flaremodel_scratchpad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import binned_statistic\n",
    "from scipy import special\n",
    "from scipy.special import erf\n",
    "from scipy.optimize import curve_fit\n",
    "import emcee\n",
    "import corner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aflare1(t, tpeak, fwhm, ampl, upsample=False, uptime=10):\n",
    "    '''\n",
    "    The Analytic Flare Model evaluated for a single-peak (classical).\n",
    "    Reference Davenport et al. (2014) http://arxiv.org/abs/1411.3723\n",
    "    Use this function for fitting classical flares with most curve_fit\n",
    "    tools.\n",
    "    Note: this model assumes the flux before the flare is zero centered\n",
    "    Parameters\n",
    "    ----------\n",
    "    t : 1-d array\n",
    "        The time array to evaluate the flare over\n",
    "    tpeak : float\n",
    "        The time of the flare peak\n",
    "    fwhm : float\n",
    "        The \"Full Width at Half Maximum\", timescale of the flare\n",
    "    ampl : float\n",
    "        The amplitude of the flare\n",
    "    upsample : bool\n",
    "        If True up-sample the model flare to ensure more precise energies.\n",
    "    uptime : float\n",
    "        How many times to up-sample the data (Default is 10)\n",
    "    Returns\n",
    "    -------\n",
    "    flare : 1-d array\n",
    "        The flux of the flare model evaluated at each time\n",
    "    '''\n",
    "    _fr = [1.00000, 1.94053, -0.175084, -2.24588, -1.12498]\n",
    "    _fd = [0.689008, -1.60053, 0.302963, -0.278318]\n",
    "\n",
    "    if upsample:\n",
    "        dt = np.nanmedian(np.diff(t))\n",
    "        timeup = np.linspace(min(t)-dt, max(t)+dt, t.size * uptime)\n",
    "\n",
    "        flareup = np.piecewise(timeup, [(timeup<= tpeak) * (timeup-tpeak)/fwhm > -1.,\n",
    "                                        (timeup > tpeak)],\n",
    "                                    [lambda x: (_fr[0]+                       # 0th order\n",
    "                                                _fr[1]*((x-tpeak)/fwhm)+      # 1st order\n",
    "                                                _fr[2]*((x-tpeak)/fwhm)**2.+  # 2nd order\n",
    "                                                _fr[3]*((x-tpeak)/fwhm)**3.+  # 3rd order\n",
    "                                                _fr[4]*((x-tpeak)/fwhm)**4. ),# 4th order\n",
    "                                     lambda x: (_fd[0]*np.exp( ((x-tpeak)/fwhm)*_fd[1] ) +\n",
    "                                                _fd[2]*np.exp( ((x-tpeak)/fwhm)*_fd[3] ))]\n",
    "                                    ) * np.abs(ampl) # amplitude\n",
    "\n",
    "        # and now downsample back to the original time...\n",
    "        ## this way might be better, but makes assumption of uniform time bins\n",
    "        # flare = np.nanmean(flareup.reshape(-1, uptime), axis=1)\n",
    "\n",
    "        ## This way does linear interp. back to any input time grid\n",
    "        # flare = np.interp(t, timeup, flareup)\n",
    "\n",
    "        ## this was uses \"binned statistic\"\n",
    "        downbins = np.concatenate((t-dt/2.,[max(t)+dt/2.]))\n",
    "        flare,_,_ = binned_statistic(timeup, flareup, statistic='mean',\n",
    "                                 bins=downbins)\n",
    "\n",
    "    else:\n",
    "        flare = np.piecewise(t, [(t<= tpeak) * (t-tpeak)/fwhm > -1.,\n",
    "                                 (t > tpeak)],\n",
    "                                [lambda x: (_fr[0]+                       # 0th order\n",
    "                                            _fr[1]*((x-tpeak)/fwhm)+      # 1st order\n",
    "                                            _fr[2]*((x-tpeak)/fwhm)**2.+  # 2nd order\n",
    "                                            _fr[3]*((x-tpeak)/fwhm)**3.+  # 3rd order\n",
    "                                            _fr[4]*((x-tpeak)/fwhm)**4. ),# 4th order\n",
    "                                 lambda x: (_fd[0]*np.exp( ((x-tpeak)/fwhm)*_fd[1] ) +\n",
    "                                            _fd[2]*np.exp( ((x-tpeak)/fwhm)*_fd[3] ))]\n",
    "                                ) * np.abs(ampl) # amplitude\n",
    "\n",
    "    return flare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ModifiedGaussian2(t,A,mu,sigma,tau):\n",
    "        x = 1./(2.*tau) * np.exp(.5*(sigma/tau)**2) * np.exp(- (t-mu)/tau)\n",
    "        s = A*x*( 1. + special.erf((t-mu-sigma**2/tau)/np.sqrt(2*sigma**2)))\n",
    "        return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _gaus(x, a, b, x0, sigma):\n",
    "    # Array or float of same type as input (x).\n",
    "    # \"\"\"\n",
    "    return a * np.exp(-(x - x0)**2 / (2 * sigma**2)) + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(-10,20,0.01)\n",
    "\n",
    "plt.plot(t, aflare1(t, 0, 1, 1), label='aflare')\n",
    "Gmodel = _gaus(t, 1, 0, 0, 0.1)\n",
    "Emodel = np.exp((-0.2-t)/1.5)  * (0.5*(np.sign(t-0)+1.0))\n",
    "\n",
    "plt.plot(t, Gmodel, label='gaus')\n",
    "plt.plot(t, Emodel, label='exp')\n",
    "\n",
    "Cmodel = np.convolve(Emodel,Gmodel,  mode='same')\n",
    "Cmodel /= np.trapz(Cmodel, x=t)\n",
    "\n",
    "plt.plot(t, Cmodel, label='Cmodel')\n",
    "\n",
    "# plt.plot(t, ModifiedGaussian2(t, 1.4, -.2, 0.1, 1.2), label='mod2')\n",
    "\n",
    "# plt.plot(t, np.convolve(Gmodel, Emodel, mode='full'), label='conv')\n",
    "plt.legend()\n",
    "plt.xlim(-1,4)\n",
    "plt.ylim(0,1.5)\n",
    "# plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa509feed90>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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up6Wp/9uvRVWdHv37aJJPsNRSuuv8j5pqX0tyZVU9kuRK4NHeBV2oqvra2fuz9DpJ8iyWwvzPqur20ep1eV6m7gj9Ah0G9iZ5dpLtwA7gHzvXNLHRE3rWG1n68HdWTHIR8ZmQ5LuTPPfsfeANzNZzsZLDwFtH998KfKpjLU/LLL5OkgT4E+ChqvqDsU3r8rzM1DdFk7wR+CNgDvgv4N6quna07TeBX2TpU+V3VNWne9W5Vkn+lKW3kQV8Gfjls/21WTCaPvY+/u8i4r/Xt6ILk+T7gE+MFjcCfz5L+5LkY8A1LJ2a9WvAu4BPAn8BbAUeBt5UVVP/YeM59uUaZux1kuRHgb8H7geeHK3+DZb66M2fl5kKdEnSuQ2l5SJJz3gGuiQNhIEuSQNhoEvSQBjokjQQBrokDYSBLkkD8b/RrAEZMPF7cgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, 0.5*(np.sign(t-0)+1.0))\n",
    "# idea from https://stackoverflow.com/questions/48285417/understanding-scipy-convolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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lMfT21qhQbE5LXGPo0nAqWxSpwSushw5Hx9BzBWfn/iN0d81l/pykxtCloRToIjVUW++8tEBXWzL6ETqSLZCIadciaSwNuYjUUBpDD8aNoZfKGg8cjtZz+cm2F7RAlzScAl2kBi+vh37s46UFunYVh1iS8UALdEnDKdBFaqi1xdzWvmF+9dwFxMw4o6NVC3RJwynQRWqoVrYI0YXRucUFvI5k84AW6JLGUqCL1BBWKVuEo5OL3J3nDh7R5CJpOAW6SA1hWLlsEY5OLsKi87RAlzSaAl2khkKFTaJLSpOL5iTjZAuuBbqk4VSHLlJDtbJFODq5aPsLw+wbGmUwk8Pdyys0isw09dBFaghDP65ksaQ0ht4/FG0W3RIzDo3k2X0wo9JFaQgFukgNoXvFCheIJhdt7TtMPBYdP1zsmZ+3uF2li9IQCnSRGgruWKUSF6LJRecsSNGZSgDRNnUXvahT66JLwyjQRWpwP36W6Firu9KcXSxTHBrJ88y+YZUuSsMo0EVqCEOvWLJYsmrxHDbvHaR0hkoXpZEU6CI1FNwrliyWbO0b5qIXzSMZD8jkCipdlIZSoIvU4F65ZLFkz0CGVDJGMh6QKzgOpJIxjaFLQyjQRWoo1ChbBEjGjI07DhIzI3RnNFdg446DJGM1niQyTRToIjXUKluEqLLFgUTMyOZD3KNeuuJcGkGBLlJDWKNsEWC04FyyYh5tyTgOxGMBl6yYx2jBZ66RIkUKdJEawrDywlwlSzpTtMTjnDM/KlMcOJLjyb1DGnKRhqgr0M1snZk9bWbbzOymGue9wswKZvZbU9dEkcaJqlyqH1+3ZjE79w+zoz+qagnDkKGRPH2HRjX9X2bchIFuZjHgZuBqYDXwDjNbXeW8zwD3THUjRRoldK9Z5dLdleasdCvptmi2qJvxqnPnc878Nk3/lxlXTw/9YmCbu2939yzwLeDaCufdCHwP2DeF7RNpqGimaO3hk9GC87IlczE7Olt0JJdX6aLMuHoCfQmwa8z93cXHysxsCfBm4JZaL2Rm681sk5lt6u/vn2xbRWbcRGWLEJUubvrlAMlYgKt0URqonkCv9K0cfwn/88BH3L3mQtDuvsHd17r72kWLFtXZRJHGmWjIBY6WLiZjAdl8CMX7inOZafVscLEbOGfM/bOBvePOWQt8q1jetRB4vZnl3f22qWikSKOEE0z9h2jIZdXiOTx/aIRMtsALh0d52ZK5Kl2UGVdPD30jsNLMlptZEng7cMfYE9x9ubsvc/dlwK3Af1WYy2wwUdkiREMuz/QNM7c1qkWf15bkmb5hDbnIjJuwh+7ueTN7P1H1Sgz4urtvMbMbisdrjpuLnMqi9dBrn1MecolH/aNcIdSQizREXXuKuvudwJ3jHqsY5O7+rpNvlkhz8Amm/sPR2aKPbD8AQO/gCEvnp+gbGp2JJoqUaaaoSA1RlUvtQF/SmWJ4pFBeImBeW5J8iPYWlRmnQBepIZxg+VwYs7doYBhwJJsHtLeozDwFukgVPb2D7HhhmF0HjvC5+7ZW7W2X9haNB4YZDI7kScRM66LLjFOgi1TQ0zvIhgd3kCuEtMQDBjM5Njy4o2qoL2pvYSQXMicZJx4YgZkmF8mMU6CLVHD35j7SqQSxYq87nUqQTiWqDqGUKl2CADLZAjv3D7N/OMvAcHZG2y2nNwW6SAV7BjJ0tMZxBysWIHa0xqsOoZQmF5W2oWuJB5zRkWRz75AujMqMUaCLVLCkM8XQSB4fU4c+NJJnSWeq6vm9g6Ms7mgBYH57C6lknHlt1Xv1IlNNgS5Swbo1ixnM5MiFjgGDmRyDmRzr1iyuev7BIzlCj9Zy2bn/CLsPZuhKt+jCqMwYBbpIBd1dadZftpyYGdlCSDqVYP1ly+nuSlc9f01XBwOZqGQxMFjYriUAZGbVNVNU5HTU3ZXmjLktzJ+T5A9/fdWE58+bk6Q9GefwSIFsPmTgSK5cmy4yE9RDF6khdJ9wca6SvqFREvGAWBCtAQNOIh5oCQCZMQp0kRoKIeUp/RM5NJKnNRFj/pwk7vCiBXNoTcQ4NJKf5laKRBToIjVEi3PVd246FcfdKYRO6PD080P0D40QHLcfjMj0UKCL1FDP4lwlq7vSdM1tYbS4a1EsMOamEgxk8qpFlxmhQBepoZ4di0rWrVnM84eypFujWoNMrsChTJ4zO5KqRZcZoSoXkRq8jtUWS7q70nS2xek/PIoRLQewsD3J84eyxPeqhy7TT4EuUkPBnTrzHIAQY1FHCyO5AtlCyP7hLO4QUy26zAANuYjUMJmyRYgujGayeXKhky845k4hDNk/PKpxdJl2CnSRGsJJlC1CdGG0NREjbtHqi0PZArHA6GiJaxxdpp0CXaSGcBJlixBdGB3I5MoXUltiAYUQcgVni8bRZZop0EVqmEzZIhSXC2hvKY+7H8kVSMYN3DXBSKadAl2khnr2FB1vTksMiBboigVGGMKB4awmGMm0U6CL1BBOssoFokqXBR0txANjNBdyJFcgCIyh0cL0NFKkSIEuUkMhdOLB5H5M0qk4YegEgeFAWyJ6vipdZLop0EVqKIRObJJd9LGVLgBDo6p0kZmhQBepIR+Gkw70cqVLsWefCEyVLjIjFOgiNYQhkw70UqVL6VmZfKhKF5kRCnSRGvJhSHyyV0WJKl3cnUQs2rFIlS4yExToIlWExXXN6+6hb74N/noNfDzNP/dezWP2Nm6w71EIC1yc/YkqXWTaKdBFqoi2kWPitVye3wx/80q49To4tAugvNriK+wpnIC3+b18M/9HzDnYo0oXmTZ1BbqZrTOzp81sm5ndVOH475jZE8VfPzGzl099U0VmViEsBnqtlRKf3wz//HbY31Px8AW2jYCQR/08umO7+Cf/CPu+++HpaK7IxIFuZjHgZuBqYDXwDjNbPe60HcDl7n4+8Elgw1Q3VGSmlQK95hj6Dz5Y7pVX0h6M0G07eTQ8D3NIEPKaA9+Bf3zrFLdWpL4e+sXANnff7u5Z4FvAtWNPcPefuPvB4t2HgbOntpkiMy9f6qFXm1h01x/Dno0VD439K+Ai28rP/cXkPAYO5hA+ey/cun6KWyynu3oCfQkwtguyu/hYNe8G7qp0wMzWm9kmM9vU399ffytFGqA85FKpg775NthYxz9EHV4RPM0RWunxpeWHDWDztxXqMqXqCfRKX+eKtVdm9mtEgf6RSsfdfYO7r3X3tYsWLaq/lSIN8FTx4uXtj+/lc/dtPfZi5gOfgjBX1+tcFGwFYGP4kuiBsT9Rm78NN/9qNBYvcpLqCfTdwDlj7p8N7B1/kpmdD3wVuNbd909N80Qao6d3kG/89DkAOlMJBjM5Njy4Iwr15zfDgWdrv8C5r8W6LqJgcKYdYJn18pCviY6N7w71b4HvvEuhLietnkDfCKw0s+VmlgTeDtwx9gQzWwp8H/g9d9869c0UmVl3b+6jvSXacjcWGOlUgnQqEa3FsvFr0Uyhai55H/zed+G9/4cPzv0btoeLeI39gofDbkb96Da+x+T6gWfgu++Znt+MnDYmDHR3zwPvB+4BeoDvuPsWM7vBzG4onvYxYAHwJTN7zMw2TVuLRWbAnoEMbcnSuubRGElHa5w9AxnYdj9QJdC7LoKrP1W+O7qwm7fEbiZOlgyt/MxXVX/T/T0aU5eTEp/4FHD3O4E7xz12y5jb7wHUvZBZY0lniucOHAGgNK9oaCTP+fHdMPQ80UD4+LGTAK79wjGPrO5K88SuAf4p+wYCCjxYeBmvCp6s/sabvw1zFh7zl4JIvTRTVKSCdWsWcygTXfQ0YDCTYzCT4w3ZO0s7RwMxjl7hDGDuWXDmmuNeJ5MLAcOJ8YPwVbhTpayg6JGbo5JIkUlSoItU0N2V5i0XRtW5g5k86VSC9ZctZ/6+RyAWBw+JisoDsDgQwMrXVnydjtY47o4DeziDv86/uRzqVXP9kS/B//tCtaMiFdU15CJyOlq6YA4A1796Ga996ZlRFcrQ88UwLw65OBDEIZaAV7y74uukU0mGMjkOjxYIgZvDt/KiQh9vif2E6svEONz/p9HNSz8wtb8xmbXUQxepojRTNF6aWbTxa4BFwy2xlqhnbgAOK688bril5IJz0mRyIbExP20fKbyf7xVeTe3RlxB+9GfqqUvdFOgiVRTGT/3fvRFS86PbYS6aw29JCJJw+X+v+jq/+6oX0ZIIKL5cOcT/xG7kMX9x7RXSPR/11BXqUgcFukgVxy3ONTIAh3vBHSxW/AW0dVbtnUM0jn7ZyoXHTbnOFkI+4b/PEHMnaIl66lIfBbpIFfni5KHALBo/HxmKwjyWgCABWHS7bcGEr3XjlStpTQTlUC/1yrfkl/LZ5Hsh3lb7BTwfhfrm2070tyOnAQW6SBWFsWPoPT+ARCoaP8+PQDganWQBzK21Vl2kuytNui15zEJfoUMudL45dAGbLviL+kL9e+9WT12qUqCLVHF0DN3g+SegkIN4ChJtUQ/dLAr5rvPrer10KlmefXrM+zjc+PhSdl3x2fpC/f6PqU5dKlKgi1RxdPlcg0N7IcxDPgM4tHRE4e4F6H5jXa9XqnapVKnYPzTKl/etgTd9eeJQx1WnLhUp0EWqyBWiMfTkwHY4vA+CAJJzonH0kQEghAXn1bwgOlap2qVSVUve4d+f6oM1b4pC3SaaIuKqfpHjKNBFqhjNR4Ge+OUD0NoJFMfPcUjOhfYuWH5p3a9Xqnappu9QNlqed82b4Mo/rSPUQ7j/T7Sgl5Qp0EWqyBWivnTL/ichVxpqmRuFuRcge6ju4ZaSG69cSSpx/Dg6ROs3/vkPigt3XfqBOkMdbZIhZQp0kSqypR56dhCCWHQhdPRQFOQAbQvrHm4p6e5K82vnVe+lP7LjwNGdkS79ALzlaxC0TvzC/Vvg79+ossbTnAJdpIpsvgBA0kIY7o/WcGmdB4k50e2WjhN63RuvXHl0stI4eR/TS4do+OU3v1LHhVJg5ADc+i5VwJzGFOgiFfT0DvKjnn0AHDiwj1wI5IZh5GB0cbRtIbXXwK2uuyvNsgXVA/on2w/wlR8/c/SB0oXSekIdj5bf/fwFGoI5DSnQRcbp6R1kw4M7GM7mAZib3cdowcnG26Px83wumvLfkj7h9/jDX19JvMpPnwOfu++ZYzelLod6nf8qGNgOX7lMvfXTjAJdZJy7N/eRTiVIFJdHzLYu4HB8PplCAIQQb4kW5KpzQlEl15y/hFeumF/1+EjeuenWJ459cM2b4D13w4Lu+t7EC+qtn2YU6CLj7BnI0NEapxA6cQoMJ8+gIxwkyB+BWBJa01AYmXSFy3gfvWY1yVjVBdF5fM8hPvGDXxz74Jlr4MaHo42oK05RqmBgO9xyKfztVQr2WU6BLjLOks4UQyN5Cu7EzVmY2c5Q0EkYT0Xli8P74MzzJ13hMl53V5qLl8+rec7fPfTcsePpJVd/Cq76RLR8b10c9mxUsM9yCnSRcdatWcxgJkcmWyBhBawwSmthiDbLQzwJyY66Vlisx0evWU0qUf3H0IHP3LW1cqhf+gF477/D3GWTeMdSsL8aPv9ylTnOMgp0kXG6u9Ksv2w5scCIex4sTlsyRiIeRAlrARx+fsre64NXvbjmD2II/OVdW48ffoHoXwkferw4BDNJA7+EW6+DT54B375OvfZZQIEuUkF3V5pzF7WTjueY39lJ8owXw8JVsGBFtJ7LyODEL1Kn916+kne9emnNcxz4+kPP8aab/++x1S8lV38Kbnhokr31osIo9NwW9do/vVTrw5zCFOgiVQyP5pkT5KLe+L6n4OBOOHIACE+qZLGSj73xZfzKORPtXASP7TrEm25+qPIQzMn01ktGB6P1YT6ehs+sULifYhToIhX09A7y7J59xPPD7C+kyAYtxQuiL0DHkpMqWazm0795PovaJ77IOZp3Pn3X1ol7610XnVyDMvuPhvvH0/CpJRqaaXLmfmKz3U7W2rVrfdOmTQ15b5FaShOLfr6lh8Uc4KuJvwIv0JJqJ9nSBolWePNXTrrKpZIfPrGHD3/ncTL5+n8uF89N8rE3rOaa8yvsnPT8Zvjue2B/zxS2ssgCSC+Fq/4sqpGXGWFmj7r72orHFOgix/rcfVsZzOS4/5HHeEmwi0+3f5t4fpgWH6UtEYczXgLX/3Da3v+HT+zhQ99+jNHC5J87L5XghiuW897LVx57YPNtcO//hEO7pqSNE0q2w7lXweX/bVr+4jud1Qr0OtbmFDm97BnI0JVuZSSMMdeGaMsNkA+S7A/OoK0jyYmu4VKvUk/7j7//BIMj4aSeezCT49N3beXTd20Fol3yzu5s5aarX8E1H9oc9djv/ij88oGpbvaxsoejC609t9U+ryUNr/lwVIIpJ009dJFxPnffVkZ2Pc7XtrbwntgP+XDi2zgBoSVpmXcWLDwPfvtfpr0dPb2D3HTrEzy+59C0vP7rgof5UOy7rLBeYkThX5p8Wucc1OYVxGH+uXDFH8+64aCTHnIxs3XA/wZiwFfd/S/HHbfi8dcDR4B3ufvPar3miQT69s0Ps/+Hf855ww/TRk5XdGXa9PtcLsndwsfjf8874/fiGIYRdJwJF74Tfu1/zFhbvvLjZ/jsPVvJTq6zPinXx37A+2K3k+bI0XA/xXn5P83FgaGgjS3Lf59XX/eJST//pIZczCwG3Az8OrAb2Ghmd7j7mEWbuRpYWfx1CfDl4v+nzPbND3Pk+3/ImvxTtFj0zZ4F3zlpUs/62QAss+cxBzOngBEc2X/Sa7hM1nsvX8llq86Y1t763xXeyN8Vot/XtcED/F7sPuZzmBQ5kuRJWp4kORIUTpmwt/J/mosBc/0Ia5/9Ig/9AycU6tXUM4Z+MbDN3bcDmNm3gGuBsYF+LfANj7r7D5tZp5l1uXvvVDW075FbWRH28lNfw6dyv1v1PK/xJ1j7WG21nlvrWzPRP4Aa0d7az639E3Cir1v782vM69YyyBxSjHCBbSs/FhBGuxY14CJfd1ea2298DT98Yg+f+mEPewZHp+29bg+v4PbwiqrHE+QxQgK8+CvEyrfH34/OK923Cn+alR6Dav9KmMTzKz5W7dxKrzt9/nPwANfH72T5jm8CMxvoS4Cxl8Z3c3zvu9I5S4BjAt3M1gPrAZYurT0zbrzY0G5ayNJBhlW2u+a51f7QomMn9rwTfl2DiSO0idp7kq97Ms870TZN1+u+LraRuXYkuuPFF1pwbs33mm7XnL+kfNH0Kz9+hi898CyDmRMohzkJHQxzoW3lbNtHDB8T4VaO77B4u/R4oXh7vOp/4VY693jVnl/p8Wp/0pXPnd6u/UIbJAA6/PCUvm49gV7pdzb+s6nnHNx9A7ABojH0Ot67rNBxNqMDSS4InuGi4Jlm/JeUzGJe+sJd+qGGtmOs916+slyeOJPhfoA09/srKibk64KHuSn2Tc62/VTeCvuoU2XoZroUgCFr58Q2MqysnkDfDZwz5v7ZwN4TOOekLL7kt+jf8xBz84O0WFjuMIlMt1JuBWve1rQVE2PDvaQRPfh7wldyT/jKCc+bTPBP1qnwF4UDBY+xY8XvcNYUvu6EVS5mFge2AlcCe4CNwG+7+5Yx51wDvJ+oyuUS4AvufnGt11WVi5wKPDByiTStl//RrK6VbtTwzVQ7z3ZyU/CPvDL2FMmKgzyNN51VLvWWLb4e+DxR2eLX3f0vzOwGAHe/pVi2+EVgHVHZ4vXuXjOtVYcuIjJ5Jz1T1N3vBO4c99gtY247cBJLvImIyMnSqIWIyCyhQBcRmSUU6CIis4QCXURklmjYaotm1g/sPMGnLwRemMLmTJVmbRc0b9vUrslRuyZnNrbrRe6+qNKBhgX6yTCzTdXKdhqpWdsFzds2tWty1K7JOd3apSEXEZFZQoEuIjJLnKqBvqHRDaiiWdsFzds2tWty1K7JOa3adUqOoYuIyPFO1R66iIiMo0AXEZklTqlAN7O/MrOnzOwJM/tXM+scc+x/mNk2M3vazF43w+16q5ltMbPQzNaOeXyZmWXM7LHir1tqvc5Mtat4rGGf17h2fNzM9oz5jF7fqLYU27Ou+JlsM7ObGtmWsczsl2b2i+Jn1LBlSs3s62a2z8w2j3lsvpndZ2bPFP8/r4na1tDvl5mdY2b/bmY9xZ/FPyg+Pj2fmbufMr+A1wLx4u3PAJ8p3l4NPA60AMuBZ4HYDLarGzgPeABYO+bxZcDmBn5e1drV0M9rXBs/Dny40d+tYltixc9iBZAsfkarG92uYtt+CSxsgnZcBlw49nsN/C/gpuLtm0o/l03StoZ+v4Au4MLi7Q6ivSVWT9dndkr10N39XnfPF+8+TLQzEkSbVH/L3UfdfQewjWhz65lqV4+7Pz1T71evGu1q6OfVxMobort7FihtiC5F7v4gcGDcw9cC/1C8/Q/Am2ayTSVV2tZQ7t7r7j8r3h4Ceoj2W56Wz+yUCvRx/gtwV/F2tU2qm8FyM/u5mf3YzF7T6MYUNdvn9f7iMNrXG/XP9aJm+1zGcuBeM3u0uNl6M1ns7r0QBRhwRoPbM15TfL/MbBlwAfAI0/SZ1bXBxUwys/uBMysc+qi7314856NAHvhm6WkVzp/Sesx62lVBL7DU3feb2UXAbWb2Unc/1OB2Tfvndcyb1Wgj8GXgk8X3/yTwWaK/rBthRj+XSXq1u+81szOA+8zsqWKPVGpriu+XmbUD3wM+6O6HbJo2Pm26QHf3q2odN7PrgDcAV3pxAIoZ2KR6onZVec4oMFq8/aiZPQusAqbsotaJtIsZ+LzGqreNZva3wL9NVzvqMKOfy2S4+97i//eZ2b8SDQ81S6D3mVmXu/eaWRewr9ENKnH3vtLtRn2/zCxBFObfdPfvFx+els/slBpyMbN1wEeA33D3I2MO3QG83cxazGw5sBL4j0a0cSwzW2RmseLtFUTt2t7YVgFN9HkVv8wlbwY2Vzt3BmwEVprZcjNLAm8n+qwayszmmFlH6TZRcUAjP6fx7gCuK96+Dqj2L8MZ1+jvl0Vd8a8BPe7+12MOTc9n1qirvyd4xXgb0RjnY8Vft4w59lGiCoWngatnuF1vJurdjQJ9wD3Fx98CbCGqlvgZ8MZmaFejP69xbfxH4BfAE8UveVeDv2OvJ6pEeJZo2KphbRnTphXF79Djxe9Tw9oF/AvRUGKu+N16N7AA+BHwTPH/85uobQ39fgGXEg33PDEmt14/XZ+Zpv6LiMwSp9SQi4iIVKdAFxGZJRToIiKzhAJdRGSWUKCLiMwSCnQRkVlCgS4iMkv8f5JQoOZkPC1tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Gaussian(t,A,mu,sigma):\n",
    "    return A/np.sqrt(2*np.pi*sigma**2)*np.exp(-(t-mu)**2/(2.*sigma**2))\n",
    "def Decay(t,tau,t0):\n",
    "    ''' Decay expoential and step function '''\n",
    "    return 1./tau*np.exp(-t/tau) * 0.5*(np.sign(t-t0)+1.0)\n",
    "def ModifiedGaussian(t,A,mu,sigma,tau):\n",
    "        ''' Modified Gaussian function, meaning the result of convolving a \n",
    "        gaussian with an expoential decay that starts at t=0'''\n",
    "        x = 1./(2.*tau) * np.exp(.5*(sigma/tau)**2) * np.exp(- (t-mu)/tau)\n",
    "        s = A*x*( 1. + erf(   (t-mu-sigma**2/tau)/np.sqrt(2*sigma**2)   ) )\n",
    "        return s\n",
    "\n",
    "# A,mu,sigma,tau,t0 = 1.4, 0.8, 0.1, 1.2, 0  # Choose some parameters for decay and gaussian\n",
    "A,mu,sigma,tau,t0 = 1.5, .5, 0.1, 1.2, 0  # Choose some parameters for decay and gaussian\n",
    "# t = np.linspace(-10,10,1000)         # Time domain of signal\n",
    "t = np.arange(-20,20,0.01)\n",
    "\n",
    "### Populate input, response, and analyitic results\n",
    "s = Gaussian(t,A,mu,sigma)\n",
    "r = Decay(t,tau,t0)\n",
    "r2 = Decay(t,tau*2,t0)\n",
    "m = ModifiedGaussian(t,A,mu,sigma,tau)\n",
    "\n",
    "### Convolve\n",
    "m_same = np.convolve(s,r,mode='same')\n",
    "\n",
    "### Normalize the discrete convolutions\n",
    "m_same /= np.trapz(m_same,x=t) / A\n",
    "\n",
    "m_same2 = np.convolve(m_same,r2,mode='same')\n",
    "m_same2 /= np.trapz(m_same2,x=t)\n",
    "\n",
    "plt.scatter(t[::5],m_same[::5],label='Discrete Convolution (Same)', alpha=0.5)\n",
    "plt.plot(t,m,label='Analytic Solution')\n",
    "\n",
    "plt.scatter(t[::5],m_same2[::5],label='Discrete Convolution (Same)', alpha=0.5)\n",
    "print(np.min(m_same2))\n",
    "# plt.yscale('log')\n",
    "# plt.ylim(1e-3,1)\n",
    "# plt.plot(t,s)\n",
    "# plt.plot(t,r)\n",
    "# plt.plot(t,r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jackman(t,A,B,C,D1,D2,f1):\n",
    "    z1 = ((2*B + (C**2 *D1))/(2*C))\n",
    "    z2 = ((2*B + C**2 *D2)/(2*C))\n",
    "    f2 = 1-f1\n",
    "    f_t2 = (1/2) * np.sqrt(np.pi) * A * C * ((f1 * np.exp((D1*(B-t)) + ((C**2 * D1**2)/4))*(special.erf(z1) - special.erf(z1 - (t/C)))) \n",
    "                                             + (f2 * np.exp((D2*(B-t)) + ((C**2 * D2**2)/4))*(special.erf(z2) - special.erf(z2 - (t/C)))))\n",
    "    return f_t2\n",
    "\n",
    "def jackman2(t,theta2):\n",
    "    A,B,C,D1,D2,f1=theta2\n",
    "    z1 = ((2*B + (C**2 *D1))/(2*C))\n",
    "    z2 = ((2*B + C**2 *D2)/(2*C))\n",
    "    f2 = 1-f1\n",
    "    double_exp = (1/2) * np.sqrt(np.pi) * A * C * ((f1 * np.exp((D1*(B-t)) + ((C**2 * D1**2)/4))*(special.erf(z1) - special.erf(z1 - (t/C)))) \n",
    "                                             + (f2 * np.exp((D2*(B-t)) + ((C**2 * D2**2)/4))*(special.erf(z2) - special.erf(z2 - (t/C)))))\n",
    "    return double_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A,B,C,D1,D2,F1\n",
    "def gg(t,A,B,C,D1,D2,F1):\n",
    "    F2 = 1-F1\n",
    "    Z1 = (2 * B + C**2 * D1) / (2*C)\n",
    "    Z2 = (2 * B + C**2 * D2) / (2*C)\n",
    "    const = (np.sqrt(np.pi) * A * C / 2)\n",
    "    bit1 = F1 * np.exp(D1*(B-t) + (C**2 * D1**2 / 4)) * (erf(Z1) - erf(Z1 - t/C))\n",
    "    bit2 = F2 * np.exp(D2*(B-t) + (C**2 * D2**2 / 4)) * (erf(Z2) - erf(Z2 - t/C))\n",
    "\n",
    "    g = const * (bit1 + bit2)\n",
    "    g[np.isnan(g)] = 0\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# p0 = [2.67610891,0.018 ,0.001,-0.019,0.869 ,3.0346]\n",
    "p0J1 =[0.53, 2.01, 0.11, 0.89, 69.83, 0.14] # jackman small flare\n",
    "p0 =[10, 1.8, 0.3, .5, 130, 0.2]\n",
    "\n",
    "plt.plot(t, gg(t,*p0))\n",
    "# plt.plot(t, jackman(t,*p0))\n",
    "# plt.plot(t, jackman2(t,p0))\n",
    "plt.plot(t, aflare1(t, 2, 1, 1), label='aflare')\n",
    "\n",
    "plt.xlim(-1,15)\n",
    "plt.grid(True)\n",
    "print(min(gg(t,*p0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 1.8, 0.3, 0.5, 130, 0.2]\n",
      "[ 10.63864914   1.67932204   0.25317745   0.6608895  130.\n",
      "   0.21524984]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py:829: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa501a08810>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(-20,20,0.01)\n",
    "ftmp = aflare1(t, 2, 1, 1)\n",
    "ftmp[np.isnan(ftmp)] = 0\n",
    "\n",
    "popt,pcov = curve_fit(gg, t, ftmp, p0=p0)\n",
    "\n",
    "print(p0)\n",
    "print(popt)\n",
    "\n",
    "plt.plot(t, ftmp, label='aflare',c='k')\n",
    "plt.plot(t, gg(t,*p0), c='C0', label='p0')\n",
    "plt.plot(t, gg(t,*popt), c='r', label='fit')\n",
    "plt.xlim(0,7)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'fake data')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ftmp = aflare1(t, 2, 1, 1.075)\n",
    "ftmp[np.isnan(ftmp)] = 0\n",
    "ftmp += np.random.standard_normal(size=len(ftmp)) *  0.075\n",
    "\n",
    "plt.plot(t, ftmp, label='aflare',c='k')\n",
    "# plt.xlim(-3,12)\n",
    "plt.title('fake data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _lnProb(theta, x, y, yerr):\n",
    "    A,B,C,D1,D2,F1 = theta\n",
    "    debug=False\n",
    "    \n",
    "    # add a finite check for all variables, else return -inf\n",
    "    if sum(np.isfinite(theta)):\n",
    "        \n",
    "        # some basic boundaries\n",
    "        if (D1 < 0.) | (F1 > 1):\n",
    "            if debug:\n",
    "                print('DEBUG: boundary')\n",
    "            return -np.inf\n",
    "\n",
    "        # in case there's a math error still\n",
    "        try:\n",
    "            model = gg(x,*theta)\n",
    "            signtrick = 0.5*(special.erf((x-0.2)*100) + 1)\n",
    "#             signtrick = 0.5*(np.sign(x -0.2)+1.0)\n",
    "            model = model * signtrick\n",
    "\n",
    "            # the dreaded negative-number cliff!\n",
    "            if min(model) < 0:\n",
    "                if debug:\n",
    "                    print('DEBUG: model<0 error')\n",
    "                return -np.inf\n",
    "\n",
    "            if (gg(np.array([-1000.]),*theta) * 0.5*(special.erf((-1000-0.2)*100) + 1)) < 0:\n",
    "#             if (gg(np.array([-1000.]),*theta) * 0.5*(np.sign(-1000. -0.2)+1.0) ) < 0:\n",
    "                if debug:\n",
    "                    print('DEBUG: -1000 error')\n",
    "                return -np.inf\n",
    "\n",
    "            chisq = np.sum(((y - model)/yerr)**2)\n",
    "            return -0.5 * chisq\n",
    "\n",
    "        except:\n",
    "            if debug: \n",
    "                print('DEBUG: ambiguous fail')\n",
    "            return -np.inf\n",
    "\n",
    "    if debug: \n",
    "        print('DEBUG: indef theta')\n",
    "    return -np.inf\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# theta = [3.00671768, 1.76101647, 0.30947892,0.31284824, 2.11848531, 0.25297672]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: overflow encountered in exp\n",
      "  import sys\n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: invalid value encountered in multiply\n",
      "  import sys\n",
      "  0%|          | 2/10000 [00:00<09:53, 16.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init:  -20725.856092213653\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 44/10000 [00:01<04:09, 39.84it/s]/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in multiply\n",
      "  \n",
      "  3%|▎         | 311/10000 [00:08<03:38, 44.40it/s]/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:33: RuntimeWarning: overflow encountered in true_divide\n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:33: RuntimeWarning: overflow encountered in square\n",
      "  3%|▎         | 331/10000 [00:08<03:49, 42.18it/s]/Users/james/opt/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "  7%|▋         | 668/10000 [00:17<03:44, 41.53it/s]/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:27: RuntimeWarning: invalid value encountered in multiply\n",
      " 14%|█▍        | 1392/10000 [00:38<12:51, 11.15it/s]/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: RuntimeWarning: overflow encountered in multiply\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "100%|██████████| 10000/10000 [04:25<00:00, 37.67it/s]\n"
     ]
    }
   ],
   "source": [
    "p0new = p0J1 ## the small flare from Jackman - not that close to our solution, needs a while to burn-in\n",
    "\n",
    "## ran p0J1 (above) for 5k steps, now starting from this solution\n",
    "# jfit = [3.03184697, 1.75743007, 0.30829131, 0.3073191,  2.05719971, 0.25313744]\n",
    "# p0new = jfit # [3.00673326, 1.76098817, 0.3094334,  0.31286115,2.11846101, 0.25295999]\n",
    "\n",
    "# safety check - does the init values give a valid probability? if not, you're not going to have fun...\n",
    "print('init: ', _lnProb(p0new, t, ftmp, 0.05))\n",
    "\n",
    "pos = p0new + 1e-4 * np.random.randn(32, len(popt))\n",
    "nwalkers, ndim = pos.shape\n",
    "\n",
    "sampler = emcee.EnsembleSampler(nwalkers, ndim, _lnProb, args=(t, ftmp, 0.05))\n",
    "sampler.run_mcmc(pos, 10000, progress=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.53, 2.01, 0.11, 0.89, 69.83, 0.14]\n",
      "[2.94490052 1.74606128 0.31504101 0.29430171 2.02416171 0.21554183]\n",
      "[8.20367443e+00 3.97477526e-02 2.64687504e-02 1.83147426e-01\n",
      " 3.35067621e+01 2.10189046e-01]\n"
     ]
    }
   ],
   "source": [
    "samples = sampler.get_chain()\n",
    "# discard X% as burn-in\n",
    "Bpct = 0.5\n",
    "flat_samples = sampler.get_chain(discard=int(samples.shape[0]*Bpct), thin=15, flat=True)\n",
    "\n",
    "print(p0new)\n",
    "print(np.median(flat_samples,axis=0))\n",
    "print(np.std(flat_samples,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tnew = np.arange(-100,20,0.01)\n",
    "\n",
    "plt.plot(t, ftmp, label='aflare',c='k', alpha=0.4)\n",
    "plt.plot(tnew, gg(tnew,*p0new), c='b', lw=2)\n",
    "plt.plot(tnew, gg(tnew,*np.median(flat_samples,axis=0)), c='r', lw=2)\n",
    "plt.ylim(-.5,1.2)\n",
    "plt.xlim(-2,12);\n",
    "\n",
    "\n",
    "#### example draws from the posterior\n",
    "# inds = np.random.randint(len(flat_samples), size=50)\n",
    "# for ind in inds:\n",
    "#     sample = flat_samples[ind]\n",
    "#     # offset these lines for clarity\n",
    "#     plt.plot(t, gg(t, *sample) - 0.3, \"C1\", alpha=0.1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.1, 0.1)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inds = np.random.randint(len(flat_samples), size=100)\n",
    "for ind in inds:\n",
    "    sample = flat_samples[ind]\n",
    "    plt.plot(t, gg(t, *sample) - gg(t,*np.median(flat_samples,axis=0)), \"C1\", alpha=0.1);\n",
    "#     plt.plot(t, gg(t, *sample) - gg(t,*p0new), \"C3\", alpha=0.1);\n",
    "\n",
    "plt.xlim(-1,20)\n",
    "plt.ylim(-.1,.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Too few points to create valid contours\n",
      "WARNING:root:Too few points to create valid contours\n",
      "WARNING:root:Too few points to create valid contours\n",
      "WARNING:root:Too few points to create valid contours\n",
      "WARNING:root:Too few points to create valid contours\n",
      "WARNING:root:Too few points to create valid contours\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000.8x1000.8 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = corner.corner(\n",
    "    flat_samples, labels=['A', 'B', 'C','D1', 'D2', 'F1'], truths=np.median(flat_samples,axis=0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in exp\n",
      "  \n",
      "/Users/james/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in multiply\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the fit I liked from a couple weeks ago\n",
    "jfit = [3.03184697, 1.75743007, 0.30829131, 0.3073191,  2.05719971, 0.25313744]\n",
    "\n",
    "tnew = np.arange(-2000,30000,.01)\n",
    "\n",
    "signtrick = 0.5*(special.erf((tnew-0.2)*100) + 1)\n",
    "# signtrick = 0.5*(np.sign(tnew -0.2)+1.0)\n",
    "\n",
    "newmodel = gg(tnew,*jfit) * signtrick\n",
    "\n",
    "plt.plot(t, ftmp, label='aflare',c='k', alpha=0.4)\n",
    "plt.plot(tnew, newmodel, c='r', lw=2)\n",
    "plt.plot(tnew, signtrick, c='b', lw=2)\n",
    "\n",
    "plt.xlim(-.1,2.5)\n",
    "print(np.nanmin(newmodel))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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