{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3461285-3f18-4ba7-9619-4ead5fa207c5",
   "metadata": {},
   "source": [
    "# Intialize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f764a698-d3ad-4991-8ca4-f8ba72b05734",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from classy_sz import Class as Class_sz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebd21063-d1ca-4721-a3f4-e66c00e0aae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosmo_params = {\n",
    "'omega_b': 0.02242,\n",
    "'omega_cdm':  0.11933,\n",
    "'H0': 67.66, # use H0 because this is what is used by the emulators and to avoid any ambiguity when comparing with camb. \n",
    "'tau_reio': 0.0561,\n",
    "'ln10^{10}A_s': 3.047,\n",
    "'n_s': 0.9665   \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f92e83-72c5-40fe-b8e4-a415bed0c6ff",
   "metadata": {},
   "source": [
    "# Notations\n",
    "\n",
    "In `class_sz`, we use $\\mathrm{Mpc}/h$ units.\n",
    "\n",
    "Let us denote the angular diameter distance $D_A$.\n",
    "\n",
    "Then, the comoving (or coordinate) distance $\\chi$ is given by:\n",
    "\n",
    "$$\\chi = D_A(1+z)$$ \n",
    "\n",
    "Note that in the flat case, this is also the conformal distance. \n",
    "\n",
    "\n",
    "## Definition\n",
    "The comoving distance is the distance traveled by light starting its journey from redshift $z$. We have \n",
    "\n",
    "$$\\chi(z) = \\int_0^z \\frac{d z^\\prime}{H(z^\\prime)}.$$\n",
    "\n",
    "For further details, we refer to Chapter 2 of [Dodelson \\& Schmidt (2021)](https://www.sciencedirect.com/book/9780128159484/modern-cosmology). \n",
    "\n",
    "\n",
    "## Comvong volume\n",
    "\n",
    "An important quantity for LSS calculations is the comvong volume,\n",
    "\n",
    "$$ \\frac{dV}{dz} = \\frac{c\\chi^2}{H}.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123d4b26-9d0b-4c46-b727-528822e389b1",
   "metadata": {},
   "source": [
    "# Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f669b4ae-a304-4a1d-9995-51bb329fb0fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 532 μs, sys: 1.84 ms, total: 2.37 ms\n",
      "Wall time: 2.71 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "classy_sz = Class_sz()\n",
    "classy_sz.set(cosmo_params)\n",
    "classy_sz.set({\n",
    "'output':' ',\n",
    "'skip_chi':0,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "588083b0-3056-43f8-bd87-54ef25c8e626",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 198 ms, sys: 77.3 ms, total: 275 ms\n",
      "Wall time: 54.7 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "classy_sz.compute_class_szfast()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "038e6221-a1c5-4b90-967b-b5cce14817ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1697.9299202204513"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = 1.\n",
    "classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122b0357-4b13-48b2-b0e2-4842869127e1",
   "metadata": {},
   "source": [
    "Update cosmological parameters and recompute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26f401df-4234-40d9-ad1c-46dd50a57f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1700.0951046348046"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosmo_params.update({'omega_cdm':  0.11833})\n",
    "classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796966e5-9cb3-4726-9896-717472231709",
   "metadata": {},
   "source": [
    "Time calculation after initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "870fbae0-f007-46f2-b36c-a8fbbb0a0276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.9 ms ± 4.76 ms per loop (mean ± std. dev. of 10 runs, 50 loops each)\n"
     ]
    }
   ],
   "source": [
    "# 50 loops 10 runs\n",
    "%timeit -n 50 -r 10 classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d4c115-0b63-4319-8a0c-6c04ccd9a5c6",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Plot distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22c1ebe3-bde8-4389-a66d-1ffda3b4f913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x329c4c8f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Ds8bIbacdIf9ZslP+9sV22V1QLs98tFme/WSLnHZUplw5daAcO6CHNWMKVLvvEei+dFa7m/rir3Y39cVf7R2N367sY3vb3dQXf7W7qS/+au/K1+yqz1s3/fv6q91NffFXu5v60lq7/nHf9LvCwe7vpr77q91NffFXu5v64q92m78vtNbupr74q91NffFXu7+fu+lnbjCMyQ3tbuqLv9q7+jWjo6PN4VaNZ2Jh6v371t93jB071lwuW7as2WOqq6tl9erV5o0ePnx4mx7j2+5sBhxsPlqfbTYfTYqJkLPH9g50d4BuIzk2Uv7vxCHy8S3T5Q+XjZfjBvc0ibi3V+2VC//whZz128/kpaU7paL6QAkgAAAAAAAABA8m/pucueFs6qtlenydeeaZ5vLll19u9ri33nrL1H6aOXNmoyyP85g333yzUV0olZWVJZ9++qn06NFDpk6dKsFIzzZWc47tKzGRYYHuDgKkaRINXSc8LFROG5Up//7eZHn3huPl4on9JCo8VNbsKZJbXl4pUx5eII+9v172FR6oBw4v4hY2Im5hK2IXNiJuYStiFzYiboGO6XYT/zk5OfL000832+CmpKREfvCDH8iXX34pmZmZMmvWrEY/v/baa03JH63X/8orrzS0Z2dny6233mqu33zzzY0eM3HiRDOpr/fRDXgduqHED3/4Q7NS4PrrrzcbVbRFa8tM3Gjr/lL5eEOOaJcvO65tpYwQfDRmNb5tit1gdWQvbxmgRXd8S24//QjpkxwjeaVV8rsPN8nUXy2QH724TJZsy2u1jm13QtzCRsQtbEXswkbELWxF7MJGxC1sFOKSeA2KzX3ffvttuf/++xtuL1682ExeTZo0qaHtrrvuMmfgb9u2TQYNGmR2aJ4wYYL06tXLJAO07E5ubq7ZhFTP4G/pLPy5c+fKnDlzzHNPnz5dUlJSZP78+WZjp9Y26N24caNMnjzZPPfo0aPN/gBLliyRLVu2yJQpU2TBggWNNuY92Oa+zgZSNrj/rbXy58+2yowRafL8VRMD3R0EyME2j0Jg1dR6ZP66LHn+823y5da8RgmCyycPkHOP7i2xkd1zGxjiFjYibmErYhc2Im5hK2IXNiJuYaPCwkIzx8zmvn6gE/d6pn5Tvm16H6WT9Xr2/aJFi2TDhg2ycOFCCQsLM8mAK6+8Un7yk59Inz59Wnyd2bNnyyeffCIPPPCAebzu2qwT+dddd51cccUVrW4WrBv83n333fLee++ZUkK66a8mIn72s58dctLfRmVVNfLfpTvN9cunDAx0dxBgQZBbDOIyQL3MsXZPkfx14TZ5bfluWbe3SO54ZZU89M46ufDYfmbFzqDU7reskriFjYhb2IrYhY2IW9iK2IWNiFugG5/xH+xsO+P/X4t3mInDASmx8uHN0yU0lIxsd6UfL5rd1LglM+9+BWVV8tLSXfL3RdtlR15ZQ/sJw9Pk8uMGyIwj0iWsG/w+E7ewEXELWxG7sBFxC1sRu7ARcQsbFXLGP4L1A9nZ1PeySQOY9AcskhwbKd89YbBcM22QfLwxR/7+xXb5cH22fLIhxxx9e8SYFQAXHdtPesRFBrq7AAAAAAAAaAVn/FvApjP+dXPQC//whURHhJpNRHUiEd2Xfrx4PB4JDQ0lM2+pHbll8o8vt8t/luyUwvJq0xYZHipnj+ktV0wZIGP6JkuwIW5hI+IWtiJ2YSPiFrYidmEj4hY2KnTJGf9M/FvAmfgPdLC0xY//9bW8uWKPOSP4VxeMCXR34AL6EcN/zvarqK6VN1bskb99sU1W7y5qaB/bL9mUATpzTC+JjgiTYEHcwkbELWxF7MJGxC1sRezCRsQtbFPkkrnc0IC9MtrN7Tma7KIKeXfVXnP9O5MHBLo7cFEtPrfHLg5NJ/XnHNtP3rxumrzywyly/jF9JDIsVFbsLJCbX1ohxz30gTzw1lrZklMitiNuYSPiFrYidmEj4ha2InZhI+IWNqpzSbxS4x9+89ry3VLjqZPxA3rIqD7uLkkEoGP0LItx/XuY4+dnHmlKAL345Q7ZXVAuz3221RxTh6bIpZMGyMkjMyQijPwyAAAAAABAV2PiH37z6cb95vKsMb0C3RUAXSA1Pkp+NGOofP/EIfLxhmz556IdsmB9tny+KdccaQlR8u0J/eTbE/tLn+SYQHcXAAAAAACg22DiH35RWVNrNvZVU4akBro7ALpQWGiInHREhjl25ZfJvxfvlH8v2Sk5xZXy2wWb5OkPN8lJR6SbVQAnDE8z9wcAAAAAAEDnYXNfC7hlQ4iDWbQlV779x0XmDOAlP/8Wm66gAZvwdE/VtR6ZtzZL/rFouyzcnNvQrmf+XzKpv9kvQFcEuBVxCxsRt7AVsQsbEbewFbELGxG3sE2RS+ZyOePfIm7O0TgTe1OGpPBhjEYx6/F4JDQ0lLjoZrS2/xmje5ljc06J2Qfg5a92mb0AHn1/vfxm3gY5dVSmXDyhv/ncCHXRKgDiFjYibmErYhc2Im5hK2IXNiJuYaM6l8zhsusi/GLhJm99f93UE/BVXFwc6C4gwIakxctdZ42UL3/2LXn8wrEyrn+y2Qj87ZV75bI/fynTH/vIlAPKLqoQtyBuYSPiFrYidmEj4ha2InZhI+IW6BjO+MdhK62skeU7C8x16vsDaE10RJjMHt/XHGv3FMm/Fu+Q177eLTvyyswqgF/P22D2Arh4Yj85cXg6ewEAAAAAAAB0EBP/OGyLt+aZs3f79YyRfj1jA90dABYY2TtR7j9vlPzsjCPl7VV75d+Ld8jS7flmXwA9eiVFy4XH9pM5x/aVvj34XAEAAAAAAGgPJv5x2BZuri/zw9n+aAE1+HAwMZFhcsH4vubYmFUs/16yU15Ztkv2FlbIUx9slN8u2CjHD0uTiyf0k5kjM8zeAV2BuIWNiFvYitiFjYhb2IrYhY2IW6BjQurcstsAXL8TdGvOePJTWbu3SJ789tFy7tF9At0dAJarrKmV99dkmVUAzsbhKjU+0pQJ+vaE/jIoNS6gfQQAAAAAAHDzXC4T/xYFS0FBgbl0k7zSKhl3/zxzfcnPZ0paQlSguwQX0Y+XmpoaCQ8PJ0OPDtmeWyr/WbJTXvpql+QUVza0TxzUU+Yc20/OGJ0psZH+XbxG3MJGxC1sRezCRsQtbEXswkbELWxUWFgoycnJAZ/475qaCQhai7Z4z8YdkZHApD9aVFpaGuguwGIDUuLk1tOOkIW3nyTPfme8zBiRJrrnr+4t8tOXVsjEX34gt89dKV9tzzdfCP2FuIWNiFvYitiFjYhb2IrYhY2IW6BjqPGPw/L5Jm99/8lDUgLdFQBBTGv7n3pUpjn2FpbLK8t2y3+X7pTtuWVmXwA9hqTFmVUA54/rI+kJ0YHuMgAAAAAAQMAw8Y/D4tTfnjqUjX0BdI1eSTHyoxlD5YfTh5gz//+7dJe8s2qvbM4plYfe/UYeeX+9zBiRLnOO7Sszjkjvsg2BAQAAAAAA3IKJf3TYnoJy2bq/1JTdmDS4Z6C7A5cKDWXSFZ1D6ztOGpxijnvPGSlvr9xrVgEs21Eg89dlmUM3BJ41rq9cOL6vDMtIaPNzE7ewEXELWxG7sBFxC1sRu7ARcQt0DJv7WsAtO0E39fJXu0yN7bH9kuX1H00NdHcAwNiUXSwvLd0lc5ftlv0lBzYEPrpfslwwvq+cPaa3JMVGBLSPAAAAAAAgOBW5ZC6XM/4t4rYczcL6+v5Tqe+Pg8RsVVWVREZGmrOzga4wND1B7jjjSPnpqSPko/U5ZhXAgm+yZfnOAnPc99ZaOfnIDJk1ro+cMDytWSkg4hY2Im5hK2IXNiJuYStiFzYibmGjOpfM4TLxjw4H8Oeb6yf+qe+PgygvLzf/QQNdTSf0Tx6ZYY6c4kp57evdMnfZLvlmX7G8vWqvObQU0LlH95HZ4/rKyN4HsvDELWxE3MJWxC5sRNzCVsQubETcAh3DxD86ZMv+UskqqpTI8FAZP6BHoLsDAAeVlhAl3z1hsFx7/CBZu7dI5n61W15frqWAquTPn201xxGZCaYU0DljewtfKQEAAAAAgM2Y+MdhlfkZ37+HREeEBbo7ANAmujT0qN5J5rjjjCPk4/U5ZhXAB+uyzUqAB95eJw+9+41MHpQsF00caFYL8BkHAAAAAABsw8Q/OuTzTbnmcupQ6vvj4MLD+ZiBe0sBzRyZYY6Csip5c+VemfvVLrMPwGeb882REB0uZ43pLbPH9TGrm6gpCTfj8xa2InZhI+IWtiJ2YSPiFuiYkDq37DYA1+8E7aj11Mm4++dJYXm1zP3BFEr9AAgqm7JL5JVlu+TVr3fL3sKKhvZ+PWPk3LF95Lxj+sjQ9PiA9hEAAAAAALhTkUvmcpn4tyhYCgoKzGWgrd5dKGf99jOJjwqX5XefLOFhoYHuElxKP14qKiokOjqaM6VhXdxGREbJl1vzzCqA99fsk9Kq2ob7jO6TJOce3dvsB5CeGB3Q/gKKz1vYitiFjYhb2IrYhY2IW9iosLBQkpOTAz7xz1oZtNvn9fX9Jw3qyaQ/DqmystL8Bw3YGLdTh6aao7yqVuaty5LXvt4tH2/IkVW7C83x4DvrzM/PP6aPnHJUpkmIAoHC5y1sRezCRsQtbEXswkbELdAxzFCg3T7f7K3vP2VoaqC7AgBdIiYyzJzdr0duSaW8vWqvKQX09Y4C+XTjfnNER6ySU0ZmmiTAtGGpZg8BAAAAAACAQGDiH+1SVeORJVvzzPUpQ9jYF0D3kxIfJZdPHmiObftL5fXle+S15btl6/5SeWPFHnP0jIuUs8f0knOO7iPj+iezJBUAAAAAAHQpJv7RLst3Fkh5da2kxEXKiIyEQHcHFoiMjAx0F4BOi9uBqXFyw8xhcv23hsqKXYWmFNCbK/ZIbmmV/PWL7ebokxwjZ9evFjiyVwJJAHQaPm9hK2IXNiJuYStiFzYiboGOYXNfC7hlJ2j1m3kb5MkPNspZY3rJ7y4ZF9C+AIAbVdd65LNN++X1r3fL/9ZmSZnPpsBD0uLknLF95Jyje8ug1LiA9hMAAAAAAATvXG5QFCD+6quv5OGHH5ZZs2ZJ3759zdmUrZ1R6fF45NNPP5Vbb71Vxo8fLwkJCRIVFSVDhgyR73//+7J169YWH/fRRx81PG9Lx3HHHddq/3bt2iVXXXWV9O7d22xGMnz4cLnnnnvMruTt4YYczRdOff8h1PdH22K2rKzMFbELdFXcam3/GSPS5YlvHyNf3XmyPH3JODn1qAyJDA+VzTml8pv5G2TGYx/JWb/9VP74yWbZU1Du9zGg++HzFrYidmEj4ha2InZhI+IWNqpzSbwGRamf+++/X15//fU23XfLli1ywgknmOuZmZly0kknSVhYmCxevFieffZZefHFF+Wdd96RadOmtfh4TRC09DNtb8mmTZtk8uTJsn//fhk1apQcf/zxsnTpUrnvvvvkgw8+MIcmHmxQVlUjX+/MN9enDqW+P9qmqqpKYmJiAt0NICBxq5sCnzmmlzmKKqrlf2uyzB4An2/aL6t3F5njwXe+kQkDe5hSQGeM7mX2EAA6gs9b2IrYhY2IW9iK2IWNiFugG0/868T6mDFjZMKECeYYOHCgVFZWtnhfPTv/5JNPlttvv11mzJjRsDJA769n/L/wwgty6aWXmgn7iIiIZo/XSX+9T1tdeeWVZtL/+uuvlyeffNK01dTUyJw5c+TVV1+Vhx56SO69916xweKteVJdW2fqVffvGRvo7gCAVRKjI+SC8X3NkVtSKe+s3idvLt8ji7flyZJt+ea49821ZuN03RPg1JGZkhTb/P8hAAAAAACAblnjX8vp6ER+e4dWXl4uvXr1MvWXtLTPiSee2PAzva2JgiuuuKLNE/+6imDSpEmSnp4uO3bsaHRmf1ZWlvTr10/i4+MlOztbwsPDD1kXqqCgwFwGyoPvrJM/frJFLhzfVx69cGzA+gF76O+g/j5p3LKhKWzR1XG7t7Bc3lqxV95cuUdW7ipsaI8IC5FpQ1PNKoBTSALgEPi8ha2IXdiIuIWtiF3YiLiFjQoLCyU5OZka/26iy4a0/r7as2fPYT/f22+/bS7PPvvsZuV8MjIyTNmf/Px8+eyzz8QGCzfvN5dTh1LfH21nSykrIFBx2yspRr57wmB547pp8uFPp8vNJw+XIzITzAqrD9fnyC0vr5RjfzlPrn5hicz9apcUlld3Wd9gFz5vYStiFzYibmErYhc2Im6Bblzqx19049/t27c31P9vycaNG+WOO+6Q3NxcSU1NNaV/TjvtNAkNbZ5DWbFihbkcN25ci8+l7QsWLJCVK1fK9OnTD9m/QGY2C8qqZM2eInNdy1AAbaExSx0+2CaQcTsoNU5+/K1h5tiUXSLvrNorb6/cK+uzimXBN9nmiAwLlROGp5p9A2YemSEJ0awEAJ+3sBexCxsRt7AVsQsbEbewUYhLVqcw8e/jX//6lym7k5aWJlOmTGnxPgsXLjSHr9GjR8vcuXNl2LBhjdq1vI/q27dvi8/ltDvJhkPRUj++5Ys0iPS2Zj59s59Oe1OH065n++vVoenxkpYQ1ez+nfGaXdXupr4E25j0emlpqSlp1RIbx+TPdjf1hTEdaNcksMZtXFycuR2ovujn7Y9PGmqOjVnF8s6qffLWqr0mITB/XbY5GpIAo3vJzJEZEh8V3m3eJ8bUuF1vl5WVSWxsbLMvmbaOyZZ2N/XFxjE53xX0M1dPpAmGMXVVu5v60t3GpEpKShq+KwTDmILxfWJM9n5faM+YbG93U1/cOibf7wraFgxjckO7m/pi85gqKyvN5tNN27XEjxsw8V9v586dcuONN5rr9913X7NlRFpL7JZbbpHZs2c3TPAvX75cfv7zn8uiRYvklFNOMbd9a/Drl0Gl/6m2RD+0VHFxcZv6OGDAgBbbb7vtNrNZsdJ+ayZUPxR1E2GHtunP9LV0Usu3D7qJse4j4BukCQkJ5o8vJ1A/Wre34Wx/fbxvnzXwddz6evq6Dn281rHSXwDdP8Gh+xnoRHBFRUWjTZgjIyPNv5XeVx/j6KwxObTvjKlzxqR91ev6PMEypmB8nxhT8zHp57c+l9P3QI8pPVrkygnp8oMTBsj2giqZu2Sb/G9djmzNLT+QBAgPlSmDkuVbw1PkxGE9JTE6POjfJ8ZU2uxLqPZP+x8MYwrG94kxNR+T9kc/c/X+wTKmYHyfGFPjMelraN+d7wrBMKZgfJ8YU/MxOfT1NAEQDGMKxveJMTUekx7O32fav2AYUzC+T911TI8++qg8+OCD4lZs7iti3ngttbN06VI577zz5NVXX23za9XW1ppNfz/99FPzRmsZIIfuF6ClgebNmyczZ85s9tjnnntOvvvd75rjj3/84yE399WVAb6Jha484/9bv/5YtuSUyrPfGS+njMzw63MHut1NfQm2Mel1Z0OTltg4Jn+2u6kvjOlAu35R8N08yo19dM58We+sBFi5V7buP/AlJjw0RCYPSZHTRmXKqUdlSkpcpKv63hnt3X1Melu/L+iXV2cSyvYx2dLupr7YOCbnu4J+5nLGP2NyU18O1u6sxm660aTNYwrG94kx2ft9oT1jsr3dTX1x65h8vytoWzCMyQ3tbupLsJ7xP2DAgIBv7tvtz/ivrq6WCy+80Ez6a73+F198sV2PDwsLM2fc68T/+++/32ji3ylv4ptJ9+VkmjRT1Rb6Iec78X8wTf8TP5z2fYUVZtI/NETkuEEpfn1ut7S7qS/+andLX5zbwTQmf7a7qS/+andTXzra7nu4uY9H9koyx00nD5dv9hXLu6v3yXur98qGrBL5dON+c9z12mo5dmBPOX1UpkkE6GbCge57Z7W7qS/+au9obHRlH9vb7qa++KvdTX3xV3tXvmZXfd666d/XX+1u6ou/2t3Ul9ba9Y/7pt8VDnZ/N/XdX+1u6ou/2t3UF3+12/x9obV2N/XFX+1u6ou/2v393E0/c4NhTG5od1Nf/NXe1a8ZHR1tDrfq1hP/elbnFVdcIe+++64cffTR8uabb3ZowxCn9M/evd5yOI7+/fvL119/Lbt27WrxcU57ayV83OLzTfvN5ag+SZIUyyaSaB824YGNbItb/fJxZK9Ec2gSYHNOibxnkgD7ZNXuQlm8Nc8cv3hzrRzdL9kkADQRMCDFW3IOwcG2uAUcxC5sRNzCVsQubETcAh3TrSf+f/zjH5sNfbUkj56t31o5kkPJz89vVLPfMXbsWHn99ddl2bJlLT7OaR8zZkybXqe1bFNnW7g511xOGZIakNeHvTRmm+6XAbhdMMTtkLR4+dGMoebYlV9mEgDvr9knS7fny/KdBeZ4+N1vTKLgtKO8KwGGZ8QH7P8ZHL5giFt0T8QubETcwlbELmxE3MJGIS752zpUuqk777xTnnnmGXNWvtbgT09P7/BzzZ0711yOGzeuUfuZZ55pLnUlge+GECorK8uUB+rRo4dMnTq1Ta/TWn3JzqSvuXCz94z/qUNTuvz1YTenhmQgYhfoqGCL2749YuXa4wfLS9+fIl/e8S154LxRMm1oqoSFhsi6vUXym/kb5NQnPpEZj30kD76zTpZuy5NaT3CMvTsJtrhF90HswkbELWxF7MJGxC1sVOeSeO2WE/+/+c1v5Je//KVkZmbK/PnzzeT/oTzxxBOyc+fOZm/is88+a55PMzk/+MEPGv184sSJZlI/Ozvb7APg0J2kf/jDH5r9Ba6//nqzQ7Vb6WaRewsrJDIsVI4d0DPQ3YGFfHdkB2wRrHGbnhgtlx03QP5x7SRZ+vOZ8ugFY+RbR6RLZHiobMstkz9+skUu+MMXMunB+XL73JWy4JssqaiuDXS30c3jFsGP2IWNiFvYitiFjYhboGNC6tySgjgMb7/9ttx///0NtxcvXmwm5SdNmtTQdtddd5kz8JcvX27OzNefT5482ZT5acm1115rNvt1DBw40NTk18cOGjRIKioqZNWqVbJ161YJDQ2VJ598Uq677rpmz7Nx40bzOrm5uTJ69GgZOXKkLFmyRLZs2SJTpkyRBQsWHHLJkmY2dVPfgoKCNm/u6y//WLRd7nxttUwa1FP+83+Tu/S1YT/9PdMdzDVu3bLMCTiU7hi3pZU18smGHPnf2iz5YF2WFFXUNPwsNjJMpo9Ik1NGZsqMEens9eJS3TFuERyIXdiIuIWtiF3YiLiFjQoLC01Jeb1MTEwMWD+CosZ/Tk6OfPnll83afdv0Pkonz51cxxdffGGOlkyfPr3RxP/NN98s//vf/2TNmjWydu1ac7Z+r1695LLLLjNn7U+YMKHVjX91g9+7775b3nvvPXn11VfNCgNNRPzsZz9zfZ2yA2V+qO8PAMEqLipcTh/dyxzVtR6zEfD/1uwziQBd9fXOqn3mCA8NkeMGp8gpR2XIySMzpFcSm2wBAAAAAOBGQXHGf7AL1Bn/Hk+djH9gnuSXVcvL358sxw6k1A/aRz9etLRVeHg4mXlYg7ht/G+xaneh/G9Nlvxv7T7ZkFXS6Oej+yTJzCMz5FtHpstRvRO7/b9XIBG3sBWxCxsRt7AVsQsbEbewUaFLzvhn4t+iif+uDpbVuwvlrN9+JnGRYbL8nlMkIqxbbgkBAPDZ92Xe2n0mEfDVjnzx/QbROylavlWfBJg8JEWiwsMC2VUAAAAAALrVXG5QlvrpLro6R/PF5lxzOXFQTyb90eGY1Q87/ZAjMw9bELetG5QaJ987YYg5coor5cNvsmXeuiz5bON+2VNYIX9ftN0cmjA+fliazByZITNGpElKvLvL2gUD4ha2InZhI+IWtiJ2YSPiFjaqc8l59kz8o1Wf19f3nzKE+v6w/8MOaA/i9tDSEqJkzoR+5qiorjV7wsxfl202B84qqpT31uwzh343H9+/h1kNcPLIdBmSFs8X9k5C3MJWxC5sRNzCVsQubETcAh3DxD9aVFXj3dxRTRmaEujuAABcLDoiTE46IsMcdeeNktW7i8xKAE0CrNlTJEu355vjV+99IwNSYuVb5r7pZkVZZDgrygAAAAAA8Dcm/tGilbsKpKyqVnrERsiRmYGrRQUAsIuezT+6b5I5bjp5uOwpKJcPvsmW+WuzTAm57bll8pfPt5pDSwJNG5YqM0aky4wj0iUjMTrQ3QcAAAAAICgw8Y8Wfb7JW99fN2gMDaUkAzouISEh0F0A2o249Z/eyTHyneMGmKOkskY+25gjH6zLlg/X58j+kkp5f02WOdRRvRMbkgBH90uWMP7/aRfiFrYidmEj4ha2InZhI+IW6Bgm/i3SlTWRqe8Pf8VsaGgo9bxhFeK288RHhctpo3qZw+OpM2WAFnyjSYBsWbGrwNzW43cfbjIrzk4cnmaSAHqZHBsZ6O67GnELWxG7sBFxC1sRu7ARcQsbhbgkXkPq2CHD9XT38qSkJCkoKDCXna28qlbG/OJ9qa6tkw9/Ol0GpcZ1+msiOOnHS2FhoYlbt3zoAYdC3AaGnv3/8fockwT4ZEOOFFXUNPxMT/wf179HQxJgZK9EVqM1QdzCVsQubETcwlbELmxE3MJGhYWFkpycbC4TEwNXQp0z/tHMkm15ZtK/V1K0DEyJDXR3AADdQGp8lMwe39ccNbUe+Wp7vikH9OE32bI+q7hhg+BH319v7qsJgBNHpMkJw1JZDQAAAAAAQBNM/KOZRVtyG8r8kE0FAHS18LBQmTQ4xRy3n36E7MovM0mAj9dny8LNuWZ1wNxlu8yhJ/6P7ZdsEgHTR6TL6D5J7A0AAAAAAOj2mPhHM+v3FZvLo/t1flkhAAAOpW+P2IYNgitrauWrbfny0QZNBOSY1QBf7ygwxxPzN5q9AY4fpkmANHOZlhAV6O4DAAAAANDlqPFvUY3/rqoLdcIjH8qOvDL513ePk8lDUjr99RDc9COGlSOwDXFrj72F5SYB8PGGHPls434prjywN4Aa1SfRrAY4YViaHNO/h0SGh0qwIm5hK2IXNiJuYStiFzYibmGboi6ey20NE/8W6MrNfXVj35H3vCcaFUvvnGnqKAMdpR8vHo9HQkND+U8a1iBu7VVd6zFn/n+8IVs+Wp8ja/YUNfp5XGSYSWjrSoDjh6WazeuD5T0mbmErYhc2Im5hK2IXNiJuYaNCNveFG23OKTGT/smxEZISx2aJOHzFxcWdnrAC/I24tVNEWKhMHNTTHLeceoRkF1fIpxv2m9UAn2/aL7mlVTJ/XbY5VJ/kGDlheKpJBEwZkmL9JsHELWxF7MJGxC1sRezCRsQt0DFM/KORTdkl5nJYejyZVACA1dITomX2+L7m8HjqZO3eIvl04375dGOOLN2WL7sLyuVfi3eaQ/cDHtM3WU4YlirHD0+To/slm0QCAAAAAAA2YuIfjWzM9m7sOzQ9IdBdAQDAb0JDQ2RUnyRz/GD6ECmrqpEvt+aZFQGaCNiYXSLLdxaY46kFmyQ+KlyOG5wi04amyLRhqTIkjYQ4AAAAAMAeTPyj1TP+AX9gogw2Im6DX2xkuMwYkW4OZ5NgXQ2gGwR/tmm/5JmyQFnmUBmJUTJ1aKpMHZJqLjOTosVtiFvYitiFjYhb2IrYhY2IW6Bj2NzXAl25E/RJj38kW3JK5e/XTDQ1jwEA6G6cskCfbMyRhZtyZfG2PKmq8TS6z9D0eJk2NNXsDXDckBRJjI4IWH8BAAAAAN1zLvdgOOPfIp2do6msqZXtuWXm+jBK/cBPMVtTUyPh4eFk6GEN4ha+ZYF+OH2oVFTXylfb881KAN0keNXuQrNCTo8XFm4z+wOM7ZdcnwhIlXEDkiUqPKxL+0zcwlbELmxE3MJWxC5sRNzCRnUuOc+eiX802La/TGo9dZIQFW5KGgD+UFpaarKcgE2IW/iKjgjzlvkZmmpuF5RVyaItufWJgFzZur9Uvt5RYI7fLtgk0RGhMmFgT7NHgK4IGN0nScK7YKNg4ha2InZhI+IWtiJ2YSPiFugYJv7RfGPfDDYwBACgNcmxkXLaqF7mULsLys1KAOfYX1Jl9gvQQ+lGwRMG9jCrASYPSZEjeyVKmC4TAAAAAACgkzDxjwYbs7wb+w5NY2NfAADaqk9yjMw5tp85dEnn+qxi+WJzrjm+3JonheXV8uH6HHOopJgImTSop0kCaDJgOAl3AAAAAICfMfGPBlqrWA3LYOIf/hMa2vnlLQB/I27RUTqBf0RmojmumjrIlNBbt7fImwjYkiuL6xMB/1ubZQ6VEhdpygJpIkAvh6TFdSgRQNzCVsQubETcwlbELmxE3AIdE1Lnlt0GEPCdoE/9zSfmLMXnr5wgM45I77TXAQCgu6qp9ZjNgTUJoMmAJdvypKLa0+g+qfFRMmmwd4+A4wb1lKHprAgAAAAAAFsUddFc7qFwxr9FOjNHoxMRW/bXl/pJ54x/+C9mq6qqJDIykkkrWIO4RWfSTX6P6d/DHD+cPlSqajyyYleBSQIs3LzfbBC8v6RS3l651xzOigBNBEwa5F0RMCw9XkKb7BFA3MJWxC5sRNzCVsQubETcwkZ1LjnPnol/GNvzyqS6tk5iIsJMrWLAX8rLy81/0IBNiFt0lcjwUJkwsKc5rv/WMKmsqZUVOwtl0RbdHyBXvtqeL7mlVfLOqn3mUD1idY+AlIZVASMyEkT/BiJuYStiFzYibmErYhc2Im4Bl0z85+TkyJYtW2Tfvn1SWloqERERkpycLP3795ehQ4dKWFiYv18S/tzYt4WzCAEAQNeICg+TiYN6mkNkmFkRsHJXQX0iIE+WbsuX/LJqeW/NPnOo5NgIOXZADxmTGSvHH1kno/okSUQYdVABAAAAoDs77Il/ndx//fXX5d1335WPP/5Ydu/e3ep9o6Ki5JhjjpFTTjlFzj//fBkzZszhvjz8ZFN2sbnU8gEAAMA9KwKOHdjTHNeJmESA7hFwIBGQJwVl1TJ/XbbMXyfy6w+3SWxkmIzr38OsItAEwjH9kyU6ghMvAAAAAKA76fDE/9dffy2//e1v5aWXXpKysrI21S+qqKiQL774QhYtWiT33XefHHXUUfKjH/1IvvOd70hsbGxHuwI/2Jhdf8Z/BhP/8K/wcCqKwT7ELdycCBg/oIc5fjRDpLrWI6t3F8rirXnyxaYc+XpXkRSWV8tnm/abQ0WEhcjoPkkycVCKTBykj+0pSTERgR4K0IDPXNiIuIWtiF3YiLgFOiakrp27DeiE/1133WXO8FfOwzMzM2XixIkyfvx4SU9Pl549e0qPHj1MHa68vDzJz8+XDRs2yJIlS2TlypVSXV3t7UBIiKSkpMitt94qP/7xj82qAHT9TtBnPPmprN1bJH+6/Fg5eWRGp7wGAADoXB5PnWzILpYlW/Nk8bZ8Wbw1V7KKKhvdR/cDOCIzUSYO7GFWEujKgMyk6ID1GQAAAACCSVEXzOX6feL/qquukr///e/i8XjM7XHjxsmll14qs2fPNjX820p34/7kk0/kn//8p7z66qvmH0MTAAMGDJC//e1vMm3atI6NJsiDpaCgwFz6W62nTkbe/Z5U1njkw59Ol0GpcX5/DXRP+vGiK32io6PN7zhgA+IWwRS32r4zr9xsFLxkW55ZGbAt17tS01ffHjFmnwBvWaEeMjw9gT1/0CX4zIWNiFvYitiFjYhb2KiwsNDseWvVxH9oaKjZRfuKK66Qm2++WYYPH37YHaisrDTlgh588EH55ptv5N5775W77777sJ83mHT2xP+O3DI54dEPTfmAtb84VcLZEBB+oh8v+iGncct/0LAFcYtgj9vsogpZUr8aYOn2fFm3t0g8Tb4NJkaHy7gB3n0CtKzQ2L7JEhPJPgHwPz5zYSPiFrYidmEj4hY2KnTJxH+7imT98Ic/lNtuu0369evntw5oaZ/LLrvMrBzQBEBtbW27n+Orr76SefPmyeLFi83hbDB8qJzGCy+8IM8884ysXbvWJDSOO+44ufPOO2XKlCmtPubzzz+XX/7yl2afAl25MHLkSLnuuuvk8ssvb/Uxu3btMuWR3n//fVP2SFdHXHzxxXLHHXeYjGWgbazf2HdwahyT/gAABLn0xGg5c0wvc6jiimpZvrPAJAO+2p4nX+8okKKKGvlofY45VHhoiIzqk9SwKkCTAWkJlGcEAAAAgKCp8e9G5513nrz++uvN2g82tBtvvFGefPJJiYmJkVNOOcUsG/rggw/MY15++WXznE3NnTtXLrroIlPq6IQTTpDU1FTzGD0TX1dAPPbYY80es2nTJpk8ebLs379fRo0aZRIFS5culS1btsjUqVPN4w+1r0Fnn/H/h483y8PvfiNnj+0tv734GL8/P7ovMvOwEXGL7h63NbUeWbe32JQG+mp7vrnMLm68T4Dq3zPWJADG9U82qwNGZCRwAgHajc9c2Ii4ha2IXdiIuIWNCl1yxr9fJv4TEhLMpLZOiuuEelf71a9+JaWlpTJhwgRzDBw40JQQam1o8+fPl5NPPtlsKvzFF1/IsGHDTLtenz59usTGxsrWrVvNG+TQM/UHDRpkJuE1ATBr1izTnpWVZfYk0An+Dz/80Dzel/5MVwlcf/31JtGgampqZM6cOWZ/g3vuuceUNwrkxP/N/10hc5ftkptOHi7Xf8v7bwH4g/4O6gbfmmDjP2jYgriFjTozbvW5d+WXy9Lted5VAdvyzQbCTb9mxUWGydh+yfXJgB5yTP9kSY6N9GtfEHz4zIWNiFvYitiFjYhb2KgwmCb+tfa/88unJW0ONZHd2bR8zsEm/s844wx599135Te/+U2zRMUNN9wgTz31lDl7X8/idzzyyCOmzNG5554rr732WqPH6AS+JgLOOussefPNNxvatezQpEmTJD09XXbs2NHozH5NGGjJpPj4eMnOzpbw8PCA7QR97u8+kxW7CuX3l46T00d7l/0DAAC0prDcWx5o2fZ8WbYjX5bvKJDiyppm9xuaHm9WBDjJgCFp8WwaDAAAACCoFXXyXG6n1Pg/FJ1ov//++831Q03+7927V/74xz/KmDFj5Pzzz5euolnCBQsWmOsXXHBBs59rm0786wS+78T/22+/3epjzjzzTJNs0JUEzk7jvo85++yzm5XzycjIkOOPP9705bPPPmu2UqAlnVGVSZ9zY3aJuT4sI97vz4/ujcw8bETcwkZdHbdJMRFy4vA0c6haj36fKDalgZZtLzDJgK37S2VTdok5/rt0V8OmwUf37yFH90s2KwKO6ceqgO6Oz1zYiLiFrYhd2Ii4hY3qXFJZ368T/zqBr2e/6+S/DvAXv/hFq/fV0jn6c10toKVvusr69evNaoC0tDTp27dvs5+PGzfOXK5cubJR+4oVKxr93JduDKyljrR2/4YNG0wy41CPcdp14l9fqy0T/51hT2GFlFXVmk37BqTEBaQPCG66Cbb+Bw3YhLiFjQIZt2GhIXJEZqI5Lp00wLTlllSajYI1CaAJgRW7vJsGf7IhxxyOwalxcrQmAbQ8UL9kOSKTvQK6Gz5zYSPiFrYidmEj4hZwwcS/TvgPHz7c1Nx/4IEHTNvBJv8DkQHRkjuqpUl/FRcXZ2ow5efnS3Fxsdm/QJdn6NKMgz1O23Xif/v27Q0T/4d6LaddH9MWTh8cmunUfz9dTeC7osBpb6ql9o1ZxeZyYGqcmfz3/XlL92/Pc9vS7qa+BNuY9LpzO1jG5M92N/WFMTVu943dQPelM9rd1BfG5J8x+euz1p/tKfFR8q0j082hqms9siGrxJQH+npngXy9I1+25ZbJlv2l5nhl2W5zv5iIMBndN8kkAbzJgCRJT4x2xZiIPf+3+37mBsuYuqrdTX3pbmNSvt8VgmFMwfg+MSY7vi/wPrmnL24dk+8RLGNyQ7ub+mLzmCorK01iqml70zncoJj4Vw899JC5bM/kf1cqKfGWtdENfFujk/+6ka4z8e885mCP08cofUxbX6ulxxzMgAHeM+ia0r0Hbr/9dnNdEwCaBdXNjn1XUmib/kxfy+PxNLRv2FdkLgf2iGoUlDpuXY3RNFC1PpU+3rfPGvjarq+nr+vQx2sdK/0F0GVZDt3PQPc20LJI+gviu3JC/630vvoYR3vHpP+uERERJmHj+4vHmLp+TNpX53qwjCkY3yfG1HxMzue30/dgGFMwvk+MqbRRu9L+af/dOqajeifJkZnxcs7IZP32IQXl1bJ6T4lsyK1qWBVQUlkri7fmmcORmRgpo3olyFG94mVs32SZNCxTQj3VrhhTd4+9wx2T9kc/c7VUZrCMKRjfJ8bUeEz6Gk5fnM9f28cUjO8TY2o+Joe+XllZWVCMKRjfJ8bUeEx6OH+faf+CYUzB+D511zE9+uij8uCDD4pb+XVz31WrVsnIkSNN2x133GEm/7X95z//udx3332NHrNmzRoZPXq0+Xltba101ea+L774olx66aUydepUU1u/tTPxd+/ebY7evXvLnj17pE+fPuZn1dXVLW7Ee9lll8k///lPc1xyySWmTVc/bNy4UebNmyczZ85s9pjnnntOvvvd75pD9zs41IYQujJAL/15xv/tr6yS/yzZKT8+aajcdPLwQ97fTZk8f7W7qS/BNia9rh+WrS3Js3FM/mx3U18Y04F2/aLg7Neit93YR94nxtS0XW/rdx/9PuBMQtk4Jo+nzpz9r6sBvKsCCmRDVrF4mjxE9wcelh4vY/slm0TA2H5JMjwjQSLDw1w3Jhvbu/I1ne8K+pmrf1MEw5i6qt1NfeluY1L6h7/zXSEYxhSM7xNjsvf7QnvGZHu7m/ri1jH5flfQtmAYkxva3dSXYD3jf8CAAcG1uW9rZ/7/8pe/NNebTv4HgmZtlG92uyknA6QZJN/HOI9r6Q1r+pi2vFZLjzkYLUHU1mBp+p94a+264Z4alpHQ4mPa2mZ7u5v64q92N/RFrx9sdY2NY/J3u5v64q92N/WlI+068dQ0bt3WR94nxtS0XW8frO6pLWMKCwsx30n0mDOhv2kvqayRVbsKzWqAFTu9h+5RtD6rxBzOxsFR4aEyqk+SSQTongFH902Wfj0PbALnhvfJpvaues2m3xWCYUxd2e6mvvir3U19OVh7a99xbR5TML5PjMne7wuttbupL/5qd1Nf/NXuz+du+l3B38/f1nY3/fv6q91NffFXe1e/ZnR0tDna+vigmfhvafJfsx+6D0Ag9e/v/SNy1y7vH4ktTcZrmZ8ePXo0TMjrZLueaa9ZGn2cs6rBl/N8vuV49LW+/vrrVl+rpcccTGtnm3SUPp9T41/PmgP8TWNMf6d0eZZbPvSAQyFuYaNgjtv4qHCZPCTFHI7sogpZockATQTsKpDlOwukuKLGlAvSQz733q9HbISM7pssY/okyZi+SWaFQEaT/QIQWMEcuwhexC1sRezCRsQtbFTn5zlcV078N538d2oeBXLyf8SIEWZZW05Ojinl45TwcSxbtsxcOhv0OsaOHSuffPKJ+XnTiX8t/7N69WqT4dHyPr6Pef311xues6nWXqur5BRXSlFFjVkuPyjVu98A4G++ddsAWxC3sFF3ilvd7PfkkXpkmNtaImhbbqk3CbCjQJbvKpR1e4okv6xaPtmQYw5HRmKUjO6jJYKSZEw/b1KgR1xkAEeD7hS7CB7ELWxF7MJGxC3g0on/1ib/v/3tb0sg6LK2k046Sd5991156aWX5MYbb2z085dfftlcnn322Y3azzzzTDPxrz/Xev6+3nrrLVNv7Kyzzmq0vEMfo+WN3nzzzYY6eo6srCz59NNPzcoC3W8gEDbWl/kZkBIn0RFhAekDAADA4QoNDZHBafHmOP+YvqatsqZW1u8rNisDVu0qkJW7Cs1+AVlFlZJVlCXz12U1PF5LAo1pWBmQLKP7JpmVBgAAAABgK7/8RaOlanTj2fZM/q9bt04C5aabbjIT/w888ICZnB82bJhp/+KLL+TZZ581tfSvueaaRo+59tprTbkiPYP/lVdekVmzZpn27OxsufXWW831m2++udFjJk6caCb1P//8c7ntttvkiSeeaMhU/vCHPzQrBa6//nqzQ3UgOGV+hlLmBwAABJmo8DDvZH7fZP22atrKqmpk7Z4ikwxYuavA7B2gmwnvzCs3x9sr95r76SryQSlxJgEwuk+S2TvgqN6JkhAdmO9sAAAAANBeIXV+KjpUXFxsznY/1CT2HXfcYSb/tS6XvrRe1tbWHtZrv/32243KBy1evNg896RJkxra7rrrLjPJ79Az/Z988kmzQcjJJ59sdmCeN2+eeZye1X/eeec1e525c+fKnDlzzH2mT58uKSkpMn/+fLMngCYTHn/88WaP2bhxo0yePFlyc3Nl9OjRpkzQkiVLZMuWLTJlyhRZsGBBo5UALSkqKjJ7DOjr6KW/3PnaKvnHoh3yg+lD5LbTjvDb8wIO/V3R363IyEhq8cEaxC1sRNx2XGF5tazZXdiQDNCVAbsLylu87+DUOJMEaEgG9EmURJIBh4XYhY2IW9iK2IWNiFvYqLCw0JxYrpe6d6z1E//t4Uz+mw74YeL/hRdekKuuuuqg93n++eflyiuvbPa43/3ud2b1gX6AHHfccSZBoBPyrdGz93WlwKJFi8wHj07kX3fddXLFFVe0+pidO3fK3XffLe+9957k5eWZTX8vvvhi+dnPftbizs+tTfz7O1guevYL+XJrnvx6zliZNc67LB4AAKC7yy2plNV7ikyJoFW7C2X17qJWkwGDGpIBifUrA5IkKYZkAAAAANBdFXXSXK4VE//q4Ycflv/+979m0r28vOU/pNC5Z/yPv3+e5JZWyZvXTTNL2QF/048XXQ2UkJBAZh7WIG5hI+K265IBq3frngGFJiHQWjJA9wwY1dtbHkgTAboyID3h0Cd7dEfELmxE3MJWxC5sRNzCRoUuOeM/YLuW3X777eY43LP90fE/XnXSXw1Jjwt0dxDEPB5PoLsAtBtxCxsRt50rJT5KThyeZg5HXmmVNxFgVgV4L3fle/cL0OPd1fsa7puWECWjnERAb+/qgL49YvgDltiFpYhb2IrYhY2IW8CyiX9HWFhYoLvQLW3KLjGX+gdnbGTAwwAAAMA6PeMi5YThaeZwFJRVmQ2E1+jqgD2F5nJLTonkFFfKh+tzzOFIjA5vSASMrE8KDE6Lk4iw0ACNCAAAAECwaNeM7yuvvCKzZs3qtM7s2bNHduzYYWrto3NtrJ/4H5YeH+iuAAAABI3k2EiZMjTVHI6yqhpZt7dY1u7x7hewZm+hrN9XLEUVNfLFllxzOCLDQ2VERoKM7OVNBuhxRGaCJLCJMAAAAIDOmvi/4IILZMyYMXLnnXea6/6im98+9NBDZgNe3fiXif+uO+N/WEZCoLuCIBcXRykp2Ie4hY2IW/fS1ZXjB/Qwh6OqxiMbs4vNioA1uwu9iYG9RVJSWWNKBunha0BKrDcZ4JMQyEyMDopSQcQubETcwlbELmxE3AJdMPE/ZMgQWblypVx00UXSv39/ueSSS8xx1FFHtfuFS0tL5dVXX5UXX3xR5s+fLzU1NRIeHm5eAy3z5x92zsT/UM74RyfHbEQEZyjCLsQtbETc2kfP7PeW+UkSObafafN46sweAWv3FppyQZoI0Ms9hRWyPbfMHL77BvSIjZAjeyWaQ1cF6OWwjHiJCrenlCaxCxsRt7AVsQsbEbewUYhLTs4JqdPtsduourpannjiCXnkkUckNze3YRDDhg0zZ+lPmDBBjjnmGElPT5cePXqYo7y8XPLy8iQ/P182bNggS5YskcWLF5ujoqLC7M6ttITQgw8+KMOHD++80VqqqKhIkpKSpKCgwFz6w6QH50tWUaW88sMpMq7/gbPPAH/S32+NX93B3C0fesChELewEXEb3PJLq2Td3gOJAL3Uso21nuZf48NCQ2RIWlxDQsAcmQlmc2E3xgaxCxsRt7AVsQsbEbewUWFhoSQnJ5tLjV0rJv4dJSUl8swzz8jTTz9tyvSYJ2rHL5/zklFRUWbC/4YbbpCJEye2txvdhr8n/gvLq2XsL/5nrq+89xRJpGYsOon+ruuHnMYt/0HDFsQtbETcdj8V1bWyMatE1u0rMkkB71Fsvue1JCUustHKgCN6JZiVn4FeHUDswkbELWxF7MJGxC1sVOiSif92lfpxxMfHy6233io//elPZd68efLf//5XPvzwQ9m2bdshHxsdHS2TJk2Sc889Vy6//HLp2bNnR7oAP5T50bqwTPoDAADYJzoiTEb3TTKH7x/G+4oqGpIAujJAr2/bXyq5pVXy2ab95vBdHTA4NU6OqE8ImKNXovROCo69AwAAAIDurEMT/47Q0FA59dRTzaF2794tCxculF27dklOTo4p8aMT/WlpaeYYPXq0HHvssdTmCrBN2cXmUmvAAgAAIDjoZH2vpBhznHRERkN7eVWtbMgqNkmAb/Z5EwLr93lXB2jJID3eXHHgeRKiw00SYIRJBmi5oAQZnpEgCZwwAgAAAHSPif+m+vTpIxdeeKE/nxKdQJeFKzb2RVdISEgIdBeAdiNuYSPiFq2JiQyTsf2SzdF0dcA3e4tNMuCbfd5kgK4MLa6okSXb8s3hq2+PGBmRkSDD61cHaDJgcFrcYZcLInZhI+IWtiJ2YSPiFnDBxD86l7+WXOtZXWpYOh+c6PyY1ZVBlAuATYhb2Ii4xeGsDphxRHpDe1WNR7bsL2mWENhbWCG78svN8cE32Y3KBQ1KjfMmBDJ0lUC8uRyQEmd+1pZ+ELuwDXELWxG7sBFxCxuFuCRemfi3SAf2YT5ojX9K/aCzsQkPbETcwkbELfwlMjzUlPfRw1dBWZVJBGzM8iYEtHSQJgSKKmrMd0s93l61t+H+UeGhZnWpJgS0ZJAmA/R2n+QYCfVJCBC7sBFxC1sRu7ARcYvuPId7uJj472ZKK2tkd0G5uT40jYl/AAAAHFpybKQcNzjFHL5/0GQVVcr6rGLZsK/YXGoyYGN2sVRUe2TNniJz+IqLDDMJgGFmhUC8DEuPl8wYkcTEOv6YBwAAAIJp4n/Tpk2yfPly+frrr83lihUrzObA6Bybc7xn+6fGR0qPuMhAdwcAAACW0on6zKRoc5w4PK2hvdZTJzvzyhoSAt9keVcKbN1fKqVVtbJiV6E5fMVHhZuEgCYDdHWAkxjITIwmIQAAAAC4eeK/qqpKVq1aZSb3nYl+vV1SUtJoCQRf7DsXG/sCAACgM2lt/4GpceY49ajMhvbqWo9szy2VDVklplTQxvpLTQiUVNbI8p0F5vCVEBUuQ3SFgFklEO9dLZCe0KxkEAAAAIAunPj/9a9/3TDRv379eqmpqWk0yR8dHW2uH3XUUXLNNdeYy1GjRnVml6zmj6QIG/uiq2OWOnywDXELGxG3sEFEmNb911r/CXLG6F4N7ZXVtbI9r8wkATQpoKsD9Pq23DIpbiUhEB0RKkPSnIRAgvd6RrwM6Bkr4WGhARgduhM+c2ErYhc2Im5hoxCXxGunTvz/9Kc/bdh9e+jQoWZSX4/Ro0ebyyFDhkhERIRMnjxZbrzxxs7sSlDwx8YQm7KLzSUb+6KrYtbj8ZjPALd86AGHQtzCRsQtbI7d8FA9KcVb4sdXVY1HtuWWmpUBum+As4nwlpzSVvcQiAgLkUGpcWZlgO5npasF9LomBqIjwrp4dAhWfObCVsQubETcwkZ13WFz38jISHOW/w033CC/+MUvJC4urjNfDu04459SP+gqxcXFJjsP2IS4hY2IWwRb7EaGh5pkgDchcGCFQE2tR3bml5uVAfrddpPPUV5dW19KyPud16HzBH17xJgEgCYETGKg/tCNi4H24jMXtiJ2YSPiFnDhxP/atWvNmfxa8ufFF1+U++67z5T0IUMXGBXVtbIjr8xcp9QPAAAAbKSlfPSsfj1OOepAu8dTJ7sLyhuSAJtz6hMCOSVSUFYtO/PKzfHR+pxGz5caHymDnWRAWrwMToszCQL2EQAAAIDNOnXif/DgwfLGG2/I+++/b876/7//+z956qmn5LHHHpNTTjmlM18aLdA/fnSlSXJshPkDBwAAAAgWOknfr2esOWYckd5oqXVuaVXjZIBezy6RPYUVsr+kSvaX5MnirXmNni8q3JtgGOKTDNBLTRLER3Xqn1EAAADAYeuSb6ynnnqqrF69Wn7zm9/IAw88IKeffrqZ+H/00Ue74uVRT//AUVpDlVUX6CrEGmxE3MJGxC1s1dmxq8+fGh9ljuMGpzT6WWlljdkzYFOOdw+BzdmlsmV/iWzbXyaVNR75Zl+xOZrKSIxqkhCIl8GpcdI7OUbCWCXQLfCZC1sRu7ARcQt0TJedqhIeHi633HKLXH755XLrrbfKP/7xD5k/fz6/vO1wuP9WzsQ/9f3RlTFLHT7YhriFjYhb2CrQsRsXFS6j+yaZw5fuI6Blg3SFgCYG9NJJCugKgayiSnMs3JzbbF+CQSneMkTO6gDvqoE49hIIIoGOW6CjiF3YiLiFjUJcMt/d5WtUMzIy5K9//av84Ac/kB//+Mfy1Vdfyb///W8ZNmyY/OQnPzEJAnTOjtAb6zc5G0p9f3RhzOoG3/p77ZYPPeBQiFvYiLiFrdwau7qPwICUOHOcdETjnxWWVcvm/d5SQVv2lzZcbs8tlaoaj6zPKjZHUz1iIxpWBgzSpECqd8VA/56xEh0R1nWDQ9DGLXAoxC5sRNyiO87h+ktIXQB7oi/95z//WX7+85/L/v37ZcSIEfLkk0/KySefHKguuVJRUZHJbhYUFBxWlvNbj38km3NK5W9XT5QThqf5tY9Aa7/jhYWFJm75Dxq2IG5hI+IWtgqm2HVWCegKAU0EbMkpka3mslT2FVW0+jgddu+kGJMEGFi/WsA5+vaIMUkIuEswxS26F2IXNiJuYaPCwkJJTk42l4mJiQHrR0BPr9df2GuvvVYuvPBCufvuu+X3v/+9qf+vmTz4l559tC23zFwflkGpHwAAAKCzVgnMaPIz3UvAJAH2l8pWkxg4kBQoqawxCQM9Pt24v/FzhoaYFQED6xMBejm4/rJXYrTZ0BgAAADokol/j8cjoaHtOytFs3Z6pv/3vvc9ufHGG/3dJYjIttxSqfXUSXxUuGQmRge6OwAAAEC3oXsJjOqTZI6mZzHqngH6XV0TAlvrL83t/aVmg2GzemB/abPn1P0EBvgmBVL08N7W7/skBQAAALq3w5r41y+qL774orzyyiuyaNEiU66ntrbWLGXQWv6TJk2SU045RU499VTp0aPHIZ/vqKOOknnz5h1Ol3DI+v7xLI1Cl2pvIhBwA+IWNiJuYavuHLv6vTwtIcocEwb2bPQzj6fOlAjSBIAe2+ovNTmwI7fMrOjdmF1ijqaiNCmgSYD60kG6CmFgaqy5npFAUsAfunPcwm7ELmxE3AJdXON/69atcu6558qaNWsa2nyfyndyOSYmRq655hq5+eabpX///h3savfl1Pg/nLpQT87fKL+Zv0EuHN9XHr1wrN/7CAAAAKDr9hPYU1BhkgCaENjWcFkmO/PKpMbT+p94TlKgf0/vCoEBmhjQlQMpcdI7OZo9BQAAAFwwlxuwif89e/bI+PHjJTs7u2GyPzIyUtLS0sxlfn6+GVjTREB0dLT89re/lauvvtq/owhy/tjc97oXl8lbK/fKz844Qr53whC/9xFoiX4GVFVVmc8FVprAFsQtbETcwlbEbudtMqyrA7bnlnlXC+R6rx8qKaB7CuiGwmaFgCYH6i/1dr+eMRIVHtalY3Er4ha2InZhI+IWNiq0eXPf73//+5KVlWWun3jiiXLnnXfK8ccfb34JHdXV1bJs2TL57LPP5C9/+YusW7dOysvL5bvf/a4sXbpUnnnmGf+NAoe0KftAqR+gK+nvve9nA2AD4hY2Im5hK2K38zYZbqrarBQoN0mA7bpKwFx6r2/P85YP0jY9Pm7yWJ1r0b0DdLNh74oBb2JgQP3tpJiIbjUhQ9zCVsQubETcAl008f/NN9/IW2+9Zb7UXX755WZSv6UveBEREabGvx5a4uf1119vSBg8++yzMmjQILnllls62G2096yfLTneDcGGpScEujsAAAAAAiCiUVIgrdmeAlnFFbJt/4FEgEkO1N8uraqVvYUV5vhya16z506IDjcJgAE9dXWANxmgSQG93iuJEkIAAACun/jXzXxVamqqPP30020+q0P3A5g8ebKcdtppsnz5crnjjjtk5syZcswxx0hX++ijj2TGjBmHvN8vfvELufvuu831e++919xuzW233SYPP/xwiz/7/PPP5Ze//KXZAFmXJ40cOVKuu+46kzjpCjv07J1aj0RHhEqf5JgueU0AAAAA9tANf3slxZhj8pCUZmUW8kqrTDJANxbWvy90pcCOvFJzPauoUooramT17iJztFRCqE+PGLNKQBMB/X0Ova2rBQAAABDgif/Fixebyf6LL75YYmNj2/XY9PR0efvtt80qgN27d8uDDz4oL730knS1zMxMueKKK1r8WW1trfzjH/8w17V8UVNTp06VoUOHNmvXPQ9aMnfuXLnooovE4/HICSecYBImH3zwgXn9lStXymOPPSadbaNPmR/9Qg90pfDwDlUUAwKKuIWNiFvYith1P/37LyU+yhzj+vdo9vPyqlrZme9NCniTA96EgF7flVduTkLylhQqa/H5deLfmwSIaUgM9OvhTQroiUuR4e5bLUDcwlbELmxE3AJdWOpH6dn7HdGrVy9zZvxll10mr776qmzfvl0GDBggXemII46QF154ocWfvfvuu2biv1+/fjJ9+vRmP7/22mvlyiuvbNPr5OXlmY2MNZmgCYBZs2aZdi13NG3aNHn88cflrLPOavF1WtLRmplOfX/K/KCraczGx7OvBOxC3MJGxC1sRewGh5jIMBmekWCOppwSQs5KAd1geEfDUS77SyqlsLxaVu0uNEdT+idQr8Ro6duQDIgxl/1TvLfTE6K6/OQm4ha2InZhI+IWNgpxyb5H7Z74LygoMJf9+/fv8Itecsklcs8998iWLVtkwYIFctVVV4lbOGf7X3rppYf9Jj333HNSVFRkyhw5k/4qIyNDHnnkEdOmk/9tnfjXJbYdsTGr2FyysS+6msZsRUWFREdHu+ZDDzgU4hY2Im5hK2K3e5UQmjS4cQkhVVZVIzvzyhuSATt9LnUVQUW1R/YUVphjcQt7C+hqgL7JMfWJAe+Kgb562cN72TMu0u+xRdzCVsQubETcwkZ1HZzDDfjEf3GxdxI5IeHwzh4/88wz5amnnpJPP/3UNRP/paWlZhNi9Z3vfOewn0/LGqkLLrigxfHrh9b8+fMbPsA6y6Yc54x/Jv7R9SorKzs1voHOQNzCRsQtbEXsdm+xkeEyIjPBHC390by/pMokAnbl1ycD8spNQkCPPQUVUlXjkS37S83R8vOHmQRA3x6NEwJ961cPaJmhjkwkEbewFbELGxG3QBdN/OuXL/1iFBp6eHUWtVa+TvxrnXu3eOWVV8zkv244rBvwtkRXKOjmxDpZ37dvXzn99NNbre+/YsUKczlu3LhmP4uMjJRRo0bJ0qVLZcOGDTJmzBjpDLq0tqHUTwtLbwEAAADAjfTvzrSEKHOMH9B8b4GaWo/sLaxoWB2gSQFNEOzK9yYHdNPhsqpa2ZBVYo6WxEeF1ycCvMkA3VNAr/epv90jtmOJAQAAgEAL2O4YWu7GqYPvtjI/Bzvb/+9//3uj23fddZfMnj3b7BngW3NMS/wUFnprVGqCoCXarhP/us9BZ0387y4oN8tjI8NCzdJXAAAAAAgG4fo3jtkUOLbFn1fW1JpVAZoY0GSAb1JAL3OKK6Wkska+2VdsjpbERDgrBg4kA/okR0tyhEdGhEabpASJAQAAEFQT/4f75aZHjx6umvjfu3evfPDBBxIWFiYXX3xxs58PHTpUHnvsMXOGv25GnJ+fL5988onceuutZuNe3cBXNyt2lJQcOKMkNrblL6JxcXGNyicdipNI8H0PdAVGVFSUOZq2qw319f0HpcWZL8Yt1Zjyvf+h2ttzX1va3dSXYBuTXo+IiGi4Hgxj8me7m/rCmBq3a9w6Pw90Xzqj3U19YUz+GZPe1tWEwTQmW9rd1Bcbx+T7mRssY+qqdjf1xa1j0pOfBqbEmqOl+1dU15oTpZykwG5zWd7Qll1cKeXVtbIxu8QcLdE9BnonRXuTAsmx0jvZe11XDujRKzlGwlvYfNhN/+7tbXdTXxhT+8Zky/eF9ozJ9nY39cWtY/L9rhAsY3JDu5v6YvOYKisrpaqqqll70zlc6yb+p02bZs5SP/roo2Xs2LHmOOqoo8x/Im3hlApq66R3Z/vXv/5lJu9PO+00yczMbPbzyy67rNmkvW5SPGPGDBk9erS89tprsmjRIjnuuOM6rY+acGjJbbfdJrfffru5rgmAmJgYU7KopqZGVu3IMe1DUmMb/r09Hk+jcegHqK5Q8A1S3cNB36OmgZqUlGQe7/u+aeBru76evq5DH5+YmGh+AcrLyxvaw8PDzeoILZekvyAOjR1Nkuh99TGOpmNyaJv+jDG5f0z62tr3YBpTML5PjMk7Jm1X1dXVQTOmYHyfGFPLY9J+B9uYgvF9YkzNx6TPE2xjCsb3KRjH1CchSoamp5sTt5qOqS40XDbu3m+SAHuLdIPhSskprTUbDesqAi0lpHsMbMstM4dIbqPxmH6GiKQnRktmQoT0SozyHknRMrR3T8mIi5CkiFqJi/L+Wc77xJi6akz6PTfYxhSM7xNjajwmjdtgG1Mwvk/dbUyPPvqoPPjgg+JWIXUtpTAOwre2v/7j+dKz5UeMGNGQCHAOp6yPrzVr1pgJc30OnXAPNK3D//XXX8s///lPM6HfHrfccotZDXDPPffIvffea9o0KDWwlAaiBlNT559/vkkYvPHGG3L22We3+vzOc2lJIOc523rG/y0vr5CXv9otP5k5TG6YOdzK7Flnt7upL8E2Jr2uH4qtrXqxcUz+bHdTXxjTgXb9oqBx6ySt3NhH3ifG1LRdb+uXU930rOn3M1vHZEu7m/pi45ic7wr6mat/ZwTDmLqq3U196W5jUmVlZRIWEWVWBegKAV0tsLugQnYXlDXc1gSBJgYOJTE6XHrrCoH6lQKaGNBLbdMVBOkJ0X5Zve2vdre9H4yp7WOy5ftCe8Zke7ub+uLWMfl+V9C2YBiTG9rd1JdgPeN/wIABrc4Ju/aM/9///vdmc1s9Vq1aZb7wODR7snbtWnPoGfSOtLS0ZskA38xOoK1bt85M+mtG57zzzmv344cNG9ZQLsihb6pO0usbvGvXrhY3C9b2g53J35Q+n+/E/8E4/4lvzC5ttLFv0//cm96/Le3+eA63tbupL/5qd0tfnLOmg2lM/mx3U1/81e6mvnS0XeNWE1bOfdzYx8Ntd1Nf/NXupr74q70999UvnM4fRF3Zx/a2u6kv/mp3U1/81d6Vr+l85nb267rp39df7W7qi7/a3dSX1tr1j3snbvunxJmjJR5PnewvrTT7DHgTA95yQiYxUFAhewrKpbC8WooqaqToIPsMaKmgjEQnGRBtygeZpEBStPRK8iYLEmPCm/W1u79P7W13U1/81W7z94XW2t3UF3+1u6kv/mr393O74e8zN/37+qvdTX3xV3tXv2Z0dLQ53KrdE///93//1+gLz4YNGxoSAc6RlZXV6DHZ2dkyf/58c7S0ciDQnA17Z82a1eqZyQej9f59a/Y7NMGh+wAsW7as2cS/fmitXr3aBMfw4cOlM+j7s7m+FuWw9AMbDwMAAAAAOldoaIg5W1+Po/slt3gf3Vx4r0kElHsTBAVl9Zd6u1z2FVZIjaeuPllwoBRBU7GRYSYZoKsFeid5EwO9kp3r0eZ2dERYJ44WAAC4TYdr/DtZDi3to8dFF13U0K4T/02TARs3bmxUp8kN5X2cyfEXX3zRXP/Od77Tocc7m/pquSBfZ555ppn4f/nll5vtEfDWW2+ZJXZnnXVWp2WG9hVVmC+SeobIgFbOQgEAAAAABEZ8VLhZne2s0G6q1lMnOU45ofpkgCYKtIyQuV5YIXmlVVJWVSubskvM0ZoesRFmhYAmBzQp0Ks+KZCZWH+ZFC1R4SQHAAAIFu2u8d9RWo9r5cqVjZIBWipI2wOZBNCJ+RNPPFH69OkjO3bsaHElQk5Ojvz3v/+Vyy+/3Gwg4dANoH7605/Ks88+azYE3rx5c6MVA3l5eTJo0CBTo3/u3LlmRYGzAmLq1KmyadMm+fDDD2X69OkH7aNT47+goKDNpX7M2DbkyOV/WSxD0+Nl/k0ntvlxgL8crIYk4FbELWxE3MJWxC5s5La4La+qlb2F3iSAJgf2FlSY2+Z6fYJAEwNtkRofaRIAJilgEgTeREFmorctIymK5IDF3Ba7QFsQt7BRYWGhJCcn21fjv6O0htykSZPM0bRUUCD94x//MJe6oW9r5Yd01+frrrtObr/9dpkwYYL06tXLJAO0hE9ubq55I/Ws/qZlgnr27Cl/+ctfZM6cOXLBBReYCf6UlBRT8kgn8W+66aZDTvr7au8H3Mb6sz2GplHmB4GhMau/+4BNiFvYiLiFrYhd2MhtcRsTGSaD0+LN0RL9u7uovEb2FnmTAnsKneSAN0HgJAcqazyyv6TKHKt3F7X6eilxTnLAu0pAEwLexMCB29onuI/bYhdoC+IWNgpxSZKqyyb+D1YqKFB052WdsFdNS/H40sn62267TRYtWmQSFQsXLpSwsDBzNv+VV14pP/nJT8yKgZbMnj3brCp44IEHzON1Ix2t96+JhCuuuKJd/W3v4oxN2d7NoYZlMPGPwNCY1cSZ7n/hlg894FCIW9iIuIWtiF3YyLa41T4mxUaY44jMxFbHVFBWfSApUFRhSgppUkD3GdAysk5yILe0yhxr9rSeHEiK0bJC0WZT4kaXDasHos19bPj3Cya2xS6giFvYqK5rCuy4e+I/0KKiokw5nkPR8j4PP/xwh19Hy/q8++670tV2F1SYy349279hMeAvNTU1ge4C0G7ELWxE3MJWxC5sFGxxq5NpPeIizXFU76SDJgdMMqDoQFLgwKW3TcsKFZZXm+Obfd6T0VoSHRFqkgC+SYFeid5VA+l6mRgtaQlREhHW8sp8dEywxS66B+IW6JhuPfEf7LKLvBP/+oUJAAAAAAB/JAdG9m595UBxZY13lYDPagFNCGT5XOqGxBXVHtmWW2aO1l9T9x2IakgQZCTWX69fOaBJgoyEaEmMCedMYAAAmmDiP4jpFyqlX5AAAAAAAOhMOvmeGB1hjuEZCa3er6K6VrKLKs0qAU0M+CYFNFmQVVRprtd46iSnuNIcq3YXtvp8MRFhJimgKwX079/MxChz6awcyKi/HR3B3gMAgO6Dif8gVVlTK/ll1ea6fskBAoVNeGAj4hY2Im5hK2IXNiJuD49OwPdPiTVHazyeOrOXgCYATEJAL+tXEOwrqjQr3PW6lh8qr6495OoBlRgd7l0loEmBhAMJAb1Mq7+t5YWiwoM3QUDswkbELdAxTPxbpD1LF/XsCRUZHmo2TQICFbO6lwZgE+IWNiJuYStiFzYibrtGaGiImYTXY1SflvcdcFYPeJMD3lUCBw7v7eziSrOKQJMDRRU1UlRRIhuySg762j1iI0xCQF/bSQw4iQKbEwTELmxE3MJGIS4pP8fEf5DuCH2gzE+Ua4IN3Y+p8VlcbDbIJg5hC+IWNiJuYStiFzYibt23emBASpw5WuPsPZBdnxAw5YSKvSsINDHgJAj0BLqqWo9ZPa/HwTYnVsmaIEjQkkLeRICTHEivb0uvb4uJdEeCgNiFjYhbBPscbmdi4j9I6ZcZpV9CgEDyeDyB7gLQbsQtbETcwlbELmxE3Nq798DQ9ISDTtRo6SAnGXAgIeBNGGQXH7isrvXeV4/1WQdPECREhzckAZyEgJMo8L3eFZsUE7uwEXELdAwT/0Gq4Yz/JCb+AQAAAAA4FJ107xEXaY4RmW1PEJjkQHGFWTGQ06StotojxRU15ticU3rQ19dSvQcSAd5kQMN1XVUQ772dEh8pEWGhnfAvAAAIJkz8Byldtqg44x8AAAAAgMAkCLwlhryrBjQZkOMkCepLC+W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06NFhn332qWEvobLiHdEha8QtWSRuySqxSxaJW7JK7JJF4hbap0OhtdsUUzarVq0KY8aMCY888kjYcccdw6hRo8Irr7ySfu7bt29YsGBB2HXXXTe5j3gn6HjlQK3vBA0AAAAAQP3Xcs34r7B4Z+e5c+eGSZMmhW7duoXZs2enwv+4cePSGv9tFf2bc46GrIkxG2/CI3bJEnFLFolbskrskkXilqwSu2SRuCWLCnUSr2b8Z+gs0fvvv58eIStieolnN2PcdujQodbdgaKIW7JI3JJVYpcsErdkldgli8QtWfTBBx+E3r17m/EPAAAAAACUj8J/BqxevbrFI2RFjNmpU6eKXTJF3JJF4pasErtkkbglq8QuWSRuyaLVdVLLtdRPBixZsiT0798/vPrqq+kRsnZpk2WqyBJxSxaJW7JK7JJF4pasErtkkbgli1577bUwYMCA9NivX7+a9cOM/wyxlhlZjVmxS5aIW7JI3JJVYpcsErdkldgli8QtWdShTuJV4T9DXJxBVmNW7JIl4pYsErdkldgli8QtWSV2ySJxSxYV6iReFf4BAAAAACBHFP4BAAAAACBHOtW6AxR/eUhDQ0NYvnx5rbsDRWuM1/hYL+ubQVvELVkkbskqsUsWiVuySuySReKWLGpoaKiLJX86FGrdA9r00ksvhUGDBtW6GwAAAAAAFOHFF18Mu+66a6gVhf8MWLduXVi6dGno2bOns5sAAAAAAHWqUCikWf877bRT6NixdivtK/wDAAAAAECOuLkvAAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMI/AAAAAADkiMJ/DXz00Udh8uTJ4XOf+1zo2rVr2GmnncL48ePD66+/XvK+li1bFs4///wwcODA0KVLl/R4wQUXhPfff78ifWfLtHLlyjB79uxw2mmnhd122y3Fbffu3cNee+0VrrrqqrBixYqS9vfZz342dOjQYaNfzz//fMXGwpZlzJgxm4y1+++/v6T9yblUw4MPPrjJuG38ivm3GHIu5fT444+HqVOnhuOOOy7069evKY7aMmPGjPCVr3wl9OjRI2y33Xbh8MMPD/Pnz29XHz755JNw0003hT333DNss802oW/fvuGkk04Kzz33XLv2R/6VErfr1q0LDz30ULj44ovDPvvsE3r27Jn+5g8aNCiceeaZYfHixSW//7hx4zaZh2+//fYyjJI8KjXnXnHFFZuMtQkTJpTcBzmXSsdtMce9Bx54YNHvL+dS7brXjDo+zu3Urq1ot1WrVqWEtWDBgrDjjjuGo48+Orz88sth+vTp4a9//Wtq33XXXYva1zvvvBP23Xff8MILL6RtjjnmmPDss8+Gm2++Odx3333hX//6Vwo42Fx33nln+O53v5u+Hzp0aDjqqKPC8uXLUyK7/PLLw+9+97swb968sP3225e037Fjx7ba3qtXr7L0Gxodf/zx6Y/w+nbeeeei9yHnUi077LDDRvNjPBD8zW9+k74fNWpUSfuVcymHKVOmhD//+c8lbRNPkMZcGT+8HHzwwel4eM6cOeHvf/97mDlzZsqnxYpF2RNPPDHMmjUr9O7dOxxxxBEpP8f93HPPPWHu3Lnpgxe0N25feumlcMABBzTl4/jZbauttgqPPvpo+PnPf56Oi++9996w//77l9yPQw45JO1zfbHAAOXKudHIkSPD4MGDN2iPJ7NKIedSjbjd2DFqFOMsxlypx72RnEs16l51f5xboKomTpxYiP/s++67b6GhoaGp/YYbbkjto0ePLnpfp556atrmuOOOK6xZs6ap/dxzz03tY8eOLXv/2TLNmDGjcMYZZxQWLlzYon3p0qWFvffeO8XbySefXPT+Bg4cmLaBSos5Ncba4sWLN3tfci714N57703x1r9//8K6deuK2kbOpZymTp1amDRpUuEvf/lL4Y033ih06dJlk/E1Z86c9HyfPn0KixYtamqfP39+oXPnzoXevXsXli1bVvT7T5s2Le1vyJAhhTfffLOpfebMmal98ODBLXI0lBq3L7zwQuGggw4qPPDAAy3y7KpVqwrjxo1L2w0YMKDw8ccfF/3+8Rghbjd37tyyjIctR6k59/LLL0/PT58+vSzvL+dSjbjdmHh80Lht82OItsi5VKvuNScDx7k+BVbR6tWrC7169Uq/rCeeeGKD54cNG5aee+yxx9rcVwy8jh07pkBqHgyNB6V9+/YtbLXVVoW33nqrrGOA9cWEFuM2/kGOMV4MRSiyVviXc6kXp5xySorpCRMmFL2NnEsltfVh/rDDDkvP33TTTRs8d95556Xnrr/++qLfb+jQoWmbWbNmbfDcUUcdlZ6LH45gU9pbhFq5cmXT57kHH3yw6O0UoSiXahf+5VxqmXN/8YtfpO1GjBhR0nZyLtWqex2WgeNca/xX0cMPPxw++OCDtD7k3nvvvcHzJ5xwQnq8++6729xXXJc6XgISL3f6zGc+0+K5uAblkUcemZYDiJehQiXF9c6i1atXh3fffbfW3YGKkHOpBx9++GHTZdPf+ta3at0dKOq+Vv/85z9bHOe299g3imurx/VN46XU8dLnzd0flCrGXrxPW7R06dJadwcqSs6l1hqXt3TcSz3WvT7KyHGuNf6r6Omnn06Pw4cPb/X5xvZnnnmmLPu64447itoXbI64Dmq09dZbl7y++XXXXRdefPHFVDjdfffdw7HHHptuXALl9stf/jL9ge7YsWP6wB7X2RswYEDR28u51IM//elPqfgfJw984QtfKHl7OZdq++9//5s+IMU4izf325xj3+a5eI899kjHHZu7PyhVnATwyiuvpO9bWze6mDx+1113pckCu+yyS5o48PnPf74CPWVLF4tRTz31VFprOubfww47rOT1/eVcaunVV19NN1qPsfeNb3yjXfuQc6lk3Ssrx7kK/1VOXFFrAdG8vfFgslr7gs0Rb2ISHXrooamYVIqLL764xc8/+MEPwi233BLGjx9f1j7C1Vdf3eLnH/7wh2HSpEnpqxhyLnmY9STnUm1t5c7u3bunG5ctW7YsNDQ0hJ49e27W/uRiKi3e2O/tt99OH/L322+/krePObe5Sy65JJx11lnpeLpTJx/NKZ9f//rXLX6Ox7zHH398mDFjRujRo0dR+5BzqaXf/va3cW2gdNKqT58+7dqHnEsl615ZOc611E8VrVixIj1269Zto0ERxYCo5r6gveKyJnEmdTwbOWXKlKK3i3dHj2ffY8JauXJl+M9//hMuvPDCdLb09NNPb1rKAjbXAQcckD74xFnOMdbiWflrrrkmHehNnjy56Q94W+Rcau2NN94IDzzwQNhqq63CySefXNK2ci610lbujBz/khWvvfZauOCCC9L3V111VUkTXuKVWrfffntYtGhRysNx5uBtt92WCgI/+9nPwo9+9KMK9pwtyeDBg8P1118fnn322ZQzY9zGAurOO++cZj6XMnlAziWrE17kXKpR91qRkeNcp7iAdnn++efDN7/5zXQWPi4f0bjmWTF++tOftvg5Ljlxww03pMvuzjjjjHQm/uijj65Ar9nSxA/mzcVlfi699NLwpS99KRxyyCHhiiuuSDEX19GDep9lGi9TjrNMSl1eQs4F2DxxmbXjjjsuvPPOO2m5wDPPPLOk7c8///wWP8clJ84+++wwevTodOn+rbfemk7I9u/fv8w9Z0sTP5+tXyg65ZRTwle/+tWw5557htmzZ4cFCxaEESNG1KyP0JYnnngiLFy4MBXq4/I8pZJzqYe6V70w47+KGi+pi2ccN3ZAGbV1+Ue59wWlev3111PxKV6yFP9grv+Htb1OO+20sP3226dZ2S+//HJZ9gmtOfjgg1Px//333w+PPPJIm6+Xc8njzc3kXCqtrdwZOf6l3q1ZsyaceOKJ4bHHHgv7779/uPPOO8u273giNl6VtXbt2nRVF1TKjjvuGL7zne+k7++///6itpFzqfVxb8y9pS4nvClyLuWse2XlOFfhv4oabyS5ZMmSVp9vbB84cGBV9wWleO+991LRNC4ZEQ8e46Wk5RJvvDpo0KCmZS2gkoYMGVJ0rMm51NJzzz0XnnzyyXQwGGealoucS6W1lTvjB5h4Anbbbbct6kOMXEwtbuY7duzYcN9994UvfvGL4e677y77VYKlHI9ANWNNzqUW4hWuv//971u9gqUc5FzKVffKynGuwn8VNV4SEi9bak1j+7Bhw6q6LyhWXHMs3lwnXnYXL3eeNm1a6NChQ1nfI55Nbb5+GVRKKbEm51IPN+iLeXdTa0i2h5xLJe22225ppt7//ve/NGtqc3NnYy6O96mIs7A3d3/QlnPPPTcttRaXCvzb3/6Wlp0oN3mYaik11uRcaiHOxI9F+VjcHDVqVNn3L+dSrrpXVo5zFf6raOTIkaFXr17pJpNPPfXUBs/PnDkzPRazhlm83CTO1HvooYfC22+/3eK5eLO+OBsl3gDw8MMPL+MI2JLFuIprQD/66KNpbfT4ISjGWDnFm1DFJSdiYSuuPQ2VEv84x/wZxXUe2yLnUitxPcnGZSXKucxPJOdSaXFm9IEHHpi+/+Mf/7hZx76Na/QOHTo0fPTRR+Gee+7Z7P3Bplx22WXpJpBxBt6cOXPS0mjlFo8hGmO5mOMR2JzjiVmzZpUUa3IutVzmJ872L/ckQzmXcta9MnOcW6CqJk6cWIj/7Pvtt19hxYoVTe033HBDah89enSL199yyy2F3XbbrTBhwoQN9nXqqaembY4//vjCmjVrmtrPO++81D527NgKj4Ytxdq1awvHHntsiqtRo0YVPvzwwza32Vjs3nPPPYUHHnhgg9c//fTThaFDh6b3iDEMm+vhhx8uzJo1K8Vvc4sXLy6MHDkyxdpRRx3V4jk5l3ozb968FF8777xz4ZNPPtno6+RcaqVLly4pjjZmzpw56fk+ffoUFi1a1NQ+f/78tG3v3r0Ly5Yta7HNI488kuL5wAMP3GB/06ZNS/sbMmRI4a233mpqv+uuu1L74MGDW+RoaE/c3njjjen5HXbYoUXcbsrG4va5554r/OpXvyqsWrWqRfvbb79dOOaYY9L77LXXXoV169a1czRsSTYVuzGmbr311sLy5ctbtDc0NBS+973vNcX0+p/l5FxqnXMbxdjs0aNHeu3zzz+/ydfKudRD3WtOBo5zO5V2moByzBz5xz/+EebPn5/WFouXLsU1o+LNJfv27RvuuOOOFq9/55130my81tYf+8lPfhIWLFgQ7rrrrjRTL96oMs7ei5eFxH3feOONVRwZeRbvet84Q+TTn/50OPvss1t9XVz3LD6/qdiNZ06vvPLKdOlevJQpzjR96aWX0mVL8SY7Y8aMCVOnTq3CqMi7RYsWpfX4dthhhzSjI16eH/Pt448/HlatWpVu7hQv22tOzqVeZz2dcsop6aqTjZFzqZY4A2nKlClNP3/88cfpccSIEU1tkyZNCkcccUT6/utf/3q6GdrNN9+c1kg/6KCD0jZxBnWcgTp9+vQNlk+JNzWL8Rxz9frGjx8f7r333nRcEnPx1772tRT/8+bNSzOv4v8znTr5iEP74zZemX3RRRc1zb675pprWt3n6aefnm7221bcvvnmm+Hb3/52+v8gHjvEz3xLly5NxyMNDQ2hX79+4Q9/+EPZZ7ay5cVuXE/6nHPOCRMmTAhf/vKX0w1941Wu8W/+u+++m3JtnDG6/rKBci61PlZoNHv27LTMSozfuIzKpsi51EPd6+tZOM4t6TQBZbFy5crCpEmTCoMGDSp07tw5nXUfN25c4bXXXtvgtZdffvkmZ5K+++67hXPPPbfQv3//tK/4GGfurX9GCTZHYxy29RVnUrcVu/HM5/jx4wt77rlnOivaqVOnwnbbbVcYM2ZMOru5/uxsaK+FCxcWzjrrrMLw4cMLffv2TbHWq1evwogRI9JVVjEXr0/OpZ7EmUrbbrttisk4Q39T5FyqZfr06W0eD8TXtLbdPvvsU+jWrVua/XTooYemK7NaM3fu3LSfgQMHtvp8jNuYx3ffffdC165dU2yfcMIJhWeffbbs42XLi9vG+Cs1zjcWt6+//nrhggsuSMcf8XPf1ltvnWa0xuOTmLvfe++9qv5bkN/YjTP9L7nkkrSKQLxSMM42jTk35sqLLrqosGTJklbfQ86lXo4VDjvssPTczTff3OZ7yLnUQ90rC8e5HeJ/SjtVAAAAAAAA1Cs39wUAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAAAAgBxR+AcAAKpil112CR06dCjqq3v37mHdunW17jIAAGSSwj8AAFBxy5cvDy+//HLRr99jjz1Cx44+rgAAQHt0atdWAAAAJejatWv497//vcnXTJ48OcyaNSt9f9JJJ1WpZwAAkD8K/wAAQMV17tw5zeLfmB//+MdNRf/LLrssXHTRRVXsHQAA5ItrZwEAgJqKRf+pU6c2Ff2nTJlS6y4BAECmKfwDAAB1UfSfOHGioj8AAJSBwj8AAFAXRf+rr7661l0CAIBcUPgHAACqTtEfAAAqR+EfAACoWdH/0ksvVfQHAIAyU/gHAABqVvS/5pprat0lAADIHYV/AACg6kX/+L2iPwAAVIbCPwAAUPWi/7XXXlvrLgEAQG4p/AMAABWl6A8AANXVoVAoFKr8ngAAwBYiFvxjsT869thjw1VXXbXJ12+33XZhp512qlLvAAAgnxT+AQCAihk+fHh48skni3795MmTw5VXXlnRPgEAQN5Z6gcAAKiItWvXhoULF5a0zbBhwyrWHwAA2FKY8Q8AAAAAADlixj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAOSIwj8AAAAAAIT8+D+xxiJTxHt+6gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_size = 15\n",
    "title_size = 20\n",
    "legend_size = 13\n",
    "handle_length = 1.5\n",
    "fig, (ax1) = plt.subplots(1,1,figsize=(18,5))\n",
    "\n",
    "ax = ax1\n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n",
    "ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n",
    "\n",
    "\n",
    "z = np.linspace(0.,20,100)\n",
    "\n",
    "ax.plot(z,classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params),ls='-',label='class_szfast')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_ylabel(r\"$D_A(z)$\",size=title_size)\n",
    "ax.set_xlabel(r\"$z$\",size=title_size)\n",
    "ax.set_xscale('linear')\n",
    "ax.set_xlim(0,20)\n",
    "ax.legend(fontsize=legend_size)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c13542-6beb-49cc-96ff-ba7c038efcdd",
   "metadata": {},
   "source": [
    "# Time computations of $D_A(z)$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f36c4b-eefc-4943-b6a0-e8e21a441fdc",
   "metadata": {},
   "source": [
    "Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "231671fe-a514-4ec5-8ac5-16224ecf919d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35 ms ± 5.7 ms per loop (mean ± std. dev. of 10 runs, 50 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 50 -r 10 classy_sz.compute_class_szfast()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e799898-eb07-4070-b96e-466dd4f7630b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 ms ± 4.6 ms per loop (mean ± std. dev. of 10 runs, 50 loops each)\n"
     ]
    }
   ],
   "source": [
    "z = np.linspace(0.,20,1000)\n",
    "%timeit -n 50  -r 10 classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e988c4-93e4-4c52-a838-6256329377d0",
   "metadata": {},
   "source": [
    "# Compute with Jax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "424c28d7-b9b0-4e9b-9cc9-73e582039cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from classy_sz import Class as Class_sz\n",
    "import jax.numpy as jnp\n",
    "\n",
    "cosmo_params = {\n",
    "'omega_b': 0.02242,\n",
    "'omega_cdm':  0.11933,\n",
    "'H0': 67.66, # use H0 because this is what is used by the emulators and to avoid any ambiguity when comparing with camb. \n",
    "'tau_reio': 0.0561,\n",
    "'ln10^{10}A_s': 3.047,\n",
    "'n_s': 0.9665   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da874626-5ba1-4bc2-bd2f-14fa660affbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 199 μs, sys: 278 μs, total: 477 μs\n",
      "Wall time: 709 μs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "classy_sz = Class_sz()\n",
    "classy_sz.set(cosmo_params)\n",
    "classy_sz.set({\n",
    "'output':' ',\n",
    "'jax' : 1,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65ec730f-d0a3-4881-a89e-66f495eee8de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 498 ms, sys: 70.2 ms, total: 569 ms\n",
      "Wall time: 592 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "classy_sz.compute_class_szfast()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9654a5d7-01de-4c04-bc17-0fbf53dab8d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(1697.92992022, dtype=float64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = 1.\n",
    "classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9726e68d-79e7-4441-a44f-da4a81aac504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(1700.09510463, dtype=float64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosmo_params.update({'omega_cdm':  0.11833})\n",
    "classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "013a5620-55fb-4c2b-886d-701a5e746be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1700.0951046348045565537177026'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = jnp.linspace(1.,20,1000)\n",
    "\"%.25f\"%classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c4478c6-2686-4b22-9f51-b3b4eb63d18a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1697.9299202204513221658999100'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosmo_params.update({'omega_cdm':  0.11933})\n",
    "z = jnp.linspace(1.,20,1000)\n",
    "\"%.25f\"%classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbae0ed4-1379-4df8-9391-8c68c34b2446",
   "metadata": {},
   "source": [
    "## Compatibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b4270d51-d465-4462-9fc8-2a4911c83110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is D_A(z) a JAX array? True\n",
      "Does D_A(z) support JAX jit? True\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import jax\n",
    "\n",
    "z = jnp.linspace(1., 20, 1000)\n",
    "daz_values = classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)\n",
    "\n",
    "# Check if it's a JAX array\n",
    "is_jax_array = isinstance(daz_values, jnp.ndarray)\n",
    "\n",
    "# Additional check: apply a JAX function to see if it supports JAX transformations\n",
    "try:\n",
    "    jitted_hubble = jax.jit(classy_sz.get_angular_distance_at_z)(z,params_values_dict = cosmo_params)\n",
    "    supports_jit = True\n",
    "except Exception as e:\n",
    "    supports_jit = False\n",
    "    print(\"Error with jax.jit:\", e)\n",
    "\n",
    "print(\"Is D_A(z) a JAX array?\", is_jax_array)\n",
    "print(\"Does D_A(z) support JAX jit?\", supports_jit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79b5b665-e791-44a8-887d-12398736adf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is get_hubble_at_z a JAX array? True\n"
     ]
    }
   ],
   "source": [
    "daz_values = classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)\n",
    "is_jax_array = isinstance(daz_values, jnp.ndarray)\n",
    "print(\"Is get_hubble_at_z a JAX array?\", is_jax_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54802845-78de-462d-815d-b85300b18fa4",
   "metadata": {},
   "source": [
    "## Gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d422e2ea-4abf-4282-afaf-a2e2495eeea2",
   "metadata": {},
   "source": [
    "### 1 dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fd950a-923b-438e-b003-58283bbdb3b4",
   "metadata": {},
   "source": [
    "At one redshift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b0cb98f1-5090-4307-97a2-56a283c6c8d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = 1.\n",
    "def DA(H0):\n",
    "    cosmo_params.update({'H0':H0})\n",
    "    daz = classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)\n",
    "    return daz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "975fb2d2-cb6e-46b0-8eaf-84030705afdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(1630.88521896, dtype=float64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA(72)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3c7f7cf4-00c1-4d7a-83dd-a089410c986b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax import grad\n",
    "# Get the derivative of f with respect to p\n",
    "dDA = grad(DA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cc672df6-2375-4af4-b9cf-3c6b7d945a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(-14.00494222, dtype=float64, weak_type=True)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dDA(76.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "697a8553-6e3e-4f99-b6b9-006598f0d895",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "h = np.geomspace(1e-10, 1e-1,50)\n",
    "dfunc = [(DA(76.+hp)-DA(76))/hp for hp in h]\n",
    "jaxdDA = dDA(76.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4cd3c43-f695-40b1-9e53-84001d1c9364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(h, dfunc, label='Numerical', linestyle='-', marker='o')  # Line plot for 'numerical'\n",
    "plt.axhline(y=jaxdDA, label='JAX', color='r', linestyle='--')   # Horizontal line for 'jax'\n",
    "\n",
    "# Logarithmic scale for x-axis\n",
    "plt.xscale('log')\n",
    "\n",
    "# Axis labels and title\n",
    "plt.xlabel('h (step size)')\n",
    "plt.ylabel('dDA (derivative)')\n",
    "plt.title('Comparison of Numerical and JAX Derivatives')\n",
    "\n",
    "# Thin grid\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Legend\n",
    "plt.legend()\n",
    "\n",
    "# Display plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871a2a6b-d174-47bc-9843-7ee0160e334f",
   "metadata": {},
   "source": [
    "On a redshift grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "21c57793-be74-4d45-a1d5-b2dda942488b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax import jacrev, jacfwd\n",
    "z = jnp.linspace(1., 20, 10)\n",
    "\n",
    "def DA(H0):\n",
    "    cosmo_params.update({'H0':H0})\n",
    "    daz = classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)\n",
    "    return daz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a4d88535-5118-4565-ad79-1ad64ad965e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dDA = jacrev(DA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "789eb2a7-02c2-4eb8-b348-5366770ab57e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.4 ms ± 2.99 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 100 dDA(72.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e1b16726-7615-4a28-a51b-7cc3b4d156d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dDA = jacfwd(DA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "643e696d-d04f-4523-9a52-a279cf1542cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.4 ms ± 4.78 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 100 dDA(72.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eb7cea8-f560-4854-9d01-675a4ab92bb4",
   "metadata": {},
   "source": [
    "Forward mode is faster. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8765559e-6068-4ee5-8e00-fb0004ba8714",
   "metadata": {},
   "source": [
    "### > 1 dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb18b16-5c92-46d6-b8ab-6a19f79a9d9e",
   "metadata": {},
   "source": [
    "At one redshift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a47a8849-8f2e-4470-a155-f5f49e901594",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = 1.\n",
    "def DA(H0,omega_cdm):\n",
    "    cosmo_params.update({'H0':H0,\n",
    "                         'omega_cdm':omega_cdm})\n",
    "    result = classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8b2da873-e580-436a-88ee-e5d2a3c2c8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(1632.84310812, dtype=float64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA(72,0.11833)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e87ce87d-1e13-4ed9-be18-ab06b6c63ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax import jacfwd\n",
    "# Get the derivative of f with respect to p\n",
    "dHubble = jacfwd(DA,argnums=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "08913457-2ac5-4b69-aee1-36ec0adf06a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array(-14.00494222, dtype=float64), Array(-1783.43448132, dtype=float64))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dHubble(76.,0.11933)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5cdaa5-7338-4782-b814-8f45f03b4a3c",
   "metadata": {},
   "source": [
    "On a redshift grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e9d2d59f-63cf-4ad5-9034-96bfc5cc5b83",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = jnp.linspace(1., 20, 10)\n",
    "\n",
    "def DA(H0,omega_cdm):\n",
    "    cosmo_params.update({'H0':H0,\n",
    "                         'omega_cdm':omega_cdm})\n",
    "    result = classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "deccbf99-be56-4637-83e7-97c57241be88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([1632.84310812, 1571.11877833, 1271.8676124 , 1053.46629496,\n",
       "        896.83513331,  780.53912212,  691.07787402,  620.22074422,\n",
       "        562.72907243,  515.15300307], dtype=float64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA(72,0.11833)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0693c119-55e1-4d5e-b612-f0b236afc4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dDA = jacfwd(DA,argnums=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cd033ffe-8170-4d7e-afe2-75af54fa86a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array([-14.00494222,  -8.6050116 ,  -5.78514655,  -4.33597964,\n",
       "         -3.4625816 ,  -2.8826046 ,  -2.46779339,  -2.15711573,\n",
       "         -1.91565929,  -1.72323142], dtype=float64),\n",
       " Array([-1783.43448132, -3081.2518757 , -2823.75760283, -2467.51538028,\n",
       "        -2163.88758335, -1920.71914021, -1723.76087679, -1562.73780216,\n",
       "        -1429.03369932, -1316.76429582], dtype=float64))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dDA(76.,0.11933)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1daf17f1-4bd5-46bd-8a9f-ad2d8832ff3a",
   "metadata": {},
   "source": [
    "## Gradient surface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3cfa8a-1f8f-419b-b9a8-9c8cfacd8461",
   "metadata": {},
   "source": [
    "Sample code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a11883ef-7160-4a9d-87ef-88fd6823784b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array(-14.00494222, dtype=float64), Array(-1783.43448132, dtype=float64))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior_H0 = (40,100)\n",
    "prior_omega_cdm = (0.08,0.2)\n",
    "\n",
    "\n",
    "z = 1\n",
    "def DA(H0,omega_cdm):\n",
    "    cosmo_params.update({'H0':H0,\n",
    "                         'omega_cdm':omega_cdm})\n",
    "    result = classy_sz.get_angular_distance_at_z(z,params_values_dict = cosmo_params)\n",
    "    return result\n",
    "\n",
    "dDA = jacfwd(DA,argnums=(0,1))\n",
    "\n",
    "# one value: \n",
    "\n",
    "dDA(76.,0.11933)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3632a7e6-023b-4ee4-bb02-599976eea8f4",
   "metadata": {},
   "source": [
    "Full grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3d7e1c43-8f2f-4305-b7c8-5299e45bd709",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from jax import jacfwd\n",
    "import classy_sz  # Assuming classy_sz is properly imported and available\n",
    "\n",
    "cosmo_params = {\n",
    "'omega_b': 0.02242,\n",
    "'omega_cdm':  0.11933,\n",
    "'H0': 67.66, # use H0 because this is what is used by the emulators and to avoid any ambiguity when comparing with camb. \n",
    "'tau_reio': 0.0561,\n",
    "'ln10^{10}A_s': 3.047,\n",
    "'n_s': 0.9665   \n",
    "}\n",
    "\n",
    "classy_sz = Class_sz()\n",
    "classy_sz.set(cosmo_params)\n",
    "classy_sz.set({\n",
    "'output':' ',\n",
    "'skip_hubble':0,\n",
    "'jax' : 1,\n",
    "})\n",
    "\n",
    "classy_sz.compute_class_szfast()\n",
    "\n",
    "# Set up prior ranges\n",
    "prior_H0 = (40, 100)\n",
    "prior_omega_cdm = (0.08, 0.2)\n",
    "\n",
    "# Create meshgrid for H0 and omega_cdm\n",
    "H0_vals = np.linspace(prior_H0[0], prior_H0[1], 100)\n",
    "omega_cdm_vals = np.linspace(prior_omega_cdm[0], prior_omega_cdm[1], 100)\n",
    "H0_grid, omega_cdm_grid = np.meshgrid(H0_vals, omega_cdm_vals)\n",
    "\n",
    "# Define cosmology parameters and set redshift\n",
    "z = 1\n",
    "\n",
    "# Define function to get DA\n",
    "def DA(H0, omega_cdm):\n",
    "    cosmo_params.update({'H0': H0, 'omega_cdm': omega_cdm})\n",
    "    result = classy_sz.get_angular_distance_at_z(z, params_values_dict=cosmo_params)\n",
    "    return result\n",
    "\n",
    "# Calculate derivatives with respect to H0 and omega_cdm\n",
    "dDA = jacfwd(DA, argnums=(0, 1))\n",
    "\n",
    "# Initialize arrays to store derivative values\n",
    "dDA_dH0_values = np.zeros_like(H0_grid)\n",
    "dDA_domega_cdm_values = np.zeros_like(omega_cdm_grid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0658eefd-1277-4599-841b-8d20f23b770f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 45s, sys: 44.2 s, total: 6min 30s\n",
      "Wall time: 3min 44s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Evaluate derivatives over the grid\n",
    "for i in range(H0_grid.shape[0]):\n",
    "    for j in range(H0_grid.shape[1]):\n",
    "        dH0, domega_cdm = dDA(H0_grid[i, j], omega_cdm_grid[i, j])\n",
    "        dDA_dH0_values[i, j] = dH0\n",
    "        dDA_domega_cdm_values[i, j] = domega_cdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c3f8d757-f91f-43d7-866f-b7b5a12433ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "# First plot: Derivative with respect to H0\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.contourf(H0_grid, omega_cdm_grid, dDA_dH0_values, cmap='viridis')\n",
    "plt.colorbar(label=r'$\\partial DA / \\partial H_0$')\n",
    "plt.xlabel(r'$H_0$')\n",
    "plt.ylabel(r'$\\omega_{cdm}$')\n",
    "plt.title(r'Derivative of $DA$ with respect to $H_0$')\n",
    "\n",
    "# Second plot: Derivative with respect to omega_cdm\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.contourf(H0_grid, omega_cdm_grid, dDA_domega_cdm_values, cmap='plasma')\n",
    "plt.colorbar(label=r'$\\partial DA / \\partial \\omega_{cdm}$')\n",
    "plt.xlabel(r'$H_0$')\n",
    "plt.ylabel(r'$\\omega_{cdm}$')\n",
    "plt.title(r'Derivative of $DA$ with respect to $\\omega_{cdm}$')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (venv_class_sz)",
   "language": "python",
   "name": "venv_class_sz312"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}