borg_velocity/tests/TFR_tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f235d0c0-d7fb-46fd-89df-3d66851f457f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bartlett/fsigma8/borg_velocity/tests/tfr_inference.py:310: SyntaxWarning: invalid escape sequence '\\s'\n",
      "  sigma_m = np.median(df['e_Kmag'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'tfr_inference' from '/home/bartlett/fsigma8/borg_velocity/tests/tfr_inference.py'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import astropy.constants\n",
    "import astropy.units as apu\n",
    "import astropy.cosmology\n",
    "from astropy.coordinates import SkyCoord\n",
    "import borg_velocity.poisson_process as poisson_process\n",
    "from astropy.cosmology import LambdaCDM, z_at_value\n",
    "import jax.numpy as jnp\n",
    "\n",
    "import importlib\n",
    "import tfr_inference\n",
    "importlib.reload(tfr_inference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7811713f-d849-4372-90d4-c01aaab5e988",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0028092475228007545 0.04950413092258361 0.011837616297618123 0.009863123740916042\n",
      "335 9990\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:3: SyntaxWarning: invalid escape sequence '\\s'\n",
      "<>:3: SyntaxWarning: invalid escape sequence '\\s'\n",
      "/tmp/ipykernel_13422/2464123570.py:3: SyntaxWarning: invalid escape sequence '\\s'\n",
      "  df = pd.read_csv(fname, sep='\\s+', names=columns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14ea40a974d0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8ilddrU0hbdju64DH8THctcl2HGkAKiMiItFmx2LwlkOrntAm03aasAhcqrnKtcFqDmkYKiMiItGm6hLNRLs5wiiw3sgo90YcfJbTSLipjIiIRJOKL2HHEv/zGC4ja3yZnDCNaKspvnFBZUREJJrsXgoVJyGlI6QPtJ0mbDTFN76ojIiIRJOzZ9E4jt0sYaYpvvFDZUREJFp4K/3ri0BMrbpam7On+KZywnIaCSeVERGRaLF3BZw+Ck1aQads22nCbj+t2ebriNsxjNAU35imMiIiEi0CN8bLnABuj90sDeSrG+dp3EgsUxkREYkGPt9XN8aL4Vk0X5cXmOLr2qApvjFMZUREJBrsXwfH90NiMnQdZTtNgwlM8W3jlJLl7LEdR8JEZUREJBoEZtH0vBYSGtnN0oAq8PCh71JAU3xjWVBlZObMmVx++eUkJyfTtm1bbr75ZrZt23be1+Tm5uI4zjmPrVu31iu4iEjcMCYuVl2tTWA11qs0xTdmBVVG8vLyuPvuu1m9ejVLliyhsrKSsWPHcvLkhe+quG3bNoqKiqoePXv2rHNoEZG4cmgrHNkF7iT/mZE4E5jiO8DZSXOOW04j4RDUcOx333232ucvvPACbdu2Ze3atYwcOfK8r23bti3NmzcPOqCISNwLnBXpfhUkJdvNYkExrdjiy6C3ax8jXZtY4BtmO5KEWL3GjJSUlADQsmXLC+47cOBA0tLSGDNmDEuXLj3vvmVlZZSWllZ7iIjErS0L/B/j8BJNQN6ZKb6jdKkmJtW5jBhjmDp1KsOHD6dv37617peWlsYzzzxDTk4O8+fPJzMzkzFjxrBs2bJaXzNz5kxSU1OrHhkZGXWNKSIS3Y7ugeJN4Lih13jbaaxZ6h0AwCiX7uIbi+q8as6UKVPYuHEjH3744Xn3y8zMJDMzs+rz7Oxs9u3bx6OPPlrrpZ0ZM2YwderUqs9LS0tVSEQkPgXWFuk8DJq2spvForWmJ6WmMa2dUi518tloutuOJCFUpzMjP/vZz1iwYAFLly6lY8eOQb9+6NCh7Nixo9avJyUlkZKSUu0hIhKXAquu9r7Rbg7LKvGwwuc/C68pvrEnqDJijGHKlCnMnz+fDz74gK5du9bpTdevX09aWlqdXisiEjeOH4CC1f7nl1xvN0sEWKq7+MasoC7T3H333bz88sv8/e9/Jzk5meLiYgBSU1Np3Lgx4L/EUlhYyEsvvQTArFmz6NKlC1lZWZSXlzNv3jxycnLIyckJ8Y8iIhJjti0EDHQYBKkdbKexLs/bHxJggLOLFpRyFJ01jxVBlZE5c+YAMHr06GrbX3jhBX7wgx8AUFRUREFBQdXXysvLmTZtGoWFhTRu3JisrCwWLlzIhAkT6pdcRCTWxeG9aM7nAC3Z4utEb1cBI1ybWOC70nYkCRHHGGNsh7iQ0tJSUlNTKSkp0fgREYkPp49R8Ug3EhwvV5U9Rr7RpW2A+z2v8lPPAuZ7hzO14qc17rPnEV3SihQX+/e37k0jIhKJdiwmwfGyzddRReQsgdVYR7k+0RTfGKIyIiISic4sdPaeb7DlIJFl3Zkpvq2c4/RzdtuOIyGiMiIiEmnKT8GO9wF4zzvEcpjIUomH5bqLb8xRGRERiTR7PoTK03xuWvOZ6Ww7TcTJPTPFV3fxjR0qIyIikSY/D4Bl3ksBx26WCJR3ZtxIP2c3LdG9y2KByoiISKQ5U0ZW+bIsB4lMB2nBZ77OuBzDSNdG23EkBFRGREQiyakj/hvjoTJyPrk+/9kRrcYaG1RGREQiSf6ZO5q37cNhUu1miWCBu/iOdG3EpSm+UU9lREQkkgTKSNea72oufutNT0pME1o6J+jv7LIdR+pJZUREJJKcGS9C11F2c0Q4L+6vpvi6NcU32qmMiIhEipJC+GInOC7oPMx2mogXmOI7yrXBag6pP5UREZFIEbhEkz4QGje3GiUaBKb4DnDtphUlltNIfaiMiIhECo0XCcohmrPJ1wVAU3yjnMqIiEgkMOasMqLxIhcrcKlG40aim8qIiEgkOLIbSj8HdyJkXGE7TdQI3MVXU3yjm8qIiEgkCMyi6TgEEpvYzRJFNpgeHDNNaeGcYICz03YcqSOVERGRSLD7TBnppks0wag+xXeD3TBSZyojIiK2+XywZ7n/uQavBi33zGqso10aNxKtVEZERGw7+Bmc+gISmkKHQbbTRJ28M/ep6efKp7Wm+EYllREREdsCs2g6DwN3gt0sUegwqWz0dQVglM6ORCWVERER2wJlRONF6kx38Y1uKiMiIjZ5K2HPCv9zjReps8C4kRGuTf5jKlFFZURExKb966H8ODRuAe0utZ0mam0wPThqmtHcOQmFa2zHkSCpjIiI2JSf6//YZQS49Cu5rny4WObr5/9kxxK7YSRo+pMvImKT7kcTMsu8Z8pIYAE5iRoqIyIitlSchoKP/M+7jbYaJRas8vXxPylcB2XH7YaRoKiMiIjYsu9j8JZBchq06mE7TdTbT2v2+NqB8cLeVbbjSBBURkREbDn7Lr2OYzdLjFgZODuiSzVRRWVERMSWwF+YGi8SMqt9Wf4ngeX1JSqojIiI2PBlqX9sA6iMhFDVuJGijXDqiN0wctFURkREbNi70j+2oWU3aJ5hO03MOERzaJ0JGP8xlqigMiIiYoOm9IZP4JgGjrFEPJUREREbqsaL6H40IacyEnVURkREGtrJw3DgU/9znRkJvS7DAQcObYETB22nkYugMiIi0tACMz3a9YWmre1miUVNWkL7vv7nmlUTFVRGREQa2m5N6Q27LrpUE01URkREGtrZi51JeFSNG9GZkWigMiIi0pBKPocju8BxQ+dhttPErs7D/Mf4yC4oKbSdRi5AZUREpCEFzoqkD4RGKXazxLJGKZA+wP9c40YinsqIiEhDCowX6aZLNGGnKb5RI6gyMnPmTC6//HKSk5Np27YtN998M9u2bbvg6/Ly8hg0aBCNGjWiW7duPPXUU3UOLCIStYzRYmcNqcsI/8f8Zf5jLxErqDKSl5fH3XffzerVq1myZAmVlZWMHTuWkydP1vqa/Px8JkyYwIgRI1i/fj0PPPAA99xzDzk5OfUOLyISVb7YBcf3gzsJMq6wnSb2dRoKrgQo2QdH99hOI+fhCWbnd999t9rnL7zwAm3btmXt2rWMHFlzy3/qqafo1KkTs2bNAqB3796sWbOGRx99lFtuuaVuqUVEolF+rv9jxhBIaGw1SlxIbAodL4eClf6zIy272k4ktajXmJGSkhIAWrZsWes+q1atYuzYsdW2jRs3jjVr1lBRUVHja8rKyigtLa32EBGJeoFLNBov0nC6nrlUo0GsEa3OZcQYw9SpUxk+fDh9+/atdb/i4mLatWtXbVu7du2orKzk8OHDNb5m5syZpKamVj0yMnRHSxGJcj7fV2teaH2RhnP2IFaNG4lYdS4jU6ZMYePGjbzyyisX3NdxnGqfmzN/IL6+PWDGjBmUlJRUPfbt21fXmCIikeHAp3D6CCQ280/rlYbR8XLwNIITB+DwdttppBZ1KiM/+9nPWLBgAUuXLqVjx47n3bd9+/YUFxdX23bw4EE8Hg+tWrWq8TVJSUmkpKRUe4iIRLXAXXo7XwnuBLtZ4onnrMHCmuIbsYIqI8YYpkyZwvz58/nggw/o2vXCg4Gys7NZsmRJtW2LFy9m8ODBJCTof0gRiROa0muP1huJeEGVkbvvvpt58+bx8ssvk5ycTHFxMcXFxZw+fbpqnxkzZnD77bdXfT558mT27t3L1KlT2bJlC88//zxz585l2rRpofspREQimbcC9q70P9fg1YYXKCN7lvvH7kjECaqMzJkzh5KSEkaPHk1aWlrV47XXXqvap6ioiIKCgqrPu3btyqJFi8jNzWXAgAH89re/5YknntC0XhGJH4XroPwENGkFbbNsp4k/6QP9Y3VOH4WDn9lOIzUIap0RcxEjkV988cVzto0aNYp169YF81YiIrEjcHmgywhw6S4cDc6dAJ2yYecS/3+L9pfaTiRfE1QZERGROggMXtV4kQbRZfrCc7bd5W7DrxJgyaI3uOvvXWp97Z5Hrg9jMqmNKrqISDhVnIZ9H/mfa30Ra1b6/JfHrnBtwY3Xchr5OpUREZFwKlgN3nJI6QCtuttOE7e2mM6UmCakOKfp6+TbjiNfozIiIhJOZ0/prWWhRwk/Hy5W+/oAkO3abDmNfJ3KiIhIOFWVEV2isW3VmTIyzKUZNZFGZUREJFy+LIH9Z2YSavCqdYFxI4Nd20mg0nIaOZvKiIhIuOxdCcYHrXpAagfbaeLedtORwyaFJk4Z/Z2dtuPIWVRGRETCZbem9EYWp2rcyDCNG4koKiMiIuGi+9FEnMClGg1ijSwqIyIi4XDi0FdLj3dRGYkUgUGsl7m2k0S55TQSoDIiIhIOe86cFWl3KTRtZTeLVMk37SkyLUlyKhnk2m47jpyhMiIiEg6BSzS6S2+EcVip9UYijsqIiEg47M71f9R4kYizWuuNRByVERGRUDuSD0f3gMsDnYfZTiNfs9LrH8Taz9lNU05bTiOgMiIiEnqBu/R2HAJJyXazyDkKaUOBrw0JjpfLXdtsxxFURkREQi9wiabbaJsp5Dy+muKrSzWRQGVERCSUfL6vFjtTGYlYWm8ksqiMiIiEUvFGOH0EEpOhw2W200gtAuuN9HX2kMIJy2lEZUREJJSqZtGMAHeC1ShSu0O0YKcvHZdjuMK11XacuKcyIiISSruX+j/qEk3EC1yq0RRf+1RGRERCpeI07F3lf64yEvFWafGziKEyIiISKvs+Am8ZJKdB616208gFrPb1BuAS1z5aUWI5TXxTGRERCZWqKb1XgeNYjSIXdpQUtvg6ATDUtcVymvimMiIiEiq7NF4k2mjcSGRQGRERCYVTR6DoE/9z3RwvagRumjdU40asUhkREQmF/GWAgbZ9ILm97TRykT729cZrHLq7imjHEdtx4pbKiIhIKGhKb1Q6ThM2ma6AZtXYpDIiIhIKuh9N1FqtcSPWqYyIiNTXkXw4ugdcHug8zHYaCVJg3Mgwt8qILSojIiL1FTgr0nEIJCVbjSLB+5cvkwrjpqNz2F8qpcGpjIiI1Jcu0US10zRig+nu/yR/md0wcUplRESkPnw+yM/zP1cZiVqB9UbIX243SJxSGRERqY/ijXD6KCQmQ4fLbKeROlp9ZtwI+cvAGLth4pDKiIhIfQSm9HYdAe4Eu1mkztb5evKlSYATxXBom+04ccdjO4CISCTrMn3heb/+vwlvMMIND37ahr9+bd89j1wfzmgSQmUk8rHvEka6N8Guf0LbS2xHiis6MyIiUkdJlDPE5f9X9Ie+vpbTSH3l+fr7n+z8p90gcUhlRESkjga7tpHkVFBkWrLLpNuOI/WUGygje1dAxWm7YeKMyoiISB0Nd30KwApfX8CxG0bqbZdJh5SOUPkl7FlhO05c0ZgREZE6uvJMGfnQW/MlmguNN5FI40CPMbDur7Dzfeh5je1AcSPoMyPLli1j4sSJpKen4zgOb7311nn3z83NxXGccx5bt26ta2YREeuac5y+zh4AVgTWqJDo1+NMAdn5vt0ccSboMnLy5En69+/PX/7yl6Bet23bNoqKiqoePXv2DPatRUQixjDXZ7gcw1ZfBodoYTuOhEq3UeC44YsdWhq+AQV9mWb8+PGMHz8+6Ddq27YtzZs3D/p1IiKRqPp4EYkZjVIh4wooWOmfVXP5j20nigsNNoB14MCBpKWlMWbMGJYuXXrefcvKyigtLa32EBGJJFXjRVRGYk+PMf6PmuLbYMJeRtLS0njmmWfIyclh/vz5ZGZmMmbMGJYtq/1mRDNnziQ1NbXqkZGREe6YIiIXLcM5QGfXQSqMm498vW3HkVALjBvJz4PKcrtZ4kTYZ9NkZmaSmZlZ9Xl2djb79u3j0UcfZeTIkTW+ZsaMGUydOrXq89LSUhUSEYkYgUs060xPTtHIchoJufb9oGkbOHkI9n3kX+pfwsrKOiNDhw5lx44dtX49KSmJlJSUag8RkUgRuESzopYpvRLlXC7oHrhUo1k1DcFKGVm/fj1paWk23lpEpF4cfFzp+gzQeJGYVjXFV+NGGkLQl2lOnDjBzp07qz7Pz89nw4YNtGzZkk6dOjFjxgwKCwt56aWXAJg1axZdunQhKyuL8vJy5s2bR05ODjk5OaH7KUREGkiWs4cWzgmOm8Z8YrrbjiPh0v0qwIEDm6C0CFL0D+hwCrqMrFmzhquuuqrq88DYjjvuuIMXX3yRoqIiCgoKqr5eXl7OtGnTKCwspHHjxmRlZbFw4UImTJgQgvgiIg0rMF5kta8PXtyW00jYNG0N6QNh/zrY9QEMvM12opgWdBkZPXo0xphav/7iiy9W+/z+++/n/vvvDzqYiEgk0pTeONLjGn8Z2fm+ykiY6UZ5IiIXKYlyhri2ASojcSEwbmT3UvB57WaJcSojIiIXabBrG0lOBUWmpf8OrxLbOgzyr8h6+ijsX287TUxTGRERuUjVl4B37IaR8HN7oNto/3NN8Q0rlRERkYs03LUJgA+1vkj80F18G4TKiIjIRWhBKVnOXkA3x4srgcXPCtfCqSN2s8QwlRERkYuQ7dqMyzFs9WVwiOa240hDSe0AbfuA8fkHskpYqIyIiFyE6uNFJK7oLr5hpzIiInIRqsaLqIzEn7PHjZxnnS2pO5UREZELyHAO0Ml1iArj5iNfb9txpKF1yoaEJnDiABz41HaamKQyIiJyAYFLNOtMT07RyHIaaXCeJOg60v9cs2rCQmVEROQCApdoVmhKb/zSXXzDSmVEROQ8XPgY5toMaLxIXAsMYi1YBWXH7WaJQSojIiLn0cfZQwvnBMdNYz4x3W3HEVtadvM/fJWQv8x2mpijMiIich6B8SKrfX3w4racRqzSaqxhozIiInIemtIrVTTFN2xURkREalNxmstd2wGVEQG6DAd3IhwrgC922U4TU1RGRERqU7CaJKeCItOSXSbddhqxLbGpf80R0KWaEFMZERGpzZl7kfiXgHfsZpHIoHEjYaEyIiJSm925AHyo9UUkIFBG9nwIFaftZokhKiMiIjU5+QUUbQR0czw5S9vekJwOladh70rbaWKGyoiISE12LwUM23wdOURz22kkUjiO7uIbBiojIiI12fx3AN73XWY5iEQcjRsJOZUREZGvKz9V9RfNO94hlsNIxOk2Ghw3HN7mn+Yr9aYyIiLydTvfh4pT0LwTn5quttNIpGncHDpe7n+uSzUhoTIiIvJ1Wxb4P/a+EU3plRrpUk1IqYyIiJytsgy2vet/3ucmu1kkcgUGse7OA2+F3SwxQGVERORsu5ZC+XFIToMOg22nkUiVNgCatPL/Wdn3se00UU9lRETkbFWXaCaCS78ipRYuF3QPTPHVpZr60v9pIiIB3grYutD/vPeNdrNI5NO4kZBRGRERCdizHL48Bk1aQ+dhttNIpOt+tf9j8UY4cdBuliinMiIiEnBmoTN63wAut90sEvmatfGPHQHY9YHVKNFOZUREBMDn1SUaCV4PjRsJBY/tACIiEaFgFZw8BI2aQ9eRttOIJV2mLwxq/8udpryeBEc2vsPgf73N7kcmhilZbNOZERERgM1nZtFccj24E+xmkaix3vSg1DSmpXOCvk6+7ThRS2VERMTngy1v+5/rEo0EoRIPK3x9ARjl+sRymuilMiIiUrgGju+HxGTofpXtNBJl8nz9ARjl3mg5SfRSGRERCcyi6TUOPEl2s0jUWebtB8BAZwecPmo5TXRSGRGR+GbMV6uu6l40Ugf7ac12XwfcjoHdubbjRCXNphGRmHah2RFZTj4Lkwo4ZZK47KVKviS42RQi4L9U08tVCDuWQNY3bMeJOjozIiJxbYL7IwByff35El2ikbr5p+8y/5Mt/4CKL+2GiUJBl5Fly5YxceJE0tPTcRyHt95664KvycvLY9CgQTRq1Ihu3brx1FNP1SWriEiIGa5z/QuAd71DLGeRaPaR7xIKTSsoK4Ed79mOE3WCLiMnT56kf//+/OUvf7mo/fPz85kwYQIjRoxg/fr1PPDAA9xzzz3k5OQEHVZEJJR6OZ/T3VVEmfHwgW+A7TgSxQwu/u690v/JJ6/ZDROFgh4zMn78eMaPH3/R+z/11FN06tSJWbNmAdC7d2/WrFnDo48+yi233BLs24uIhMx418cALPP14wRNLKeRaPemdzg/9SyAHYvh1BFo0tJ2pKgR9jEjq1atYuzYsdW2jRs3jjVr1lBRUVHja8rKyigtLa32EBEJtevc/jKiSzQSCjtMR2h/Kfgq4LP5tuNElbCXkeLiYtq1a1dtW7t27aisrOTw4cM1vmbmzJmkpqZWPTIyMsIdU0TiTFeniN6ufVQYN0sCgw9F6qvfd/0fN/7Nbo4o0yCzaRzHqfa5MabG7QEzZsygpKSk6rFv376wZxSR+BK4RLPK14dSmllOIzHj0m+B44J9H8ER3avmYoW9jLRv357i4uJq2w4ePIjH46FVq1Y1viYpKYmUlJRqDxGRUApcolnku8JyEokpye2h22j/c50duWhhLyPZ2dksWbKk2rbFixczePBgEhJ0Z0wRaXgdnUP0c+XjNQ6LvYNtx5FY0+9W/8eNr/lX+JULCrqMnDhxgg0bNrBhwwbAP3V3w4YNFBQUAP5LLLfffnvV/pMnT2bv3r1MnTqVLVu28PzzzzN37lymTZsWmp9ARCRI485covnY15sj6MyrhNglN0BCEziyCwrX2k4TFYIuI2vWrGHgwIEMHDgQgKlTpzJw4ED+67/+C4CioqKqYgLQtWtXFi1aRG5uLgMGDOC3v/0tTzzxhKb1iog1E85connHd7nlJBKTkpr5Cwn4z47IBQW9zsjo0aOrBqDW5MUXXzxn26hRo1i3bl2wbyUiEnLtOMIg1w5AU3oljPrdCpv+Bp/mwLjfg1vDEs5H96YRkbgyzu1f/n2NrxcHaWE5jcSsbqOhaVs49QXs/KftNBFPZURE4sr4M/eiecerSzQSRm6Pf5ov6FLNRVAZEZG40YoShri2APCeT5doJMz6fcf/cdsi+LLEbpYIpzIiInFjrHsNbsew0deVz00b23Ek1qUNgNaZUPklbHnbdpqIpjIiInEjsOrqO14tdCYNwHG+Ojvyyat2s0Q4lRERiQspnCDbtRnQlF5pQIEysudDKPncbpYIpjIiInHhWtc6EhwvW3wZ7DFptuNIvGjeCTpfCRjY9IbtNBFLZURE4sJ490eA1hYRC7Q8/AWpjIhIzGvGKUa4NgG6MZ5Y0OcmcCfCwc1w4FPbaSKSyoiIxLyrXRtIcirZ5Utjh+lgO47Em8bNodd1/ucayFojlRERiXnXVd2LZgjg2A0j8an/d/0fN70BPq/dLBFIZUREYlpjvmS06xMA3tF4EbGlx7XQuAWcKIb8PNtpIo7KiIjEtFGujTRxyijwteEz08V2HIlXnkTI+qb/+ca/2c0SgVRGRCSmjdclGokUgVk1W96G8pN2s0QYlRERiV2VZVztWg9oSq9EgIwh0KILlJ+ArYtsp4koKiMiErt2LSXZOU2RackG0912Gol3jlN9zRGpojIiIrFrywIA3vVejtGvO4kEgTKy6wM4cdBulgii/ztFJDadPgab/WVEs2gkYrTqDh0Gg/HCpzm200QMlRERiU1rX4Dy42z1ZfCxucR2GpGv6FLNOVRGRCT2VJbB6qcAeLbyejSLRiJK32+CywP718Oh7bbTRASVERGJPZte9y8ulZzOAt8w22lEqmvaGnpc43+usyMAeGwHEBG5kC7TF170vg4+3kv8Pb1c8Psjo6jQrzmJRP2+A9vf9S+AdtWvwBXf5wbi+6cXkZgz2vUJvVyFHDeNecU7xnYckZplToDEZCgpgH2rbaexTmVERGLKTzz/AOBl79Ucp4nlNCK1SGgMfW7yP9elGpUREYkd/ZxdDHVtocK4eaHyOttxRM6v/5lZNZ+9CRVf2s1imcqIiMSMfz9zVmSBbxjFtLKcRuQCOg+HlA7wZQnsWGw7jVUqIyISEzKcA4x3+W+K90zl9ZbTiFwElwsu/Zb/eZxfqlEZEZGYcKd7EW7HkOvtzzbTyXYckYvT77v+j9vfg1NH7GaxSGVERKJeC0r5jjsPgKe9N1hOIxKEdn2g3aXgq/CPHYlTKiMiEvUmud+nsVPOJl8XVvn62I4jEpzAQNaNf7ObwyKVERGJao0o4w7PewA8U3kDWvpdok7fb4Hj8q83cnin7TRWqIyISFT7lnsZrZzj7PO1YZHvCttxRIKXkgY9rvU/z3vEbhZLVEZEJGq58HGnexEAc73j8eK2nEikjq56wP9x0+tQtNFuFgtURkQkao11raGL6wBHTTNe8462HUek7tIHQNY3/c8/+K3VKDaojIhIlDJVS7//r/caTtPIch6Rerr61+Dy+BdA27PCdpoGpTIiIlHpcmcbA107KTMJvFQ5znYckfpr1R0uu93//J8PgTF28zQglRERiUqBpd9zvCM4TKrlNCIhMvJ+8DSGfR/Btndsp2kwKiMiEnW6O4Vc616Hzzg869XS7xJDUtJg6GT/838+DD6v3TwNxGM7gIhIsO5yLwRgiW8Q+SbNchqRr3SZvrDOr93zyJlifeXPYc3zcGiLfyG0Af8WonSRq05nRmbPnk3Xrl1p1KgRgwYNYvny5bXum5ubi+M45zy2bt1a59AiEr/acJRvuD8E4OlKLf0uMahxCxg+1f986e+hssxungYQdBl57bXXuPfee/nVr37F+vXrGTFiBOPHj6egoOC8r9u2bRtFRUVVj549e9Y5tIjErx943iPJqWSNrxfrTC/bcUTCY8i/Q3IalBTAmhdspwm7oMvIH//4R3784x9z55130rt3b2bNmkVGRgZz5sw57+vatm1L+/btqx5utxYnEpHgNOU033e/D8AzlRorIjEssQmM+qX/+bL/gbLjdvOEWVBlpLy8nLVr1zJ27Nhq28eOHcvKlSvP+9qBAweSlpbGmDFjWLp0afBJRSTu3erOJdU5xS5fGkt8g2zHEQmvgd+Hlt3h1GFY9aTtNGEVVBk5fPgwXq+Xdu3aVdverl07iouLa3xNWloazzzzDDk5OcyfP5/MzEzGjBnDsmXLan2fsrIySktLqz1EJL55qORHHv9Ux+e8EzCaDCixzp3gXwgNYOWf4eRhu3nCqE6zaRyn+l0xjTHnbAvIzMwkMzOz6vPs7Gz27dvHo48+ysiRI2t8zcyZM3nooYfqEk1EYtT1rtV0dA5zyKQw3zvCdhyRhtHnZkh7HIo2wPLH4LqZthOFRVD/tGjdujVut/ucsyAHDx4852zJ+QwdOpQdO3bU+vUZM2ZQUlJS9di3b18wMUUk5hh+4vFPmfxr5TjKSLScR6SBuFxwzYP+5/96Do6df7JItAqqjCQmJjJo0CCWLFlSbfuSJUsYNmzYRX+f9evXk5ZW+9oASUlJpKSkVHuISPwa7vqUPq69nDJJzPNeYzuOSMPqdhV0HQneclgam2dGgr5MM3XqVCZNmsTgwYPJzs7mmWeeoaCggMmT/SvGzZgxg8LCQl566SUAZs2aRZcuXcjKyqK8vJx58+aRk5NDTk5OaH8SEYlZ/+72L/3+mnc0x0i2nEakgTkOjPkNPHc1fPIKDPsZtOtjO1VIBV1Gbr31Vr744gsefvhhioqK6Nu3L4sWLaJz584AFBUVVVtzpLy8nGnTplFYWEjjxo3Jyspi4cKFTJgwIXQ/hYjErqKNjHRvwmsc5nrH204jYkfHQdB7Imx5Gz74f/BvL9tOFFKOMZF/W8DS0lJSU1MpKSnRJRuReJNzF2z6G297h/KzintspxEJm6rl4GtzaDvMvgKMD368BDKGNEywerjYv791bxoRCbu63q8jncMsS3oDj6Ol30Vo0wsG3Abr/xfe/w38YKH/Ek4M0ER9EYlIDj5+lzAXj+NjhTeLT00325FE7Bs9HdxJsHcF7HzfdpqQURkRkYj0Y/c7XOX+hC9NAg9V3m47jkhkSO0IQ+7yP3//IfD57OYJEZUREYk4/Zxd3O95FYCHK29nu8mwnEgkgoz4BSSlwIFN8Nl822lCQmVERCJKM07x54Q/k+h4Wegdwsveq21HEoksTVrClWcGc3/wW6gst5snBFRGRCSCGH6X8DydXQf53LRmRsVdQGwM0BMJqSv+A5q2haN7YN1fbaepN5UREYkY33bncZN7JZXGxT3lUyilqe1IIpEpqRmMut//PO+/ofyk3Tz1pDIiIhGhu1PIQx7/v/D+WPlt1plelhOJRLjL7oAWXeDkQVg9x3aaelEZERHrkijnLwl/polTxofeLOZ4J9qOJBL5PIlw1a/9z1c8DqeO2M1TDyojImLdDM/L9HYVcNikcF/FTzH61SRycfreAu36Qlkp5P3Bdpo60//xImLVta41/MCzGIBfVPwHh2hhOZFIFHG54JqH/M8/egrWvWQ3Tx2pjIiINWl8wf8kPA3A05XXk+frbzmRSBTqeQ2MmOZ//va9sP09q3HqQmVERKxw42VW4pM0d06ywdeNRytvtR1JJHpd/Wvo/z0wXnj9B/D5WtuJgqIyIiJW3ON5kytcWzluGnNPxc+o0H07RerOceDGJ6D7GKg4BS9/G77YZTvVRVMZEZEGN9S1mSnuNwH4VcWPKTDtLCcSiQHuBPjOS5A2AE59AfNugROHbKe6KCojItKgWlDKrIQncTuGv1WOYoFvmO1IIrEjqRnc9jo07wxH8/1nSMpO2E51QSojItKADP+T8DTtnaPs8qXxYOUdtgOJxJ5mbeH786FxS9i/3j+GxFthO9V56SKtiFyULtMX1vt7/ND9Lte411NmEphScQ+naRSCZCJyjtY94Ht/g79OhJ1L4B/3wo1/8Y8tiUA6MyIiDSLLyWe65xUA/l/lbWwxnS0nEolxGZfDt18AxwXr50HuTNuJaqUzIyJRor5nJvY8cn2IkgSvKaf5c8KfSXIqec87mP/1Xmsti0hcyRwP1//Rf2Yk7w+QnAaDf2g71Tl0ZkREwu7hhBfp5ipmv2nJ/RX/DkTmqWKRmDT4hzDyzB1+F06Fbe/YzVMDlRERCatvuJZzi3s5XuPw8/IplNDMdiSR+HPVAzDg+2B88PoPYd+/bCeqRmVERMLEcKd7If+d8AwAj1fewr/MJZYzicQpx4GJs6DHtVB5Gl65FQ7vtJ2qisqIiIRcCid4JuGP/Drh/0hwvLzpvZK/eG+2HUskvrkT4NsvQvrAM4uifRNOHLSdClAZEZEQ6+fsYmHirxjrXkuZ8fCrih9xX8VP8enXjYh9Sc3ge69Diy5wbC/837eg7LjtVPrtICKhYrjD/R5vJP6GDNch9vra8s3yh/g/7zVowKpIBGnWxr8oWpNWUPQJ/O0O64uiqYyISL0lc4onEx7noYS/kuh4ecd7OTeU/57PTFfb0USkJq26+8+QJDSBXf+EBfeAMdbiaJ0RkTgRihVUa5Ll7OHJhMfp4jpAuXHz+8rbeNE7Dp0NEYlwHQf5x5C88m/wycv+VVtH/MJKFJUREakjw/fcH/Cg5yWSnAo+N625u/wePjE9bAcTiUpWFjbsNQ5u+BOs/DP0vaVe718fKiMiErQmfMnvE57jZvdKAJZ4L2NaxWStISISjQbdAf1uhQR794pSGRGRoGQ6BcxOeJzuriIqjYs/VH6XZ73Xo8syIlHMYhEBlRERCcK33bk87HmRxk45RaYlU8p/xlqTaTuWiEQ5lRERuaBGlPFbzwt827MMgDxvP+6r+ClHSLGcTERigcqIiNQqgUpGuzYwzfM3Ml2f4zUOj1V+mzneGzFaGUBEQkRlRES+xpDl7OFb7mXc6F5JK8e/OuNB05x7Kqaw2tfHcj4RiTUqIyICQBuOcZN7Bbe4l9Hbta9q+0HTnDe9V/Js5Q0cJtViQhGJVSojInEsiXLGuNbxLfcyRro24nF8AJSZBBb7BpHjHcly36V4cVtOKiKxTGVEpAGFaxXU4BgGOju5xb2Mie5VpDqnqr6y1teTHO9I/uG9glKtGSIiDURlRCROtOcLvun+kFvcy+juKqravt+0ZL53BPO9I9ht0i0mFJH6qM8/duq0emsIqYyIxBxDGkfo6fqcnk4hPZxCMl37GODswuX4b4R12iTyjm8IOd4RrPJl4dPMGBGxqE5lZPbs2fzP//wPRUVFZGVlMWvWLEaMGFHr/nl5eUydOpXPPvuM9PR07r//fiZPnlzn0CICDj46OIfp6RTS0/EXj54uf/lo5nxZ42s+8l3CG96RvOMdwgmaNHBiEZGaBV1GXnvtNe69915mz57NlVdeydNPP8348ePZvHkznTp1Omf//Px8JkyYwF133cW8efNYsWIFP/3pT2nTpg233GLvpjwikS6JclI4SapzkhRO0cYpofuZwtHTKaS7s5/GTnmNr60wbvaY9uwwHdhhOrDT14H1piefmzYN/FOIiFyYY4wxwbzgiiuu4LLLLmPOnDlV23r37s3NN9/MzJkzz9n/l7/8JQsWLGDLli1V2yZPnswnn3zCqlWrLuo9S0tLSU1NpaSkhJQUrfgo9ROy66rGgK8SvOVnHhVnfayoYXs5k59fRopzklROkuKcIrWqbHxVOgIfk5yKC+YpMx52m3R/6fB1YIfpyA7Tgb2mHZW6CisiFylcY0Yu9u/voH5blZeXs3btWqZPn15t+9ixY1m5cmWNr1m1ahVjx46ttm3cuHHMnTuXiooKEhISznlNWVkZZWVlVZ+XlJQA/h8qpJY9xtrlb4f2ewZhUEYLa+9dX2v3Ha3zax3gsozmte9wgX684fOjVbdkcwjsa6q+t1P1/Kttgc8d4HUMDgYXvjPbAp+feThfff7VRx8uDAXTfSRQiQcfSU5l0D/7f38V9bzKzjy8xuE4jSk1TTlGU/aY9uz2pbPLpLHLpFNo2tQy7bb8zENE5MJC/vfr177vhc57BFVGDh8+jNfrpV27dtW2t2vXjuLi4hpfU1xcXOP+lZWVHD58mLS0tHNeM3PmTB566KFztmdkZAQTVyRGnP1LYpO1FCISu1Jnhff7Hz9+nNTU2hdNrNN5XMepfqtwY8w52y60f03bA2bMmMHUqVOrPvf5fBw5coRWrVqd933OVlpaSkZGBvv27dOlnYukY1Y3Om7B0zGrGx234OmYBS+Ux8wYw/Hjx0lPP/+yAUGVkdatW+N2u885C3Lw4MFzzn4EtG/fvsb9PR4PrVq1qvE1SUlJJCUlVdvWvHnzYKJWSUlJ0R/AIOmY1Y2OW/B0zOpGxy14OmbBC9UxO98ZkYCgFhdITExk0KBBLFmypNr2JUuWMGzYsBpfk52dfc7+ixcvZvDgwTWOFxEREZH4EvRKR1OnTuW5557j+eefZ8uWLdx3330UFBRUrRsyY8YMbr/99qr9J0+ezN69e5k6dSpbtmzh+eefZ+7cuUybNi10P4WIiIhEraDHjNx666188cUXPPzwwxQVFdG3b18WLVpE586dASgqKqKgoKBq/65du7Jo0SLuu+8+nnzySdLT03niiSfCvsZIUlISDz744DmXe6R2OmZ1o+MWPB2zutFxC56OWfBsHLOg1xkRERERCSXdkEJERESsUhkRERERq1RGRERExCqVEREREbEqpsrI0aNHmTRpEqmpqaSmpjJp0iSOHTt23tecOHGCKVOm0LFjRxo3bkzv3r2r3QQw1tXlmAFs2bKFG2+8kdTUVJKTkxk6dGi1WVSxrK7HLOAnP/kJjuMwa9assGWMRMEet4qKCn75y19y6aWX0rRpU9LT07n99tvZv39/w4VuYLNnz6Zr1640atSIQYMGsXz58vPun5eXx6BBg2jUqBHdunXjqaeeaqCkkSWY4zZ//nyuvfZa2rRpQ0pKCtnZ2bz33nsNmDYyBPtnLWDFihV4PB4GDBgQ2kAmhlx33XWmb9++ZuXKlWblypWmb9++5oYbbjjva+68807TvXt3s3TpUpOfn2+efvpp43a7zVtvvdVAqe2qyzHbuXOnadmypfnP//xPs27dOrNr1y7zj3/8wxw4cKCBUttVl2MW8Oabb5r+/fub9PR086c//Sm8QSNMsMft2LFj5pprrjGvvfaa2bp1q1m1apW54oorzKBBgxowdcN59dVXTUJCgnn22WfN5s2bzc9//nPTtGlTs3fv3hr33717t2nSpIn5+c9/bjZv3myeffZZk5CQYN54440GTm5XsMft5z//ufnDH/5gPv74Y7N9+3YzY8YMk5CQYNatW9fAye0J9pgFHDt2zHTr1s2MHTvW9O/fP6SZYqaMbN682QBm9erVVdtWrVplALN169ZaX5eVlWUefvjhatsuu+wy8+tf/zpsWSNFXY/Zrbfear7//e83RMSIU9djZowxn3/+uenQoYP59NNPTefOneOqjNTnuJ3t448/NsAFf2lGoyFDhpjJkydX23bJJZeY6dOn17j//fffby655JJq237yk5+YoUOHhi1jJAr2uNWkT58+5qGHHgp1tIhV12N26623ml//+tfmwQcfDHkZiZnLNKtWrSI1NZUrrriiatvQoUNJTU1l5cqVtb5u+PDhLFiwgMLCQowxLF26lO3btzNu3LiGiG1VXY6Zz+dj4cKF9OrVi3HjxtG2bVuuuOIK3nrrrQZKbVdd/5z5fD4mTZrEf/7nf5KVldUQUSNKXY/b15WUlOA4Tp3vVRWpysvLWbt2LWPHjq22fezYsbUen1WrVp2z/7hx41izZg0VFRVhyxpJ6nLcvs7n83H8+HFatmwZjogRp67H7IUXXmDXrl08+OCDYckVM2WkuLiYtm3bnrO9bdu259yo72xPPPEEffr0oWPHjiQmJnLdddcxe/Zshg8fHs64EaEux+zgwYOcOHGCRx55hOuuu47FixfzjW98g29+85vk5eWFO7J1df1z9oc//AGPx8M999wTzngRq67H7Wxffvkl06dP53vf+17M3fDs8OHDeL3ec2442q5du1qPT3FxcY37V1ZWcvjw4bBljSR1OW5f99hjj3Hy5Em+853vhCNixKnLMduxYwfTp0/n//7v//B4gl64/aJEfBn5zW9+g+M4532sWbMGAMdxznm9MabG7QFPPPEEq1evZsGCBaxdu5bHHnuMn/70p7z//vth+5nCLZzHzOfzAXDTTTdx3333MWDAAKZPn84NN9wQ1YPnwnnM1q5dy+OPP86LL7543j+L0Sjc/38GVFRU8N3vfhefz8fs2bND/nNEiq8fiwsdn5r2r2l7rAv2uAW88sor/OY3v+G1116rsSzHsos9Zl6vl+9973s89NBD9OrVK2x5wlNxQmjKlCl897vfPe8+Xbp0YePGjRw4cOCcrx06dOicBhhw+vRpHnjgAd58802uv/56APr168eGDRt49NFHueaaa+r/A1gQzmPWunVrPB4Pffr0qba9d+/efPjhh3UPbVk4j9ny5cs5ePAgnTp1qtrm9Xr5xS9+waxZs9izZ0+9stsUzuMWUFFRwXe+8x3y8/P54IMPYu6sCPj/v3K73ef8y/TgwYO1Hp/27dvXuL/H46FVq1ZhyxpJ6nLcAl577TV+/OMf8/rrr0ft7/q6CPaYHT9+nDVr1rB+/XqmTJkC+P9RaozB4/GwePFirr766nrnivgy0rp1a1q3bn3B/bKzsykpKeHjjz9myJAhAHz00UeUlJQwbNiwGl9TUVFBRUUFLlf1E0Rut7vqDEA0CucxS0xM5PLLL2fbtm3Vtm/fvr3qZonRKJzHbNKkSef8shs3bhyTJk3ihz/8Yf3DWxTO4wZfFZEdO3awdOnSmP1LNjExkUGDBrFkyRK+8Y1vVG1fsmQJN910U42vyc7O5u233662bfHixQwePJiEhISw5o0UdTlu4D8j8qMf/YhXXnml6h+i8SLYY5aSksKmTZuqbZs9ezYffPABb7zxBl27dg1NsJAOh7XsuuuuM/369TOrVq0yq1atMpdeeuk5UwczMzPN/Pnzqz4fNWqUycrKMkuXLjW7d+82L7zwgmnUqJGZPXt2Q8e3oi7HbP78+SYhIcE888wzZseOHebPf/6zcbvdZvny5Q0d34q6HLOvi7fZNMYEf9wqKirMjTfeaDp27Gg2bNhgioqKqh5lZWU2foSwCky3nDt3rtm8ebO59957TdOmTc2ePXuMMcZMnz7dTJo0qWr/wNTe++67z2zevNnMnTs3rqf2Xuxxe/nll43H4zFPPvlktT9Tx44ds/UjNLhgj9nXhWM2TUyVkS+++MLcdtttJjk52SQnJ5vbbrvNHD16tNo+gHnhhReqPi8qKjI/+MEPTHp6umnUqJHJzMw0jz32mPH5fA0b3pK6HDNjjJk7d67p0aOHadSokenfv3/crMtiTN2P2dnisYwEe9zy8/MNUONj6dKlDZ6/ITz55JOmc+fOJjEx0Vx22WUmLy+v6mt33HGHGTVqVLX9c3NzzcCBA01iYqLp0qWLmTNnTgMnjgzBHLdRo0bV+GfqjjvuaPjgFgX7Z+1s4SgjjjFnRjyJiIiIWBDxs2lEREQktqmMiIiIiFUqIyIiImKVyoiIiIhYpTIiIiIiVqmMiIiIiFUqIyIiImKVyoiIiIhYpTIiIiIiVqmMiIiIiFUqIyIiImKVyoiIiIhY9f8DDBA+ryLZMHIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['ID', 'RAdeg', 'DEdeg', 'cz2mrs', 'Kmag', 'Hmag', 'Jmag', 'e_Kmag', 'e_Hmah', 'e_Jmag', 'WHIc', 'e_WHIc']\n",
    "fname = '/data101/bartlett/fsigma8/PV_data/2MASS/table1.dat'\n",
    "df = pd.read_csv(fname, sep='\\s+', names=columns)\n",
    "    \n",
    "eta = np.log10(df['WHIc']) - 2.5\n",
    "\n",
    "bins = np.linspace(eta.min(), eta.max(), 30)\n",
    "plt.hist(eta, bins=bins, density=True)\n",
    "\n",
    "mu = np.median(eta)\n",
    "sigma = (np.percentile(eta, 84) - np.percentile(eta, 16)) / 2\n",
    "g = np.exp(- (bins - mu) ** 2 / 2 / sigma**2) / np.sqrt(2 * np.pi) / sigma\n",
    "plt.plot(bins, g)\n",
    "\n",
    "sigma_eta = df['e_WHIc'] / df['WHIc'] / np.log(10)\n",
    "print(sigma_eta.min(), sigma_eta.max(), sigma_eta.mean(), sigma_eta.median())\n",
    "\n",
    "print(df['cz2mrs'].min(),  df['cz2mrs'].max())\n",
    "z = df['cz2mrs'] / astropy.constants.c.to('km/s').value\n",
    "cosmo = astropy.cosmology.Planck18\n",
    "dL = cosmo.luminosity_distance(z).to(apu.Mpc).value # Mpc\n",
    "mu = 5 * np.log10(dL) + 25\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(eta, mu, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "952f0354-76a0-4b76-a310-f47c7dbd48e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:20: SyntaxWarning: invalid escape sequence '\\s'\n",
      "<>:34: SyntaxWarning: invalid escape sequence '\\s'\n",
      "<>:20: SyntaxWarning: invalid escape sequence '\\s'\n",
      "<>:34: SyntaxWarning: invalid escape sequence '\\s'\n",
      "/tmp/ipykernel_13422/2032762086.py:20: SyntaxWarning: invalid escape sequence '\\s'\n",
      "  df = pd.read_csv(fname, sep='\\s+', names=columns)\n",
      "/tmp/ipykernel_13422/2032762086.py:34: SyntaxWarning: invalid escape sequence '\\s'\n",
      "  df = pd.read_csv(fname, sep='\\s+', names=columns)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zcosmo 0.0039389587876126975 0.05462965177877669\n",
      "etatrue: -0.41705506563366274 0.5555464714961542\n",
      "muTFR 31.441390386741705 37.174221031390445\n",
      "mutrue 30.852170311886482 36.64413989655261\n",
      "sigma_TFR 0.3\n",
      "mtrue 5.590206617138733 16.490061642329344\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'm')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def estimate_data_parameters():\n",
    "    \n",
    "    \"\"\"\n",
    "    ID   2MASS XSC ID name (HHMMSSss+DDMMSSs)\n",
    "    RAdeg   Right ascension (J2000)\n",
    "    DEdeg   Declination (J2000)\n",
    "    cz2mrs  Heliocentric redshift from the 2MRS (km/s)\n",
    "    Kmag    NIR magnitudes in the K band from the 2MRS (mag)\n",
    "    Hmag    NIR magnitudes in the H band from the 2MRS (mag)\n",
    "    Jmag    NIR magnitudes in the J band from the 2MRS (mag)\n",
    "    e_Kmag    Error of the NIR magnitudes in K band from the (mag)\n",
    "    e_Hmag    Error of the NIR magnitudes in H band from the (mag)\n",
    "    e_Jmag    Error of the NIR magnitudes in J band from the (mag)\n",
    "    WHIc    Corrected HI width (km/s)\n",
    "    e_WHIc    Error of corrected HI width (km/s)\n",
    "    \"\"\"\n",
    "    \n",
    "    columns = ['ID', 'RAdeg', 'DEdeg', 'cz2mrs', 'Kmag', 'Hmag', 'Jmag', 'e_Kmag', 'e_Hmah', 'e_Jmag', 'WHIc', 'e_WHIc']\n",
    "    fname = '/data101/bartlett/fsigma8/PV_data/2MASS/table1.dat'\n",
    "    df = pd.read_csv(fname, sep='\\s+', names=columns)\n",
    "    \n",
    "    sigma_m = np.median(df['e_Kmag'])\n",
    "\n",
    "    eta = np.log10(df['WHIc']) - 2.5\n",
    "    sigma_eta = np.median(df['e_WHIc'] / df['WHIc'] / np.log(10))\n",
    "    \n",
    "    hyper_eta_mu = np.median(eta)\n",
    "    hyper_eta_sigma = (np.percentile(eta, 84) - np.percentile(eta, 16)) / 2\n",
    "    \n",
    "    return sigma_m, sigma_eta, hyper_eta_mu, hyper_eta_sigma\n",
    "\n",
    "columns = ['ID', 'RAdeg', 'DEdeg', 'cz2mrs', 'Kmag', 'Hmag', 'Jmag', 'e_Kmag', 'e_Hmah', 'e_Jmag', 'WHIc', 'e_WHIc']\n",
    "fname = '/data101/bartlett/fsigma8/PV_data/2MASS/table1.dat'\n",
    "df = pd.read_csv(fname, sep='\\s+', names=columns)\n",
    "\n",
    "# Get some parameters from the data\n",
    "sigma_m, sigma_eta, hyper_eta_mu, hyper_eta_sigma = estimate_data_parameters()\n",
    "\n",
    "# cosmo = astropy.cosmology.Planck18\n",
    "cosmo = LambdaCDM(H0 = 80, Om0 = 0.3, Ode0 = 0.7)\n",
    "\n",
    "# Other parameters to use\n",
    "L = 200.0\n",
    "N = 64\n",
    "xmin = -L/2\n",
    "Rmax = 100\n",
    "Nt = 2062\n",
    "alpha = 1.4\n",
    "mthresh = 11.25\n",
    "a_TFR = -23\n",
    "b_TFR = -8.2\n",
    "sigma_TFR = 0.3\n",
    "bias_epsilon = 1e-7\n",
    "\n",
    "dens = np.load('dens.npy')\n",
    "vel = np.load('vel.npy')\n",
    "N = dens.shape[0]\n",
    "\n",
    "phi = (1. + dens + bias_epsilon) ** alpha\n",
    "\n",
    " # Generate positions (comoving)\n",
    "xtrue = poisson_process.sample_3d(phi, Nt, L, (xmin, xmin, xmin))\n",
    "\n",
    "# Convert to RA, Dec, Distance (comoving)\n",
    "rtrue = np.sqrt(np.sum(xtrue** 2, axis=0))  # Mpc/h\n",
    "c = SkyCoord(x=xtrue[0], y=xtrue[1], z=xtrue[2], representation_type='cartesian')\n",
    "RA = c.spherical.lon.degree\n",
    "Dec = c.spherical.lat.degree\n",
    "r_hat = np.array(SkyCoord(ra=RA*apu.deg, dec=Dec*apu.deg).cartesian.xyz)\n",
    "\n",
    "# Compute cosmological redshift\n",
    "zcosmo = z_at_value(cosmo.comoving_distance, rtrue * apu.Mpc / cosmo.h).value\n",
    "\n",
    "print('zcosmo', zcosmo.min(), zcosmo.max())\n",
    "        \n",
    "# Compute luminosity distance\n",
    "# DO I NEED TO DO /h???\n",
    "# dL = (1 + zcosmo) * rtrue  # Mpc/h\n",
    "dL = cosmo.luminosity_distance(zcosmo).to(apu.Mpc).value # Mpc\n",
    "\n",
    "# Compute true distance modulus\n",
    "mutrue = 5 * np.log10(dL) + 25\n",
    "\n",
    " # Sample true linewidth (eta) from its prior\n",
    "etatrue = hyper_eta_mu + hyper_eta_sigma * np.random.randn(Nt)\n",
    "\n",
    "print('etatrue:', etatrue.min(), etatrue.max())\n",
    "\n",
    "# Obtain muTFR from mutrue using the intrinsic scatter\n",
    "muTFR = mutrue + sigma_TFR * np.random.randn(Nt)\n",
    "\n",
    "print('muTFR', muTFR.min(), muTFR.max())\n",
    "print('mutrue', mutrue.min(), mutrue.max())\n",
    "print('sigma_TFR', sigma_TFR)\n",
    "\n",
    "# Obtain apparent magnitude from the TFR\n",
    "mtrue = muTFR + (a_TFR + b_TFR * etatrue)\n",
    "\n",
    "print('mtrue', mtrue.min(), mtrue.max())\n",
    "\n",
    "# Scatter true observed apparent magnitudes and linewidths\n",
    "mobs = mtrue + sigma_m * np.random.randn(Nt)\n",
    "etaobs = etatrue + sigma_eta * np.random.randn(Nt)\n",
    "\n",
    "# Apply apparement magnitude cut\n",
    "m = mobs <= mthresh\n",
    "mobs = mobs[m]\n",
    "etaobs = etaobs[m]\n",
    "xtrue = xtrue[:,m]\n",
    "\n",
    "data_eta = np.log10(df['WHIc']) - 2.5\n",
    "plt.hist(etaobs, label='synthetic', histtype='step', density=True, bins=20)\n",
    "plt.hist(data_eta, label='data', histtype='step', density=True, bins=20)\n",
    "plt.legend()\n",
    "plt.title('eta')\n",
    "\n",
    "plt.figure()\n",
    "data_m = df['Kmag']\n",
    "plt.hist(mobs, label='synthetic', histtype='step', density=True, bins=20)\n",
    "plt.hist(data_m, label='data', histtype='step', density=True, bins=20)\n",
    "plt.legend()\n",
    "plt.title('m')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b7b1015b-9cc2-4f64-9da7-48601b0a159f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "203.125\n",
      "(26, 26, 26)\n",
      "-101.5625\n",
      "7.8125\n",
      "7.8125\n"
     ]
    }
   ],
   "source": [
    "L = 500\n",
    "xmin = - 250\n",
    "Rmax = 100\n",
    "N = 64\n",
    "x = np.linspace(xmin, xmin + L, N+1)\n",
    "i0 = np.argmin(np.abs(x + Rmax))\n",
    "i1 = np.argmin(np.abs(x - Rmax))\n",
    "\n",
    "Lsmall = x[i1] - x[i0]\n",
    "dens_small = dens[i0:i1, i0:i1, i0:i1]\n",
    "xmin_small = x[i0]\n",
    "print(Lsmall)\n",
    "print(dens_small.shape)\n",
    "print(xmin_small)\n",
    "\n",
    "print(x[1] - x[0])\n",
    "print(L / N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e2f630-355c-477c-8b35-ec570eb97382",
   "metadata": {},
   "source": [
    "# Check MB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f6103c09-b314-4898-b615-33676f7fc66d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[STD    ] Building gravity model lpt\n",
      "[STD    ] | |     ___________                              \n",
      "[STD    ] | |    /-/_\"/-/_/-/|     __________________________ \n",
      "[STD    ] | |   /\"-/-_\"/-_//||                \u001b[34;1mBORG3\u001b[39;0m model\n",
      "[STD    ] | |  /__________/|/|     (c) Jens Jasche 2012 - 2019\n",
      "[STD    ] | |  |\"|_'='-]:+|/||        Guilhem Lavaux 2014 - 2019\n",
      "[STD    ] | |  |-+-|.|_'-\"||//     __________________________ \n",
      "[STD    ] | |  |[\".[:!+-'=|//     \n",
      "[STD    ] | |  |='!+|-:]|-|/       \n",
      "[STD    ] | |   ----------         \n",
      "[STD    ] | | \n",
      "[STD    ] | | Please acknowledge the following papers:\n",
      "[STD    ] | |   - Jasche & Lavaux (A&A, 2019, arXiv 1806.11117)\n",
      "[STD    ] | |   - Jasche & Wandelt (MNRAS, 2012, arXiv 1203.3639)\n",
      "[STD    ] | |   - Jasche & Kitaura (MNRAS, 2010, arXiv 0911.2496)\n",
      "[STD    ] | |   - Lavaux, Jasche & Leclercq (arXiV 1909.06396)\n",
      "[STD    ] | |   - And relevant papers depending on the used sub-module/contribution\n",
      "[STD    ] | | \n",
      "\n",
      "[STD    ] | | This is BORG version 6b1404bfd8011f03ee1b8635275f68d14d16bef3\n",
      "[\u001b[35;1mWARNING\u001b[39;0m] | | | Entering [/home/bartlett/borg/libLSS/physics/forwards/primordial_as.cpp]virtual void LibLSS::ForwardPrimordial_As::updateCosmo()\n",
      "[\u001b[35;1mWARNING\u001b[39;0m] | | | | Sigma8 is set, but sigma8 normalization is not supported for PRIMORDIAL_AS. Ignoring the supplied sigma8.\n",
      "[\u001b[35;1mWARNING\u001b[39;0m] | | | Done (in 2.1512e-05 seconds) (ctx='[/home/bartlett/borg/libLSS/physics/forwards/primordial_as.cpp]virtual void LibLSS::ForwardPrimordial_As::updateCosmo()')\n",
      "[STD    ] | Smoothing field with rsmooth=4\n",
      "[STD    ] | | Smoothing field with rsmooth=4\n",
      "[STD    ] | | Smoothing field with rsmooth=4\n",
      "[STD    ] | | Smoothing field with rsmooth=4\n",
      "[STD    ] \tMade 668 of 2000\n",
      "[STD    ] \tMade 1345 of 2000\n",
      "[STD    ] \tMade 2000 of 2000\n",
      "[STD    ] Obtaining peculiar velocities\n",
      "[STD    ] Radial projection\n",
      "[STD    ] Making MB data\n",
      "26.779654 162.9076 97.677765\n",
      "27.107544 164.64821 92.31098\n",
      "16.885567 111.88842 61.495857\n",
      "0\n",
      "0\n",
      "Low: 14.289446\n",
      "High 16.209558\n",
      "0.078125 250.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x146c1c6ac3e0>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importlib.reload(tfr_inference)\n",
    "\n",
    "# Get some parameters from the data\n",
    "sigma_m, sigma_eta, hyper_eta_mu, hyper_eta_sigma = tfr_inference.estimate_data_parameters()\n",
    "\n",
    "# Other parameters to use\n",
    "L = 500.0\n",
    "N = 64\n",
    "xmin = -L/2\n",
    "R_lim = L / 2\n",
    "Rmax = 100\n",
    "Nt = 2000\n",
    "alpha = 1.4\n",
    "mthresh = 11.25\n",
    "a_TFR = -23\n",
    "b_TFR = -8.2\n",
    "sigma_TFR = 0.3\n",
    "sigma_v = 150\n",
    "Nint_points = 201 \n",
    "Nsig = 10\n",
    "frac_sigma_r = 0.07  # WANT A BETTER WAY OF DOING THIS - ESTIMATE THROUGH SIGMAS FROM TFR\n",
    "interp_order = 1\n",
    "bias_epsilon = 1.e-7\n",
    "\n",
    "cpar, dens, vel = tfr_inference.get_fields(L, N, xmin)\n",
    "\n",
    "RA, Dec, czCMB, m_true, eta_true, m_obs, eta_obs, xtrue = tfr_inference.create_mock(\n",
    "        Nt, L, xmin, cpar, dens, vel, Rmax, alpha, mthresh,\n",
    "        a_TFR, b_TFR, sigma_TFR, sigma_m, sigma_eta, \n",
    "        hyper_eta_mu, hyper_eta_sigma, sigma_v, \n",
    "        interp_order=interp_order, bias_epsilon=bias_epsilon)\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "rtrue = jnp.sqrt(jnp.sum(xtrue ** 2, axis=0))\n",
    "robs = czCMB / 100 \n",
    "plt.plot(rtrue, robs, '.')\n",
    "    \n",
    "MB_pos = tfr_inference.generateMBData(RA, Dec, czCMB, L, N, R_lim, Nsig, Nint_points, sigma_v, frac_sigma_r)\n",
    "r = jnp.sqrt(jnp.sum(MB_pos ** 2, axis=0))\n",
    "\n",
    "rtrue = jnp.sqrt(jnp.sum(xtrue ** 2, axis=0))\n",
    "\n",
    "for i in range(3):\n",
    "    print(r[i,0], r[i,-1], rtrue[i])\n",
    "    \n",
    "# Check truth always inside the range of the MB pos\n",
    "dr_low = rtrue - r[:,0]\n",
    "dr_high = r[:,-1] - rtrue\n",
    "m = dr_low < 0\n",
    "print(m.sum())\n",
    "m = dr_high < 0\n",
    "print(m.sum())\n",
    "print('Low:', dr_low.min())\n",
    "print('High', dr_high.min())\n",
    "print(r.min(), r.max())\n",
    "\n",
    "czCMB_new = ((1 + zcosmo) * (1 + vr_noised / tfr_inference.utils.speed_of_light) - 1) * tfr_inference. utils.speed_of_light\n",
    "\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67759f3d-0cb3-4771-bb07-f3d5bfc6848c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c0b73c6-70d7-4ba7-9c6f-e98a53a1358a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([107., 113.,  94., 108., 100.,  93., 110.,  95.,  86.,  94.]),\n",
       " array([-1.99562025, -1.59607887, -1.19653749, -0.79699612, -0.39745474,\n",
       "         0.00208664,  0.40162802,  0.8011694 ,  1.20071077,  1.60025215,\n",
       "         1.99979353]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.loadtxt('a.txt')\n",
    "plt.hist(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeb7135d-0fc7-43c1-81c9-1fd32e5f05d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "borg_new",
   "language": "python",
   "name": "borg_new"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}