOOPJAZX7NgSFUTEnIhJuW3+B9271bgx+wZOgHZOIiEih/vbJIpZsTOfmzlUq/W0ICqNiTkQknPbthrevh5gYuOpNiE8KOpGIiEhE+mLhRkb8sIohp7SgyzE6O6wwKuZERMLFOZhwF2xaBFe8AnWOCzqRiIhIRNq4K5Pfjp9Dx8Y1+V3/tkHHiVgq5kREwuWHf8P8d6Hf/8HxZwWdRkREJCLl5jnue3s2mdl5PDewOwlxug1BUXS8UkQkHFZ8A58/Au0uhFN+E3QaERGRiPWfr3/h+1+28o8runB8g+pBx4loOjInIlLedqbBOzdCvVZw6Uu64ImIiEgRZq7eztOf/8yFXRoxoFfToONEPBVzIiLlKTvTu+BJzj4YOAYSdaPTys7M7jOzBWY238zeMrNEM6trZp+b2VL/uU7QOUVEwm1XZjZ3vzWLRrUS+evlnXUbglJQMSciUl6cg49/A+tmweX/hfqtg04kATOzJsDdQC/nXCcgFhgIPARMds61Bib74yIilYZzjt+/N4/1OzP51zXdqZkYH3SkqKBiTkSkvPz0CsweDac/CO0uCDqNRI44IMnM4oCqwDrgEmCEP30EcGkw0UREgvH2T2v4aO56fnN2G3ocq84JpaULoIiIlIdV38Okh6BNfzhdB1nE45xba2ZPAquBvcBnzrnPzCzZObfen2e9mTUobHkzGwoMBUhOTiY1NTVMyY9MRkZGxGcsjHKHl3KHVyTmXpuRx5++30vHejG0Zw2pqWmHzBOJuUujvHOrmBMRKWs718K4QVCnOVw+3LtBuAjgnwt3CdAC2AG8Y2bXl3Z559xwYDhAr169XEpKSjmkLDupqalEesbCKHd4KXd4RVruzOxcLnnhO2pWrcLrvz6VBjUSC50v0nKXVnnn1l8YIiJlKf+CJ9l74erRkFgr6EQSWc4CVjjnNjvnsoH3gJOAjWbWCMB/3hRgRhGRsPnLRwtZsjGdp67qVmQhJ0VTMSciUlacg4/ug3Uz4bL/QoN2QSeSyLMa6GtmVc27TFs/YBEwARjszzMY+DCgfCIiYfPJvPWMnrqaW09vyeltjgk6TlRSN0sRkbIybTjMGeOdI9f+wqDTSARyzk01s/HATCAHmIXXbbI6MM7MhuAVfAOCSykiUv5Wb93Dg+Pn0q1Zbe4/u23QcaKWijkRkbKw4luY9DC0vcC7eqVIEZxzjwKPHtSchXeUTkSkwtuXk8ddb80Eg+ev6U6VOHUWPFIq5kREjtb2VfDOYKh3PFz2H13wREREpBj//HQxc9J28p/re9CsbtWg40Q1/cUhInI09u2GsddBXg5c8xYk1gw6kYiISMT6cvFGXv52BYNOPI7+nRoFHSfq6ciciMiRcg4+uB02LYDr3oF6rYJOJCIiErHW7djL/ePm0KFRTX5/fvug41QIKuZERI7Ut0/Bwg/g7L/A8WcFnUZERCRiZefmcfdbs9iXk8e/r+tBYnxs0JEqhFJ1szSz2mY23swWm9kiMzvRzOqa2edmttR/rhMy/8NmtszMlpjZuSHtPc1snj/tX/5lmUVEos+SifDl49D5KjjprqDTiIiIRLSnP/+Z6au289fLO9OifrWg41QYpT1n7jlgknOuHdAV7544DwGTnXOtgcn+OGbWARgIdAT6Ay+aWX7p/RIwFGjtP/qX0fsQEQmfTYvg3ZuhUVe4+F+g76VERESKlLpkEy+l/sI1JzTjkm5Ngo5ToZRYzJlZTeA04FUA59w+59wO4BJghD/bCOBSf/gSYKxzLss5twJYBpxgZo2Ams65H5xzDhgZsoyISHTYsw3eGghVqsHAMRCfFHQiERGRiLVhZya/GTeHdg1r8OhFHYOOU+GU5shcS2Az8LqZzTKzV8ysGpDsnFsP4D838OdvAqwJWT7Nb2viDx/cLiISHXKzvVsQ7FoHV4+GWvoVJiIiUpQc/zy5zOxcXrhW58mVh9JcACUO6AHc5ZybambP4XepLEJh/Y1cMe2HrsBsKF53TJKTk0lNTS1FzGBkZGREdL6iKHd4RWtuiN7s5ZH7+KXDabr2Gxa1u4eNv+yGX8p2/aDtLSIiFcdTn//MtJXbePbqbhzfoHrQcSqk0hRzaUCac26qPz4er5jbaGaNnHPr/S6Um0LmbxayfFNgnd/etJD2QzjnhgPDAXr16uVSUlJK924CkJqaSiTnK4pyh1e05obozV7muae/Dms/hhPvpP25f6a8Lqis7S0iIhXBV4v3nyd3aXf1ZCkvJXazdM5tANaYWVu/qR+wEJgADPbbBgMf+sMTgIFmlmBmLfAudDLN74qZbmZ9/atYDgpZRkQkcq2cAp88AMefDWf/Oeg0IiIiEW3djr38Ztxs2jeqqfPkyllp7zN3FzDazKoAy4Gb8ArBcWY2BFgNDABwzi0ws3F4BV8OcIdzLtdfz23AG0ASMNF/iIhErm0r4O0boG5LuPJViFF/fxERkaJk5+ZxV/795K7trvPkylmpijnn3GygVyGT+hUx/zBgWCHt04FOh5FPRCQ4mbvgrWvA5cE1YyGxVtCJREREIto/Ji1mxqrtPH9Nd1oeo/Pkyltpj8yJiFQuebneveS2/Aw3vAf1WgWdSEREJKJ9tmADL3+7gkEnHsdFXRsHHadSUDEnIlKYzx+BpZ/CBU9By5Sg04iIiES01Vv3cP87c+jStBZ/uKC8LhMmByvNfeZERCqXmW/CDy/ACUOh981BpxEREYlomdm53D5mBgb8+9oeJMTpPLlw0ZE5EZFQK7+Dj+6DlmfAuX8LOo2IiEjE+8tHC5m/dhcvD+pFs7pVg45TqejInIhIvm3L4e3roc5xMOB1iNX3XSIiIsV5f1Yao6eu5tbTW3J2h+Sg41Q6KuZERAD27oAxA70rV147DpLqBJ1IREQkov28MZ3fvzefE1rU5bfntC15ASlz+tpZRCQ3B8bfBNt+gRs+0JUrRURESpCRlcOvR82gWkIcL1zTnbhYHSMKgoo5EZFJD8EvX8JF/4IWpwadRkREJKI553jo3bms3LKb0Tf3pUHNxKAjVVoqoUWkcpv6X/jpZTjpLug5OOg0IiIiEe+N71fy0dz13H9OW05sVS/oOJWaijkRqbx+/sw7Ktf2fDjrT0GnERERiXgzVm1j2MeLOKt9MredrtMSgqZiTkQqpw3zvfPkkjvB5S9DjO6JIyIiUpzN6VncPnomjWsn8dRVXYmJsaAjVXo6Z05EKp/0jTDmakioAde+DQnVg04kIiIS0XJy87j7rVns2JPNe7f3plZSfNCRBBVzIlLZ7NsDY6+BvdvgpolQs3HQiURERCLek5/9zA/Lt/KPK7vQsXGtoOOIT8WciFQeeXnw/q2wdiYMHA2NuwWdSEREJOJNmr+B/3z9C9eccCxX9WoWdBwJoWJORCqPyY/Boglw7l+h3QVBpxEREYl4yzdn8MA7c+jatBaPXdwh6DhyEF0ARUQqhxlvwHfPQe+boe/tQacRERGJeLv9G4PHxxovXt+ThDhdLCzS6MiciFR8yybDR7+B48+G/n8H09W3REREiuOc46H35rFsUwYjf9WHJrWTgo4khdCRORGp2DYugHGDoUF7uPI1iNV3WCIiIiV57buV/G/OOu4/py2ntK4fdBwpgoo5Eam4dq2H0QO8Ww9cOw4SawadSEREJOL9uHwrf/1kEed2TOb2FN0YPJLpK2oRqZBic/bAmAGQudO7BUGtJkFHEhERiXjrd+7lzjEzOa5eVZ4c0BXTqQkRTcWciFQ8uTl0WPgkbF/o3RS8UZegE4mIiES8rJxcbh89k737chk7tC81EnVj8EinYk5EKhbn4JP7qbdtBlz4DLQ+O+hEIiIiUeFP/1vIrNU7eOm6HhzfoEbQcaQUdM6ciFQsU56BGW+w6tgroNevgk4jIiISFd6atpoxU1dzW0orzuvcKOg4Ukoq5kSk4pg3Hib/CTpdwYoW1wedRkREJCrMWr2dRz9cwKmt6/PAOW2DjiOHQcWciFQMK6fAB7fBcSfDpS+B6debiIhISTalZ3LbqJkk10rg+Wu6ExujC55EE50zJyLRb9NiGHst1GkOV4+CuISgE4mIiES8fTl53DF6Jjv27uO9206mdtUqQUeSw6RiTkSi2671MPpKiEuE68ZD1bpBJxIREYkKf/loIT+t3M5zA7vRobHuxRqNVMyJSPTKSvfuJbdnG9z0CdQ5LuhEIiIiUeHtn1bz5o+rGHpaSy7ppnuxRisVcyISnXL2wbhBsHEhXDsOGncLOpGIiEhUmLl6O//3gXfBk9+dqwueRDNdIUBEoo9zMOEu+OVLuOg5aH1W0IlERESiwo7MPG4bNaPggidxsSoHopmOzIlI9Jn8Z5g7Fs74A/S4Ieg0IiIiUSErJ5fnZ2Wxa6/x3u0n6YInFYCKORGJLtNehilPQ88b4bTfBp1GREQkKjjneOSDBfyyM48Xr+tB+0a64ElFoOOqIhI9Fv0PPvkttDkPzn8KTPfCERERKY2RP6zi7elruKhVPOd3bhR0HCkjKuZEJDqs/A7GD4GmveDK1yBWHQtERERK44dftvLnjxZyVvsGXHZ8fNBxpAyVqpgzs5VmNs/MZpvZdL+trpl9bmZL/ec6IfM/bGbLzGyJmZ0b0t7TX88yM/uXmb5WF5FS2LgA3rrGu/XAteOgStWgE4mIiESFNdv2cPvoGTSvV5Vnru5GjP78rlAO58jcGc65bs65Xv74Q8Bk51xrYLI/jpl1AAYCHYH+wItmFusv8xIwFGjtP/of/VsQkQptxxoYdaVXwF3/rm4KLiIiUkq7s3K4ZeR0cvMcrwzuTY1EHZWraI6mm+UlwAh/eARwaUj7WOdclnNuBbAMOMHMGgE1nXM/OOccMDJkGRGRQ+3ZBqOugH27vUKu9rFBJxIREYkKeXmO+8fN4eeN6bxwbQ9a1K8WdCQpB6U96cQBn5mZA/7rnBsOJDvn1gM459abWQN/3ibAjyHLpvlt2f7wwe2HMLOheEfwSE5OJjU1tZQxwy8jIyOi8xVFucMrWnNDcNljcjPpOucRaqQvZ07Xx9i5aDMsKn2OaN3myi0iImXhX18uZdKCDfzxgvac1uaYoONIOSltMXeyc26dX7B9bmaLi5m3sI64rpj2Qxu9YnE4QK9evVxKSkopY4ZfamoqkZyvKModXtGaGwLKnpvtnSOXvhSuGkn39hcd9iqidZsrt4iIHK2J89bz7BdLuaJHU4ac0iLoOFKOStXN0jm3zn/eBLwPnABs9LtO4j9v8mdPA5qFLN4UWOe3Ny2kXURkv7w8mHAXLPscLngajqCQExERqawWrNvJb8bNocextRl2WSd0vcGKrcRizsyqmVmN/GHgHGA+MAEY7M82GPjQH54ADDSzBDNrgXehk2l+l8x0M+vrX8VyUMgyIiLgHHz+fzDnLTjjD9DrpqATiYiIRI3N6VncMmI6tavG858bepIYH1vyQhLVStPNMhl436/q44AxzrlJZvYTMM7MhgCrgQEAzrkFZjYOWAjkAHc453L9dd0GvAEkARP9h4iI57vn4IcX4IShcNpvg04jIiISNbJycvn1qBls27OP8b8+iQY1EoOOJGFQYjHnnFsOdC2kfSvQr4hlhgHDCmmfDnQ6/JgiUuHNfBO+eBQ6XQH9/w7qFiIiIlIqzjn+8P58Zqzazr+v7UGnJrWCjiRhcjS3JhARKRuLP4b/3Q2t+sGl/4EY/WoSEREprZe/Xc74GWnc0681F3RpFHQcCSP9xSQiwVrxLbxzEzTuAVe/CXFVgk4kIiISNb5YuJG/TVzMBV0acU+/1kHHkTBTMSciwVk3y7sFQd0WcN07UEU3NBURESmtxRt2cc/YWXRuUosnr+xKTIxOUahsVMyJSDA2/wyjroCqdeCG96Fq3aATiYiIRI0tGVkMeWM61RLiGH5DL5Kq6MqVlZGKOREJvx1r4M3LwGLghg+gZuOgE4mEjZnVNrPxZrbYzBaZ2YlmVtfMPjezpf5znaBzikjkyszO5dY3Z7B1dxavDO5Fw1q6cmVlpWJORMIrYzO8eSlkpcP170G9VkEnEgm354BJzrl2eFeLXgQ8BEx2zrUGJvvjIiKHcM7x0LtzmbFqO09f1Y0uTWsHHUkCpGJORMJn7w4YdRnsXAvXjYNGXYJOJBJWZlYTOA14FcA5t885twO4BBjhzzYCuDSIfCIS+f791TI+mL2OB85pw/mddeXKyq40Nw0XETl6+/bAmKth02K4diwc2zfoRCJBaAlsBl43s67ADOAeINk5tx7AObfezBoUtrCZDQWGAiQnJ5OamhqW0EcqIyMj4jMWRrnDS7lLb9qGHF6cncWJjWPpaGmkpq497HVoe4dXeedWMSci5S8nC96+HtKmwZWvwfFnBZ1IJChxQA/gLufcVDN7jsPoUumcGw4MB+jVq5dLSUkpl5BlJTU1lUjPWBjlDi/lLp3Za3bw6hc/0PO4OrxxSx8S4o7sgifa3uFV3rnVzVJEylduDrx7M/wyGS56DjpeFnQikSClAWnOuan++Hi84m6jmTUC8J83BZRPRCJQ2vY93DxiOg1qJjD8hp5HXMhJxaNiTkTKT14eTLgLFk2Ac/8GPQYFnUgkUM65DcAaM2vrN/UDFgITgMF+22DgwwDiiUgESs/MZsgb08nKyeW1wb2pVz0h6EgSQdTNUkTKh3Mw6UGYMwbO+AOceHvQiUQixV3AaDOrAiwHbsL7cnWcmQ0BVgMDAswnIhEiJzePu96axbLNGYy46QRaJ9cIOpJEGBVzIlL2nIPJf4Jpw+HEO+G03wadSCRiOOdmA70KmdQvzFFEJII55/jT/xaSumQzf72sM6e0rh90JIlA6mYpImXv2ydhyjPQ61dwzuNgFnQiERGRqPLadyt588dV3HpaS67tc2zQcSRCqZgTkbL1w4vw5ePQZSCc/5QKORERkcP02YINPP7xQs7r1JAH+7cLOo5EMBVzIlJ2ZrwBnz4M7S+GS/4NMfoVIyIicjjmpe3knrGz6dK0Nk9f1Y2YGH0pKkXTX1oiUjbmjIX/3QvHnw1XvAqxOiVXRETkcKzdsZdfjfiJutWq8PKgniRV0S0IpHj6a0tEjt6C9+GD26DFqXD1mxBXJehEIiIiUWVXZjY3vT6NzOxcxtzchwY1EoOOJFFAR+ZE5Ogs/sS7KXizPnDNWIhPCjqRiIhIVNmXk8dto2awYstu/nt9T92CQEpNR+ZE5Mgt+wLeGQyNusK146BKtaATiYiIRBXnHH94fx7fLdvKUwO6ctLxugWBlJ6OzInIkVmeCmOvg2PawvXvQmLNoBOJiIhEnX9NXsY7M9K4p19rrujZNOg4EmVUzInI4Vv5HYwZCHVbwg0fQlKdoBOJiIhEnfEz0njmi5+5okdT7j2rddBxJAqpmBORw7N6Koy5Cmo3g0EToFq9oBOJiIhEnW+Xbuahd+dyyvH1+dvlnTHdl1WOgIo5ESm9tOkw6gqo3sAr5KofE3QiERGRqLNo/S5uGzWT4xtU56Xre1AlTn+Sy5HRJ0dESmftDHjzMu9I3OCPoGajoBOJiIhEnXU79nLT6z9RPSGO12/qTY3E+KAjSRRTMSciJVs32yvkkup4hVytJkEnEhERiTo792Zz4+vT2J2Vwxu/6k2jWrqdjxwdFXMiUqzq6cvhzUshoRbc+JF3rpyIiIgclqycXIaOnO7dS25QT9o11FWg5eipmBORoq2fS9c5j0B8NRg8AWofG3QiERGRqJOX53jgnblMXbGNJwd05aRWupeclA0VcyJSuA3zYOTF5MZW8Y7I1W0RdCIREZGo9NdPFvG/Oet46Lx2XNJNpypI2VExJyKH2jAfRlwM8VWZ3W2YCjkREZEj9Mq3y3llygpuPKk5t57WMug4UsGomBORA22YByMugrhEGPw/MpN01UoREZEj8eHstTz+8SLO79yQ/7uwg+4lJ2VOxZyI7Ld+rn9ELsnrWlmvVdCJREREotL3y7bwwDtz6NOiLk9f1Y3YGBVyUvZUzImIZ/0cGOl1rVQhJyIicuTmr93J0Ddn0LJ+dYYP6kVifGzQkaSCKnUxZ2axZjbLzD7yx+ua2edmttR/rhMy78NmtszMlpjZuSHtPc1snj/tX6ZjzSKRYd1sGHkJVKnuX+xEffpFRESOxKqtu7nx9Z+olRTPiF+dQK0k3RRcys/hHJm7B1gUMv4QMNk51xqY7I9jZh2AgUBHoD/wopnlfx3xEjAUaO0/+h9VehE5emtneEfkqtTQVStFRESOwub0LAa9No2cvDxG/OoEGtZKDDqSVHClKubMrClwAfBKSPMlwAh/eARwaUj7WOdclnNuBbAMOMHMGgE1nXM/OOccMDJkGREJwpqfYOSlkFQHbvoY6jQPOpGIiEhUysjK4aY3prFxVyav3dib4xtUDzqSVAKlPTL3LPA7IC+kLdk5tx7Af27gtzcB1oTMl+a3NfGHD24XkSCsngpvXgbV6sONH+uG4CIiIkcoKyeXoSOns2h9Oi9e14Mex9YpeSGRMhBX0gxmdiGwyTk3w8xSSrHOws6Dc8W0F/aaQ/G6Y5KcnExqamopXjYYGRkZEZ2vKModXpGWu/b2eXSe9zhZCXWZ3faP7Ju1DO8g+qEiLXtpKXd4RWtuEZGjlZvnuO/t2Xz/y1aeGtCVM9slBx1JKpESizngZOBiMzsfSARqmtkoYKOZNXLOrfe7UG7y508DmoUs3xRY57c3LaT9EM654cBwgF69ermUlJTSv6MwS01NJZLzFUW5wyuicv/yJUwZBvWaU3XQBE6qUfxOJ6KyHwblDq9ozS0icjScczzy4Xw+mbeBP17Qnit6Ni15IZEyVGI3S+fcw865ps655ngXNvnSOXc9MAEY7M82GPjQH54ADDSzBDNrgXehk2l+V8x0M+vrX8VyUMgyIhIOP38KYwZCveO9rpUlFHIiIiJStGe+WMroqav59emtuPlUXQlawq80R+aK8gQwzsyGAKuBAQDOuQVmNg5YCOQAdzjncv1lbgPeAJKAif5DRMJh0f/gnZsguSPc8D5UrRt0IhERkaj1+cpsRi9eylW9mvJg/7ZBx5FK6rCKOedcKpDqD28F+hUx3zBgWCHt04FOhxtSRI7SvPHw3lBo0hOueweSagedSEREJGp9MGstoxfv49yOyfz1ss7o1skSlMO5z5yIRKOZb8K7N8NxJ3lH5FTIiYiIHLEvF2/kgXfm0L5uDM8N7E5crP6cluAcTTdLEYl0016GTx6AVv3g6lFQpWrQiURERKLWtBXbuH30TNo3qsnt7bNJjI8NOpJUcvoqQaSimvKMV8i1PR+ueUuFnIiIyFGYv3YnQ974iSa1k3jjpt4kxalrpQRPR+ZEKhrn4MvH4dsnodOVcNl/IDY+6FQiIiJR65fNGQx+bRo1k+J5c0gf6lVPCDqSCKBiTqRiycuDT38PU1+CHoPgwmchRl1AREREjtTaHXu54ZWpmMGom/vQuHZS0JFECqiYE6ko8nJhwt0wexT0vR3O/Svo6loiIiJHbHN6Fte/MpX0rBzGDu1Li/rVgo4kcgAVcyIVQc4+eO9mWPghnP4QpDykQk5EROQo7NizjxtencqGnZmMuvkEOjauFXQkkUOomBOJdvv2wLgbYNkX3tG4E+8IOpGIiEhUy8jK4cbXf2L55t28emMveh5XN+hIIoVSMScSzfbugLcGwuof4eLnvfPkRERE5IhlZucydOR05q3dyb+v7cGprY8JOpJIkVTMiUSrjE3w5uWweTFc+Rp0ujzoRCIiIlFtX04et4+eyfe/bOXpq7rSv1PDoCOJFEvFnEg02r4K3rwU0jfAtWPh+LOCTiQiIhLVcnLzuO/t2Xy5eBOPX9qJy3s0DTqSSIlUzIlEm02LvCNy2bth0IfQ7ISgE4mIiES1vDzHg+/O4+N56/njBe25vu9xQUcSKRUVcyLRZM1PMPpKiEuAGz+Bhp2CTiQiIhLVnHM8OmEB785M476z2nDzqS2DjiRSajFBBxCRUlr6BYy8GJLqwJDPVMiJiIgcJeccwz5exJs/ruLW01pyd7/jg44kclhUzIlEg3nj4a2roW4rr5Cr0zzoRCIiIlHvqc9+5pUpK7jxpOY8dF47TPdolSijYk4k0v34Erw7BJr1gZs+huoNgk4kIiIS9V74cikvfLWMgb2b8ciFHVTISVTSOXMikco5mPxnmPI0tLsQrngV4hODTiUiIhL1hn/zC09+9jOXdW/CsMs6ExOjQk6ik4o5kUiUmwMf3QOzRkHPG+GCpyEmNuhUIiIiUe/171bw108Wc0GXRvzzyi7EqpCTKKZiTiTS7NsD42+CnyfB6Q9CysOgrh8iIiJHbdSPq/jT/xZybsdknr26G3GxOuNIopuKOZFIsnurd6GTtTO8o3G9hwSdSEREpEJ4+6fV/PGD+fRr14Dnr+lBvAo5qQBUzIlEiu2rYNQVsGM1XDUS2l8UdCIREZEK4Z3pa3jovXmc1uYYXry+B1XiVMhJxaBiTiQSrJ8DowdATiYM+hCOOzHoRCIiIhXCezPT+N27cznl+PoMv6EnCXE6B10qDn0tIRK0ZZPh9fMhJh5+9ZkKORERkTLy4ey1PPDOHE5sWY/hN/QiMV6FnFQsKuZEgjR7DIy5yrsJ+M1fQIN2QScSERGpECbMWcd9b8/mhBZ1eXVwb5KqqJCTikfFnEgQnIOv/wEf3AbHnQw3fQI1GwWdSkREpEKYMGcd946dRa/mdXntRhVyUnHpnDmRcMvNho/ug1lvQpeBcPHzEFcl6FQiIiIVQmgh98ZNvalaRX/uSsWlT7dIOGWlw7jB8MtkOO13cMbvdQ85ERGRMqJCTiobfcJFwmXnWhhzNWxaCBe/AD1uCDqRiIhIhfHh7LXc9/ZsFXJSqehTLhIG1dOXwyu/hqwMuO4dOL5f0JFEREQqjPdnpXH/uDmc0MI7R06FnFQW+qSLlLeln9Nt9sNQrT4M+RSSOwadSEREpMIYPyON3473bj+gq1ZKZaOrWYqUp2kvw5ir2JvU2Lv1gAo5ERGRMjPupzX8dvwcTm5VX4WcVEoq5kTKQ14uTHoYPnkAWp/L7G5/1a0HRKSAmcWa2Swz+8gfr2tmn5vZUv+5TtAZRSLd6Kmr+N27czm19TG8MriXCjmplFTMiZS1rAx4+3r48UXocxsMHE1uXFLQqUQkstwDLAoZfwiY7JxrDUz2x0WkCCO+X8kf3p/Pme0aMPyGniTGq5CTyknFnEhZ2rkWXu8PP0+C85+E856AGO1gRGQ/M2sKXAC8EtJ8CTDCHx4BXBrmWCJR45Vvl/PohAWc0yGZ/1yvQk4qtxIvgGJmicA3QII//3jn3KNmVhd4G2gOrASucs5t95d5GBgC5AJ3O+c+9dt7Am8AScAnwD3OOVe2b0kkIGtnwlvXwL7dcO04aH120IlEJDI9C/wOqBHSluycWw/gnFtvZg0KW9DMhgJDAZKTk0lNTS3fpEcpIyMj4jMWRrnD63By/++Xfby7NJteybFc1TSd76d8U77hilEZtnckUe7CleZqllnAmc65DDOLB6aY2UTgcrwuIU+Y2UN4XUIeNLMOwECgI9AY+MLM2jjncoGX8HZCP+IVc/2BiWX+rkTCbcEH8P6vofoxcMP7kNwh6EQiEoHM7EJgk3NuhpmlHO7yzrnhwHCAXr16uZSUw15FWKWmphLpGQuj3OFVmtzOOZ75/GfeXbqMy7o34Z9XdiEuNtgOZhV5e0ci5S5ciT8FzpPhj8b7D0fRXUIuAcY657KccyuAZcAJZtYIqOmc+8E/GjcSdSORaOccfP0PeGcwNOoCN3+pQk5EinMycLGZrQTGAmea2Shgo7+fxH/eFFxEkcjinOOJiYv515fLGNi7GU8O6Bp4IScSKUp1nzkziwVmAMcD/3bOTTWzorqENME78pYvzW/L9ocPbi/s9aKmG4kO+YZXJOWOyc2i7ZLnSd70LRuSU/i5+R3kTV9Q6LyRlPtwRWt25Q6vaM0dbs65h4GHAfwjcw845643s38Cg4En/OcPg8ooEkny8hyPTljAmz+uYtCJx/HYRR2JibGgY4lEjFIVc34XyW5mVht438w6FTN7YT9hrpj2wl4varqR6JBveEVM7l3rYey1sGkW9HuUhqfcR0MreucSMbmPQLRmV+7witbcEeQJYJyZDQFWAwMCziMSuJzcPB58dx7vzkzj1tNb8lD/dlgx+1qRyqhUxVw+59wOM0vFO9dto5k18o/KhXYJSQOahSzWFFjntzctpF0kuqydAWOvg8xdMHA0tLsg6EQiEoWcc6lAqj+8FegXZB6RSLIvJ4/73p7Nx/PWc99Zbbi73/Eq5EQKUWKHYzM7xj8ih5klAWcBi4EJeF1B4MAuIROAgWaWYGYtgNbANL9LZrqZ9TXvp3EQ6kYi0WbuO/D6+RAbDzd/rkJORESkjGVm5/LrUTP4eN56/nB+e+45q7UKOZEilObIXCNghH/eXAwwzjn3kZn9QCFdQpxzC8xsHLAQyAHu8LtpAtzG/lsTTERXspRokZcLX/4FpjwDx50MV42EavWDTiUiIlKhZGTlcPOIn5i6YhuPX9qJ6/seF3QkkYhWYjHnnJsLdC+kvcguIc65YcCwQtqnA8WdbycSeTJ3wru3wNJPoedNcN4/IK5K0KlEREQqlB179jH49Z+Yv3Ynz17djUu6FXqdPBEJcVjnzIlUOluWejcC374CLngaeg8JOpGIiEiFs2lXJoNem8byLbv5z/U9ObtDctCRRKKCijmRovz8Kbx7M8RWgUEToPnJQScSERGpcDbvyWPAf39gc3oWr9/Ym5OP12kMIqWlYk7kYHl58O1T8NUwaNjZu2Jl7WODTiUiIlLh/LwxnWFTM3ExcYy+uQ/dj60TdCSRqKJiTiRUVjq8/2tY/BF0vgoueg6qVA06lYiISIUze80Obnx9GgDjbj2Rtg1rBJxIJPqomBPJt2UpvH2993zuX6Hv7aBLIYuIiJS5b5du5tY3Z1C/egJ3dopTISdyhEq8z5xIpbD4E3j5TNi9GW54H068Q4WciIhIOfh47np+9cZPHFu3KuN/fSINqurPUZEjpZ8eqdzycuHLYTD2GqjXCoZ+DS1PDzqViIhIhTR66irufGsm3ZrV5u1bT6RBzcSgI4lENXWzlMprzzZ47xZY9gV0ux4ueAritVMREREpa845/jV5Gc988TNntmvAv6/tQVKV2KBjiUQ9FXNSOa2bDeNugPQN3v3jev1K3SpFRETKQW6e47EJC3jzx1Vc0aMpT1zRmfhYdQ4TKQsq5qTymfkmfHw/VDsGbpoETXsGnUhERKRCysrJ5Tdvz+Hjeeu59bSWPHReO0xfnoqUGRVzUnlk74VPHoBZo6BlClzxGlSrF3QqERGRCmlXZja3jpzBD8u38ofz23PLaS2DjiRS4aiYk8ph6y8wbjBsnAen/RZSHoYY9dUXEREpDxt3ZTL4tWks25TBM1d35bLuTYOOJFIhqZiTim/R/+CD28Fi4Np3oM05QScSERGpsH7ZnMGgV6exY88+XruxN6e1OSboSCIVloo5qbhy9sEXj8KPL0Lj7nDVSKh9bNCpREREKqwZq7YxZMR04mKMsUNPpHPTWkFHEqnQVMxJxbRjDYy/CdJ+ghNuhXP+AnEJQacSERGpsCbN38A9Y2fRqFYiI351AsfVqxZ0JJEKT8WcVDxLJsEHv4bcHBjwBnS8LOhEIiIiFdqI71fy2P8W0LVpbV4d3It61fUFqkg4qJiTiiM3G754DH54ARp2hgEjoF6roFOJiIhUWHl5jr9PWsx/v1nOWe2Tef6a7roZuEgYqZiTimHHahj/K69bZe+b4ZxhEJ8YdCoREZEKKzM7l/vfmcPHc9dzXZ9j+dPFHYnTzcBFwkrFnES/hRNgwp3gnLpVioiIhMGOPfu4ZeR0flq5nYfOa8etp7XUzcBFAqBiTqJXdiZ89gf46RVo3AOufA3qtgg6lYiISIW2autubnrjJ9K27eX5a7pzUdfGQUcSqbRUzEl02rzE61a5cT6ceCf0exTiqgSdSkREpEKbsWo7t4ycTp5zjLq5Dye0qBt0JJFKTcWcRBfnYMYbMPEhqFIVrh0Hbc4NOpWIiEiF9/Hc9dw3bjaNayXy2o29aXlM9aAjiVR6KuYkeuzdToeF/4DN30OL0+Hy4VCjYdCpREREKjTnHC99/Qv/mLSEXsfVYfigXtStpt4wIpFAxZxEh5XfwXtDqZ++Hs56DE66B2J0xSwREZHytC8njz+8P493ZqRxUdfG/PPKLiTG69YDIpFCxZxEttxsSH0CpjwNdZozq/sT9DxlaNCpREREKrwde/bx61Ez+HH5Nu7u15r7zmqtK1aKRBgVcxK5tv4C7w2FtdOh2/Vw3t9J/2F60KlEREQqvBVbdjPkjZ9I276XZ67uymXdmwYdSUQKoWJOIo9zMHMkTHoYYuPgyteh0+VBpxIREakUvl+2hdtGzyTG0BUrRSKcijmJLLu3wv/uhsUfQYvT4NL/QK0mQacSERGpFMZMXc0jH86nRf1qvDq4N8fWqxp0JBEphoo5iRw/fwof3gmZO+Ccx6HvHbrIiYiISBjk5Obx108W89p3Kzi9zTE8f213aibGBx1LREqgYk6Cl5UBn/3Bu39cg45ww/vQsFPQqURERCqFnXuzueutWXzz82ZuPKk5f7ygPXGx+jJVJBqomJNgrf4R3v81bF8JJ90NZ/4R4hKCTiUiIlIprNiymyEjfmLNtj08cXlnBp5wbNCRROQwqJiTYGRnQupf4bt/Qe1mcOPH0PzkoFOJiIhUGt/8vJk7x8wkLjaGUUP60KdlvaAjichhUjEn4bd+jnc0btNC6DEYzh0GCTWCTiUiIlIpOOd4dcoK/vrJItok1+DlQb1oVlcXOhGJRirmJHxy9sG3T8G3T0LV+nDtO9DmnKBTiYiIVBqZ2bn84f35vDszjf4dG/LUVV2plqA/B0WiVYlnt5pZMzP7yswWmdkCM7vHb69rZp+b2VL/uU7IMg+b2TIzW2Jm54a09zSzef60f5mZlc/bkoizYT68ciZ8/QR0ugJu/0GFnIiISBit37mXq//7A+/OTOO+s9rw4nU9VMiJRLnSXKooB7jfOdce6AvcYWYdgIeAyc651sBkfxx/2kCgI9AfeNHMYv11vQQMBVr7j/5l+F4kEuVmQ+rfYXgKpG+Aq0fD5cOhqm5AKiIiEi7TV27joue/Y9mmDP57Q0/uOas1MTH6Tl0k2pX4dYxzbj2w3h9ON7NFQBPgEiDFn20EkAo86LePdc5lASvMbBlwgpmtBGo6534AMLORwKXAxLJ7OxJR1s+FD26HjfOg05Vw3j+gmk6uFhERCRfnHGOmreaxCQtoUjuJMbf0oU2yzlMXqSgO69i6mTUHugNTgWS/0MM5t97MGvizNQF+DFkszW/L9ocPbi/sdYbiHcEjOTmZ1NTUw4kZVhkZGRGdryjlmdvysjlu1TiOXf0uOXE1+Lnjw2yp3xd+mnfU69b2Dr9oza7c4RWtuUUqsszsXB79cAFvT1/D6W2O4V8Du1Orqm4ELlKRlLqYM7PqwLvAvc65XcWc7lbYBFdM+6GNzg0HhgP06tXLpaSklDZm2KWmphLJ+YpSbrnX/AQT7oTNi6HLQKr0/xudyrBLpbZ3+EVrduUOr2jNLVJRrduxl9tGz2TOmh3cecbx3Hd2G2LVrVKkwilVMWdm8XiF3Gjn3Ht+80Yza+QflWsEbPLb04BmIYs3Bdb57U0LaZeKYN9u+HIY/Pgi1GwC142H1mcHnUpERKTS+XH5Vu4cM5PM7Dz+c31P+ndqGHQkESknpbmapQGvAoucc0+HTJoADPaHBwMfhrQPNLMEM2uBd6GTaX6XzHQz6+uvc1DIMhLNln0BL/aFH/8NvX7lXalShZyIiEhYOecY/s0vXPfKVGolxfPBHSerkBOp4EpzZO5k4AZgnpnN9tt+DzwBjDOzIcBqYACAc26BmY0DFuJdCfMO51yuv9xtwBtAEt6FT3Txk2i2eyt8+nuYOxbqt4GbJsFxJwadSkREpNLJyMrh37OzmL5xMed1asg/B3Slum47IFLhleZqllMo/Hw3gH5FLDMMGFZI+3Sg0+EElAjkHMwZ6xVyWbvgtN/BqfdDfGLQyURERCqdnzem8+tRM1ixOZffn9+OW05tiW7lK1I56CsbOTxbf4GP7oMVX0PTE+Ci5yC5Q9CpREREKqUPZ6/loXfnUS0hlt/1TmToaa2CjiQiYaRiTkonJwu+ew6+eRLiEuCCp6HnTRBTmvvOi4iISFnKysll2MeLGPnDKno3r8ML1/Zg0cwfS15QRCoUFXNSshXfekfjti6FDpdC/yegZqOgU4mIiFRKa7bt4c4xM5mTtpNbTm3B7/q3Iz42hkVBBxORsFMxJ0XL2ASfPwJz3oLax+l2AyIiIgH7fOFG7h83Gwe67YCIqJiTQuTlwozXYfKfYd8e7+Impz4AVaoGnUxERKRSys7N48lPl/Dfb5bTqUlN/n1tD46rVy3oWCISMBVzcqC0GfDJ/bBuFrQ4Dc5/Co5pE3QqERGRSmvtjr3cNWYmM1fv4Lo+x/J/F3YgMT426FgiEgFUzIln91aY/BjMfBOqN4DLX4HOV4IubSwiIhKYLxZu5P535pCb53jh2u5c2KVx0JFEJIKomKvsCrpU/gWy0uHEO+D0ByGxZtDJREREKq2snFz+MWkJr05ZQacmNXnhmh40r69ulSJyIBVzldmq7+GT38HGedD8VDj/n9CgfdCpREREKrWVW3Zz11uzmLd2Jzee1JyHz29HQpy6VYrIoVTMVUY719J+4VOQ+g3UbAoD3vBuOaAulSIiIoH6YNZa/vD+POJiYxh+Q0/O6airVYpI0VTMVSbZe+H752HKM9TPzYHTfgen3KerVIqIiAQsIyuHRz6cz3sz19K7eR2eG9idxrWTgo4lIhFOxVxl4BwseB8+fxR2roYOl/JTjfPpe+bVQScTERGp9Oas2cE9Y2exetse7unXmrvOPJ642JigY4lIFFAxV9GtnQGTfg9rfoTkznDpR9DiVDJTU4NOJiIiUqnl5jmGf7Ocpz5bQoMaCYwdeiIntKgbdCwRiSIq5iqqnWneFSrnjoVqDeCif0H36yFGJ1CLiIgEbd2Ovfxm3Gx+XL6N8zs35G+XdaFW1figY4lIlFExV9FkpcOUZ+GHF7zulafcB6f8RrcaEBGJAGbWDBgJNATygOHOuefMrC7wNtAcWAlc5ZzbHlROKV+fzFvPw+/NIzs3j39c2YUBPZtiugiZiBwBFXMVRW42zBwJqU/A7k3QeQD0ewRqHxt0MhER2S8HuN85N9PMagAzzOxz4EZgsnPuCTN7CHgIeDDAnFIOdmVm89iEBbw3cy1dm9bi2YHdaaF7x4nIUVAxF+2cg8UfwxePwdalcOxJcM1YaNoz6GQiInIQ59x6YL0/nG5mi4AmwCVAij/bCCAVFXMVyrQV27jv7dms37mXu888nrv6tSZeFzkRkaOkYi6arf7Ru0Llmh+hfhsY+Ba0PU/3ixMRiQJm1hzoDkwFkv1CD+fcejNrUMQyQ4GhAMnJyaRG+MWsMjIyIj5jYcoyd3ae4/2l2Uxckc0xVY3fn5DI8VXW892368tk/aG0vcNLucNLuQunYi4abVoEk/8MSz6B6slw4TPQfRDE6r9TRCQamFl14F3gXufcrtKeL+WcGw4MB+jVq5dLSUkpt4xlITU1lUjPWJiyyr1o/S7ue3s2izfsYWDvZvzxwg5UTyi/fXVl397hptzhpdyF01//0WTHau+cuDlvQZXqcOb/Qd/boIr624uIRAszi8cr5EY7597zmzeaWSP/qFwjYFNwCeVo5d9y4OnPl1ArqQqvDu5Fv/bJQccSkQpIxVw0yNgM3z4J018DDPreDqfeD1V1LxoRkWhi3iG4V4FFzrmnQyZNAAYDT/jPHwYQT8rA8s0ZPPDOHGau3sF5nRoy7LLO1K1WJehYIlJBqZiLZHu3w3f/gqn/hZxM6H4dnP4g1GoadDIRETkyJwM3APPMbLbf9nu8Im6cmQ0BVgMDgoknRyovzzHih5X8fdJiEuJieW5gNy7u2li3HBCRcqViLhJlpcOPL8H3L0DWTuh0BaQ8DPVbB51MRESOgnNuClDUX/f9wplFys6qrbv57fi5TFuxjTPbNeBvl3cmuWZi0LFEpBJQMRdJsjLgp5fhu+e8o3JtL4Azfg8NOwWdTERERA6SfzTuH5OWEBdrugG4iISdirlIsG83/PSqV8Tt2QKtz4GUh6CJ7hUnIiISiVZs2c2D4+cybeU2Utoew98u70yjWklBxxKRSkbFXJCyMuCnV+D7570iruUZcMYfoFnvoJOJiIhIIXJy83jtuxU89dnPJMTF6GiciARKxVwQstJh2svwwwuwZyu0OhNOfwiO7RN0MhERESnCkg3p/G78HOak7eTsDsk8fmknnRsnIoFSMRdOe3fAtOHww78hcwe06ud1p2x2QtDJREREpAhZObn8+8tlvJj6CzWT4nn+mu5c2KWRjsaJSOBUzIVDxmb48UWvS2XWLmh7Ppz6ADTVOXEiIiKRbPrKbTz47lx+2byby7o34f8u7KD7xolIxFAxV552rPG6Us4Y4d0nrsMl3s2+G3UJOpmIiIgUY+febP4xaTGjp66mSe0k3ripNyltGwQdS0TkACrmysPmJTDlWZg3zhvvcjWccp/uEyciIhLhnHP8tCGH3z39NVsyshhySgt+c3YbqiXoTyYRiTz6zVSW1kzzbi+w+COIS4LeN8OJd0DtY4NOJiIiIiVYs20Pj05YwJeLs+jYuCavDu5N56a1go4lIlIkFXNHy+XBkoleEbf6B0isDaf9Dvr8GqrVCzqdiIiIlCA7N49Xp6zg2S9+JsaMq9tWYdigk4mLjQk6mohIsUos5szsNeBCYJNzrpPfVhd4G2gOrASucs5t96c9DAwBcoG7nXOf+u09gTeAJOAT4B7nnCvbtxNG2Zkw9216//QP2JMGtZpB/79D9+shoXrQ6URERKQUflq5jT++P58lG9M5p0Myj17ckaWzp6qQE5GoUJojc28ALwAjQ9oeAiY7554ws4f88QfNrAMwEOgINAa+MLM2zrlc4CVgKPAjXjHXH5hYVm8kbHZvgemvebcY2L2ZvOot4fKXoeNlEBsfdDoREREphS0ZWTwxcTHjZ6TRpHYSw2/oyTkdGwKwNOBsIiKlVWIx55z7xsyaH9R8CZDiD48AUoEH/faxzrksYIWZLQNOMLOVQE3n3A8AZjYSuJRoKuY2L/FuLzBnrHdlyuPPhpPvZsbKXFK6nBF0OhERESmF3DzHmGmrefLTJezOyuG2lFbcdebxVK2iM09EJPoc6W+uZOfcegDn3Hozy79WbxO8I2/50vy2bH/44PZCmdlQvKN4JCcnk5qaeoQxj5Jz1Nk+i6Zp/6PetpnkxlRhY/IZpDW9iD3VmsGqPDJ27w4u31HIyMhQ7jCK1twQvdmVO7yiNbdULjNWbeeRD+ezYN0u+rasy18u6UTr5BpBxxIROWJl/TWUFdLmimkvlHNuODAcoFevXi4lJaVMwpXavt3eEbip/4EtP0P1ZDjjD8T2+hWNq9WnccisqamphD1fGVDu8IrW3BC92ZU7vKI1t1QOm9Iz+cekJYyfkUZyzQSev6Y7F3ZphFlhf56IiESPIy3mNppZI/+oXCNgk9+eBjQLma8psM5vb1pIe2TZvhKmvQyz3oTMndCoG1w23DsfLq5K0OlERILjHGRkwJYtsHWr9wgZrh8TAyrmJMLsy8ljxPcreW7yUrJycrn19JbcfWZr3TNORCqMI/1tNgEYDDzhP38Y0j7GzJ7GuwBKa2Cacy7XzNLNrC8wFRgEPH9UycuKc7D8K6+IWzIRLAY6XOLdWqDZCaBv7USkonEOdu4svDAr7jk7u/D1mVH3wgvD+x5ESpC6ZBN/+Wghv2zeTUrbY3jkwg60PEZXmxaRiqU0tyZ4C+9iJ/XNLA14FK+IG2dmQ4DVwAAA59wCMxsHLARygDv8K1kC3Mb+WxNMJOiLn2Tu8rpS/vSy15Wyan049X7o9SuoVeTpfCIikSUnB7ZvP7AwK6pAC33k5ha+vthYqFdv/+P446FPH2+4fn3vkT8tf7hOHX7+9tsDuqCLBOWXzRkM+3gRXy7eRPN6VXntxl6c2S456FgiIuWiNFezvKaISf2KmH8YMKyQ9ulAp8NKVx42LfYKuDljYV8GNO4Bl/0XOlwK8YlBpxORyiwzkyqbN8PcuUUXYQcXaDt2FL2+KlUOLL46djywCDu4KKtXD2rVghjdX0uiz8492fzry6WM+H4lSfGx/P78dgw+qTkJcbFBRxMRKTeVo9N4bjYs/hh+egVWfguxVaDTFdD7FmjaM+h0IlLROAe7dh1aiJVUoO3Zw0lFrbNatQMLrxYtii/K6tf3llFXcangsnPzGPXjKp6bvJRde7O5uncz7j+nLfWrJwQdTUSk3FX8Yu7bp70bfKevh1rNoN8j0GMwVKsfdDIRiQbZ2bBt26GFWGFtodNycgpfnxnUqbO/6GrcGDp3LijElmzZQtuTTjq0QEvQH6YioZxzfLFoE3+buIjlm3dz8vH1+OMFHWjfqGbQ0UREwqbiF3Mb5kGD9nDB09DmXIhRdwuRSsk5SE8v/GhZYcVY/vCuXUWv8+BujB067B+uW7fwI2e1a3vnpRVhfWoqbXVVSJFizVmzg2GfLGLaim20PKYarw7uxZntGuhWAyJS6VT8Yu7ylyG24r9NkUolM/PAguvgAmzbNjotWeIdBSvN0TLwiqzQoqtt2wMLsYO7Mdatq26MImG2Ztse/vnpEibMWUe9alX4y6WdGNi7GfGxOs9TRCqnil/lqJATiVz5V2I8qBAr9Dn0sWdP0etMTIR69UhMSIBjjz3waFl+EXZwkVanDsTpd4VIpNq2ex/Pf7mUUT+uIjbGuOOMVvz69FbUSIwPOpqISKD014uIHL3cXO++ZfnFV1EFWejztm3FX4kxJmZ/4VW3LjRtCl27Fl2Y5Q9XrQrA9NRUUtRdUSSq7c7K4bUpKxj+zXJ278vh6t7NuPesNiTX1NWnRURAxZyIhMovykKKrgZTpniXyj+4SAsd3rHDOyetMGZeF8a6dfefR9a27YEXAQktxvKfa9bUJfJFKql9OXm8NW01z3+5lC0Z+zinQzK/69+W4xvUCDqaiEhEUTEnUhFlZ+/vvng4j0KKsg6hI6FFWb160KrVoUVYaJFWp473KOaCHyIi+XJy83h/1lqem7yUtO176duyLsMHtaPHsXWCjiYiEpFUzIlEKue8c8PyC62Di7PixtPTi15v/qXx69bdX3i1br1/OL9Y84uzqUuX0ue881SUiUi5yctzfDJ/PU9//jPLN++mc5NaDLusM6e1rq8rVIqIFEPFnEh527ePKtu2waJFXsGV/wgtwIp6zs4uer3x8fuLrjp1oEkT735lBxdl+eP5R8lKuDT+wfZmZnpdI0VEyphzjs8WbuSZz39m8YZ0Wjeozn+u78G5HRuqiBMRKQUVcyKlsW/fgYVYcY/8Yiz/sWcPJxW37lq19hdadepAp077i7DQo2Sh43Xq6LL4IhK1nHN8tWQTT3/+M/PX7qJF/Wo8e3U3LuramNgY/V4TESktFXNSOeTfMHrHDu+xffuhw/nFV2FtxV0KH7zCqm5d76hXnTr7zyXzC7Sft2yhTZ8++wu2/KKsVi1dEl9EKo38Iu7ZL5YyN20nzeom8c8ru3BZ9ybE6V5xIiKHTX9FSnTIy4OMDBI2boR58/YXYjt2eFdfDB0PfYQWbXl5xb9GrVr7i7E6dfafRxbaPTF0PLS9SpViV70uNZU2uky+iFRSzjm+WLSJ57/cX8T944ouXNajiW74LSJyFFTMSflzDnbv9oqu/MeuXQeO5z/yi7PQ4R07vPnz8jixuNdJStpfXNWuDQ0bQrt23nBoV8b8oix/vvzpuriHiEiZynOOT+at5/kvl7Fo/S6OrVtVRZyISBlSMSdFy82FjAyve+KuXYc+H/zIL9JCh/OfSzoqFhPj3VesVq39R8iOO+7A8Vq1WLJhA2379t1fgIUWYyUcHRMRkfDIzs3jf3PW8eSUvazbPZOW9avx1ICuXNKtsbpTioiUIRVzFUVenndeV0aGdxRs9+79wxkZhzxaLVwIY8Z4hVl+wXbwY/fu0r12YqJXiNWosb/4atVqf3EWWqQd3JZfiFWvXqqLeaxPTaWtuiuKiESkzOxcxs9I4z9f/0La9r00rW48N7AbF3bRhU1ERMpDxS/mvv/eK0qqV/ce1ap5xUf+IyHBOyp0tFcFdA5ycrxHdrb32Ldv/3NW1v7n/Edm5v7nzEzYu9d7hA7v2bP/+eDH7t37n/fuPay4jRMTvUKqRg1vu9SoAY0aQZs23vDBj1q1DhzOL95q1NARMRGRSm5XZjajf1zNq1NWsCUji27NavPYRR2J2biQM7s1CTqeiEiFVfGLuQcfhClTSp4vPt67qmBsrFfc5T9COecdAXPO64KYl8dpOTleW25u2eZOSPDOAct/VK26/5Gc7BWl+ePVqh34yC9aq1XbX6zlt9WoAUlJfPvNN6ToCJeIiByFTbsyef37lYz6YRXpWTmc2ro+t53ejRNb1cPMSN20KOiIIiIVWsUv5l55BTZvPrDLYeiRsPyjaPlH1PLy9hdnzu1fj3P7j+CZFRR9aWvXcmyLFvuLwbg4b7hKFe85Pt4rzKpU8R4JCQc+kpL2HyHML9wSEw8tJEVERCLEsk0ZvPzNct6ftZacvDzO69SI21Ja0alJraCjiYhUKhW/mGvb1nuUk+WpqRyrI1wiIlLBOeeYumIbL3+znMmLN5EQF8PVvZtx86ktOK5etaDjiYhUShW/mBMREZEjlp2bx8T5G3jl2+XMTdtJ3WpVuLtfawafeBz1qicEHU9EpFJTMSciIiKH2Lknm7d+Ws2I71eyfmcmLetXY9hlnbiiR1MS43VfThGRSKBiTkRERAos35zBG9+vZPyMNPbsy+XElvV4/NJOnNG2ATG6vYCISERRMSciIlLJOeeYsmwLr3+3ki8Xb6JKbAwXdm3EkFNa0LGxLmoiIhKpVMyJiIhUUnv25fDezLW88f1Klm3KoH71KtzTrzXX9T2WBjUSg44nIiIlUDEnIiJSCc1YtY1fvTGdnXuz6dSkJk8N6MoFXRrpfDgRkSiiYk5ERKQSatuwJme2a8B1fY6l53F1MNP5cCIi0UbFnIiISCVUPSGOZ67uFnQMERE5CirmRERERETkqDnnyHN5ONwhww5/vJDhkpZzzrExcyMrd6w8oK2odYfjGSh2nvztMXfrXHrt60X1KtXLZZurmBMRERGRsHPOkety2Ze3j937dpPrcsnNyyXX5ZKTl1MwHPqck5dzSFthz3kuj9w8//mg6flthc1T2LT8R357/rSVq1YyMXtiodNCl8tzeeSRd2hbKR/5xU2h03DFzhs6PX/a3sy9VJld5YB5Q4uhg8dLOy2/gClXU8v/JcrDZadfRrv67cpl3SrmRERERCJQnssjOzeb7Lxs9uXuK3hk5+4fz5+WnZvNjO0z2LN0T8Ey+c85eTmHtBU2LX84Jy+HHJdz4PgRPkILs4Pb8lze/jf7bXDb+UjEWiwxxBC7PhbDiI2JJdZiMTNiLZbYmFhiLKZg2sHDpZ1mWME6YyyGuJi4gnEzO2QdJU2LsRg2bdxEo4aNDnit/Gn54zEWU7COo51W2PDB8xa3XP7zkiVL6NCuwwFtRa0vXM9AsfMAzJwxk+NqHVdun0UVcyIiFVBeniM7L4+cXEdOniMnN4/cPEe2P+y1OXIK5tk/b3ZuSJs/X3bBMnlkHzQtJzcPtz2HlKDftEgZyHN57M3dy9Y9W9mbs5e92XvJzMks8pGVm3XgeE4WWblZZOVkFUzPH9+Xu++Q4X25+wrG8x/57Tl5OYf/BuYe3uwxFkN8TDzxsfHEx8QTGxNbMB5rsQXtcTFxBdPyhxPjEomLiSt0WsGwX9gUzGexBfPkj69auYo2x7c5YN784YOfi5t28HN+kVHY9IJpIePFTQstqPL/SE9NTSUlJeXw/48CFrW5d6WS0j0l6BiHbc/SPSTFJ5Xb+lXMiUilkJfnyHWO3DyvYMnN9Yqd0PGc0PG8/UVQ6HiuX/QUN1927kHrOWCav45CxnNyHRs3Z/LGimmHrOfQoizPfw8HZfTfR14YeruEOq2pdidS/nLycti9bze7s3eTsS+D3fv85+zd7N63mz3Ze9id7T2X5pFfrIU+78vd573YlCPLaBgJcQkkxCaQEJdAYlwiCbEJVImtUtBeJbYKtRJrFQyHTouPiSchbn976Hh8TPz+dr/ICh2fP2c+fXr1KZhW0nN+wRO0VJdKyskpQccQiUra+4pUEM4vVPILltw8R14eB4znOq9oyW/Lc94f/3khRU5oW06eY87mHHIWbtw/Lc+Rl7f/ObeQtvyiJzcP79kdPG3/49Dx/cuVbt4Di6r818jalw2TJxa0uzAXNweLjzViY4y4mBhiY+yA8biCYWPvXkdulX3E+dPiY2NIjPemxcbEEB9rxMXG+NONuNgD17l/Wow/zfxp+8f3TztwufgYf/mDlztg+ZiC7PnTYmOMr7/+OtgNLBFrX+4+dmXtYmfmTu85y3sOfaRnpXvP+9JJ35dOxr4M0rP2D+c/MnMyD+u142PiqValGlXjq5IUl0TV+KrecHwSDao1KBhOivMf/vC61evo2LYjSXFJJMYlkhTvP8clkRCXUPCcEJtQMC2/eIuPiQ/sNg8xq2Lo07RPIK8tIsEIezFnZv2B54BY4BXn3BPhziDlxzs5FvKc94e/cxQUDXnOOzqSP+yd+HxguzdvyLS8/evKc7BsRy7VV24rmC9/Wm7e/tfKdc4vbEKX9cfzX6fQ1/XackOKlDz/dXLz15O3f70Hvtah7Xl5+9/P5i2ZvLZ8WkHb/nU7cv0cuSFZ8qeHFkyh6y8oitz+6eVarMyYflSLx8UYMTFGrFlB4VIwbEZsbP54DLHmzRsfMl9MjJEQH0OShRRAMaHL+esJKW7Wr03juOOOLSh6YmL2F0T54/EHZNlfmMTF7B+PiTHiY/avO3/5uNhClgspkEILrRij1H/ced1fTjmq7S1Slpxz7Mnew7a92w54bM/czva92wued2TtYEfmgY9te7ax7+t9Jb5GrMVSI6EGNRNqUqNKDWok1KB6leo0rN6Q6lWqH/CoFl9t/3CValSLr3bIc37RFh8bf0TvOTU1lZQ+KUe0rIhIOIW1mDOzWODfwNlAGvCTmU1wzi0sr9cc/sNk0nZuAQcO/CvueMM4Rx4UXF40v4jwhr1r8njz+pcZDWnLn75hwwZGb/zloHkpmDfPf7H8aXmFvlb+fP4yBesAHP5VkJw/jP9abv+z85fJC3l99hdF+fPl5edwjr2Ze0mY9mNBwXXA/AXbyh3wnvKLIkLef57/xvMLobJRwoqmpR7ZckfEYQYxln9Cbv6w9xwTAzEFJ+saFuN9S2ExRgzedMPYl5VJVUvyl2f//KHrid0/HhtjxID37L/uwa8fG5N/ovNB+QqW8deHI8YvKLzp+cXF/tcy9i8X67+PGIzYGFi5YgXHt2rlZbSYggwFuQteN4YYICYGYmMOnK/wLVu+h8osawFtmqcf1jI5/iMrvyHPf5RC/s/z0VqweQGbF2wuk3WF07b0baTorLmokp6Vzsz1M9m0exObdm9i857NbN69mS17t7Blz/7H1j1bycrNKnI9cTFx1E6sTZ3EOtROrE3txNo0q9mMWgm1SN+cTqfWnaiVUItaibWomVCTWgnec82EmtRIqEGthFokxiXqpuUiIkcg3EfmTgCWOeeWA5jZWOASoNyKuT98dT9bsueU1+o928p39eVm7xEsY/5DPKX8Qx+Aw6srIsuqoAMcoXL7zVLOojD3+Q3P51ZuDTqGHIaFmxeSMiLlgLa6SXU5puox1K9an+a1m9OrUS/qVa1HvaR61E2qS72q9aiTWIc6SXWom1SXOol1qF6lepGFWGpqKimnpRQ6TUREjl64i7kmwJqQ8TTgkM7dZjYUGAqQnJxMamrqEb/gw+1uIz17N+AdlQDvSIn3z0LaQuoUvy3Wn8/L5B2pOGDYYO/evSQlHXiFGium2il22hF+K3kkr7dn7x6qJlUtk9cvTnHZjmS5wnIfsFyEfrO7d89ekqru/5wc6XY5UkfzeiVt86NRnv9fe/bsoWrV8sldnqI1d0xWzFH9rpbw63BMByYPmkyDag04puox1Ktaj7gYnUovIhJNwv1bu7C/3A7pm+ScGw4MB+jVq5c7msunlne3n6i9vKtyh1W05oboza7c4RWtuSuzGgk1OLPFmUHHEBGRoxDu69GmAc1CxpsC68KcQUREREREJOqFu5j7CWhtZi3MrAowEJgQ5gwiIiIiIiJRL6zFnHMuB7gT+BRYBIxzzi0IZwYREZFIZGb9zWyJmS0zs4eCziMiIpEv7Gc6O+c+AT4J9+uKiIhEqiBu3SMiItEv3N0sRURE5FAFt+5xzu0D8m/dIyIiUiRdg1hERCR4Yb91TzhkZGREfMbCKHd4KXd4KXd4lXduFXMiIiLBC/ute8IhWm9ZodzhpdzhpdzhVd651c1SREQkeLp1j4iIHDYVcyIiIsHTrXtEROSwqZuliIhIwJxzOWaWf+ueWOA13bpHRERKomJOREQkAujWPSIicrjUzVJERERERCQKqZgTERERERGJQirmREREREREopCKORERERERkShkzh1yT9KIYmabgVVB5yhGfWBL0CGOgHKHV7TmhujNrtzhVRa5j3POHVMWYSqDKNg/QuX+PAdBucNLucOrsucudB8Z8cVcpDOz6c65XkHnOFzKHV7RmhuiN7tyh1e05pbyFa2fC+UOL+UOL+UOr/LOrW6WIiIiIiIiUUjFnIiIiIiISBRSMXf0hgcd4Agpd3hFa26I3uzKHV7RmlvKV7R+LpQ7vJQ7vJQ7vMo1t86ZExERERERiUI6MiciIiIiIhKFVMyJiIiIiIhEIRVzR8jMEs1smpnNMbMFZvanoDOVlpnFmtksM/so6CyHw8xWmtk8M5ttZtODzlNaZlbbzMab2WIzW2RmJwadqSRm1tbfzvmPXWZ2b9C5SsPM7vN/Jueb2Vtmlhh0ptIws3v8zAsifVub2WtmtsnM5oe01TWzz81sqf9cJ8iMEpxo3j9CdO4jo3X/CNpHhpv2keUriP2jirkjlwWc6ZzrCnQD+ptZ32Ajldo9wKKgQxyhM5xz3aLsPiPPAZOcc+2ArkTBtnfOLfG3czegJ7AHeD/YVCUzsybA3UAv51wnIBYYGGyqkplZJ+AW4AS8z8iFZtY62FTFegPof1DbQ8Bk51xrYLI/LpVTNO8fIXr3kdG4fwTtI8NG+8iweIMw7x9VzB0h58nwR+P9R8RfTcbMmgIXAK8EnaUyMLOawGnAqwDOuX3OuR2Bhjp8/YBfnHOrgg5SSnFAkpnFAVWBdQHnKY32wI/OuT3OuRzga+CygDMVyTn3DbDtoOZLgBH+8Ajg0nBmksgRrftH0D4y3LSPDIT2keUoiP2jirmj4HfFmA1sAj53zk0NOFJpPAv8DsgLOMeRcMBnZjbDzIYGHaaUWgKbgdf9bjuvmFm1oEMdpoHAW0GHKA3n3FrgSWA1sB7Y6Zz7LNhUpTIfOM3M6plZVeB8oFnAmQ5XsnNuPYD/3CDgPBKgKN0/QvTuI6Nx/wjaR4aV9pGBKdf9o4q5o+Ccy/UPsTcFTvAPA0csM7sQ2OScmxF0liN0snOuB3AecIeZnRZ0oFKIA3oALznnugO7iaLuZ2ZWBbgYeCfoLKXh90O/BGgBNAaqmdn1waYqmXNuEfB34HNgEjAHyAk0lMhRiLb9I0T9PjIa94+gfWRYaR9ZMamYKwN+l4BUDu0jG2lOBi42s5XAWOBMMxsVbKTSc86t85834fVNPyHYRKWSBqSFfCs9Hm/HFS3OA2Y65zYGHaSUzgJWOOc2O+eygfeAkwLOVCrOuVedcz2cc6fhddFYGnSmw7TRzBoB+M+bAs4jESCK9o8QxfvIKN0/gvaR4aZ9ZDDKdf+oYu4ImdkxZlbbH07C+wFZHGioEjjnHnbONXXONcfrFvClcy7iv5EBMLNqZlYjfxg4B++we0Rzzm0A1phZW7+pH7AwwEiH6xqipPuIbzXQ18yqmpnhbe+IP5kewMwa+M/HApcTXdsdYAIw2B8eDHwYYBYJUDTuHyF695HRun8E7SMDoH1kMMp1/xhXliurZBoBI8wsFq8oHueci5rLGEehZOB973cPccAY59ykYCOV2l3AaL87xnLgpoDzlIrfL/1s4Nags5SWc26qmY0HZuJ1wZgFDA82Vam9a2b1gGzgDufc9qADFcXM3gJSgPpmlgY8CjwBjDOzIXh/MAwILqEETPvH8Irm/SNoHxk22keWvyD2j+ZcVFxgSkREREREREKom6WIiIiIiEgUUjEnIiIiIiIShVTMiYiIiIiIRCEVcyIiIiIiIlFIxZyIiIiIiEgUUjEnAphZczObHzJe379xbKVgZivN7NuD2maHbhMREamctI/UPlIil4o5EclXw8yaAZhZ+6DDiIiIRBDtIyUiqZgT8WQCVQqbYGYpZvaRP1zXzHaa2QP++PFm9oWZzTGzmWbWKmSZnf43dxtC5u9nZrPMbJ6ZvWZmCYW8XqqZ/RAy/nczc/5wdfv/9u0eRK4yisP483cXXI1BEERwCwMKiiguESGxCGx6iQYkikSIjYhgbyFCFEFBSBNI0phAPjAxLBgLFSxSaJMU4geKFiKIuhYRFGUh4rG478J1mLu7Vjtjnl9133MP877cYg7nzpnko7bX50n2tPhDST5LMpdkS5Ivk9w3lD/gLLCvXT8JnPmPz1CS9P9kjbRGakLZzEmdZWDLaqFZw4vA9731KeBwVT0APAz81OIzwMWqWgCOACSZA44D+6rqfmAWeG5gn+uS3JtkBtgO/NHiK8BjVbUdWATeTJKqugS8C7wKvAGcrKovhvIH9nwH2NuuHwEurPMsJEnXBmukNVITanazDyBNgqqqJM8C59v3+MxoTpJ5YAew1NZbgfmqWmqfsdJLv4GuSPTdDXxXVd+09QngeeDQmCO9BRwALgLvAztXjwG8lmQX8DcwD9wG/AwcBC61fV/YQP6oK8CvSZ4AvgL+HJMjSbrGWCMBa6QmlL/MSU1VvVdVC+1N4eKYlJeBV4Bq66G3dwC3Az+OxNbKH3UZWACeAU724k8BtwIPtnMuA3Pt3i3ATcDWXmyt/HHeBg7j+IgkqccaCVgjNYFs5qSNuRPYVlUfrgaq6jfghySPAiS5PsmNbexjL/DxyGd8DWxLcldb76d7qzjkHHClqpZ7sZuBX6rqapJF4I7evWPAS3RjLa9vIH+cJboRlA/WyZMkaZU1UtokjllKG3MP3UjHqP3A0SQHgavA43Qz+d8C5/uJVbWS5ABwLsks3bjHkaENq+oYXfHpOwVcSHIZ+JSu+JHkaeCvqjrdCuUnSXYP5a+x5++0Ijf8twFJkv7FGiltklTV+lmSJEmSpInimKUkSZIkTSGbOUmSJEmaQjZzkiRJkjSFbOYkSZIkaQrZzEmSJEnSFLKZkyRJkqQpZDMnSZIkSVPoH9vzOCSxGkuXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.subplot(121)\n",
    "plt.plot(l_100, np.array(r_p)/100000, label='P, атм')\n",
    "plt.plot(l_100, r_t, label='T, К')\n",
    "plt.plot(l_100, r_rho, label='$rho$, кг/$m^3$', color='green')\n",
    "plt.plot(l_100, r_v, label='v, м/с', color='red')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xlabel('Число маха М')\n",
    "plt.title('Зависимость параметров за скачком от маха')\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(l_100, np.array(r_p)/100000, label='P, атм')\n",
    "plt.plot(l_100, r_rho, label='$rho$, кг/$m^3$', color='green')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.title('Зависимость параметров за скачком от маха')\n",
    "plt.xlabel('Число маха М')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Модель с диссоциацией"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left( 26233333.3333334, \\  6.57391304347826, \\  903.525331516144\\right)$"
      ],
      "text/plain": [
       "(26233333.3333334, 6.57391304347826, 903.525331516144)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans = solve_eq(15, 7/5) # p, rho, v\n",
    "ans.args[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Эмпирические данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "diss = {4500: 0.0001, 5000: 0.05, 5500: 0.13, 5750: 0.2, 6000: 0.255, 6250: 0.35, 6500: 0.45, 6750: 0.56, 7000: 0.66, 7250: 0.75, 7500: 0.81, 7750: 0.85, 8000: 0.9, 8500: 0.95, 10000: 1} # T: \\alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 6.02*10**23  # Число Авогадро\n",
    "T_start = 4500  # температура с которой начинается распад молекул\n",
    "diss_mol_once = 1e19  # число диссоциирующих молекул за раз - отвечает за шаг расчета, начинает 1e9\n",
    "T = float(ans.args[1][0])*mol/(8.31*float(ans.args[1][1]))\n",
    "count_mol = float(ans.args[1][1])/mol * N\n",
    "count_atom = 0\n",
    "nu = float(ans.args[1][1])/mol\n",
    "nu_ = 0\n",
    "R = 8.31\n",
    "dT = 942000/N * diss_mol_once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13445.796378006613"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6499.998432073813\n",
      "Tkon = 6499.998432073813, alpha = 0.39000147531297696\n"
     ]
    }
   ],
   "source": [
    "alpha = 0\n",
    "alpha0 = 1\n",
    "while T > T_start and count_mol > 0:\n",
    "    i = 5 if T < 3340 else 7\n",
    "    U = (i/2*R*T*nu + 3/2*R*T*nu_ - dT)  # вычисление уменьшения температуры для шага молекул\n",
    "    count_atom += diss_mol_once\n",
    "    count_mol -= diss_mol_once\n",
    "    T = U/(i/2*R*count_mol/N + 3/2*R*count_atom/N)\n",
    "    nu = count_mol / N\n",
    "    nu_ = count_atom / N\n",
    "    alpha = count_atom*2 / (count_atom*2 + count_mol)  # вычисляем степень диссоциации\n",
    "    for j in range(len(list(diss.keys())) - 1):\n",
    "        if list(diss.keys())[j] < T < list(diss.keys())[j + 1]:\n",
    "            alpha0 = diss[list(diss.keys())[j]]\n",
    "            break\n",
    "    if alpha > alpha0:\n",
    "        print(T)\n",
    "        break\n",
    "print('Tkon = {}, alpha = {}'.format(T, alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "V = 1\n",
    "nu = count_mol / N\n",
    "nu_ = count_atom / N\n",
    "gamma = 9/7\n",
    "gamma3 = alpha*3/2 + (1 - alpha)*gamma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Параметры за скачком"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M2 = 1.41497657585387, p2 = 17.72*10^5, rho2 = 0.81, T2 = 6500.0, alpha = 39.0%\n"
     ]
    }
   ],
   "source": [
    "p = float(ans.args[1][0])\n",
    "V1 = (p*V**gamma/(nu+nu_)*1/(R*T))**(1/(gamma3-1)) # для адиабатической ставить 1 вместо выражения\n",
    "\n",
    "p_sum = (nu+nu_)*R*T/V1\n",
    "\n",
    "rho_sum = (nu*0.028 + nu_*0.014)/V1\n",
    "\n",
    "v3 = rho1*v1/rho_sum\n",
    "M2 = v3/sqrt(gamma3*p_sum/rho_sum)\n",
    "\n",
    "print('M2 = {}, p2 = {}*10^5, rho2 = {}, T2 = {}, alpha = {}%'.format(M2, round(p_sum/10**5, 2), round(rho_sum, 2), round(T, 2), round(alpha*100, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\honor\\anaconda3\\lib\\site-packages\\sympy\\core\\relational.py:486: SymPyDeprecationWarning: \n",
      "\n",
      "Eq(expr) with rhs default to 0 has been deprecated since SymPy 1.5.\n",
      "Use Eq(expr, 0) instead. See\n",
      "https://github.com/sympy/sympy/issues/16587 for more info.\n",
      "\n",
      "  SymPyDeprecationWarning(\n"
     ]
    }
   ],
   "source": [
    "r_p = []\n",
    "r_t = []\n",
    "r_rho = []\n",
    "r_v = []\n",
    "for x in np.arange(2.5, 13, 0.1):\n",
    "    v1 = M1 * sqrt(gamma1*p1/rho1)\n",
    "    ans = solve_eq(x, 7/5) # p, rho, v\n",
    "    diss_mol_once = 1e19 # число диссоциирующих молекул за раз - отвечает за шаг расчета, начинает 1e9\n",
    "    T = float(ans.args[1][0])*mol/(8.31*float(ans.args[1][1]))\n",
    "    count_mol = float(ans.args[1][1])/mol * N\n",
    "    count_atom = 0\n",
    "    nu = float(ans.args[1][1])/mol\n",
    "    nu_ = 0\n",
    "    dT = 942000/N * diss_mol_once\n",
    "    alpha = 0\n",
    "    alpha0 = 1\n",
    "    while T > T_start and count_mol > 0:\n",
    "        i = 5 if T < 3340 else 7\n",
    "        U = (i/2*R*T*nu + 3/2*R*T*nu_ - dT)  # вычисление уменьшения температуры для шага молекул\n",
    "        count_atom += diss_mol_once\n",
    "        count_mol -= diss_mol_once\n",
    "        T = U/(i/2*R*count_mol/N + 3/2*R*count_atom/N)\n",
    "        nu = count_mol / N\n",
    "        nu_ = count_atom / N\n",
    "        alpha = count_atom*2 / (count_atom*2 + count_mol)  # вычисляем степень диссоциации\n",
    "        for j in range(len(list(diss.keys())) - 1):\n",
    "            if list(diss.keys())[j] < T < list(diss.keys())[j + 1]:\n",
    "                alpha0 = diss[list(diss.keys())[j]]\n",
    "                break\n",
    "        if alpha > alpha0:\n",
    "            break\n",
    "    p = float(ans.args[1][0])\n",
    "    V = 1\n",
    "    nu = count_mol / N\n",
    "    nu_ = count_atom / N\n",
    "    gamma = 9/7\n",
    "    gamma3 = alpha*3/2 + (1 - alpha)*gamma\n",
    "    V1 = (p*V**gamma/(nu+nu_)*1/(R*T))**(1/(gamma3-1))  # для адиабатической ставить 1 вместо выражения\n",
    "\n",
    "    p_sum = (nu+nu_)*R*T/V1\n",
    "\n",
    "    rho_sum = (nu*0.028 + nu_*0.014)/V1\n",
    "\n",
    "    v4 = rho1*v1/rho_sum\n",
    "    M2 = v4/sqrt(gamma3*p_sum/rho_sum)\n",
    "    r_p.append(p_sum)\n",
    "    r_t.append(T)\n",
    "    r_rho.append(rho_sum)\n",
    "    r_v.append(v4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l_100 = np.linspace(2.5, 13, 105)\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.subplot(121)\n",
    "plt.plot(l_100, np.array(r_p)/100000, label='P, атм')\n",
    "plt.plot(l_100, r_t, label='T, К')\n",
    "plt.plot(l_100, r_rho, label='$rho$, кг/$m^3$', color='green')\n",
    "plt.plot(l_100, r_v, label='v, м/с', color='red')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xlabel('Число маха М')\n",
    "plt.title('Зависимость параметров за скачком от маха')\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(l_100, np.array(r_p)/100000, label='P, атм')\n",
    "plt.plot(l_100, r_rho, label='$rho$, кг/$m^3$', color='green')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.title('Зависимость параметров за скачком от маха')\n",
    "plt.xlabel('Число маха М')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\honor\\anaconda3\\lib\\site-packages\\sympy\\core\\relational.py:486: SymPyDeprecationWarning: \n",
      "\n",
      "Eq(expr) with rhs default to 0 has been deprecated since SymPy 1.5.\n",
      "Use Eq(expr, 0) instead. See\n",
      "https://github.com/sympy/sympy/issues/16587 for more info.\n",
      "\n",
      "  SymPyDeprecationWarning(\n"
     ]
    }
   ],
   "source": [
    "p1 = 1*10**5\n",
    "rho1 = 1.12\n",
    "gamma1 = 7/5\n",
    "v1 = M1 * sqrt(gamma1*p1/rho1)\n",
    "mol = 0.028\n",
    "p2, rho2, v2 = symbols('p2 rho2 v2')\n",
    "r_p = []\n",
    "r_t1 = []\n",
    "r_rho = []\n",
    "r_v = []\n",
    "for x in np.arange(2.5, 13, 0.1):\n",
    "    ans = solve_eq(x, 7/5)\n",
    "    r_p.append(ans.args[1][0])\n",
    "    r_t1.append(ans.args[1][0]*mol/(8.31*ans.args[1][1]))\n",
    "    r_rho.append(ans.args[1][1])\n",
    "    r_v.append(ans.args[1][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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": [
    "l_100 = np.linspace(2.5, 13, 105)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(l_100, r_t1, label='без диссоциации, T, К')\n",
    "plt.plot(l_100, r_t, label='с диссоциацией, Т, К')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xlabel('Число маха М')\n",
    "plt.title('Зависимость температуры за скачком от маха')\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
}