diff --git a/papers/Kilpatrick2024_Alopeke/Analysis/Quick_and_Dirty.ipynb b/papers/Kilpatrick2024_Alopeke/Analysis/Quick_and_Dirty.ipynb
new file mode 100644
index 00000000..579d1bc1
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Analysis/Quick_and_Dirty.ipynb
@@ -0,0 +1,524 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quick and Dirty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import numpy as np\n",
+    "\n",
+    "from astropy import units\n",
+    "from astropy import constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def flux_from_m(m):\n",
+    "    f_nu = 10**((8.9-m)/2.5) * units.Jy\n",
+    "    return f_nu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Numbers from NT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp_time = 0.010418 # s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star_i = 163866.09 # counts / s\n",
+    "star_i_err = 6863.84 \n",
+    "frb_i = 8626.39 \n",
+    "frb_i_err = 1387.42\n",
+    "#\n",
+    "star_r = 105084.78\n",
+    "star_r_err = 5508.96 \n",
+    "frb_r = 7730.56 \n",
+    "frb_r_err = 1316.59"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Magnitudes -- Assuming AB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star_r_mag = 15.75\n",
+    "star_i_mag = 15.14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Zero points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28.303849548380075, 28.176222727892828)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ZP_r = star_r_mag + 2.5*np.log10(star_r)#/exp_time)\n",
+    "ZP_i = star_i_mag + 2.5*np.log10(star_i)#/exp_time)\n",
+    "#\n",
+    "ZP_r, ZP_i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Flux at FRB location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18.583322160252827"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frb_r_mag = ZP_r - 2.5*np.log10(frb_r) # /exp_time)\n",
+    "frb_r_mag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.13386626 \\; \\mathrm{mJy}$"
+      ],
+      "text/plain": [
+       "<Quantity 0.13386626 mJy>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frb_r_nu = flux_from_m(frb_r_mag)\n",
+    "frb_r_nu.to('mJy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## $i$-band"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18.336650006010252"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frb_i_mag = ZP_i - 2.5*np.log10(frb_i) # /exp_time)\n",
+    "frb_i_mag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.16801188 \\; \\mathrm{mJy}$"
+      ],
+      "text/plain": [
+       "<Quantity 0.16801188 mJy>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frb_i_nu = flux_from_m(frb_i_mag)\n",
+    "frb_i_nu.to('mJy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Limits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.068396202 \\; \\mathrm{mJy}$"
+      ],
+      "text/plain": [
+       "<Quantity 0.0683962 mJy>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lim_3sig_r = frb_r_nu * (frb_r_err/frb_r) * 3\n",
+    "lim_3sig_r.to('mJy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.11399367 \\; \\mathrm{mJy}$"
+      ],
+      "text/plain": [
+       "<Quantity 0.11399367 mJy>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lim_5sig_r = frb_r_nu * (frb_r_err/frb_r) * 5\n",
+    "lim_5sig_r.to('mJy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.081066256 \\; \\mathrm{mJy}$"
+      ],
+      "text/plain": [
+       "<Quantity 0.08106626 mJy>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lim_3sig_i = frb_i_nu * (frb_i_err/frb_i) * 3\n",
+    "lim_3sig_i.to('mJy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fluence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.0011875861 \\; \\mathrm{Jy\\,ms}$"
+      ],
+      "text/plain": [
+       "<Quantity 0.00118759 Jy ms>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fluence_5sig_r = lim_5sig_r * (exp_time*units.s)\n",
+    "fluence_5sig_r.to('Jy ms')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.00071255163 \\; \\mathrm{Jy\\,ms}$"
+      ],
+      "text/plain": [
+       "<Quantity 0.00071255 Jy ms>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fluence_3sig_r = lim_3sig_r * (exp_time*units.s)\n",
+    "fluence_3sig_r.to('Jy ms')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$0.00084454825 \\; \\mathrm{Jy\\,ms}$"
+      ],
+      "text/plain": [
+       "<Quantity 0.00084455 Jy ms>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fluence_3sig_i = lim_3sig_i * (exp_time*units.s)\n",
+    "fluence_3sig_i.to('Jy ms')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## FRB fluence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$4.2827494 \\times 10^{14} \\; \\mathrm{Hz}$"
+      ],
+      "text/plain": [
+       "<Quantity 4.2827494e+14 Hz>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nu_r = constants.c / (700*units.nm)\n",
+    "nu_r.to('Hz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$3.5269701 \\times 10^{14} \\; \\mathrm{Hz}$"
+      ],
+      "text/plain": [
+       "<Quantity 3.52697009e+14 Hz>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nu_i = constants.c / (850*units.nm)\n",
+    "nu_i.to('Hz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frb_fluence = 4.4 * units.Jy * units.ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$69.356366 \\; \\mathrm{}$"
+      ],
+      "text/plain": [
+       "<Quantity 69.35636557>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eta_r = fluence_3sig_r * nu_r / (frb_fluence * 1*units.GHz)\n",
+    "eta_r.to('')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## $i$-band"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$67.697646 \\; \\mathrm{}$"
+      ],
+      "text/plain": [
+       "<Quantity 67.69764608>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eta_i = fluence_3sig_i * nu_i / (frb_fluence * 1*units.GHz)\n",
+    "eta_i.to('')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/papers/Kilpatrick2024_Alopeke/Analysis/Red_analysis.ipynb b/papers/Kilpatrick2024_Alopeke/Analysis/Red_analysis.ipynb
new file mode 100644
index 00000000..59737c2e
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Analysis/Red_analysis.ipynb
@@ -0,0 +1,770 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Count me"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import numpy as np\n",
+    "from scipy.stats import norm\n",
+    "from scipy.stats import poisson\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from astropy.io import fits\n",
+    "from astropy.table import Table\n",
+    "from astropy.time import Time\n",
+    "from astropy import units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def set_fontsize(ax, fsz):\n",
+    "    \"\"\"\n",
+    "    Set the fontsize throughout an Axis\n",
+    "\n",
+    "    Args:\n",
+    "        ax (Matplotlib Axis):\n",
+    "        fsz (float): Font size\n",
+    "\n",
+    "    Returns:\n",
+    "\n",
+    "    \"\"\"\n",
+    "    for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
+    "                 ax.get_xticklabels() + ax.get_yticklabels()):\n",
+    "        item.set_fontsize(fsz)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = Table.read('../Data/master_table_redcam.fits')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=5</i>\n",
+       "<table id=\"table139671042050256\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>frame</th><th>UTC</th><th>flux_1FWHM</th><th>fluxerr_1FWHM</th><th>flux_2FWHM</th><th>fluxerr_2FWHM</th><th>flux_star1_1FWHM</th><th>flux_star1_2FWHM</th><th>x_star1</th><th>y_star1</th><th>SN_star1</th><th>flux_star2_1FWHM</th><th>flux_star2_2FWHM</th><th>x_star2</th><th>y_star2</th><th>SN_star2</th><th>flux_star3_1FWHM</th><th>flux_star3_2FWHM</th><th>x_star3</th><th>y_star3</th><th>SN_star3</th><th>MJD</th><th>file</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>bytes10</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bytes29</th></tr></thead>\n",
+       "<tr><td>1</td><td>07:42:37.5</td><td>41.09619966387744</td><td>204.3105572221791</td><td>708.1392292153837</td><td>410.30599587347393</td><td>161.15999999999994</td><td>733.4000000000004</td><td>67.4775429326288</td><td>208.52146631439894</td><td>27.081358902388935</td><td>867.96</td><td>1154.96</td><td>77.45429740791269</td><td>247.66507503410642</td><td>33.98470244095128</td><td>699.7200000000001</td><td>911.920000000001</td><td>219.01283585336668</td><td>241.54313064288044</td><td>30.19801317967791</td><td>59145.320596604084</td><td>count_table_reduced_01_r.fits</td></tr>\n",
+       "<tr><td>2</td><td>07:42:37.5</td><td>1319.3480070877076</td><td>303.7042626786741</td><td>7842.342931165101</td><td>611.0096404430687</td><td>126832.08</td><td>171249.24000000002</td><td>70.72577147578328</td><td>201.1659431072298</td><td>413.8227156645706</td><td>17255.280000000002</td><td>26214.279999999995</td><td>79.11964937144057</td><td>240.93737442662723</td><td>161.90824562078362</td><td>53108.119999999995</td><td>71879.63999999997</td><td>219.48939617146652</td><td>242.7834837014834</td><td>268.1037858740528</td><td>59145.32059673827</td><td>count_table_reduced_01_r.fits</td></tr>\n",
+       "<tr><td>3</td><td>07:42:37.5</td><td>1931.2358083868028</td><td>300.70813484360747</td><td>10838.24466142595</td><td>605.4659129476277</td><td>133895.48</td><td>179014.07999999996</td><td>70.8221427896834</td><td>201.17037593311719</td><td>423.10055542388494</td><td>14986.520000000004</td><td>25638.399999999998</td><td>79.07610279962599</td><td>241.3169001980089</td><td>160.11995503371838</td><td>43805.08</td><td>64405.24000000001</td><td>219.9698534068555</td><td>242.0887384018314</td><td>253.78187484530886</td><td>59145.32059687244</td><td>count_table_reduced_01_r.fits</td></tr>\n",
+       "<tr><td>4</td><td>07:42:37.5</td><td>1748.7043463277814</td><td>298.45045507817014</td><td>7231.171118897203</td><td>598.5630554163253</td><td>127669.60000000002</td><td>173886.63999999998</td><td>71.11263135383966</td><td>201.21414842962898</td><td>416.99717025418767</td><td>18244.360000000004</td><td>29723.960000000003</td><td>78.16490023557878</td><td>240.55132076585238</td><td>172.4063803923741</td><td>45356.76000000001</td><td>62081.56000000001</td><td>219.38856152387743</td><td>242.3897359425101</td><td>249.1617145550255</td><td>59145.32059700662</td><td>count_table_reduced_01_r.fits</td></tr>\n",
+       "<tr><td>5</td><td>07:42:37.5</td><td>2266.8467990481854</td><td>299.00791510123736</td><td>9563.821881459062</td><td>599.8886049234525</td><td>122921.68</td><td>168733.08000000007</td><td>71.02556898451927</td><td>200.9617680031375</td><td>410.77132324445444</td><td>17142.360000000004</td><td>27718.240000000023</td><td>79.2684134369226</td><td>241.70508670777588</td><td>166.48795752245871</td><td>48848.44000000001</td><td>68021.36</td><td>219.90539603876448</td><td>242.32537963253435</td><td>260.80904892277033</td><td>59145.320597140795</td><td>count_table_reduced_01_r.fits</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=5>\n",
+       "frame    UTC     ...        MJD                      file            \n",
+       "int64  bytes10   ...      float64                  bytes29           \n",
+       "----- ---------- ... ------------------ -----------------------------\n",
+       "    1 07:42:37.5 ... 59145.320596604084 count_table_reduced_01_r.fits\n",
+       "    2 07:42:37.5 ...  59145.32059673827 count_table_reduced_01_r.fits\n",
+       "    3 07:42:37.5 ...  59145.32059687244 count_table_reduced_01_r.fits\n",
+       "    4 07:42:37.5 ...  59145.32059700662 count_table_reduced_01_r.fits\n",
+       "    5 07:42:37.5 ... 59145.320597140795 count_table_reduced_01_r.fits"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100000"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ndata = len(data)\n",
+    "ndata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Diagnostics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gd_frame = data['frame'] > 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C_gal = data['flux_2FWHM'].data[gd_frame]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.011593429371714592"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt = data['MJD'][1] - data['MJD'][0]\n",
+    "dt * 24 * 60 * 60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2020-10-23T07:41:39.547'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t0 = Time(data['MJD'][0], format='mjd')\n",
+    "t0.isot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time evolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-6.84400548e-04,  4.35452300e+01])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = (data['MJD'][gd_frame]-59145)*24*60*60\n",
+    "y = C_gal*5.6/2000\n",
+    "p, V = np.polyfit(x, y, 1, cov=True)\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = np.poly1d(p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(43.32581343143557, 43.315896421440115)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(xval[0]), f(xval[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,10))\n",
+    "ax = plt.gca()\n",
+    "ax.scatter(x, y, s=1)\n",
+    "# Fit\n",
+    "xval = np.linspace(x.min(), x.max(), 10000)\n",
+    "ax.plot(xval, f(xval), 'r-')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gaussian Galaxy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_gal, std_gal = norm.fit(C_gal)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(8,8))\n",
+    "plt.hist(C_gal, bins=30, density=True)\n",
+    "xmin, xmax = plt.xlim()\n",
+    "x = np.linspace(xmin, xmax, 1000)\n",
+    "y = norm.pdf(x, mean_gal, std_gal)\n",
+    "plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examine the tails"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isort_gal = np.argsort(C_gal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cdf = (np.arange(isort_gal.size)+1) / isort_gal.size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### High end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9999978242613436"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm.cdf(15000., loc=mean_gal, scale=std_gal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xval = np.linspace(mean_gal, C_gal.max(), 100000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PBYjZqIf9cc57DPHejWAB6PZfOLRnzGNs2LaTzo9OY1nONqpVSOXDG9pStUJqzHOIiEj0aNRDIjLzFpK6fDrUOAhGD4C3r4x5R8dK5VKYdlM7TjqoBms276DjI1OZu3xDTDOIiIg/VCgkgqqNvX4LJ93krUj5zImwdFZMIyQnBXj1oqP5R8eDWLt5B10em07bBz9m9abtMc0hIiKxpUcPBYibRw8FWfS5t17ExuXQ7nY44XoIxLbe+/XPTTz64S+898NyAI5uVJWbOjcju1HVmOYQEZGSo5kZCyGuCwXwZnR893r4aSw0aedN2pRRM+Yx3v72D+6bOJeVG7y7Cq0bVWHEwOMIaEZHEZGEo0KhEOK+UACvo+M3r8DEmyGtEvR8Fpq29yXKd0vW0+PJz/a8f6Tv4fQ8qp4vWUREpGjUmbG0MYNWF8KlH0P5qvBaT/hoMOTujHmUI+pX5ue7O+95f8PI2TS65T2WrtsS8ywiIlKyVCgkulotvGLhqPPh00fhpa6wfnHMY6QlJ7Hw/tOYdP2Je7a1eeBj3pwR+ywiIlJyVCiUBqnl4YzHofeLsGqeN/3znHd8idK8diUW3n8al53UBIBbxvxA5/9O05oRIiIJSoVCaXJoL7hsGlRtCiP7w3uDYOc2X6Lc2vVg7jnzUADmrdhI41snkJunYkFEJNGoM2MIM+sGdMvKyrp0/vz5fscpul07YMoQ+Px/UOtQ6P2SN2GTD3bsyuOgOybutW3+PV1ISVKNKiIST9SZMQLOufHOuYGZmZl+Ryme5FQ45W44Z5Q338LQtvDtMG+kRIylJgeYd1dnLm7TeM+2A2+fyAOT5sU8i4iIFJ7uKBQgIYZHRmrDchhzKSycDof1hdMfgbSKvkTJy3Nc+upMJs/7c8+2ly5sTbvmsZ8DQkRE9qY7CmVVpTpw/jhvFscfR8OzJ8Gy73yJEggYL1zYmlGXH7dn24CXv+b7pet9ySMiIvunQqEsCCRB25vgwve8zo0vdIKvX/DlUQRA60ZVWXj/adx5egsAznjiM8569gvy1NlRRCTuqFAoSxoeD5d/Co1PgvdugLcujvlKlKEubtOYu3p4IyO++n0tTW6bwOe/rfYtj4iI/J36KBSgVPVRKEheHnz2KEy5G6o2gT6vQO1DfYuzK9cbGbH7hkKNimlM+2c70lOTfMskIlLWqI+C/CUQgBMHwQXjYfsmeL4DfPOqb48ikpMCLLjvNO7reRgAqzZu5+B/TeK+CXP1OEJExGe6o1CAUn9HIdSmP71REQs+gZb9vFERqRV8i7MrN48bR83m7e+W7dl2WdsmDOrUjNRk1bUiItGi1SMLoUwVCgB5uTDtQfjkfqjRzHsUUbO5r5H+WL+VE+6fste2QZ0O4ur2WZhpGWsRkZKmQqEQylyhsNuCT+CtS2DHZjj9UTi8n9+JyNm6kxtGfLfX3AsHZJajbpV0MtNTeaDXYVTLSPMxoYhI6aBCoRDKbKEAsHEFjL4YFn0KR/aHrg9CSrrfqfht1SbufW8uy3O2MWf5hr0+u7VLcy49sQmBgO40iIgUlQqFQijThQJA7i745D6Y/pC3VkSfV6B6lt+p/qbvM18wY+HaPe//r1sLBpzQeB/fISIi4ahQKIQyXyjsNv8jr6Nj7g7o9hgc1tvvRH8z4/e1PPzBz3z1+18Fw13dD6H/cY38CyUikoBUKBSCCoUQOUth9EWw5CtofSmceq+36FSc+fzX1Zzz/Fd7bfv1ni4ka5VKEZGIqFCIQKlZZrqk5e6EjwbDF09A3Wzo+wpk1vM7VYHmLt9Al8em77Vt1OXH0bpRVZ8SiYgkBhUKhaA7CmHMGQdvX+XdUej1AjRt53eiAu3KzeOKYd+wPGcrP/7xV8fHw+tl8shZR9C0RoaP6URE4pMKhUJQobAPq+fDiPNg1c/Q/nZoM8ib6TFODZ32G6NnLeWXlZv2bJs8qK2KBRGRfFQoFIIKhf3YvgnGX+ctW31QFzjzaUiv4neq/TrnuS/5/Lc1ALx0YWvaNa/pcyIRkfihtR6k5KRlQK/nocuD8OtHMPRkWP6936n2641Lj+XiNt7wyQEvf+1zGhGRxKBCQYrGDI4ZCAMmwK4d8EIn+HaY36n2687TW1A9wxu1MXnuSnbm5vmcSEQkvqlQkOKpfzRcNs37c9yV8M61sHOb36n26d4zvVUqL35lJgfePpGPQ6aHFhGRvalQkOLLqAH93/aWrv7mFXjxVFi3yO9UYXVqUYuXB7TmqAaVAe8xxDNTf/M3lIhInFJnxgKoM2MxzJsAYy/3Hk30eh4O7OR3on367NfVnBucqCkjLZmr2mXRJ7se1bXQlIiUMerMKLHRvCtc9glk1odhfeDj+7xlrOPUCVnVebZ/KwA2bd/FA5PmkX33RzwxZT45W3f6nE5ExH+6o1AA3VEoATu2wHuDYPYbkNUJej0X90Moc7bs5IwnP2XRmi17tt3Q6SCuPLmppoIWkVJP8ygUggqFEuIczHwRJt7sTfnc7w2o1cLvVPu1fssOzn9xBt8vzdmzbcG9XbWMtYiUanr0ILFnBq0vhgvfg51b4PmO8NNYv1PtV+XyqbxzdRvmDDl1z7Ymt03g5c9+Z+3mHT4mExGJPd1RKIDuKETBxhUw8nxvFcoTroMO/weBJL9T7VfOlp10enQqf27cvtf27wefQqVyKT6lEhEpebqjIP6qWBsueBeyL4bPHoPXe8GWtX6n2q/M8inMuL0jn93SnkGdDtqzveXgD1CRLSJlgQoFiZ3kVDj9ETjjf7DoMxjaNiGmfgaoWzmdazocyML7TyM12LGx8a0T2LJjl8/JRESiS4WCxN5R58OASZC7C144Bb4f5XeiQplxe4c9X7f41/us2bR9H3uLiCQ2FQrij3qt4LKpUPcoGHMJTLrNKxwSQOXyqcy7qzNZNb2lqlvd/RHzVmzwOZWISHSoUBD/ZNSE88fBMVfAl0/Caz1g0yq/U0WkXEoS713bhoy0ZAA6/3c6l7zyNbl56rcgIqWLCgXxV1IKdLkfzhwKS7/2lqz+4xu/U0UkLTmJH/99KqceUguAj+b+SdPbJrBhm2Z0FJHSo9QPjzSzqUBlwIBfgIucc/u8T6zhkT5Z9h2M6A+bVsLpj8KR5/qdKGK7cvPIun3invdNqldgwnUnUi4l/oeAiohA2R4eeYZz7nDnXEtgMfBPvwNJGAccAQM/gQbHektWT7gJchPjt/PkpAA/392ZW7o0B2DB6s00v3MSY75ZqmGUIpLQYl4omFmWmT1rZrPNLNfMPgmzXwszm2xmW8xsmZkNMbNC/3rmnMsJHi8AVAD0UzueVagG542B466GGc/C6z0TYr4F8B5FXN62Kb/e02XPthtGzqbxrRO47LWZ6r8gIgnJjzsKhwBd8R4D/FLQDmZWBfgI7x/17sAQYBDw76Kc0MwmACuBZsB/inIMiaGkZDj1HujxDCz+yuu3sPInv1NFLDkpwML7T2P6Te04qkFlAN7/aSUH/2uSigURSTh+FArjnXP1nXN9gHA//S8H0oGezrkPnXPP4BUJN5hZpd07mdmnZrawgNcLoQdzznUFagMzgCuj0iopeUecDQMmwK7t8HwnmDve70SFUr9qecZceQLz7uoMwI5decxatM7nVCIihRPzQsE5lxfBbl2A9/N1OnwTr3hoG3KsNs65RgW8Li7gvLnAK8D5xWyCxFK9bK/fQs3mMOI8+OQByIvkr1D8KJeSxJsDjwWg77NfcN/EuT4nEhGJXLx2ZmwOzAvd4JxbDGwJfhYRM6tiZrVCNvUCfgyz70Azm2lmM1etSoyx/GVGpTpw4QQ4/Gz45F4YdQFs3+R3qkI5pnFVzjmmAQDPTl3Aas3mKCIJIl4LhSrA+gK2rwt+VpjjvGdm35vZD8ChwHUF7eicG+qcy3bOZdeoUaOweSXaUspBj6fh1Hth3rvw4qmwbpHfqSJmZtx75mFc2z4LgOy7P+KP9Vt9TiUisn/xWihAwaMTLMz2gg/g3ILgP/4tnXOHOef6OudWllxEiSkzOO4qOHc05CzxOjn+Pt3vVIXyj04H0aKO183mhPuncPbQL7n+zW/5c8M2n5OJiBQsXguFdXiTJOWXScF3GqQsyeoAl34MFWp40z7PeA4SZK4CM2PCdSdy5pF1yW5YhS8WrOHt75Zx9L2T2ZWbWH0vRKRsiNdCYR75+iKYWX28eRDmFfgdJcDMupnZ0JycnGidQkpKtaZwyUeQ1REm3AjvXg+7dvidKmKPnnUEo684np/+feqebR0emcr2Xbk+phIR+bt4LRQmAqeaWcWQbWcBW4Gp0Tqpc268c25gZmZmtE4hJalcJej3BrS5AWa9DK+ekTCLSu1WIS2ZWXd0BGDRmi00u2MSfZ75XPMtiEjc8GNmxvJm1tvMegN1gRq735tZ+eBuzwDbgTFm1tHMBgKDgUf2t06DlDGBJOj4f9D7RW+tiKEnw/LZfqcqlGoZaXz3r050auEN0Pl64Tom/LDc51QiIp6YLwplZo2A38N83Ng5tzC4XwvgCeA4vH4JzwODg/MhRJUWhUpQy76DN8+FrWuh51A4uJvfiQpt2fqtHH//FAC+vr0jVcqnkJwUrzf+RKQ0iZtFoZxzC51zFua1MGS/Oc659s65dOdcHefcnbEoEiSBHXAEXDoFarbwJmea/nDCdHLc7YDK6TSpUQGA1vd8RNbtE3n5s3B1tYhI9OlXlRDqzFgKVKwFF74Hh/WByUNg7GWwM7GGHn5w/Unc1/OwPetEDB4/h1//TKwJpkSk9FChEEKdGUuJlHLQ8zlofwd8PwJe6Qab/vQ7VcSSkwKcfXQDxlx5Ak+fexQAz0z9zedUIlJWqVCQ0skMTvon9H0VVvwAz7WHFQXO3h3XTm5WE4DRs5ayIiex7oyISOmgQkFKtxbd4aJJkJcLL5wC8yb4nahQ0lOT6H9sQwCOvW+yhk2KSMzFfNRDItCoh1Jow3J482xvZETHwXDCdd5dhwTR6Jb39nx90QmNqVkpjfKpSfTNrk+5lCQfk4lIaRFu1IMKhRBm1g3olpWVden8+fP9jiMlbccWGHcl/DQWjjgXTn8UktP8ThWRjdt2ctvYHxk/e9le28unJnFfz8M4pnE1ameW8ymdiJQGKhQKQXcUSjHnYOoD8Ml90OA4OOt1qFDd71QR274rl7w82LErj8OHfLDXZwfXqcRT5x5F4+oVfEonIolMhUIhqFAoA358C96+EjJqwtkjoFYLvxMV2tYduSzP2cqrXyxi8ryVLFm7lfSUJObe1dnvaCKSgOJmwiWRuHBoLxgwwVtI6oVO8Mv7ficqtPTUJJrUyGDwGYcw7Z/tANi6M5dTH52GfgEQkZKiQkHKrrqtvJkcqzWFN86CL55MuJkcdzMzPvjHSQD8vHIjN43+3udEIlJaqFCQsi2zLgyYCAefDu/fBu8NgtxdfqcqkoNqVeSLW9sDMGrWUu56dw5PfvyrhlSKSLGoUAihKZzLqNQK0OdVOP5amPkCDD8LtiXmIqV1MtO547SDMYMXPv2dB9//mUEjv/M7logkMHVmLIA6M5Zhs16Gd2+AGs3hnBFQub7fiYps7eYdHHXXh1TPSOOs1vW4tsOBpCVrzgURKZg6M4pEotWFcN5oyFkCz3eAP77xO1GRVa2Qyq1dmrN603ae/Pg3Lnzxaxau3ux3LBFJMLqjUADdURD+nAvD+sLmVdDrea8PQ4LasG0nLQf/NedC71b1aFkvk/7HNsQSaHZKEYku3VEQKYyaB8Olk735FUacB5//L2FHRFQql8LHN57MPzoeBHgLTP1r3E+c9ODHrNq43ed0IhLv9ntHwcw+AK5xzv0csq098JVzrlTex9QdBdljxxYYexnMfQdaDYCuD0FSst+pisw5x9J1Wznzqc9YvWkHAM+fn80RDSpTPSMxprMWkegozh2FjkBmyIGSgA+BZiUXLz5o1IP8TWp56POKt4jUrJfgjb4JOyICvPkW6lctz+e3dKBZrYoAXPLqTLo/8ZnPyUQkXhX10UOpfLDpnBvvnBuYmZm5/52l7AgEoNMQ6PYYLPgEXjwV1i/2O1WxpCYHeO/aNrx7TRsA/li/VbM5ikiB1EdBJFKtLoTz3oKcpfBcB/hjlt+JiiU5KcChdTPp06oeAI1vncBTn/zqcyoRiTeRFgoF/aqhXz+k7GnaDi7+AJLLwUunwdzxficqtn90OogrTm4KwH8m/awOjiKyl0gLhffN7E8z+xNYHtw2efe20FeUcorEjz0jIg6BEf0TekQEwAGV07m5c3POProBAGO+WepzIhGJJ5F03/531FOIJJqMmnDhu96IiA/u8PosdL4fAok78+GVJzdl+IzFPDP1Ny5r29TvOCISJ/ZbKDjnVCiIFCQlHXq/DB/eCV88ATl/eJMzpZb3O1mR1K9anirlU1i3ZScXvjQDgOSAMeiUZhxcp5LP6UTEL4XuzGhmSWZWI/hK3F+fREpCIACn3gOdH4CfJ8Ar3WDzar9TFdm/ux/K4fUyWbd5B2s37+CjuX8yee5Kv2OJiI8insLZzPoDVwCt+OtOxE5gFvCUc25YVBL6QBMuSZHMHQ9vXQIV63ijI6ol/u37xre+h3Pw+31dNd2zSClXrCmczex54BW8+RMewCsYrgT+AyQBr5rZ0JKL6w9NuCTFcnA3uOBd2L4Bnu8IS2b4najYGlb1HqN0e+JTdubm+ZxGRPyw30LBzE4HBgAXO+eOc879yzk31Dn3bPDrY4FLgYvMrGu0A0eTJlySYqvfGi7+ENIre48h5rzjd6JieWnA0RzTuCo//rGBjdt2+R1HRHwQyVoPbwHbnXPn7Ge/4UCKc653CebzhR49SLFtXg3D+8HSmdD5Pjj2Cr8TFdmwrxZx+9gfyUxPIRB8+nB526YaGSFSyhTn0UM2EMmvReOA1oUNJlIqVagO578DzU+DSbfApFshLzFv3Xc6uBYXt2lM9yMOoNvhBxAwY8bva/2OJSIxEsk8CjWAJRHstzS4r4iAN0yy76vw/m3w5VPe1M89h3rDKhNIzUrluPP0Fnvez1uxkS8WrKHzf6dxS5fmnNyspo/pRCTaIrmjUA5vdMP+7AS0Tq1IqEASdHkATr3XW6r61e6weY3fqYrl/OMactKBNZi3YiMXvvS1pnwWKeUi6aOQBzwILNrPsRoBg5xzCT+3gvooSFT8NBbGXAaZ9eC80VC1id+JiqXnU5/xzeL1nHRQDQYc34h2zXVnQSSRheujEGmhECmnQkFkHxZ9AW+eDZYE54yEeq38TlRkqzdt56xnv2Dx2i3UrZxO/+MacWyTqhxygEYNiSSiIndmdM4FCvFK+CJBJKoaHucNn0ytAC+fBr+873eiIquekcbkQSfT7fADWLhmC3e9O4crXv/G71giUsIKPYWziBRT9QPhko+gRjMYfjZ885rfiYrlod6HM/v/TuHAmhksXrtFfRZESplIJlyqZmZvmdmp+9jn1OA+ekgpEondq082aQvvXA3THkzYpaoDASMzPWXPMtUnP/gxS9Zu8TmViJSUSO4oXA80AT7Yxz4fAI2BQSWQyTeawlliKq0inD0CDusLU+6GCTdCXq7fqYqsV6t69Gtdn807crnwpRns0pTPIqVCJIVCX+AZt49ej8HPngW6l1QwP2gKZ4m55FQ481k4/lr4+nkYdQHs3OZ3qiLJTE/hhlMOAuC3VZtZqUcQIqVCJIVCQ2BOBPvNxRsiKSKFEQjAKXfBqfd5K1C+3hO2rvc7VZHUrFiO//RqCcDOXXnszM0j0hVqRSQ+RVIobAUqRbBfRnBfESmK466EXi94q06+1AVy/vA7UZEkJ3kLQpz80CccePtErh/xnb+BRKRYIikUvgHOiGC/7sF9RaSoDusN570F65fAC6fAn/P8TlRoHVvU4rauzbnxlINoXL0C0+evZvA7P7E8R79HiCSiSAqFJ4GLzeyCcDuY2fl4S1E/UVLBRMqsJm1hwATI2wkvngqLv/Q7UaFUKpfCwJOacnX7A+l1VF0AXv58IaNnLvU5mYgUxX5nZgQws4eBfwCzgEnAYsABDYBT8VaYfNQ5d2P0osaOZmaUuLBuIbzWEzb8Ab1f9FaiTEDrt+zgiCEfAjDuqhM4vH5lfwOJSIGKs8w0zrlBeI8WNgA34o1wGAr8E9gIdC8tRYJI3KjSCC7+AGodAiPOg5kv+Z2oSCqXT+UfHb3REI9Pns/gd37i43l/+pxKRCIV8cyMwaGDHYCKQJ3gq6JzrqNz7t1oBRQp0ypUhwvGQ1ZHePd6+Pi+hJyY6ZxjGlCvSjozF63j9S8X8fiU+X5HEpEIRfToIew3m50EzHLObS65SP7ToweJO7k7Yfx18N0wOOoCOO0RSEr2O1WRXPjSDD75eRVpyd7vKdd2OJCr2mX5nEpEwj16KPJPGjNLAj4GWqPRDiLRlZQC3Z+EinVg+kOwZY03lDKlnN/JCu3qdlk0q10RgBFfL2Hqz6tocUAlWtSpRK1KidcekdKuuL+SWImkEJH9M4MOd0KFGjDpZhjWG/q9AeUimeYkfmQ3qkp2o6oAfLVgLTMWrmXGS2s58cDqvHbxMT6nE5H8tHqkSKI59nLo+Tws/gJeOR02rfI7UZG9eGFrxl55PC3rZTJ/5Saem7ZAMzmKxJniFAoOmIo36kFEYqllHzj7TVj1C7x4Cqxb5HeiIqlaIZUjG1Th6EZVWbVpO/dMmMuiNVp5UiSeFLlQcM7lOefaOefmA5hZOTNrUHLRRGSfDuwE54/z+iu8cAqsjGRJlvh0x+kteKTv4QDc/vYPPPrhLz4nEpHdSvLRw2nA7yV4vJjTMtOScBocAwMmef0XXuoMi7/yO1GRHXJAJofXr8y85Rt5eupvfscRkaBiDY/c60BmvYCRzrmkEjmgjzQ8UhLOukXw2pmwYRmc9Zp3tyFB3T9xHs9O+41jGlf922f9Wjegx5F1fUglUvoVeXikmU2J8Bw1Cp1KREpGlYZw0fveEtXD+0GPp6FlX79TFcnJzWrw3ZJ15OX7HebHP3LISFumQkEkxvZ7R8HMdgE/A/t7AFoXOEZ3FER8tG0DvHkOLJwOnR/wRkiUEqc9Pp2VG7bRsl7lAj+vXyWdwWccgplGbYsURXEmXPoR+Nk5d9Z+TtAbGFHEfCJSEspVgnNHw1sXe3MtbFkD7W7z+jAkuK6H1WHSjytYtXH73z5btXE7U+b9yT87NycjLTFnrBSJV5H8H/UV0DmC/RyagEnEfynloM8r3toQ0/4DW1ZD14cgkNg3+65qlxV2qufnpi3gnglzeej9n0lJ8n4MpSYHuOiExlTLSItlTJFSJ5JC4T/AexHsNwFoXLw4IlIikpLhjP9B+Wrw2X9hy1roORSSS+c/mlm1MqhYLpmRM5cAkOcc23bm0bBaBfpm1/c5nUhi22+h4Jz7DdjvWCXn3FYgMWd9ESmNzKDTv70VKD+4A7ath7Neh7SKficrce2a1eSHwafueb9s/VaOv38Kv/65ic9/W71ne4XUZFrWy1Q/BpFC0MM8kdLu+Gu8OwvjroZXu3t9GMr/fehhaZKekoQZDJ22gKHTFuz12dgrj+fIBlV8SiaSeFQoiJQFR5wD5SrDqAvh5dOg/1ioWNvvVFFTpUIqH1x/Ems279izbd7yDQweP4fN23N9TCaSeFQoiJQVzbvCuSNh+DnwYmdv+ucqDf1OFTUH1qrIgSHvkwLe44Zbx35PxbQUBp1yEB0OruVPOJEEotUjRcqSJid7BcLWtV6xsOpnvxPFzMF1KtHzqLocXLsSP6/cyBe/rfE7kkhCUKEgUtbUbw0XToC8XfBSF1j2nd+JYiIjLZlH+h7B0POzKZcc4M+N2/luyfo9r8VatVKkQHr0IFIW1T4ULprkdW58pRucMxIaHud3qphJT03mndnLeGf2sj3bAgaz7uhElQqpPiYTiT8qFETKqmpNg8VCD29BqbNehwM7+p0qJt4ceCxL1v51B2H6/NW8+NnvbN6xS4WCSD4qFETKssx6MGAivH6mt5hUr+fhkB5+p4q6rJoZZNXM2PN+1SZvWuhtO/P8iiQSt9RHQaSsy6gBF7wLdVvB6AHwzWt+J4q5QHACpp+W5ficRCT+qFAQEUivDP3HeKMi3rkavnjK70QxdUT9TOCvgkFE/qJHDyLiSa0AZ7/prTz5/q2wfQO0vblUrDwZqc9/W83m7bvCfl4uJYnOh9amXEpiL7AlUhgqFETkL8lp0PtlGH8tfHIfbNsAp95T6ouFzPRUUpKM4TOWMHzGkn3u+1xaNp1aaKImKTvKTKFgZk8BVzjnSvdPPJHiSkqGM57wFo/68knYngPdHk/4Zar3pUbFNGbe0YktO8LfTViwajPnPv8VO3apw6OULWWiUDCzE4EKfucQSRiBAHS+H8plwtQHYPsm6PkcJJfeoYOZ6SlkpqeE/XzjNq+I2LZTa0VI2RLzzoxmlmVmz5rZbDPLNbNPwuzXwswmm9kWM1tmZkPMrNC/0phZGnA/cGMxo4uULWbQ7jY45R6Y8zaM7A87t/mdyjcpSd6Py0GjZvucRCS2/Bj1cAjQFfgl+PobM6sCfAQ4oDswBBgE/LsI5/sX8IJzblWR0oqUdcdfDac9Ar9MguFnwY7NfifyRaNq5WlYrTypSRosJmWLH3/jxzvn6jvn+gA/hdnnciAd6Omc+9A59wxekXCDmVXavZOZfWpmCwt4vRD8vCVwDPBSdJskUsq1vhh6PA2/T4PXe3udHMsYM6PTwbVISVI3JylbYt5HwTkXSU+gLsD7zrnQn0ZvAg8AbYHxwWO12c9xTgBaAL9bsNe2mS0EWusOg0ghHXEOJJeDMZfCaz3g3NFQvqrfqWJuR24ej3xY4M3QAh3VoDInN6sZxUQi0RWvnRmbA1NCNzjnFpvZluBn4yM5iHPuaeDp3e/NzDnnGhW0r5kNBAYCNGjQoGipRUq7Q3t6xcKoC+CVM+D8t6FCdb9TxUzTmhnk5jkenzw/4u9pUr2CCgVJaPFaKFQB1hewfV3wsxLnnBsKDAXIzs520TiHSKnQvKs3MdOb58JLXeH8cVCpjt+pYuLsoxtw9tGR/yJx/Zvf8u2S9dELJBID8dwrp6B/rC3M9sgOqDkUREpGVgc4bzRs+ANe6gLrF/udSESiJF7vKKwDKhewPZOC7zSISKw1agP934bXe/11Z6FaU79TxZ11m3fwn0nzCvU95VOTuLhNE9JTS+8kV5I44rVQmIfXF2EPM6uPN2lS4f6PKwQz6wZ0y8rKitYpREqX+q3hgnfgtTO9YuGCd6BGM79TxY1mtSvx3g/LeW76goi/J89Bbp6jZb3KnHRQjSimE4mMOeff43gzGw1Ud86dnG/7rcA/gYbOuY3BbTfizadQO99oiBKXnZ3tZs6cGc1TiJQuf871Oje6PK+DY+3D/E6UsL5ZvI6eT33OywNaqxOkxJSZzXLOZeff7sfMjOXNrLeZ9QbqAjV2vzez8sHdngG2A2PMrGNwRMJg4JFoFwkiUgQ1D4YBE71FpV4+DZbO8juRiJQQPzoz1gRGBV/H4s1zsPt9TQDn3DqgA5CENxTy38CjwP/5kFdEIlE9yysWylWGV7vDos/9TpTQNm3fRV6eBmCJ/2JeKDjnFjrnLMxrYch+c5xz7Z1z6c65Os65O51zWo1FJJ5VaQgXTYKKtb1Ojr997HeihJMS8H4sX/3Gt9z81vc+pxGJ7+GRMWdm3cxsaE5Ojt9RRBJXpQNgwASo0hjeOAt+ed/vRAmlxQGVeKjP4dSvms6ynK1+xxFRoRDKOTfeOTcwMzPT7ygiiS2jJlz4rtd34c1zYd57fidKGEkBo3eretSqWM7vKCKACgURiZbyVb25Feq0hJHnw5x3/E4kIkWgQkFEoie9MvQfCwccBaMuhB/H+J0ooeTlwfZduXu9duVGsq6eSMmJ1wmXRKS0KJcJ/cfAsD7w1sWQlwst+/idKu4lBYwvFqyh2R2T9tqelhxgwnUn0rRGhk/JpKxRoRBCMzOKRElaRW9Z6jfOgrEDweXC4f38ThXXbunSnM9/W7PXtqXrtjB8xhJW5mxToSAxo0IhhHNuPDA+Ozv7Ur+ziJQ6aRlw7kgY3g/GXg65O+Go/n6niltHNqjCkQ32Xiz3qwVrGD5jiU+JpKxSHwURiZ3UCnDOSGjaDt65Gma+5HciEdkPFQoiElsp6dBvOGR1gnevhxnP+Z1IRPZBjx5EJPZSykG/YTDyAphwo9fB8djL/U6VMH5btYkKaXv/+D6wVgblU/UjXUqe/laJiD+S06DvqzB6AEy6GfJ2wfFX+50qru0uBO4c99PfPut5ZF0eOeuIGCeSskCFQgiNehCJseRU6PMyvHUJfHA75O2ENv/wO1XcOrRuJUYMPJbNO3bttf3/3vmJDdt2hfkukeJRoRBCox5EfJCUAr1egEASfDTYu7Nw0j/9ThWXzIxjmlT72/aH3v/FhzRSVqhQEBH/JSXDmUMhkAxT7vb6LLS9Gcz8TiZS5qlQEJH4kJQMPZ72ioVP7vPmWWh/h4oFEZ+pUBCR+BFIgjOe8IqF6Q9521QsiPhKhYKIxJdAAE7/L+C8YsEM2t2uYmE//ty4jUk/Li/WMapnpJHdqGoJJZLSQoVCCI16EIkTgQCc/pj39bQHAYN2t6lYCCMzPYUvFqzh8te/Kfaxvr2zE1UqpJZAKiktVCiE0KgHkTiyV7HwH+9PFQsFeu6CbJas3VKsY0z8cQWPT57P9l1axlr2pkJBROLX7mLBOa9YsOCdBdlLRloyB9epVKxjfLdkfcmEkVJHhYKIxLdAALo9DjiY+gDeY4hb/U4lUmaoUBCR+BcIQLf/gQOm3u/dWTj5Fr9TiZQJKhREJDEEAnDG/wDnzbOAwck3+51KpNRToSAiiWN3seAcfHKvd2eh7U1+pxIp1VQoiEhiCSRB9ycABx/fAxi01doQItES8DuAiEihBZKg+5PQsh98fDdMfdDvRKXGLWO+9zuCxBndUQihCZdEEkggCXo85X398d1gaNXJYji5WQ0A5izb4HMSiTe6oxDCOTfeOTcwMzPT7ygiEondxULLft6qk9Me8jtRwqqTmU7f7HokBTShlexNdxREJLHtubPgYMpdXgfHEwf5nUqk1FChICKJL5DkLVHtHEweAhaANv/wO5VIqaBCQURKh0ASnPkMuDz4aDAkpcJxV/mdSiThqVAQkdIjkARnPgt5O+H927xi4Wit8SZSHCoURKR0SUqGXi9A7i6YcCMEkiF7gN+pRBKWRj2ISOmTlAJ9XoIDT4F3r4dvX/c7kUjCUqEgIqVTchr0fQ2atINxV8P3I/1OJJKQ9OhBREqvlHLQ7w14oy+Mvcy703DImX6nimvOwY5deX7H2EtSwDS/g49UKIhI6ZZaHs4ZAa/3gtEXe30WDu7md6q4lBQwVmzYxkF3TPQ7yl4qlUtm2k3tqFw+1e8oZZIKhRCawlmklEqtAOeOgtfOhFED4KzXoVlnv1PFnUtPbEK9KuX9jrGXOcs28N4Py1m7eYcKBZ+Yc87vDHEnOzvbzZw50+8YIlLStq6H13rAyp/g7OGQ1dHvRLIf4777g+ve/I4pg9rSpEaG33FKNTOb5ZzLzr9dnRlFpOxIrwznjYEazeDNc2HBVL8TicQ9FQoiUraUrwr9x0HVJjC8Hyz8zO9EInFNhYKIlD0VqsH54yCzHgzrA4u/8juRSNxSoSAiZVNGTbhgPFSsDcN6wx+z/E4kEpdUKIhI2VWxtlcslK/qjYhYPtvvRCJxR4WCiJRtmXW9YiGtErza3RsRISJ7qFAQEancAC54B5LLwas9YPWvficSiRsqFEREwBsFcf474PLg1TNg3SK/E4nEBRUKIiK71TgIzn8bdmzyioUNy/xOJOI7FQoiIqFqH+ZNyrR5tddnYfNqvxOJ+EqFgohIfvWy4ZyRsH6J12dh6zq/E4n4RoWCiEhBGp0A/V6H1T/D671h+0a/E4n4QqtHioiEk9UR+rwMI/rDG/28FShT42t1xbLiljE/UCE1KaJ9y6UkMfiMQ6hVqVyUU5UNuqMQwsy6mdnQnJwcv6OISLxofhr0HAqLPoOR/WHXdr8TlSmH1c2kdaMqbNuZy5rNO/b7WrpuKxN/XMG3i/W4qKTojkII59x4YHx2dvalfmcRkThyWG/YuQXeuQZGXwR9XoEk/fiMhSY1Mhh1+fER7z93+Qa6PDY9ionKHt1REBGJxFHnQ+cHYN678PblkJfrdyKRmFBJLCISqWMvh52bYfIQSEmHbo+Dmd+pRKJKhYKISGGcOAh2bIHpD0FKBeh8n4oFKdVUKIiIFFb7O7w+C18+BakVoMOdficSiRoVCiIihWUGp94LOzZ7dxZSy3t3GkRKIRUKIiJFYQanPwo7twb7LJSHY6/wO5VIiVOhICJSVIEk6PG09xhi0i2QVhGOPM/vVCIlSsMjRUSKIykZer8ITdp58yzMecfvRCIlSoWCiEhxJadBv2FQN9ubkOnXyX4nEikxKhREREpCagU4dyTUaAYjzoPFX/mdSKREqFAQESkp6VWg/1ioWBve6AMrfvA7kUixqVAQESlJGTXh/HGQmgGvnQlrfvM7kUixqFAQESlplRtA/7fB5cGr3SFnqd+JRIpMhYKISDTUOAjOGwPbcuDVHrB5td+JRIpEhYKISLQccAScMwJylniPIbbl+J1IpNBUKIiIRFPD4+Gs1+HPOfBGP29BKZEEokJBRCTaDuwEPYfC4i9g5Pmwa4ffiUQipkJBRCQWDu0F3f4Lv34IYy+DvFy/E4lEpEys9WBmC4EtwO4y/hzn3Bz/EolImdTqQq+fwof/8taF6PaYt7iUSBwrE4VCUFfn3EK/Q4hIGXfCdV6xMP1hKJcJnYaoWJC45sujBzPLMrNnzWy2meWa2Sdh9mthZpPNbIuZLTOzIWaWFOO4IiIlq/2d0PoS+Pxx+PQRv9OI7JNfdxQOAboCXwKpBe1gZlWAj4A5QHegKfAwXnFzRxHO+baZGfAuMNg5t7MIxxARKT4z6PIgbNsAk4dAWiU4+lK/U4kUyK9CYbxzbhyAmY0Gqhewz+VAOtDTObcB+NDMKgGDzew/wW2Y2adAvQK+f7Jz7uLg122cc0vNLAN4DbgRuK9kmyQiUgiBAPR4CrZvhAn/hPJVvQ6PInHGl0LBOZcXwW5dgPd3FwRBbwIPAG2B8cFjtYngfEuDf24ysxeAywodWkSkpCWlQJ+X4LWeMOYyKFcZsjr4napUGPvtH8xeWromuKqcnsKlJzYhEIhtn5Z47szYHJgSusE5t9jMtgQ/Gx/JQcysApDknNtgZslAL+D7AvYbCAwEaNCgQTGji4hEKCUdzh4OL58OI/rDBe9AvWy/UyWsmhXTqJ6RxsfzVvHxvFV+xykxuc6Rm+do37wmB9aqGNNzx3OhUAVYX8D2dcHPIlULGGNmASAJ+AK4J/9OzrmhwFCA7OxsV9iwIiJFll4ZznsLXjwFhvWGi96HGs38TpWQqmWkMfOOjn7HKHETfljOlcO+Ic+Hf53ifcKlgv6TWJjtBR/AuQXOuSOccy2dc4c45y5xzmkOVRGJLxVrQf+xEEjx1oXQipMSJ+K5UFgHVC5geyYF32kQEUlsVZt4dxa2b/SKhc1r/E4kEteFwjy8vgh7mFl9oELwsxJnZt3MbGhOTunqACMiCaROSzj7TVi/GN7oA9s3+Z1Iyrh4LhQmAqeaWWivjbOArcDUaJzQOTfeOTcwMzMzGocXEYlMoxOg90uw7DsYcZ4WkRJf+TUzY3kz621mvYG6QI3d782sfHC3Z4DteB0ROwZHJQwGHsk3ZFJEpPRp3hXOeBwWfKxFpMRXfo16qAmMyrdt9/vGwELn3Doz6wA8gTcUcj3wKF6xICJS+h15HmxZ4y0iVb4qdH1I60JIzPk14dJCvNEL+9tvDtA+6oFEROLVCdfB5tXeuhDlq0O7W/1OJGVMPM+jEHNm1g3olpWV5XcUEZG/dBoCW9bC1PuhQnWtCyExFc+dGWNOnRlFJC6ZQbfHoFlXb12IH0b7nUjKEBUKIiKJICkZer8IDY+HsZfDr5P9TiRlhAoFEZFEsXtdiBrNvXUhls70O5GUASoUREQSSblMb/bGjJreuhCrfvY7kZRyKhRCaGZGEUkIu9eFSEr1lqjO+cPvRFKKqVAIoc6MIpIwqjaGc0fDthzvzsLWdX4nklJKhYKISKKq0xL6DYPV82H4ObBzm9+JpBRSoSAiksiatIWez8Liz2HMJZrqWUqcCgURkUR3aC/ofD/MHQ8TbwLn/E4kpYhmZhQRKQ2OvQI2LofPHoOKteGkf/qdSEoJFQohNIWziCS0DoNh40qYcjdk1IKjzvc7kZQCevQQQqMeRCShBQLQ/Qlo2gHGXwc/T/Q7kZQCKhREREqTpBTo+yrUORxGDYAlM/xOJAlOhYKISGmTlgHnjIJKdeCNvrDqF78TSQJToSAiUhpl1IDzxkAgGV7vCRuW+51IEpQKBRGR0mr37I1b18HrvWDrer8TSQJSoRBCaz2ISKlzwBFw1muw+hd481zN3iiFpkIhhEY9iEip1LQ99HgaFn0KYwdq9kYpFBUKIiJlQcs+cMo9MGccTLxZszdKxDThkohIWXH81d7sjV884Y2IOHGQ34kkAahQEBEpSzrdBZtWwuQhkFEbjjzX70QS51QoiIiUJYEAdH8KNq+C8dd660JkdfA7lcQx9VEQESlrklOh72tQozmMPB+Wf+93IoljKhRERMqicpXg3FFQLhOG9YH1i/1OJHFKhYKISFlV6QBvQqadW+H13t7ETCL5qFAIoQmXRKTMqdUC+r0Oaxd4EzLt2u53IokzKhRCaMIlESmTGp8UnJDpMxh7OeTl+Z1I4ohGPYiIiDch04Y/4KP/g8x6cMpdfieSOKFCQUREPCdcBzlL4PPHIbM+HDPQ70QSB1QoiIiIxwy6/MdbknriTd7sjQd38zuV+Ex9FERE5C+BJOj1PNRtBW9dAktm+J1IfKZCQURE9pZaHs4Z4Q2ffOMsWP2r34nERyoURETk7ypU9+ZYMINhvWDTKr8TiU9UKIiISMGqNYVzRsLGlfBGX9ix2e9E4gMVCiIiEl69bOj9Iiz/DkZfBLm7/E4kMaZCQURE9q15V+j6IPwyCSbcCM75nUhiSMMjQ5hZN6BbVlaW31FEROJL60tg/RL47L9QuT6cOMjvRBIjuqMQQlM4i4jsQ4f/g8P6wOQh8P1Iv9NIjOiOgoiIRCYQgO5PwsYVMO4qb/hkozZ+p5Io0x0FERGJXHIanPUaVGnkrTa5er7fiSTKVCiIiEjhpFeBc0dBUgoM6w2bV/udSKJIhYKIiBRelUZw9pveY4jh/WDnVr8TSZSoUBARkaKplw09n4OlM2HsZZCX53ciiQIVCiIiUnQtzoBT7oI542DyYL/TSBRo1IOIiBTPcVfDuoXw2WPeI4nsi/xOJCVIhYKIiBSPGXR+ANYvhvduhMwGcGBHv1NJCdGjBxERKb6kZOj9EtRqAaMugBU/+J1ISogKBRERKRlpGd5qk2mVYFhf2LDM70RSAlQoiIhIyal0gDfHwvaNXrGwfaPfiaSYVCiIiEjJqn0o9H0Z/pwDowZoaeoEp0JBRERKXlZHOO1h+PVDmHiTlqZOYBr1EELLTIuIlKDsAbDud2/YZNXGcPw1fieSItAdhRBaZlpEpIR1GAwtesAHd3qTMknC0R0FERGJnkAAznzGGwExZiBUPADqt/Y7lRSC7iiIiEh0paTD2cOhYm1vAam1v/udSApBhYKIiERfhepw7mjI2wVv9IWt6/1OJBFSoSAiIrFR/UDoN8y7ozDqAsjd6XciiYAKBRERiZ1GbaDbY7DgE5jwTw2bTADqzCgiIrF15LmwZj58+qh3l+G4q/xOJPugQkFERGKv/b9gzW/w/u1QtQk06+J3IglDjx5ERCT2AgE481k44AgYfTEs/97vRBKGCgUREfFHank4+01Ir+wNm9y4wu9EUgAVCiIi4p+KteGcEd5wyeH9YMcWvxNJPioURETEX7UPg94vwLLvYOxAyMvzO5GEUKEgIiL+a9YFTr0H5o6HKUP8TiMhNOpBRETiw7FXwurgsMlqWXDkeX4nElQoiIhIvDCDrg/CuoUw/nqo0siboEl8pUcPIiISP5JSoM/L3twKI87z5loQX6lQEBGR+JJe2RsJYQEY1ge2rPU7UZmmQkFEROJP1cbQ7w3IWQIjz4ddO/xOVGapUBARkfjU4Fjo/iQsnA7v/UMLSPmk1BcKZlbBzF42s5/NbJ6ZXel3JhERiVDLvnDSTfDt6/DZY36nKZPKwqiHh4FfnHMXmpkBNf0OJCIihdDuNljzK3w0GKo1hYO7+Z2oTIn5HQUzyzKzZ81stpnlmtknYfZrYWaTzWyLmS0zsyFmllTIc1UEegAPAjjPyuK2QUREYsgMejwFdVvBmIGwfLbficoUPx49HAJ0BX4Jvv7GzKoAHwEO6A4MAQYB/y7kuZoAq4DHzOwbMxtnZo2KmFtERPySku51bkyvCsPPho36nS9W/CgUxjvn6jvn+gA/hdnnciAd6Omc+9A59wxekXCDmVXavZOZfWpmCwt4vRDcJQU4FBjnnDsKGAe8ErWWiYhI9FSsBWcPh63r4M1zYOc2vxOVCTEvFJxzkaz20QV43zm3IWTbm3jFQ9uQY7VxzjUq4HVxcJclQI5z7v2QY7QqgWaIiIgf6rSEM5+FP2bCO9doJEQMxGtnxubAlNANzrnFZrYl+Nn4SA7inFtpZt+bWWvn3NdAJ+CHgvY1s4HAwODb7Wb2Y5HTJ4bqwGq/Q0SR2pf4Snsb1b5iez748k3Mr2HzB6J6+IYFbYzXQqEKsL6A7euCnxXG5cDzZpYRPOZFBe3knBsKDAUws5nOuexCniehlPY2qn2Jr7S3Ue1LfGWhjRC/hQJ4HRnzszDbwx/EuTnA8SWSSEREpIyJ1wmX1gGVC9ieScF3GkRERCQK4rVQmIfXF2EPM6sPVAh+Fm1DY3AOv5X2Nqp9ia+0t1HtS3xloY2Y87HHqJmNBqo7507Ot/1W4J9AQ+fcxuC2G/HmU6idbzSEiIiIREnM+yiYWXm8CZcA6gKVzKx38P0E59wW4BngWmCMmT2AN3HSYOARFQkiIiKxE/M7CsGZEX8P83Fj59zC4H4tgCeA4/D6JTwPDHbO5UY/pYiIiADgnEvYF5AFPAvMBnKBT/J9XgdvnYfZwCa8CZheAQ4o4FgtgMnAFmAZ3mOOpHz7GHBb8DhbgWnAEUU5VqzbCFyIN2Ik/+tyP9sYQftSgZHAgmCeVcBEoFUiXMOSal+8Xr9I2ljA/v8NZn+oNFzDSNsXr9cwkvYBCwvIvSIRrl9JtjFer2G0X/E8PDISu9eN+BLvB25+rYAz8e5GfAXUwnuE8bmZHeqc2wR7rS0xB29tiaZ4q04GgDtCjncLcCde/4l5wA3AR8FjrSjksWLaxhDt8f7i7rYg3+exbuP+2peE9z/ifcBvQCXgH8AUMzvSObegkJkSsn0h4u36RdLGPYJ3Ci8C/vYIMYGvYUTtCxFv1zDS9r0B/C/k/Y7QD+P4+pVYG0PE2zWMLr8rleK8gEDI16P5+29rlYHkfNsOwvvBfEHItlvxhmRWCtl2E16VVyn4vhyQA/wrZJ8KeL8B3l2YY/nUxguD2zL2ca6Yt3F/7QvzPRnAduCGeL+GJdi+uLx+hW0j3g/Gu/B+e8v/G3fCX8P9tC8ur2Ek7SuoPQXsE5fXr4TbGJfXMNqveB0eGRG3n3UjnHPrnXO78m37Be9C1AzZHMnaEsfj/bY3MuRYm/Gmk+5SyGNFrATbGImYt3F/7QtjM7CNvX8ziMtrWILti0Rc/h3dLdhp+WDg/jC7JPQ1jKB9kYjb9kUgLq9f8Bwl1cZI+NLGaEroQqEozKwlUB7vds9uzck3P4NzbjHeP7bNQ/bJBebnO+Rc9p7zIZJjRVWYNu72m5ntMrOfzeyyfJ/FbRvNk2xmtYH/BHMOL2SmRG7fbol6/dLxbqveEvyhWZCEvYYRtm+3hLyGwEVmtsPMcsxstJk1zPd5wl6/EPtr426Jeg2LJNH7KBSKmQWAx/Au4AchH0WytkQVYJP7+6iLdUB5M0t1zu2I8FhRs482Lsd7ZjYD77n42cAzZlbeOfdocJ94buPNeM/xwbuF19U5tyjk80S/hvtrX6Jfv1vx2vD6PvZJ5GsYSfsS+RqOw3u+vxTvrsn/AdPN7DDnXE5I9v1litf2QWRtTORrWGRlqlDA+0F8HNDWObcz32eugP3zry0Rbp/8n0VyrGgpsI3OW2r7/ZD9JppZGnCHmT0WcmsuXtv4Mt7z3zrAlcC7ZnaS89byKEymhGxfIl8/M2sM3Ai0d8EHsfuQcNcw0vYl8jV0zl0X8na6mX0OfAcMwBvlUZhMcdc+iKyNiXwNi6PMPHowsyvxeqBe4Jz7Kt/H69j/2hLrgIpmlpRvn8rAlpB/lCM5VlTsp40FGQ1UBRoF38dtG51zK5xzM51z44FuwBq8nsW7JfQ1jKB9BUmU63c/3pDPeWZW2cwq4/3sSQu+3/0DNFGvYaTtK0iiXMO9OOd+BH4GjgrZnKjXr0Bh2liQhLyGhVEmCgUz64U35OUm59yIAnaJZG2JeXi3mrLyfW/+50y+rFMRQRv3ZXf1Gtdt3C3YefMHvBk7C5Mpkdu3z28J/hmv7WsG9MT7wbj7VR+4Ovh13ULkisc2Rtq+fYn3axhO6G++iXr99ifS3+4T9RruV6kvFMzsZGAY8IRz7qEwu00ETjWziiHbzsIbJzs1+P5zvLHRfUKOXR7vt7+JhTxWiYqwjQXpBawGdj8Lj9s2hjKzcnhV/u+FzJTI7StIoly/S4B2+V4r8XqFt8PrkxFprnhsY6TtK0iiXMO9mNmheAXSrEJmSoj2BXMV1MaCJOQ1LJRIx1HG4wuvZ3/v4OsL4KeQ9+XxOqSsx3vOdDxwbMirachxquB1UvkQ6AgMxJvl8O5857sVr0fqVUAH4D28vyC1CnssH9r4Fl6HuS7A6cBreBXwNX62MYL2nQ28CpwDnBx8Px3vf6Yj4/0almD74vL6RdLGMN+zkL/PM5CQ17AQ7YvLaxjB39HT8EbgnItX+FwB/IE3yVClwmaKx7+jhWhjXF7DaL98D1Cs8N4zIRfm1Yjw02064OV8x2oBTMH7Ab0cb9KUgqYevR2vV+xWvB/oRxaQa7/HinUbgXvxnrdtCeaaBfQv4HwxbWME7Tsq+D/ZCrxJiBYCI4BDipIpUdsXr9cvkjaG+Z6FhJ/COaGuYaTti9drGMHf0ZZ40wyvAnYG/66+TPip8OPq+pVkG+P1Gkb75esy0yIiIhLfSn0fBRERESk6FQoiIiISlgoFERERCUuFgoiIiISlQkFERETCUqEgIiIiYalQEBERkbBUKIiIiEhYKhREJCbMLMXM/mFmM8wsx8y2mtms4LZUv/OJSME0M6OIRJ2ZVQE+AprirXL6WfCjLsDleNPgjvQpnojsgwoFEYkqMzO8+eybA+2cc/PyfZ4NrHHO/e5HPhHZt2S/A4hIqXcB3sqYPfIXCQDOuZkxTyQiEdMdBRGJKjP7Hkh2zrXwO4uIFJ46M4pI1JhZQ+AwYJjfWUSkaFQoiEg0HRb888doHNzMdEtUJMpUKIhINGUG/1zpawoRKTIVCiISTX8G/zxgfzua2XlmNt/MppnZY2b2Schno81sppn9aGaPB0dSiEgMqFAQkWj6AtgADCjoQzNrE/yzNvAgcBLQFqiVb9fLnHPZQEugAdA1WoFFZG8qFEQkapxzm4CbgdPNbJyZ9TaztmZ2kZlNBB4I7noM8IVzbrnzhmK9ke9Ql5rZt8Bs4Fj+6vsgIlGmeRREJKqcc8+Y2XLgRuDl4ObFwDTgxeB7AwrsmGhmbYF+QFvnXI6ZPQyUi2poEdlDhYKIRJ1zbhwwbh+7fAU8bWa1nXMrgLNCPssE1gMbzKwa0Iu/Cg4RiTI9ehAR3znnluM9ophuZtOAFUBO8ONJeIXCT3jzMUzzI6NIWaWZGUUkLphZhnNuU3BEwzPAYufcPX7nEinrdEdBROLFrcEOi3OASsBjPucREXRHQURERPZBdxREREQkLBUKIiIiEpYKBREREQlLhYKIiIiEpUJBREREwlKhICIiImGpUBAREZGwVCiIiIhIWP8PbeeXZwUfjQYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(8,8))\n",
+    "ax = plt.gca()\n",
+    "# Data\n",
+    "ax.step(C_gal[isort_gal], 1.-cdf)\n",
+    "ax.plot(xval, 1-norm.cdf(xval, loc=mean_gal, scale=std_gal))\n",
+    "#\n",
+    "ax.set_xlim(12000., np.max(C_gal))\n",
+    "ax.set_ylim(1e-6, 1e-1)\n",
+    "#\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xlabel(r'$C_{\\rm gal}$')\n",
+    "ax.set_ylabel('1-CDF')\n",
+    "set_fontsize(ax, 15.)\n",
+    "#\n",
+    "#\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Low tail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8626.391053122208"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_gal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xval2 = np.linspace(C_gal.min(), mean_gal, 100000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(8,8))\n",
+    "ax = plt.gca()\n",
+    "# Data\n",
+    "ax.step(C_gal[isort_gal], cdf)\n",
+    "ax.plot(xval2, norm.cdf(xval2, loc=mean_gal, scale=std_gal))\n",
+    "#\n",
+    "ax.set_xlim(0., 6000.)\n",
+    "ax.set_ylim(1e-6, 1e-1)\n",
+    "#\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xlabel(r'$C_{\\rm gal}$')\n",
+    "ax.set_ylabel('CDF')\n",
+    "set_fontsize(ax, 15.)\n",
+    "#\n",
+    "#\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FRB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "59145.325250898924"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fake\n",
+    "FRB_time = Time('2020-10-23T07:48:30.777667') - 9.1*units.s\n",
+    "FRB_time.mjd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Alopeke error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ata = 163*units.s/1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mjd_low = (FRB_time-ata/2.).mjd\n",
+    "mjd_high = (FRB_time+ata/2.).mjd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i_FRB = (data['MJD']>=mjd_low) & (data['MJD']<=mjd_high)\n",
+    "np.sum(i_FRB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Also made up\n",
+    "C_obs_FRB = C_gal[i_FRB]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare to Gaussian"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CMJMkScqEYSZJkpQJw0ySJCkThpkkSVImDDNJkqRMGGaSJEmZMMwkSZIyYZhJkiRlwjCTJEnKhGEmSZKUiYbCLCLOiIhlEbE8ImZ38nxExGXl849ExNFdrRsRQyPi1oh4srzde7vXHBMRGyPiz9/LG5QkSeopugyziKgClwMzgcnAJyJi8naLzQQmlNPFwLcbWHc2MC+lNAGYVz7u6BvATTvxniRJknqkRvaYTQOWp5RWpJS2AtcAs7ZbZhbwvVSYDwyJiJFdrDsLuLq8fzVw7m9eLCLOBVYAS3bqXUmSJPVAjYTZAcBzHR6vKuc1ssyO1h2RUloNUN4OB4iIQcDngS819hYkSZJ6h0bCLDqZlxpcppF1t/cl4BsppY073KiIiyNiYUQsXLNmTRcvKUmSlL+WBpZZBYzu8HgU8HyDy7TtYN0XI2JkSml1edjzpXL+ccDHIuKrwBCgHhFvpJS+2fEPppSuBK4EmDp1alexJ0mSlL1G9pjdD0yIiHER0QacD8zZbpk5wAXlrzOnA+vLw5M7WncOcGF5/0LgOoCU0okppbEppbHA/wL+YfsokyRJ6o263GOWUmqPiM8CNwNV4KqU0pKIuKR8/grgRuBMYDmwCbhoR+uWL30p8NOI+DTwLPDxbn1nkiRJPUwjhzJJKd1IEV8d513R4X4CPtPouuX8dcCMLv7u3zayfZIkSb2BZ/6XJEnKhGEmSZKUCcNMkiQpE4aZJElSJgwzSZKkTBhmkiRJmTDMJEmSMmGYSZIkZcIwkyRJyoRhJkmSlAnDTJIkKROGmSRJUiYMM0mSpEwYZpIkSZkwzCRJkjJhmEmSJGXCMJMkScqEYSZJkpQJw0ySJCkThpkkSVImDDNJkqRMGGaSJEmZMMwkSZIyYZhJkiRlwjCTJEnKhGEmSZKUCcNMkiQpE4aZJElSJgwzSZKkTBhmkiRJmTDMJEmSMmGYSZIkZcIwkyRJyoRhJkmSlAnDTJIkKROGmSRJUiYMM0mSpEwYZpIkSZkwzCRJkjJhmEmSJGXCMJMkScqEYSZJkpQJw0ySJCkThpkkSVImDDNJkqRMGGaSJEmZMMwkSZIyYZhJkiRloqXZGyCp+7TSzgC20J+tDIgtDGBr8Ti2MpA36M82WminhTotUaOFGlVqtJa3LdRooU4l6qQU1AkSxS289TgBdSpso4WttLA1tRa3tLLlN7eptXzcCq88A/32hLY9oKWt2cMkSdkyzKSsJPZgM0PjNfZlPfvEBobERgazicGxicG8Xt5uYnC8zp5sZnC8zmA2MYAttEat2W+gc//f59+6X20rAq3fHtC2Z3m7B/TfCwbsDQOHFrcDhr79cf+9oOo/W5J6L/+Fk3aDNrYxIl5mP15hZLzMiHiZYbGefWI9+/Aa+8T6MsY20C+2dfoa9RRsZAAbGMiGNIgNDGRV2pcN6UBeSwPYyAA2pzbeoB+baWNzKm7foB+bUxub6ccWWst9ZhXaU3lLlRpVtnW4TUS5bwwq5f6yCnXY7nEr7bTRTltso412+rGNfmzrMG8b/dnG5R87BLZuhC0bYetr5e3r5bzX4I1X4dVnYPMrxZTq7zyYA/eBPfaDPYbDnuXtHiPemn4zr99giOju/yklaZcyzKT3qEKd/XiZ0bGGUbGGkbHuzfgqbl9h39jwtvXeSK2sZS/WpcGsSUNYlsawNg1mXRrMy2kw6xjM2jSYV9mT9WkQG+lP2o1fC+14yLKBhXc47/Kjz2r8D9frsGV9EWibylDb/DJserm43fhSOb0A65bDxhehtvXtr9O2B+w1artp9Fu3g/eHamvj2yVJu4FhJjVgCK9xYLzI6FhTTi8xqrw9INbStt0hxHVpT15IQ3khDWVR/WBeSHvzAkPfnPdCGsprDADco/M2lUp56HJvGNrA8ikV8bbxpSLSNr4Ir70AG56H9c/B+lXw/CLYtHa7FQP2HAlDxsDQ8eU0rpzGF39fknYzw0wq9WMrB8aLjIvVHBSrGV9Zzfh4nnHxAnvHxt9adl3ak+fSMBancdxUP47n0jCeS8N5Lg3jhTSULfgF990movge2sChMPzQd15u66bfjrX1q4r7rzwDK+6Ah3/028v3H7JdsB0EwybCvocU34uTpF3AMFPfs/lVjollTKys4uD4NeOjCLBRsZZKvHX87YW0NyvqI7mxfhwr0kieSSN4Lg1jVRrG6wxo4hvQTmkbCPseXEyd2bqp+J7byys6TCth1f2w5Nrf/t7b4FEw7BDYd2IRa8MmFvcH7bN73oukXsswU++17Q1YuwxeWgovLiluX1oKG1bxH/2KRV5P/ViZRrIoHcy19RNZUd+fFWk/VqaRxldf0zYQhk8qpu21b4VXni4+T2sehzVPFPcf+C60b35ruYH7FIE2YjKMmFJOk6Ft0O56F5J6OMNMvcNrL8Lqh+GFh2H1I0WAvfzUW3s5qm3FfzDHngDDJ3HRja+zrD6a59kHv+elLrW0FXvIhh0Ck855a369XhwOXfsErFlWhtsyePgnsPVfyoWiOBy635S3Ym2/KcUPEPzVqKTtGGZ6T8bOvmE3/8XEAaxlSuVpDqus5LB4himVlYyIV99cYmV9BI+nMTyRzmVZfRTL0mieTvtRe70Kz+zmzVWP9u4+3+PK6QyCOgfEWibHM0yKZzl0zbNMWjufsY9d9+bSG9JAlqYxPFofxyP18TyaxvN0GtEtv7x9+tJ38StY9Wm7/9/w7tGbP+OGmTJWRNj7Kk9xRGUlh8VKplSefvOL+O2pwvJ0AHfXD2dxfSyL6+NYmsawkYFN3m71dYkKq9JwVqXh3MKxUP5odxCbmRjPManyLIfGsxxWeZpPVX9B/5bi3HUb0gAW18fxSBrPo/XxPJLG8Vwajnt1pb7DMFM29mATR1RWcGQs56jKUxxZWc6wWA/AltTCsjSam2rHsiSNY3F9LI+nMf76UT3K6wzgwXQID9YOeXNelRoT4tccXlnBEbGCwysruKjyc/q1tAPwahpU7lEbx0P1CTxYn8DLDG7WW5C0ixlmaooqNSbGcxxZeYojYzlHVpZzcDz/5q8in6qP5K764SyqH8yi+sE8nsawzY+reqEaVR5PY3i8NoZ/42SguObpIfEcR1RWcHis4IjKSi6u3EBrS7Hr7en6CB5MRaQ9VJ/A42k0NapNfBeSuov/pdNuMYA3OLLyFMfGMo6tPM7RlScZFFsAeDntwaL6wcytHc+idBAP1w9iPZ4nSn3XNlpYksaxpDaOHzMDKM6zNyVWckzlCY6uLOcDlcV8tHo3UPy6+OH6QW/GGq9P99QdUg9lmGmX2Jf1HFNZxrGVZUytLGNKPE1L1Kmn4PE0hn+vnVT8v/00gWf9Do3UpS208UCayAO1ieV31hKjYi1Hx5McXXmCoytPcknlelpa6vC1rxcnwj3w/XDgCcXtXqOa/RYkNcAwUzdIjI6XmF5ZytR4gmMrjzO+8gJQXA/y4XQQV9TOYWF9Ig/WJ7ABz+kkvXfBqvKEx3Pq7wegP1s4Ilbw0zMr8Ox8WHxtca41KC499ZtIO/CE4hQenq5Dyo5hpp1yAGs4vvoYx1ce47jKUkZFcR3CV9IeLKxP5Jptp7CwPpHFaRxb8ULR0u7wBv1YkCbBieWpBOq14uTKz/wKnrkHnrwVHv5x8dweI357j9qwScV1SiU1lWGmhozgZY6vPFZOSxhTWQMU3w+bX5/MP9XPZn59MsvT/t1yHiZJ3aBShZFHFNP0S4oLvq99soi038Takp8Vyw7cF8adBOM/WNzuPc49alITGGbq1L6s5/2VJUyvPMb0ymNvHpp8NQ3ivvokrto2k3vrk3kijTLEpJ4i4q0rGEy9qAi1V5+Fp++GlXfByjuL64IC7DUGxp8E48pQ23O/5m671EcYZgKKX3xNrSzjxMqjnFR5lMmV4hT5G9IAFtQP5YfbZjC/fhhL0xjqhpjUO0TA3gcW01H/5a09aivvLKalc+GhHxTLDjv0rUgb+wEYMKSpmy71VoZZn5WYGM+VIfYI0yqP0z+2sTVVeaA+ka9u+13uqU9hcRrn+ZGkvqLjHrVpf1B8R+2FR2BFGWoPfg8W/BNEBQ44Bg6aAQfPKO5X/HdC6g6GWR8yjFc5obKYE6uPcGJlMcPL60s+WT+AH9Vm8Mv64dxXn8Qm+jd3QyXloVKF/Y8qpg98Dtq3wKqFsOIOeOo2uPMrcOel0H8IjD+5iLSDZsBeBzR3u6UezDDrxVpoZ2rlCU6uLOKDlUeYVHkWKL6wf3f9cH5ZP5xf1g7nBTwRpaQGtPSDsScU04f+Gja9DCtuh+W3wVPz4LH/LJYbNqmMtA8Vv/ps9f/sSY0yzHqZYbzCydWHOaWyiA9UHmVwbGZrqrKwPpGvbDufu+qH81g60C/sS3rvBg6FKecVU0rw0mOwfF4RaQuuhHu/CS0DipA7aAZMOA32PbjZWy1lzTDr4SrUOTKWc3J1EadUFnF45WkAVqehzK1N5476kdxTn8LrDGjuhkrq3SJgxGHFdMIfw9bX4el7ikhbPg9u/kIxDR0Ph5xRRNqBJ0BLW7O3XMqKYdYDDeE1Plh5mFOqxSHKvWMjtRQ8kA7hq9t+j9vqR/F4Go2XOZLUNG2D4JDTignglWfgyVvgiZvh/u/A/G9B255w0MlFqB38YdhzRFM3WcqBYdYjJA6N5zi18gCnVBdxVCynEom1aTC31Y/i9tqR3FU/nA1e+FtSrvY+sPil57Q/KPamrbyriLQnboal1xfL7H8Uf1Idz231o1icxvqVC/VJhlmmWmhnWuVxPlx5gFMrDzK6PNP+ovp4Lqt9hNtqR/FoGuc/XJJ6nrZBMHFmMaUELy6GJ34OT9zCn7Rcy5/Gf/BSGsLttSO5rX4Uv6wf7q/F1WcYZhkZzOt8sPIwp1Yf5JTKIgbHJt5Irfyyfjjf3HYut9WOYg1Dmr2ZktR9ImC/w4vppL9g6uwfc3JlER+qLmJm9T5+r+UOtqQW7qlP4db6Mfyidoz/DqpXM8ya7dVnYdlNfL/1e0yvLKU1aqxNg7mpNo1f1I/m7voUNvv/FCX1ES8zmGvrJ3Ft/aQ3T/lzauUBTqss5EOti/j7lqtYlA7iltpUbq0fw1PJc6apdzHMdreU4PmHYNlNxfTiowDsFwfwL7UzubV2DIvSwV72SFKf104L8+uTmV+fzJf5FBPjOU6rLOTD1QeY3XoNs7mGp+ojubU+lVtqx/BQOtivd6jHM8x2h1o7PHNP8QXXx2+A154vLmky5ng47csw8Uw+/LXHm72VkpSxYFkaw7LaGP537aOMZB2nVos9aZ+u3sglLdezJu3FrbWjubU+lV/VD2MLnopDPY9htqtse6O4bMnS62HZDbD5leJEiwfPgEP/Bg45vTg545sMM0lq1Gr24fu10/h+7TQG8zonVxZxWvUBfqd6L59suZ3XUz/urL+PW2pTua1+FBsY1OxNlhpimHWnLa/Bk7cWMfbkLbB1I/TbCyaeAZPOKc583Taw2VspSb3KBgYxp34Cc+on0MY2jq88xmmVhZxafYAzqwvYmqrcU5/CTfVp3Fo7hlcY3OxNlt6RYfZebXq5+K7Y0uuLi/rWtsCgYXD4x4oYG3uSZ7aWpN1kK63cWX8fd9bfx39vv4gj4ynOqC5gZmUBX239Z2ot/8J99UncVJ/GzbVjeYm9m73J0m8xzHbGhtXw+Nwixp6+G1IN9hoNx366iLHRx0Gl2uytlKQ+LVHhoTSBh9on8D/5JIfFM29G2v9o/S5farmaB9MEbqody89r0/g1w5q9yZJh1rCXVxYhtvR6WLWgmLfPBPjA54oYG3lkcT4eSVKGgiVpLEvax/KP/C4Hxa+ZWVnAzOoCvtj6Q77Y+kMeqY/j57Vp3FSfxso0stkbrD7KMGvE+lVw2ZHF/f2OgFP+exFjww9t6mZJknbOU+kAvln7CN+sfYQx8SJnVBYws3o/f9n6E/6Sn/B4fTQ/rx/LTbVpLPPaw9qNDLNG7DUKZl0OYz8Ae49t9tZIkrrRs2kEV9bO4craOYxkHadX72dmdQF/XP0Zn2u5lhX1/bipPo0ba9NZkg7ESNOu1NCZ+CLijIhYFhHLI2J2J89HRFxWPv9IRBzd1boRMTQibo2IJ8vbvcv5H46IByLi0fL2Q93xRt+zoz5llElSL7eaffhu7Qx+b+vfMG3Lt/irbZ9mVRrGH1bnckO/v+L2tj/jz1t+wqR4BkjN3lz1Ql3uMYuIKnA58GFgFXB/RMxJKT3WYbGZwIRyOg74NnBcF+vOBuallC4tg2028HlgLXBOSun5iJgC3Ax4zQ1J0m61lr34UW0GP6rNYG82cFr1Ac6qzOeS6vV8tuU6nqqP5Ib6cdxQm15c1cXvGasbNHIocxqwPKW0AiAirgFmAR3DbBbwvZRSAuZHxJCIGAmM3cG6s4CTy/WvBu4APp9SeqjD6y4B+kdEv5TSlp16h5IkvUevMJif1E7hJ7VTGMoGTq/ez1mV+Xymeh1/3PKfcPlVcNhHimn4pGZvrnqwRsLsAOC5Do9XUewV62qZA7pYd0RKaTVASml1RAzv5G+fBzxklEmScvEyg/lxbQY/rs1gH9ZzRvV+/n7P5XDX1+DOr8C+EztEmj8S07vTyHfMOts3u/2B9XdappF1O/+jEYcBXwH+8B2evzgiFkbEwjVr1jTykpIkdat17MUPa6fChdfDf1sGZ/0j7DG8CLRvHQeXT4c7vgJrnmj2pqqHaGSP2SpgdIfHo4DnG1ymbQfrvhgRI8u9ZSOBl36zUESMAn4GXJBSeqqzjUopXQlcCTB16lS/gSlJaq49hsOx/2cxvfZCcd7LJT+DO/4n3PEPMPywck/aubDvhGZvrTLVyB6z+4EJETEuItqA84E52y0zB7ig/HXmdGB9eZhyR+vOAS4s718IXAcQEUOAG4AvpJTu2fm3JklSk+y5H0z7A7joRvizpTDzq9B/MNz+ZfjmVPj2CcWhz3Wd7ntQH9blHrOUUntEfJbi15FV4KqU0pKIuKR8/grgRuBMYDmwCbhoR+uWL30p8NOI+DTwLPDxcv5ngYOBL0bEF8t5p6WU3tyjJklSjzF4JBz3h8W04Xl47LpiT9ptXy6m/Y+Cwz5a7E0bMrrr11Ov1tAJZlNKN1LEV8d5V3S4n4DPNLpuOX8dMKOT+V8GvtzIdkmS1KMM3h+m/1/FtH5VEWiL/wNu/WIxjZ4OU86DybNgzxHN3lo1gWf+lySpGfYaBe//v4tp3VOw5FpYfC3c9Bfw88/D2BOLSJt0Dgwc2uyt1W7S0Jn/JUnSLrTPQXDSX8Af3Qt/NB9O/HNY/xxc/8fw9Qnww9+Fh6+BNzY0e0u1i7nHTJKknAyfBB/6azjlr2D1w8WhziU/g5/dDNV+cMhpxZ60CadD28Bmb626mWEmSVKOImD/I4vp1C/BqvuLw51LflaciqN1EBx6ZhFpB30IWvo1e4vVDQwzSZJyV6nAmOOK6fR/gGfuKfakPXYdPPpv0G+v4rtoUz4K4z4IVf/z3lP5v5wkST1JpQrjTiqmM78OK+4oIm3pHFj0Axi4b/GrzinnwZjji6hTj2GYSZLUU1VbYcKHi2nbG7D8F0WkLfoRLPwO7DmyOEfalPPggKOLw6PKmmEmSVJv0NofJp1dTFs2whM/L06/cf8/w/zLYciBxaHOKefBiClGWqYMM0mSept+e8DhHyumza/CshuLPWn3XAZ3fwP2PQSmnMdBMZSn0gHN3lp1YJhJktSbDRgCR36ymF5fB0uvK/ak3XEp8/olHqsfyPW147m+Pp1VaXizt7bP8xuBkiT1FYP2gan/B/z+XPizpfzttgvYTBufb72Gu/t9jp+1/Q2frt7Ifqxr9pb2We4xkySpLxo8ku/WzuC7tTMYFWs4qzKfc6r38sXWH/DF1h+woD6RubXp3FQ7jjUMafbW9hmGmSRJfdyqNIx/qp3DP9XOYVys5qzKfM6uzufvWq/m/2n5HvfVJzG3fjw31Y7lFQY3e3N7NcNMkiS9aWUayTdrH+GbtY8wIVZxdnU+Z1fu5R9av8Pftfwrv6ofxvX147m5NpUN7NHsze11DDNJktSpJ9MovtH+Mb7BeUyKZzm7ei9nV+bztdYr+fuW7/DL+hHMrU3n1voxbMTrdnYHw0ySJHUhWJoOZGn7gXyN3+PwWMnZ1Xs5q3ofM6oPsSW1ckf9fcytTecX9aPZTP9mb3CPZZhJkqR3IXg0jefR9vFc2v4JjorlnF2dz5nV+zi9upDNqY159aOYWzue2+tHsoW2Zm9wj2KYSZKknZKo8GA6hAfbD+F/tH+KY2MZZ1fnM7N6H2dX72Nj6s8v6kczt3Y8d9WPYCutzd7k7BlmkiTpPUtUWJAmsaB9El9qv4DjKks5u3IvM6v3c271V2xIA7mlPpW5tencXZ9CuwnSKUdFkiR1qxpVflWfwq/qU/ib9os4obKEsyv3cnp1IR+r3sUraQ9+XjuWufXpzK9Ppka12ZucDcNMkiTtMu20cGf9fdxZfx9/3b6NEyuPcHa1OJntJ1puZ20azE21acytHc/9aSL1Pn5RIsNMkiTtFltpZV79GObVj6EfWzm5sohzqvM5r/pL/mvLL3gxDeGm2jRuqh3XZyPNMJMkSbvdFtq4uT6Nm+vTGMAbzKg8xFnV+ZxfvZ3fb7mFNWkwN9eO5Yb6dBbUD+0zhzsNM0mS1FSb6c/c+vHMrR/PQN7g5Moizqwu4KPVu/lUyzzWpT25uTaVm+rHcW99crM3d5cyzCRJUjY20Z8b69O5sT6d/mzhg5WHObO6gN+p3ssnW27n1TQI/vMWOOxcGPdBaOld50kzzCRJUpbeoN+bhzv7sZUTK48ys3of5y2dA4t+AP32gkPPhMmz4KAPQUu/Zm/ye2aYSZKk7G2hjV/Uj+EX9WM47y9OhRV3wGPXweNz4eEfQ9ueMHFmEWkHz4DWAc3e5J1imEmSpJ6lpR8ccnoxtf8vWHkXPPafRaQ9+lNoHVQ8d9i5cPCHoa3nXGDdMJMkST1XSxtMOLWYzv4GPH13sSdt6fWw5FpoHQgHnwqTzoEJp8GAIc3e4h0yzCRJUu9QbYWDTimmM78Oz/6qjLS5sHQOVFph/Afh0LPh0LNgj+HN3uK3McwkSVLvU22BcScV08yvwa8fKOJs6fUw93Mw909hzPHFnrRJZ8OQMc3eYsAwkyRJvV2lAqOPLaYP/x28uKT4PtrS6+HmLxTTyPeVkfY7MGxi0zbVMJMkSX1HBOw3pZhOng3rnnor0m77Mqz8JVw4p2mbZ5hJkqS+a5+D4IQ/KaYNz8PmV5u6OYaZJEkSwOD9i6mJ+t5l2yVJkjJlmEmSJGXCMJMkScqEYSZJkpQJw0ySJCkThpkkSVImDDNJkqRMGGaSJEmZMMwkSZIyYZhJkiRlwjCTJEnKhGEmSZKUCcNMkiQpE4aZJElSJgwzSZKkTBhmkiRJmTDMJEmSMmGYSZIkZcIwkyRJyoRhJkmSlAnDTJIkKROGmSRJUiYMM0mSpEwYZpIkSZkwzCRJkjJhmEmSJGXCMJMkScqEYSZJkpQJw0ySJCkThpkkSVImDDNJkqRMGGaSJEmZMMwkSZIyYZhJkiRlwjCTJEnKhGEmSZKUCcNMkiQpE4aZJElSJgwzSZKkTBhmkiRJmTDMJEmSMmGYSZIkZcIwkyRJyoRhJkmSlAnDTJIkKROGmSRJUiYMM0mSpEwYZpIkSZkwzCRJkjJhmEmSJGXCMJMkScqEYSZJkpSJhsIsIs6IiGURsTwiZnfyfETEZeXzj0TE0V2tGxFDI+LWiHiyvN27w3NfKJdfFhGnv9c3KUmS1BN0GWYRUQUuB2YCk4FPRMTk7RabCUwop4uBbzew7mxgXkppAjCvfEz5/PnAYcAZwLfK15EkSerVGtljNg1YnlJakVLaClwDzNpumVnA91JhPjAkIkZ2se4s4Ory/tXAuR3mX5NS2pJSWgksL19HkiSpV2skzA4AnuvweFU5r5FldrTuiJTSaoDydvi7+HuSJEm9TksDy0Qn81KDyzSy7s78PSLiYorDpgAbI2JZF6/bqH2Btd30Wvptju2uk/XYxleavQXvWdbju70eNt49amzfSaZj3ivGtjOZjPd7Gd8D3+mJRsJsFTC6w+NRwPMNLtO2g3VfjIiRKaXV5WHPl97F3yOldCVwZQPb/65ExMKU0tTufl05truSY7trOb67jmO76zi2u9auGt9GDmXeD0yIiHER0Ubxxfw52y0zB7ig/HXmdGB9eXhyR+vOAS4s718IXNdh/vkR0S8ixlH8oGDBTr4/SZKkHqPLPWYppfaI+CxwM1AFrkopLYmIS8rnrwBuBM6k+KL+JuCiHa1bvvSlwE8j4tPAs8DHy3WWRMRPgceAduAzKaVad71hSZKkXEVKXX3lq2+JiIvLw6TqZo7truPY7lqO767j2O46ju2utavG1zCTJEnKhJdkkiRJykSvD7OImBgRizpMGyLicxHxtxHx6w7zz+ywTqeXhIqIYyLi0fK5yyKis1N79CkR8acRsSQiFkfEjyOi/85cbsuxfbt3GFs/t90kIv6kHNslEfG5cp6f3W7wDmPrZ3cnRcRVEfFSRCzuMK/bPqtR/NjuJ+X8+yJi7G59g030bsY2IsZGxOYOn+ErOqzTfWObUuozE8UPEF6gOH/I3wJ/3skyk4GHgX7AOOApoFo+twA4nuJcazcBM5v9npo8ngcAK4EB5eOfAr8PfBWYXc6bDXzFse22sfVz2z3jOwVYDAyk+BHULyh+Ae5nd9eNrZ/dnR/Tk4CjgcUd5nXbZxX4I+CK8v75wE+a/Z4zHduxHZfb7nW6bWx7/R6z7cwAnkopPbODZTq9JFQU51obnFK6NxUj/D3euoxUX9YCDIiIFop/iJ/nXV5uy7F9R52N7TtxbN+dScD8lNKmlFI7cCfwEfzsdod3Gtt34th2IaV0F/DydrO787Pa8bX+HZjRV/ZOvsux7VR3j21fC7PzgR93ePzZiHik3JX5m93AO7q81KpO5vdZKaVfA1+nON3Jaorz193Cu7/clmO7nR2MLfi57Q6LgZMiYp+IGEhxup/R+NntDu80tuBntzt152f1zXXKmF4P7LPLtjx/7zS2AOMi4qGIuDMiTizndevY9pkwi+IEt78D/Fs569vAQcCRFP/h+8ffLNrJ6jt7ealerfyHdRbF7vL9gUER8akdrdLJPMe2EzsYWz+33SCltBT4CnAr8HOKQz/tO1jF8W3QDsbWz+7usTPj6Vg3ZjUwJqV0FPBnwI8iYjDdPLZ9JsyAmcCDKaUXAVJKL6aUaimlOvDPwLRyuXe6JNSq8v728/uyU4GVKaU1KaVtwLXA+ykvtwVv7uLt6nJbju3bdTq2fm67T0rpOymlo1NKJ1EcyngSP7vdorOx9bPb7brzs/rmOuVXJ/bi7Yf3+pJOx7Y8PLyuvP8Axff3DqGbx7Yvhdkn6HAY8zeDXvoIxe53eIdLQpW7M1+LiOnl8eELeOsyUn3Vs8D0iBhYjskMYCnv8nJbjm2nOh1bP7fdJyKGl7djgI9S/PvgZ7cbdDa2fna7XXd+Vju+1seA28rvSvVVnY5tRAyLiGp5fzzF2K7o9rF9r79o6AkTxRen1wF7dZj3feBR4JFy4EZ2eO6vKUp4GR1+BQRMpfjH5Cngm5Qn6O3LE/Al4PFyXL5P8UugfYB5FHsg5gFDHdtuG1s/t903vr+kuPTbw8CMcp6f3V03tn52d348f0xxGG0bxR6YT3fnZxXoT/E1n+UUvy4c3+z3nOPYAucBS8rP9YPAObtibD3zvyRJUib60qFMSZKkrBlmkiRJmTDMJEmSMmGYSZIkZcIwkyRJyoRhJkmSlAnDTJIkKROGmSRJUib+f4WRhgnq7tAqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,10))\n",
+    "plt.hist(C_obs_FRB, bins=10, density=True)\n",
+    "xmin, xmax = plt.xlim()\n",
+    "x = np.linspace(xmin, xmax, 1000)\n",
+    "y = norm.pdf(x, mean_gal, std_gal)\n",
+    "plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10311.480634150505"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maxC_obs_FRB = np.max(C_obs_FRB)\n",
+    "maxC_obs_FRB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monte Carlo me"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C_FRB = 5000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rand_C_FRB = poisson.rvs(C_FRB, size=(ndata, 1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add em"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rand_C_obs = np.outer(C_gal, np.ones(1000)) + rand_C_FRB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100000, 1000)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rand_C_obs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.99542852"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(rand_C_obs > maxC_obs_FRB) / rand_C_obs.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_analy.py b/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_analy.py
new file mode 100644
index 00000000..35515e4e
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_analy.py
@@ -0,0 +1,439 @@
+""" Analysis methods """
+import sys, os
+import numpy as np
+import requests
+
+from scipy.stats import poisson
+from scipy.interpolate import interp1d
+
+from astropy import units
+from astropy.time import Time
+
+from frb.galaxies import photom, nebular
+
+sys.path.append(os.path.abspath("../../Analysis/py"))
+import alopeke_defs
+import alopeke_utils2
+
+from IPython import embed
+
+import warnings
+warnings.filterwarnings('ignore')
+
+def dust_extinction(camera:str):
+    """Calculate the dust extinction 
+
+    Args:
+        camera (str): [description]
+
+    Raises:
+        IOError: [description]
+
+    Returns:
+        [type]: [description]
+    """
+    try:
+        EBV = float(nebular.get_ebv(alopeke_defs.frb180916.coord,
+                              definition='SandF')['meanValue'])
+    except requests.exceptions.ConnectionError:
+        raise IOError("No internet connection!")
+
+    if camera == 'red':
+        return EBV, photom.extinction_correction('GMOS_N_i', EBV)
+    elif camera == 'blue':
+        return EBV, photom.extinction_correction('GMOS_N_r', EBV)
+    else:
+        raise IOError("Bad camera!")
+
+def time_obs(date:str, camera:str):
+    # Load
+    data_dict = alopeke_utils2.load_camera(camera, date)
+
+    # t0
+    FRB_MJD = alopeke_defs.chime_time_arrival[date].mjd
+    t_start = (FRB_MJD - data_dict['MJD_gal'][0])*24*3600 / 60. # min
+    t_end = (data_dict['MJD_gal'][-1] - FRB_MJD)*24*3600 / 60. # min
+
+    time_of_start = Time(data_dict['MJD_gal'][0], format='mjd')
+    sec_frac =  float(time_of_start.datetime.strftime(".%f"))
+    sec_frac = '%.3f'%sec_frac
+    sec_frac = sec_frac[1:]
+    time_of_start_str = time_of_start.datetime.strftime("%Y-%m-%dT%H:%M:%S")+sec_frac
+    
+    # Report
+    print(f"This camera = {camera}")
+    print(f"Time of start (UTC) {time_of_start_str}")
+    print(f"We started {t_start} minutes before")
+    print(f"And ended {t_end} minutes after")
+
+def time_evolution(date:str, camera:str, t_rel=59145):
+    # Load
+    data_dict = alopeke_utils2.load_camera(camera, date)
+
+    time_array = (data_dict['MJD_gal'].data-alopeke_defs.FRB_time[date].mjd)*24*3600
+    mask = np.abs(time_array) < 1.0
+
+    # Fit
+    p, V = np.polyfit(
+        time_array[mask], data_dict['C_star1'][mask], 1,
+        cov=True)
+    
+    # Report
+    print("Intercept is: {0} electrons/s".format(p[1]))
+    print("Slope is: {} electrons/s".format(p[0]))
+    return p, V
+
+def prob_of_max(date:str, camera:str, nsamp=10000):
+    data_dict = alopeke_utils2.load_camera(camera, date, cut=True)
+
+    n_event = len(data_dict['C_FRB'])
+    max_CFRB = data_dict['C_FRB'].data.max()
+
+    # Random draws from C_gal
+    n_gal = len(data_dict['C_gal'])
+    rand_num = np.random.choice(data_dict['C_gal'], size=(n_event, nsamp), replace=True)
+
+    exceed = np.sum(rand_num > max_CFRB, axis=0)
+    f_exceed = np.sum(exceed > 0)/exceed.size
+
+    # Return
+    print("Fraction exceeding: {}".format(f_exceed))
+    return f_exceed
+
+def values_around_frb(date:str, camera:str):
+
+    # Load
+    data_dict = alopeke_utils2.load_camera(camera, date)
+
+    n_values = len(data_dict['C_FRB'])
+    max_flux = np.max(data_dict['C_FRB'])
+    std_flux = np.std(data_dict['C_FRB'])
+    mean_flux = np.mean(data_dict['C_FRB'])
+
+    ata = alopeke_defs.ata
+
+    print(f'N values (+/-{ata}): {n_values}')
+    print(f'Max flux: {max_flux}')
+    print(f'Mean flux: {mean_flux}')
+    print(f'Standard deviation: {std_flux}')
+
+def sensitivity_function(date:str, camera:str):
+
+    # Load
+    data_dict = alopeke_utils2.load_camera(camera, date)
+
+    zpt = np.nanmedian(data_dict['zeropoint'])
+    zpt_err = np.nanmedian(data_dict['zeropoint_error'])
+
+    print(f'Zeropoint: {zpt} AB mag')
+    print(f'Zeropoint error: {zpt_err} mag')
+    
+def total_time(camera, date):
+    # Load
+    data_dict = alopeke_utils2.load_camera(camera, date)
+    Dt = data_dict['MJD_gal'][-1] - data_dict['MJD_gal'][0]
+    Dt *= 24*3600
+    print("Total observing = {}s".format(Dt))
+
+def upper_limit(date:str, camera:str, nsamp:int=100, step:int=2, cl:float=2.7e-3):
+    """ Calculate the upper limit to the counts
+
+    Args:
+        camera (str): [description]
+        nsamp (int, optional): [description]. Defaults to 100.
+        step (int, optional): [description]. Defaults to 2.
+        cl (float, optional): [description]. Defaults to 1e-3.
+
+    Returns:
+        [type]: [description]
+    """
+                
+    if camera == 'red':
+        assert alopeke_defs.EM == 1000
+        # Range of analysis
+        min_FRB=20
+        max_FRB=160
+    elif camera == 'blue':
+        assert alopeke_defs.EM == 1000
+        # Range of analysis
+        min_FRB=20
+        max_FRB=160
+    else:
+        raise IOError(f"Bad camera: {camera}")
+
+    data_dict = alopeke_utils2.load_camera(camera, date)
+    C_gal = data_dict['C_gal']
+    C_grid = np.outer(C_gal, np.ones(nsamp))
+
+    # Max observed
+    max_C_obs_FRB = np.max(data_dict['C_FRB'])
+
+    C_FRB_val = np.arange(min_FRB, max_FRB+step, step=step)
+    p_exceed = []
+
+    for C_FRB in C_FRB_val:
+        rand_C_FRB = poisson.rvs(C_FRB, size=(C_gal.size, nsamp))
+        rand_C_obs = rand_C_FRB + C_grid
+        # Stats
+        p_exceed.append(np.sum(rand_C_obs > max_C_obs_FRB)/rand_C_obs.size)
+    p_exceed = np.array(p_exceed)
+
+    # Interpolate
+    f_int = interp1d(p_exceed, C_FRB_val)
+    # import pdb;pdb.set_trace()
+    upper_FRB = float(f_int(1-cl))
+
+    print(f"The upper limit is {upper_FRB}")
+
+    # Return
+    return C_FRB_val, p_exceed, upper_FRB
+
+def calc_fluence(camera:str, date:str):
+    # Grab the values
+    data_dict = alopeke_utils2.load_camera(camera, date)
+    C_FRB_val, p_exceed, upper_FRB = upper_limit(date, camera)
+    EBV, A = dust_extinction(camera)
+
+    # Magnitude
+    Amag = 2.5*np.log10(A)
+
+    # Star-1
+    mean_1_tot = data_dict['mean_star1']
+    bkg_median = np.median(data_dict['mean_bkg']) # Background
+    mean_1 = mean_1_tot - bkg_median #(alopeke_defs.C_red_bg if camera == 'red' else alopeke_defs.C_blue_bg)
+
+    # Reference Zero point
+    ZP = 2.5*np.log10(mean_1) + (alopeke_defs.i_1 if camera == 'red' else alopeke_defs.r_1)
+
+    # Convert FRB limit to apparent magnitude and apply extinction
+    mag_FRB = -2.5*np.log10(upper_FRB) + ZP - Amag
+    mag_FRB_uncorr = -2.5*np.log10(upper_FRB) + ZP
+
+    # AB -- Include a color term??
+    fnu = 10**(-(48.6+mag_FRB)/2.5) * units.erg/units.s/units.Hz/units.cm**2
+    fnu_uncorr = 10**(-(48.6+mag_FRB_uncorr)/2.5) * units.erg/units.s/units.Hz/units.cm**2
+
+    # Fluence
+    fluence = (fnu * alopeke_defs.dt_alopeke).to('uJy s')
+    fluence_uncorr = (fnu_uncorr * alopeke_defs.dt_alopeke).to('uJy s')
+
+    freq = None
+    if camera=='blue':
+        freq = alopeke_defs.r_nu
+    elif camera=='red':
+        freq = alopeke_defs.i_nu
+    else:
+        raise IOError("Bad camera!")
+
+    energy = fnu * alopeke_defs.dt_alopeke * freq * 4 * np.pi * alopeke_defs.distance**2
+    energy = energy.to('erg')
+
+    # Get the radio fluence
+    radio_fluence = alopeke_defs.radio_data[date]["fit_statistics"]["bestfit_parameters"]["fluence"][0]
+    # Radio fluence is in Jy ms
+    radio_fluence = radio_fluence * 1.0e-23 * 1.0e-3 * units.erg/units.Hz/units.cm**2
+
+    fluence_ratio = fnu * alopeke_defs.dt_alopeke / radio_fluence
+
+    print(f"Extinction (A_filter): {Amag}")
+    print(f"AB mag (no dust correction): {mag_FRB_uncorr}")
+    print(f"AB mag (MW dust correction): {mag_FRB}")
+    print(f"fluence (no dust correction): {fluence_uncorr}")
+    print(f"fluence (MW dust correction): {fluence}")
+    print(f"fluence ratio (opt/radio): {fluence_ratio}")
+    print(f"Equivalent isotropic energy: {energy}")
+    return fluence, mag_FRB
+
+# See equation 63 from Metzger paper, defined piecewise in time-evolving
+# cooling and synchrotron frequency.
+# Assume t in seconds, nu in Hz
+def luminosity(nu, t, E43=100.0, sigma=0.3, beta=0.5, M21=1.0, T=1.4e6, dt=1e-4, nH=None):
+    # nu_syn is defined piecewise.  See equations 56-57 in Metzger
+    nu_syn_0 = 1.38e22 * (10 * sigma)**0.5 * E43**0.5 * (1e3 * dt)**-1.5
+    # Note that t_dec in eqn 56-57 is approximately dt (eqn 13)
+    if t < dt:
+        nu_syn = nu_syn_0 * (t/dt)**-1.0
+    else:
+        nu_syn = nu_syn_0 * (t/dt)**-1.5
+
+    # Can use circumburst density instead of T
+    if nH:
+        T = 1e5 * np.sqrt((4e3/nH) * M21 * (beta/0.5)**-3)
+
+    nu_c = 2.17e18 * (10 * sigma)**-1.5 * (2 * beta)**3 * M21**-1 *\
+        (1.0e3 * t)**-0.5 * (1.0e-5 * T)**2 # in Hz
+
+    L_pk = 1e45 * E43 * (1.0e3 * t)**-1 # in erg/s
+    tc = 6.4 * (10 * sigma)**2 * E43**0.5 * (2 * beta)**-3 * M21 * (1.0e-5*T)**-2
+
+    if nu > nu_syn:
+        L_pk = L_pk * np.exp(-(nu/nu_syn-1))
+
+    if nu < nu_c:
+        return(L_pk*(nu/nu_c)**(1.333333333)*(nu_c/nu_syn)**0.5)
+    else:
+        return(L_pk*(nu/nu_syn)**0.5)
+
+def afterglow_limits(date:str, Erange=[-1.8, 4.414], nrange=[1,6.2]):
+
+    Evals = np.linspace(*Erange, 400)
+    nvals = np.linspace(*nrange, 300)
+
+    grid = np.zeros((len(nvals),len(Evals)))
+
+    print(f'Getting fluence limits for {date}')
+    #rfluence, rmag = calc_fluence('red', date)
+    #bfluence, bmag = calc_fluence('blue', date)
+
+    rmag=16.63982358813184
+    bmag=16.38716776637639
+
+    rfreq = alopeke_defs.i_nu
+    bfreq = alopeke_defs.r_nu
+
+    # Convert magnitude to a luminosity, i.e., nu * Lnu
+    r_flux = 3631.0 * 1.0e-23 * 10**(-0.4 * rmag)
+    b_flux = 3631.0 * 1.0e-23 * 10**(-0.4 * bmag)
+
+    r_flux = r_flux * units.erg / units.s / units.Hz / (units.cm)**2
+    b_flux = b_flux * units.erg / units.s / units.Hz / (units.cm)**2
+
+    r_lum = r_flux * rfreq * 4 * np.pi * alopeke_defs.distance**2
+    b_lum = b_flux * bfreq * 4 * np.pi * alopeke_defs.distance**2
+
+    r_lum = r_lum.to(units.erg/units.second).value
+    b_lum = b_lum.to(units.erg/units.second).value
+
+    for i,modE in enumerate(Evals):
+        for j,modn in enumerate(nvals):
+            nval = 10**modn
+            Eval = 10**modE
+
+            model_lum_r = luminosity(rfreq.to('Hz').value,0.01,E43=Eval,nH=nval)
+            model_lum_b = luminosity(bfreq.to('Hz').value,0.01,E43=Eval,nH=nval)
+
+            if model_lum_r > r_lum or model_lum_b > b_lum:
+                grid[j,i] = 1
+
+    return(Evals, nvals, grid)
+
+def summary_dict(date, calc_upper=False, t_rel=59145):
+
+    summary = {}
+
+    for camera in ['blue', 'red']: 
+        summary[camera] = {}
+        data_dict = alopeke_utils2.load_camera(camera, date)
+
+        # Extinction
+        EBV, A = dust_extinction(camera)
+        summary[camera]['A'] = {}
+        summary[camera]['A']['variable'] = 'A'
+        summary[camera]['A']['value'] = 2.5*np.log10(A)  # Magnitudes! 
+        summary[camera]['A']['vformat'] = '{:0.1f}'
+        summary[camera]['A']['error'] = ''
+        summary[camera]['A']['eformat'] = '{}'
+        summary[camera]['A']['units'] = 'mag'
+        summary[camera]['A']['desc'] = 'Galactic extinction'
+
+        # Star 1
+        summary[camera]['C_1'] = {}
+        summary[camera]['C_1']['variable'] = '\\cstar'
+        summary[camera]['C_1']['value'] = data_dict['Gauss_star1'][0]
+        summary[camera]['C_1']['vformat'] = '{:0.1f}'
+        summary[camera]['C_1']['error'] = data_dict['Gauss_star1'][1] / np.sqrt(len(data_dict['C_star1']))
+        summary[camera]['C_1']['eformat'] = '{:0.3f}'
+        summary[camera]['C_1']['units'] = '\\cunit'
+        summary[camera]['C_1']['desc'] = 'Mean count rate of \\refstar'
+
+        # counts at galaxy, away from FRB event
+        summary[camera]['C_gal'] = {}
+        summary[camera]['C_gal']['variable'] = '\\ctfrb'
+        summary[camera]['C_gal']['value'] = data_dict['Gauss_gal'][0]
+        summary[camera]['C_gal']['vformat'] = '{:0.1f}'
+        summary[camera]['C_gal']['error'] = '' # We report the RMS not an error
+        summary[camera]['C_gal']['eformat'] = '{}'
+        summary[camera]['C_gal']['units'] = '\\cunit'
+        summary[camera]['C_gal']['desc'] = 'Mean count rate of galaxy at FRB location'
+
+        summary[camera]['sC_gal'] = {}
+        summary[camera]['sC_gal']['variable'] = '\\sgal'
+        summary[camera]['sC_gal']['value'] = data_dict['Gauss_gal'][1]
+        summary[camera]['sC_gal']['vformat'] = '{:0.1f}'
+        summary[camera]['sC_gal']['error'] = '' # We report the RMS not an error
+        summary[camera]['sC_gal']['eformat'] = '{}'
+        summary[camera]['sC_gal']['units'] = '\\cunit'
+        summary[camera]['sC_gal']['desc'] = 'RMS in count rate of galaxy at FRB location'
+
+        # Drift in C_gal
+        p, V = time_evolution(camera, t_rel=t_rel)
+        summary[camera]['dCdt'] = {}
+        summary[camera]['dCdt']['variable'] = '$d\\ctfrb/dt$'
+        summary[camera]['dCdt']['value'] = p[0]*1e3
+        summary[camera]['dCdt']['vformat'] = '{:0.1f}'
+        summary[camera]['dCdt']['error'] = np.sqrt(V[0,0])*1e3
+        summary[camera]['dCdt']['eformat'] = '{:0.2f}'
+        summary[camera]['dCdt']['units'] = '$10^{-3}$ counts s$^{-1}$'
+        summary[camera]['dCdt']['desc'] = 'Drift in count rate during the observations'
+
+        # Maximum C during FRB event, including galaxy
+        summary[camera]['mxFRB'] = {}
+        summary[camera]['mxFRB']['variable'] = '\\mxfrb'
+        summary[camera]['mxFRB']['value'] = np.max(data_dict['C_FRB'])
+        summary[camera]['mxFRB']['vformat'] = '{:0.1f}'
+        summary[camera]['mxFRB']['error'] = '' # We report the RMS not an error
+        summary[camera]['mxFRB']['eformat'] = '{}'
+        summary[camera]['mxFRB']['units'] = '\\cunit'
+        summary[camera]['mxFRB']['desc'] = 'Maximum count rate at FRB location during event'
+
+        # Upper limit
+        if calc_upper:
+            _, _, upper_FRB = upper_limit(date, camera)
+            summary[camera]['uppFRB'] = {}
+            summary[camera]['uppFRB']['variable'] = '\\umufrb'
+            summary[camera]['uppFRB']['value'] = upper_FRB
+            summary[camera]['uppFRB']['vformat'] = '{:0.1f}'
+            summary[camera]['uppFRB']['error'] = '' # We report the RMS not an error
+            summary[camera]['uppFRB']['eformat'] = '{}'
+            summary[camera]['uppFRB']['units'] = '\\cunit'
+            summary[camera]['uppFRB']['desc'] = 'Upper limit (99.9\%) on count rate of FRB emission'
+
+        # Fluences
+        fluence, mag_frb = calc_fluence(camera)
+        summary[camera]['uppFlu'] = {}
+        summary[camera]['mag_lim'] = {}
+        if camera == 'red':
+            summary[camera]['uppFlu']['variable'] = '\\iflufrb'
+        else:
+            summary[camera]['uppFlu']['variable'] = '\\rflufrb'
+        summary[camera]['uppFlu']['value'] = fluence.to('uJy s').value
+        summary[camera]['uppFlu']['vformat'] = '{:0.3f}'
+        summary[camera]['uppFlu']['error'] = '' # We report upper limit
+        summary[camera]['uppFlu']['eformat'] = '{}'
+        summary[camera]['uppFlu']['units'] = '\\fluunit'
+        summary[camera]['uppFlu']['desc'] = 'Upper limit (99.9\%) to the fluence'
+        summary[camera]['mag_lim']['value'] = mag_frb
+    # Return
+    return summary
+
+
+if __name__ == '__main__':
+    camera = ['red', 'blue']
+
+    date = sys.argv[1]
+    year = date[0:4]
+    month = date[4:6]
+    day = date[6:8]
+
+    t = Time(year+'-'+month+'-'+day)
+    t_rel=t.mjd
+    for cam in camera:
+        time_obs(date, cam)
+        values_around_frb(date, cam)
+        sensitivity_function(date, cam)
+        calc_fluence(cam, date)
+        time_evolution(date, cam, t_rel=t_rel)
+        upper_limit(date, cam)
+        prob_of_max(date, cam)
+        total_time(cam, date)
+        dust_extinction(cam)
diff --git a/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_defs.py b/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_defs.py
new file mode 100644
index 00000000..b4a4701f
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_defs.py
@@ -0,0 +1,88 @@
+import numpy as np
+from astropy.time import Time
+from astropy import units
+import json
+
+from frb.frb import FRB
+frb180916 = FRB.by_name('FRB20180916B')
+
+# CHIME
+# Tendulkar (Feb 11, 2021): 
+# This pulse arrival time is topocentric at 400 MHz.
+# In the database I see 2020-10-23 07:48:30.777667 UTC+00:00 
+# The uncertainty is 1 ms. 
+
+chime_time_arrival = {
+    '20201023':Time('2020-10-23T07:48:30.777667'),
+    '20220908':Time('2022-09-08T10:53:26.888596'),
+}
+ata_chime = 1*units.s/1000.  # see comment above
+
+#  Alopeke absolute time accuracy (from email Feb 8, 2021 Nic Scott)
+#  Nic: The major contributor in this uncertainty is thought to be the variable lag
+#  between the computer receipt from the NTP server and the triggering of the
+#  cameras.
+ata_alopeke = 163*units.s/1000.
+
+# final absolute time accuracy (1 sigma)
+ata = np.sqrt(ata_alopeke**2 + ata_chime**2)  # quadrature of absolute uncertainties
+
+# According to the calculation of Kilpatrick, from 400 MHz to optical
+# frequencies, a delay of 9.1s should be applied to the radio.
+# Also account for light-travel time for CHIME to Alopeke (14.8ms)
+chime_time_arrival_optical = {}
+FRB_time = {}
+mjd_low = {}
+mjd_high = {}
+for key in chime_time_arrival.keys():
+    chime_time_arrival_optical[key]=chime_time_arrival[key] - 9.08*units.s - 0.0148*units.s
+    FRB_time[key]=chime_time_arrival_optical[key]
+    mjd_low[key] = (FRB_time[key]-ata).mjd
+    mjd_high[key] = (FRB_time[key]+ata).mjd
+
+# Gains 
+gain_red = 5.61
+gain_blue = 5.54
+EM = 1000
+
+# Center of band
+XSDSS_r = 620. # nm
+XSDSS_i = 765. # nm
+
+# Exposure time
+#dt_alopeke = 11.6 * (1e-3 * units.s)
+dt_alopeke = 10.419 * (1e-3 * units.s)  # on-sky exposure time
+
+# Photometry Star-1
+# From Charlie Kilpatrick #frb180916-speckle channel (5 deb 2021)
+# r=15.7481+/-0.00017 (PS1)
+# i=15.1387+/-0.00237 (PS1)
+r_1 = 15.7481  # Panstarrs-1 magnitude
+i_1 = 15.1387  # Panstarrs-1 magnitude
+
+r_2 = 17.1016  # Panstarrs-1 magnitude
+i_2 = 16.6247  # Panstarrs-1 magnitude
+
+redshift = 0.0337
+
+r_nu = (2.998e18 * units.angstrom/units.second) / (6231 * units.angstrom)
+i_nu = (2.998e18 * units.angstrom/units.second) / (7625 * units.angstrom)
+
+distance = 150.0e6 * units.parsec
+
+# Radio data from CHIME (email from Emmanuel Fonseca, 2023-04-21)
+burst1_file = '../../Data/results_R3/results_fitburst_139459007.json'
+burst2_file = '../../Data/results_R3/results_fitburst_244202260.json'
+
+burst1_data = {}
+burst2_data = {}
+
+with open(burst1_file, 'r') as f:
+    burst1_data = json.load(f)
+with open(burst2_file, 'r') as f:
+    burst2_data = json.load(f)
+
+radio_data = {
+    '20201023':burst1_data,
+    '20220908':burst2_data,
+}
diff --git a/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_utils2.py b/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_utils2.py
new file mode 100644
index 00000000..729bfe2d
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Analysis/py/alopeke_utils2.py
@@ -0,0 +1,130 @@
+import sys, os
+import numpy as np
+from scipy.stats import norm
+
+from astropy.table import Table
+
+sys.path.append(os.path.abspath("../Analysis/py"))
+import alopeke_defs
+
+def calc_zeropoint(data_dict, camera:str):
+
+    # Instrumental magnitude for star 1 and star 2
+    m_star1 = -2.5 * np.log10(data_dict['C_star1_full']-data_dict['C_bkg_full'])
+    m_star2 = -2.5 * np.log10(data_dict['C_star2_full']-data_dict['C_bkg_full'])
+
+    # Uncertainties on instrumental magnitudes - assume Poisson
+    merr_star1 = 1.086 * np.sqrt(data_dict['C_star1_full'])/data_dict['C_star1_full']
+    merr_star2 = 1.086 * np.sqrt(data_dict['C_star1_full'])/data_dict['C_star1_full']
+
+    # Now get zero points
+    zpt_star1 = 0.
+    zpt_star2 = 0.
+    if camera=='red':
+        zpt_star1 = alopeke_defs.i_1 - m_star1
+        zpt_star2 = alopeke_defs.i_2 - m_star2
+    elif camera=='blue':
+        zpt_star1 = alopeke_defs.r_1 - m_star1
+        zpt_star2 = alopeke_defs.r_2 - m_star2
+    else:
+        raise Exception(f'ERROR: Unrecognized camera {camera}')
+
+    zpt = (merr_star1 * zpt_star1 + merr_star2 * zpt_star2)/(merr_star1 + merr_star2)
+    zpt_err = 1./np.sqrt(2) * np.sqrt(merr_star1**2 + merr_star2**2)
+
+    return(zpt, zpt_err)
+
+def get_star_flux(date:str, data_dict):
+    mask = np.abs(data_dict['MJD_star1_full']-alopeke_defs.FRB_time[date].mjd)*24*3600<alopeke_defs.ata.to('s').value
+
+    data_dict['mean_bkg'] = np.mean(data_dict['C_bkg_full'][mask])
+    data_dict['mean_star1'] = np.mean(data_dict['C_star1_full'][mask])
+    data_dict['mean_star2'] = np.mean(data_dict['C_star2_full'][mask])
+    data_dict['mean_star3'] = np.mean(data_dict['C_star3_full'][mask])
+
+    return(data_dict)
+
+def load_camera(camera:str, date:str, cut=True):
+
+    if camera == 'red':
+        data = Table.read(f'../../Data/master_table_{date}_r.fits')
+    elif camera == 'blue':
+        data = Table.read(f'../../Data/master_table_{date}_b.fits')
+    else:
+        raise IOError("Bad camera")
+
+    # Cut down to Galaxy
+    i_FRB = (data['MJD']>=alopeke_defs.mjd_low[date]) & (
+        data['MJD']<=alopeke_defs.mjd_high[date])
+    i_gal = np.invert(i_FRB)
+
+    # Expunge first reads
+    if cut:
+        gd_data = data['frame'] > 1
+    else:
+        gd_data = np.ones(len(data), dtype='bool')
+
+    # ADUs
+    C_gal = data['flux_2FWHM'][i_gal & gd_data]
+    C_gal_full = data['flux_2FWHM'][gd_data]
+    C_frb = data['flux_2FWHM'][i_FRB & gd_data]
+
+    C_star1 = data['flux_star1_2FWHM'][i_gal & gd_data]
+    C_star1_full = data['flux_star1_2FWHM'][gd_data]
+
+    C_star2 = data['flux_star2_2FWHM'][i_gal & gd_data]
+    C_star2_full = data['flux_star2_2FWHM'][gd_data]
+
+    C_star3 = data['flux_star3_2FWHM'][i_gal & gd_data]
+    C_star3_full = data['flux_star3_2FWHM'][gd_data]
+
+    C_bkg = data['flux_bkg_2FWHM'][i_gal & gd_data]
+    C_bkg_full = data['flux_bkg_2FWHM'][gd_data]
+    
+
+    out_dict = {}
+
+    # Time
+    out_dict['MJD_gal'] = data['MJD'][i_gal & gd_data]
+    out_dict['MJD_FRB'] = data['MJD'][i_FRB & gd_data]
+    out_dict['MJD_star1'] = data['MJD'][i_gal & gd_data]
+    out_dict['MJD_star1_full'] = data['MJD'][gd_data]
+    out_dict['MJD_star2'] = data['MJD'][i_gal & gd_data]
+    out_dict['MJD_star2_full'] = data['MJD'][gd_data]
+    out_dict['MJD_star3'] = data['MJD'][i_gal & gd_data]
+    out_dict['MJD_star3_full'] = data['MJD'][gd_data]
+    out_dict['dt'] = (data['MJD'][2]-data['MJD'][1])*24*3600 # seconds
+
+    # Counts
+    gain = alopeke_defs.gain_red if camera == 'red' else alopeke_defs.gain_blue
+    out_dict['C_FRB'] = C_frb * gain / alopeke_defs.EM
+    out_dict['C_gal'] = C_gal* gain / alopeke_defs.EM
+    out_dict['C_gal_full'] = C_gal_full * gain / alopeke_defs.EM
+
+    out_dict['C_star1'] = C_star1 * gain / alopeke_defs.EM
+    out_dict['C_star1_full'] = C_star1_full * gain / alopeke_defs.EM
+
+    out_dict['C_star2'] = C_star2 * gain / alopeke_defs.EM
+    out_dict['C_star2_full'] = C_star2_full * gain / alopeke_defs.EM
+
+    out_dict['C_star3'] = C_star3 * gain / alopeke_defs.EM
+    out_dict['C_star3_full'] = C_star3_full * gain / alopeke_defs.EM
+
+    out_dict['C_bkg'] = C_bkg * gain / alopeke_defs.EM
+    out_dict['C_bkg_full'] = C_bkg_full * gain / alopeke_defs.EM
+    
+
+    # Gauss
+    out_dict['Gauss_gal'] = norm.fit(out_dict['C_gal'])
+    out_dict['Gauss_star1'] = norm.fit(out_dict['C_star1'])
+    out_dict['Gauss_star2'] = norm.fit(out_dict['C_star2'])
+    out_dict['Gauss_star3'] = norm.fit(out_dict['C_star3'])
+
+    out_dict = get_star_flux(date, out_dict)
+
+    zpt, zpt_err = calc_zeropoint(out_dict, camera)
+    out_dict['zeropoint'] = zpt
+    out_dict['zeropoint_error'] = zpt_err
+
+    # Return
+    return out_dict
diff --git a/papers/Kilpatrick2024_Alopeke/Analysis/py/analysis_data_20201023.txt b/papers/Kilpatrick2024_Alopeke/Analysis/py/analysis_data_20201023.txt
new file mode 100644
index 00000000..301e7a2a
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Analysis/py/analysis_data_20201023.txt
@@ -0,0 +1,46 @@
+This camera = red
+Time of start (UTC) 2020-10-23T07:41:40.265
+We started 6.841878353152424 minutes before
+And ended 12.09422416635789 minutes after
+N values (+/-0.16300306745579976 s): 28
+Max flux: 72.33394791687047
+Mean flux: 47.82070429999975
+Standard deviation: 9.000776067207367
+Zeropoint: 22.136190941626683 AB mag
+Zeropoint error: 0.035881878355318225 mag
+The upper limit is 51.61662613981765
+Extinction (A_filter): 1.5465383884105393
+AB mag (no dust correction): 18.18696094759817
+AB mag (MW dust correction): 16.64042255918763
+fluence (no dust correction): 2.0092855408094445 s uJy
+fluence (MW dust correction): 8.349433582310564 s uJy
+fluence ratio (opt/radio): 0.003211320608580986
+Equivalent isotropic energy: 8.837761298901163e+40 erg
+Intercept is: 908.6946900880495 electrons/s
+Slope is: -3.99180150411254 electrons/s
+The upper limit is 51.66037543041208
+Fraction exceeding: 0.0678
+Total observing = 1136.1661511706188s
+This camera = blue
+Time of start (UTC) 2020-10-23T07:41:40.265
+We started 6.84188335086219 minutes before
+And ended 12.095297552878037 minutes after
+N values (+/-0.16300306745579976 s): 28
+Max flux: 56.42290357139614
+Mean flux: 43.515621034091886
+Standard deviation: 7.255609825974984
+Zeropoint: 22.151109428099474 AB mag
+Zeropoint error: 0.04506495957178212 mag
+The upper limit is 38.949428571428534
+Extinction (A_filter): 2.205121094427004
+AB mag (no dust correction): 18.59129404177557
+AB mag (MW dust correction): 16.386172947348566
+fluence (no dust correction): 1.3845492902092602 s uJy
+fluence (MW dust correction): 10.552536404938659 s uJy
+fluence ratio (opt/radio): 0.00405866784805333
+Equivalent isotropic energy: 1.366860549177359e+41 erg
+Intercept is: 576.6310231475677 electrons/s
+Slope is: 4.910245350656469 electrons/s
+The upper limit is 38.94579361689137
+Fraction exceeding: 0.6364
+Total observing = 1136.2308542244136s
diff --git a/papers/Kilpatrick2024_Alopeke/Analysis/py/analysis_data_20220908.txt b/papers/Kilpatrick2024_Alopeke/Analysis/py/analysis_data_20220908.txt
new file mode 100644
index 00000000..16b7269d
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Analysis/py/analysis_data_20220908.txt
@@ -0,0 +1,46 @@
+This camera = red
+Time of start (UTC) 2022-09-08T10:39:04.629
+We started 14.370993955526501 minutes before
+And ended 7.545006988802925 minutes after
+N values (+/-0.16300306745579976 s): 28
+Max flux: 99.15800454694453
+Mean flux: 88.44920933888261
+Standard deviation: 8.789814983629809
+Zeropoint: 22.118906012299913 AB mag
+Zeropoint error: 0.03531093383954578 mag
+The upper limit is 41.997860785932886
+Extinction (A_filter): 1.5465383884105393
+AB mag (no dust correction): 18.279726116280635
+AB mag (MW dust correction): 16.733187727870096
+fluence (no dust correction): 1.8447418125932844 s uJy
+fluence (MW dust correction): 7.665684606755237 s uJy
+fluence ratio (opt/radio): 0.002190195601930068
+Equivalent isotropic energy: 8.114022356042966e+40 erg
+Intercept is: 825.3673651910062 electrons/s
+Slope is: 7.763991414138952 electrons/s
+The upper limit is 41.988571154215386
+Fraction exceeding: 0.9929
+Total observing = 1314.9600566597655s
+This camera = blue
+Time of start (UTC) 2022-09-08T10:39:04.629
+We started 14.370995422359556 minutes before
+And ended 7.546799898846075 minutes after
+N values (+/-0.16300306745579976 s): 28
+Max flux: 134.17818092168486
+Mean flux: 112.8543244232961
+Standard deviation: 16.212168141402877
+Zeropoint: 21.993747416781208 AB mag
+Zeropoint error: 0.042791267583366197 mag
+The upper limit is 76.1798582600194
+Extinction (A_filter): 2.205121094427004
+AB mag (no dust correction): 17.659275130073198
+AB mag (MW dust correction): 15.454154035646194
+fluence (no dust correction): 3.266750648610154 s uJy
+fluence (MW dust correction): 24.89799777377808 s uJy
+fluence ratio (opt/radio): 0.007113713649650881
+Equivalent isotropic energy: 3.2250152574271826e+41 erg
+Intercept is: 555.8676614392847 electrons/s
+Slope is: -1.9068632522228652 electrons/s
+The upper limit is 76.23242942144554
+Fraction exceeding: 0.9591
+Total observing = 1315.0677192723379s
diff --git a/papers/Kilpatrick2024_Alopeke/Data/README b/papers/Kilpatrick2024_Alopeke/Data/README
new file mode 100644
index 00000000..ffec1783
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Data/README
@@ -0,0 +1,3 @@
+Grab the files from the Drive.
+
+Don't add to the Repo
diff --git a/papers/Kilpatrick2024_Alopeke/Data/results_R3/results_fitburst_139459007.json b/papers/Kilpatrick2024_Alopeke/Data/results_R3/results_fitburst_139459007.json
new file mode 100644
index 00000000..b228daac
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Data/results_R3/results_fitburst_139459007.json
@@ -0,0 +1,118 @@
+{
+    "model_parameters": {
+        "amplitude": [
+            -2.1554888830049914
+        ],
+        "arrival_time": [
+            847456.7816011407
+        ],
+        "burst_width": [
+            0.002704256345508544
+        ],
+        "dm": [
+            350.47945440662073
+        ],
+        "dm_index": [
+            -2.0
+        ],
+        "scattering_timescale": [
+            0.0
+        ],
+        "scattering_index": [
+            -4.0
+        ],
+        "spectral_index": [
+            0.9836707442829146
+        ],
+        "spectral_running": [
+            -2.2612435431981592
+        ],
+        "ref_freq": [
+            400.1953125
+        ]
+    },
+    "fit_statistics": {
+        "num_freq": 16384,
+        "num_freq_good": 12492,
+        "num_fit_parameters": 6,
+        "num_observations": 2023698,
+        "num_time": 162,
+        "chisq_initial": 2023703.9999999998,
+        "chisq_final": 2023571.4985833005,
+        "chisq_final_reduced": 0.9999374899729606,
+        "snr": 11.510925970540438,
+        "bestfit_parameters": {
+            "amplitude": [
+                -2.1554888830049914
+            ],
+            "peak_flux": [
+                0.5
+            ],
+            "fluence": [
+                2.6
+            ],
+            "arrival_time": [
+                847456.7816011407
+            ],
+            "burst_width": [
+                0.002704256345508544
+            ],
+            "dm": [
+                0.0011134504300374516
+            ],
+            "spectral_index": [
+                0.9836707442829146
+            ],
+            "spectral_running": [
+                -2.2612435431981592
+            ]
+        },
+        "bestfit_uncertainties": {
+            "amplitude": [
+                0.11713145949915273
+            ],
+            "peak_flux": [
+                0.2
+            ],
+            "fluence": [
+                0.8
+            ],
+            "arrival_time": [
+                0.0007959071832493715
+            ],
+            "burst_width": [
+                0.0003277596214531834
+            ],
+            "dm": [
+                0.06172069076343659
+            ],
+            "spectral_index": [
+                1.7548582270156299
+            ],
+            "spectral_running": [
+                2.523951199207993
+            ]
+        },
+        "bestfit_covariance": null
+    },
+    "fit_logistics": {
+        "dm_incoherent": [
+            350.4783409561907
+        ],
+        "factor_freq_upsample": 8,
+        "factor_time_upsample": 4,
+        "is_repeater": true,
+        "normalize_variance": false,
+        "spectrum_window": 0.08,
+        "variance_range": [
+            0.85,
+            1.15
+        ],
+        "variance_weight": 0.020833333333333332
+    },
+    "derived_parameters": {
+        "arrival_time_UTC": [
+            "2020-10-23 07:48:30.782084"
+        ]
+    }
+}
diff --git a/papers/Kilpatrick2024_Alopeke/Data/results_R3/results_fitburst_244202260.json b/papers/Kilpatrick2024_Alopeke/Data/results_R3/results_fitburst_244202260.json
new file mode 100644
index 00000000..00ade133
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Data/results_R3/results_fitburst_244202260.json
@@ -0,0 +1,118 @@
+{
+    "model_parameters": {
+        "amplitude": [
+            -2.3867582869350814
+        ],
+        "arrival_time": [
+            1270684.8964626202
+        ],
+        "burst_width": [
+            0.0027468856073360618
+        ],
+        "dm": [
+            349.9345159524652
+        ],
+        "dm_index": [
+            -2.0
+        ],
+        "scattering_timescale": [
+            0.0
+        ],
+        "scattering_index": [
+            -4.0
+        ],
+        "spectral_index": [
+            21.457180284733205
+        ],
+        "spectral_running": [
+            -58.66944617541479
+        ],
+        "ref_freq": [
+            400.1953125
+        ]
+    },
+    "fit_statistics": {
+        "num_freq": 16384,
+        "num_freq_good": 10832,
+        "num_fit_parameters": 6,
+        "num_observations": 1754778,
+        "num_time": 162,
+        "chisq_initial": 1754783.9999999998,
+        "chisq_final": 1754330.1705535743,
+        "chisq_final_reduced": 0.9997447942438157,
+        "snr": 21.303273138779137,
+        "bestfit_parameters": {
+            "amplitude": [
+                -2.3867582869350814
+            ],
+            "peak_flux": [
+                0.5
+            ],
+            "fluence": [
+                3.5
+            ],
+            "arrival_time": [
+                1270684.8964626202
+            ],
+            "burst_width": [
+                0.0027468856073360618
+            ],
+            "dm": [
+                -0.011583182214040137
+            ],
+            "spectral_index": [
+                21.457180284733205
+            ],
+            "spectral_running": [
+                -58.66944617541479
+            ]
+        },
+        "bestfit_uncertainties": {
+            "amplitude": [
+                0.12275319439771248
+            ],
+            "peak_flux": [
+                0.2
+            ],
+            "fluence": [
+                0.8
+            ],
+            "arrival_time": [
+                0.0006242139225926881
+            ],
+            "burst_width": [
+                0.00017966657020020762
+            ],
+            "dm": [
+                0.07693552535645061
+            ],
+            "spectral_index": [
+                3.0836709596285234
+            ],
+            "spectral_running": [
+                8.097462123965938
+            ]
+        },
+        "bestfit_covariance": null
+    },
+    "fit_logistics": {
+        "dm_incoherent": [
+            349.94609913467923
+        ],
+        "factor_freq_upsample": 8,
+        "factor_time_upsample": 4,
+        "is_repeater": true,
+        "normalize_variance": false,
+        "spectrum_window": 0.08,
+        "variance_range": [
+            0.85,
+            1.15
+        ],
+        "variance_weight": 0.020833333333333332
+    },
+    "derived_parameters": {
+        "arrival_time_UTC": [
+            "2022-09-08 10:53:26.896729"
+        ]
+    }
+}
diff --git a/papers/Kilpatrick2024_Alopeke/Figures/py/Summary_Plot.ipynb b/papers/Kilpatrick2024_Alopeke/Figures/py/Summary_Plot.ipynb
new file mode 100644
index 00000000..e48f4147
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Figures/py/Summary_Plot.ipynb
@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efa778a0-e4c4-4e14-a536-0d1388efcc7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import IO\n",
+    "import numpy as np\n",
+    "\n",
+    "import glob, os, sys, json\n",
+    "import pdb\n",
+    "\n",
+    "from cycler import cycler\n",
+    "import matplotlib as mpl\n",
+    "from numpy.core.fromnumeric import mean\n",
+    "\n",
+    "import pandas\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "from matplotlib.patches import ConnectionPatch\n",
+    "\n",
+    "import matplotlib\n",
+    "from matplotlib import rc\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.colors as mcolors\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "from scipy.interpolate import interp1d\n",
+    "from scipy.stats import kstest\n",
+    "from scipy.stats import norm, poisson\n",
+    "from scipy.signal import convolve\n",
+    "\n",
+    "from astropy import units as u\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "from astropy.io import fits\n",
+    "from astropy.wcs import WCS\n",
+    "from astropy.time import Time\n",
+    "\n",
+    "from IPython import embed\n",
+    "\n",
+    "# Local\n",
+    "sys.path.append(os.path.abspath(\"../../Analysis/py\"))\n",
+    "import alopeke_defs\n",
+    "import alopeke_utils2\n",
+    "import alopeke_analy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39cbb07c-d2c8-4289-b107-42dce51cea23",
+   "metadata": {},
+   "source": [
+    "### Estimating the optical fluence ratio for the Galactic Magnetar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d42c49b8-76b9-49ff-8144-b721feb7ad22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Isotropic equivalent radio energy is: 2.03524182621986e+35\n",
+      "Optical fluence is: 4494.824141336007 Jy ms\n",
+      "Optical/radio fluence ratio is: 0.0029965494275573383\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Expected optical emission from SGR1935+2154, from FRB200428\n",
+    "# From this paper: https://arxiv.org/pdf/2007.02978.pdf\n",
+    "# Expect frequency dependence on fluence is nu^-0.46\n",
+    "spectral_index = -0.46\n",
+    "\n",
+    "# STARE2 burst for 200428 was at 1.4 GHz and had a fluence of 1.5e6 Jy ms\n",
+    "radio_freq = 1.4e9\n",
+    "radio_fluence = 1.5e6\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Estimate what the optical fluence would be at 7000 angstroms\n",
+    "# Fluence = F_0 * nu^-0.46 -> F_0 = Fluence / (nu^-0.46)\n",
+    "optical_freq = 2.998e18 / (7000)\n",
+    "optical_fluence = (radio_fluence / radio_freq**spectral_index) * optical_freq ** spectral_index\n",
+    "\n",
+    "# Distance to SGR1935+2154 based on association with SNR G57.2+0.8\n",
+    "# https://iopscience.iop.org/article/10.3847/2041-8213/aba262\n",
+    "sgr_distance = 9.0e3\n",
+    "\n",
+    "sgr_isotropic_radio_energy = 4*np.pi*(sgr_distance * 3.08568025e18)**2 * radio_fluence * 1.0e-26 * radio_freq\n",
+    "\n",
+    "sgr_fluence_ratio = optical_fluence/radio_fluence\n",
+    "\n",
+    "print('Isotropic equivalent radio energy is:',sgr_isotropic_radio_energy)\n",
+    "print('Optical fluence is:',optical_fluence,'Jy ms')\n",
+    "print('Optical/radio fluence ratio is:',optical_fluence/radio_fluence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de3f0d6e-7c1b-4385-9abb-222dfadb4d76",
+   "metadata": {},
+   "source": [
+    "### Getting optical fluence ratios of Galactic optical pulsars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "02b5a38c-c182-4ae8-9dd9-fbef6745dabf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.table import Table\n",
+    "\n",
+    "all_pulsar_data = []\n",
+    "\n",
+    "# Crab Pulsar\n",
+    "data = Table.read('data/crab_data.csv',format='csv')\n",
+    "optical_idx = np.argmin(np.abs(data['frequency']-2.998e18/7000))\n",
+    "radio_idx = np.argmin(np.abs(data['frequency']-400e6))\n",
+    "optical_fluence=data[optical_idx]['fluence']/data[optical_idx]['frequency']\n",
+    "radio_fluence=data[radio_idx]['fluence']/data[radio_idx]['frequency']\n",
+    "fluence_ratio=optical_fluence/radio_fluence\n",
+    "radio_energy=4*np.pi*(2.0e3*3.08568025e18)**2 * data[radio_idx]['fluence']*300e-6\n",
+    "\n",
+    "all_pulsar_data.append({'name':'crab','radio_freq':400.0e6,'optical_freq':2.998e18/7000,\n",
+    "                        'optical_fluence':optical_fluence,'radio_fluence':radio_fluence,\n",
+    "                        'fluence_ratio':optical_fluence/radio_fluence,\n",
+    "                        'distance':2.0e3,\n",
+    "                        'duration':300.0e-6,\n",
+    "                        'radio_energy':4*np.pi*(2.0e3*3.08568025e18)**2 * data[radio_idx]['fluence']*300e-6})\n",
+    "\n",
+    "# Geminga\n",
+    "data = Table.read('data/geminga_data.csv',format='csv')\n",
+    "optical_idx = np.argmin(np.abs(data['frequency']-2.998e18/7000))\n",
+    "radio_idx = np.argmin(np.abs(data['frequency']-400e6))\n",
+    "optical_fluence=data[optical_idx]['flux']\n",
+    "radio_fluence=data[radio_idx]['flux']\n",
+    "fluence_ratio=optical_fluence/radio_fluence\n",
+    "radio_energy=4*np.pi*(250.0*3.08568025e18)**2 * data[radio_idx]['flux']*1.0e-6*1.0e-23*400.0e6*300e-3\n",
+    "\n",
+    "all_pulsar_data.append({'name':'geminga','radio_freq':400.0e6,'optical_freq':2.998e18/7000,\n",
+    "                        'optical_fluence':optical_fluence,'radio_fluence':radio_fluence,\n",
+    "                        'fluence_ratio':optical_fluence/radio_fluence,\n",
+    "                        'distance':250.0,\n",
+    "                        'duration':300.0e-3,\n",
+    "                        'radio_energy':4*np.pi*(250.0*3.08568025e18)**2 * data[radio_idx]['flux']*1.0e-6*1.0e-23*400.0e6*300e-3})\n",
+    "\n",
+    "# Vela Pulsar\n",
+    "data = Table.read('data/vela_data.csv',format='csv')\n",
+    "optical_idx = np.argmin(np.abs(data['frequency']-2.998e18/7000))\n",
+    "radio_idx = np.argmin(np.abs(data['frequency']-400e6))\n",
+    "optical_fluence=data[optical_idx]['flux']\n",
+    "radio_fluence=data[radio_idx]['flux']\n",
+    "fluence_ratio=optical_fluence/radio_fluence\n",
+    "radio_energy=4*np.pi*(294.0*3.08568025e18)**2 * data[radio_idx]['flux']*1.0e-6*1.0e-23*400.0e6*300e-3\n",
+    "\n",
+    "all_pulsar_data.append({'name':'vela','radio_freq':400.0e6,'optical_freq':2.998e18/7000,\n",
+    "                        'optical_fluence':optical_fluence,'radio_fluence':radio_fluence,\n",
+    "                        'fluence_ratio':optical_fluence/radio_fluence,\n",
+    "                        'distance':294.0,\n",
+    "                        'duration':300.0e-3,\n",
+    "                        'radio_energy':4*np.pi*(294.0*3.08568025e18)**2 * data[radio_idx]['flux']*1.0e-6*1.0e-23*400.0e6*300e-3})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9111bdd-a49e-4b9f-9fdd-3de18921e72e",
+   "metadata": {},
+   "source": [
+    "### Plotting empirical and theoretical data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64423682-b7e0-446d-a688-83cb46ab6969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "from matplotlib import rc\n",
+    "from matplotlib.ticker import MultipleLocator,AutoMinorLocator\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "def ccolor(r,g,b):\n",
+    "    return((r/255.,g/255.,b/255., 1.0))\n",
+    "\n",
+    "lightblue=ccolor(135, 206, 235)\n",
+    "red=ccolor(255,0,0)\n",
+    "blue=ccolor(0,0,255)\n",
+    "black=ccolor(0,0,0)\n",
+    "red=ccolor(255,0,0)\n",
+    "blue=ccolor(10,0,255)\n",
+    "green=ccolor(12,83,0)\n",
+    "magenta=ccolor(204,0,204)\n",
+    "goldenrod=ccolor(239,139,8)\n",
+    "orange=ccolor(204,102,0)\n",
+    "lightred=ccolor(255,178,178)\n",
+    "\n",
+    "figsize=10\n",
+    "rc('font',**{'family':'serif','serif':['Times'],'size':5.0*figsize})\n",
+    "rc('text', usetex=True, color='k')\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for i in ax.spines.keys(): ax.spines[i].set_linewidth(0.6*figsize)\n",
+    "fig.set_size_inches(1.8*figsize, 1.5*figsize)\n",
+    "\n",
+    "ax.xaxis.set_minor_locator(AutoMinorLocator())\n",
+    "ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
+    "\n",
+    "ax.tick_params(direction='in', length=2*figsize,\n",
+    "            width=0.6*figsize, which='major', axis='both', colors=black,\n",
+    "            pad=2*figsize, top=True, bottom=True, left=True, right=True)\n",
+    "ax.tick_params(direction='in', length=figsize,\n",
+    "            width=0.6*figsize, which='minor', axis='both', colors=black,\n",
+    "            pad=0.4*figsize, top=True, bottom=True, left=True, right=True)\n",
+    "\n",
+    "# Burst radio energies at 400 MHz and our limits\n",
+    "radio_freq=400e6\n",
+    "burst1=2.6 # Jy ms\n",
+    "burst2=3.5 # Jy ms\n",
+    "frb_distance=150e6\n",
+    "burst1_energy=4*np.pi*(frb_distance * 3.08568025e18)**2 * burst1 * 1.0e-26 * radio_freq\n",
+    "burst2_energy=4*np.pi*(frb_distance * 3.08568025e18)**2 * burst2 * 1.0e-26 * radio_freq\n",
+    "\n",
+    "# Optical fluence ratios\n",
+    "burst1_r_ratio=0.0041\n",
+    "burst1_i_ratio=0.0032\n",
+    "burst2_r_ratio=0.0071\n",
+    "burst2_i_ratio=0.0022\n",
+    "\n",
+    "# Plot all limits as downward arrows\n",
+    "i=0\n",
+    "for energy,ratio,color,error in zip([burst1_energy,burst1_energy,burst2_energy,burst2_energy],\n",
+    "                        [burst1_r_ratio,burst1_i_ratio,burst2_r_ratio,burst2_i_ratio],\n",
+    "                        [blue,red,blue,red],\n",
+    "                        [0.8/burst1,0.8/burst1,0.8/burst2,0.8/burst2]):\n",
+    "    if i==0:\n",
+    "        label='r-band Limit'\n",
+    "    elif i==1:\n",
+    "        label='i-band Limit'\n",
+    "    else:\n",
+    "        label=None\n",
+    "    ax.quiver(energy,ratio,0,-0.01,color=color,edgecolor='k',linewidth=2,scale=0.01,angles='xy',scale_units='inches')#,\n",
+    "            #head_length=0.1*ratio,head_width=0.5*energy)\n",
+    "    ax.errorbar([energy],[ratio],xerr=[2*error*energy],yerr=0,uplims=[0],color=color,\n",
+    "               linewidth=0.4*figsize)\n",
+    "    i=i+1\n",
+    "\n",
+    "label='Optical Pulsars'\n",
+    "for pulsar in all_pulsar_data:\n",
+    "    radio_energy = pulsar['radio_energy']\n",
+    "    fluence_ratio = pulsar['fluence_ratio']\n",
+    "    name = pulsar['name']\n",
+    "    uppername = name[0].upper() + name[1:]\n",
+    "    name = uppername\n",
+    "    ax.scatter([radio_energy],[fluence_ratio],marker='o',s=[300*figsize],\n",
+    "               color=orange,edgecolor='k',linewidth=1,label=label)\n",
+    "    ax.annotate(name, xy=(float(radio_energy)*10**(1.5), fluence_ratio), xycoords='data',\n",
+    "                fontsize=2.0*figsize)\n",
+    "    label = None\n",
+    "\n",
+    "# SGR1935\n",
+    "ax.scatter([sgr_isotropic_radio_energy],[sgr_fluence_ratio],marker='*',s=[300*figsize],\n",
+    "           color=lightblue,edgecolor='k',linewidth=1,label='SGR 1935+2154')\n",
+    "\n",
+    "# Model comparison - from https://arxiv.org/pdf/1905.02429.pdf\n",
+    "ax.text(3e35,4.5e-2,'Pulsar Magnetosphere',color=black,va='center',ha='center',fontsize=4.0*figsize)\n",
+    "ax.hlines(3e-2,1e34,1e44,linestyle='dashed',color=black,linewidth=0.5*figsize,zorder=0)\n",
+    "\n",
+    "ax.text(7e33,3e-3,'Young SNR',color=orange,va='center',ha='right',fontsize=4.0*figsize)\n",
+    "ax.hlines(3e-3,1e34,1e44,linestyle='dashed',color=orange,linewidth=0.5*figsize,zorder=0)\n",
+    "\n",
+    "ax.text(7e33,2e-6,'Maser Outflow Emission',color=magenta,va='center',ha='right',fontsize=4.0*figsize)\n",
+    "ax.hlines(2e-6,1e34,1e44,linestyle='dashed',color=magenta,linewidth=0.5*figsize)\n",
+    "\n",
+    "ax.text(2.5e33,0.006,'Our Limits',color='k')\n",
+    "\n",
+    "# Finalize plot and save\n",
+    "\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xscale('log')\n",
+    "\n",
+    "ax.set_xlim([1e21,1e39])\n",
+    "ax.set_ylim([1.0e-7,0.2])\n",
+    "\n",
+    "ax.set_ylabel('Optical-to-Radio Fluence Ratio')\n",
+    "ax.set_xlabel('Burst Radio Energy (erg)')\n",
+    "\n",
+    "plt.legend(fontsize=4.0*figsize,loc='upper left')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('fluence_ratio.png', format='png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a566e73f-ad7a-4ea9-9b5c-204bd3622795",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/papers/Kilpatrick2024_Alopeke/Figures/py/figs_alopeke.py b/papers/Kilpatrick2024_Alopeke/Figures/py/figs_alopeke.py
new file mode 100644
index 00000000..16b09154
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Figures/py/figs_alopeke.py
@@ -0,0 +1,812 @@
+from typing import IO
+import numpy as np
+
+import glob, os, sys, json
+import pdb
+
+#import healpy as hp
+
+from cycler import cycler
+import matplotlib as mpl
+from numpy.core.fromnumeric import mean
+import seaborn as sns
+
+import pandas
+import aplpy
+
+from matplotlib import pyplot as plt
+import matplotlib.gridspec as gridspec
+from matplotlib.patches import ConnectionPatch
+
+import matplotlib
+from matplotlib import rc
+import matplotlib.cm as cm
+import matplotlib.colors as mcolors
+from matplotlib.colors import ListedColormap
+
+from scipy.interpolate import interp1d
+from scipy.stats import kstest
+from scipy.stats import norm, poisson
+from scipy.signal import convolve
+
+from astropy import units as u
+from astropy.coordinates import SkyCoord
+from astropy.io import fits
+from astropy.wcs import WCS
+from astropy.time import Time
+
+from IPython import embed
+
+# Local
+sys.path.append(os.path.abspath("../../Analysis/py"))
+import alopeke_defs
+import alopeke_utils2
+import alopeke_analy
+
+# Globals
+handletextpad=0.3
+
+def ccolor(r,g,b):
+    return((r/255.,g/255.,b/255., 1.0))
+
+
+def fig_fov(outroot='fov_gmos_alopeke'):
+    # images
+    #gmos_image = '/media/ntejos/caladan01/projects/FRBs/FRB180916-R3/GMOS/FRB180916_Z_1320s_GaiaDR2ac.fits'
+    #alopeke_image = '/media/ntejos/caladan01/data/FRBs/R3/Alopeke/science/N20201023A0015r.fits'  # just as an example
+    gmos_image = '/Users/ckilpatrick/Dropbox/Data/FRB/FRB180916/Data/Gemini/frb180916.r.ut190713_0001.fits'
+    alopeke_image = '/Users/ckilpatrick/Dropbox/Data/FRB/FRB180916/Data/Alopeke/20220908/N20220908A0017r_reduced.fits'
+
+    # define positions of objects
+    # We use the position from Marcote+ paper because the one in the repo is slightly different
+    frb_coord = alopeke_defs.frb180916.coord  # in Gaia DR2 frame (as given by Marcote+20; ok in our Repo)
+    
+    star1_coord = SkyCoord(29.4954470, 65.7189804, unit='deg')  # From GaiaDR2 frame (to match that of the FRB)
+    star2_coord = SkyCoord(29.4908603, 65.7128539, unit='deg')
+
+    star1_xy_red = (61.515341,197.51952)
+
+    bkg_coord = SkyCoord(frb_coord.ra.value,star1_coord.dec.value, unit='deg')  # place holder check with consuelo
+    
+    # need to add WCS to Alopeke image (blue)
+    hdul_alopeke = fits.open(alopeke_image)
+
+    #update header properly
+    alopeke_header = hdul_alopeke[0].header
+    alopeke_header['CDELT1']=-np.sqrt(alopeke_header['PC1_1']**2+alopeke_header['PC1_2']**2)
+    alopeke_header['CDELT2']=-np.sqrt(alopeke_header['PC2_1']**2+alopeke_header['PC2_2']**2)
+    alopeke_header['CD1_1'] = alopeke_header['PC1_1']
+    alopeke_header['CD2_2'] = alopeke_header['PC2_2']
+    alopeke_header['CD2_1'] = alopeke_header['PC2_1']
+    alopeke_header['CD1_2'] = alopeke_header['PC1_2']
+    alopeke_header['CRPIX1'] = star1_xy_red[0]
+    alopeke_header['CRPIX2'] = star1_xy_red[1]
+    alopeke_header['CRVAL1'] = star1_coord.dec.value  # x is declination for red camera
+    alopeke_header['CRVAL2'] = star1_coord.ra.value  # y is RA for red camera
+
+    # Write new HDU as image, getitng only the frame 45 as reference
+    alopeke_header['NAXIS'] = 2
+    alopeke_header.remove('NAXIS3')
+    hdul_alopeke[0].data = hdul_alopeke[0].data[45]
+    hdul_alopeke[:-1].writeto('data/alopeke_aux.fits', overwrite=True)
+
+    wt = WCS(naxis=2)
+    wt.wcs.ctype = ['DEC--TAN', 'RA---TAN']
+    wt.wcs.crval = alopeke_header['CRVAL1'], alopeke_header['CRVAL2']
+    wt.wcs.crpix = alopeke_header['CRPIX1'], alopeke_header['CRPIX2']
+    wt.wcs.cdelt = alopeke_header['CDELT1'], alopeke_header['CDELT2']
+    header = wt.to_header()
+
+    hdu = fits.PrimaryHDU(hdul_alopeke[0].data, header=header)
+    hdul = fits.HDUList([hdu])
+    hdul.writeto('data/alopeke_aux.fits', overwrite=True)
+
+    # new string pointing to this new file, used for Fig.2 of paper
+    alopeke_image_example = './data/alopeke_aux.fits'
+
+    # Global things
+    lw = 1  # lw of square and connector
+    color= 'k' # color square and connector
+    ls = 'dashed'
+    c_frb = 'r'
+    ls_frb='solid'
+    radius_frb = 1.2/3600
+    c_ref = 'orange'
+    ls_ref='solid'
+    radius_ref = 1.2/3600
+    c2_ref = 'magenta'
+    lw_c = 1.5 # lw of circles
+    ls_bkg = 'solid'
+    c_bkg = 'y'
+    radius_bkg = 1.2/3600
+
+    # cmap = 'afmhot_r'
+    cmap = 'Blues'
+
+    # relative geometry
+    # center_gmos = (29.5017349,65.7145370)
+    # center_alopeke = (29.5024181,65.7164417)
+    center_alopeke = wt.pixel_to_world(128,128)
+    center_alopeke = center_alopeke.ra.value, center_alopeke.dec.value
+    center_gmos = center_alopeke[0],center_alopeke[1] + 2/3600.
+    size_alopeke = 37/3600.  # 37"
+    size_gmos = 60/3600. # 1 arcmin
+    vmin_gmos = 0.
+    vmax_gmos = 600.
+    vmin_alopeke = -50.
+    vmax_alopeke= 500.
+
+    # Test for cuts
+    if 0:
+        vmin,vmax = 500,1000
+        f3 = aplpy.FITSFigure(alopeke_image_example)
+        f3.show_colorscale(vmin=vmin,vmax=vmax, cmap=cmap)
+        # f3.recenter(center_gmos[0],center_gmos[1], radius=size_gmos/2.)
+        f3.add_colorbar()
+        plt.show()
+        stop
+
+    # MAIN FIG
+    fig = plt.figure(figsize=(7,4))
+    fig.subplots_adjust(left=0.15,right=0.95,bottom=0.15,top=0.95,wspace=0.25,hspace=0.25)
+
+    f1 = aplpy.FITSFigure(gmos_image, figure=fig, subplot=(1,2,1), north=True)
+    f2 = aplpy.FITSFigure(alopeke_image_example, figure=fig, subplot=(1,2,2), north=True)
+
+    f1.show_colorscale(vmin=vmin_gmos,vmax=vmax_gmos, cmap=cmap)
+    f1.recenter(center_gmos[0],center_gmos[1], radius=size_gmos/2.)
+    f2.recenter(center_alopeke[0], center_alopeke[1], radius=size_alopeke/2.)
+    f2.show_colorscale(vmin=vmin_alopeke,vmax=vmax_alopeke, cmap=cmap)
+
+    #add rectangle to the fig1 with alopeke Zoom
+    xw = center_alopeke[0]
+    yw = center_alopeke[1]
+    f1.show_rectangles(xw, yw, size_alopeke, size_alopeke, zorder=1, color=color, lw=lw, ls=ls)
+
+    #connectors AX1 to AX2
+    axA = f1.ax
+    axB = f2.ax
+    xyA1 = f1.world2pixel(xw-size_alopeke/2./np.cos(yw*u.deg), yw+size_alopeke/2.)
+    xyB1 = (0,1)
+    xyA2 = f1.world2pixel(xw-size_alopeke/2./np.cos(yw*u.deg), yw-size_alopeke/2.)
+    xyB2 = (0,0)        
+    con1 = ConnectionPatch(xyA=xyA1, xyB=xyB1, coordsA="data", coordsB="axes fraction", \
+                        axesA=axA, axesB=axB, color=color, lw=lw, ls=ls)
+    con2 = ConnectionPatch(xyA=xyA2, xyB=xyB2, coordsA="data", coordsB="axes fraction", \
+                        axesA=axA, axesB=axB, color=color, lw=lw, ls=ls)
+    axA.add_artist(con1)
+    axA.add_artist(con2)
+
+    # f1.add_scalebar((10*u.arcsec).to('deg').value)
+    f2.add_scalebar((10*u.arcsec).to('deg').value)
+    f2.scalebar.set_corner('bottom left')
+    f2.scalebar.set_label('10"')
+    f2.scalebar.set_color('k')
+    f2.scalebar.set_font_size('large')
+
+    # add FRB position and reference star
+    frb_ra = frb_coord.ra.value
+    frb_dec = frb_coord.dec.value
+    star_ra = star1_coord.ra.value
+    star_dec = star1_coord.dec.value
+    star2_ra = star2_coord.ra.value
+    star2_dec = star2_coord.dec.value
+    bkg_ra = bkg_coord.ra.value 
+    bkg_dec = bkg_coord.dec.value
+
+    f2.show_circles(frb_ra, frb_dec, radius_frb, zorder=1, color=c_frb, lw=lw_c, ls=ls_frb)
+    f1.show_circles(frb_ra, frb_dec, radius_frb, zorder=1, color=c_frb, lw=lw_c, ls=ls_frb)
+    f2.show_circles(star_ra, star_dec, radius_ref, zorder=1, color=c_ref, lw=lw_c, ls=ls_ref)
+    f1.show_circles(star_ra, star_dec, radius_ref, zorder=1, color=c_ref, lw=lw_c, ls=ls_ref)
+    f2.show_circles(star2_ra+0.0005, star2_dec, radius_ref, zorder=1, color=c2_ref, lw=lw_c, ls=ls_ref)
+    f1.show_circles(star2_ra, star2_dec, radius_ref, zorder=1, color=c2_ref, lw=lw_c, ls=ls_ref)
+
+    f1.add_label(frb_ra, frb_dec+3*radius_frb, 'FRB pos.', color=c_frb)
+    f1.add_label(star_ra+0.5*radius_ref, star_dec-3.5*radius_ref, 'Star-1', color=c_ref)
+    f1.add_label(star2_ra+0.5*radius_ref, star2_dec+3.5*radius_ref, 'Star-2', color=c2_ref)
+
+    # ADD labels
+    f1.add_label(0.53,0.97,'GMOS r-band', relative='axes', va='top')
+    f2.add_label(0.5,0.97,'`Alopeke i-band\n(~10 ms)\n 2020-10-23', relative='axes', va='top')
+
+    #Hide tick labels for f2
+    f2.tick_labels.hide()
+    f2.axis_labels.hide()
+
+    # write
+    fig.savefig(outroot+'.pdf', format='pdf')
+
+
+def fig_chk_gauss(date, outroot='fig_chk_gauss_', camera='red',
+                  show_poisson=False):
+    """
+
+    Args:
+        outfile:
+
+    Returns:
+
+    """
+    set_mplrc()
+    
+    # Load data
+    if camera == 'red':
+        clr = 'r'
+    elif camera == 'blue':
+        clr = 'b'
+    else:
+        raise IOError(f'Bad camera: {camera}')
+    
+    outfile = f'{outroot}{camera}_{date}.pdf'
+        
+    # Load up
+    data_dict = alopeke_utils2.load_camera(camera, date, cut=True)
+    C_gal = data_dict['C_gal']
+    mean_gal, std_gal = data_dict['Gauss_gal'] # Gaussian
+    print("mean={}, std={}".format(mean_gal, std_gal))
+
+    poiss_gal = np.mean(C_gal)
+
+    # Plot
+    psz = 13.
+    plt.figure(figsize=(6, 9))
+    gs = gridspec.GridSpec(3,1)
+
+    # Main Gaussian
+    ax0 = plt.subplot(gs[0])
+    ax0.hist(C_gal, bins=50, density=True, color=clr)
+    xmin, xmax = C_gal.min(), C_gal.max()
+    x = np.linspace(xmin, xmax, 1000)
+    y = norm.pdf(x, mean_gal, std_gal)
+    ax0.plot(x, y, 'k')
+    if show_poisson:
+        x2 = np.arange(int(xmin), int(xmax)+1)
+        y2 = poisson.pmf(x2, poiss_gal)
+        ax0.plot(x2, y2, 'k--')
+
+    # Label me
+    xlbl = r'$C_{\rm FRB}^{\rm Tot} \; (\rm e^-/exposure)$'
+    ax0.set_xlabel(xlbl)
+    ax0.set_ylabel('PDF')
+    set_fontsize(ax0, psz)
+
+    # High-tail
+    isort_gal = np.argsort(C_gal)
+    cdf_gal = (np.arange(isort_gal.size)+1) / isort_gal.size
+
+    ax1 = plt.subplot(gs[2])
+    xval = np.linspace(mean_gal+std_gal, C_gal.max(), 100000)
+    ax1.step(C_gal[isort_gal], 1.-cdf_gal, color=clr, where='mid')
+    ax1.plot(xval, 1-norm.cdf(xval, loc=mean_gal, scale=std_gal),
+             color='k')
+    #
+    ax1.set_xlim(mean_gal+std_gal, np.max(C_gal))
+    ax1.set_ylim(1e-6, 1e-1)
+    #
+    ax1.set_yscale('log')
+    ax1.set_xlabel(xlbl)
+    ax1.set_ylabel('1-CDF')
+    set_fontsize(ax1, psz)
+
+    # Low tail
+    ax2 = plt.subplot(gs[1])
+    xval2 = np.linspace(C_gal.min(), mean_gal, 100000)
+    
+    # Data
+    ax2.step(C_gal[isort_gal], cdf_gal, color=clr, where='mid')
+    ax2.plot(xval2, norm.cdf(xval2, loc=mean_gal, scale=std_gal),
+             color='k')
+    #
+    ax2.set_xlim(C_gal.min(), mean_gal-std_gal)
+    ax2.set_ylim(1e-6, 1e-1)
+
+    ax2.set_yscale('log')
+    ax2.set_xlabel(xlbl)
+    ax2.set_ylabel('CDF')
+    set_fontsize(ax2, psz)
+    
+    # End
+    plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
+    print('Writing {:s}'.format(outfile))
+    kwargs = {}
+    if 'png' in outfile:
+        kwargs['dpi'] = 700
+    elif 'pdf' in outfile:
+        kwargs['format'] = 'pdf'
+    plt.savefig(outfile, **kwargs)
+    plt.close()
+
+
+def fig_upper_limit(date, outroot='fig_upper_limit_', camera='red',
+                    step=200, nsamp=100):
+    if camera == 'red':
+        clr = 'r'
+        ylim = (1e-4, 1e-1)
+    elif camera == 'blue':
+        clr = 'b'
+        ylim = (1e-4, 3e-1)
+    elif camera == 'both':
+        ylim = (1e-4, 1e-1)
+    else:
+        raise IOError(f'Bad camera: {camera}')
+
+    outfile = f'{outroot}{camera}_{date}.pdf'
+
+    # Analyze
+    if camera == 'both':
+        C_FRB_val_blue, p_exceed_blue, upper_FRB_blue = alopeke_analy.upper_limit(date, camera='blue')
+        C_FRB_val_red, p_exceed_red, upper_FRB_red = alopeke_analy.upper_limit(date, camera='red')
+    else:
+        C_FRB_val, p_exceed, upper_FRB = alopeke_analy.upper_limit(date, camera)
+
+    
+    date_str=alopeke_defs.FRB_time[date].datetime.strftime('%Y-%m-%d')
+
+    # Figure time
+    psz = 13.
+    plt.figure(figsize=(6, 5))
+    gs = gridspec.GridSpec(1,1)
+
+    ax = plt.subplot(gs[0])
+
+    if camera == 'both':
+        ax.plot(C_FRB_val_blue, 1-p_exceed_blue, color='blue')
+        ax.plot(C_FRB_val_red, 1-p_exceed_red, color='red')
+    else:
+        ax.plot(C_FRB_val, 1-p_exceed, color=clr)
+
+    ax.axhline(0.0027, color='k', ls='--')
+
+    ax.set_xlabel(r'$\mu_{\rm FRB} \; (\rm e^-)$')
+    ax.set_ylabel('Fraction of Events')
+
+    ax.set_ylim(ylim)
+    ax.set_yscale('log')
+
+    set_fontsize(ax, psz)
+
+    ax.text(0.8, 0.9, date_str, color='k', transform=ax.transAxes)
+    
+    # End
+    plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
+    print('Writing {:s}'.format(outfile))
+    kwargs = {}
+    if 'png' in outfile:
+        kwargs['dpi'] = 700
+    elif 'pdf' in outfile:
+        kwargs['format'] = 'pdf'
+    plt.savefig(outfile, **kwargs)
+    plt.close()
+
+
+def fig_gal_star_corr(date, outroot='fig_gal_star_corr_', camera='red',
+                      cut=True):
+    if camera == 'red':
+        clr = 'r'
+    elif camera == 'blue':
+        clr = 'b'
+    else:
+        raise IOError(f'Bad camera: {camera}')
+
+    outfile = f'{outroot}{camera}_{date}.pdf'
+
+    # Load up
+    data_dict = alopeke_utils2.load_camera(camera, date, cut=cut)
+    C_gal = data_dict['C_gal']
+    #C_star = data_dict['C_star1']
+    C_star = data_dict['C_star2']
+
+    # Fit Gaussian
+    mean_gal, std_gal = norm.fit(C_gal)
+    mean_star, std_star = norm.fit(C_star)
+
+    # Convert to sig
+    nsig_gal = (C_gal-mean_gal)/std_gal
+    nsig_star = (C_star-mean_star)/std_star
+
+    df = pandas.DataFrame(dict(nsig_gal=nsig_gal,
+                               nsig_star=nsig_star))
+    
+    # Figure time
+    psz = 13.
+    plt.figure(figsize=(6, 5))
+    
+    fg = sns.displot(df, x='nsig_gal', y='nsig_star',
+                    color=clr)
+    fg.ax.set_xlabel(r'$\Delta C_{\rm FRB}^{\rm Tot} \; (\sigma_{\rm FRB})$')
+    fg.ax.set_ylabel(r'$\Delta C_{\rm 1}^{\rm Tot} \; (\sigma_{\rm 1})$')
+
+    fg.ax.set_aspect('equal')
+
+    fg.ax.set_xlim([-6,6])
+    fg.ax.set_ylim([-6,6])
+
+    set_fontsize(fg.ax, psz)
+    
+    # End
+    plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
+    print('Writing {:s}'.format(outfile))
+    kwargs = {}
+    if 'png' in outfile:
+        kwargs['dpi'] = 700
+    elif 'pdf' in outfile:
+        kwargs['format'] = 'pdf'
+    plt.savefig(outfile, **kwargs)
+    plt.close()
+
+
+def fig_frb_counts(date, outfile='fig_frb_counts', camera='red',
+                    step=200, nsamp=100):
+    set_mplrc()
+
+
+    # Figure time
+    psz = 13.
+    plt.figure(figsize=(6, 5))
+    gs = gridspec.GridSpec(2,1)
+
+    for ss, clr, camera in zip(range(2), ['r', 'b'], ['red', 'blue']):
+        # Load up
+        data_dict = alopeke_utils2.load_camera(camera, date, cut=True)
+
+        ax = plt.subplot(gs[ss])
+
+        date_str=alopeke_defs.FRB_time[date].datetime.strftime('%Y-%m-%d')
+
+        # All points
+        ax.scatter((data_dict['MJD_gal']-alopeke_defs.FRB_time[date].mjd)*24*3600,
+                data_dict['C_gal'], color='k')
+        # In time window
+        ax.scatter((data_dict['MJD_FRB']-alopeke_defs.FRB_time[date].mjd)*24*3600,
+                data_dict['C_FRB'], color=clr)
+
+        # Limits
+        ax.set_xlim(-1., 1.)
+        ax.xaxis.set_major_locator(plt.MultipleLocator(0.5))
+        ax.minorticks_on()
+
+        print(data_dict['MJD_FRB']-alopeke_defs.FRB_time[date].mjd)
+
+        mask = np.abs(data_dict['MJD_FRB']-alopeke_defs.FRB_time[date].mjd)<163.0/2/86400/1000
+        print(f'{camera} data:')
+        for idx in np.where(mask)[0]:
+            mjd = data_dict['MJD_FRB'][idx]
+            mjd = '%5.8f'%mjd
+            filt = 'r'
+            if camera=='r' or camera=='red':
+                filt = 'i'
+            bkg = '%.2f'%data_dict['C_bkg'][idx]
+            star1 = '%.2f'%data_dict['C_star1'][idx]
+            star2 = '%.2f'%data_dict['C_star2'][idx]
+            frb = '%.2f'%data_dict['C_FRB'][idx]
+
+            # Output measurements for latex table
+            print(f'{mjd} & {bkg} & {star1} & {star2} & {frb} \\\\')
+
+        ax.text(0.02, 0.9, date_str, color='k', transform=ax.transAxes)
+
+        ax.set_ylabel(r'$C_{\rm FRB}^{\rm TOT} \, ({\rm e^-})$')
+
+        if ss == 0:
+            ax.axes.xaxis.set_ticklabels([])
+        else:
+            ax.set_xlabel(r'$t - t_{\rm FRB} \;$ (seconds)')
+
+        #ax.set_ylim(1e-4, 1)
+        #ax.set_yscale('log')
+
+        set_fontsize(ax, psz)
+        # Stats
+        print(f"Camera: {camera}")
+        print("Nobs = {}".format(len(data_dict['C_FRB'])))
+        print("Max = {}".format(max(data_dict['C_FRB'])))
+        
+    # End
+    plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
+    print('Writing {:s}'.format(outfile))
+    kwargs = {}
+    outfile=outfile+'_'+date+'.pdf'
+    if 'png' in outfile:
+        kwargs['dpi'] = 700
+    elif 'pdf' in outfile:
+        kwargs['format'] = 'pdf'
+    plt.savefig(outfile, **kwargs)
+    plt.close()
+
+
+
+def fig_all_counts(date, outfile='fig_all_counts.pdf', zoom=False):
+    set_mplrc()
+
+    # Load up
+    data_red = alopeke_utils2.load_camera('red', date, cut=True)
+    data_blue = alopeke_utils2.load_camera('blue', date, cut=True)
+
+    mjd_day = int(data_red['MJD_gal'][0])
+
+    mjd_FRB = alopeke_defs.FRB_time[date].mjd
+    print("MJD FRB = {}".format(mjd_FRB))
+
+    # Figure time
+    psz = 13.
+    plt.figure(figsize=(12, 5))
+    gs = gridspec.GridSpec(2,1)
+
+
+    for ss, clr, data in zip([0,1], ['b', 'r'], [data_blue, data_red]):
+        ax = plt.subplot(gs[ss])
+        # FRB
+        ax.axvline(mjd_FRB-mjd_day, ls='--', color='gray', zorder=4)
+        ax.axvspan(alopeke_defs.mjd_low[date] - mjd_day, alopeke_defs.mjd_high[date] - mjd_day, 0,100, color='gray', alpha=0.5, zorder=4)
+
+        # Data
+        ax.scatter(data['MJD_gal']-mjd_day, data['C_gal'], color=clr, s=1, zorder=1)
+        ax.scatter(data['MJD_FRB']-mjd_day, data['C_FRB'], color=clr, s=1, zorder=1)
+        if ss == 0:
+            ax.get_xaxis().set_ticks([])
+        set_fontsize(ax, psz)
+        ax.set_ylabel(r'$C_{\rm FRB}^{\rm Tot} \, ({\rm e^-/exposure})$')
+
+        # Limits
+        if zoom is True:
+            xmin = (alopeke_defs.FRB_time - 7*alopeke_defs.ata).mjd - mjd_day
+            xmax = (alopeke_defs.FRB_time + 7*alopeke_defs.ata).mjd - mjd_day
+            ax.set_xlim(xmin,xmax)
+
+    ax.set_xlabel('MJD - {}'.format(mjd_day))
+
+    
+    # End
+    plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
+    if zoom is True:
+        outfile = outfile.replace('.','_zoom.')
+    print('Writing {:s}'.format(outfile))
+    kwargs = {}
+    if 'png' in outfile:
+        kwargs['dpi'] = 500
+    elif 'pdf' in outfile:
+        kwargs['format'] = 'pdf'
+    plt.savefig(outfile, **kwargs)
+    plt.close()
+
+
+def fig_star_gal_counts(date, outfileroot='fig_star_gal_counts.pdf', cam='red',
+    star_factors=[6], use_stars=['star1'], ymax=200):
+    set_mplrc()
+
+    # Load up
+    data = alopeke_utils2.load_camera(cam, date, cut=True)
+
+    mjd_day = int(data['MJD_FRB'][0])
+
+    mjd_FRB = alopeke_defs.FRB_time[date].mjd
+    print("MJD FRB = {}".format(mjd_FRB))
+
+    # Figure time
+    psz = 13.
+    plt.figure(figsize=(9, 3))
+    gs = gridspec.GridSpec(1,1)
+
+
+
+    #for ss, clr, data in zip([0,1], ['b', 'r'], [data_blue, data_red]):
+    if 1:
+        clr = cam
+        ax = plt.subplot(gs[0])
+        # FRB
+        ax.axvline(mjd_FRB-mjd_day, ls='--', color='gray', zorder=4)
+        ax.axvspan(alopeke_defs.mjd_low[date] - mjd_day, alopeke_defs.mjd_high[date] - mjd_day, 0,100, color='gray', alpha=0.5, zorder=4)
+
+        # Data
+        ax.scatter(data['MJD_gal']-mjd_day, data['C_gal'], color=clr, s=1, zorder=1, label='FRB position')
+        ax.scatter(data['MJD_FRB']-mjd_day, data['C_FRB'], color=clr, s=1, zorder=1)
+        colors = ['orange','purple']
+        i=0
+        for star,fact in zip(use_stars,star_factors):
+            nstar=star.replace('star','')
+            ax.scatter(data[f'MJD_{star}_full']-mjd_day,
+                data[f'C_{star}_full']/fact, color=colors[i],
+                alpha=0.8, s=1, zorder=1,
+                label=f'Ref. star {nstar} (scaled by 1/{fact})')
+            i=i+1
+        
+        # label camera and filter
+        frb_date = Time(mjd_FRB, format='mjd')
+        date_str = frb_date.datetime.strftime('%Y-%m-%d')
+        if cam == 'red':
+            label = 'Red camera (i-band) \n'+date_str
+        elif cam == 'blue':
+            label = 'Blue camera (r-band) \n'+date_str
+        ax.text(0.05, 0.85, label, color='k', transform=ax.transAxes)
+
+        set_fontsize(ax, psz)
+        ax.set_ylabel(r'$ {\rm Total \ counts} \ ({\rm e^-/exposure})$')
+        
+
+    ax.set_xlabel('MJD - {}'.format(mjd_day))
+
+    ax.set_ylim(0, ymax)
+
+    # minorticks
+    ax.minorticks_on()
+    
+    # End
+    plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
+    outfile = outfileroot.replace('.pdf', '_{}.pdf'.format(cam+'_'+date))
+    print('Writing {:s}'.format(outfile))
+    plt.legend(loc='upper right')
+    kwargs = {}
+    if 'png' in outfile:
+        kwargs['dpi'] = 500
+    elif 'pdf' in outfile:
+        kwargs['format'] = 'pdf'
+    plt.savefig(outfile, **kwargs)
+
+    plt.close()
+
+
+def fig_model_afterglow_limits(date, outfileroot='fig_model_afterglow_limits.pdf'):
+
+    set_mplrc()
+
+    # Load up
+    Evals, nvals, grid = alopeke_analy.afterglow_limits(date)
+
+    # Figure time
+    psz = 13.
+    plt.figure(figsize=(3.5, 3.5))
+    gs = gridspec.GridSpec(1,1)
+
+    ax = plt.subplot(gs[0])
+
+    ncols = 60
+    colarray=np.array([ccolor(l,l,l) for l in np.flip(np.arange(255-ncols,255))])
+    cm = ListedColormap(colarray)
+
+    norm = matplotlib.colors.Normalize(vmin=0, vmax=1)
+    levels = np.linspace(0, 1, ncols)
+
+    Erange = 10**Evals*1e43
+    nrange = 10**nvals
+
+    cf=ax.contourf(Erange, nrange, grid, cmap=cm, norm=norm, levels=levels)
+    C=ax.contour(Erange, nrange, grid, linewidths=2, levels=levels,
+        zorder=5, colors=('red'))
+
+    ax.set_xscale('log')
+    ax.set_yscale('log')
+    ax.set_xlim([np.min(Erange), np.max(Erange)])
+    ax.set_ylim([np.min(nrange), np.max(nrange)])
+
+    set_fontsize(ax, psz)
+
+    ax.set_xlabel('Burst Energy (erg)')
+    ax.set_ylabel(r'Circumburst Density (cm$^{-3}$)')
+
+    # minorticks
+    ax.minorticks_on()
+
+    # End
+    plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
+    outfile = outfileroot.replace('.pdf', '_{}.pdf'.format(date))
+    print('Writing {:s}'.format(outfile))
+    kwargs = {}
+    if 'png' in outfile:
+        kwargs['dpi'] = 500
+    elif 'pdf' in outfile:
+        kwargs['format'] = 'pdf'
+    plt.savefig(outfile, **kwargs)
+
+    plt.close()
+
+
+def set_mplrc():
+    mpl.rcParams['mathtext.default'] = 'it'
+    mpl.rcParams['font.size'] = 12
+    mpl.rc('font',family='Times New Roman')
+    mpl.rcParams['text.latex.preamble'] = [r'\boldmath']
+    mpl.rc('text', usetex=True)
+
+
+def set_fontsize(ax,fsz):
+    '''
+    Parameters
+    ----------
+    ax : Matplotlib ax class
+    fsz : float
+      Font size
+    '''
+    for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
+                ax.get_xticklabels() + ax.get_yticklabels()):
+        item.set_fontsize(fsz)
+
+def set_mplrc():
+    mpl.rcParams['mathtext.default'] = 'it'
+    mpl.rcParams['font.size'] = 12
+    #mpl.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
+    #mpl.rc('font',family='Times New Roman')
+    #mpl.rcParams['text.latex.preamble'] = [r'\boldmath']
+    mpl.rcParams['mathtext.fontset'] = 'custom'
+    mpl.rcParams['mathtext.rm'] = 'Bitstream Vera Sans'
+    mpl.rcParams['mathtext.it'] = 'Bitstream Vera Sans:italic'
+    mpl.rcParams['mathtext.bf'] = 'Bitstream Vera Sans:bold'
+    mpl.rc('text', usetex=True)
+
+
+#### ########################## #########################
+def main(flg_fig, date, cameras=['blue','red']):
+
+    if flg_fig == 'all':
+        flg_fig = np.sum( np.array( [2**ii for ii in range(25)] ))
+    else:
+        flg_fig = int(flg_fig)
+
+    # Check Gaussian
+    if flg_fig & (2**0):
+        for camera in cameras:
+            fig_chk_gauss(date, camera=camera)
+
+    # Upper limit
+    if flg_fig & (2**1):
+        if 'blue' in cameras and 'red' in cameras:
+            fig_upper_limit(date, camera='both')
+        else:
+            for camera in cameras:
+                fig_upper_limit(date, camera=camera)
+
+    # Correlation?
+    if flg_fig & (2**2):
+        for camera in cameras:
+            fig_gal_star_corr(date, camera=camera)
+
+    # FRB counts
+    if flg_fig & (2**3):
+        if 'blue' in cameras and 'red' in cameras:
+            fig_frb_counts(date)
+
+    # All counts: FRB counts for both cameras
+    if flg_fig & (2**4):
+        if 'blue' in cameras and 'red' in cameras:
+            fig_all_counts(date)
+        
+    # counts for FRB + star in the same panel for each camera
+    if flg_fig & (2**5):
+        for camera in cameras:
+            if camera == 'blue':
+                ymax = 120
+                use_stars = ['star1','star2']
+                star_factors = [34, 20]
+            elif camera == 'red':
+                ymax = 120
+                use_stars = ['star1','star2']
+                star_factors = [45, 30]
+            fig_star_gal_counts(date, cam=camera, ymax=ymax, star_factors=star_factors,
+                use_stars=use_stars)
+
+    # FOV
+    if flg_fig & (2**6):
+        fig_fov()
+
+    if flg_fig & (2**7):
+        fig_model_afterglow_limits(date)
+    
+
+
+# Command line execution
+if __name__ == '__main__':
+
+    date = sys.argv[1]
+
+    if len(sys.argv) == 2:
+        flg_fig = 0
+        flg_fig += 2**0   # Check Gaussianity
+        flg_fig += 2**1   # Upper limit
+        flg_fig += 2**2   # Star/gal correlation
+        flg_fig += 2**3   # FRB counts
+        flg_fig += 2**4   # All counts (both cameras)
+        flg_fig += 2**5   # FRB counts + Star counts (each camera)
+        flg_fig += 2**6   # FOV
+    else:
+        flg_fig = sys.argv[2]
+
+    main(flg_fig, date, cameras=['blue','red'])
diff --git a/papers/Kilpatrick2024_Alopeke/Tables/py/UTILS.py b/papers/Kilpatrick2024_Alopeke/Tables/py/UTILS.py
new file mode 100644
index 00000000..157ef879
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Tables/py/UTILS.py
@@ -0,0 +1,221 @@
+import copy
+import csv
+import math
+import os
+
+import fitsio
+import matplotlib.pyplot as plt
+import numpy as np
+import sep
+from astropy import units as u
+from astropy.coordinates import SkyCoord
+from astropy.io import ascii, fits
+from astropy.table import Table
+from astropy.wcs import WCS
+from astropy.wcs.utils import pixel_to_skycoord
+from matplotlib import rcParams
+from matplotlib.patches import Ellipse
+from scipy import signal
+
+
+def get_star_norm(size, FWHM):
+	"""This function gets a normalized gaussian matrix"""
+
+	std = FWHM/2.35482
+	get_star_1d = signal.gaussian(size, std=std).reshape(size, 1)
+	get_star_2d = np.outer(get_star_1d, get_star_1d)
+	return get_star_2d
+
+def get_star(star_norm, flux):
+	"""This function gets a gaussian matrix with a determined flux"""
+
+	# valor A que debo multiplicar a la matriz para que la suma de elementos sea igual al flujo:
+	A = flux / np.sum(star_norm)
+	star = star_norm * A
+	return star
+
+def get_mag_rec(image_diff, CC, circle_radio, x, y):
+	"""This function gets the magnitude of a star at position x, y of a differentiated image"""
+
+	data = fitsio.read(image_diff)
+    # sustraccion background
+	bkg = sep.Background(data)     # estima el background
+	data_sub = data - bkg    # resta el background estimado
+	objects = sep.extract(data_sub, 1.5, err=bkg.globalrms)    # detecta objetos
+	flux_s, fluxerr_s, flag_s = sep.sum_circle(data_sub, x, y, circle_radio, err=bkg.globalrms, gain=1.0)
+
+	flux_s = np.where(flux_s>0, flux_s, 1)
+	mas = flux_s + fluxerr_s
+	menos = flux_s - fluxerr_s
+	mas = np.where(mas>0, mas, 1)
+	menos = np.where(menos>0, menos,1)
+	mag = CC -2.5 * math.log10(flux_s)
+	mag_m = CC -2.5 * math.log10(mas)    # porque mas flujo es menos magnitude
+	mag_p = CC -2.5 * math.log10(menos)
+
+	err_mag_m = mag - mag_m
+	err_mag_p = mag_p - mag
+
+	SN = flux_s/fluxerr_s
+	
+	return (mag, err_mag_p, err_mag_m, SN)
+
+def mag_rec(image, skymapper, out_image_star, filtro, mag, x, y, h_size, FWHM, circle_radio, image_template, image_diff):
+	"""This function makes an artificial star of a given magnitude at the x, y position of an image, differentiates this 
+	image (ToO) with a template from a different epoch and calculates the recovered magnitude of the artificial star in 
+	the difference image ."""
+
+	CC = get_CC(image, skymapper, filtro)
+	flux = get_flux(mag, CC)
+	star_norm = get_star_norm(2*h_size, FWHM)
+	star = get_star(star_norm, flux)
+
+	zeros = np.zeros((4096, 4096))
+	y_menos = y - h_size
+	y_mas = y + h_size
+	x_menos = x - h_size
+	x_mas = x + h_size
+	zeros[y_menos:y_mas, x_menos:x_mas] = star
+
+	data = fitsio.read(image)
+	copy_data = copy.deepcopy(data)
+	copy_data_star = copy_data + zeros
+	hdu = fits.PrimaryHDU(copy_data_star)
+	hdulist = fits.HDUList([hdu])
+	hdulist.writeto(out_image_star)
+
+	image_ToO = out_image_star
+
+	os.system("hotpants "\
+	 			"-inim {} "\
+ 				"-tmplim {} "\
+ 				"-outim {} "\
+ 				"-c i "\
+ 				"-tu 50000 "\
+ 				"-iu 50000 "\
+ 				"-tl -500 "\
+ 				"-il -500 "\
+ 				"-tr 9.5 "\
+ 				"-ir 9.5".format(image_ToO, image_template, image_diff))
+
+	mag_tup = get_mag_rec(image_diff, CC, circle_radio, x, y) 
+	return mag_tup
+
+
+
+
+
+
+
+
+
+
+def create_median(cube):
+	cube_array = fitsio.read(cube)
+	median = np.median(cube_array, axis=0)
+	return median
+
+def get_fits_images(array_image, suffix):
+	hdu = fits.PrimaryHDU(array_image)
+	hdulist = fits.HDUList([hdu])                # hdul = header data unit list
+	fits_path = '/home/consuelo/projects/FRBs/alopeke/images/image' + '_' + suffix + '.fits'
+	hdulist.writeto(fits_path, overwrite = True)
+	return fits_path
+
+def get_diff(image, template, image_diff, convolve_with='i'):
+	os.system("hotpants "\
+ 				"-inim {} "\
+				"-tmplim {} "\
+				"-outim {} "\
+				"-c {} "\
+				"-tu 60000 "\
+				"-iu 60000 "\
+				"-tl -10 "\
+				"-il -10 ".format(image, template, image_diff, convolve_with))
+
+
+def search_obj(image, plot = False):
+	img = fitsio.read(image)
+	bkg = sep.Background(img)
+	img_sub = img - bkg
+	
+	objects = sep.extract(img_sub, 3, err=bkg.globalrms)
+
+	if plot:
+		fig, ax = plt.subplots()
+		m = np.mean(img_sub)
+		s = np.std(img_sub)
+		im = ax.imshow(img_sub, interpolation='nearest', cmap='gray', vmin = m-s, vmax = m+s, origin='lower')
+
+		# plot an ellipse for each object
+		for i in range(len(objects)):
+		    e = Ellipse(xy=(objects['x'][i], objects['y'][i]),
+		                width=6*objects['a'][i],
+		                height=6*objects['b'][i],
+		                angle=objects['theta'][i] * 180. / np.pi)
+		    e.set_facecolor('none')
+		    e.set_edgecolor('red')
+		    ax.add_artist(e)
+
+		plt.show()
+
+	return objects
+
+
+def check_objects_in_region(image, objects, xmin, xmax, ymin, ymax):
+	all_objects = objects
+	bool_aux = False
+	for x,y in zip(all_objects['x'],all_objects['y']):
+		cond = (xmin < x < xmax) & (ymin < y < ymax)
+		if cond:
+			bool_aux = True
+			# maskeo de estrella brillante
+			cond2 = (178 < x < 190) & (189 < y < 201)
+			if cond2:
+				bool_aux = False
+				break
+	return bool_aux
+
+def get_master(cube, filename):
+	cube_array = fitsio.read(cube)
+	master = np.median(cube_array, axis=0)
+	hdu = fits.PrimaryHDU(master)
+	hdulist = fits.HDUList([hdu])                # hdul = header data unit list
+	hdulist.writeto('/home/consuelo/projects/FRBs/alopeke/reductions/' + filename + '.fits', overwrite = True)
+
+
+def get_cube_reduced(cube, n_cube, master_bias, master_dark, flat):
+	data = fitsio.read(cube)
+	master_flat_bias = flat - master_bias
+	#master_flat_dark = flat - master_dark
+
+	reduced_bias = []
+	#reduced_dark = []
+	for i in range(1,5000):
+		reduced_bias += [(data[i] - master_bias) / master_flat_bias]
+		#reduced_dark += [(data[i] - master_dark) / master_flat_dark]
+
+	reduced_bias = np.array(reduced_bias)
+	#reduced_dark = np.array(reduced_dark)
+	fits.writeto('/home/consuelo/projects/FRBs/alopeke/reductions/data_reduced/cube' + str(n_cube) + '_bias.fits', reduced_bias)
+	#fits.writeto('/home/consuelo/projects/FRBs/alopeke/reductions/data_reduced/cube' + str(n_cube) + '_dark.fits', reduced_dark)
+
+
+# for tables
+
+def create_mask(x, y, sizebox):
+	x = int(x)
+	y = int(y)
+	mask = np.zeros([256,256]).astype('int')
+	mask[y-int(sizebox/2):y+int(sizebox/2), x-int(sizebox/2):x+int(sizebox/2)] = 1
+	return mask
+
+def bright_star_parameters(image, mask, rad_aperture_flux):
+	image_masked = (image*mask).astype('int32')
+	objects = sep.extract(image_masked, thresh=5, minarea=10)
+	if len(objects) > 1:
+		cond = np.where(objects['flux'] == np.max(objects['flux']))
+		objects = objects[cond]
+	flux, fluxerr, flag = sep.sum_circle(image_masked, objects['x'], objects['y'], rad_aperture_flux, gain=1.0)
+	SN = flux/fluxerr
+	return(flux, objects['x'], objects['y'], SN)
diff --git a/papers/Kilpatrick2024_Alopeke/Tables/py/count_tables.py b/papers/Kilpatrick2024_Alopeke/Tables/py/count_tables.py
new file mode 100644
index 00000000..d832ce4b
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Tables/py/count_tables.py
@@ -0,0 +1,373 @@
+from astropy.io import fits
+from astropy.table import Table, vstack
+import sep
+import numpy as np
+import UTILS as ut
+import os
+import sys
+import glob
+import copy
+
+from astropy.time import Time
+from astropy import units as u
+from astropy.wcs import WCS, utils
+from astropy.coordinates import SkyCoord
+
+from astroquery.mast import Catalogs
+
+from photutils.detection import DAOStarFinder
+from astropy.stats import sigma_clipped_stats
+
+import progressbar
+
+def correct_masked_median_columns(fitsfile, camera):
+
+    image_data = np.median(fitsfile[0].data, axis=0)
+
+    for i in np.arange(fitsfile[0].header['NAXIS2']):
+        med = np.median(image_data[:,i])
+        fitsfile[0].data[:,:,i] = fitsfile[0].data[:,:,i]-med
+
+    return(fitsfile)
+
+
+def get_frame_alignment(fitsfile, coord, camera, catalog, save_stack=''):
+
+    image_data = np.median(fitsfile[0].data, axis=0)
+
+    newhdu = fits.PrimaryHDU()
+    newhdu.data = image_data
+
+    # Initial WCS guess
+    newhdu.header['CTYPE1']='RA---TAN'
+    newhdu.header['CTYPE2']='DEC--TAN'
+    newhdu.header['CRVAL1']=coord.ra.degree
+    newhdu.header['CRVAL2']=coord.dec.degree
+    if camera=='r':
+        newhdu.header['CRPIX1']=113.92306
+        newhdu.header['CRPIX2']=117.73306
+        newhdu.header['CD1_1']=0.
+        newhdu.header['CD1_2']=-0.00004138888
+        newhdu.header['CD2_1']=-0.00004138888
+        newhdu.header['CD2_2']=0.
+    if camera=='b':
+        newhdu.header['CRPIX1']=128.0
+        newhdu.header['CRPIX2']=128.0
+        newhdu.header['CD1_1']=0.
+        newhdu.header['CD1_2']=-0.00004138888
+        newhdu.header['CD2_1']=0.00004138888
+        newhdu.header['CD2_2']=0.
+
+    wcs = WCS(newhdu.header)
+
+    # Get xy coordinates of all sources in catalog
+    coords = SkyCoord(catalog['ra'], catalog['dec'], unit='deg')
+    xs, ys = utils.skycoord_to_pixel(coords, wcs, origin=1)
+
+    mask = (xs > 0) & (xs < fitsfile[0].header['NAXIS1']) &\
+        (ys > 0) & (ys < fitsfile[0].header['NAXIS2'])
+
+    catalog = catalog[mask]
+
+    n=len(catalog)
+    print(f'Got {n} sources for alignment')
+
+    mean, median, std = sigma_clipped_stats(image_data, sigma=3.0)
+    daofind = DAOStarFinder(fwhm=3.0, threshold=5.*std)
+    sources = daofind(image_data - median)
+
+    # Do matching from catalog->sources
+    catalog.sort('phot_g_mean_mag')
+
+    temp_sources = copy.copy(sources)
+    matched_sources = []
+    for row in catalog:
+        if len(temp_sources)==0: break
+        c = SkyCoord(row['ra'], row['dec'], unit='deg')
+        x,y=utils.skycoord_to_pixel(c, wcs, origin=1)
+
+        sep = (temp_sources['xcentroid']-x)**2+(temp_sources['ycentroid']-y)**2
+        idx = np.argmin(sep.data)
+
+        if sep[idx]>400: continue
+
+        matched_sources.append((row['ra'], row['dec'],
+            temp_sources[idx]['xcentroid'], temp_sources[idx]['ycentroid']))
+
+        temp_sources.remove_row(idx)
+
+    n=len(matched_sources)
+    print(f'Got {n} matched sources for alignment')
+
+    coords = SkyCoord([m[0] for m in matched_sources],
+        [m[1] for m in matched_sources], unit='deg')
+    c = coords[0]
+
+    xy = np.array([[m[2] for m in matched_sources],[m[3]
+        for m in matched_sources]])
+
+    wcs = utils.fit_wcs_from_points(xy=xy, world_coords=coords, proj_point=c,
+        projection='TAN')
+    wcs_head = wcs.to_header()
+    for key in wcs_head.keys():
+        newhdu.header[key] = wcs_head[key]
+        fitsfile[0].header[key] = wcs_head[key]
+
+    if save_stack:
+        newhdu.writeto(save_stack, overwrite=True, output_verify='silentfix')
+
+    return(fitsfile)
+
+tipo = "reduced"     #change filename
+
+data_path = sys.argv[1]
+date = sys.argv[2]
+camera = sys.argv[3]
+
+star1_ra = 29.4956219
+star1_dec = 65.7191098
+star1 = SkyCoord(star1_ra, star1_dec, unit='deg')
+star2_ra = 29.4916103
+star2_dec = 65.7187927
+star2 = SkyCoord(star2_ra, star2_dec, unit='deg')
+star3_ra = 29.4911121
+star3_dec = 65.7129930
+star3 = SkyCoord(star3_ra, star3_dec, unit='deg')
+
+frb_ra = 29.5031258
+frb_dec = 65.7167542
+frb = SkyCoord(frb_ra, frb_dec, unit='deg')
+
+bkg_ra = 29.5076445
+bkg_dec = 65.7183129
+bkg_coord = SkyCoord(bkg_ra, bkg_dec, unit='deg')
+
+radio = 2
+FWHM = 2*radio
+sizebox = 20
+
+master = None
+
+for filename in sorted(glob.glob(os.path.join(data_path, date,
+    f'N{date}A*{camera}_{tipo}.fits'))):
+
+    basefile = os.path.basename(filename)
+    n = basefile.replace(f'{camera}_{tipo}.fits','')
+    n = n.replace(f'N{date}A','')
+
+    origfile = basefile.replace(f'_{tipo}','')
+    origfile = os.path.join(data_path, date, 'raw', origfile)
+
+    if not os.path.exists(filename):
+        raise Exception(f'{filename} does not exist.  Stop!')
+    if not os.path.exists(origfile):
+        raise Exception(f'{origfile} does not exist.  Stop!')
+
+    fitsfile = fits.open(filename)
+    print(f'Correcting bias rows in {filename}...')
+    fitsfile = correct_masked_median_columns(fitsfile, camera)
+    print('Done.')
+    orighdu = fits.open(origfile)
+
+    print(f'Getting WCS alignment for stacked frame {filename}...')
+    coord = SkyCoord(orighdu[0].header['RA'],
+        orighdu[0].header['DEC'], unit=(u.hour, u.deg))
+    catalog_data = Catalogs.query_region(coord, radius=0.1,
+            catalog="Gaia", version=2)
+    mask = catalog_data['phot_g_mean_mag'] < 21.0
+
+    basepath = os.path.dirname(filename)
+    stackpath = os.path.join(basepath, 'stack')
+    if not os.path.exists(stackpath):
+        os.makedirs(stackpath)
+    save_stack = os.path.join(stackpath,basefile.replace('.fits','.stack.fits'))
+    fitsfile = get_frame_alignment(fitsfile, coord, camera, catalog_data,
+        save_stack=save_stack)
+
+    fitsfile.writeto(filename, overwrite=True, output_verify='silentfix')
+
+    wcs_head = WCS(fitsfile[0].header).to_header()
+
+    # Start time for all frames
+    start_utc = Time(orighdu[0].header['OBSTIME'], format='unix')
+    end_utc = Time(orighdu[0].header['EXPENDTM'], format='unix')
+
+    # In principle, the total time per frame should be (start_utc-end_utc)/num_frames
+    time_per_frame = (start_utc-end_utc)/orighdu[0].header['NAXIS3']
+
+    # We also want the exposure time, which is slightly different accounting for dead time
+    time_per_exposure = orighdu[0].header['EXPTIME']
+
+    data = fitsfile[0].data
+    data = data.astype(float)
+
+    ind = []
+    flux_1FWHM = []
+    flux_2FWHM = []
+    fluxerr_1FWHM = []
+    fluxerr_2FWHM = []
+
+    flux_star1_1FWHM_recov = []
+    flux_star1_2FWHM_recov = []
+    x_star1_recov = []
+    y_star1_recov = []
+    SN_star1_recov = []
+
+    flux_star2_1FWHM_recov = []
+    flux_star2_2FWHM_recov = []
+    x_star2_recov = []
+    y_star2_recov = []
+    SN_star2_recov = []
+
+    flux_star3_1FWHM_recov = []
+    flux_star3_2FWHM_recov = []
+    x_star3_recov = []
+    y_star3_recov = []
+    SN_star3_recov = []
+
+    flux_bkg_1FWHM_recov = []
+    flux_bkg_2FWHM_recov = []
+    x_bkg_recov = []
+    y_bkg_recov = []
+
+    mjd = []
+    utc = []
+    exptime = []
+
+    bar = progressbar.ProgressBar(maxval=data.shape[0]).start()
+
+    print(data.shape[0])
+
+    for i in range(data.shape[0]):
+        bar.update(i)
+        ind += [i+1]
+        image = data[i]
+        bkg = sep.Background(image)
+        image_sub = image - bkg
+
+        mean, median, std = sigma_clipped_stats(image_sub, sigma=3.0)
+        daofind = DAOStarFinder(fwhm=6.0, threshold=5.*std)
+        sources = daofind(image_sub)
+        sources.sort('flux')
+        sources.reverse()
+
+        mask = (sources['xcentroid']-wcs_head['CRPIX1'])**2 +\
+            (sources['ycentroid']-wcs_head['CRPIX2'])**2 < 100
+        sources = sources[mask]
+
+        if len(sources)!=0:
+            wcs_copy = copy.copy(wcs_head)
+            wcs_copy['CRPIX1']=sources[0]['xcentroid']
+            wcs_copy['CRPIX2']=sources[0]['ycentroid']
+
+        wcs = WCS(wcs_copy)
+
+        # Start at frame index=1 (as opposed to 0) because we skip the first
+        # blank image frame in the _reduced.fits data
+        curr_time = start_utc + (i+1) * time_per_frame
+
+        mjd.append(curr_time.mjd)
+        utc.append(curr_time.datetime.strftime('%Y-%m-%d %H:%M:%S.%f'))
+        exptime.append(time_per_exposure)
+
+        x_FRB, y_FRB = utils.skycoord_to_pixel(frb, wcs, origin=1)
+
+        flux_2FWHM_, fluxerr_2FWHM_, flag_2FWHM_ = sep.sum_circle(image_sub, [x_FRB], [y_FRB], 2*FWHM, err=bkg.globalrms, gain=1.0)
+        flux_1FWHM_, fluxerr_1FWHM_, flag_1FWHM_ = sep.sum_circle(image_sub, [x_FRB], [y_FRB], FWHM, err=bkg.globalrms, gain=1.0)
+
+        flux_1FWHM += [flux_1FWHM_[0]]
+        flux_2FWHM += [flux_2FWHM_[0]]
+        fluxerr_1FWHM += [fluxerr_1FWHM_[0]]
+        fluxerr_2FWHM += [fluxerr_2FWHM_[0]]
+
+        x_star1, y_star1 = utils.skycoord_to_pixel(star1, wcs, origin=1)
+
+        mask1 = ut.create_mask(x_star1, y_star1, sizebox)
+        flux_star1_1FWHM_recov_, x_star1_recov_, y_star1_recov_, SN_star1_recov_ = ut.bright_star_parameters(image_sub, mask1, FWHM)
+        flux_star1_2FWHM_recov_, x_star1_recov_, y_star1_recov_, SN_star1_recov_ = ut.bright_star_parameters(image_sub, mask1, 2*FWHM)
+
+        flux_star1_1FWHM_recov += [flux_star1_1FWHM_recov_[0]]
+        flux_star1_2FWHM_recov += [flux_star1_2FWHM_recov_[0]]
+        x_star1_recov += [x_star1_recov_[0]]
+        y_star1_recov += [y_star1_recov_[0]]
+        SN_star1_recov += [SN_star1_recov_[0]]
+
+        x_star2, y_star2 = utils.skycoord_to_pixel(star2, wcs, origin=1)
+
+        mask2 = ut.create_mask(x_star2, y_star2, sizebox)
+        flux_star2_1FWHM_recov_, x_star2_recov_, y_star2_recov_, SN_star2_recov_ = ut.bright_star_parameters(image_sub, mask2, FWHM)
+        flux_star2_2FWHM_recov_, x_star2_recov_, y_star2_recov_, SN_star2_recov_ = ut.bright_star_parameters(image_sub, mask2, 2*FWHM)
+
+        flux_star2_1FWHM_recov += [flux_star2_1FWHM_recov_[0]]
+        flux_star2_2FWHM_recov += [flux_star2_2FWHM_recov_[0]]
+        x_star2_recov += [x_star2_recov_[0]]
+        y_star2_recov += [y_star2_recov_[0]]
+        SN_star2_recov += [SN_star2_recov_[0]]
+
+        x_star3, y_star3 = utils.skycoord_to_pixel(star3, wcs, origin=1)
+
+        mask3 = ut.create_mask(x_star3, y_star3, sizebox)
+        flux_star3_1FWHM_recov_, x_star3_recov_, y_star3_recov_, SN_star3_recov_ = ut.bright_star_parameters(image_sub, mask3, FWHM)
+        flux_star3_2FWHM_recov_, x_star3_recov_, y_star3_recov_, SN_star3_recov_ = ut.bright_star_parameters(image_sub, mask3, 2*FWHM)
+
+        flux_star3_1FWHM_recov += [flux_star3_1FWHM_recov_[0]]
+        flux_star3_2FWHM_recov += [flux_star3_2FWHM_recov_[0]]
+        x_star3_recov += [x_star3_recov_[0]]
+        y_star3_recov += [y_star3_recov_[0]]
+        SN_star3_recov += [SN_star3_recov_[0]]
+
+        x_bkg, y_bkg = utils.skycoord_to_pixel(bkg_coord, wcs, origin=1)
+
+        flux_bkg_2FWHM_, fluxerr_bkg_2FWHM_, flag_bkg_2FWHM_ = sep.sum_circle(image_sub, [x_bkg], [y_bkg], 2*FWHM, err=bkg.globalrms, gain=1.0)
+        flux_bkg_1FWHM_, fluxerr_bkg_1FWHM_, flag_bkg_1FWHM_ = sep.sum_circle(image_sub, [x_bkg], [y_bkg], FWHM, err=bkg.globalrms, gain=1.0)
+
+        flux_bkg_1FWHM_recov += [flux_bkg_1FWHM_[0]]
+        flux_bkg_2FWHM_recov += [flux_bkg_2FWHM_[0]]
+
+    bar.finish()
+
+    tab = Table()
+    tab['frame'] = ind
+    tab['file'] = [os.path.basename(basefile)]*len(flux_1FWHM)
+    tab['UTC'] = utc # UTC at start of exposure
+    tab['MJD'] = mjd # MJD at start of exposure
+    tab['exptime'] = exptime
+
+    tab['flux_1FWHM'] = flux_1FWHM
+    tab['fluxerr_1FWHM'] = fluxerr_1FWHM
+    tab['flux_2FWHM'] = flux_2FWHM
+    tab['fluxerr_2FWHM'] = fluxerr_2FWHM
+
+    tab['flux_star1_1FWHM'] = flux_star1_1FWHM_recov
+    tab['flux_star1_2FWHM'] = flux_star1_2FWHM_recov
+    tab['x_star1'] = x_star1_recov
+    tab['y_star1'] = y_star1_recov
+    tab['SN_star1'] = SN_star1_recov
+
+    tab['flux_star2_1FWHM'] = flux_star2_1FWHM_recov
+    tab['flux_star2_2FWHM'] = flux_star2_2FWHM_recov
+    tab['x_star2'] = x_star2_recov
+    tab['y_star2'] = y_star2_recov
+    tab['SN_star2'] = SN_star2_recov
+
+    tab['flux_star3_1FWHM'] = flux_star3_1FWHM_recov
+    tab['flux_star3_2FWHM'] = flux_star3_2FWHM_recov
+    tab['x_star3'] = x_star3_recov
+    tab['y_star3'] = y_star3_recov
+    tab['SN_star3'] = SN_star3_recov
+
+    tab['flux_bkg_1FWHM'] = flux_bkg_1FWHM_recov
+    tab['flux_bkg_2FWHM'] = flux_bkg_2FWHM_recov
+
+    if master is None:
+        master = tab
+    else:
+        master = vstack([master, tab])
+
+    tabfile = f'count_table_{date}_{n}_{camera}.fits'
+    tabfile = os.path.join('..','..','Data',tabfile)
+    print(f'Writing out {tabfile}')
+    tab.write(tabfile, overwrite=True)
+
+masterfile = os.path.join('..','..','Data',f'master_table_{date}_{camera}.fits')
+master.write(masterfile, overwrite=True)
diff --git a/papers/Kilpatrick2024_Alopeke/Tables/py/generate_all_count_tables.sh b/papers/Kilpatrick2024_Alopeke/Tables/py/generate_all_count_tables.sh
new file mode 100755
index 00000000..877a6f17
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Tables/py/generate_all_count_tables.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+
+conda activate astroconda37
+python count_tables.py /Users/ckilpatrick/Dropbox/Data/FRB/FRB180916/Data/Alopeke 20201023 r
+python count_tables.py /Users/ckilpatrick/Dropbox/Data/FRB/FRB180916/Data/Alopeke 20201023 b
+python count_tables.py /Users/ckilpatrick/Dropbox/Data/FRB/FRB180916/Data/Alopeke 20220908 r
+python count_tables.py /Users/ckilpatrick/Dropbox/Data/FRB/FRB180916/Data/Alopeke 20220908 b
diff --git a/papers/Kilpatrick2024_Alopeke/Tables/py/sources.cat b/papers/Kilpatrick2024_Alopeke/Tables/py/sources.cat
new file mode 100644
index 00000000..2a714dfb
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Tables/py/sources.cat
@@ -0,0 +1,7 @@
+237.6792630177016 52.339930354765116
+214.9063750150557 93.72669696967193
+60.461516733247045 196.53942142939343
+44.31454964946594 202.26631001478998
+26.567437103418577 213.21818490703401
+69.29481747303787 236.56473530935705
+209.94894696438934 236.9092106449319
diff --git a/papers/Kilpatrick2024_Alopeke/Tables/py/tabs_alopeke.py b/papers/Kilpatrick2024_Alopeke/Tables/py/tabs_alopeke.py
new file mode 100644
index 00000000..3ec11f2b
--- /dev/null
+++ b/papers/Kilpatrick2024_Alopeke/Tables/py/tabs_alopeke.py
@@ -0,0 +1,174 @@
+""" Module for Tables for the Alopeke paper """
+# Imports
+from signal import raise_signal
+import numpy as np
+import os, sys
+import copy
+
+import pandas
+
+from astropy import units
+from astropy.table import Table 
+
+
+# Local
+sys.path.append(os.path.abspath("../../Analysis/py"))
+import alopeke_defs
+import alopeke_utils2
+import alopeke_analy
+
+from IPython import embed
+
+# Summary table of results
+def mktab_photom(camera, outroot='tab_photom_', sub=False):
+
+    # Open
+    if sub:
+        outfile = outroot+camera+'.tex'
+    else:
+        outfile = outroot+camera+'_sub.tex'
+    tbfil = open(outfile, 'w')
+    lbl = 'Red' if camera == 'red' else 'Blue'
+    filt = '$i$' if camera == 'red' else '$r$'
+    gain = alopeke_defs.gain_red if camera == 'red' else alopeke_defs.gain_blue
+
+    # Load
+    #data = Table.read('../Data/master_table_redcam.fits')
+    data_dict = alopeke_utils2.load_camera(camera)
+
+    # Header
+    #tbfil.write('\\clearpage\n')
+    tbfil.write('\\begin{deluxetable}{cccccccccccccccc}\n')
+    #tbfil.write('\\rotate\n')
+    tbfil.write('\\tablewidth{0pc}\n')
+    tbfil.write('\\tablecaption{Photometry for '+lbl+' Camera \\label{tab:photom_'+camera+'}}\n')
+    tbfil.write('\\tabletypesize{\\footnotesize}\n')
+    tbfil.write('\\tablehead{\\colhead{MJD} & \\colhead{Filter} \n')
+    #tbfil.write('& \\colhead{$x_1$} & \\colhead{$y_1$} & \\colhead{FWHM$_1$}\n')
+    tbfil.write('& \\colhead{\cbkg} & \\colhead{\cstar} & \\colhead{\ctfrb} \n')
+    #tbfil.write("& (deg) & (deg) & ($''$) & & (deg) & (deg) & ($''$) & ($''$) & ($''$) & ($''$) & (mag)\n")
+    #tbfil.write("\\\\ (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) & (14) & (15)")
+    tbfil.write('} \n')
+
+    tbfil.write('\\startdata \n')
+
+    # Loop 
+    for kk in range(len(data_dict['C_star1_full'])):
+        if sub and kk > 10:
+            continue
+        #if data_dict['frame'][kk] == 1:
+        #    raise ValueError("Bad frame value!!")
+
+        sline = ''
+
+        # Name and filter
+        sline += '{}'.format(data_dict['MJD_star1'][kk]) + '& '+filt
+
+        # x1, y1
+        #sline += '&' + '{:0.2f}'.format(row['x_star1'])
+        #sline += '&' + '{:0.2f}'.format(row['y_star1'])
+
+        # FWHM
+        #sline += '&' + 'TBD'
+
+        # C_bkg
+        sline += '&' + '{:0.2f}'.format(data_dict['C_bkg_full'][kk])
+        # C_star
+        sline += '&' + '{:0.2f}'.format(data_dict['C_star1_full'][kk])
+        # C FRB
+        sline += '&' + '{:0.2f}'.format(data_dict['C_gal_full'][kk])
+
+        tbfil.write(sline + '\\\\ \n')
+
+    # End end
+    tbfil.write('\\hline \n')
+
+
+    tbfil.write('\\enddata \n')
+
+    #tbfil.write('\\tablenotetext{a}{Spectroscopic redshifts are reported to 4 significant digits.  Photometric to 2.} \n')
+    #tbfil.write('the gas below the line $\\rm DEC_{\\rm off} = \\aslope RA_{\\rm off} \\ayoff$}\n')
+    #tbfil.write('\\tablecomments{Column 1: FRB source. Columns 2 and 3: R.A. and Decl. of the FRB (J2000). Column 4: FRB error ellipse. Column 5: FRB classication. Repeating = yes(y)/no(n). Column 6 and 7: R.A. and Dec. of the associated host galaxy (J2000). Column 8: projected angular offset of the FRB to the host galaxy center. Column 9: association radius $\delta x$ \citep{Tunnicliffe14}. Column 10: angular effective radius of the host measured from a sersic model using GALFIT \citep{galfit} on the $i$-band images (or equivalent). Column 11: effective search radius \citep{Bloom02}. Column 12: measured apparent magnitude of the host. Column 13: filter used for the magnitude measurement. Column 14: probability of chance coincidence using the \citet{Bloom02} formalism. Column 15: sample designations following the criteria outlined in $\S$~\\ref{ssec:associate}.}\n')
+    # End
+    tbfil.write('\\end{deluxetable} \n')
+
+    tbfil.close()
+    print('Wrote {:s}'.format(outfile))
+
+
+
+# Summary table of results
+def mktab_summary(outfile='tab_summary.tex'):
+
+    # Load up
+    summary = alopeke_analy.summary_dict(calc_upper=True)
+
+    # Items to show
+    items = ['A', 'C_1', 'C_gal', 'sC_gal', #'dCdt',
+             'mxFRB', 'uppFRB', 'uppFlu']
+
+    # Header
+    #tbfil.write('\\clearpage\n')
+    tbfil = open(outfile, 'w')
+    tbfil.write('\\begin{deluxetable*}{cccccccccccccccc}\n')
+    #tbfil.write('\\rotate\n')
+    tbfil.write('\\tablewidth{0pc}\n')
+    tbfil.write('\\tablecaption{Summary of Results\\label{tab:summary}}\n')
+    tbfil.write('\\tabletypesize{\\footnotesize}\n')
+    tbfil.write('\\tablehead{\\colhead{Item} & \\colhead{Camera} \n')
+    tbfil.write('& \\colhead{Value} & \\colhead{Error} & \\colhead{Unit}\n')
+    tbfil.write('& \\colhead{Desc.} \n')
+    #tbfil.write("& (deg) & (deg) & ($''$) & & (deg) & (deg) & ($''$) & ($''$) & ($''$) & ($''$) & (mag)\n")
+    #tbfil.write("\\\\ (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) & (14) & (15)")
+    tbfil.write('} \n')
+
+    tbfil.write('\\startdata \n')
+
+    # Loop 
+    for item in items:
+        for camera in ['blue', 'red']:
+            sline = ''
+
+            # Name and filter
+            sline += summary[camera][item]['variable'] + '&'+camera
+ 
+            # Value 
+            sline += '&' + summary[camera][item]['vformat'].format(
+                summary[camera][item]['value'])
+            # Error
+            sline += '&' + summary[camera][item]['eformat'].format(
+                summary[camera][item]['error'])
+            # Unit
+            sline += '&' + summary[camera][item]['units']
+            # Description
+            sline += '&' + summary[camera][item]['desc']
+
+            tbfil.write(sline + '\\\\ \n')
+
+    # End end
+    tbfil.write('\\hline \n')
+
+
+    tbfil.write('\\enddata \n')
+
+    #tbfil.write('\\tablenotetext{a}{Spectroscopic redshifts are reported to 4 significant digits.  Photometric to 2.} \n')
+    #tbfil.write('the gas below the line $\\rm DEC_{\\rm off} = \\aslope RA_{\\rm off} \\ayoff$}\n')
+    #tbfil.write('\\tablecomments{Column 1: FRB source. Columns 2 and 3: R.A. and Decl. of the FRB (J2000). Column 4: FRB error ellipse. Column 5: FRB classication. Repeating = yes(y)/no(n). Column 6 and 7: R.A. and Dec. of the associated host galaxy (J2000). Column 8: projected angular offset of the FRB to the host galaxy center. Column 9: association radius $\delta x$ \citep{Tunnicliffe14}. Column 10: angular effective radius of the host measured from a sersic model using GALFIT \citep{galfit} on the $i$-band images (or equivalent). Column 11: effective search radius \citep{Bloom02}. Column 12: measured apparent magnitude of the host. Column 13: filter used for the magnitude measurement. Column 14: probability of chance coincidence using the \citet{Bloom02} formalism. Column 15: sample designations following the criteria outlined in $\S$~\\ref{ssec:associate}.}\n')
+    # End
+    tbfil.write('\\end{deluxetable*} \n')
+
+    tbfil.close()
+    print('Wrote {:s}'.format(outfile))
+
+
+
+#### ########################## #########################
+#### ########################## #########################
+#### ########################## #########################
+
+# Command line execution
+if __name__ == '__main__':
+
+    #mktab_photom('blue', sub=True)
+    #mktab_photom('red', sub=True)
+    mktab_summary()