plots_RubinToO2024.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rubin ToO Strategies\n",
    "\n",
    "Notebook template author: Igor Andreoni <igor.andreoni@gmail.com>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 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.\n",
    "\n",
    "### Requirements\n",
    "\n",
    "Python3\n",
    "\n",
    "- numpy\n",
    "- matplotlib\n",
    "- math\n",
    "- collections\n",
    "\n",
    "Custom scripts\n",
    "- chart.py\n",
    "- etc.py\n",
    "\n",
    "### Run the cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\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"
   ]
  },
  {
   "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` 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> If both \"depths\" and \"exposures\" are given, \"depths\" will dominate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "strategies = {\n",
    "    'Strategy 1 blue': {\"cadence_hr\": [1, 4, 24, 48],\n",
    "                   \"exptimes\": [30, 30, 60, 180],\n",
    "                   \"depths\": [24.5, 24.5, 24.5, 24.5],\n",
    "                   \"filters\": ['ugr', 'ugr', 'g', 'g']},\n",
    "    'Strategy 1 red': {\"cadence_hr\": [1, 4, 24, 48],\n",
    "                   \"exptimes\": [30, 30, 60, 180],\n",
    "                   \"depths\": [24.5, 24.5, 24.5, 24.5],\n",
    "                   \"filters\": ['izy', 'izy', 'z', 'z']},\n",
    "    'Fabulous Strategy': {\"cadence_hr\": [4, 24, 48],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths\": [24.5, 25, 25],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy', 'ugrizy']},\n",
    "    #'Strategy 3': {\"cadence_hr\": [1, 4, 24],\n",
    "    #               \"exptimes\": [],\n",
    "    #               \"depths\": [24, 24, 24],\n",
    "    #               \"filters\": ['ugrizy', 'ugrizy', 'gz']},\n",
    "}\n",
    "\n",
    "strategies = {\n",
    "    'Fabulous Strategy': {\"cadence_hr\": [4, 24, 48],\n",
    "                   \"exptimes_median\": [],\n",
    "                   \"exptimes\": [],\n",
    "                   \"depths\": [24.5, 25, 25],\n",
    "                   \"filters\": ['ugrizy', 'ugrizy', 'ugrizy']}\n",
    "}\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\", '^']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Gantt chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using DEPTHS to calculate the exposure times\n",
      "    Epoch: 24.5 hr\n",
      "        Filter: u Depth: 24.5mag, exptime: 131s\n",
      "        Filter: g Depth: 24.5mag, exptime: 13s\n",
      "        Filter: r Depth: 24.5mag, exptime: 29s\n",
      "        Filter: i Depth: 24.5mag, exptime: 60s\n",
      "        Filter: z Depth: 24.5mag, exptime: 170s\n",
      "        Filter: y Depth: 24.5mag, exptime: 1088s\n",
      "Median exposure time: 96s\n",
      "    Epoch: 25 hr\n",
      "        Filter: u Depth: 25mag, exptime: 330s\n",
      "        Filter: g Depth: 25mag, exptime: 32s\n",
      "        Filter: r Depth: 25mag, exptime: 73s\n",
      "        Filter: i Depth: 25mag, exptime: 150s\n",
      "        Filter: z Depth: 25mag, exptime: 428s\n",
      "        Filter: y Depth: 25mag, exptime: 2732s\n",
      "Median exposure time: 240s\n",
      "    Epoch: 25 hr\n",
      "        Filter: u Depth: 25mag, exptime: 330s\n",
      "        Filter: g Depth: 25mag, exptime: 32s\n",
      "        Filter: r Depth: 25mag, exptime: 73s\n",
      "        Filter: i Depth: 25mag, exptime: 150s\n",
      "        Filter: z Depth: 25mag, exptime: 428s\n",
      "        Filter: y Depth: 25mag, exptime: 2732s\n",
      "Median exposure time: 240s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If depths are given, then compute the exposure times\n",
    "strategy_names = list(strategies.keys())\n",
    "for i in range(len(strategy_names)):\n",
    "    # Check if depths are given, as they will dominate\n",
    "    if len(strategies[strategy_names[i]][\"depths\"]) > 0:\n",
    "        print(\"Using DEPTHS to calculate the exposure times\")\n",
    "        # Initiate the exposure times list\n",
    "        strategies[strategy_names[i]][\"exptimes\"] = []\n",
    "        #Get exposure times, print results:\n",
    "        for j in np.arange(len(strategies[strategy_names[i]][\"depths\"])):\n",
    "            depth = strategies[strategy_names[i]][\"depths\"][j]\n",
    "            # Iterate over the filters\n",
    "            exptimes_epoch = [] # Get the average exposure time for a given epoch \n",
    "            print(f\"    Epoch: {strategies[strategy_names[i]]['depths'][j]} hr\")\n",
    "            for filt in strategies[strategy_names[i]][\"filters\"][j]:\n",
    "                exptime = int(np.round(etc.get_exptime(depth, filt)))\n",
    "                print(f\"        Filter: {filt} Depth: {depth}mag, exptime: {exptime}s\")\n",
    "                exptimes_epoch.append(exptime)\n",
    "            # Add the average exposure time to the strategies dictionary\n",
    "            strategies[strategy_names[i]][\"exptimes_median\"].append(int(np.round(np.median(exptimes_epoch))))\n",
    "            print(f\"Median exposure time: {int(np.round(np.median(exptimes_epoch)))}s\")\n",
    "            # Detailed exposure times\n",
    "            strategies[strategy_names[i]][\"exptimes\"].append([int(np.round(x)) for x in exptimes_epoch])\n",
    "          \n",
    "    else:\n",
    "        print(\"Using EXPOSURE TIMES to calculate the depths\")\n",
    "        # TO BE CODED .....\n",
    "\n",
    "# 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": [
    "See how the `strategies` dictionary has been populated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Fabulous Strategy': {'cadence_hr': [4, 24, 48],\n",
       "  'exptimes_median': [96, 240, 240],\n",
       "  'exptimes': [[131, 13, 29, 60, 170, 1088],\n",
       "   [330, 32, 73, 150, 428, 2732],\n",
       "   [330, 32, 73, 150, 428, 2732]],\n",
       "  'depths': [24.5, 25, 25],\n",
       "  'filters': ['ugrizy', 'ugrizy', 'ugrizy']}}"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Strategy name: Fabulous Strategy\n",
      "    Epoch: 24.5 hr\n",
      "        Filter: u Depth: 24.5mag, exptime: 131s\n",
      "        Filter: g Depth: 24.5mag, exptime: 13s\n",
      "        Filter: r Depth: 24.5mag, exptime: 29s\n",
      "        Filter: i Depth: 24.5mag, exptime: 60s\n",
      "        Filter: z Depth: 24.5mag, exptime: 170s\n",
      "        Filter: y Depth: 24.5mag, exptime: 1088s\n",
      "        Median exposure time: 240s\n",
      "    Epoch: 25 hr\n",
      "        Filter: u Depth: 25mag, exptime: 330s\n",
      "        Filter: g Depth: 25mag, exptime: 32s\n",
      "        Filter: r Depth: 25mag, exptime: 73s\n",
      "        Filter: i Depth: 25mag, exptime: 150s\n",
      "        Filter: z Depth: 25mag, exptime: 428s\n",
      "        Filter: y Depth: 25mag, exptime: 2732s\n",
      "        Median exposure time: 240s\n",
      "    Epoch: 25 hr\n",
      "        Filter: u Depth: 25mag, exptime: 330s\n",
      "        Filter: g Depth: 25mag, exptime: 32s\n",
      "        Filter: r Depth: 25mag, exptime: 73s\n",
      "        Filter: i Depth: 25mag, exptime: 150s\n",
      "        Filter: z Depth: 25mag, exptime: 428s\n",
      "        Filter: y Depth: 25mag, exptime: 2732s\n",
      "        Median exposure time: 240s\n",
      "--\n"
     ]
    }
   ],
   "source": [
    "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",
    "            depth = strategies[strategy_names[i]][\"depths\"][j]\n",
    "            print(f\"    Epoch: {strategies[strategy_names[i]]['depths'][j]} hr\")\n",
    "            # Iterate over the filters\n",
    "            for filt, exptime in zip(strategies[strategy_names[i]][\"filters\"][j], strategies[strategy_names[i]][\"exptimes\"][j]):\n",
    "                print(f\"        Filter: {filt} Depth: {depth}mag, exptime: {exptime}s\")\n",
    "            print(f\"        Median exposure time: {int(np.round(np.median(exptimes_epoch)))}s\")\n",
    "    print(\"--\")\n",
    "    "
   ]
  },
  {
   "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": 6,
   "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": [
    "For plotting kilonovae, choose relevant parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models come in CSV or ASCII files. check out the `/models` folder and copy the format if you want to add your favorite model.\n",
    "\n",
    "One important parameter 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "delay_hr = 6.\n",
    "\n",
    "model_filename = \"models/kilonova_Bulla/nph1.0e+06_dyn0.001-0.15-0.20_wind0.050-0.03_theta25.84_z0.2.dat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Strategy name: Fabulous Strategy\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",
    "# Do plot \n",
    "plot_lc.doPlotLc(strategies, t_model,\n",
    "                 delay_hr,  # delay time between merger and observing window\n",
    "                 xlim=[0.01, 4], # x axis limit in apparent mag\n",
    "                 ylim=[26, 21],  # y axis limit in apparent mag\n",
    "                 n_interp=100,  # 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 overheads between exposures and rates.\n",
    "\n",
    "Overheads are calculated by multiplying the overhead per exposure * the number of exposures, which accounts for slewing to the first field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Overheads\n",
    "overhead_between_exposures = 10.  # in seconds\n",
    "\n",
    "# Overhead first slew per epoch, assuming it may be further away than tiling\n",
    "overhead_first_slew = 30\n",
    "\n",
    "# Number of fields\n",
    "n_fields = 10\n",
    "\n",
    "# Number of events to follow-up\n",
    "n_events = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Strategy name: Fabulous Strategy\n",
      "Total exposure time per pointing: 2.5hr\n",
      "Total overheads per pointing: 0.07 (0.028135483172816788% of total budget)\n",
      "Total time budget per pointing: 2.6hr\n",
      "Total time for 10 fields: 25.7hr\n",
      "Total time for 10 fields and 5 events: 128.3hr\n",
      "-- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(strategy_names)):\n",
    "    exptimes = strategies[strategy_names[i]][\"exptimes\"]\n",
    "    # Number of exposures\n",
    "    n_exposures = len(np.concatenate(exptimes))\n",
    "    # calculate the overheads, convert overhead from s hours\n",
    "    overheads = np.sum(overhead_between_exposures/60/60 * (n_exposures-1)) + overhead_first_slew * len(exptimes)/60/60\n",
    "    # calculate the total exposure time per field/pointing in hours\n",
    "    total_exposure_time_hr = np.sum(np.concatenate(exptimes))/60/60\n",
    "    # calculate the time budget in hours\n",
    "    budget_strategy_hr = total_exposure_time_hr + overheads\n",
    "    {'{:.1f}'.format(budget_strategy_hr)}\n",
    "    \n",
    "    print(f\"\\n Strategy name: {strategy_names[i]}\")\n",
    "    print(f\"Total exposure time per pointing: {'{:.1f}'.format(total_exposure_time_hr)}hr\")\n",
    "    print(f\"Total overheads per pointing: {'{:.2f}'.format(overheads)} ({overheads/budget_strategy_hr}% of total budget)\")\n",
    "    print(f\"Total time budget per pointing: {'{:.1f}'.format(budget_strategy_hr)}hr\")\n",
    "    print(f\"Total time for {n_fields} fields: {'{:.1f}'.format(budget_strategy_hr * n_fields)}hr\")\n",
    "    print(f\"Total time for {n_fields} fields and {n_events} events: {'{:.1f}'.format(budget_strategy_hr * n_fields * 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the custom exposure time calculator\n",
    "import etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filter, mag_limit, exptime (s)\n",
      "u, 24.5, 131\n",
      "g, 24.5, 13\n",
      "r, 24.5, 29\n",
      "i, 24.5, 60\n",
      "z, 24.5, 170\n",
      "y, 24.5, 1088\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)))\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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define filters and exp times\n",
    "filters = \"ugrizy\"\n",
    "exptimes = [30, 30, 60, 60, 60, 60]  # in seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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)\n",
    "    print(f\"{filt}, {'{:.1f}'.format(mag)}, {exptime}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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