plots_RubinToO2024.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rubin ToO Strategies\n",
    "This notebook was put together to aid visualization of possible strategies to adopt for Target of Opportunity (ToO) observations with Vera C. Rubin Observatory. The notebook provides code for:\n",
    "- visualization of the ToO observing strategies\n",
    "- light curves plots\n",
    "- exposure time calculator (ETC) to convert exposure times to limiting magnitudes and vice-versa\n",
    "- time budget calculations\n",
    "\n",
    "Notebook template author: Igor Andreoni <<igor.andreoni@gmail.com>> <br>\n",
    "Code reviewed by Meet Vyas and Ritwik Sharma with equal merit.\n",
    "\n",
    "### Requirements\n",
    "\n",
    "Python3\n",
    "\n",
    "- numpy\n",
    "- matplotlib\n",
    "- math\n",
    "- scipy\n",
    "- collections\n",
    "- astropy\n",
    "\n",
    "Custom scripts\n",
    "- chart.py\n",
    "- etc.py\n",
    "- plot_lc.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "import pandas as pd\n",
    "\n",
    "# Import the custom exposure time calculator\n",
    "import etc\n",
    "\n",
    "# Import the function to make the Gantt chart\n",
    "from chart import makeChart\n",
    "\n",
    "# Import function to complete the strategy dictionary with automatic calculations\n",
    "from chart import complete_strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a dictionary with the strategies that you want to be seen on the same plot. \n",
    "The keys of the dictionary will be the nicknames of the strategies (for example, `strategy1`, `strategy2`).\n",
    "\n",
    "Notes:\n",
    "- The LSSTCam filter wheel holds 5 filters on a given night. The bluer and redder filters, u and y, will be swapped depending on the lunation. If all 6 filters are added to the plot, we shall assume that 5 available filters will be used in the ToOs.\n",
    "- The length of the exposure time list (`exptimes_median` key) must be the same length as the cadence list (`cadence_hr` key) and the filters list (`filters` key);\n",
    "- The cadence must be given in hours, the exposure times in seconds, the filters as a list or in the form `'ugrizy'`\n",
    "- The `depths` are 5sigma limiting magnitudes in all bands, the corresponding exptime will be the median of the exposure times for the filters provided in each strategy\n",
    "- <b>Important:</b> Order of \"domination\" of the inputs: individual `depths` (list of lists of depths per individual filters) dominate over `depths_median` (list of median depths per epoch), which dominates over individual `exptimes` (list of lists of exposure times per individual filters), which dominates over `exptimes_median` (list of median exposure times per epoch). All the other values will be calculated automatically using the ETC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fabulous Strategy: Using INDIVIDUAL DEPTHS to calculate the exposure times\n",
      "Less Fabulous: Using MEDIAN EXPOSURE TIMES to calculate the depths\n",
      "Mixed bag: Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the stratergy for the original stratergies\n",
    "strategies = {\n",
    "    'Fabulous Strategy': {\"cadence_hr\": [4, 24, 48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[23, 24.5, 24.5, 24.5, 24., 22],\n",
    "                              [23, 24.5, 24.5, 24.5, 24., 22],\n",
    "                              [24, 25, 25, 25, 25., 23]\n",
    "                              ],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy', 'ugrizy']},\n",
    "    'Less Fabulous': {\"cadence_hr\": [6, 48],\n",
    "                   \"exptimes_median\": [30, 30],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['grz', 'grz']},\n",
    "    'Mixed bag': {\"cadence_hr\": [6, 24, 48],\n",
    "                   \"exptimes_median\": [], # if this list is given\n",
    "                   \"exptimes\": [[120, 30, 30, 30, 120, 200], [30, 30], [120, 120]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugrizy', 'gz', 'gz']}\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimal (Area <20 sq degrees): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Preferred (Area <20 sq degrees): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Minimal (20 sq degrees < Area < 100 sq deg): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Preferred (20 sq degrees < Area < 100 sq deg): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the stratergy for the minimal and preferred stratergies\n",
    "strategies = {\n",
    "    'Minimal (Area   <20 sq degrees)' : {\"cadence_hr\": [1,4,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30,30,30],\n",
    "                                [30,30,30,30,30,30],\n",
    "                                [180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy', 'gzi']},\n",
    "    'Preferred (Area <20 sq degrees)' : {\"cadence_hr\": [1,2,4,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30,30,30],[30,30,30,30,30,30],[30,30,30,30,30,30],[180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy', 'ugrizy','gzi']},\n",
    "    'Minimal (20 sq degrees < Area < 100 sq deg)': {\"cadence_hr\": [1,4,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30],[30,30,30],[180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['gzi','gzi','gzi']},\n",
    "    'Preferred (20 sq degrees < Area < 100 sq deg)': {\"cadence_hr\": [1,2,4,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30,30,30],[30,30,30,30,30,30],[30,30,30,30,30,30],[180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy','ugrizy','gzi']},\n",
    "\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NSBH Minimal (Area <20 sq degrees): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "NSBH Preferred (Area <20 sq degrees): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "NSBH Minimal (20 sq degrees < Area < 100 sq deg): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "NSBH Preferred (20 sq degrees < Area < 100 sq deg): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the stratergy for the NSBH science case \n",
    "strategies = {\n",
    "    'NSBH Minimal (Area <20 sq degrees)' : {\"cadence_hr\": [1,4,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[180,180,180,180,180,180],\n",
    "                                [180,180,180,180,180,180],\n",
    "                                [180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy', 'gzi']},\n",
    "        'NSBH Preferred (Area <20 sq degrees)' : {\"cadence_hr\": [1,4,24,48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[180,180,180,180,180,180],[180,180,180,180,180,180],[180,180,180,180,180,180],[180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy', 'ugrizy','gzi']},\n",
    "    'NSBH Minimal (20 sq degrees < Area < 100 sq deg)': {\"cadence_hr\": [1,4,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[180,180,180],[180,180,180],[180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['gzi','gzi','gzi']},\n",
    "    'NSBH Preferred (20 sq degrees < Area < 100 sq deg)': {\"cadence_hr\": [1,4,24,48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[180,180,180,180,180,180],[180,180,180,180,180,180],[180,180,180],[180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy','gzi','gzi']},\n",
    "\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workshop Baseline (BNS): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Workshop Baseline + u band(Day 1)(BNS): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Poor Localisation: Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Unidentified GW Source: Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the baseline stratergies for the workshop along with the poor localisation and unidentified GW source \n",
    "strategies = {\n",
    "    'Workshop Baseline (BNS)' : {\"cadence_hr\": [1,2,5,24,48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30,30],\n",
    "                                [30,30,30,30,30],\n",
    "                                [30,30,30,30,30],\n",
    "                                [180,180],\n",
    "                                [180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugriz', 'ugriz', 'ugriz','gz','gz']},\n",
    "    'Workshop Baseline + u band(Day 1)(BNS)' : {\"cadence_hr\": [1,2,5,24,48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30,30],\n",
    "                                [30,30,30,30,30],\n",
    "                                [30,30,30,30,30],\n",
    "                                [180,180,180],\n",
    "                                [180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['ugriz', 'ugriz', 'ugriz','gzu','gz']},\n",
    "    'Poor Localisation': {\"cadence_hr\": [1,2,5,24,48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30,],[30,30,30,30],[30,30,30,30],[180,180],[180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['griz','griz','griz','gz','gz']},\n",
    "    'Unidentified GW Source': {\"cadence_hr\": [1,3,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30],[30,30,30],[180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['giz', 'giz','giz']},\n",
    "\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workshop Baseline (BNS): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Workshop Baseline + u band =(BNS): Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Poor Localisation: Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Unidentified GW Source: Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the baseline stratergies for the workshop along with the poor localisation and unidentified GW source \n",
    "strategies = {\n",
    "    'Workshop Baseline (BNS)' : {\"cadence_hr\": [1,2,5,24,48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30],\n",
    "                                [30,30,30,30],\n",
    "                                [30,30,30,30],\n",
    "                                [180,180],\n",
    "                                [180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['griz', 'griz', 'griz','gz','gz']},\n",
    "    'Workshop Baseline + u band 2nd day (BNS)' : {\"cadence_hr\": [1,2,5,24,48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30],\n",
    "                                [30,30,30,30],\n",
    "                                [30,30,30,30],\n",
    "                                [180,180,180],\n",
    "                                [180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['griz', 'griz', 'griz','gzu','gz']},\n",
    "    'Poor Localisation': {\"cadence_hr\": [1,2,5,24,48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30,30,],[30,30,30,30],[30,30,30,30],[180,180],[180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['griz','griz','griz','gz','gz']},\n",
    "    'Unidentified GW Source': {\"cadence_hr\": [1,3,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[30,30,30],[30,30,30],[180,180,180]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['giz', 'giz','giz']},\n",
    "\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 Filter Stratergy + u filter(griu)(BNS): Using INDIVIDUAL DEPTHS to calculate the exposure times\n",
      "3 Filter Stratergy + u filter(grzu)(BNS): Using INDIVIDUAL DEPTHS to calculate the exposure times\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the 3 filter stratergies for the workshop\n",
    "strategies = {\n",
    "    '3 Filter Stratergy + u filter(griu)(BNS)' : {\"cadence_hr\": [4,8,12,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,25,25,25],\n",
    "                              [25,25,25,25],\n",
    "                              [25,25,25,25],\n",
    "                              [25,25,25,25]],\n",
    "                   \"filters\": ['griu', 'griu', 'griu','griu']},\n",
    "    '3 Filter Stratergy + u filter(grzu)(BNS)' : {\"cadence_hr\": [4,8,12,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,25,25,25],\n",
    "                              [25,25,25,25],\n",
    "                              [25,25,25,25],\n",
    "                              [25,25,25,25]],\n",
    "                   \"filters\": ['grzu', 'grzu', 'grzu','grzu']},\n",
    "     '3 Filter Stratergy(2 epochs day 1)(gru)(BNS)': {\"cadence_hr\": [4,8,24],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,25,25],\n",
    "                             [25,25,25],\n",
    "                             [25,25,25]],\n",
    "                    \"filters\": ['gru','gru','gru','gru']},\n",
    "     '3 Filter Stratergy(3 epochs day 1)(gru)(BNS)': {\"cadence_hr\": [4,8,12,24],\n",
    "                    \"exptimes_median\": [],\n",
    "                    \"exptimes\": [],\n",
    "                    \"depths_median\": [],\n",
    "                    \"depths\": [[25,25,25],\n",
    "                               [25,25,25],\n",
    "                               [25,25,25],\n",
    "                               [25,25,25]],\n",
    "                    \"filters\": ['gru','gru','gru','gru']},\n",
    "\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lensed KN Ω=1000deg2: Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Lensed KN Ω=500deg2: Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n",
      "Lensed KN Ω=100deg2: Using INDIVIDUAL EXPOSURE TIMES to calculate median exposure times and depths\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the Lensed KN stratergies for the workshop\n",
    "strategies = {\n",
    "    'Lensed KN Ω=1000deg2' : {\"cadence_hr\": [24,48,120],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[60,60],\n",
    "                                [60,60],\n",
    "                                [60,60]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['gi','gi','gi']},\n",
    "    'Lensed KN Ω=500deg2' : {\"cadence_hr\": [24,48,120],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[120,120],\n",
    "                                [120,120],\n",
    "                                [120,120]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],\n",
    "                   \"filters\": ['gi','gi','gi']},\n",
    "    'Lensed KN Ω=100deg2' : {\"cadence_hr\": [24,48,120],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [[600,600],\n",
    "                                [600,600],\n",
    "                                [600,600]],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [],    \n",
    "                   \"filters\": ['gi','gi','gi']},\n",
    "\n",
    "\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strategy dictionary for the Golden Strategy for 350 Mpc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gold Events: 3 Filter Deep Strategy (350 Mpc) Case 1: Using INDIVIDUAL DEPTHS to calculate the exposure times\n",
      "Gold Events: 3 Filter Deep Strategy (350 Mpc) Case 2: Using INDIVIDUAL DEPTHS to calculate the exposure times\n",
      "Gold Events: 3 Filter Deep Strategy (350 Mpc) Case 3: Using INDIVIDUAL DEPTHS to calculate the exposure times\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the 3 filter stratergies for the workshop\n",
    "strategies = {\n",
    "        'Gold Events: 3 Filter Deep Strategy (350 Mpc) Case 1' : {\"cadence_hr\": [1,5,8,24,48,72],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,24.5,23],\n",
    "                              [25,24.5,23],\n",
    "                              [25,24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23]],\n",
    "                   \"filters\": ['gri', 'gri', 'gri','ri','ri','ri']},\n",
    "\n",
    "        'Gold Events: 3 Filter Deep Strategy (350 Mpc) Case 2' : {\"cadence_hr\": [1,5,24,48,72],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,24.5,23],\n",
    "                              [25,24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23]],\n",
    "                   \"filters\": ['gri', 'gri', 'ri', 'ri','ri']},\n",
    "\n",
    "    'Gold Events: 3 Filter Deep Strategy (350 Mpc) Case 3' : {\"cadence_hr\": [4,6,24,48,72],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,24.5,23],\n",
    "                              [24.5],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23]],\n",
    "                   \"filters\": ['gri','r','ri', 'ri','ri']},\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strategy dictionary for the Golden Strategy for 700 Mpc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 1: Using INDIVIDUAL DEPTHS to calculate the exposure times\n",
      "Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 2: Using INDIVIDUAL DEPTHS to calculate the exposure times\n",
      "Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 3: Using INDIVIDUAL DEPTHS to calculate the exposure times\n"
     ]
    }
   ],
   "source": [
    "# Run this block to register the 3 filter stratergies for the workshop\n",
    "strategies = {\n",
    "        'Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 1' : {\"cadence_hr\": [1,5,8,24,48,72],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,24.5,23],\n",
    "                              [25,24.5,23],\n",
    "                              [25,24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23]],\n",
    "                   \"filters\": ['gri', 'gri', 'gri','ri','ri','ri']},\n",
    "\n",
    "        'Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 2' : {\"cadence_hr\": [1,5,24,48,72],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,24.5,23],\n",
    "                              [25,24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23]],\n",
    "                   \"filters\": ['gri', 'gri', 'ri', 'ri','ri']},\n",
    "\n",
    "    'Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 3' : {\"cadence_hr\": [4,6,24,48,72],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths_median\": [],\n",
    "                   \"depths\": [[25,24.5,23],\n",
    "                              [24.5],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23],\n",
    "                              [24.5,23]],\n",
    "                   \"filters\": ['gri', 'r','ri', 'ri','ri']},\n",
    "}\n",
    "\n",
    "# Airmass for the observations (>= 1.)\n",
    "airmass = 1.2\n",
    "\n",
    "# When is the timeline starting? e.g., Rubin Trigger, NS-NS merger, ...\n",
    "event_name = \"start of Rubin observing window\"\n",
    "\n",
    "# Output file name\n",
    "filename_out = \"plot_strategy.pdf\"\n",
    "\n",
    "# Define the colors for the Rubin filters\n",
    "filters_color_dict = {'u':'b', 'g':'g', 'r':'r', 'i':'yellow', 'z':'k', 'y':'orange'}\n",
    "\n",
    "# Define the sequence of markers for different exposure times\n",
    "# (the sequence will be ordered by increasing exposure time)\n",
    "exptimes_marker_list = [\"o\", \"s\", \"p\", \"h\", \"8\", '^']\n",
    "\n",
    "# Complete the strategies using the ETC [do not modify the next line]\n",
    "strategies = complete_strategies(strategies, airmass=airmass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how the `strategies` dictionary has been populated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 1': {'cadence_hr': [1,\n",
       "   5,\n",
       "   8,\n",
       "   24,\n",
       "   48,\n",
       "   72],\n",
       "  'exptimes_median': [31, 31, 31, 18, 18, 18],\n",
       "  'exptimes': [[34, 31, 4],\n",
       "   [34, 31, 4],\n",
       "   [34, 31, 4],\n",
       "   [31, 4],\n",
       "   [31, 4],\n",
       "   [31, 4]],\n",
       "  'depths_median': [24.5, 24.5, 24.5, 23.75, 23.75, 23.75],\n",
       "  'depths': [[25, 24.5, 23],\n",
       "   [25, 24.5, 23],\n",
       "   [25, 24.5, 23],\n",
       "   [24.5, 23],\n",
       "   [24.5, 23],\n",
       "   [24.5, 23]],\n",
       "  'filters': ['gri', 'gri', 'gri', 'ri', 'ri', 'ri']},\n",
       " 'Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 2': {'cadence_hr': [1,\n",
       "   5,\n",
       "   24,\n",
       "   48,\n",
       "   72],\n",
       "  'exptimes_median': [31, 31, 18, 18, 18],\n",
       "  'exptimes': [[34, 31, 4], [34, 31, 4], [31, 4], [31, 4], [31, 4]],\n",
       "  'depths_median': [24.5, 24.5, 23.75, 23.75, 23.75],\n",
       "  'depths': [[25, 24.5, 23],\n",
       "   [25, 24.5, 23],\n",
       "   [24.5, 23],\n",
       "   [24.5, 23],\n",
       "   [24.5, 23]],\n",
       "  'filters': ['gri', 'gri', 'ri', 'ri', 'ri']},\n",
       " 'Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 3': {'cadence_hr': [4,\n",
       "   6,\n",
       "   24,\n",
       "   48,\n",
       "   72],\n",
       "  'exptimes_median': [31, 31, 18, 18, 18],\n",
       "  'exptimes': [[34, 31, 4], [31], [31, 4], [31, 4], [31, 4]],\n",
       "  'depths_median': [24.5, 24.5, 23.75, 23.75, 23.75],\n",
       "  'depths': [[25, 24.5, 23], [24.5], [24.5, 23], [24.5, 23], [24.5, 23]],\n",
       "  'filters': ['gri', 'r', 'ri', 'ri', 'ri']}}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the results in a more readable format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 1\n",
      "    Epoch: 1 hr\n",
      "        Filter: g Depth: 25mag, exptime: 34s\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 31s\n",
      "    Epoch: 5 hr\n",
      "        Filter: g Depth: 25mag, exptime: 34s\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 31s\n",
      "    Epoch: 8 hr\n",
      "        Filter: g Depth: 25mag, exptime: 34s\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 31s\n",
      "    Epoch: 24 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "    Epoch: 48 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "    Epoch: 72 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "--\n",
      "\n",
      " Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 2\n",
      "    Epoch: 1 hr\n",
      "        Filter: g Depth: 25mag, exptime: 34s\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 31s\n",
      "    Epoch: 5 hr\n",
      "        Filter: g Depth: 25mag, exptime: 34s\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 31s\n",
      "    Epoch: 24 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "    Epoch: 48 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "    Epoch: 72 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "--\n",
      "\n",
      " Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 3\n",
      "    Epoch: 4 hr\n",
      "        Filter: g Depth: 25mag, exptime: 34s\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 31s\n",
      "    Epoch: 6 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Median exposure time: 31s\n",
      "    Epoch: 24 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "    Epoch: 48 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "    Epoch: 72 hr\n",
      "        Filter: r Depth: 24.5mag, exptime: 31s\n",
      "        Filter: i Depth: 23mag, exptime: 4s\n",
      "        Median exposure time: 18s\n",
      "--\n"
     ]
    }
   ],
   "source": [
    "\n",
    "strategy_names = list(strategies.keys())\n",
    "for i in range(len(strategy_names)):\n",
    "    print(f\"\\n Strategy name: {strategy_names[i]}\")\n",
    "    # Iterate over the epochs\n",
    "    for j in np.arange(len(strategies[strategy_names[i]][\"depths\"])):\n",
    "            print(f\"    Epoch: {strategies[strategy_names[i]]['cadence_hr'][j]} hr\")\n",
    "            # Iterate over the filters\n",
    "            for filt, exptime, depth in zip(strategies[strategy_names[i]][\"filters\"][j],\n",
    "                                            strategies[strategy_names[i]][\"exptimes\"][j],\n",
    "                                            strategies[strategy_names[i]][\"depths\"][j]):\n",
    "                print(f\"        Filter: {filt} Depth: {depth}mag, exptime: {exptime}s\")\n",
    "            print(f\"        Median exposure time: {strategies[strategy_names[i]]['exptimes_median'][j]}s\")\n",
    "    print(\"--\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survey Strategy Visualization\n",
    "\n",
    "The template code was used to produce some figures published in Andreoni et al. (2022), The Astrophysical Journal Supplement Series, Volume 260, Issue 1, id.18, 17 pp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.0, 168.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the gantt chart\n",
    "fig, ax = makeChart(strategies, event=event_name,\n",
    "                    filters_color_dict=filters_color_dict,\n",
    "                    exptimes_marker_list=exptimes_marker_list)\n",
    "\n",
    "# # Save the figure and show the plot\n",
    "# plt.savefig(filename_out, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot transient light curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the light curve of a transient given the strategy defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "import numpy as np\n",
    "from astropy.io import ascii\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from astropy.cosmology import Planck18 as cosmo\n",
    "import astropy.cosmology as ac\n",
    "import astropy.units as u\n",
    "\n",
    "import plot_lc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One important variable to be set is `delay_hr`, which is the delay time between the onset of the transient (e.g., the merger of two compact objects) and the start of the observability window. Note that `cadence_hr` starts from the beginning of the observability window, since we cannot predict when the onset is, if the event happens during the Chilean daytime.\n",
    "\n",
    "Light curve models come in ASCII or CSV files. Check out the `models/` folder and use the same format, if you want to add your favorite model. Simply assign the path to the model to the `model_filename` variable. \n",
    "\n",
    "A grid of kilonova light curves is available, which were obtained with 3D Monte Carlo radiative transfer code (Bulla 2019, 2023; Anand et al. 2023). Four parameters can be chosen: the dynamic ejecta mass `mej_dyn`, the disk wind ejecta mass `mej_wind`, the half opening angle of the lanthanide-rich dynamical-ejecta component `phi` and the viewing angle `theta` (see Dietrich et al. 2020, for more details about the adopted geometry). The grid is available for kilonovae at distances from 100Mpc to 700Mpc, which is twice the upper bound of the projected horizon for BNS mergers in O5, at intervals of 100Mpc. More of such light curves can be produced on demand (ask Igor). The best-fit parameters to GW170817, ignoring the distance, are:\n",
    "\n",
    "```\n",
    "inj_params_list = [{'mej_dyn': 0.005,\n",
    "              'mej_wind': 0.050,\n",
    "              'phi': 30,\n",
    "              'theta': 25.8,\n",
    "              'dist_Mpc': 300  #  Distance (Mpc), available from 100Mpc to 700Mpc at 100Mpc intervals\n",
    "                   }]\n",
    "```\n",
    "Suggestions for parameters that can be used to probe a good range of models can be found in Table 2 in\n",
    "[Andreoni et al. (2023)](https://ui.adsabs.harvard.edu/abs/2024APh...15502904A/abstract) and the models used in that paper are given here as a dictionary.\n",
    "\n",
    "<b>Lensed kilonova</b> light curves are also available, from [Smith et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023MNRAS.520..702S/abstract). The brighter one is 170817-like in its SED and evolution and the fainter one is a more conservatively viewed and redder model. Both are placed at z=1.6. All details can be found in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected model file name: \n",
      " models/kilonova_Bulla/nph1.0e+06_dyn0.001-0.15-0.20_wind0.050-0.03_theta25.84_dMpc700.dat\n"
     ]
    }
   ],
   "source": [
    "# Delay between transient onset and start of the observing window\n",
    "delay_hr = 6.\n",
    "\n",
    "# Distance in Mpc, available from 100Mpc to 700Mpc at 100Mpc intervals\n",
    "distance_Mpc = 700\n",
    "\n",
    "# Here are some selected models from the Bulla+19, Anand+23 model grid. Do not modify unless necessary\n",
    "# Please look at models/kilonova_Bulla/ folder for more options.\n",
    "models = {\"GW170817 best fit polar, theta=26deg\": f\"models/kilonova_Bulla/nph1.0e+06_dyn0.001-0.15-0.20_wind0.050-0.03_theta25.84_dMpc{distance_Mpc}.dat\",\n",
    "          \"GW170817 best fit equatorial, theta=90deg\": f\"models/kilonova_Bulla/nph1.0e+06_dyn0.001-0.15-0.20_wind0.050-0.03_theta90.00_dMpc{distance_Mpc}.dat\",\n",
    "          \"Kilonova low Y_e and M_ej, theta=45deg\": f\"models/kilonova_Bulla/nph1.0e+06_dyn0.001-0.15-0.15_wind0.010-0.03_theta45.57_dMpc{distance_Mpc}.dat\",\n",
    "          \"Kilonova low Y_e, theta=26deg\": f\"models/kilonova_Bulla/nph1.0e+06_dyn0.001-0.15-0.15_wind0.050-0.03_theta25.84_dMpc{distance_Mpc}.dat\",\n",
    "          \"Kilonova low M_e, theta=26deg\": f\"models/kilonova_Bulla/nph1.0e+06_dyn0.010-0.15-0.20_wind0.010-0.03_theta25.84_dMpc{distance_Mpc}.dat\",\n",
    "          \"Kilonova high M_ej, theta=26deg\": f\"models/kilonova_Bulla/nph1.0e+06_dyn0.020-0.15-0.20_wind0.090-0.03_theta25.84_dMpc{distance_Mpc}.dat\",\n",
    "          \"Lensed kilonova z=1.6 bright\": f\"models/kilonova_lensed_Nicholl/M1.188_q0.92_z1.6_C24_d0.12_v32_forIgor.txt\",\n",
    "          \"Lensed kilonova z=1.6 faint\": f\"models/kilonova_lensed_Nicholl/M1.200_q0.90_z1.6_C0_d0.15_v60_forIgor.txt\",\n",
    "}\n",
    "\n",
    "# Pick the model\n",
    "model_filename = models[\"GW170817 best fit polar, theta=26deg\"]\n",
    "\n",
    "# Verbose\n",
    "print(f\"Selected model file name: \\n {model_filename}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def doPlotLc2(strategies, t, delay_hr, xlim=[0, 7], ylim=[28, 18],\n",
    "             doShow=True, doSave=True, n_interp=120,\n",
    "             offset_filt_hr=0,\n",
    "             outfile_base=\"plot_lc\", outfile_format='pdf',\n",
    "             event_name=\"merger\",\n",
    "             filters_color_dict={'u': 'b', 'g': 'g', 'r': 'r', 'i': 'yellow',\n",
    "                                 'z': 'k', 'y': 'orange'},\n",
    "             linestyle=\"-\"):\n",
    "    # Make sure the time starts from zero in the model\n",
    "    if t[\"t[days]\"][0] == 0:\n",
    "        pass\n",
    "    else:\n",
    "        # Add one row at the top\n",
    "        t.add_row([0.] + [99. for x in np.arange(len(t.colnames) - 1)])\n",
    "        # Re-sort\n",
    "        t.sort(\"t[days]\")\n",
    "\n",
    "    # For each strategy make a plot\n",
    "    strategy_names = list(strategies.keys())\n",
    "    for i in range(len(strategy_names)):\n",
    "        print(f\"\\n Strategy name: {strategy_names[i]}\")\n",
    "        # Epochs of the strategy in days\n",
    "        days_strategy = np.array(strategies[strategy_names[i]][\"cadence_hr\"]) / 24\n",
    "        depths_strategy = strategies[strategy_names[i]][\"depths\"]\n",
    "        filters_strategy = strategies[strategy_names[i]][\"filters\"]\n",
    "        # Initialize detections and non-detections\n",
    "        detections = []\n",
    "        non_detections = []\n",
    "        # Initialize the possible time offset by filter\n",
    "        offset_filt_hr_tot = 0\n",
    "\n",
    "        # Initialize the figure\n",
    "        fig, ax = plt.subplots()\n",
    "        # Blank plot for the detections and non-detections\n",
    "        ax.scatter([], [], label=\"Det.\", color=\"k\",\n",
    "                marker=\"o\", s=100, edgecolor='black')\n",
    "        ax.scatter([], [], label=\"UL\", color=\"k\",\n",
    "                marker=\"v\", s=100,edgecolor='blue')\n",
    "\n",
    "        for filt in t.colnames:\n",
    "            # Ignore anything not LSST\n",
    "            if filt[0] != \"l\":\n",
    "                continue\n",
    "            # Ignore time column\n",
    "            if filt == \"t[days]\":\n",
    "                continue\n",
    "            # Add the offset by filter\n",
    "            offset_filt_hr_tot += offset_filt_hr\n",
    "            # Interpolate to reduce noise\n",
    "            idx = [i for i in np.arange(len(t)) if np.isnan(t[filt][i]) == False]\n",
    "            idx = [i for i in idx if not t[filt][i] == np.inf]\n",
    "            xnew = np.linspace(t[\"t[days]\"][idx].min(), t[\"t[days]\"][idx].max(), n_interp)\n",
    "            f = interpolate.interp1d(t[\"t[days]\"][idx], t[filt][idx])\n",
    "            label = f\"{filt.replace('lsst', '')}\"\n",
    "            # Plot the model\n",
    "            ax.plot(xnew, f(xnew), linestyle=linestyle, label=label,\n",
    "                    color=filters_color_dict[filt.replace(\"lsst\", \"\")])\n",
    "            # for each epoch, check if there is the given filter\n",
    "            for day_strategy, filter_strategy, depth_strategy_epoch in zip(days_strategy, filters_strategy, depths_strategy):\n",
    "                # Apply the delay between the event and the obs. window\n",
    "                day_strategy += delay_hr / 24  # from hours to days\n",
    "                # Apply a delay between filters to show overlapping points\n",
    "                day_strategy += offset_filt_hr_tot / 24\n",
    "                if not (filt.replace('lsst', '') in filter_strategy):\n",
    "                    continue\n",
    "                else:\n",
    "                    # Find depth for the given filter\n",
    "                    idx = filter_strategy.index(filt.replace('lsst', ''))\n",
    "                    depth = depth_strategy_epoch[idx]\n",
    "                # Detection or non-detection?\n",
    "                if f(day_strategy) <= depth:\n",
    "                    detections.append((filt.replace('lsst', ''), day_strategy, f(day_strategy)))\n",
    "                    # Plot the detection using scatter with black edge color\n",
    "                    ax.scatter(day_strategy, f(day_strategy), marker=\"o\", s=100, edgecolor='black',\n",
    "                               color=filters_color_dict[filt.replace(\"lsst\", \"\")])\n",
    "                else:\n",
    "                    non_detections.append((filt.replace('lsst', ''), day_strategy, depth))\n",
    "                    # Plot the non-detection using scatter with blue edge color\n",
    "                    ax.scatter(day_strategy, depth, marker=\"v\", s=100, edgecolor='blue',\n",
    "                               color=filters_color_dict[filt.replace(\"lsst\", \"\")])\n",
    "\n",
    "        # Add horizontal dashed lines for fiveSigmaDepth magnitudes for the lsstr, lssti, and lsstg bands\n",
    "        ax.axhline(y=24.5, color='r', linestyle='dotted', linewidth=2)  # lsstr\n",
    "        ax.axhline(y=23, color='yellow', linestyle='dotted', linewidth=2)  # lssti\n",
    "        ax.axhline(y=25, color='g', linestyle='dotted', linewidth=2)  # lsstg\n",
    "\n",
    "        # Set plot parameters\n",
    "        plt.rcParams['xtick.labelsize'] = 20\n",
    "        ax.legend(fontsize=11, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "        ax.set_xlim(xlim)\n",
    "        ax.set_ylim(ylim)\n",
    "        ax.set_xlabel(f\"Time from {event_name} (days)\", fontsize=13)\n",
    "        ax.set_ylabel(\"AB Magnitude\", fontsize=13)\n",
    "        ax.set_title(strategy_names[i], fontsize=13,)\n",
    "        ax.tick_params(axis='both', labelsize=13)\n",
    "        ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "        # Add the annotation \"Faint KN\" at the top-right corner\n",
    "        plt.text(6.5, 19, \"Bright KN\", fontsize=20, \n",
    "        horizontalalignment='right', verticalalignment='top')\n",
    "        \n",
    "        # Save to file\n",
    "        if doSave is True:\n",
    "            # Generate a valid filename by replacing invalid characters such as spaces with underscores\n",
    "            out_filename = f\"{outfile_base}_{strategy_names[i]}.{outfile_format}\"\n",
    "            out_filename = out_filename.replace(\" \", \"_\")  # Replace spaces with underscores\n",
    "            out_filename = out_filename.replace(\"<\", \"lt\")  # Replace < with 'lt' or any other valid character\n",
    "            out_filename = out_filename.replace(\">\", \"gt\")  # Replace > with 'gt' or any other valid character\n",
    "            # Add any additional filename sanitization steps if necessary\n",
    "            plt.savefig(out_filename, bbox_inches='tight')\n",
    "\n",
    "        if doShow is True:\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def printDetectionsNonDetections(strategies, t, delay_hr, offset_filt_hr=0):\n",
    "    \"\"\"Prints the detections and non-detections for each strategy in a structured table format.\"\"\"\n",
    "    \n",
    "    # For each strategy, create a structured table (DataFrame) for detections and non-detections\n",
    "    strategy_names = list(strategies.keys())\n",
    "    \n",
    "    for i in range(len(strategy_names)):\n",
    "        print(f\"\\nStrategy name: {strategy_names[i]}\")\n",
    "        \n",
    "        # Epochs of the strategy in days\n",
    "        days_strategy = np.array(strategies[strategy_names[i]][\"cadence_hr\"]) / 24\n",
    "        depths_strategy = strategies[strategy_names[i]][\"depths\"]\n",
    "        filters_strategy = strategies[strategy_names[i]][\"filters\"]\n",
    "        \n",
    "        # Initialize detections and non-detections as lists to populate the DataFrame later\n",
    "        detection_data = []\n",
    "        non_detection_data = []\n",
    "        \n",
    "        # Initialize the possible time offset by filter\n",
    "        offset_filt_hr_tot = 0\n",
    "\n",
    "        for filt in t.colnames:\n",
    "            # Ignore anything not LSST\n",
    "            if filt[0] != \"l\":\n",
    "                continue\n",
    "            # Ignore time column\n",
    "            if filt == \"t[days]\":\n",
    "                continue\n",
    "            # Add the offset by filter\n",
    "            offset_filt_hr_tot += offset_filt_hr\n",
    "            # Interpolate to reduce noise\n",
    "            idx = [i for i in np.arange(len(t)) if not np.isnan(t[filt][i])]\n",
    "            idx = [i for i in idx if not t[filt][i] == np.inf]\n",
    "            f = interpolate.interp1d(t[\"t[days]\"][idx], t[filt][idx])\n",
    "\n",
    "            # For each epoch, check if there is the given filter\n",
    "            for day_strategy, filter_strategy, depth_strategy_epoch in zip(days_strategy, filters_strategy, depths_strategy):\n",
    "                # Apply the delay between the event and the obs. window\n",
    "                day_strategy += delay_hr / 24  # from hours to days\n",
    "                # Apply a delay between filters to show overlapping points\n",
    "                day_strategy += offset_filt_hr_tot / 24\n",
    "                if not (filt.replace('lsst', '') in filter_strategy):\n",
    "                    continue\n",
    "                else:\n",
    "                    # Find depth for the given filter\n",
    "                    idx = filter_strategy.index(filt.replace('lsst', ''))\n",
    "                    depth = depth_strategy_epoch[idx]\n",
    "                \n",
    "                # Detection or non-detection?\n",
    "                if f(day_strategy) <= depth:\n",
    "                    detection_data.append({\n",
    "                        'Filter': filt.replace('lsst', ''),\n",
    "                        'Day': day_strategy,\n",
    "                        'Magnitude': f(day_strategy)\n",
    "                    })\n",
    "                else:\n",
    "                    non_detection_data.append({\n",
    "                        'Filter': filt.replace('lsst', ''),\n",
    "                        'Day': day_strategy,\n",
    "                        'Limiting Magnitude': depth\n",
    "                    })\n",
    "\n",
    "        # Convert detections and non-detections to DataFrames\n",
    "        detections_df = pd.DataFrame(detection_data)\n",
    "        non_detections_df = pd.DataFrame(non_detection_data)\n",
    "\n",
    "        # Print the results in a structured DataFrame format\n",
    "        print(\"\\nDetections:\")\n",
    "        if not detections_df.empty:\n",
    "            print(detections_df.to_string(index=False))\n",
    "        else:\n",
    "            print(\"No detections.\")\n",
    "\n",
    "        print(\"\\nNon-Detections:\")\n",
    "        if not non_detections_df.empty:\n",
    "            print(non_detections_df.to_string(index=False))\n",
    "        else:\n",
    "            print(\"No non-detections.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 1\n",
      "\n",
      "Detections:\n",
      "Filter      Day          Magnitude\n",
      "     g 0.333333 21.797631578947367\n",
      "     g 0.500000  22.09148275862069\n",
      "     g 0.625000  22.20183783783784\n",
      "     r 0.354167  22.42488888888889\n",
      "     r 0.520833 22.224822222222222\n",
      "     r 0.645833   22.1664009009009\n",
      "     r 1.312500 23.527272727272727\n",
      "     i 0.375000              22.68\n",
      "     i 0.541667 22.417355555555556\n",
      "     i 0.666667 22.227701754385965\n",
      "\n",
      "Non-Detections:\n",
      "Filter      Day  Limiting Magnitude\n",
      "     r 2.312500                24.5\n",
      "     r 3.312500                24.5\n",
      "     i 1.333333                23.0\n",
      "     i 2.333333                23.0\n",
      "     i 3.333333                23.0\n",
      "\n",
      "Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 2\n",
      "\n",
      "Detections:\n",
      "Filter      Day          Magnitude\n",
      "     g 0.333333 21.797631578947367\n",
      "     g 0.500000  22.09148275862069\n",
      "     r 0.354167  22.42488888888889\n",
      "     r 0.520833 22.224822222222222\n",
      "     r 1.312500 23.527272727272727\n",
      "     i 0.375000              22.68\n",
      "     i 0.541667 22.417355555555556\n",
      "\n",
      "Non-Detections:\n",
      "Filter      Day  Limiting Magnitude\n",
      "     r 2.312500                24.5\n",
      "     r 3.312500                24.5\n",
      "     i 1.333333                23.0\n",
      "     i 2.333333                23.0\n",
      "     i 3.333333                23.0\n",
      "\n",
      "Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 3\n",
      "\n",
      "Detections:\n",
      "Filter      Day          Magnitude\n",
      "     g 0.458333             21.978\n",
      "     r 0.479167 22.254283950617282\n",
      "     r 0.562500 22.164772727272727\n",
      "     r 1.312500 23.527272727272727\n",
      "     i 0.500000 22.484862068965516\n",
      "\n",
      "Non-Detections:\n",
      "Filter      Day  Limiting Magnitude\n",
      "     r 2.312500                24.5\n",
      "     r 3.312500                24.5\n",
      "     i 1.333333                23.0\n",
      "     i 2.333333                23.0\n",
      "     i 3.333333                23.0\n"
     ]
    }
   ],
   "source": [
    "# Read the model\n",
    "t_model = ascii.read(model_filename)\n",
    "\n",
    "# Example usage of the printDetectionsNonDetections function\n",
    "printDetectionsNonDetections(strategies, t_model, delay_hr=6, offset_filt_hr=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Strategy name: Gold Events: 3 Filter Deep Strategy (700 Mpc) Case 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the model\n",
    "t_model = ascii.read(model_filename)\n",
    "\n",
    "# Plot the lightcurve using the plot_lc module\n",
    "doPlotLc2(strategies, t_model,\n",
    "                 delay_hr,  # delay time between merger and observing window\n",
    "                 xlim=[0, 7], # x axis limit in days\n",
    "                 ylim=[28,18],  # y axis limit in apparent mag\n",
    "                 n_interp=120,  # Number of points for the interpolation (e.g. 150)\n",
    "                 offset_filt_hr=0.5,  # offset in hours between filters (to show overlapping points)\n",
    "                 outfile_base=\"plot_lc\",  # Base name for the file to be saved\n",
    "                 outfile_format='pdf',  # Format of the output plot (pdf, png, etc)\n",
    "                 doSave=True,  # If set to False, the plot will not be saved\n",
    "                 event_name=event_name,  # Name of the event for the X label\n",
    "                 linestyle=\"-\",\n",
    "                 filters_color_dict=filters_color_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time budget\n",
    "\n",
    "Based on the strategies adopted, compute the time budget. Some parameters need to be defined, such as:\n",
    "- overheads between exposures (readout)\n",
    "- overheads for the first slew\n",
    "- overheads for the filter change\n",
    "- number of fields (i.e., number of tiles)\n",
    "- number of events (i.e., number of GW events or other)\n",
    "- ratio of number of days in which u-band is mounted (10/30 for 10 days per month)\n",
    "\n",
    "IMPORTANT: the filter change and first slew overhead will be divided by the number of fields, assuming that Rubin will complete a full pass of the tiling before changing filter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Overheads\n",
    "overhead_between_exposures = 7.  # in seconds\n",
    "\n",
    "# Overhead first slew per epoch, assuming it may be further away than tiling\n",
    "overhead_first_slew = 30\n",
    "\n",
    "# Overhead for filter change\n",
    "overhead_filter_change = 120.  # in seconds\n",
    "\n",
    "# Number of fields\n",
    "n_fields = 14\n",
    "\n",
    "# Number of separate compact mergers (or other type of events) to follow-up\n",
    "n_events = 1\n",
    "\n",
    "# If 6 filters are given, one must be dropped. \n",
    "# Indicate here the number of days per month in which u band is used.\n",
    "# Y band will be assumed for the other epochs. The total time budget will be a combination of the two.\n",
    "u_days_ratio = 14./30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at what `strategies` are being used again for verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Lensed KN Ω=100deg2': {'cadence_hr': [24, 48, 120],\n",
       "  'exptimes_median': [600, 600, 600],\n",
       "  'exptimes': [[600, 600], [600, 600], [600, 600]],\n",
       "  'depths_median': [26.200000000000003,\n",
       "   26.200000000000003,\n",
       "   26.200000000000003],\n",
       "  'depths': [[26.64, 25.76], [26.64, 25.76], [26.64, 25.76]],\n",
       "  'filters': ['gi', 'gi', 'gi']}}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now calculate the total time for the fields and events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " >> Strategy name: Lensed KN Ω=100deg2\n",
      "  Epoch 24 hr:\n",
      "    exposure times: 1200s\n",
      "    overhead change filter: 240.0s\n",
      "    overhead between exposures: 7.0s\n",
      "  Epoch 48 hr:\n",
      "    exposure times: 1200s\n",
      "    overhead change filter: 240.0s\n",
      "    overhead between exposures: 7.0s\n",
      "  Epoch 120 hr:\n",
      "    exposure times: 1200s\n",
      "    overhead change filter: 240.0s\n",
      "    overhead between exposures: 7.0s\n",
      "Total exposure time per pointing: 1.00hr\n",
      "Total time for 14 fields: 14.10hr\n",
      "Total time for 14 fields and 1 events: 14.10hr\n",
      "-- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(strategy_names)):\n",
    "    print(f\"\\n >> Strategy name: {strategy_names[i]}\")\n",
    "    # Initialize total overhead slews\n",
    "    overhead_first_slew_total = 0\n",
    "    # Initialize total overhead readouts\n",
    "    overhead_filter_change_total = 0\n",
    "    # Initialize total overhead readouts\n",
    "    overhead_between_exposures_total = 0\n",
    "    # Initialize total exposure time\n",
    "    exptime_total = 0\n",
    "    \n",
    "    # Iterate over epochs\n",
    "    for cadence_hr, exptimes_epoch, filters_epoch in zip(strategies[strategy_names[i]][\"cadence_hr\"],\n",
    "                                             strategies[strategy_names[i]][\"exptimes\"],\n",
    "                                             strategies[strategy_names[i]][\"filters\"]):\n",
    "        # Add overheads for the first slew\n",
    "        overhead_first_slew_total += overhead_first_slew \n",
    "        # Initialize\n",
    "        exptime_epoch = 0\n",
    "        # Check if 6 filters are given\n",
    "        if len(filters_epoch) == 6:\n",
    "            # add overhead between exposures\n",
    "            overhead_between_exposures_epoch = overhead_between_exposures * 4\n",
    "            # Add overhead change of filter, assuming the first filter changes, too\n",
    "            overhead_filter_change_epoch =  overhead_filter_change * 5\n",
    "            # Combine exposure times between u and y\n",
    "            for filt, exptime in zip(filters_epoch, exptimes_epoch):\n",
    "                if filt == \"u\":\n",
    "                    exptime_epoch += exptime * u_days_ratio\n",
    "                elif filt == \"y\":\n",
    "                    exptime_epoch += exptime * (1-u_days_ratio)\n",
    "                else:\n",
    "                    exptime_epoch += exptime\n",
    "        # In case <= 5 filters are given for this epoch    \n",
    "        else:\n",
    "            overhead_between_exposures_epoch = overhead_between_exposures * (len(exptimes_epoch) - 1)\n",
    "            # Add overhead change of filter, assuming the first filter changes, too\n",
    "            overhead_filter_change_epoch = overhead_filter_change * len(exptimes_epoch)\n",
    "            # Exposure time\n",
    "            exptime_epoch = np.sum(exptimes_epoch)\n",
    "\n",
    "        # Add the exposure times to the epoch to the total\n",
    "        exptime_total += exptime_epoch\n",
    "        # Add overheads\n",
    "        overhead_between_exposures_total += overhead_between_exposures_epoch\n",
    "        overhead_filter_change_total += overhead_filter_change_epoch\n",
    "        # Print results for the epoch\n",
    "        print(f\"  Epoch {cadence_hr} hr:\")\n",
    "        print(f\"    exposure times: {'{:.0f}'.format(exptime_epoch)}s\")\n",
    "        print(f\"    overhead change filter: {overhead_filter_change_epoch}s\")\n",
    "        print(f\"    overhead between exposures: {overhead_between_exposures_epoch}s\")\n",
    "        \n",
    "    # calculate the total overheads, convert overhead from seconds to hours\n",
    "    # IMPORTANT: the filter change and first slew overhead must to be divided by the number of fields,\n",
    "    # if we complete the tiling before changing filters   \n",
    "    overheads_total_nfields = np.sum((overhead_between_exposures_total * n_fields)/60/60 +\n",
    "                       overhead_first_slew_total/n_fields/60/60 +\n",
    "                       overhead_filter_change_total/n_fields/60/60)\n",
    "    \n",
    "    # calculate the total exposure time per field/pointing in hours\n",
    "    total_exposure_time_hr = exptime_total/60/60\n",
    "\n",
    "    # Calculate the total time budget in hours per event\n",
    "    budget_strategy_nfields_hr = total_exposure_time_hr * n_fields + overheads_total_nfields\n",
    "    \n",
    "    print(f\"Total exposure time per pointing: {'{:.2f}'.format(total_exposure_time_hr)}hr\")\n",
    "    #print(f\"Total overheads per pointing, assuming {n_fields} fields: {'{:.2f}'.format(overheads_total_nfields)}hr ({'{:.2f}'.format(100*overheads/budget_strategy_hr)}% of total budget)\")\n",
    "    print(f\"Total time for {n_fields} fields: {'{:.2f}'.format(budget_strategy_nfields_hr)}hr\")\n",
    "    print(f\"Total time for {n_fields} fields and {n_events} events: {'{:.2f}'.format(budget_strategy_nfields_hr * n_events)}hr\")\n",
    "    print(\"-- \\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exposure Time Calculator\n",
    "\n",
    "From a given filter and magnitude limit (5 sigma), get the exposure time. The formulae were obtained from Formulae from [SMTN 002](https://smtn-002.lsst.io/v/OPSIM-1134/index.html).\n",
    "\n",
    "The formula to go from the magnitude limit to the exposure time was slightly simplified, but it is accurate within 0.28s for 30s exposures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the custom exposure time calculator\n",
    "import etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define filters and magnitude limits\n",
    "filters = \"ugrizy\"\n",
    "mag_limits = [24.5, 24.5, 24.5, 24.5, 24.5, 24.5]  # in AB mag\n",
    "\n",
    "# Define airmass (>= 1)\n",
    "airmass = 1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filter, mag_limit, exptime (s)\n",
      "u, 24.5, 156\n",
      "g, 24.5, 14\n",
      "r, 24.5, 31\n",
      "i, 24.5, 62\n",
      "z, 24.5, 175\n",
      "y, 24.5, 1158\n"
     ]
    }
   ],
   "source": [
    "print(\"filter, mag_limit, exptime (s)\")\n",
    "for filt, mag in zip(filters, mag_limits):\n",
    "    exptime = int(np.round(etc.get_exptime(mag, filt, X=airmass)))\n",
    "    print(f\"{filt}, {mag}, {exptime}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now from a given filter and exposure time, get the magnitude limit (5 sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define filters and exp times\n",
    "filters = \"ugrizy\"\n",
    "exptimes = [30, 30, 60, 60, 60, 60]  # in seconds\n",
    "\n",
    "# Define airmass (>= 1)\n",
    "airmass = 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filter, mag_limit, exptime (s)\n",
      "u, 23.7, 30\n",
      "g, 25.0, 30\n",
      "r, 24.9, 60\n",
      "i, 24.5, 60\n",
      "z, 23.9, 60\n",
      "y, 22.9, 60\n"
     ]
    }
   ],
   "source": [
    "print(\"filter, mag_limit, exptime (s)\")\n",
    "for filt, exptime in zip(filters, exptimes):\n",
    "    mag = etc.get_m5(exptime, filt, X=airmass)\n",
    "    print(f\"{filt}, {'{:.1f}'.format(mag)}, {exptime}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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