Vatira_eval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c9e3da4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: The update_default_config function is deprecated and may be removed in a future version. [sncosmo]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import rubin_sim.maf as maf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0033728e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lynnej/lsst_repos/rubin_sim/rubin_sim/maf/slicers/base_slicer.py:390: UserWarning: Cannot use saved slicer init values; falling back to defaults for baseline_v3_0_10yrs_DiscoveryNChances_Vatira_1_quad_in_1_night_detection_loss_MOOB.npz\n",
      "  warnings.warn(\n",
      "/Users/lynnej/lsst_repos/rubin_sim/rubin_sim/maf/slicers/base_slicer.py:390: UserWarning: Cannot use saved slicer init values; falling back to defaults for baseline_v3_0_10yrs_DiscoveryNChances_Vatira_Single_detection_detection_loss_MOOB.npz\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "nchances = maf.MetricBundle.load('baseline_v3_0_10yrs_DiscoveryNChances_Vatira_1_quad_in_1_night_detection_loss_MOOB.npz')\n",
    "nchances1 = maf.MetricBundle.load('baseline_v3_0_10yrs_DiscoveryNChances_Vatira_Single_detection_detection_loss_MOOB.npz')\n",
    "diff = maf.MetricBundle.load('baseline_v3_0_10yrs_DifferentialCompleteness_Vatira_1_quad_in_1_night_detection_loss_MOOB.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b0a6ebd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = np.where(nchances.metric_values.data > 0)[0]\n",
    "dd = np.unique(xx)\n",
    "xx1 = np.where(nchances1.metric_values.data > 0)[0]\n",
    "dd1 = np.unique(xx1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fa39c2cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "biggest = nchances.metric_values[dd].data.swapaxes(0, 1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5d996b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([357.,  11.,   3.,   3.,   0.,   1.,   0.,   0.,   0.,   1.])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9UlJSlJGRofT0dG3dulWlpaUaOHCgKioqau7MAABAreUyxphL3fk///mPGjVqpM2bN6tbt24yxig+Pl4pKSmaPHmypK/ulsTGxur555/XI488oqKiIjVs2FBLlizRsGHDJEnHjh1TQkKC1q5dq379+l30uMXFxfJ6vSoqKpLH47nU8s/r+ilranzMK+3IjAHBLgEAgAuqzuv3Zc1BKSoqkiRFRUVJkg4fPqyCggL17dvX6eN2u9W9e3dlZWVJkrZv365Tp04F9ImPj1dSUpLT51x+v1/FxcUBCwAAuHpdckAxxmj8+PHq2rWrkpKSJEkFBQWSpNjY2IC+sbGxzraCggKFhoaqQYMG5+1zrrS0NHm9XmdJSEi41LIBAEAtcMkBZezYsfr444/12muvVdrmcrkC1o0xldrOdaE+U6dOVVFRkbPk5eVdatkAAKAWuKSAMm7cOL3++ut699131bhxY6fd5/NJUqU7IYWFhc5dFZ/Pp/Lycp04ceK8fc7ldrvl8XgCFgAAcPWqVkAxxmjs2LFatWqVNm7cqGbNmgVsb9asmXw+nzIzM5228vJybd68WV26dJEkdejQQXXr1g3ok5+fr507dzp9AADAd1ud6nQeM2aMli9frr/+9a+KjIx07pR4vV6Fh4fL5XIpJSVFqampSkxMVGJiolJTU1WvXj0NHz7c6Ttq1ChNmDBB0dHRioqK0sSJE5WcnKzevXvX/BkCAIBap1oBZf78+ZKkHj16BLQvWrRII0eOlCRNmjRJZWVlGj16tE6cOKFbb71V69evV2RkpNN/zpw5qlOnjoYOHaqysjL16tVLixcvVkhIyOWdDQAAuCpc1uegBAufg1IZn4MCALDdt/Y5KAAAAFcCAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdaodULZs2aJBgwYpPj5eLpdLq1evDtg+cuRIuVyugKVTp04Bffx+v8aNG6eYmBhFRERo8ODBOnr06GWdCAAAuHpUO6CcPHlS7dq107x5887b584771R+fr6zrF27NmB7SkqKMjIylJ6erq1bt6q0tFQDBw5URUVF9c8AAABcdepUd4f+/furf//+F+zjdrvl8/mq3FZUVKSFCxdqyZIl6t27tyRp6dKlSkhI0IYNG9SvX7/qlgQAAK4yV2QOyqZNm9SoUSO1aNFCDz/8sAoLC51t27dv16lTp9S3b1+nLT4+XklJScrKyqpyPL/fr+Li4oAFAABcvWo8oPTv31/Lli3Txo0bNWvWLGVnZ6tnz57y+/2SpIKCAoWGhqpBgwYB+8XGxqqgoKDKMdPS0uT1ep0lISGhpssGAAAWqfYjnosZNmyY8+ekpCR17NhRTZs21Zo1azRkyJDz7meMkcvlqnLb1KlTNX78eGe9uLiYkAIAwFXsir/NOC4uTk2bNtX+/fslST6fT+Xl5Tpx4kRAv8LCQsXGxlY5htvtlsfjCVgAAMDV64oHlOPHjysvL09xcXGSpA4dOqhu3brKzMx0+uTn52vnzp3q0qXLlS4HAADUAtV+xFNaWqoDBw4464cPH1ZOTo6ioqIUFRWl6dOn64c//KHi4uJ05MgRTZs2TTExMbrnnnskSV6vV6NGjdKECRMUHR2tqKgoTZw4UcnJyc67egAAwHdbtQPKhx9+qDvuuMNZPzs3ZMSIEZo/f75yc3P16quv6osvvlBcXJzuuOMOrVixQpGRkc4+c+bMUZ06dTR06FCVlZWpV69eWrx4sUJCQmrglAAAQG3nMsaYYBdRXcXFxfJ6vSoqKroi81Gun7Kmxse80o7MGBDsEgAAuKDqvH7zXTwAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA61Q7oGzZskWDBg1SfHy8XC6XVq9eHbDdGKPp06crPj5e4eHh6tGjh3bt2hXQx+/3a9y4cYqJiVFERIQGDx6so0ePXtaJAACAq0e1A8rJkyfVrl07zZs3r8rtM2fO1OzZszVv3jxlZ2fL5/OpT58+KikpcfqkpKQoIyND6enp2rp1q0pLSzVw4EBVVFRc+pkAAICrRp3q7tC/f3/179+/ym3GGM2dO1dPPvmkhgwZIkl65ZVXFBsbq+XLl+uRRx5RUVGRFi5cqCVLlqh3796SpKVLlyohIUEbNmxQv379Ko3r9/vl9/ud9eLi4uqWDQAAapEanYNy+PBhFRQUqG/fvk6b2+1W9+7dlZWVJUnavn27Tp06FdAnPj5eSUlJTp9zpaWlyev1OktCQkJNlg0AACxTowGloKBAkhQbGxvQHhsb62wrKChQaGioGjRocN4+55o6daqKioqcJS8vrybLBgAAlqn2I55vwuVyBawbYyq1netCfdxut9xud43VBwAA7Fajd1B8Pp8kVboTUlhY6NxV8fl8Ki8v14kTJ87bBwAAfLfVaEBp1qyZfD6fMjMznbby8nJt3rxZXbp0kSR16NBBdevWDeiTn5+vnTt3On0AAMB3W7Uf8ZSWlurAgQPO+uHDh5WTk6OoqCg1adJEKSkpSk1NVWJiohITE5Wamqp69epp+PDhkiSv16tRo0ZpwoQJio6OVlRUlCZOnKjk5GTnXT0AAOC7rdoB5cMPP9Qdd9zhrI8fP16SNGLECC1evFiTJk1SWVmZRo8erRMnTujWW2/V+vXrFRkZ6ewzZ84c1alTR0OHDlVZWZl69eqlxYsXKyQkpAZOCQAA1HYuY4wJdhHVVVxcLK/Xq6KiInk8nhof//opa2p8zCvtyIwBwS4BAIALqs7rN9/FAwAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGCdGg8o06dPl8vlClh8Pp+z3Rij6dOnKz4+XuHh4erRo4d27dpV02UAAIBa7IrcQWnTpo3y8/OdJTc319k2c+ZMzZ49W/PmzVN2drZ8Pp/69OmjkpKSK1EKAACohepckUHr1Am4a3KWMUZz587Vk08+qSFDhkiSXnnlFcXGxmr58uV65JFHqhzP7/fL7/c768XFxVeibAAAYIkrcgdl//79io+PV7NmzXTvvffq0KFDkqTDhw+roKBAffv2dfq63W51795dWVlZ5x0vLS1NXq/XWRISEq5E2QAAwBI1HlBuvfVWvfrqq3r77bf10ksvqaCgQF26dNHx48dVUFAgSYqNjQ3YJzY21tlWlalTp6qoqMhZ8vLyarpsAABgkRp/xNO/f3/nz8nJyercubO+973v6ZVXXlGnTp0kSS6XK2AfY0yltq9zu91yu901XSoAALDUFX+bcUREhJKTk7V//35nXsq5d0sKCwsr3VUBAADfXVc8oPj9fu3Zs0dxcXFq1qyZfD6fMjMzne3l5eXavHmzunTpcqVLAQAAtUSNP+KZOHGiBg0apCZNmqiwsFC/+c1vVFxcrBEjRsjlciklJUWpqalKTExUYmKiUlNTVa9ePQ0fPrymSwEAALVUjQeUo0eP6r777tNnn32mhg0bqlOnTtq2bZuaNm0qSZo0aZLKyso0evRonThxQrfeeqvWr1+vyMjImi4FAADUUi5jjAl2EdVVXFwsr9eroqIieTyeGh//+ilranzMK+3IjAHBLgEAgAuqzus338UDAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALBOnWAXgJpx/ZQ1wS6h2o7MGBDsEgAAluIOCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdYIaUF544QU1a9ZMYWFh6tChg957771glgMAACwRtM9BWbFihVJSUvTCCy/otttu04svvqj+/ftr9+7datKkSbDKwreoNn52S23E580AqI2Cdgdl9uzZGjVqlH72s5+pVatWmjt3rhISEjR//vxglQQAACwRlDso5eXl2r59u6ZMmRLQ3rdvX2VlZVXq7/f75ff7nfWioiJJUnFx8RWp74z/yysyLhAMV+rfyZWU9NTbwS6h2nY+3S/YJcBi/Ex/5ezvI2PMRfsGJaB89tlnqqioUGxsbEB7bGysCgoKKvVPS0vT008/Xak9ISHhitUIXC28c4NdwXcD1xlXmyv5M11SUiKv13vBPkH9Lh6XyxWwboyp1CZJU6dO1fjx4531M2fO6PPPP1d0dHSV/a92xcXFSkhIUF5enjweT7DLqbW4jjWD61gzuI41g+tYM67UdTTGqKSkRPHx8RftG5SAEhMTo5CQkEp3SwoLCyvdVZEkt9stt9sd0HbttddeyRJrBY/Hwz/AGsB1rBlcx5rBdawZXMeacSWu48XunJwVlEmyoaGh6tChgzIzMwPaMzMz1aVLl2CUBAAALBK0Rzzjx4/XAw88oI4dO6pz585asGCB/vnPf+rRRx8NVkkAAMASQQsow4YN0/Hjx/XMM88oPz9fSUlJWrt2rZo2bRqskmoNt9utp556qtJjL1QP17FmcB1rBtexZnAda4YN19Flvsl7fQAAAL5FfBcPAACwDgEFAABYh4ACAACsQ0ABAADWIaDUEmlpabr55psVGRmpRo0a6e6779bevXuDXVatl5aWJpfLpZSUlGCXUuv861//0v3336/o6GjVq1dP7du31/bt24NdVq1y+vRp/fKXv1SzZs0UHh6uG264Qc8884zOnDkT7NKst2XLFg0aNEjx8fFyuVxavXp1wHZjjKZPn674+HiFh4erR48e2rVrV3CKtdSFruGpU6c0efJkJScnKyIiQvHx8XrwwQd17Nixb60+AkotsXnzZo0ZM0bbtm1TZmamTp8+rb59++rkyZPBLq3Wys7O1oIFC9S2bdtgl1LrnDhxQrfddpvq1q2rt956S7t379asWbP4hOdqev755/WnP/1J8+bN0549ezRz5kz99re/1R/+8Idgl2a9kydPql27dpo3b16V22fOnKnZs2dr3rx5ys7Ols/nU58+fVRSUvItV2qvC13DL7/8Ujt27NCvfvUr7dixQ6tWrdK+ffs0ePDgb69Ag1qpsLDQSDKbN28Odim1UklJiUlMTDSZmZmme/fu5vHHHw92SbXK5MmTTdeuXYNdRq03YMAA89BDDwW0DRkyxNx///1Bqqh2kmQyMjKc9TNnzhifz2dmzJjhtP33v/81Xq/X/OlPfwpChfY79xpW5YMPPjCSzKeffvqt1MQdlFqqqKhIkhQVFRXkSmqnMWPGaMCAAerdu3ewS6mVXn/9dXXs2FE//vGP1ahRI91000166aWXgl1WrdO1a1e988472rdvnyTpH//4h7Zu3aof/OAHQa6sdjt8+LAKCgrUt29fp83tdqt79+7KysoKYmW1W1FRkVwu17d2pzSo32aMS2OM0fjx49W1a1clJSUFu5xaJz09XTt27FB2dnawS6m1Dh06pPnz52v8+PGaNm2aPvjgAz322GNyu9168MEHg11erTF58mQVFRXpxhtvVEhIiCoqKvTcc8/pvvvuC3ZptdrZL6I998tnY2Nj9emnnwajpFrvv//9r6ZMmaLhw4d/a1/CSECphcaOHauPP/5YW7duDXYptU5eXp4ef/xxrV+/XmFhYcEup9Y6c+aMOnbsqNTUVEnSTTfdpF27dmn+/PkElGpYsWKFli5dquXLl6tNmzbKyclRSkqK4uPjNWLEiGCXV+u5XK6AdWNMpTZc3KlTp3TvvffqzJkzeuGFF7614xJQaplx48bp9ddf15YtW9S4ceNgl1PrbN++XYWFherQoYPTVlFRoS1btmjevHny+/0KCQkJYoW1Q1xcnFq3bh3Q1qpVK61cuTJIFdVO//M//6MpU6bo3nvvlSQlJyfr008/VVpaGgHlMvh8Pklf3UmJi4tz2gsLCyvdVcGFnTp1SkOHDtXhw4e1cePGb+3uicS7eGoNY4zGjh2rVatWaePGjWrWrFmwS6qVevXqpdzcXOXk5DhLx44d9ZOf/EQ5OTmEk2/otttuq/Q293379vFln9X05Zdf6pprAn8Nh4SE8Dbjy9SsWTP5fD5lZmY6beXl5dq8ebO6dOkSxMpql7PhZP/+/dqwYYOio6O/1eNzB6WWGDNmjJYvX66//vWvioyMdJ6xer1ehYeHB7m62iMyMrLSvJ2IiAhFR0czn6cannjiCXXp0kWpqakaOnSoPvjgAy1YsEALFiwIdmm1yqBBg/Tcc8+pSZMmatOmjT766CPNnj1bDz30ULBLs15paakOHDjgrB8+fFg5OTmKiopSkyZNlJKSotTUVCUmJioxMVGpqamqV6+ehg8fHsSq7XKhaxgfH68f/ehH2rFjh958801VVFQ4rztRUVEKDQ298gV+K+8VwmWTVOWyaNGiYJdW6/E240vzxhtvmKSkJON2u82NN95oFixYEOySap3i4mLz+OOPmyZNmpiwsDBzww03mCeffNL4/f5gl2a9d999t8rfiSNGjDDGfPVW46eeesr4fD7jdrtNt27dTG5ubnCLtsyFruHhw4fP+7rz7rvvfiv1uYwx5srHIAAAgG+OOSgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKMBFLF68WNdee22wywiKTz75RJ06dVJYWJjat2//jffr0aOHUlJSnPXrr79ec+fOrfH6aoPy8nI1b95cf/vb34JdymU5cuSIXC6XcnJyLtq3sLBQDRs21L/+9a8rXxiuWgQUXDVGjhwpl8ulGTNmBLSvXr2ar1i/RE899ZQiIiK0d+9evfPOO5c8TnZ2tn7+85/XYGW1x4IFC9S0aVPddtttTpvL5VJYWJg+/fTTgL533323Ro4c+S1XWPMaNWqkBx54QE899VSwS0EtRkDBVSUsLEzPP/+8Tpw4EexSrgoHDx5U165d1bRp08v6JtOGDRuqXr16NVjZt+vUqVOXvO8f/vAH/exnP6vU7nK59Otf//pyyrLaT3/6Uy1btox/i7hkBBRcVXr37i2fz6e0tLRq7ffFF1/o5z//uWJjYxUWFqakpCS9+eabAX3efvtttWrVSvXr19edd96p/Px8Z1t2drb69OmjmJgYeb1ede/eXTt27AjY3+Vy6c9//rPuuece1atXT4mJiXr99dcD+uzatUsDBgyQx+NRZGSkbr/9dh08eNDZvmjRIrVq1UphYWG68cYb9cILLzjbysvLNXbsWMXFxSksLEzXX3/9Ba/DmTNn9Mwzz6hx48Zyu91q37691q1bF1Dv9u3b9cwzz8jlcmn69OlVjnPy5Ek9+OCDql+/vuLi4jRr1qxKfc59xDN9+nQ1adJEbrdb8fHxeuyxx5xtfr9fkyZNUkJCgtxutxITE7Vw4UJn++bNm3XLLbfI7XYrLi5OU6ZM0enTpyVJL774oq677jqdOXMm4PiDBw/WiBEjnPU33nhDHTp0UFhYmG644QY9/fTTzhhnz/1Pf/qT7rrrLkVEROg3v/mNmjdvrt/97ncB4+7cuVPXXHNNwN/R1+3YsUMHDhzQgAEDKm0bN26cli5dqtzc3Cr3PZ/FixerSZMmqlevnu655x7NmjUr4BHkyJEjdffddwfsk5KSoh49ejjr69atU9euXXXttdcqOjpaAwcOrHQOH3zwgW666SaFhYWpY8eO+uijjwK2nzhxQj/5yU/UsGFDhYeHKzExUYsWLXK2Jycny+fzKSMjo1rnBzi+la8kBL4FI0aMMHfddZdZtWqVCQsLM3l5ecYYYzIyMsyFftQrKipMp06dTJs2bcz69evNwYMHzRtvvGHWrl1rjDFm0aJFpm7duqZ3794mOzvbbN++3bRq1coMHz7cGeOdd94xS5YsMbt37za7d+82o0aNMrGxsaa4uNjpI8k0btzYLF++3Ozfv9889thjpn79+ub48ePGGGOOHj1qoqKizJAhQ0x2drbZu3evefnll80nn3xijDFmwYIFJi4uzqxcudIcOnTIrFy50kRFRZnFixcbY4z57W9/axISEsyWLVvMkSNHzHvvvWeWL19+3vOePXu28Xg85rXXXjOffPKJmTRpkqlbt67Zt2+fMcaY/Px806ZNGzNhwgSTn59vSkpKqhznF7/4hWncuLFZv369+fjjj83AgQNN/fr1A74humnTpmbOnDnGGGP+8pe/GI/HY9auXWs+/fRT8/777wd8E/LQoUNNQkKCWbVqlTl48KDZsGGDSU9Pd65RvXr1zOjRo82ePXtMRkaGiYmJMU899ZQxxpjjx4+b0NBQs2HDBme8zz//3ISGhpq3337bGGPMunXrjMfjMYsXLzYHDx4069evN9dff72ZPn16wN9Vo0aNzMKFC83BgwfNkSNHzHPPPWdat24dcO5PPPGE6dat23mv8Zw5c8yNN95YqV2SycjIMIMHDzYDBgxw2u+66y7n23irsm3bNuNyuUxaWprZu3ev+f3vf2+uvfZa4/V6nT5n/x183eOPP266d+/urP/f//2fWblypdm3b5/56KOPzKBBg0xycrKpqKgwxhhTWlpqGjZsaIYNG2Z27txp3njjDXPDDTcYSeajjz4yxhgzZswY0759e5OdnW0OHz5sMjMzzeuvvx5w3KFDh5qRI0ee93yACyGg4Krx9V/MnTp1Mg899JAx5uIB5e233zbXXHON2bt3b5XbFy1aZCSZAwcOOG1//OMfTWxs7HnHPH36tImMjDRvvPGG0ybJ/PKXv3TWS0tLjcvlMm+99ZYxxpipU6eaZs2amfLy8irHTEhIqBQ4nn32WdO5c2djjDHjxo0zPXv2NGfOnDlvXV8XHx9vnnvuuYC2m2++2YwePdpZb9eunfPiX5WSkhITGhrqBAhjvgoJ4eHh5w0os2bNMi1atKjyPPfu3WskmczMzCqPN23aNNOyZcuAc/zjH/9o6tev77y4Dh482Pm7N8aYF1980fh8PnP69GljjDG33367SU1NDRh3yZIlJi4uzlmXZFJSUgL6HDt2zISEhJj333/fGGNMeXm5adiwoRMQq/L444+bnj17Vmo/G1B27dplQkJCzJYtW4wxFw8o9913n7nzzjsD2oYNG1btgHKuwsJCI8nk5uYaY766ZlFRUebkyZNOn/nz5wcElEGDBpmf/vSn5x3TmK8CXI8ePS7YBzgfHvHgqvT888/rlVde0e7duy/aNycnR40bN1aLFi3O26devXr63ve+56zHxcWpsLDQWS8sLNSjjz6qFi1ayOv1yuv1qrS0VP/85z8Dxmnbtq3z54iICEVGRjrj5OTk6Pbbb1fdunUrHf8///mP8vLyNGrUKNWvX99ZfvOb3zi35keOHKmcnBy1bNlSjz32mNavX3/e8ykuLtaxY8cCJm5K0m233aY9e/acd79zHTx4UOXl5ercubPTFhUVpZYtW553nx//+McqKyvTDTfcoIcfflgZGRnO45WcnByFhISoe/fuVe67Z88ede7cOWDS82233abS0lIdPXpUkvSTn/xEK1eulN/vlyQtW7ZM9957r0JCQiTJeWz19ev48MMPKz8/X19++aUzbseOHQOOHRcXpwEDBujll1+WJL355pv673//qx//+MfnPdeysjKFhYWdd3vr1q314IMPavLkyeftU9X5f92569/EwYMHNXz4cN1www3yeDxq1qyZJDk/r3v27FG7du0C5g2de5xf/OIXSk9PV/v27TVp0iRlZWVVOk54eHjANQWqg4CCq1K3bt3Ur18/TZs27aJ9w8PDL9rn3NDgcrlkjHHWR44cqe3bt2vu3LnKyspSTk6OoqOjVV5eftFxzs6XuFAdZ/u89NJLysnJcZadO3dq27ZtkqTvf//7Onz4sJ599lmVlZVp6NCh+tGPfnTB8zr33U3GmGq94+nr1+CbSkhI0N69e/XHP/5R4eHhGj16tLp166ZTp05d9O+iqvrO1nC2fdCgQTpz5ozWrFmjvLw8vffee7r//vud/mfOnNHTTz8dcB1zc3O1f//+gDARERFR6fg/+9nPlJ6errKyMi1atEjDhg274OTfmJiYi04Sffrpp/XRRx9p9erVF+z39XO9kGuuuaZSv3Mn+Q4aNEjHjx/XSy+9pPfff1/vv/++JDk/r9/kOP3799enn36qlJQUHTt2TL169dLEiRMD+nz++edq2LDhRccCqkJAwVVrxowZeuONN6r8n93XtW3bVkePHtW+ffsu+VjvvfeeHnvsMf3gBz9QmzZt5Ha79dlnn1VrjLZt2+q9996r8h0jsbGxuu6663To0CE1b948YDn7v19J8ng8GjZsmF566SWtWLFCK1eu1Oeff15pPI/Ho/j4eG3dujWgPSsrS61atfrGNTdv3lx169Z1QpL01eTJi13L8PBwDR48WP/7v/+rTZs26e9//7tyc3OVnJysM2fOaPPmzVXu17p1a2VlZQW8gGZlZSkyMlLXXXedM/aQIUO0bNkyvfbaa2rRooU6dOjg9P/+97+vvXv3VrqOzZs31zXXXPhX4g9+8ANFRERo/vz5euutt/TQQw9dsP9NN92kTz755IIv+AkJCRo7dqymTZumioqKC47XunXrgGstqdJ6w4YNAyZwSwr47JLjx49rz549+uUvf6levXqpVatWlUJU69at9Y9//ENlZWXnPc7ZY40cOVJLly7V3LlztWDBgoDtO3fu1E033XTBcwLOK1jPloCaVtWz9wceeMCEhYVdcA6KMcb06NHDJCUlmfXr15tDhw6ZtWvXOnNDFi1aFPCM35jK81rat29v+vTpY3bv3m22bdtmbr/9dhMeHu7MuzDm/887+Dqv12sWLVpkjDHms88+M9HR0c4k2X379plXX33VmST70ksvmfDwcDN37lyzd+9e8/HHH5uXX37ZzJo1yxjz1aTX1157zezZs8fs3bvXjBo1yvh8PmduxrnmzJljPB6PSU9PN5988omZPHlywCRZYy4+B8UYYx599FHTpEkTs2HDBpObm2sGDx58wUmyixYtMn/+859Nbm6uOXjwoHnyySdNeHi4+eyzz4wxxowcOdIkJCSYjIwMc+jQIfPuu++aFStWGGP+/yTZMWPGmD179pjVq1cHTJI9a/369cbtdpuWLVuaZ599NmDbunXrTJ06dcxTTz1ldu7caXbv3m3S09PNk08+ecG/q7OmTZtmQkNDq5z8eq7PPvvMhIaGOnM7zjf+8ePHjdfrNWFhYRecg/L3v//duFwu8/zzz5u9e/eaP/zhD5Umya5bt864XC7zyiuvmH379plf//rXxuPxOHNQKioqTHR0tLn//vvN/v37zTvvvGNuvvnmgJpKSkpMTEyMue+++8yuXbvMmjVrTPPmzQPmoPzqV78yq1evNvv37zc7d+40AwcONLfccotTx8mTJ014eLgzvwaoLgIKrhpVBZQjR44Yt9t90YBy/Phx89Of/tRER0ebsLAwk5SUZN58801jzDcLKDt27DAdO3Y0brfbJCYmmr/85S8BL8rGXDygGGPMP/7xD9O3b19Tr149ExkZaW6//XZz8OBBZ/uyZctM+/btTWhoqGnQoIHp1q2bWbVqlTHmq3f5tG/f3kRERBiPx2N69eplduzYcd5zrqioME8//bS57rrrTN26dU27du2cUHbWNwkoJSUl5v777zf16tUzsbGxZubMmaZ79+7nDSgZGRnm1ltvNR6Px0RERJhOnToFvOumrKzMPPHEEyYuLs6Ehoaa5s2bm5dfftnZvmnTJnPzzTeb0NBQ4/P5zOTJk82pU6cCajp9+rSJi4szkgKu31nr1q0zXbp0MeHh4cbj8Zhbbrkl4J1EFwooBw8eNJLMzJkzL3hdzrr33nvNlClTAtqqGj81NdVIumBAMcaYhQsXmsaNG5vw8HAzaNAg87vf/a7Sz+evf/1rExsba7xer3niiSfM2LFjAybJZmZmmlatWhm3223atm1rNm3aVKmmv//976Zdu3YmNDTUtG/f3qxcuTIgoDz77LOmVatWJjw83ERFRZm77rrLHDp0yNl/+fLlpmXLlt/oGgFVcRlzCQ+RAeA76m9/+5t69Oiho0ePKjY29qL9c3Nz1bt3bx04cECRkZE1Xs/ixYuVkpKiL774osbHvhy33HKLUlJSNHz48GCXglqKOSgA8A34/X4dOHBAv/rVrzR06NBvFE6krz6wbObMmTpy5MiVLdAihYWF+tGPfqT77rsv2KWgFiOgAMA38Nprr6lly5YqKirSzJkzq7XviBEjlJycfIUqs0+jRo00adIkvgMLl4VHPAAAwDrcQQEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArPP/AFWpDowxHKU8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n,b,p = plt.hist(biggest,bins=10)\n",
    "plt.xlabel(\"N chances of discovery (N quads)\")\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "27673bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(376, 530)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dd), len(dd1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7cd87838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([486.,  25.,   9.,   3.,   4.,   1.,   1.,   0.,   0.,   1.])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "biggest1 = nchances1.metric_values[dd1].data.swapaxes(0, 1)[0]\n",
    "n,b,p = plt.hist(biggest1,bins=10)\n",
    "plt.xlabel(\"N chances of discovery (N quads)\")\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0c8ba078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16. , 16.2, 16.4, 16.6, 16.8, 17. , 17.2, 17.4, 17.6, 17.8, 18. ,\n",
       "       18.2, 18.4, 18.6, 18.8, 19. , 19.2, 19.4, 19.6, 19.8, 20. , 20.2,\n",
       "       20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. , 22.2, 22.4,\n",
       "       22.6, 22.8, 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2, 24.4, 24.6,\n",
       "       24.8, 25. , 25.2, 25.4, 25.6, 25.8, 26. , 26.2, 26.4, 26.6, 26.8,\n",
       "       27. , 27.2, 27.4, 27.6, 27.8, 28. ])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nchances.slicer.slice_points['H']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "07773b2e",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>objId</th>\n",
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       "      <th>inc</th>\n",
       "      <th>argPeri</th>\n",
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       "      <td>0.15</td>\n",
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       "      <td>54466</td>\n",
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       "      <td>263.521706</td>\n",
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       "      <td>9999</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       objId         q         e        inc     argPeri       Omega  \\\n",
       "0     V00000  0.420118  0.197929  22.087133  235.327905  358.024868   \n",
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       "2     V00002  0.507951  0.189877  44.810933  282.879508   10.805294   \n",
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       "4     V00004  0.390396  0.256538   8.190931  288.505112  242.228175   \n",
       "...      ...       ...       ...        ...         ...         ...   \n",
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       "9997  V09997  0.519704  0.079071  43.929587   86.009789  305.652382   \n",
       "9998  V09998  0.344178  0.340393   6.078806  122.714118  210.664320   \n",
       "9999  V09999  0.388282  0.221601  23.405729  173.005580   23.107340   \n",
       "\n",
       "             tPeri  epoch          H         a  meanAnomaly     g  \\\n",
       "0     54528.093879  54466  24.302256  0.523791   198.558482  0.15   \n",
       "1     54472.149743  54466  24.800761  0.594590   346.779917  0.15   \n",
       "2     54638.027541  54466  24.248234  0.627005    18.496333  0.15   \n",
       "3     54545.953831  54466  24.476993  0.515537   147.110859  0.15   \n",
       "4     54472.827649  54466  20.489163  0.525105   342.314968  0.15   \n",
       "...            ...    ...        ...       ...          ...   ...   \n",
       "9995  54540.523119  54466  24.578710  0.517639   162.778119  0.15   \n",
       "9996  54563.488762  54466  23.750308  0.583242   144.282633  0.15   \n",
       "9997  54588.812682  54466  24.668028  0.564326    74.469574  0.15   \n",
       "9998  54534.674429  54466  19.843221  0.521793   180.422374  0.15   \n",
       "9999  54500.485986  54466  24.622836  0.498821   263.521706  0.15   \n",
       "\n",
       "     sed_filename  obj_id  \n",
       "0           S.dat       0  \n",
       "1           S.dat       1  \n",
       "2           S.dat       2  \n",
       "3           S.dat       3  \n",
       "4           S.dat       4  \n",
       "...           ...     ...  \n",
       "9995        S.dat    9995  \n",
       "9996        S.dat    9996  \n",
       "9997        S.dat    9997  \n",
       "9998        S.dat    9998  \n",
       "9999        S.dat    9999  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nchances.slicer.slice_points['orbits']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a2590d5d",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   obj_id          time          ra        dec     dradt    ddecdt  \\\n",
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       "2      22  63674.401541  329.366527 -33.677273  0.801230  0.375649   \n",
       "3      22  63675.408781  330.338429 -33.279460  0.802071  0.397036   \n",
       "4      26  62260.419611    0.143246   3.998665  0.775070  0.507439   \n",
       "\n",
       "        phase  solarelon  helio_dist  geo_dist  ...  night  \\\n",
       "0   90.777979  41.582800    0.665119  0.740436  ...   2749   \n",
       "1   90.423639  41.886436    0.669232  0.741200  ...   2750   \n",
       "2   82.593321  42.227152    0.674560  0.823929  ...   3456   \n",
       "3   81.883445  42.236281    0.676011  0.832516  ...   3457   \n",
       "4  103.043368  42.223382    0.695625  0.589708  ...   2042   \n",
       "\n",
       "   observationStartMJD   rotSkyPos  seeingFwhmEff  seeingFwhmGeom solarElong  \\\n",
       "0         62967.414922   49.883936       1.269010        1.095126  42.944912   \n",
       "1         62968.416111   79.752346       1.251011        1.080331  41.662135   \n",
       "2         63674.401541  181.241227       1.340649        1.154013  42.262080   \n",
       "3         63675.408781  174.853887       1.205235        1.042703  42.154766   \n",
       "4         62260.419611  147.127361       1.728888        1.473146  43.476731   \n",
       "\n",
       "   visitExposureTime  dmag_color  dmag_trail  dmag_detect  \n",
       "0               30.0   -0.399899    0.150709     0.170063  \n",
       "1               30.0   -0.399899    0.159705     0.183700  \n",
       "2               30.0   -0.399899    0.155128     0.176724  \n",
       "3               30.0   -0.399899    0.178728     0.213525  \n",
       "4               30.0   -0.399899    0.119291     0.124996  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = pd.read_csv('baseline_v3.0_10yrs__vatiras_granvik_10k_obs.txt', comment='#', delim_whitespace=True)\n",
    "obs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "f06059cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_m = obs.join(nchances.slicer.slice_points['orbits'], on='obj_id', how='left', lsuffix='', rsuffix='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7612dfc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 19\n",
    "app_mag = obs_m.magV + obs_m.dmag_color + obs_m.dmag_detect + m  - obs_m.H\n",
    "obs_m['appMag'] = app_mag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ae1d458e",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_m = obs_m.query('appMag <= fiveSigmaDepth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "2825c5b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6100\n",
      "1461\n"
     ]
    }
   ],
   "source": [
    "print(len(obs_m))\n",
    "print(len(obs_m.groupby(['obj_id'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "ab1e8ff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "obj_id\n",
       "31      1\n",
       "41      1\n",
       "72      1\n",
       "92      1\n",
       "102     1\n",
       "       ..\n",
       "9927    1\n",
       "9940    1\n",
       "9956    1\n",
       "9965    3\n",
       "9976    1\n",
       "Name: time, Length: 382, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_m.groupby(['obj_id', 'night']).count().query('time >= 4')['time'].groupby('obj_id').count().hist()\n",
    "obs_m.groupby(['obj_id', 'night']).count().query('time >= 4')['time'].groupby('obj_id').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "0f552399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_m.groupby(['obj_id', 'night']).count()['time'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3595547f",
   "metadata": {},
   "outputs": [],
   "source": [
    "streak_idx = np.array([2403,  219, 1513, 2380, 5169, 2691, 2815, 5494, 4452, 4290, 2165,\n",
    "       5391, 5776, 2514, 4659, 5493, 4803, 2881, 2080, 5169, 4354, 5969,\n",
    "       2140,  552, 3480, 2871, 1723, 4771, 5129, 5372,  878,  870, 2643,\n",
    "       5610, 2761, 3275, 4995, 4214,  446, 1216, 2153,  457, 2016, 5004,\n",
    "       3084, 1059,  662, 1997, 3270, 3283, 3582,  818, 2594, 3749, 4290,\n",
    "        990, 2989, 5721,  541, 6090, 2656, 3305, 3999, 3403, 2876, 2803,\n",
    "       5846, 1361,  597, 2012, 4068, 4098,  742, 6089, 3046,  653, 5303,\n",
    "        231, 4018, 4104, 2066, 3746, 1519, 4190, 2510,  487, 3245, 3823,\n",
    "       2706, 3047,  384, 4888, 5425, 1558, 5165, 4909, 5367, 5950, 1614,\n",
    "        173, 2919,  326,  837, 2683, 2280, 1300,  480, 5843, 1637, 4231,\n",
    "       5701, 2983, 5774, 4189, 1854, 4410, 1434, 4773, 4615, 2035, 2006,\n",
    "       5754, 3961, 3885,  599, 4625, 6063,  514,  661,  546, 1046, 5026,\n",
    "       5158, 3860, 1261, 2019, 1428, 3071, 2911, 3009, 1786, 3892, 4513,\n",
    "       5492, 2806,  867, 2941, 5158, 3000,  395, 1403, 3181, 5468, 4560,\n",
    "       1862, 1640, 1672, 3085, 2811,  782, 3839,  391, 1385,  432, 1575,\n",
    "       1614, 1996, 3282, 5021,  492, 2070, 4671, 2656, 2783, 3305,  984,\n",
    "       2466, 3368, 5538, 4379, 5145, 2247, 2710, 3338,  527, 3114, 5638,\n",
    "       6064, 5198,   96, 3269, 4027, 4317, 2193, 1890, 2928, 2897,  527,\n",
    "       5684, 4161, 2008, 3653, 3617, 4159, 3547,  169, 5564,  862, 2273,\n",
    "       3187, 6002, 2945, 4107, 4627, 4013, 3917, 4007,  182, 4202,  642,\n",
    "        960, 3008, 3916, 1559, 1204, 5410, 2303, 2310, 3018, 2475,  697,\n",
    "       5900,  198,  193, 1518, 1141,  643, 1869,  368, 2422, 3766, 3530,\n",
    "       2905,   88,   39, 1269, 1699, 6089, 5519, 6029, 2105, 3921, 5761,\n",
    "       1719, 1087, 1086, 1712,  447, 3337, 3583,  454, 1831, 2195, 3177,\n",
    "       4971, 5184, 1914, 1378, 2278, 3540,   92, 5590, 1499, 2960, 6067,\n",
    "       1314,  439, 5151, 3782,   36,  949,  306, 2467, 1068, 5621, 2252,\n",
    "       4356, 5366, 5648, 2793, 5142, 1147, 2082,  213, 5662, 6015, 5566,\n",
    "       3314,  753, 3106, 4486,  666, 4570, 5678,  673, 2446, 2688, 5948,\n",
    "         73, 1017, 2086, 1037, 3744,  669, 1978, 3834, 4391, 2397, 5106,\n",
    "       1502, 2395, 4060, 5552, 5533, 5731, 4084, 5654,  676,  834, 2626,\n",
    "        915, 1868, 3571, 2536, 4407, 3306, 4232, 5697,  773,  924,  178,\n",
    "       1783,   55, 5525, 5836, 1890, 2793, 5932])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "c87d0145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5765"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streak_obs  = obs.drop(index=streak_idx)\n",
    "len(streak_obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "b74c231f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5762\n",
      "1427\n"
     ]
    }
   ],
   "source": [
    "sobs_m = streak_obs.join(nchances.slicer.slice_points['orbits'], on='obj_id', how='left', lsuffix='', rsuffix='o')\n",
    "m = 19\n",
    "app_mag = sobs_m.magV + sobs_m.dmag_color + sobs_m.dmag_detect + m  - sobs_m.H\n",
    "sobs_m['appMag'] = app_mag\n",
    "sobs_m = sobs_m.query('appMag <= fiveSigmaDepth')\n",
    "print(len(sobs_m))\n",
    "print(len(sobs_m.groupby(['obj_id'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "386d353e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sobs_m.groupby(['obj_id', 'night']).count()['time'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "cd74abc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "obj_id\n",
       "31      1\n",
       "41      1\n",
       "72      1\n",
       "92      1\n",
       "102     1\n",
       "       ..\n",
       "9921    2\n",
       "9927    1\n",
       "9940    1\n",
       "9965    2\n",
       "9976    1\n",
       "Name: time, Length: 337, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o8vLyJEmDBw+W2+1WMBiMrKmtrdWBAwfOGCgAAODSEvMjKC0tLTp8+HDk+pEjR/TOO+8oOztb2dnZCgQCuuuuu5SXl6ePP/5Y8+bNU58+fXTnnXdKknr27KnZs2frscceU+/evZWdna05c+Zo4MCBkXf1AACAS1vMgbJv3z6NGTMmcr3ztSEzZszQihUrtH//fq1du1ZfffWV8vLyNGbMGG3cuFGZmZmRr3nhhReUmpqqqVOn6uTJkxo7dqxWr16tlJQUG0YCAACJLuZAGT16tM72xp+tW7d+6zl69OihyspKVVZWxvrtAQDAJYDP4gEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABgn5kDZtWuXJk2aJJ/PJ5fLpU2bNkXdblmWAoGAfD6f0tLSNHr0aB08eDBqTSgUUllZmfr06aOMjAzdfvvtOnbs2AUNAgAAkkfMgXLixAkNGjRIy5cvP+3tS5Ys0dKlS7V8+XLt3btXXq9XJSUlam5ujqwpLy9XdXW1NmzYoN27d6ulpUUTJ05Ue3v7+U8CAACSRmqsXzBhwgRNmDDhtLdZlqVly5Zp/vz5mjJliiRpzZo1ys3NVVVVlR588EE1Njbq5Zdf1rp16zRu3DhJ0vr165Wfn6/t27dr/PjxFzAOAABIBjEHytkcOXJEdXV18vv9kWMej0ejRo3Snj179OCDD6qmpkbhcDhqjc/nU1FRkfbs2XPaQAmFQgqFQpHrTU1NkqRwOKxwOGznCJHzebpZtp433s71fuhcZ/f9ZpJkn5H5El+yz8h8iS9eM8ZyPlsDpa6uTpKUm5sbdTw3N1effPJJZE337t3Vq1evLms6v/6bFi9erAULFnQ5vm3bNqWnp9ux9S6eHdIRl/PGy+bNm2NaHwwG47QTcyT7jMyX+JJ9RuZLfHbP2Nraes5rbQ2UTi6XK+q6ZVldjn3T2dbMnTtXFRUVketNTU3Kz8+X3+9XVlbWhW/4v4TDYQWDQT21r5tCHWffs0kOBM7tqbHO+UpKSuR2u+O8K2ck+4zMl/iSfUbmS3zxmrHzGZBzYWugeL1eSV8/SpKXlxc5Xl9fH3lUxev1qq2tTQ0NDVGPotTX16u4uPi05/V4PPJ4PF2Ou93uuP1whDpcCrUnTqDEej/E874zRbLPyHyJL9lnZL7EZ/eMsZzL1t+DUlhYKK/XG/WQUFtbm3bu3BmJj8GDB8vtdketqa2t1YEDB84YKAAA4NIS8yMoLS0tOnz4cOT6kSNH9M477yg7O1v9+vVTeXm5Fi1apP79+6t///5atGiR0tPTNX36dElSz549NXv2bD322GPq3bu3srOzNWfOHA0cODDyrh4AAHBpizlQ9u3bpzFjxkSud742ZMaMGVq9erUef/xxnTx5Ug8//LAaGho0bNgwbdu2TZmZmZGveeGFF5SamqqpU6fq5MmTGjt2rFavXq2UlBQbRgIAAIku5kAZPXq0LOvMb8F1uVwKBAIKBAJnXNOjRw9VVlaqsrIy1m8PAAAuAXwWDwAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMk+r0BmCP7zz5+jmt86RYWvK/UlFgq0Ltrjjv6uw+/uVtjn5/AIC5eAQFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAc2wMlEAjI5XJFXbxeb+R2y7IUCATk8/mUlpam0aNH6+DBg3ZvAwAAJLC4PIIyYMAA1dbWRi779++P3LZkyRItXbpUy5cv1969e+X1elVSUqLm5uZ4bAUAACSguARKamqqvF5v5HLFFVdI+vrRk2XLlmn+/PmaMmWKioqKtGbNGrW2tqqqqioeWwEAAAkoLoFy6NAh+Xw+FRYW6p577tFHH30kSTpy5Ijq6urk9/sjaz0ej0aNGqU9e/bEYysAACABpdp9wmHDhmnt2rW6+uqr9cUXX+i5555TcXGxDh48qLq6OklSbm5u1Nfk5ubqk08+OeM5Q6GQQqFQ5HpTU5MkKRwOKxwO27r/zvN5ulm2ntcUnXOZMJ/df3bfPG+8zu805kt8yT4j8yW+eM0Yy/lclmXF9W+qEydO6KqrrtLjjz+u4cOH68Ybb9Tnn3+uvLy8yJof//jHOnr0qLZs2XLacwQCAS1YsKDL8aqqKqWnp8dt7wAAwD6tra2aPn26GhsblZWVdda1tj+C8k0ZGRkaOHCgDh06pMmTJ0uS6urqogKlvr6+y6Mq/23u3LmqqKiIXG9qalJ+fr78fv+3DhircDisYDCop/Z1U6jDZeu5TeDpZunZIR1GzHcgMD4u5+38MywpKZHb7Y7L93AS8yW+ZJ+R+RJfvGbsfAbkXMQ9UEKhkN5//33ddNNNKiwslNfrVTAY1A033CBJamtr086dO/X888+f8Rwej0cej6fLcbfbHbcfjlCHS6H25AuUTibMF+9/seP582EC5kt8yT4j8yU+u2eM5Vy2B8qcOXM0adIk9evXT/X19XruuefU1NSkGTNmyOVyqby8XIsWLVL//v3Vv39/LVq0SOnp6Zo+fbrdWwEAAAnK9kA5duyY7r33Xv373//WFVdcoeHDh+vNN99UQUGBJOnxxx/XyZMn9fDDD6uhoUHDhg3Ttm3blJmZafdWAABAgrI9UDZs2HDW210ulwKBgAKBgN3fGgAAJAk+iwcAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGCfV6Q3g0vWdJ1+Py3k9KZaW/K9UFNiqULvL1nN//MvbbD0fAOD0eAQFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMZx9G3GL774on71q1+ptrZWAwYM0LJly3TTTTc5uSXgrOL11uhYxPo2at4aDSAROfYIysaNG1VeXq758+fr7bff1k033aQJEybo008/dWpLAADAEI4FytKlSzV79mw98MADuu6667Rs2TLl5+drxYoVTm0JAAAYwpGneNra2lRTU6Mnn3wy6rjf79eePXu6rA+FQgqFQpHrjY2NkqQvv/xS4XDY1r2Fw2G1trYqNdxN7R32/hZSE6R2WGpt7Uja+aTknzHW+Y4fP34RdmWfzn8Hvz//9wol4Z+fJHm6WfrFDR1GzPj3uWNtP2fnn+Hx48fldrttP7/Tzme+YYv/FOdd2avzZ9TuP8Pm5mZJkmVZ377YcsBnn31mSbL+9re/RR1fuHChdfXVV3dZ//TTT1uSuHDhwoULFy5JcDl69Oi3toKjL5J1uaL/z8GyrC7HJGnu3LmqqKiIXO/o6NCXX36p3r17n3b9hWhqalJ+fr6OHj2qrKwsW89tgmSfT0r+GZkv8SX7jMyX+OI1o2VZam5uls/n+9a1jgRKnz59lJKSorq6uqjj9fX1ys3N7bLe4/HI4/FEHbv88svjuUVlZWUl7Q+elPzzSck/I/MlvmSfkfkSXzxm7Nmz5zmtc+RFst27d9fgwYMVDAajjgeDQRUXFzuxJQAAYBDHnuKpqKjQD3/4Qw0ZMkQjRozQypUr9emnn+qhhx5yaksAAMAQjgXKtGnTdPz4cT3zzDOqra1VUVGRNm/erIKCAqe2JOnrp5OefvrpLk8pJYtkn09K/hmZL/El+4zMl/hMmNFlWefyXh8AAICLh8/iAQAAxiFQAACAcQgUAABgHAIFAAAYh0D5f7t27dKkSZPk8/nkcrm0adMmp7dkq8WLF2vo0KHKzMxUTk6OJk+erA8++MDpbdlmxYoVuv766yO/VGjEiBF64403nN5W3CxevFgul0vl5eVOb8U2gUBALpcr6uL1ep3elq0+++wz3Xffferdu7fS09P1/e9/XzU1NU5vyzbf+c53uvwZulwulZaWOr01W5w6dUq/+MUvVFhYqLS0NF155ZV65pln1NHR4fTWbNPc3Kzy8nIVFBQoLS1NxcXF2rt3ryN7cfRX3ZvkxIkTGjRokGbNmqW77rrL6e3YbufOnSotLdXQoUN16tQpzZ8/X36/X++9954yMjKc3t4F69u3r375y1/qu9/9riRpzZo1uuOOO/T2229rwIABDu/OXnv37tXKlSt1/fXXO70V2w0YMEDbt2+PXE9JSXFwN/ZqaGjQjTfeqDFjxuiNN95QTk6O/vWvf8X9t2JfTHv37lV7e3vk+oEDB1RSUqK7777bwV3Z5/nnn9dvfvMbrVmzRgMGDNC+ffs0a9Ys9ezZU48++qjT27PFAw88oAMHDmjdunXy+Xxav369xo0bp/fee0//8z//c3E3Y8un/yUZSVZ1dbXT24ir+vp6S5K1c+dOp7cSN7169bJ+97vfOb0NWzU3N1v9+/e3gsGgNWrUKOvRRx91eku2efrpp61BgwY5vY24eeKJJ6yRI0c6vY2L6tFHH7Wuuuoqq6Ojw+mt2OK2226z7r///qhjU6ZMse677z6HdmSv1tZWKyUlxfrjH/8YdXzQoEHW/PnzL/p+eIrnEtXY2ChJys7Odngn9mtvb9eGDRt04sQJjRgxwunt2Kq0tFS33Xabxo0b5/RW4uLQoUPy+XwqLCzUPffco48++sjpLdnmtdde05AhQ3T33XcrJydHN9xwg3772986va24aWtr0/r163X//ffb/qGuThk5cqT+9Kc/6cMPP5Qk/fOf/9Tu3bt16623Orwze5w6dUrt7e3q0aNH1PG0tDTt3r37ou+Hp3guQZZlqaKiQiNHjlRRUZHT27HN/v37NWLECP3nP//RZZddpurqan3ve99zelu22bBhg/7xj3849nxwvA0bNkxr167V1VdfrS+++ELPPfeciouLdfDgQfXu3dvp7V2wjz76SCtWrFBFRYXmzZunt956Sz/96U/l8Xj0ox/9yOnt2W7Tpk366quvNHPmTKe3YpsnnnhCjY2Nuvbaa5WSkqL29nYtXLhQ9957r9Nbs0VmZqZGjBihZ599Vtddd51yc3P1yiuv6O9//7v69+9/8Td00R+zSQBK8qd4Hn74YaugoMA6evSo01uxVSgUsg4dOmTt3bvXevLJJ60+ffpYBw8edHpbtvj000+tnJwc65133okcS7aneL6ppaXFys3NtX796187vRVbuN1ua8SIEVHHysrKrOHDhzu0o/jy+/3WxIkTnd6GrV555RWrb9++1iuvvGK9++671tq1a63s7Gxr9erVTm/NNocPH7ZuvvlmS5KVkpJiDR061PrBD35gXXfddRd9LwTKaSRzoDzyyCNW3759rY8++sjprcTd2LFjrZ/85CdOb8MW1dXVkf9gdF4kWS6Xy0pJSbFOnTrl9BbjYty4cdZDDz3k9DZs0a9fP2v27NlRx1588UXL5/M5tKP4+fjjj61u3bpZmzZtcnorturbt6+1fPnyqGPPPvusdc011zi0o/hpaWmxPv/8c8uyLGvq1KnWrbfeetH3wFM8lwjLslRWVqbq6mrt2LFDhYWFTm8p7izLUigUcnobthg7dqz2798fdWzWrFm69tpr9cQTTyTVu106hUIhvf/++7rpppuc3ootbrzxxi5v7f/www8d/4DUeFi1apVycnJ02223Ob0VW7W2tqpbt+iXbqakpCTV24w7ZWRkKCMjQw0NDdq6dauWLFly0fdAoPy/lpYWHT58OHL9yJEjeuedd5Sdna1+/fo5uDN7lJaWqqqqSq+++qoyMzNVV1cnSerZs6fS0tIc3t2FmzdvniZMmKD8/Hw1Nzdrw4YN2rFjh7Zs2eL01myRmZnZ5fVCGRkZ6t27d9K8jmjOnDmaNGmS+vXrp/r6ej333HNqamrSjBkznN6aLX72s5+puLhYixYt0tSpU/XWW29p5cqVWrlypdNbs1VHR4dWrVqlGTNmKDU1uf6KmTRpkhYuXKh+/fppwIABevvtt7V06VLdf//9Tm/NNlu3bpVlWbrmmmt0+PBh/fznP9c111yjWbNmXfzNXPTHbAz1l7/8xZLU5TJjxgynt2aL080myVq1apXTW7PF/fffbxUUFFjdu3e3rrjiCmvs2LHWtm3bnN5WXCXba1CmTZtm5eXlWW632/L5fNaUKVOS5jVEnf7whz9YRUVFlsfjsa699lpr5cqVTm/Jdlu3brUkWR988IHTW7FdU1OT9eijj1r9+vWzevToYV155ZXW/PnzrVAo5PTWbLNx40bryiuvtLp37255vV6rtLTU+uqrrxzZi8uyLOviZxEAAMCZ8XtQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxvk/FoZvkcfTAtoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sobs_m.groupby(['obj_id', 'night']).count().query('time >= 4')['time'].groupby('obj_id').count().hist()\n",
    "sobs_m.groupby(['obj_id', 'night']).count().query('time >= 4')['time'].groupby('obj_id').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a1c494a",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}