notebooks/region_search/Region Searching Workbook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d94d8d2b",
   "metadata": {},
   "source": [
    "# Region Searching for KBMOD\n",
    "\n",
    "The point of this notebook is to do step-by-step exploration of the DEEP dataset that was first run through KBMOD for the first set of papers. That work was led by Hayden Smotherman, hence the reference to that name.\n",
    "\n",
    "The \"Steven\" name that appears is Steven Stetzler, who inherited the responsibilities of properly re/processing the entire DEEP dataset through data acquired through 2023.\n",
    "\n",
    "### Basic Idea\n",
    "Here we are connecting to an existing Butler/repository with the intent of carrying out queries to find data suitable for KBMOD to use.\n",
    "\n",
    "#### FAQ\n",
    "\n",
    "Q: Who wrote this, and when?\\\n",
    "A: Colin Orion Chandler (coc123@uw.edu), late January 2024.\n",
    "\n",
    "Q: What are the basic requirements to run this?\\\n",
    "A: (a) Epyc access, (b) with the appropriate (LSST w_2022_06) weekly build active, (c) permissions to access the repo_path mentioned below, and (d) an acceptable Kernel in the Jupyter-Hub of Epyc (user or global).\n",
    "\n",
    "Q: Why don't we just query the DB (e.g., PostGres, SQLite) to get what we need?\\\n",
    "A: Because (a) the underlying schema can (and has/does) change, and (b) the underlying DB does not contain views, meaning that we would have to recreate the very complex relational mapping ourselves. Moreover, aside from (b) being very challenging, the relationship mapping recreation would fail due to the reasons mentioned in (a)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ea4ebd7",
   "metadata": {},
   "source": [
    "### Initial Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0189084",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages needed to run the notebook\n",
    "import lsst\n",
    "import lsst.daf.butler as dafButler\n",
    "import lsst.sphgeom as sphgeom\n",
    "\n",
    "import os\n",
    "import glob\n",
    "\n",
    "import multiprocessing\n",
    "from concurrent.futures import ProcessPoolExecutor, as_completed\n",
    "\n",
    "import time\n",
    "from dateutil import parser\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.cm import get_cmap\n",
    "from matplotlib.colors import Normalize\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "import progressbar\n",
    "\n",
    "from astropy.time import Time  # for converting Butler visitInfo.date (TAI) to UTC strings\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "import astropy.io.fits as fits\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ceeec168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96 CPUs were reported as available by the multiprocessing module.\n"
     ]
    }
   ],
   "source": [
    "# We will use some kind of multiprocessing in a few places. Let's see what the sytsem thinks we have available.\n",
    "# NOTE: we could set limits on executors later using this value, if desired. 2/6/2024 COC\n",
    "\n",
    "available_cpus = multiprocessing.cpu_count()\n",
    "print(f\"{available_cpus} CPUs were reported as available by the multiprocessing module.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "693492d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this code will run in different environments, so we need somewhere (not the working directory) to save output\n",
    "basedir = f'{os.environ[\"HOME\"]}/kbmod_tmp'\n",
    "os.makedirs(basedir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b13eb927",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the Butler\n",
    "# NOTE: the repo path contains configs that point us to the underlying registry (DB)\n",
    "# NOTE: do NOT assume there is write protection! (i.e., don't make changes)\n",
    "repo_path = f\"/epyc/users/smotherh/DEEP/PointingGroups/butler-repo\"\n",
    "butler = dafButler.Butler(repo_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85cc47d8",
   "metadata": {},
   "source": [
    "#### Collections\n",
    "1. Explore the available collections\n",
    "2. Construct a list of the collections containing the data we care about.\n",
    "\n",
    "Different collections hold different datasets.\n",
    "\n",
    "For the Hayden DEEP repo, the collections we are concerned with are organized by pointing groups (discrete regions on the sky).\n",
    "\n",
    "We want to figure out what those are, how they are named, and what else is available.\n",
    "\n",
    "We will also dump a full list of collection names to disk. \\\n",
    "NOTE: as of 2/1/2024, there are 1,292 named collections in the repo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6a546f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_collection_names(butler, basedir, verbose=False, export=True):\n",
    "    \"\"\"\n",
    "    Making this a function 2/6/2024 COC.\n",
    "    \"\"\"\n",
    "    all_collection_names = []\n",
    "\n",
    "    for c in sorted(butler.registry.queryCollections(\"*\")):\n",
    "        all_collection_names.append(c)\n",
    "\n",
    "    if export == True:\n",
    "        outfile = f\"{basedir}/all_collection_names.lst\"\n",
    "        with open(outfile, \"w\") as f:\n",
    "            for c in all_collection_names:\n",
    "                print(c, file=f)\n",
    "\n",
    "    if verbose:\n",
    "        message = f\"Found {len(all_collection_names)} collections in the Butler.\"\n",
    "        if export == True:\n",
    "            message += f' Wrote to \"{outfile}\".'\n",
    "        print(message)\n",
    "    return all_collection_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3006e4fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1292 collections in the Butler. Wrote to \"/astro/users/coc123/kbmod_tmp/all_collection_names.lst\".\n"
     ]
    }
   ],
   "source": [
    "all_collection_names = get_collection_names(butler=butler, basedir=basedir, verbose=True, export=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b409c810",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We looked through the collections already.\n",
    "# We will string manipulate to get to what we need.\n",
    "# Previously, we used a list file on disk. (This could be a better option for some users.)\n",
    "\n",
    "\n",
    "def get_desired_collections(all_collections_list, desired_collection_list=None):\n",
    "    \"\"\"\n",
    "    Produce a list of collections that will be used for querying the Butler.\n",
    "\n",
    "    If desired_collection_list is None, then a hard-wired \"default\" approach\n",
    "    (for Haden/DEEP) is carried out, requiring:\n",
    "        1. \"Pointing\" must be in the collection name.\n",
    "        2. \"/imdiff_r/\" must be in the collection name.\n",
    "        3. \"/2021\" may not be in the collection name.\n",
    "\n",
    "    Otherwise, desired_collection_list can be either\n",
    "        1. a Python list of desired collection names, or\n",
    "        2. a filename (ending in .lst) that specifies the desired collections.\n",
    "    Either way, the collection names are verified against the (required) collections_list.\n",
    "\n",
    "    Made this into a function 2/6/2024 COC.\n",
    "\n",
    "    NOTE/TODO: untested are the supplied list and list file approaches.\n",
    "    \"\"\"\n",
    "\n",
    "    desired_collections = []\n",
    "\n",
    "    if desired_collection_list == None:\n",
    "        for collection_name in all_collection_names:\n",
    "            if (\n",
    "                \"Pointing\" in collection_name\n",
    "                and \"/imdiff_r\" in collection_name\n",
    "                and \"/2021\" not in collection_name\n",
    "            ):\n",
    "                desired_collections.append(collection_name)\n",
    "    else:\n",
    "        if type(desired_collection_list) == type(\"\"):\n",
    "            with open(desired_collection_list, \"r\") as f:\n",
    "                for line in f:\n",
    "                    desired_collections.append(line.strip())\n",
    "        else:\n",
    "            for entry in desired_collection_list:\n",
    "                desired_collections.append(entry)\n",
    "    #\n",
    "    # Validate entries\n",
    "    for entry in desired_collections:\n",
    "        if entry not in all_collections_list:\n",
    "            raise KeyError(f'\"{entry}\" is not in the master list of collections supplied.')\n",
    "    #\n",
    "    return desired_collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc9a4efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PointingGroup006/imdiff_r',\n",
       " 'PointingGroup008/imdiff_r',\n",
       " 'PointingGroup009/imdiff_r',\n",
       " 'PointingGroup016/imdiff_r',\n",
       " 'PointingGroup018/imdiff_r',\n",
       " 'PointingGroup019/imdiff_r',\n",
       " 'PointingGroup021/imdiff_r',\n",
       " 'PointingGroup023/imdiff_r']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desired_collections = get_desired_collections(all_collections_list=all_collection_names)\n",
    "desired_collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4537e06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ASIDE: there is a set of collections with dates in their names\n",
    "#   (e.g., PointingGroup006/imdiff_r/20211110T184421Z).\n",
    "# Those (only?) have the following datasetTypes, (so we use the name without the date for all the datasetTypes):\n",
    "# Across all collections, we see the following numbers by datasetType:\n",
    "# 47383      deepDiff_diaSrc\n",
    "# 8          deepDiff_diaSrc_schema\n",
    "# 47383      deepDiff_differenceExp\n",
    "# 29445      deepDiff_warpedExp\n",
    "# 8          imageDifference_config\n",
    "# 17942      imageDifference_log\n",
    "# 47383      imageDifference_metadata\n",
    "# 8          packages\n",
    "\n",
    "# which is too little? we we will snag the parent Collections (stripping dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fcdae7b",
   "metadata": {},
   "source": [
    "#### datasetTypes\n",
    "\n",
    "Here we explore another dimension of the Butler: datasetType.\n",
    "\n",
    "tldr; we just care about one currently: deepDiff_differenceExp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47c8c37c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TIMING NOTE: about 2 minutes here\n",
    "#\n",
    "# Maybe a KBMOD user must know which datasetType(s) they need?\n",
    "# Here we show how to explore them to figure that out.\n",
    "#\n",
    "# NOTE: we tested two approaches,\n",
    "#   (a) iterating over desired_collections, and\n",
    "#   (b) supply desired_collections.\n",
    "# The output was the same, but the iterating method took 268s, and the supplied method took 97s.\n",
    "# There *was* a point where we thought we were seeing different results depending on the approach,\n",
    "# but this has been sorted out. (COC suspects a testing break in a loop somwhere.)\n",
    "# COC NTS: [TODO] function, [TODO] caching\n",
    "\n",
    "\n",
    "def getDatasetTypeStats(butler, overwrite=False):\n",
    "    \"\"\"\n",
    "    Get information on all datasetTypes found in a Butler.\n",
    "    TODO implement caching if desired. If not, get rid of overwrite option.\n",
    "    2/1/2024 COC\n",
    "    \"\"\"\n",
    "    datasetTypes = {}\n",
    "\n",
    "    cache_file = f\"{basedir}/dataset_types.csv\"\n",
    "    cache_exists = False\n",
    "    if len(glob.glob(cache_file)) > 0:\n",
    "        cache_exists = True\n",
    "\n",
    "    if overwrite == False and cache_exists == True:\n",
    "        print(f\"Recycling {cache_file} as overwrite was False...\")\n",
    "        with open(cache_file, \"r\") as f:\n",
    "            for line in f:\n",
    "                print(line)\n",
    "                line = line.strip().split(\",\")\n",
    "                datasetTypes[line[0]] = int(line[1])\n",
    "        print(f\"Read {len(datasetTypes)} datasetTypes from disk.\")\n",
    "        return datasetTypes\n",
    "\n",
    "    q = sorted(butler.registry.queryDatasetTypes())\n",
    "\n",
    "    with progressbar.ProgressBar(max_value=len(q)) as bar:\n",
    "        for j, dt in enumerate(q):\n",
    "            n = 0\n",
    "            for i, ref in enumerate(\n",
    "                butler.registry.queryDatasets(datasetType=dt, collections=desired_collections)\n",
    "            ):\n",
    "                n += 1\n",
    "            if n > 0:\n",
    "                if dt.name not in datasetTypes:\n",
    "                    datasetTypes[dt.name] = 0\n",
    "                datasetTypes[dt.name] += n\n",
    "            bar.update(j)\n",
    "\n",
    "    if cache_exists == False or overwrite == True:\n",
    "        print(f\"Saving {len(datasetTypes)} datasetTypes to {cache_file} now...\")\n",
    "        with open(cache_file, \"w\") as f:\n",
    "            for key in datasetTypes:\n",
    "                print(f\"{key},{datasetTypes[key]}\", file=f)\n",
    "    else:\n",
    "        print(f\"Saw {len(datasetTypes)} datasetTypes.\")\n",
    "    return datasetTypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b741015f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recycling /astro/users/coc123/kbmod_tmp/dataset_types.csv as overwrite was False...\n",
      "assembleCoadd_config,8\n",
      "\n",
      "assembleCoadd_log,268\n",
      "\n",
      "assembleCoadd_metadata,700\n",
      "\n",
      "cal_ref_cat,122856\n",
      "\n",
      "calexp,47403\n",
      "\n",
      "calexpBackground,47403\n",
      "\n",
      "calibrate_config,8\n",
      "\n",
      "calibrate_log,17961\n",
      "\n",
      "calibrate_metadata,47403\n",
      "\n",
      "characterizeImage_config,8\n",
      "\n",
      "characterizeImage_log,18290\n",
      "\n",
      "characterizeImage_metadata,47423\n",
      "\n",
      "deepCoadd,693\n",
      "\n",
      "deepCoadd_directWarp,167085\n",
      "\n",
      "deepCoadd_inputMap,693\n",
      "\n",
      "deepCoadd_psfMatchedWarp,167085\n",
      "\n",
      "deepDiff_diaSrc,47383\n",
      "\n",
      "deepDiff_diaSrc_schema,8\n",
      "\n",
      "deepDiff_differenceExp,47383\n",
      "\n",
      "deepDiff_warpedExp,29445\n",
      "\n",
      "gaia_DR1_v1,524283\n",
      "\n",
      "icExp,47423\n",
      "\n",
      "icExpBackground,47423\n",
      "\n",
      "icSrc,47423\n",
      "\n",
      "icSrc_schema,8\n",
      "\n",
      "imageDifference_config,8\n",
      "\n",
      "imageDifference_log,17942\n",
      "\n",
      "imageDifference_metadata,47383\n",
      "\n",
      "isr_config,8\n",
      "\n",
      "isr_log,18290\n",
      "\n",
      "isr_metadata,48422\n",
      "\n",
      "makeWarp_config,8\n",
      "\n",
      "makeWarp_log,64924\n",
      "\n",
      "makeWarp_metadata,167085\n",
      "\n",
      "overscanRaw,48422\n",
      "\n",
      "overscan_config,8\n",
      "\n",
      "overscan_log,18290\n",
      "\n",
      "overscan_metadata,48422\n",
      "\n",
      "packages,32\n",
      "\n",
      "postISRCCD,48422\n",
      "\n",
      "ps1_pv3_3pi_20170110,130924\n",
      "\n",
      "raw,48422\n",
      "\n",
      "skyMap,1\n",
      "\n",
      "src,47403\n",
      "\n",
      "srcMatch,47403\n",
      "\n",
      "src_schema,8\n",
      "\n",
      "Read 46 datasetTypes from disk.\n",
      "CPU times: user 1.66 ms, sys: 0 ns, total: 1.66 ms\n",
      "Wall time: 1.58 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# TIMING NOTE: this takes roughly 2.5 minutes without cache (2/6/2024 COC)\n",
    "datasetTypes = getDatasetTypeStats(butler=butler, overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6406f04c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Across all collections, we see the following numbers by datasetType: \n",
      "8          assembleCoadd_config\n",
      "268        assembleCoadd_log\n",
      "700        assembleCoadd_metadata\n",
      "122856     cal_ref_cat\n",
      "47403      calexp\n",
      "47403      calexpBackground\n",
      "8          calibrate_config\n",
      "17961      calibrate_log\n",
      "47403      calibrate_metadata\n",
      "8          characterizeImage_config\n",
      "18290      characterizeImage_log\n",
      "47423      characterizeImage_metadata\n",
      "693        deepCoadd\n",
      "167085     deepCoadd_directWarp\n",
      "693        deepCoadd_inputMap\n",
      "167085     deepCoadd_psfMatchedWarp\n",
      "47383      deepDiff_diaSrc\n",
      "8          deepDiff_diaSrc_schema\n",
      "47383      deepDiff_differenceExp\n",
      "29445      deepDiff_warpedExp\n",
      "524283     gaia_DR1_v1\n",
      "47423      icExp\n",
      "47423      icExpBackground\n",
      "47423      icSrc\n",
      "8          icSrc_schema\n",
      "8          imageDifference_config\n",
      "17942      imageDifference_log\n",
      "47383      imageDifference_metadata\n",
      "8          isr_config\n",
      "18290      isr_log\n",
      "48422      isr_metadata\n",
      "8          makeWarp_config\n",
      "64924      makeWarp_log\n",
      "167085     makeWarp_metadata\n",
      "48422      overscanRaw\n",
      "8          overscan_config\n",
      "18290      overscan_log\n",
      "48422      overscan_metadata\n",
      "32         packages\n",
      "48422      postISRCCD\n",
      "130924     ps1_pv3_3pi_20170110\n",
      "48422      raw\n",
      "1          skyMap\n",
      "47403      src\n",
      "47403      srcMatch\n",
      "8          src_schema\n"
     ]
    }
   ],
   "source": [
    "# The number of records for each datasetType.\n",
    "# This may be especially useful for users who do not yet know which datasetType(s) they need.\n",
    "\n",
    "print(f\"Across all collections, we see the following numbers by datasetType: \")\n",
    "for dt in datasetTypes:\n",
    "    print(f\"{datasetTypes[dt]!s:10} {dt}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "53a03c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the datasetType that we care about.\n",
    "# NOTE: we should allow this to be a list in case a user needs multiple.\n",
    "desired_datasetTypes = [\"deepDiff_differenceExp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c351fc7",
   "metadata": {},
   "source": [
    "### Visit Detector Region (VDR) Querying\n",
    "\n",
    "Here we will query the Butler and extract all of the \"visit_detector_region\" information.\\\n",
    "At this stage we are gathering:\n",
    "1. dataIds for all future queries\n",
    "2. lsst.sphgeom.region objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f3f393e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataId(dataId, verbose=True):\n",
    "    \"\"\"\n",
    "    We need a way to extract a portable (non-LSST object) version of the DataId.\n",
    "    A dataId normally looks like a dictionary with keys of instrument, detector, visit.\n",
    "    4/22/2024 COC\n",
    "    \"\"\"\n",
    "    fields = [\"instrument\", \"detector\", \"visit\"]\n",
    "    clean_id = {}\n",
    "    #\n",
    "    if type(dataId) == type({}) and list(dataId.keys()) == fields:\n",
    "        if verbose:\n",
    "            print(f\"dataId was already a properly formatted dict: {dataId}.\")\n",
    "        return dataId\n",
    "    #\n",
    "    try:\n",
    "        dataId = dataId.values_tuple()\n",
    "    except AttributeError as msg:\n",
    "        if verbose:\n",
    "            print(f\"dataId did not have a values_tuple() attribute. Message was {msg}.\")\n",
    "        pass\n",
    "    #\n",
    "    if type(dataId) == type((1, 1, 1)):\n",
    "        for i, label in enumerate(fields):\n",
    "            clean_id[label] = dataId[i]\n",
    "        if verbose:\n",
    "            print(f\"Derived {clean_id} from tuple {dataId}.\")\n",
    "        return clean_id\n",
    "    #\n",
    "    for i, label in enumerate(fields):\n",
    "        try:\n",
    "            clean_id[label] = dataId[label]\n",
    "        except KeyError as msg:\n",
    "            print(f\"KeyError for {label} with dataId= {dataId}. Message was: {msg}.\")\n",
    "            break\n",
    "    #\n",
    "    if clean_id == {}:\n",
    "        raise ValueError(f\"Failed to create clean_id for dataId={dataId}.\")\n",
    "    #\n",
    "    return clean_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7d7482e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataId_to_dataIdStr(dataId, force_clean=False, verbose=False):\n",
    "    \"\"\"\n",
    "    We will store a shortened, concatenated version of this unique triple.\n",
    "    This is needed because dict values in a column are not hashable.\n",
    "    Example: \"{instrument: 'DECam', detector: 1, visit: 898287}\" becomes \"DECam_1_898287\"\n",
    "    4/23/2024 COC\n",
    "    \"\"\"\n",
    "    if force_clean:\n",
    "        dataId = clean_dataId(dataId=dataId, verbose=verbose)\n",
    "    #\n",
    "    dataId_str = f\"{dataId['instrument']}_{dataId['detector']}_{dataId['visit']}\"\n",
    "    #\n",
    "    if verbose:\n",
    "        print(f\"dataId {dataId} became: {dataId_str}\")\n",
    "    #\n",
    "    return dataId_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0fc27d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataId was already a properly formatted dict: {'instrument': 'DECam', 'detector': 1, 'visit': 898287}.\n",
      "dataId {'instrument': 'DECam', 'detector': 1, 'visit': 898287} became: DECam_1_898287\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'DECam_1_898287'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test with the case of a dataId that is already a valid dict\n",
    "dataId_to_dataIdStr(\n",
    "    dataId={\"instrument\": \"DECam\", \"detector\": 1, \"visit\": 898287}, force_clean=True, verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c47e5588",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vdr_data(butler, desired_collections, desired_datasetTypes, verbose=False):\n",
    "    \"\"\"\n",
    "    Made as function 2/6/2024 COC.\n",
    "    \"\"\"\n",
    "    # VDR === Visit Detector Region\n",
    "    # VDRs hold what we need in terms of region hashes and unique dataIds.\n",
    "    # NOTE: this typically takes < 5s to run 2/1/2024 COC\n",
    "    # NOTE: tried iterating over desired_collections vs supplying desired_collections; same output 2/1/2024 COC\n",
    "\n",
    "    vdr_dict = {\"data_id\": [], \"region\": [], \"detector\": []}\n",
    "    #     vdr_ids = []\n",
    "    #     vdr_regions = []\n",
    "    #     vdr_detectors = []\n",
    "\n",
    "    for dt in desired_datasetTypes:\n",
    "        datasetRefs = butler.registry.queryDimensionRecords(\n",
    "            \"visit_detector_region\", datasets=dt, collections=desired_collections\n",
    "        )\n",
    "        for ref in datasetRefs:\n",
    "            vdr_dict[\"data_id\"].append(clean_dataId(ref.dataId, verbose=verbose))\n",
    "            vdr_dict[\"region\"].append(\n",
    "                ref.region\n",
    "            )  # keeping as objects for now; should .encode() for caching/export\n",
    "            vdr_dict[\"detector\"].append(ref.detector)  # 2/2/2024 COC\n",
    "            # BUT if we decided to export this or cache this, we should write the encode() version to disk\n",
    "            #\n",
    "            example_vdr_ref = ref  # this leaves a VDR Python object we can play with\n",
    "            # other data available:\n",
    "            #    id = ref.id# id -- e.g., 1592350 (for DEEP dataset, I think UUIDs for newer Butlers)\n",
    "            #    visit = ref.dataId.full['visit'] # e.g., 946725\n",
    "            # vdr_filters.append(ref.dataId.full['band']) # e.g., VR\n",
    "            # vdr_detectors.append(ref.dataId.full['detector']) # e.g., 1\n",
    "    df = pd.DataFrame.from_dict(vdr_dict)\n",
    "    return df, example_vdr_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eb222e02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.05 s, sys: 79.6 ms, total: 2.13 s\n",
      "Wall time: 2.64 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# TIMING NOTE: requires about 2 seconds 2/6/2024 COC\n",
    "df, example_vdr_ref = get_vdr_data(\n",
    "    butler=butler, desired_collections=desired_collections, desired_datasetTypes=desired_datasetTypes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d7d6ada2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lsst.daf.butler.core.dimensions._coordinate._BasicTupleDataCoordinate"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# notice the dataId type is not a plain tuple; so we created a function clean_dataId() to handle this.\n",
    "type(example_vdr_ref.dataId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c2125e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We also need a string version of the dataId that is hashable. 4/23/2024 COC\n",
    "df[\"data_id_str\"] = [dataId_to_dataIdStr(dataId=dataId) for dataId in df[\"data_id\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e9462e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_id</th>\n",
       "      <th>region</th>\n",
       "      <th>detector</th>\n",
       "      <th>data_id_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847372525065534...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847381014554984...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847383417970056...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847382159041213...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847381374341414...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47378</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 62, 'visit...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.987608537646486,...</td>\n",
       "      <td>62</td>\n",
       "      <td>DECam_62_946172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47379</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 62, 'visit...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9876085083003562...</td>\n",
       "      <td>62</td>\n",
       "      <td>DECam_62_946173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47380</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 62, 'visit...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9876085761885252...</td>\n",
       "      <td>62</td>\n",
       "      <td>DECam_62_946174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47381</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 62, 'visit...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9876085761885252...</td>\n",
       "      <td>62</td>\n",
       "      <td>DECam_62_946175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47382</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 62, 'visit...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9876086828694174...</td>\n",
       "      <td>62</td>\n",
       "      <td>DECam_62_946176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47383 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 data_id   \n",
       "0      {'instrument': 'DECam', 'detector': 1, 'visit'...  \\\n",
       "1      {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "2      {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "3      {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "4      {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "...                                                  ...   \n",
       "47378  {'instrument': 'DECam', 'detector': 62, 'visit...   \n",
       "47379  {'instrument': 'DECam', 'detector': 62, 'visit...   \n",
       "47380  {'instrument': 'DECam', 'detector': 62, 'visit...   \n",
       "47381  {'instrument': 'DECam', 'detector': 62, 'visit...   \n",
       "47382  {'instrument': 'DECam', 'detector': 62, 'visit...   \n",
       "\n",
       "                                                  region  detector   \n",
       "0      ConvexPolygon([UnitVector3d(0.9847372525065534...         1  \\\n",
       "1      ConvexPolygon([UnitVector3d(0.9847381014554984...         1   \n",
       "2      ConvexPolygon([UnitVector3d(0.9847383417970056...         1   \n",
       "3      ConvexPolygon([UnitVector3d(0.9847382159041213...         1   \n",
       "4      ConvexPolygon([UnitVector3d(0.9847381374341414...         1   \n",
       "...                                                  ...       ...   \n",
       "47378  ConvexPolygon([UnitVector3d(0.987608537646486,...        62   \n",
       "47379  ConvexPolygon([UnitVector3d(0.9876085083003562...        62   \n",
       "47380  ConvexPolygon([UnitVector3d(0.9876085761885252...        62   \n",
       "47381  ConvexPolygon([UnitVector3d(0.9876085761885252...        62   \n",
       "47382  ConvexPolygon([UnitVector3d(0.9876086828694174...        62   \n",
       "\n",
       "           data_id_str  \n",
       "0       DECam_1_898286  \n",
       "1       DECam_1_898287  \n",
       "2       DECam_1_898288  \n",
       "3       DECam_1_898289  \n",
       "4       DECam_1_898290  \n",
       "...                ...  \n",
       "47378  DECam_62_946172  \n",
       "47379  DECam_62_946173  \n",
       "47380  DECam_62_946174  \n",
       "47381  DECam_62_946175  \n",
       "47382  DECam_62_946176  \n",
       "\n",
       "[47383 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e04e2f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 47383 regions spanning the 8 desired collections.\n"
     ]
    }
   ],
   "source": [
    "print(f\"We found {len(df['data_id'])} regions spanning the {len(desired_collections)} desired collections.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "08d5b443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>visit_detector_region:\n",
       "  instrument: 'DECam'\n",
       "  detector: 62\n",
       "  visit: 946176\n",
       "  region: ConvexPolygon([UnitVector3d(0.9876086828694174, -0.13336028508776862, -0.08272922024438323), UnitVector3d(0.9873378171284917, -0.13332652431396907, -0.08595389916869185), UnitVector3d(0.9881047366097594, -0.12752395595185462, -0.08594573955553172), UnitVector3d(0.9883760335240734, -0.12755303452468866, -0.0827226676235914)])<pre>"
      ],
      "text/plain": [
       "visit_detector_region.RecordClass(instrument='DECam', detector=62, visit=946176, region=ConvexPolygon([UnitVector3d(0.9876086828694174, -0.13336028508776862, -0.08272922024438323), UnitVector3d(0.9873378171284917, -0.13332652431396907, -0.08595389916869185), UnitVector3d(0.9881047366097594, -0.12752395595185462, -0.08594573955553172), UnitVector3d(0.9883760335240734, -0.12755303452468866, -0.0827226676235914)]))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For demonstration purposes we still have the last \"ref\" from the last cell's iteration\n",
    "example_vdr_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e28031c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{instrument: 'DECam', detector: 62, visit: 946176}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the unique dataId we would need to retrieve this specific image later\n",
    "example_vdr_ref.dataId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "761f3610",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example_vdr_ref.instrument = DECam,     example_vdr_ref.detector = 62,     example_vdr_ref.visit = 946176\n"
     ]
    }
   ],
   "source": [
    "# The unique dataId is made up of the following attributes:\n",
    "print(\n",
    "    f\"example_vdr_ref.instrument = {example_vdr_ref.instrument}, \\\n",
    "    example_vdr_ref.detector = {example_vdr_ref.detector}, \\\n",
    "    example_vdr_ref.visit = {example_vdr_ref.visit}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5d6b2106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'p\\xddnE\\x86}\\x9a\\xef?\\x0f\\xc3\\x84\\'\\xf3\\x11\\xc1\\xbf\\x80\\x8a_\\xff\\xbd-\\xb5\\xbf\\x04xUzE\\x98\\xef?\\x94\\x15\\xcf\\xf2\\xd7\\x10\\xc1\\xbf\\x9d\\xa9\\xe4!\\x13\\x01\\xb6\\xbf\\x1d_\\x18\\xd3\\x8d\\x9e\\xef?\\x80\\x87\"z\\xb4R\\xc0\\xbff\\x1d\\x9f<\\x8a\\x00\\xb6\\xbf\\xe4Z\\x84\\xc6\\xc6\\xa0\\xef?\\x1f\\x01\\xe5g\\xa8S\\xc0\\xbf\\xba\\xc9\\x14\\x10P-\\xb5\\xbf'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the region hash we would use for \"region matching\" later\n",
    "example_vdr_ref.region.encode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aa653a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We check to see if we can crawl to other data from the dataId (False means no).\n",
    "example_vdr_ref.dataId.hasRecords()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f49186c",
   "metadata": {},
   "source": [
    "# Dataframe Assembly\n",
    "\n",
    "This used to contain more, but after I moved everything to a dictionary, then a DF, this became less important.\n",
    "TODO determine where this should go. Or remove."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "acda18f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 112 ms, sys: 20.9 ms, total: 133 ms\n",
      "Wall time: 196 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Save the dataframe to a pickle file for easier resuming of the notebook\n",
    "# Size was < 20 Mb 2/5/2024 COC\n",
    "# COC Note: this was not that useful, maybe TODO remove\n",
    "df_filename = f\"{basedir}/region_search_df.pickle\"\n",
    "df.to_pickle(df_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bb076b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the pickle\n",
    "# df = pd.read_pickle(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bdaaf00",
   "metadata": {},
   "source": [
    "### Instrument handling\n",
    "Some Butler queries require the instrument to be specified.\\\n",
    "For now, we are just supplying the one we care about (KLUDGE).\\\n",
    "It's a list so we can mix-and-match shift-and-stack across instruments(!).\\\n",
    "NOTE: will leave this as a list for future-proofing purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0228bb4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getInstruments(butler, vdr_ids, first_instrument_only=True, datasetType=desired_datasetTypes[0]):\n",
    "    \"\"\"Iterate through our records to determine which instrument(s) are involved.\n",
    "    Return a list of the identified instruments.\n",
    "    If first_instrument_only is True, stop as soon as we found an instrument.\n",
    "    \"\"\"\n",
    "    # KLUDGE: snag the instrument name of the first record we find in a visitInfo query.\n",
    "    instrument_names = []\n",
    "    for i, dataId in enumerate(vdr_ids):\n",
    "        visitInfo = butler.get(f\"{datasetType}.visitInfo\", dataId=dataId, collections=desired_collections)\n",
    "        instrument_name = visitInfo.instrumentLabel\n",
    "        if instrument_name not in instrument_names:\n",
    "            print(f'Found {instrument_name}. Adding to \"desired_instruments\" now.')\n",
    "            instrument_names.append(instrument_name)\n",
    "        if first_instrument_only == True and len(instrument_names) > 0:\n",
    "            print(\n",
    "                f\"WARNING: we are not iterating over all rows to find instruments, just taking the first one.\"\n",
    "            )\n",
    "            break\n",
    "    return instrument_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6c3bb244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found DECam. Adding to \"desired_instruments\" now.\n",
      "WARNING: we are not iterating over all rows to find instruments, just taking the first one.\n",
      "CPU times: user 1.07 s, sys: 99 ms, total: 1.17 s\n",
      "Wall time: 1.19 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "desired_instruments = getInstruments(butler=butler, vdr_ids=df[\"data_id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a434b2",
   "metadata": {},
   "source": [
    "### Butler Retrieval Example\n",
    "\n",
    "A quick stop to see how we can grab the full Butler record via a dataId."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dfc37b54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 947 ms, sys: 96.7 ms, total: 1.04 s\n",
      "Wall time: 1.06 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Back to the dataId, we can actually fetch the image from the Butler\n",
    "# TIMING NOTE: it takes 1 to 2 seconds to do this, so we won't be able to do this at scale\n",
    "\n",
    "example_butler_get = butler.get(\n",
    "    desired_datasetTypes[0], collections=desired_collections, dataId=example_vdr_ref.dataId\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "477c6e38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The example_vdr_ref has a .dataId of type <class 'lsst.daf.butler.core.dimensions._coordinate._BasicTupleDataCoordinate'>.\n",
      "ValuesView({instrument: 'DECam', detector: 62, visit: 946176})\n",
      "dataId did not have a values_tuple() attribute. Message was '_BasicTupleDataCoordinate' object has no attribute 'values_tuple'.\n",
      "Our manual vdr ref: {'instrument': 'DECam', 'detector': 62, 'visit': 946176}.\n"
     ]
    }
   ],
   "source": [
    "# let us try this without the special DataID Butler object 4/22/2024 COC\n",
    "print(f\"The example_vdr_ref has a .dataId of type {type(example_vdr_ref.dataId)}.\")  # .values_tuple()\n",
    "print(example_vdr_ref.dataId.values())\n",
    "example_vdr_ref_dataId_manual = clean_dataId(dataId=example_vdr_ref.dataId, verbose=True)\n",
    "# example_vdr_ref_dataId_manual = {}\n",
    "# for i in ['instrument', 'detector', 'visit']:\n",
    "#     example_vdr_ref_dataId_manual[i] = example_vdr_ref.dataId[i]\n",
    "print(f\"Our manual vdr ref: {example_vdr_ref_dataId_manual}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5814458",
   "metadata": {},
   "outputs": [],
   "source": [
    "example_butler_get_manual_dataId = butler.get(\n",
    "    desired_datasetTypes[0], collections=desired_collections, dataId=example_vdr_ref_dataId_manual\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "791b60ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with our alternate (manual) approach:\n",
    "example_butler_get_manual_dataId.detector.getId()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "789f1d42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which matches the same attribute via our original query:\n",
    "example_butler_get.detector.getId()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "351e8d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'VR'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_butler_get.filterLabel.bandLabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1c1e5431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0e1a87f520>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can even view the image right here\n",
    "plt.imshow(example_butler_get.image.array, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ba1b7ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FITS standard SkyWcs:\n",
       "Sky Origin: (352.4630539808, -4.8516830845)\n",
       "Pixel Origin: (1126.14, 1991.36)\n",
       "Pixel Scale: 0.262593 arcsec/pixel"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There IS a WCS here, which we did not see in the earlier VDR\n",
    "example_butler_get.wcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4270995e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are all the keywords available from this image:\n",
      "NEXTEND\n",
      "PROCTYPE\n",
      "PRODTYPE\n",
      "PIXSCAL1\n",
      "PIXSCAL2\n",
      "FILENAME\n",
      "TELESCOP\n",
      "OBSERVAT\n",
      "INSTRUME\n",
      "EXPREQ\n",
      "OBSID\n",
      "TIME-OBS\n",
      "OPENSHUT\n",
      "EXPNUM\n",
      "OBJECT\n",
      "OBSTYPE\n",
      "CAMSHUT\n",
      "PROGRAM\n",
      "OBSERVER\n",
      "PROPOSER\n",
      "DTPI\n",
      "PROPID\n",
      "EXCLUDED\n",
      "SEQID\n",
      "SEQNUM\n",
      "SEQTOT\n",
      "AOS\n",
      "BCAM\n",
      "GUIDER\n",
      "SKYSTAT\n",
      "FILTER\n",
      "INSTANCE\n",
      "ERRORS\n",
      "TELEQUIN\n",
      "TELSTAT\n",
      "RA\n",
      "DEC\n",
      "TELRA\n",
      "TELDEC\n",
      "HA\n",
      "ZD\n",
      "AZ\n",
      "DOMEAZ\n",
      "ZPDELRA\n",
      "ZPDELDEC\n",
      "TELFOCUS\n",
      "VSUB\n",
      "GSKYPHOT\n",
      "LSKYPHOT\n",
      "WINDSPD\n",
      "WINDDIR\n",
      "PRESSURE\n",
      "DIMMSEE\n",
      "DIMM2SEE\n",
      "MASS2\n",
      "ASTIG1\n",
      "ASTIG2\n",
      "OUTTEMP\n",
      "AIRMASS\n",
      "GSKYVAR\n",
      "GSKYHOT\n",
      "LSKYVAR\n",
      "LSKYHOT\n",
      "LSKYPOW\n",
      "MSURTEMP\n",
      "MAIRTEMP\n",
      "UPTRTEMP\n",
      "LWTRTEMP\n",
      "PMOSTEMP\n",
      "UTN-TEMP\n",
      "UTS-TEMP\n",
      "UTW-TEMP\n",
      "UTE-TEMP\n",
      "PMN-TEMP\n",
      "PMS-TEMP\n",
      "PMW-TEMP\n",
      "PME-TEMP\n",
      "DOMELOW\n",
      "DOMEHIGH\n",
      "DOMEFLOR\n",
      "G-MEANX\n",
      "G-MEANY\n",
      "DONUTFS4\n",
      "DONUTFS3\n",
      "DONUTFS2\n",
      "DONUTFS1\n",
      "G-FLXVAR\n",
      "G-MEANXY\n",
      "DONUTFN1\n",
      "DONUTFN2\n",
      "DONUTFN3\n",
      "DONUTFN4\n",
      "TIME_RECORDED\n",
      "G-FEEDBK\n",
      "G-CCDNUM\n",
      "DOXT\n",
      "G-MAXX\n",
      "FADZ\n",
      "FADY\n",
      "FADX\n",
      "G-MODE\n",
      "FAYT\n",
      "DODZ\n",
      "DODY\n",
      "DODX\n",
      "MULTIEXP\n",
      "SKYUPDAT\n",
      "G-SEEING\n",
      "G-TRANSP\n",
      "G-MEANY2\n",
      "DOYT\n",
      "G-LATENC\n",
      "LUTVER\n",
      "FAXT\n",
      "G-MAXY\n",
      "G-MEANX2\n",
      "SISPIVER\n",
      "CONSTVER\n",
      "HDRVER\n",
      "DTPROPID\n",
      "DTCALDAT\n",
      "DTSITE\n",
      "DTTELESC\n",
      "DTACQNAM\n",
      "DTINSTRU\n",
      "ODATEOBS\n",
      "DTNSANAM\n",
      "HISTORY\n",
      "COMMENT\n",
      "ZTENSION\n",
      "ZPCOUNT\n",
      "ZGCOUNT\n",
      "BUNIT\n",
      "DETSIZE\n",
      "DETSEC\n",
      "CCDSEC\n",
      "DETSECA\n",
      "CCDSECA\n",
      "AMPSECA\n",
      "DETSECB\n",
      "CCDSECB\n",
      "AMPSECB\n",
      "DETECTOR\n",
      "CCDNUM\n",
      "DETPOS\n",
      "EXTNAME\n",
      "GAINA\n",
      "RDNOISEA\n",
      "SATURATA\n",
      "GAINB\n",
      "RDNOISEB\n",
      "SATURATB\n",
      "FPA\n",
      "INHERIT\n",
      "CCDBIN1\n",
      "CCDBIN2\n",
      "DHEINF\n",
      "DHEFIRM\n",
      "SLOT00\n",
      "SLOT01\n",
      "SLOT02\n",
      "SLOT03\n",
      "SLOT04\n",
      "SLOT05\n",
      "LTV2\n",
      "LTV1\n",
      "VALIDA\n",
      "VALIDB\n",
      "NDONUTS\n",
      "CHECKVER\n",
      "ASTRO METADATA FIX MODIFIED\n",
      "ASTRO METADATA FIX DATE\n",
      "ISR_OSCAN_LEVELA\n",
      "ISR_OSCAN_SIGMAA\n",
      "OVERSCAN\n",
      "ISR_OSCAN_LEVELB\n",
      "ISR_OSCAN_SIGMAB\n",
      "SKYLEVEL\n",
      "SKYSIGMA\n",
      "FLATNESS_PP\n",
      "FLATNESS_RMS\n",
      "FLATNESS_NGRIDS\n",
      "FLATNESS_MESHX\n",
      "FLATNESS_MESHY\n",
      "BGMEAN\n",
      "BGVAR\n",
      "SFM_ASTROM_OFFSET_MEAN\n",
      "SFM_ASTROM_OFFSET_STD\n",
      "MAGZERO\n",
      "MAGZERO_RMS\n",
      "MAGZERO_NOBJ\n",
      "COLORTERM1\n",
      "COLORTERM2\n",
      "COLORTERM3\n"
     ]
    }
   ],
   "source": [
    "# We can access all kinds of metadata this way.\n",
    "# NOTE: keywords are source data-dependent.\n",
    "# NOTE: this linkage works with butler.get() but not necessarily elsewhere.\n",
    "meta = example_butler_get.getInfo().getMetadata()\n",
    "\n",
    "print(f\"Here are all the keywords available from this image:\")\n",
    "for k in meta.keys():\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f8d811",
   "metadata": {},
   "source": [
    "### URI / URL / Path Handling\n",
    "Here we grab URIs for the dataIds we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7bbc916e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: getURIs() does not work as you'd think (i.e., it only handles a single dataId)\n",
    "# butler.getURIs(desired_datasetTypes[0], vdr_ids, collections=desired_collections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e0f8f8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The single-thead approach (below) requires some 2 hours to execute. So instead we will multiprocess.\n",
    "# paths = [butler.getURI(desired_datasetTypes[0], dataId=dataId, collections=desired_collections) for dataId in vdr_ids]\n",
    "\n",
    "\n",
    "def chunked_dataIds(dataIds, chunk_size=200):\n",
    "    \"\"\"Yield successive chunk_size chunks from dataIds.\"\"\"\n",
    "    for i in range(0, len(dataIds), chunk_size):\n",
    "        yield dataIds[i : i + chunk_size]\n",
    "\n",
    "\n",
    "def get_uris(dataIds_chunk, repo_path, desired_datasetTypes, desired_collections):\n",
    "    \"\"\"Fetch URIs for a list of dataIds.\"\"\"\n",
    "    chunk_uris = []\n",
    "    butler = dafButler.Butler(repo_path)\n",
    "    for dataId in dataIds_chunk:\n",
    "        try:\n",
    "            uri = butler.getURI(desired_datasetTypes[0], dataId=dataId, collections=desired_collections)\n",
    "            uri = uri.geturl()  # Convert to URL string\n",
    "            chunk_uris.append(uri)\n",
    "        except Exception as e:\n",
    "            print(f\"Failed to retrieve path for dataId {dataId}: {e}\")\n",
    "    return chunk_uris\n",
    "\n",
    "\n",
    "def getURIs(butler, dataIds, repo_path, desired_datasetTypes, desired_collections, overwrite=False):\n",
    "    \"\"\"\n",
    "    Get URIs from a Butler for a set of dataIDs.\n",
    "    Cache results to disk for future runs.\n",
    "    TODO: consider exporting as CSV so we can validate URIs against dataIds. 2/6/2024 COC\n",
    "    Updated 2/5/2024 COC\n",
    "    \"\"\"\n",
    "    paths = []\n",
    "\n",
    "    cache_file = f\"{basedir}/uri_cache.lst\"\n",
    "    cached_exists = False\n",
    "    if len(glob.glob(cache_file)) > 0:\n",
    "        cached_exists = True\n",
    "\n",
    "    if cached_exists == True and overwrite == False:\n",
    "        with open(cache_file, \"r\") as f:\n",
    "            for line in f:\n",
    "                paths.append(line.strip())\n",
    "        print(f\"Recycled {len(paths)} paths from {cache_file} as overwrite was {overwrite}.\")\n",
    "        return paths\n",
    "\n",
    "    # Prepare dataId chunks\n",
    "    dataId_chunks = list(chunked_dataIds(dataIds))\n",
    "\n",
    "    # Execute get_uris in parallel and preserve order\n",
    "    with ProcessPoolExecutor() as executor:\n",
    "        # Initialize progress bar\n",
    "        with progressbar.ProgressBar(max_value=len(dataId_chunks)) as bar:\n",
    "            # Use map to execute get_uris on each chunk and maintain order\n",
    "            result_chunks = list(\n",
    "                executor.map(\n",
    "                    get_uris,\n",
    "                    dataId_chunks,\n",
    "                    [repo_path] * len(dataId_chunks),\n",
    "                    [desired_datasetTypes] * len(dataId_chunks),\n",
    "                    [desired_collections] * len(dataId_chunks),\n",
    "                )\n",
    "            )\n",
    "\n",
    "            for i, chunk_uris in enumerate(result_chunks):\n",
    "                paths.extend(chunk_uris)  # Add the retrieved URIs to the main list\n",
    "                bar.update(i)\n",
    "\n",
    "    with open(cache_file, \"w\") as f:\n",
    "        for path in paths:\n",
    "            print(path, file=f)\n",
    "        print(f\"Wrote {len(paths)} paths to disk for caching purposes.\")\n",
    "\n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6dd2ea6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recycled 47383 paths from /astro/users/coc123/kbmod_tmp/uri_cache.lst as overwrite was False.\n",
      "CPU times: user 16.6 ms, sys: 18.7 ms, total: 35.3 ms\n",
      "Wall time: 34.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# TIMING NOTE: This required 90s uncached 2/5/2024 COC\n",
    "\n",
    "df[\"uri\"] = getURIs(\n",
    "    butler=butler,\n",
    "    dataIds=df[\"data_id\"],\n",
    "    repo_path=repo_path,\n",
    "    desired_datasetTypes=desired_datasetTypes,\n",
    "    desired_collections=desired_collections,\n",
    "    overwrite=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eae8d366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///epyc/users/smotherh/DEEP/PointingGroups/butler-repo/PointingGroup021/imdiff_r/20210723T174135Z/deepDiff_differenceExp/20190927/VR/VR_DECam_c0007_6300.0_2600.0/898286/deepDiff_differenceExp_DECam_VR_VR_DECam_c0007_6300_0_2600_0_898286_S29_PointingGroup021_imdiff_r_20210723T174135Z.fits'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example URI\n",
    "df[\"uri\"].iloc()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb728cee",
   "metadata": {},
   "source": [
    "### Timestamp Handling\n",
    "Here we will access the timestamp (datetime) information from the Butler for our records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "df7ff31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 DateTime(\"2019-09-27T00:20:59.932016000\", TAI) 120.0 (351.3806941054, -5.2403083277)\n",
      "CPU times: user 92.1 ms, sys: 8.71 ms, total: 101 ms\n",
      "Wall time: 125 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Now we want to get metadata like datetime, exposure time, etc.\n",
    "for i, dataId in enumerate(df[\"data_id\"]):\n",
    "    visitInfo = butler.get(\"calexp.visitInfo\", dataId=dataId, collections=desired_collections)\n",
    "    print(i, visitInfo.date, visitInfo.exposureTime, visitInfo.boresightRaDec)\n",
    "    break\n",
    "# We have the visitInfo object for the exploration below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3ccadfaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lsst.daf.base.dateTime.dateTime.DateTime"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# That visitInfo.date format may not look familiar. Let's find out why:\n",
    "type(visitInfo.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d73d8119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-09-27T00:20:22.932'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's convert to a plain string, UTC (handles 37 s offset).\n",
    "t = Time(str(visitInfo.date).split('\"')[1], format=\"isot\", scale=\"tai\")\n",
    "str(t.utc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4cb53994",
   "metadata": {},
   "outputs": [],
   "source": [
    "# New parallel version with order-preservation redone 2/7/2024 COC\n",
    "\n",
    "\n",
    "# Define get_timestamps at the top level of your module\n",
    "def get_timestamps(dataIds_chunk, repo_path, desired_collections, datasetType=desired_datasetTypes[0]):\n",
    "    \"\"\"\n",
    "    Adding datasetType 4/22/2024 COC\n",
    "    \"\"\"\n",
    "    chunked_data = []\n",
    "    butler = dafButler.Butler(repo_path)\n",
    "    for dataId in dataIds_chunk:\n",
    "        try:\n",
    "            visitInfo = butler.get(f\"{datasetType}.visitInfo\", dataId=dataId, collections=desired_collections)\n",
    "            t = Time(str(visitInfo.date).split('\"')[1], format=\"isot\", scale=\"tai\")\n",
    "            tutc = str(t.utc)\n",
    "            chunked_data.append(tutc)\n",
    "        except Exception as e:\n",
    "            print(f\"Failed to retrieve timestamp for dataId {dataId}: {e}\")\n",
    "    return chunked_data\n",
    "\n",
    "\n",
    "def getTimestamps(dataIds, overwrite=False):\n",
    "    timestamps = []\n",
    "    cache_file = f\"{basedir}/vdr_timestamps.lst\"\n",
    "\n",
    "    if not overwrite and glob.glob(cache_file):\n",
    "        print(\"Overwrite is False, so we will read the timestamps from file now...\")\n",
    "        with open(cache_file, \"r\") as f:\n",
    "            timestamps = [line.strip() for line in f]\n",
    "        print(f\"Recycled {len(timestamps)} from {cache_file}.\")\n",
    "        return timestamps\n",
    "\n",
    "    def chunked_dataIds(dataIds, chunk_size=200):\n",
    "        for i in range(0, len(dataIds), chunk_size):\n",
    "            yield dataIds[i : i + chunk_size]\n",
    "\n",
    "    dataId_chunks = list(chunked_dataIds(dataIds))\n",
    "\n",
    "    with ProcessPoolExecutor() as executor:\n",
    "        # Adjust the executor.map call to pass additional arguments to get_timestamps\n",
    "        result_chunks = list(\n",
    "            executor.map(\n",
    "                get_timestamps,\n",
    "                dataId_chunks,\n",
    "                [repo_path] * len(dataId_chunks),\n",
    "                [desired_collections] * len(dataId_chunks),\n",
    "            )\n",
    "        )\n",
    "\n",
    "    timestamps = [timestamp for chunk in result_chunks for timestamp in chunk]\n",
    "\n",
    "    if overwrite or not glob.glob(cache_file):\n",
    "        with open(cache_file, \"w\") as f:\n",
    "            for ts in timestamps:\n",
    "                print(ts, file=f)\n",
    "        print(f\"Wrote {len(timestamps)} lines to {cache_file} for future use.\")\n",
    "\n",
    "    print(f\"Obtained {len(timestamps)} timestamps.\")\n",
    "    return timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7e0d33c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwrite is False, so we will read the timestamps from file now...\n",
      "Recycled 47383 from /astro/users/coc123/kbmod_tmp/vdr_timestamps.lst.\n",
      "CPU times: user 22.5 ms, sys: 2.42 ms, total: 24.9 ms\n",
      "Wall time: 24.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# TIMING NOTE: this took < 5 minutes 2/5/2024 COC\n",
    "df[\"ut\"] = getTimestamps(dataIds=df[\"data_id\"], overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "adddf1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2019-09-27T00:20:22.932\n",
       "1        2019-09-27T00:22:51.015\n",
       "2        2019-09-27T00:25:19.136\n",
       "3        2019-09-27T00:27:47.118\n",
       "4        2019-09-27T00:30:15.537\n",
       "                  ...           \n",
       "47378    2020-10-17T04:00:51.409\n",
       "47379    2020-10-17T04:03:19.873\n",
       "47380    2020-10-17T04:05:48.949\n",
       "47381    2020-10-17T04:08:17.445\n",
       "47382    2020-10-17T04:10:46.218\n",
       "Name: ut, Length: 47383, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ut\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "55ffd374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-09-27T00:20:22.932'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the format of the ut column shows that it is just a string:\n",
    "df[\"ut\"].iloc()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "faea89df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert \"ut\" column to datetime\n",
    "df[\"ut_datetime\"] = pd.to_datetime(df[\"ut\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c3beca0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# assert len(df[\"data_id\"]) == len(set(df[\"data_id\"]))\n",
    "# assert len(df[\"data_id\"]) == len(set([str(df[\"data_id\"].iloc()[i]) for i in range(0,len(df.index))]))\n",
    "assert df[\"data_id\"].apply(str).is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b45d3fa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def look_for_duplicates(df, verbose=True):\n",
    "    \"\"\"\n",
    "    As a reality check, we make sure there are no duplicate pairs of identical dataId + UT.\n",
    "    3/11/2024 COC\n",
    "    \"\"\"\n",
    "    if verbose:\n",
    "        print(f\"Starting look_for_duplicates()...\")\n",
    "    # make sure no duplicate URIs first (easy + fast)\n",
    "    if len(df[\"uri\"]) != len(set(df[\"uri\"])):\n",
    "        raise AttributeError(\n",
    "            f'There were only {len(set(df[\"uri\"]))} unique URIs. Should have been {len(df[\"uri\"])}.'\n",
    "        )\n",
    "    #\n",
    "    if (\n",
    "        not df[\"data_id\"].apply(str).is_unique\n",
    "    ):  # len(df[\"data_id\"]) != len(set([str(df[\"data_id\"].iloc()[i]) for i in range(0,len(df.index))])):\n",
    "        raise AttributeError(f\"Encountered non-unique set of dataIds.\")\n",
    "    #\n",
    "    dataId_uts = df[\"ut\"].astype(str) + df[\"data_id\"].astype(\n",
    "        str\n",
    "    )  # e.g., \"2019-09-27T00:20:22.932{instrument: 'DECam', detector: 1, visit: 898286}\"\n",
    "    if len(dataId_uts) != len(set(dataId_uts)):\n",
    "        raise AttributeError(f\"dataID + UT combinations were not unique\")\n",
    "    if verbose:\n",
    "        print(f\"No duplication found amongst the {len(df.index)} dataframe rows.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "83fe7b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting look_for_duplicates()...\n",
      "No duplication found amongst the 47383 dataframe rows.\n"
     ]
    }
   ],
   "source": [
    "look_for_duplicates(df=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "381e68de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['data_id', 'region', 'detector', 'data_id_str', 'uri', 'ut',\n",
       "       'ut_datetime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1f33735a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"2019-09-27T00:20:22.932{'instrument': 'DECam', 'detector': 1, 'visit': 898286}\""
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataId_uts = df[\"ut\"].astype(str) + df[\"data_id\"].astype(str)\n",
    "dataId_uts.iloc()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "72aa75be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataId_uts) == len(set(dataId_uts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a789a331",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is for coming back to later to make sure the stamps line up\n",
    "# IGNORE FOR NOW 2/6/2024 COC\n",
    "# for i in range(0,len(vdr_ids),1000):\n",
    "#     dataId = vdr_ids[i]\n",
    "#     visitInfo = butler.get(\"calexp.visitInfo\", dataId=dataId, collections=desired_collections)\n",
    "#     t = Time(str(visitInfo.date).split('\"')[1], format=\"isot\", scale=\"tai\")\n",
    "#     tutc = str(t.utc)\n",
    "#     print(f\"{i!s:5}: {tutc} for {dataId}. Dict had: {id_to_date[dataId]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f355bcf",
   "metadata": {},
   "source": [
    "### Working with the Regions\n",
    "\n",
    "We still don't know what will be ideal to do for Region Search.\\\n",
    "So, to start with, we will work on extract the (RA, Dec) of the (1) center coordinate of a chip, and (2) four corners associated with a chip."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f060a724",
   "metadata": {},
   "source": [
    "#### We will start with corners (i.e., quadilateral vertices from the convexPolygon sphgeom.regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e45d7199",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getRegionCorners(region):\n",
    "    \"\"\"\n",
    "    Using the 2D boundingBox() from an input region (convexPolygon), we\n",
    "    extract the (RA, Dec) coordinates of each vertex.\n",
    "    As there are four vertices, the input object is a quadrilateral.\n",
    "    2/2/2024 COC\n",
    "    \"\"\"\n",
    "    corners = []\n",
    "    bbox = region.getBoundingBox()\n",
    "    corners.append((bbox.getLon().getA().asDegrees(), bbox.getLat().getA().asDegrees()))\n",
    "    corners.append((bbox.getLon().getA().asDegrees(), bbox.getLat().getB().asDegrees()))\n",
    "    corners.append((bbox.getLon().getB().asDegrees(), bbox.getLat().getA().asDegrees()))\n",
    "    corners.append((bbox.getLon().getB().asDegrees(), bbox.getLat().getB().asDegrees()))\n",
    "    return corners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9b278b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(352.30950511932116, -4.930880058593892),\n",
       " (352.30950511932116, -4.7450820235469715),\n",
       " (352.64644359666477, -4.930880058593892),\n",
       " (352.64644359666477, -4.7450820235469715)]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example\n",
    "corners = getRegionCorners(example_vdr_ref.region)\n",
    "corners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b37bfe47",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMinMaxRaDec(ra_dec_touples, verbose=False):\n",
    "    \"\"\"\n",
    "    Highly unoptimized way to find and return\n",
    "    (minRA, maxRA) and (minDec, MaxDec).\n",
    "    2/2/2024 COC\n",
    "    \"\"\"\n",
    "    min_ra = min([i[0] for i in ra_dec_touples])\n",
    "    min_dec = min([i[1] for i in ra_dec_touples])\n",
    "    max_ra = max([i[0] for i in ra_dec_touples])\n",
    "    max_dec = max([i[1] for i in ra_dec_touples])\n",
    "    if verbose:\n",
    "        print(f\"RA range: {min_ra} to {max_ra}\")\n",
    "        print(f\"Dec range: {min_dec} to {max_dec}\")\n",
    "    return (min_ra, max_ra), (min_dec, max_dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ef329736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((352.30950511932116, 352.64644359666477),\n",
       " (-4.930880058593892, -4.7450820235469715))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example\n",
    "getMinMaxRaDec(ra_dec_touples=corners)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95593bb",
   "metadata": {},
   "source": [
    "#### Centers\n",
    "\n",
    "It may be easer to just work with center (RA, Dec) coordinates.\\\n",
    "We can later store this in a DB (e.g., Postgres) for cone searches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4c89be9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCenterRaDec(region):\n",
    "    \"\"\"\n",
    "    We pull the 2D boundingBox (not the boundingBox3d) from a region.\n",
    "    Then we extract the center's (RA, Dec) coordinates.\n",
    "    2/2/2024 COC\n",
    "    \"\"\"\n",
    "    bbox_center = region.getBoundingBox().getCenter()\n",
    "    ra = bbox_center.getLon().asDegrees()\n",
    "    dec = bbox_center.getLat().asDegrees()\n",
    "    return (ra, dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8e1e764c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(352.477974357993, -4.837981041070432)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getCenterRaDec(example_vdr_ref.region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8e54db29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 277 ms, sys: 1.25 ms, total: 278 ms\n",
      "Wall time: 278 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df[\"center_coord\"] = [getCenterRaDec(i) for i in df[\"region\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "64908868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47383"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"center_coord\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "625de417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(351.0694028401149, -4.336598368890197)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"center_coord\"].iloc()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "67b57215",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.26 s, sys: 27.4 ms, total: 1.29 s\n",
      "Wall time: 1.29 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0e1064b580>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sWYMRI0ZIlne5XHC5XO7HSveMtEjrwyRar0+JupSoj6g3+pQkpk+fjnfeeQdFRUV4++23ERYWhnHjxuEf//gHUlNT5Y4RQgjk5ORgwYIFSE5ORkNDQ6+Oy8rKwty5c5GQkID6+nq8+OKLSE9Px2effQaz2dztMcXFxSgsLJQxelJSYv4uWU+ectWn9UTG+Q3qrT5fAjtt2jRMm9a/D5fNZvN6Qq6pqUF1dTWcTicKCgp8qv/JJ590/3vs2LFITk5GQkICdu3ahVmzZnV7TEFBAZ5//nn3Y6fTibi4OJ9el8jf5D7p31pkT846mZj0qc9Joq2tDW+//TZOnTqFF154AUOHDsXnn3+OqKgo3HXXXb2qIy8vD9nZ2T2WSUxMxOrVq3Hw4MEuf/0nJyfjqaee6nIFkxSr1YqEhAQcP35csozZbJbsZQQKrQ+TNKyd1mVJBy3VZwRcXI9u6VOSOHr0KKZMmYIhQ4agoaEBP/vZzzB06FBUVlbi9OnT2Lp1a6/qsVgssFgsXstt2rQJq1evdj9uamrCY489hvLycqSkpPQ67vPnz6OxsRFWq7XXx5A26SGR6bm+kKC+JwouUWKsXlKfksTzzz+PnJwcrFu3DhEREe79WVlZmDdvnmzB3RIfH+/xeNCgQQCAUaNGITY21r0/KSkJxcXFmDlzJi5dugSbzYbZs2fDarWioaEBy5cvh8ViwcyZM2WPkYxJDycTPcQod13kP31KEjU1NfjDH/7QZf9dd92FlpaWfgfVV3a7HQ6HAwAQHByMuro6bN26FW1tbbBarUhLS0N5eblHYiMiIml9ShJ33HFHt5eG2u12DB8+vN9BeZOYmIjuVhPpvC8sLAy7d+9WPBYivdJDj0EPMSpRn5b0KUnMmDEDq1atwvbt2wEAJpMJZ86cQX5+PmbPni1rgOQbPQw96CFGvdSnRJ1GPuGR7/qUJDZs2IAf/OAHGDFiBDo6OpCamoqWlhZMmjQJa9askTtG0jitn/QDuT656qTA1ackMXjwYHz66af4+OOP8dlnn+HmzZuYMGECpkyZInd8RKQQPSQPPcRodD4niZs3b2LLli3YsWMHGhoaYDKZMHLkSERHR0MIAZPJpESc1Et6GHrQQ4xE9C2ffk9CCIHHH38c77//Ph544AEkJSVBCIF///vfqKurcy/XYSRG+j0JPQ2TBEJ9RGpR7PcktmzZgk8++QQfffQR0tLSPJ7bu3cvnnjiCWzduhU/+clPfI+aSEFc44mob3z6PYm//vWvWL58eZcEAQDp6enIz8/Htm3bZAuOiIjU5VNP4ujRo1i3bp3k81lZWdi0aVO/gyJlaH0ugGs8EWmPT3MSoaGhOH36tOTaR01NTRg5cqTH7zHonZHmJIiIAAXnJG7cuIEBA6QPCQ4OxjfffONLlUSkE0rOn+zM+x7Gxd4pa51anOPR45yRT0ni1o//SC2lbaQeBJEvAv3O9P769MQ5WZIEyc+nJPHMM894LcMrm4jIV5Pv8f6TAaQOn5LE5s2blYqD/EzJv1IHhQbhy1VZstaptb98tUzrPZD0DR/j1LkrstbJhQCV0+dfpiP/0tMJ89I1bf6smZ4WAlSiTq18bjonCNI+Jgnqt9svCx0U6tPtN17r6y8l69PyX8G36tZKcrjlbks4E4WOMEkEKKVOHEb96zeQyf3/YO8LXW/G7S9+TpTDJKET/BLIg+8jkW98upkuEOnhZjqtD38Ecn1y1UkkJ1/Oa/0bPCYyGK0uu5GYv8u9EfkTkwQREUnicJMXehhuov7T0xAWh6+ov3w5rzFJeMEkQUan9YQ2qmAXbnQ6S2kxRq3XdzvOSRCRYdzgn7GqYpIgIk0LNqkdQWDjcJMXHG4irdD6kAZXwtUPxX5PguSn9S9BIH7xeSIh+j9MEiRJT2soab0+ra/JpMU1nkgbdDMnkZiYCJPJ5LHl5+f3eIwQAjabDTExMQgLC8MjjzyCY8eO+SliInlpfV2shrXT3JtW61QiRqPTVU9i1apVyM3NdT8eNGhQj+XXrVuHjRs3YsuWLRgzZgxWr16NqVOnwm63IyIiQulwe0XrX3yl6g20+oj0SldJIiIiAtHR0b0qK4TAq6++ihUrVmDWrFkAgNLSUkRFReGtt97C/PnzlQzVr7Q+Jh9o9Y1evgvXO/2kBn8Eh/RMN8NNAFBSUoJhw4bhwQcfxJo1a3Dt2jXJsvX19WhpaUFGRoZ7n9lsRmpqKqqrqyWPc7lccDqdHhuRL65r8zeXiPpENz2JxYsXY8KECYiMjMShQ4dQUFCA+vp6vPnmm92Wb2lpAQBERUV57I+KisLp06clX6e4uBiFhYXyBU4BJySIiYKMQ9X7JGw2m9cTck1NDZKTk7vsr6iowJw5c3Du3DkMGzasy/PV1dX43ve+h6amJlitVvf+3NxcNDY24sMPP+z29VwuF1wul/ux0+lEXFwc75MgIsPQzX0SeXl5yM7O7rFMYmJit/sfeughAMCJEye6TRK35i5aWlo8kkRra2uX3kVnZrMZZrPZW+hERAFB1SRhsVhgsVj6dOyRI0cAwCMBdDZy5EhER0djz549GD9+PADg2rVr2L9/P0pKSvoWMJEBaf1CACV+xElPbVb74gJdTFwfOHAAr7zyCmpra1FfX4/t27dj/vz5mD59OuLj493lkpKSUFlZCQAwmUxYsmQJioqKUFlZiS+//BI5OTkIDw/HvHnz1GoKEZGu6GLi2mw2o7y8HIWFhXC5XEhISEBubi6WLl3qUc5ut8PhcLgfL126FB0dHVi4cCEuXryIlJQUVFVVaeYeCSIireMCf14ovcCf1rupXLtJ+a6+1uPnZ8B495Lw9ySIiEgWuhhuInXoaQE9rdcXKBO4ZDxMEirTyzILWo9T6/X587W0Xp8SdWq9Pj3jcBMREUnixLUXav4yndYn4wKtPiXq5HAPqYET10REJAsmCSIiksThJi/UHG4iIlKCbhb4IyLlaH3+JBDnjLReX3c43ERERJKYJIiISBLnJLzgnAQpTetDEBwWMt6lyZyT0ACtf2jlrm/apk9wrKld1jq13majn0iIACYJkoGe1lDSYn1KSszfxQRG/cIkQWQwDWunyZrIuHZTYGOSUIjWP7RcCFCb9RFpDZOEhml9DD2Q61OiTjkTDpMXyYWXwBIRkSQmCSIiksT7JLzgfRJEZDRcKpyIiGTBiWsig9LThQC8k1sb9XWHPQkiIpLEJEFERJI4ce1FXyeutd6t1Hp9SvB3m7X+HitZ3/CBIah5MUPWOrXeZq1+7rvDBf7IEPT6BSTgf5evqx0CyYRJgvpN6wvo6ak+rU7g+mr4wBC/vyYpQzdJIjExEadPn/bYt2zZMqxdu1bymJycHJSWlnrsS0lJwcGDBxWJsTOtrxGkl7Wb5ObP91Hr/8+0Xp8SdWq9Pi3STZIAgFWrViE3N9f9eNCgQV6PyczMxObNm92PQ0NDFYmNlBEIX0IiLdNVkoiIiEB0dLRPx5jNZp+P0Ru1J+O8lQm0+nytUw9/gVPg0tUlsCUlJRg2bBgefPBBrFmzBteuXfN6zL59+zBixAiMGTMGubm5aG1t7bG8y+WC0+n02IiIApVuehKLFy/GhAkTEBkZiUOHDqGgoAD19fV48803JY/JysrC3LlzkZCQgPr6erz44otIT0/HZ599BrPZ3O0xxcXFKCwsVKoZRES6oup9EjabzesJuaamBsnJyV32V1RUYM6cOTh37hyGDRvWq9drbm5GQkICysrKMGvWrG7LuFwuuFwu92On04m4uDgu8EdEhqGb+yTy8vKQnZ3dY5nExMRu9z/00EMAgBMnTvQ6SVitViQkJOD48eOSZcxms2Qvg8iI1J7T8qU+JeoMhPr6Q9UkYbFYYLFY+nTskSNHAHx74u+t8+fPo7Gx0adjiIgCmS4mrg8cOIBXXnkFtbW1qK+vx/bt2zF//nxMnz4d8fHx7nJJSUmorKwEAFy6dAkvvPACDhw4gIaGBuzbtw+PP/44LBYLZs6cqVZTiIh0RRcT12azGeXl5SgsLITL5UJCQgJyc3OxdOlSj3J2ux0OhwMAEBwcjLq6OmzduhVtbW2wWq1IS0tDeXk5IiIi1GgGBRitD0FovT4l6tR6fUrU1V+6SBITJkzo1V3Snefgw8LCsHv3biXDkoXWP7T84mvny0qkBl0kCTIGPa2hpHWJ+buYwMgvmCSIApQe1jHSeoyBkKiZJFSm9Q+tXhYC1Hp9RHrFJGEAWh+TD7T6lKqTiYvUoItLYImISB1MEkREJIm/ce1FX3/jmohIq3w5r7EnQUREkjhxTRTgtH4hABf4U/eCBfYkiIhIEpMEERFJ4sS1F0pPXGu9m6qlbq8UvbdZ6/Hr4T4SrdenNbr50SEKTEb/AhIZCZMEaY7W/ypkkqNAwiShMq2vOaSHk2Agtrkneng/tB6j3j8DcmKSIL/jF5BIP5gkAojWh130VJ9cdcpZD5ESeAksERFJYpIgIiJJvE/CCy7wR0RGw/skiEh2Wp8zUqJOrdfnDxxuIiIiSUwSREQkicNNRCrT+pCGHi4l1nqblajLX5gkdELrX4JA/uITGRmTBOmW1pMIkxIZAZMEkcq0vu6Q1utTok4m9f+jqySxa9curFq1CkePHsXAgQPx/e9/Hzt27JAsL4RAYWEh3njjDVy8eBEpKSl4/fXXcf/99/sxanlo/UvALz6RMekmSVRUVCA3NxdFRUVIT0+HEAJ1dXU9HrNu3Tps3LgRW7ZswZgxY7B69WpMnToVdrsdERERfopcf7Q+7KL1+pSoi0gtukgS33zzDRYvXoz169fj2Wefde+/9957JY8RQuDVV1/FihUrMGvWLABAaWkpoqKi8NZbb2H+/PmKx01EpHe6uE/i888/x9mzZxEUFITx48fDarUiKysLx44dkzymvr4eLS0tyMjIcO8zm81ITU1FdXW15HEulwtOp9NjIyIKVLroSZw6dQoAYLPZsHHjRiQmJuLll19Gamoq/vvf/2Lo0KFdjmlpaQEAREVFeeyPiorC6dOnJV+ruLgYhYWFMkavP1qfC9B6fURGompPwmazwWQy9bgdPnwYN2/eBACsWLECs2fPxsSJE7F582aYTCb87W9/6/E1TCaTx2MhRJd9nRUUFMDhcLi3xsbG/jeUiEinVO1J5OXlITs7u8cyiYmJaG9vBwDcd9997v1msxl33303zpw50+1x0dHRAL7tUVitVvf+1tbWLr2LzsxmM8xmc6/bQESeeKOmsXqmqiYJi8UCi8XitdzEiRNhNptht9sxefJkAMD169fR0NCAhISEbo8ZOXIkoqOjsWfPHowfPx4AcO3aNezfvx8lJSXyNYKIyMB0MXE9ePBgLFiwACtXrkRVVRXsdjuee+45AMDcuXPd5ZKSklBZWQng22GmJUuWoKioCJWVlfjyyy+Rk5OD8PBwzJs3T5V2EBHpjS4mrgFg/fr1GDBgAH784x+jo6MDKSkp2Lt3LyIjI91l7HY7HA6H+/HSpUvR0dGBhQsXum+mq6qqMvQ9EnroRushRi3S+vsWyJ8VI3/udJMkQkJCsGHDBmzYsEGyzO0/smcymWCz2WCz2RSOjojImHQx3EREROrgb1x7wd+4JiKj8eW8xp4EERFJ0s2cBGmP1icVA2VCm0hJ7EkQEZEkJgkiIpLEiWsvOHFNREbjy3mNcxJEpIpAvvlOTzjcREREkpgkiIhIEoebiAxGD0MueohR7rr0ikkiQCn5JTUHA/Y12v3iE1HvcbiJZOe6oXYERCQX9iRIduZgtSMIbHr4DXA9xEjf4n0SXvA+CSIyGi7wR0REsmCSICIiSUwSREQkiUmCiIgkMUkQEZEkJgkiIpLEJEFERJKYJIiISBLvuCYKUHpYZI/rd6mPPQkiIpLEJEFERJK4dpMXXLuJiIzGsGs37dq1CykpKQgLC4PFYsGsWbN6LJ+TkwOTyeSxPfTQQ36KlohI/3QzcV1RUYHc3FwUFRUhPT0dQgjU1dV5PS4zMxObN292Pw4NDVUyTCIiQ9FFkvjmm2+wePFirF+/Hs8++6x7/7333uv1WLPZjOjoaCXDIyIyLF0MN33++ec4e/YsgoKCMH78eFitVmRlZeHYsWNej923bx9GjBiBMWPGIDc3F62trT2Wd7lccDqdHhsRUaDSRZI4deoUAMBms+E3v/kN3nvvPURGRiI1NRUXLlyQPC4rKwvbtm3D3r178fLLL6Ompgbp6elwuVySxxQXF2PIkCHuLS4uTvb2EBHphlDRypUrBYAet5qaGrFt2zYBQPzhD39wH3v16lVhsVjE73//+16/XlNTkwgJCREVFRWSZa5evSocDod7a2xsFACEw+HoV1uJiLTC4XD0+rym6pxEXl4esrOzeyyTmJiI9vZ2AMB9993n3m82m3H33XfjzJkzvX49q9WKhIQEHD9+XLKM2WyG2WzudZ1EREamapKwWCywWCxey02cOBFmsxl2ux2TJ08GAFy/fh0NDQ1ISEjo9eudP38ejY2NsFqtfY6ZiCiQ6GJOYvDgwViwYAFWrlyJqqoq2O12PPfccwCAuXPnusslJSWhsrISAHDp0iW88MILOHDgABoaGrBv3z48/vjjsFgsmDlzpirtICLSG11cAgsA69evx4ABA/DjH/8YHR0dSElJwd69exEZGekuY7fb4XA4AADBwcGoq6vD1q1b0dbWBqvVirS0NJSXlyMiIkKtZhAR6QqX5fDC4XDgzjvvRGNjI5flICJDcDqdiIuLQ1tbG4YMGdJjWd30JNRya9Kcl8ISkdG0t7d7TRLsSXhx8+ZNNDU1QQiB+Pj4gO5R3PrrI1Dfg0BvP8D3wCjtF0Kgvb0dMTExCArqeWqaPQkvgoKCEBsb677zevDgwbr+cMgh0N+DQG8/wPfACO331oO4RRdXNxERkTqYJIiISBKTRC+ZzWasXLkyoO/GDvT3INDbD/A9CMT2c+KaiIgksSdBRESSmCSIiEgSkwQREUlikiAiIkkBnSR+97vfYdy4ce4bYyZNmoQPPvjA/XxOTg5MJpPH9tBDD3nU4XK5sGjRIlgsFgwcOBDTp0/HV1995e+m9Ikc7X/jjTfwyCOPYPDgwTCZTGhra/NzK/qnv+/BhQsXsGjRItx7770IDw9HfHw8fvGLX7gXmtQ6OT4D8+fPx6hRoxAWFobhw4djxowZ+M9//uPvpvSZHO/BLUIIZGVlwWQy4Z133vFTC5QV0EkiNjYWa9euxeHDh3H48GGkp6djxowZHr+dnZmZiebmZvf2/vvve9SxZMkSVFZWoqysDJ9++ikuXbqEH/7wh7hx44a/m+MzOdp/5coVZGZmYvny5f4OXxb9fQ+amprQ1NSEDRs2oK6uDlu2bMGHH36IZ599Vo3m+EyOz8DEiROxefNm/Pvf/8bu3bshhEBGRoYuvgOAPO/BLa+++ipMJpO/QvcPxX4fT6ciIyPFm2++KYQQ4plnnhEzZsyQLNvW1iZCQkJEWVmZe9/Zs2dFUFCQ+PDDD5UOVRG+tL+zjz/+WAAQFy9eVC44P+nre3DL9u3bRWhoqLh+/boC0Smvv+3/4osvBABx4sQJBaLzj768B7W1tSI2NlY0NzcLAKKyslLZIP0koHsSnd24cQNlZWW4fPkyJk2a5N6/b98+jBgxAmPGjEFubi5aW1vdz3322We4fv06MjIy3PtiYmIwduxYVFdX+zX+/upL+41GrvfA4XBg8ODBGDBAX0ujydH+y5cvY/PmzRg5cqQuV07u63tw5coV/OhHP8Jrr72G6Ohof4etLLWzlNqOHj0qBg4cKIKDg8WQIUPErl273M+VlZWJ9957T9TV1YmdO3eKBx54QNx///3i6tWrQgghtm3bJkJDQ7vUOXXqVPHzn//cb23oj/60vzM99yTkeg+EEOLcuXMiPj5erFixwl/h95sc7X/99dfFwIEDBQCRlJSku15Ef9+Dn//85+LZZ591P4aBehIBnyRcLpc4fvy4qKmpEfn5+cJisYhjx451W7apqUmEhISIiooKIYR0kpgyZYqYP3++onHLpT/t70zPSUKu98DhcIiUlBSRmZkprl27pnTYspGj/W1tbeK///2v2L9/v3j88cfFhAkTREdHhz/Cl0V/3oN3331X3HPPPaK9vd1dhknCwB599NEeewH33HOPWLt2rRBCiI8++kgAEBcuXPAoM27cOPHSSy8pGqdSfGl/Z3pOErfry3vgdDrFpEmTxKOPPqqrk2N3+voZuMXlconw8HDx1ltvKRGeX/jyHixevFiYTCYRHBzs3gCIoKAgkZqa6qeIlcM5idsIIeByubp97vz582hsbITVagXw7VUdISEh2LNnj7tMc3MzvvzySzz88MN+iVduvrTfqHx9D5xOJzIyMhAaGoqdO3fijjvu8FeoipDjM9BTHXrgy3uQn5+Po0ePora21r0BwCuvvILNmzf7K2TlqJmh1FZQUCA++eQTUV9fL44ePSqWL18ugoKCRFVVlWhvbxe/+tWvRHV1taivrxcff/yxmDRpkrjrrruE0+l017FgwQIRGxsr/vGPf4jPP/9cpKeniwceeEB88803Krasd+Rof3Nzszhy5Ij44x//KACITz75RBw5ckScP39exZb1Xn/fA6fTKVJSUsR3v/tdceLECdHc3OzeAuEzcPLkSVFUVCQOHz4sTp8+Laqrq8WMGTPE0KFDxddff61y63pHju/B7cDhJmP46U9/KhISEkRoaKgYPny4ePTRR0VVVZUQQogrV66IjIwMMXz4cBESEiLi4+PFM888I86cOeNRR0dHh8jLyxNDhw4VYWFh4oc//GGXMlolR/tXrlwpAHTZNm/erEKLfNff9+DWMFt3W319vUqt6r3+tv/s2bMiKytLjBgxQoSEhIjY2Fgxb9488Z///EetJvlMju/B7YyUJLhUOBERSeKcBBERSWKSICIiSUwSREQkiUmCiIgkMUkQEZEkJgkiIpLEJEFERJKYJIiISBKTBJEfdf4pzAEDBiA+Ph7PPfccLl686FGuo6MDkZGRGDp0KDo6OlSKlohJgsjvbv0UZkNDA9588038/e9/x8KFCz3KVFRUYOzYsbjvvvuwY8cOlSIlAvT101lEBmA2m92/XhYbG4snn3wSW7Zs8Sjzpz/9CU8//TSEEPjTn/6Ep556SoVIiZgkiFR16tQpfPjhhwgJCXHvO3nyJA4cOIAdO3ZACIElS5bg1KlTuPvuu1WMlAIVh5uI/Oy9997DoEGDEBYWhlGjRuFf//oXli1b5n7+z3/+M7KystxzEpmZmfjzn/+sYsQUyJgkiPwsLS0NtbW1+Oc//4lFixbhsccew6JFiwAAN27cQGlpKZ5++ml3+aeffhqlpaW4ceOGWiFTAGOSIPKzgQMH4p577sG4ceOwadMmuFwuFBYWAgB2796Ns2fP4sknn8SAAQMwYMAAZGdn46uvvkJVVZXKkVMg4u9JEPlRTk4O2tra8M4777j37du3D1lZWTh58iQWLVqE0NBQrFixwuO4tWvX4urVq3j77bf9HDEFOk5cE6nskUcewf333481a9bg73//O3bu3ImxY8d6lHnmmWcwbdo0/O9//8Pw4cNVipQCEYebiDTg+eefxxtvvIHr16/j0Ucf7fJ8WloaIiIi8Je//EWF6CiQcbiJiIgksSdBRESSmCSIiEgSkwQREUlikiAiIklMEkREJIlJgoiIJDFJEBGRJCYJIiKSxCRBRESSmCSIiEgSkwQREUlikiAiIkn/HyByXtMGv8u+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Let us visualize where on the sky our chip centers reside\n",
    "fig = plt.figure(figsize=(4, 4))\n",
    "plt.xlabel(\"RA\")\n",
    "plt.ylabel(\"Dec\")\n",
    "plt.scatter(\n",
    "    [i[0] for i in df[\"center_coord\"].iloc()], [i[1] for i in df[\"center_coord\"].iloc()], s=1, alpha=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d4fd38",
   "metadata": {},
   "source": [
    "The DECam mosaic shape is clearly visible, thrice.\\\n",
    "There is an offset from each of the three pointings, too.\n",
    "\n",
    "In the most simple approach, each of these \"dots\" represents a set of images that can be fed to KBMOD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c2dc4e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instrument': 'DECam', 'detector': 1, 'visit': 898286}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the first dataId, which does not show up in a pretty way in the DF\n",
    "df[\"data_id\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "580ac4ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0e105dab80>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us look at just detector 62 pointings.\n",
    "# This will give us a decent idea of how many pointings there are (here, 8).\n",
    "\n",
    "df62 = df[df[\"detector\"] == 62]\n",
    "plt.clf()\n",
    "plt.scatter(*zip(*df62[\"center_coord\"]), s=1, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6ee544b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# chip62_coord0 = df62['center_coord'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7d441d",
   "metadata": {},
   "source": [
    "### Region Matching\n",
    "Here region matching means comparing two lsst.sphgeom.region objects.\\\n",
    "Purpose: to see if they have any overlap whatsoever. \\\n",
    "NOTE: a partial or full overlap is called an \"intersection\" in sphgeom lingo.\n",
    "\n",
    "NOTE: Work in progress, but pausing this avenue for now. 2/2/2024 COC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2c770982",
   "metadata": {},
   "outputs": [],
   "source": [
    "# manual region-region matching between all chips\n",
    "# NOTE: estimate time is 1 hour, so skipping this for now\n",
    "\n",
    "doit = False\n",
    "if doit == True:\n",
    "    lastTime = time.time()\n",
    "\n",
    "    matches = {}\n",
    "\n",
    "    with progressbar.ProgressBar(max_value=len(vdr_regions)) as bar:\n",
    "        for i, l in enumerate(vdr_regions):\n",
    "            my_id = vdr_ids[i]\n",
    "            matches[my_id] = []\n",
    "            for j, r in enumerate(vdr_regions):\n",
    "                if i == j:\n",
    "                    continue\n",
    "                if l.intersects(r):\n",
    "                    matches[my_id].append(vdr_ids[j])\n",
    "            bar.update(i)\n",
    "    elapsed = round(time.time() - lastTime, 1)\n",
    "    print(f\"It took {elapsed} seconds.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8db514",
   "metadata": {},
   "source": [
    "To be continued..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00d9b2f",
   "metadata": {},
   "source": [
    "### HTM Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2fcdd8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colin playing 1/15/2024 COC\n",
    "def getHTMstuff(ra, dec, level=7, verbose=False):\n",
    "    \"\"\"\n",
    "    1/16/2024 COC inception.\n",
    "    A function that\n",
    "        1. Fetches an HTM ID for a pixelization of a user-supplied level.\n",
    "        2. Determines the angular size (radius, in arcseconds) of the pixelization level.\n",
    "    LSST stores the unique HTM ID for level 7 pixelization in the Butler, hence the default level=7.\n",
    "    \"\"\"\n",
    "    pixelization = lsst.sphgeom.HtmPixelization(level)\n",
    "\n",
    "    try:  # kludges; use the Butler way, but if being passed normal numbers, use those\n",
    "        ra = ra.asDegrees()\n",
    "    except AttributeError as msg:\n",
    "        pass\n",
    "    try:\n",
    "        dec = dec.asDegrees()\n",
    "    except AttributeError as msg:\n",
    "        pass\n",
    "\n",
    "    htm_id = pixelization.index(\n",
    "        lsst.sphgeom.UnitVector3d(\n",
    "            # sphgeom.LonLat.fromDegrees(ra.asDegrees(), dec.asDegrees())\n",
    "            lsst.sphgeom.LonLat.fromDegrees(ra, dec)\n",
    "        )\n",
    "    )\n",
    "    circle = pixelization.triangle(htm_id).getBoundingCircle()\n",
    "    scale = circle.getOpeningAngle().asDegrees() * 3600.0\n",
    "    level = pixelization.getLevel()\n",
    "    if verbose:\n",
    "        print(f\"HTM ID={htm_id} at level={level} is bounded by a circle of radius ~{scale:0.2f} arcsec.\")\n",
    "    return (htm_id, scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c0916372",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTM ID=189361 at level=7 is bounded by a circle of radius ~1895.11 arcsec.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(189361, 1895.111766130883)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getHTMstuff(df[\"center_coord\"].iloc()[0][0], df[\"center_coord\"].iloc()[0][1], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "47c447c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "dsRefs = butler.registry.queryDatasets(\n",
    "    datasetType=desired_datasetTypes[0], htm7=147116, collections=desired_collections\n",
    ")\n",
    "print(len(sorted(dsRefs)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d59bcdde",
   "metadata": {},
   "source": [
    "To be continued..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10339ac6",
   "metadata": {},
   "source": [
    "### Brute force approach for (small) discrete datasets\n",
    "\n",
    "The idea here is that we can organize discrete piles of images, such as DEEP, DDF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "048e38e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_overlapping_coords(df, uncertainty_radius, overwrite=False):\n",
    "    \"\"\"\n",
    "\n",
    "    Added caching 2/5/2024 COC\n",
    "    \"\"\"\n",
    "    cache_file = f\"{basedir}/overlapping_sets.pickle\"\n",
    "\n",
    "    cache_exists = False\n",
    "    if len(glob.glob(cache_file)) > 0:\n",
    "        cache_exists = True\n",
    "\n",
    "    if overwrite == False and cache_exists == True:\n",
    "        with open(cache_file, \"rb\") as f:\n",
    "            print(f\"Recycling {cache_file} as overwrite={overwrite}.\")\n",
    "            overlapping_sets = pickle.load(f)\n",
    "            return overlapping_sets\n",
    "\n",
    "    df_copy = df.copy()\n",
    "\n",
    "    # Assuming uncertainty_radius is provided as a float in arcseconds\n",
    "    uncertainty_radius_as = uncertainty_radius * u.arcsec\n",
    "\n",
    "    all_coords = SkyCoord(\n",
    "        ra=[x[0] for x in df_copy[\"center_coord\"]] * u.degree,\n",
    "        dec=[x[1] for x in df_copy[\"center_coord\"]] * u.degree,\n",
    "    )\n",
    "\n",
    "    overlapping_sets = {}\n",
    "    set_counter = 1\n",
    "    processed_data_ids = []\n",
    "\n",
    "    periodic_update_counter = 0\n",
    "    with progressbar.ProgressBar(max_value=len(all_coords)) as bar:\n",
    "        for index, coord in enumerate(all_coords):\n",
    "            data_id = df_copy.iloc[index][\"data_id\"]\n",
    "            if data_id not in processed_data_ids:\n",
    "                distances = (\n",
    "                    coord.separation(all_coords).to(u.arcsec).value\n",
    "                )  # Convert distances to arcseconds as numeric values\n",
    "\n",
    "                # Perform comparison as numeric values, bypassing direct unit comparison\n",
    "                within_radius = (distances <= uncertainty_radius_as.value) & (distances > 0)\n",
    "\n",
    "                if any(within_radius):\n",
    "                    overlapping_indices = [\n",
    "                        i\n",
    "                        for i, distance in enumerate(distances)\n",
    "                        if (distance <= uncertainty_radius_as.value) and i != index\n",
    "                    ]\n",
    "                    overlapping_data_ids = df_copy.iloc[overlapping_indices][\"data_id\"].tolist()\n",
    "                    overlapping_data_ids.append(data_id)\n",
    "\n",
    "                    processed_data_ids.extend(overlapping_data_ids)\n",
    "\n",
    "                    overlapping_sets[f\"set_{set_counter}\"] = overlapping_data_ids\n",
    "                    set_counter += 1\n",
    "            #\n",
    "            # mitigate too much output 2/5/2024 COC\n",
    "            periodic_update_counter += 1\n",
    "            if periodic_update_counter >= 250:\n",
    "                periodic_update_counter = 0\n",
    "                bar.update(index)\n",
    "\n",
    "    with open(cache_file, \"wb\") as f:\n",
    "        pickle.dump(overlapping_sets, f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "        print(f\"Saved overlapping_sets to {cache_file} for caching purposes.\")\n",
    "\n",
    "    return overlapping_sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "25710a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recycling /astro/users/coc123/kbmod_tmp/overlapping_sets.pickle as overwrite=False.\n",
      "CPU times: user 263 ms, sys: 31.5 ms, total: 295 ms\n",
      "Wall time: 293 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# TIMING NOTE: this takes about 1.25 hours [TODO update that number]\n",
    "# TODO test caching\n",
    "\n",
    "overlapping_sets = find_overlapping_coords(df=df, uncertainty_radius=30, overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5f2d5a03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 488 discrete chip-level pointings.\n"
     ]
    }
   ],
   "source": [
    "print(f\"There are {len(overlapping_sets.keys())} discrete chip-level pointings.\")  # should be 488"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e362e41",
   "metadata": {},
   "source": [
    "#### Exploring the overlapping_sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "38d96b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create lookup tables for data_id_str to center_coord and ut_datetime for vast speed-ups.\n",
    "id_to_coord = df.set_index(\"data_id_str\")[\"center_coord\"].to_dict()\n",
    "id_to_date = df.set_index(\"data_id_str\")[\"ut_datetime\"].dt.date.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9ed24e28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 26.4 ms, sys: 4.71 ms, total: 31.1 ms\n",
      "Wall time: 29.1 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0e127591f0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KaW5udpJcc3NzKssH0EXPbtnrJi/7nXt2y17yPcwPQg3W+eieZJ6/M/bxAHv37tXo0aO1fft2jRs3LnF7JBLRmjVrVFlZ2dpM6f7779eTTz6pL774QmVlZXr88cc1duzYLuXw8QAAAIRPMs/ffLYREACt8wnzysfopolF5JPvVT4g8dlGQOhYn6Mnn3xmRBAmNC9AAPg63Es++UB3cNoIAACY47QRAADIWjQvQJpxwSy+BwBSi+YFSDOGIfkeAEgtmhcgzRiG5HsAILUY2AUAAOYY2AUAAFmL5gU4C+shU9/zg1CD7/lAUNG8AGdhPWTqa/6ZT9gWNZBv3zAFoQYEG80LcBbWQ6a+5p/5hG1RA/n27wwLQg0INgZ2AQSK9YcEkm//IY1BqAGZx6dK07wAABAqvNsICBnrc/zkk8+MCcKE5gUIAOtz/OSTz4wJwoTmBQgAX4dzyScf6A5mXgAAgDlmXgAAQNaieQHOIQjDjNY1+J4PIFhoXoBzCMIwo3UNvucDCBaaF+AcgjDMaF2D7/kAgoWBXQAAYI6BXYQeMw58DwDgbGheEEhBmHGwbh5WbmhQ0/EWrdzQYJJvvf3W+UGowTofCCqaFwRSEGYcgtBAWbLe/u7mp/IJvzs1ZFN+d1jnww80LwikmyYW6d1FFaafKGvdQN35g4s1YnCO7vzBxSb51tvf3fxUNl3dqSGb8rvDOh9+YGAXQFZ5bus+PVG3W/PKx5g0v+Tb5iO8knn+pnkBAHQZzQnfg3Th3UaAZ6znDMj3J5/TQnwPgoDmBcgC1n9Myfcn33oWKgj4HtijeQGygPUfU/L9yQ/CML01vgf2mHkBAADmmHlBWlif1w8CvgcAYI/mBV1mfV6/u3y/aFgQavA9H0BqZaR5icfjGjdunCKRiHbs2NHpupWVlYpEIm2WiRMnZqJMnIP1ef3u8v2iYUGowfd8AKmVkeblrrvu0vDhw7u8/rRp03Tw4MHE8vrrr6exOnRVWIfUUtl0ded7EISmz7oG3/MBpFbaB3bfeOMN/cM//IPWr1+vSy+9VNu3b9e4cePOun5lZaWOHz+u3/72t93KY2AXAIDwCczA7ueff66qqio9++yzGjBgQJfvV1dXp6FDh6qkpERVVVU6fPjwWdeNx+OKxWJtFtiznjHwPT8oNQBAOqSteXHOqbKyUnPnztWECRO6fL/p06dr7dq1qq2t1SOPPKL6+npVVFQoHo93uP6yZcuUm5ubWAoLC1O1CegB6xmDoOQvefkDs+Zh5YYGNR1v0coNDSb5QWierGuwzgeyVdLNS3V1dbuB2m8v27Zt069//WvFYjEtXrw4qcf/6U9/qh/+8IcaO3asZsyYoTfeeEMfffSRXnvttQ7XX7x4sZqbmxNLY2NjspuENLCeMQhCfu+I9Ccnb4dErRvI7tbg+7vTrPOBruiT7B3mz5+vG264odN1iouL9cADD2jr1q2KRqNtvjZhwgTdeOONeuaZZ7qUV1BQoKKiIn388ccdfj0ajbbLgL2bJhaZDvYGIV9S4sPbLNz5g4tN8+eVjzHN724NZzYcPf0Zss7vDut8oCvSNrC7f//+NvMnBw4c0A9+8AP953/+p8rKyjRy5MguPc7Ro0c1YsQIPfXUU7rlllvOuT4DuwB6wvoTg33Ph7+Sef7O2McD7N27V6NHj273bqPS0lItW7ZM1157rb788ktVV1frb//2b1VQUKC9e/fq7rvv1v79+/Xhhx9q4MCB58yheQGAs7NuTnzPD0oNQRSYdxt1RUNDg5qbmyVJvXv31s6dOzVz5kyVlJRo9uzZKikp0ZYtW7rUuADoHus5B/Izl289i+R7flBqCLuMNS/FxcVyzrW7xkvru5IkKScnRxs2bNDhw4d16tQp7du3T08//TTvIALSzPqPKfmZyw/CMLvP+UGpIez4VGkA5oexyec0AhDImZdMoXk5O+s/kNb5QajBOh82fN/vvm8/uiZUMy/IHJ8OjQe1But82PB9v/u+/Ug9mhePWJ9n7W5+KocZu1NDNuV3l3UNYc+3/t2z5vv2I/U4bYTAu2J5rZqOt2jE4By9u6iCfAPWNfieD/iA00bIKtav2nzPD0INvucDaIsjLwAAwBxHXtBjYZ8xCHt+EGqwzgeAs6F5QYes3x3ge74krdzQoKbjLVq5ocHL/CA0T0GoAUB7NC/okPU5ft/zEYwGMgg1AGiPmRcgoKwv7OV7flBqAHzBFXZpXgAACBUGdgEgS1nP4fieH4QarPODgOYFQI9Z/zH1Kd96Dsf3/CDUYJ0fBDQvAHrM+o+pT/nWw+S+5wehBuv8IGDmBUCPWQ+2+p4PZANmXrKUT4fGg1qD7/lnc9PEIr27qMLsiTvd+ef6vlvnp5vv+QgempcQ8enQeFBr8D3fV9bfd/L5uUdbNC8hYn2e0yr/zFddFjX4nv/tGixY548vGqLekW/+a8HX3/2g5CN4mHlB4F2xvFZNx1s0YnCO3l1UQb4B6xp8zwd8wMwLsor1qy7f84NQg+/5ANriyAsAADDHkReYzwiQb//uCOsarPMBZC+alyxlPZ1Pvv27I1ZuaFDT8Rat3NDgZb5EAwVkK5qXLGV9jp58ZiSCIAhNJIDUY+YFyFLWV321zg9KDQC6Jpnnb5oXAABgjoFdAAgo6zkc3/ODUIPv+alA8wIAGWQ9h+N7fhBq8D0/FWheACCDrIe5fc8PQg2+56cCMy8AAMAcMy8BZX2e0ff8INTge74V6+0m3+98pB7NSwZZn2f0PT8INfieb8V6u8n3Ox+pR/OSQdbnGa3yW1/1jC8aYpr/3NZ9Jt8D6/wzawjCPrAwvmiIeke++a+P+b7+7QlKPlIvrTMvxcXF2rev7R+rhQsXavny5We9j3NO999/v5566il98cUXKisr0+OPP65LL720S5nMvATPFctr1XS8RSMG5+jdRRXkG7CugXz7nwEg6AI18/JP//RPOnjwYGK59957O13/oYce0qOPPqrVq1ervr5e+fn5uvrqq3XixIl0l4o0sX7V43t+EGog3/5nAMgmaT/ysmDBAi1YsKBL6zvnNHz4cC1YsEALFy6UJMXjcQ0bNkwrVqzQnDlzzvkYHHkBACB8AnXkZcWKFbrgggs0btw4Pfjggzp16tRZ192zZ48OHTqkqVOnJm6LRqOaMmWKNm/e3OF94vG4YrFYmwX2Mwa+5wehBt/zAWSvtDYvd9xxh9atW6dNmzZp/vz5WrVqlW6//fazrn/o0CFJ0rBhw9rcPmzYsMTXvm3ZsmXKzc1NLIWFhanbgBCznq73PT8INfieDyB7Jd28VFdXKxKJdLps27ZNkvTLX/5SU6ZM0WWXXaaf//zn+pd/+Rf9+7//u44ePdppRiQSafNv51y721otXrxYzc3NiaWxsTHZTcpK1ufYfc8PQg2+5wPIXknPvBw5ckRHjhzpdJ3i4mL179+/3e1NTU0aOXKktm7dqrKysnZf//TTTzVmzBi99957uvzyyxO3z5w5U4MHD9YzzzxzzvqYeQEAIHySef7uk+yD5+XlKS8vr1uFbd++XZJUUFDQ4ddHjx6t/Px8vfXWW4nm5dSpU3r77be1YsWKbmUCAIDskraZly1btuixxx7Tjh07tGfPHr344ouaM2eOrrnmGo0aNSqxXmlpqWpqaiR9c7powYIFWrp0qWpqavTBBx+osrJSAwYM0KxZs9JVKgBkjPUgs+/5QajB9/xUSFvzEo1G9cILL6i8vFyXXHKJ7rvvPlVVVek3v/lNm/UaGhrU3Nyc+Pddd92lBQsW6Pbbb9eECRPU1NSkjRs3auDAgekqFQAyxnqQ2ff8INTge34qJH3aqKu+//3va+vWredc79sjN5FIRNXV1aqurk5TZQBgZ175GD1Rt9t0kNrn/CDU4Ht+KqT1InUWGNgFACB8AnWROvw/6/OMvucHoQbf87vLum7yyQ/j7002o3nJIOvzjL7nB6EG3/O7y7pu8skP4+9NNqN5ySDri3ZZ5be+ahlfNMQ0/7mt+0y+B9b5Z9YQhH3QHT39vvme31Pkc8HFoGHmBWl3xfJaNR1v0YjBOXp3UQX5BqxrIN/+ZwAIOmZeECjWr1p8zw9CDeTb/wwA2YQjLwAAwBxHXrKA9XQ7+fbvLrCuwfd8AMFF8xJQ1tPt5Nu/u8C6Bt/zAQQXzUtAWZ8jJ99+RsG6Bt/zAQQXMy8AAMAcMy8AACBr0bwAQBKsB4nJtx/ktq7B93yJ5gUAkmI9SEy+/SC3dQ2+50s0LwCQFOtBYvLtB7mta/A9X2JgFwAABAADu2lifZ7P9/wg1EC+Tb6v200++egYzUsSrM/z+Z4fhBrIt8n3dbvJJx8do3lJgvV5Pl/zz3zVY1GD7/ln1jC+aIhJ/viiIeod+ea/Fqzzff3dJx9nw8wLAu+K5bVqOt6iEYNz9O6iCvINWNfgez7gA2ZekFWsX/X4nh+EGnzPB9AWR14AAIA5jrxkAevpdvLt311gXYPv+QCCi+YloKyn28m3f3eBdQ2+5wMILpqXgLI+x06+/YyDdQ2+5wMILmZeAACAOWZeAABA1qJ5AYAkWA8Sk28/yG1dg+/5Es0LACTFepCYfPtBbusafM+XaF4AICnWg8Tk2w9yW9fge77EwC4AAAgABnbTxPo8n+/5QaiB/OTzrWsOQg3k+50flBqyCc1LEqzP8/meH4QayE8+37rmINRAvt/5Qakhm9C8JMH6PJ+v+We+YrGowff8M2sYXzQk6fxU1NzTV609rcE6v6fIt5/RCEIN2YSZFwTeFctr1XS8RSMG5+jdRRXkG7Cuwfd8wAeBmXkpLi5WJBJpsyxatKjT+1RWVra7z8SJE9NZJgLO+hWL7/lBqMH3fABtpfXIS3FxsW699VZVVVUlbvvOd76j73znO2e9T2VlpT7//HOtWbMmcVu/fv10/vnndymTIy8AAIRPYI68SNLAgQOVn5+fWDprXFpFo9E29+lq44L/Zz3Z7nt+EGrwPR9A9kp787JixQpdcMEFGjdunB588EGdOnXqnPepq6vT0KFDVVJSoqqqKh0+fPis68bjccVisTYL7Cfbfc8PQg2+5wPIXmltXu644w6tW7dOmzZt0vz587Vq1Srdfvvtnd5n+vTpWrt2rWpra/XII4+ovr5eFRUVisfjHa6/bNky5ebmJpbCwsJ0bEroWJ+j9z0/CDX4ng8geyU981JdXa3777+/03Xq6+s1YcKEdrevX79e119/vY4cOaILLrigS3kHDx5UUVGR1q1bp+uuu67d1+PxeJvGJhaLqbCwkJkXAABCJJmZlz7JPvj8+fN1ww03dLpOcXFxh7e3vmvok08+6XLzUlBQoKKiIn388ccdfj0ajSoajXbpsQAAQPglfdooLy9PpaWlnS79+/fv8L7bt2+X9E1D0lVHjx5VY2NjUvcBgKCyHmT2PT8INfienwppm3nZsmWLHnvsMe3YsUN79uzRiy++qDlz5uiaa67RqFGjEuuVlpaqpqZGkvTll1/qzjvv1JYtW7R3717V1dVpxowZysvL07XXXpuuUgEgY6wHmX3PD0INvuenQtqal2g0qhdeeEHl5eW65JJLdN9996mqqkq/+c1v2qzX0NCg5uZmSVLv3r21c+dOzZw5UyUlJZo9e7ZKSkq0ZcsWDRw4MF2lAkDGWA8y+54fhBp8z08FPh4AAACYC9RF6vD/rM8z+p4fhBp8zPdxm4NWA/n8DGQbmpcMsj7P6Ht+EGrwMd/HbQ5aDeTzM5BtaF4yyPo8o6/5Z77isajBOv/MGsYXDcl4/rzyMRqc01dfxb82edVpnd9ag4+/e+QHq4ZswswLst4Vy2vVdLxFIwbn6N1FFd7lB6EG3/MBnBszL8AZrF/xWOcHoQbf8wGkFkdeAACAOY68wHyynXz7dxZY12CdDyB70bxkKevJdvLt31lgXYN1PoDsRfOSpazP8ZNvP2NhXYN1PoDsxcwLAAAwx8wLAADIWjQvAJBB1oPM5NsPklvXYJ2fCjQvAJBB1oPM5NsPklvXYJ2fCjQvAJBB1oPM5NsPklvXYJ2fCgzsAgAAcwzsBpT1eUbf84NQg+/53RGEmq1rIN/vfLRH85JB1ucZfc8PQg2+53dHEGq2roF8v/PRHs1LBlmfZ/Q9v7s1pPJVVxjzU11DsuaVj9HgnL76Kv612Stf659d8v3OR3vMvADncMXyWjUdb9GIwTl6d1GFd/lBqME6H0D6MfMCpJD1qy7r/CDUYJ0PIFg48gIAAMxx5AU9Zj1d73t+EGqwzgeAs6F5QYesp+t9zw9CDdb5AHA2NC/okPWMge/5QajBOh8AzoaZFwAAYI6ZFwAAkLVoXgAgRKwHqX3PD0IN1vlBQPMCACFiPUjte34QarDODwKaFwAIEetBat/zg1CDdX4QMLALAADMMbCbpazPc1rnB6EG3/MtBGGbrWsgn58BtEXzEiLW5zmt84NQg+/5FoKwzdY1kM/PANqieQkR6/Oc1vlBqKE7+al8xdbd7bd+1diTfOt9HoQayOdnAG0x8wKk2RXLa9V0vEUjBufo3UUVXtZgnQ8g+AI18/Laa6+prKxMOTk5ysvL03XXXdfp+s45VVdXa/jw4crJyVF5ebl27dqV7jKBtAnCKzbrGqzzAWSXtB55Wb9+vaqqqrR06VJVVFTIOaedO3fq+uuvP+t9VqxYoQcffFBPP/20SkpK9MADD+idd95RQ0ODBg4ceM5MjrwAABA+gTjy8vXXX+uOO+7Qww8/rLlz56qkpEQXX3xxp42Lc06rVq3SPffco+uuu05jx47VM888o5MnT+r5559PV6lIgzDPWGRDflBqAIB0SFvz8t5776mpqUm9evXS5ZdfroKCAk2fPr3TU0B79uzRoUOHNHXq1MRt0WhUU6ZM0ebNmzu8TzweVywWa7PAnvVkvu/5QakBANIhbc3Lp59+Kkmqrq7Wvffeq1dffVVDhgzRlClTdOzYsQ7vc+jQIUnSsGHD2tw+bNiwxNe+bdmyZcrNzU0shYWFKdwKdJf1jIPv+UGpAQDSIemZl+rqat1///2drlNfX6+PPvpIN954o5588knddtttkr45SjJy5Eg98MADmjNnTrv7bd68WVdccYUOHDiggoKCxO1VVVVqbGzUm2++2e4+8Xhc8Xg88e9YLKbCwkJmXgAACJFkZl76JPvg8+fP1w033NDpOsXFxTpx4oQk6ZJLLkncHo1G9d3vflf79+/v8H75+fmSvjkCc2bzcvjw4XZHY858zGg0mtQ2AACA8Er6tFFeXp5KS0s7Xfr376/x48crGo2qoaEhcd/Tp09r7969Kioq6vCxR48erfz8fL311luJ206dOqW3335bkydP7sbmAQDOZD3I7Xt+UGoIu7TNvAwaNEhz587VkiVLtHHjRjU0NGjevHmSpJ/85CeJ9UpLS1VTUyNJikQiWrBggZYuXaqamhp98MEHqqys1IABAzRr1qx0lQoA3rAe5PY9Pyg1hF3Sp42S8fDDD6tPnz66+eab1dLSorKyMtXW1mrIkCGJdRoaGtTc3Jz491133aWWlhbdfvvt+uKLL1RWVqaNGzd26RovAIDOzSsfoyfqdpsOs/ucH5Qawo6PBwAAAOYCcZE6BI/1eVbr/CDUYJ3vK+vvO/l+5yP1aF48Yn2e1To/CDVY5/vK+vtOvt/5SD2aF49YX7TMOj8INVjn+8r6+06+3/lIPWZeAACAOWZeAABA1qJ5AQAAoULzgkCyfneAdX5QagCAIKJ5QSBZvzvAOj8oNQBAENG8IJCs3x1gnR+UGgAgiHi3EQAAMMe7jQAAQNaieQEAdJn1ILl1flBq8B3NCwCgy6wHya3zg1KD72heAABdZj1Ibp0flBp8x8AuAAAwx8Au0iII53mta7DOhw3r/U4+v3doi+YFXRaE87zWNVjnw4b1fief3zu0RfOCLgvCeV7rGqzzYcN6v5PP7x3aYuYFAACYY+YFAABkLZoXAAAQKjQvAAAgVGheAABAqNC8AACAUKF5AQAAoULzAgAAQoXmBQAAhArNCwAACBWaFwAAECo0LwCQZtafiux7PrIPzQsApJn1pyL7no/sQ/MCAGlm/anIvucj+/Cp0gAAwByfKg0AALIWzQsAAAiVtDcvr732msrKypSTk6O8vDxdd911na5fWVmpSCTSZpk4cWK6ywQAACHRJ50Pvn79elVVVWnp0qWqqKiQc047d+485/2mTZumNWvWJP7dr1+/dJYJAABCJG3Ny9dff6077rhDDz/8sG699dbE7RdffPE57xuNRpWfn5+u0gAAQIil7bTRe++9p6amJvXq1UuXX365CgoKNH36dO3ateuc962rq9PQoUNVUlKiqqoqHT58+KzrxuNxxWKxNgsAAMheaWtePv30U0lSdXW17r33Xr366qsaMmSIpkyZomPHjp31ftOnT9fatWtVW1urRx55RPX19aqoqFA8Hu9w/WXLlik3NzexFBYWpmV7AABAMCR9nZfq6mrdf//9na5TX1+vjz76SDfeeKOefPJJ3XbbbZK+OUoycuRIPfDAA5ozZ06X8g4ePKiioiKtW7euw2HfeDzeprGJxWIqLCzkOi8AAIRIMtd5SXrmZf78+brhhhs6Xae4uFgnTpyQJF1yySWJ26PRqL773e9q//79Xc4rKChQUVGRPv744w6/Ho1GFY1Gu/x4AAAg3JJuXvLy8pSXl3fO9caPH69oNKqGhgb99V//tSTp9OnT2rt3r4qKirqcd/ToUTU2NqqgoCDZUgEAQBZK28zLoEGDNHfuXC1ZskQbN25UQ0OD5s2bJ0n6yU9+klivtLRUNTU1kqQvv/xSd955p7Zs2aK9e/eqrq5OM2bMUF5enq699tp0lQoAAEIkrdd5efjhh9WnTx/dfPPNamlpUVlZmWprazVkyJDEOg0NDWpubpYk9e7dWzt37tR//Md/6Pjx4yooKNCVV16pF154QQMHDkxnqQAAICT4YEYAAGAurQO7Qdfai3G9FwAAwqP1ebsrx1SyrnlpfZcT13sBACB8Tpw4odzc3E7XybrTRn/+85914MABDRw4UJFIJHHdl8bGRk4jBQz7JrjYN8HG/gku9k33Oed04sQJDR8+XL16df5+oqw78tKrVy+NHDmy3e2DBg3iBymg2DfBxb4JNvZPcLFvuudcR1xape2t0gAAAOlA8wIAAEIl65uXaDSqJUuW8BECAcS+CS72TbCxf4KLfZMZWTewCwAAslvWH3kBAADZheYFAACECs0LAAAIFZoXAAAQKqFsXp544glddtlliYsATZo0SW+88Ubi65WVlYpEIm2WiRMntnmMeDyuX/ziF8rLy9N5552na665Rp999lmmNyXrpGLfPPXUUyovL9egQYMUiUR0/PjxDG9Fdurpvjl27Jh+8Ytf6OKLL9aAAQM0atQo/f3f/33iU+HRfan4vZkzZ47GjBmjnJwcXXjhhZo5c6b++Mc/ZnpTsk4q9k0r55ymT5+uSCSi3/72txnaguwUyuZl5MiRWr58ubZt26Zt27apoqJCM2fO1K5duxLrTJs2TQcPHkwsr7/+epvHWLBggWpqarRu3Tr993//t7788kv96Ec/0p/+9KdMb05WScW+OXnypKZNm6a777470+VntZ7umwMHDujAgQNauXKldu7cqaefflpvvvmmbr31VovNySqp+L0ZP3681qxZow8//FAbNmyQc05Tp07lb1oPpWLftFq1apUikUimSs9uLksMGTLE/du//ZtzzrnZs2e7mTNnnnXd48ePu759+7p169YlbmtqanK9evVyb775ZrpL9U4y++ZMmzZtcpLcF198kb7iPNfdfdPqxRdfdP369XOnT59OQ3V+6+m++cMf/uAkuU8++SQN1fmtO/tmx44dbuTIke7gwYNOkqupqUlvkVkulEdezvSnP/1J69at01dffaVJkyYlbq+rq9PQoUNVUlKiqqoqHT58OPG1//mf/9Hp06c1derUxG3Dhw/X2LFjtXnz5ozWn826s2+QGanaN83NzRo0aJD69Mm6j0kzk4p989VXX2nNmjUaPXq0CgsLM1G2F7q7b06ePKmf/exnWr16tfLz8zNddnay7p666/3333fnnXee6927t8vNzXWvvfZa4mvr1q1zr776qtu5c6d75ZVX3Pe+9z136aWXuv/93/91zjm3du1a169fv3aPefXVV7vbbrstY9uQrXqyb87EkZfUS9W+cc65I0eOuFGjRrl77rknU+VntVTsm8cff9ydd955TpIrLS3lqEuK9HTf3Hbbbe7WW29N/Fsceemx0DYv8Xjcffzxx66+vt4tWrTI5eXluV27dnW47oEDB1zfvn3d+vXrnXNnb17+5m/+xs2ZMyetdfugJ/vmTDQvqZeqfdPc3OzKysrctGnT3KlTp9JdthdSsW+OHz/uPvroI/f222+7GTNmuO9///uupaUlE+VntZ7sm5dfftlddNFF7sSJE4l1aF56LrTNy7ddddVVnR41ueiii9zy5cudc8797ne/c5LcsWPH2qxz2WWXufvuuy+tdfoomX1zJpqX9OvOvonFYm7SpEnuqquu4okxjbr7e9MqHo+7AQMGuOeffz4d5XktmX1zxx13uEgk4nr37p1YJLlevXq5KVOmZKji7BP6mZdWzjnF4/EOv3b06FE1NjaqoKBA0jdT+X379tVbb72VWOfgwYP64IMPNHny5IzU65Nk9g0yK9l9E4vFNHXqVPXr10+vvPKK+vfvn6lSvZOK35vOHgPdl8y+WbRokd5//33t2LEjsUjSY489pjVr1mSq5Oxj2Tl11+LFi90777zj9uzZ495//3139913u169ermNGze6EydOuH/8x390mzdvdnv27HGbNm1ykyZNciNGjHCxWCzxGHPnznUjR450//Vf/+Xee+89V1FR4b73ve+5r7/+2nDLwi8V++bgwYNu+/bt7l//9V+dJPfOO++47du3u6NHjxpuWfj1dN/EYjFXVlbm/uqv/sp98skn7uDBg4mF35ue6em+2b17t1u6dKnbtm2b27dvn9u8ebObOXOmO//8893nn39uvHXhloq/ad8mThv1WCibl7/7u79zRUVFrl+/fu7CCy90V111ldu4caNzzrmTJ0+6qVOnugsvvND17dvXjRo1ys2ePdvt37+/zWO0tLS4+fPnu/PPP9/l5OS4H/3oR+3WQfJSsW+WLFniJLVb1qxZY7BF2aOn+6b1NF5Hy549e4y2Kjv0dN80NTW56dOnu6FDh7q+ffu6kSNHulmzZrk//vGPVpuUNVLxN+3baF56LuKccxZHfAAAALoja2ZeAACAH2heAABAqNC8AACAUKF5AQAAoULzAgAAQoXmBQAAhArNCwAACBWaFwAAECo0LwAAIFRoXgAAQKjQvAAAgFCheQEAAKHyf+l78ybIIQDcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Looking at the processed data.\n",
    "\n",
    "# Preparing for bulk plotting (if every point uses the same label, adjust as needed)\n",
    "coords = [id_to_coord[dataId_to_dataIdStr(overlapping_sets[p][0])] for p in overlapping_sets]\n",
    "x_coords, y_coords = zip(*coords)  # Assuming coords are tuples or lists\n",
    "\n",
    "# Plotting in bulk\n",
    "plt.scatter(x_coords, y_coords, s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fcd39c41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<timed exec>:7: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.5 s, sys: 532 ms, total: 6.03 s\n",
      "Wall time: 5.48 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# TIMING NOTE: this requires about 7 seconds\n",
    "\n",
    "# Extract unique dates and create a color map\n",
    "unique_dates = sorted(set(id_to_date.values()))\n",
    "date_to_color = {date: i for i, date in enumerate(unique_dates)}\n",
    "norm = Normalize(vmin=0, vmax=len(unique_dates) - 1)\n",
    "cmap = get_cmap(\"tab20\", len(unique_dates))  # Choose a colormap that fits the data\n",
    "\n",
    "# Preparing data for plotting\n",
    "coords = [id_to_coord[dataId_to_dataIdStr(overlapping_sets[p][0])] for p in overlapping_sets]\n",
    "dates = [id_to_date[dataId_to_dataIdStr(overlapping_sets[p][0])] for p in overlapping_sets]\n",
    "colors = [cmap(norm(date_to_color[date])) for date in dates]\n",
    "\n",
    "# Plotting\n",
    "lcount = 0\n",
    "for (x, y), color, date in zip(coords, colors, dates):\n",
    "    lcount += 1\n",
    "    plt.scatter(x, y, color=color, label=date.strftime(\"%Y-%m-%d\"), alpha=0.75, s=2)\n",
    "\n",
    "# To avoid duplicate labels in the legend, handle legend entries manually\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = dict(zip(labels, handles))  # Removing duplicates\n",
    "\n",
    "current_handles, current_labels = plt.gca().get_legend_handles_labels()\n",
    "\n",
    "current_handles = list(by_label.values())\n",
    "current_labels = list(by_label.keys())\n",
    "# sort or reorder the labels and handles\n",
    "sorted_ix = sorted(range(len(current_labels)), key=lambda k: current_labels[k])\n",
    "sorted_handles = [current_handles[i] for i in sorted_ix]  # list(current_handles)\n",
    "sorted_labels = [current_labels[i] for i in sorted_ix]  # list(current_labels)\n",
    "\n",
    "# call plt.legend() with the new values\n",
    "plt.legend(sorted_handles, sorted_labels)\n",
    "\n",
    "# plt.legend(by_label.values(), by_label.keys())\n",
    "plt.savefig(f\"{basedir}/pointings.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d06ed515",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-08-28\n",
      "2019-08-29\n",
      "2019-08-30\n",
      "2019-09-27\n",
      "2019-09-28\n",
      "2019-09-29\n",
      "2020-10-17\n",
      "2020-10-19\n"
     ]
    }
   ],
   "source": [
    "# Here are the unique dates found in the discrete dataset\n",
    "unique_dates = sorted(set(id_to_date.values()))\n",
    "print(\"\\n\".join([str(dt) for dt in unique_dates]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc0bb58",
   "metadata": {},
   "source": [
    "##### Double-checking a single date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "58158f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data_id         {'instrument': 'DECam', 'detector': 1, 'visit'...\n",
       "region          ConvexPolygon([UnitVector3d(0.9847372525065534...\n",
       "detector                                                        1\n",
       "data_id_str                                        DECam_1_898286\n",
       "uri             file:///epyc/users/smotherh/DEEP/PointingGroup...\n",
       "ut                                        2019-09-27T00:20:22.932\n",
       "ut_datetime                            2019-09-27 00:20:22.932000\n",
       "center_coord              (351.0694028401149, -4.336598368890197)\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2ff625e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2019, 9, 27)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_to_date[dataId_to_dataIdStr(df[\"data_id\"].iloc()[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c3b48b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(351.0694028401149, -4.336598368890197)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_to_coord[dataId_to_dataIdStr(df[\"data_id\"].iloc()[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "16506478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6267"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will make a dataframe with just the date we are checking\n",
    "df20190828 = df[df[\"ut_datetime\"].dt.date == parser.parse(\"2019-08-28\").date()]\n",
    "len(df20190828)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "166c9137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 954 ms, sys: 14.5 ms, total: 969 ms\n",
      "Wall time: 968 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Visualize all of the unique dates, one at a time.\n",
    "for dt in unique_dates:\n",
    "    fig = plt.figure()\n",
    "    tmpdf = df[df[\"ut_datetime\"].dt.date == parser.parse(\"2019-08-28\").date()]\n",
    "    plt.scatter(*zip(*tmpdf[\"center_coord\"]), s=1, alpha=0.5)\n",
    "    plt.title(f\"{dt}\")\n",
    "del tmpdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fe95f3f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping set_1 because only one date.\n",
      "Skipping set_2 because only one date.\n",
      "Skipping set_3 because only one date.\n",
      "Skipping set_4 because only one date.\n",
      "Skipping set_5 because only one date.\n",
      "Skipping set_6 because only one date.\n",
      "Skipping set_7 because only one date.\n",
      "Skipping set_8 because only one date.\n",
      "Skipping set_9 because only one date.\n",
      "Skipping set_10 because only one date.\n",
      "Skipping set_11 because only one date.\n",
      "Skipping set_12 because only one date.\n",
      "Skipping set_13 because only one date.\n",
      "Skipping set_14 because only one date.\n",
      "Skipping set_15 because only one date.\n",
      "Skipping set_16 because only one date.\n",
      "Skipping set_17 because only one date.\n",
      "Skipping set_18 because only one date.\n",
      "Skipping set_19 because only one date.\n",
      "Skipping set_20 because only one date.\n",
      "Skipping set_21 because only one date.\n",
      "Skipping set_22 because only one date.\n",
      "Skipping set_23 because only one date.\n",
      "Skipping set_24 because only one date.\n",
      "Skipping set_25 because only one date.\n",
      "Skipping set_26 because only one date.\n",
      "Skipping set_27 because only one date.\n",
      "Skipping set_28 because only one date.\n",
      "Skipping set_29 because only one date.\n",
      "Skipping set_30 because only one date.\n",
      "Skipping set_31 because only one date.\n",
      "Skipping set_32 because only one date.\n",
      "Skipping set_33 because only one date.\n",
      "Skipping set_34 because only one date.\n",
      "Skipping set_35 because only one date.\n",
      "Skipping set_36 because only one date.\n",
      "Skipping set_37 because only one date.\n",
      "Skipping set_38 because only one date.\n",
      "Skipping set_39 because only one date.\n",
      "Skipping set_40 because only one date.\n",
      "Skipping set_41 because only one date.\n",
      "Skipping set_42 because only one date.\n",
      "Skipping set_43 because only one date.\n",
      "Skipping set_44 because only one date.\n",
      "Skipping set_45 because only one date.\n",
      "Skipping set_46 because only one date.\n",
      "Skipping set_47 because only one date.\n",
      "Skipping set_48 because only one date.\n",
      "Skipping set_49 because only one date.\n",
      "Skipping set_50 because only one date.\n",
      "Skipping set_51 because only one date.\n",
      "Skipping set_52 because only one date.\n",
      "Skipping set_53 because only one date.\n",
      "Skipping set_54 because only one date.\n",
      "Skipping set_55 because only one date.\n",
      "Skipping set_56 because only one date.\n",
      "Skipping set_57 because only one date.\n",
      "Skipping set_58 because only one date.\n",
      "Skipping set_59 because only one date.\n",
      "Skipping set_60 because only one date.\n",
      "Skipping set_61 because only one date.\n",
      "Skipping set_62 because only one date.\n",
      "Skipping set_63 because only one date.\n",
      "Skipping set_64 because only one date.\n",
      "Skipping set_65 because only one date.\n",
      "Skipping set_66 because only one date.\n",
      "Skipping set_67 because only one date.\n",
      "Skipping set_68 because only one date.\n",
      "Skipping set_69 because only one date.\n",
      "Skipping set_70 because only one date.\n",
      "Skipping set_71 because only one date.\n",
      "Skipping set_72 because only one date.\n",
      "Skipping set_73 because only one date.\n",
      "Skipping set_74 because only one date.\n",
      "Skipping set_75 because only one date.\n",
      "Skipping set_76 because only one date.\n",
      "Skipping set_77 because only one date.\n",
      "Skipping set_78 because only one date.\n",
      "Skipping set_79 because only one date.\n",
      "Skipping set_80 because only one date.\n",
      "Skipping set_81 because only one date.\n",
      "Skipping set_82 because only one date.\n",
      "Skipping set_83 because only one date.\n",
      "Skipping set_84 because only one date.\n",
      "Skipping set_85 because only one date.\n",
      "Skipping set_86 because only one date.\n",
      "Skipping set_87 because only one date.\n",
      "Skipping set_88 because only one date.\n",
      "Skipping set_89 because only one date.\n",
      "Skipping set_90 because only one date.\n",
      "Skipping set_91 because only one date.\n",
      "Skipping set_92 because only one date.\n",
      "Skipping set_93 because only one date.\n",
      "Skipping set_94 because only one date.\n",
      "Skipping set_95 because only one date.\n",
      "Skipping set_96 because only one date.\n",
      "Skipping set_97 because only one date.\n",
      "Skipping set_98 because only one date.\n",
      "Skipping set_99 because only one date.\n",
      "Skipping set_100 because only one date.\n",
      "Skipping set_101 because only one date.\n",
      "Skipping set_102 because only one date.\n",
      "Skipping set_103 because only one date.\n",
      "Skipping set_104 because only one date.\n",
      "Skipping set_105 because only one date.\n",
      "Skipping set_106 because only one date.\n",
      "Skipping set_107 because only one date.\n",
      "Skipping set_108 because only one date.\n",
      "Skipping set_109 because only one date.\n",
      "Skipping set_110 because only one date.\n",
      "Skipping set_111 because only one date.\n",
      "Skipping set_112 because only one date.\n",
      "Skipping set_113 because only one date.\n",
      "Skipping set_114 because only one date.\n",
      "Skipping set_115 because only one date.\n",
      "Skipping set_116 because only one date.\n",
      "Skipping set_117 because only one date.\n",
      "Skipping set_118 because only one date.\n",
      "Skipping set_119 because only one date.\n",
      "Skipping set_120 because only one date.\n",
      "Skipping set_121 because only one date.\n",
      "Skipping set_122 because only one date.\n",
      "Skipping set_123 because only one date.\n",
      "Skipping set_124 because only one date.\n",
      "Skipping set_125 because only one date.\n",
      "Skipping set_126 because only one date.\n",
      "Skipping set_127 because only one date.\n",
      "Skipping set_128 because only one date.\n",
      "Skipping set_129 because only one date.\n",
      "Skipping set_130 because only one date.\n",
      "Skipping set_131 because only one date.\n",
      "Skipping set_132 because only one date.\n",
      "Skipping set_133 because only one date.\n",
      "Skipping set_134 because only one date.\n",
      "Skipping set_135 because only one date.\n",
      "Skipping set_136 because only one date.\n",
      "Skipping set_137 because only one date.\n",
      "Skipping set_138 because only one date.\n",
      "Skipping set_139 because only one date.\n",
      "Skipping set_140 because only one date.\n",
      "Skipping set_141 because only one date.\n",
      "Skipping set_142 because only one date.\n",
      "Skipping set_143 because only one date.\n",
      "Skipping set_144 because only one date.\n",
      "Skipping set_145 because only one date.\n",
      "Skipping set_146 because only one date.\n",
      "Skipping set_147 because only one date.\n",
      "Skipping set_148 because only one date.\n",
      "Skipping set_149 because only one date.\n",
      "Skipping set_150 because only one date.\n",
      "Skipping set_151 because only one date.\n",
      "Skipping set_152 because only one date.\n",
      "Skipping set_153 because only one date.\n",
      "Skipping set_154 because only one date.\n",
      "Skipping set_155 because only one date.\n",
      "Skipping set_156 because only one date.\n",
      "Skipping set_157 because only one date.\n",
      "Skipping set_158 because only one date.\n",
      "Skipping set_159 because only one date.\n",
      "Skipping set_160 because only one date.\n",
      "Skipping set_161 because only one date.\n",
      "Skipping set_162 because only one date.\n",
      "Skipping set_163 because only one date.\n",
      "Skipping set_164 because only one date.\n",
      "Skipping set_165 because only one date.\n",
      "Skipping set_166 because only one date.\n",
      "Skipping set_167 because only one date.\n",
      "Skipping set_168 because only one date.\n",
      "Skipping set_169 because only one date.\n",
      "Skipping set_170 because only one date.\n",
      "Skipping set_171 because only one date.\n",
      "Skipping set_172 because only one date.\n",
      "Skipping set_173 because only one date.\n",
      "Skipping set_174 because only one date.\n",
      "Skipping set_175 because only one date.\n",
      "Skipping set_176 because only one date.\n",
      "Skipping set_177 because only one date.\n",
      "Skipping set_178 because only one date.\n",
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      "Skipping set_184 because only one date.\n",
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      "Skipping set_188 because only one date.\n",
      "Skipping set_189 because only one date.\n",
      "Skipping set_190 because only one date.\n",
      "Skipping set_191 because only one date.\n",
      "Skipping set_192 because only one date.\n",
      "Skipping set_193 because only one date.\n",
      "Skipping set_194 because only one date.\n",
      "Skipping set_195 because only one date.\n",
      "Skipping set_196 because only one date.\n",
      "Skipping set_197 because only one date.\n",
      "Skipping set_198 because only one date.\n",
      "Skipping set_199 because only one date.\n",
      "Skipping set_200 because only one date.\n",
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      "Skipping set_202 because only one date.\n",
      "Skipping set_203 because only one date.\n",
      "Skipping set_204 because only one date.\n",
      "Skipping set_205 because only one date.\n",
      "Skipping set_206 because only one date.\n",
      "Skipping set_207 because only one date.\n",
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      "Skipping set_209 because only one date.\n",
      "Skipping set_210 because only one date.\n",
      "Skipping set_211 because only one date.\n",
      "Skipping set_212 because only one date.\n",
      "Skipping set_213 because only one date.\n",
      "Skipping set_214 because only one date.\n",
      "Skipping set_215 because only one date.\n",
      "Skipping set_216 because only one date.\n",
      "Skipping set_217 because only one date.\n",
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      "Skipping set_219 because only one date.\n",
      "Skipping set_220 because only one date.\n",
      "Skipping set_221 because only one date.\n",
      "Skipping set_222 because only one date.\n",
      "Skipping set_223 because only one date.\n",
      "Skipping set_224 because only one date.\n",
      "Skipping set_225 because only one date.\n",
      "Skipping set_226 because only one date.\n",
      "Skipping set_227 because only one date.\n",
      "Skipping set_228 because only one date.\n",
      "Skipping set_229 because only one date.\n",
      "Skipping set_230 because only one date.\n",
      "Skipping set_231 because only one date.\n",
      "Skipping set_232 because only one date.\n",
      "Skipping set_233 because only one date.\n",
      "Skipping set_234 because only one date.\n",
      "Skipping set_235 because only one date.\n",
      "Skipping set_236 because only one date.\n",
      "Skipping set_237 because only one date.\n",
      "Skipping set_238 because only one date.\n",
      "Skipping set_239 because only one date.\n",
      "Skipping set_240 because only one date.\n",
      "Skipping set_241 because only one date.\n",
      "Skipping set_242 because only one date.\n",
      "Skipping set_243 because only one date.\n",
      "Skipping set_244 because only one date.\n",
      "Skipping set_245 because only one date.\n",
      "Skipping set_246 because only one date.\n",
      "Skipping set_247 because only one date.\n",
      "Skipping set_248 because only one date.\n",
      "Skipping set_249 because only one date.\n",
      "Skipping set_250 because only one date.\n",
      "Skipping set_251 because only one date.\n",
      "Skipping set_252 because only one date.\n",
      "Skipping set_253 because only one date.\n",
      "Skipping set_254 because only one date.\n",
      "Skipping set_255 because only one date.\n",
      "Skipping set_256 because only one date.\n",
      "Skipping set_257 because only one date.\n",
      "Skipping set_258 because only one date.\n",
      "Skipping set_259 because only one date.\n",
      "Skipping set_260 because only one date.\n",
      "Skipping set_261 because only one date.\n",
      "Skipping set_262 because only one date.\n",
      "Skipping set_263 because only one date.\n",
      "Skipping set_264 because only one date.\n",
      "Skipping set_265 because only one date.\n",
      "Skipping set_266 because only one date.\n",
      "Skipping set_267 because only one date.\n",
      "Skipping set_268 because only one date.\n",
      "Skipping set_269 because only one date.\n",
      "Skipping set_270 because only one date.\n",
      "Skipping set_271 because only one date.\n",
      "Skipping set_272 because only one date.\n",
      "Skipping set_273 because only one date.\n",
      "Skipping set_274 because only one date.\n",
      "Skipping set_275 because only one date.\n",
      "Skipping set_276 because only one date.\n",
      "Skipping set_277 because only one date.\n",
      "Skipping set_278 because only one date.\n",
      "Skipping set_279 because only one date.\n",
      "Skipping set_280 because only one date.\n",
      "Skipping set_281 because only one date.\n",
      "Skipping set_282 because only one date.\n",
      "Skipping set_283 because only one date.\n",
      "Skipping set_284 because only one date.\n",
      "Skipping set_285 because only one date.\n",
      "Skipping set_286 because only one date.\n",
      "Skipping set_287 because only one date.\n",
      "Skipping set_288 because only one date.\n",
      "Skipping set_289 because only one date.\n",
      "Skipping set_290 because only one date.\n",
      "Skipping set_291 because only one date.\n",
      "Skipping set_292 because only one date.\n",
      "Skipping set_293 because only one date.\n",
      "Skipping set_294 because only one date.\n",
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      "Skipping set_298 because only one date.\n",
      "Skipping set_299 because only one date.\n",
      "Skipping set_300 because only one date.\n",
      "Skipping set_301 because only one date.\n",
      "Skipping set_302 because only one date.\n",
      "Skipping set_303 because only one date.\n",
      "Skipping set_304 because only one date.\n",
      "Skipping set_305 because only one date.\n",
      "Skipping set_306 because only one date.\n",
      "Skipping set_307 because only one date.\n",
      "Skipping set_308 because only one date.\n",
      "Skipping set_309 because only one date.\n",
      "Skipping set_310 because only one date.\n",
      "Skipping set_311 because only one date.\n",
      "Skipping set_312 because only one date.\n",
      "Skipping set_313 because only one date.\n",
      "Skipping set_314 because only one date.\n",
      "Skipping set_315 because only one date.\n",
      "Skipping set_316 because only one date.\n",
      "Skipping set_317 because only one date.\n",
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      "Skipping set_321 because only one date.\n",
      "Skipping set_322 because only one date.\n",
      "Skipping set_323 because only one date.\n",
      "Skipping set_324 because only one date.\n",
      "Skipping set_325 because only one date.\n",
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      "Skipping set_327 because only one date.\n",
      "Skipping set_328 because only one date.\n",
      "Skipping set_329 because only one date.\n",
      "Skipping set_330 because only one date.\n",
      "Skipping set_331 because only one date.\n",
      "Skipping set_332 because only one date.\n",
      "Skipping set_333 because only one date.\n",
      "Skipping set_334 because only one date.\n",
      "Skipping set_335 because only one date.\n",
      "Skipping set_336 because only one date.\n",
      "Skipping set_337 because only one date.\n",
      "Skipping set_338 because only one date.\n",
      "Skipping set_339 because only one date.\n",
      "Skipping set_340 because only one date.\n",
      "Skipping set_341 because only one date.\n",
      "Skipping set_342 because only one date.\n",
      "Skipping set_343 because only one date.\n",
      "Skipping set_344 because only one date.\n",
      "Skipping set_345 because only one date.\n",
      "Skipping set_346 because only one date.\n",
      "Skipping set_347 because only one date.\n",
      "Skipping set_348 because only one date.\n",
      "Skipping set_349 because only one date.\n",
      "Skipping set_350 because only one date.\n",
      "Skipping set_351 because only one date.\n",
      "Skipping set_352 because only one date.\n",
      "Skipping set_353 because only one date.\n",
      "Skipping set_354 because only one date.\n",
      "Skipping set_355 because only one date.\n",
      "Skipping set_356 because only one date.\n",
      "Skipping set_357 because only one date.\n",
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      "Skipping set_360 because only one date.\n",
      "Skipping set_361 because only one date.\n",
      "Skipping set_362 because only one date.\n",
      "Skipping set_363 because only one date.\n",
      "Skipping set_364 because only one date.\n",
      "Skipping set_365 because only one date.\n",
      "Skipping set_366 because only one date.\n",
      "Skipping set_367 because only one date.\n",
      "Skipping set_368 because only one date.\n",
      "Skipping set_369 because only one date.\n",
      "Skipping set_370 because only one date.\n",
      "Skipping set_371 because only one date.\n",
      "Skipping set_372 because only one date.\n",
      "Skipping set_373 because only one date.\n",
      "Skipping set_374 because only one date.\n",
      "Skipping set_375 because only one date.\n",
      "Skipping set_376 because only one date.\n",
      "Skipping set_377 because only one date.\n",
      "Skipping set_378 because only one date.\n",
      "Skipping set_379 because only one date.\n",
      "Skipping set_380 because only one date.\n",
      "Skipping set_381 because only one date.\n",
      "Skipping set_382 because only one date.\n",
      "Skipping set_383 because only one date.\n",
      "Skipping set_384 because only one date.\n",
      "Skipping set_385 because only one date.\n",
      "Skipping set_386 because only one date.\n",
      "Skipping set_387 because only one date.\n",
      "Skipping set_388 because only one date.\n",
      "Skipping set_389 because only one date.\n",
      "Skipping set_390 because only one date.\n",
      "Skipping set_391 because only one date.\n",
      "Skipping set_392 because only one date.\n",
      "Skipping set_393 because only one date.\n",
      "Skipping set_394 because only one date.\n",
      "Skipping set_395 because only one date.\n",
      "Skipping set_396 because only one date.\n",
      "Skipping set_397 because only one date.\n",
      "Skipping set_398 because only one date.\n",
      "Skipping set_399 because only one date.\n",
      "Skipping set_400 because only one date.\n",
      "Skipping set_401 because only one date.\n",
      "Skipping set_402 because only one date.\n",
      "Skipping set_403 because only one date.\n",
      "Skipping set_404 because only one date.\n",
      "Skipping set_405 because only one date.\n",
      "Skipping set_406 because only one date.\n",
      "Skipping set_407 because only one date.\n",
      "Skipping set_408 because only one date.\n",
      "Skipping set_409 because only one date.\n",
      "Skipping set_410 because only one date.\n",
      "Skipping set_411 because only one date.\n",
      "Skipping set_412 because only one date.\n",
      "Skipping set_413 because only one date.\n",
      "Skipping set_414 because only one date.\n",
      "Skipping set_415 because only one date.\n",
      "Skipping set_416 because only one date.\n",
      "Skipping set_417 because only one date.\n",
      "Skipping set_418 because only one date.\n",
      "Skipping set_419 because only one date.\n",
      "Skipping set_420 because only one date.\n",
      "Skipping set_421 because only one date.\n",
      "Skipping set_422 because only one date.\n",
      "Skipping set_423 because only one date.\n",
      "Skipping set_424 because only one date.\n",
      "Skipping set_425 because only one date.\n",
      "Skipping set_426 because only one date.\n",
      "Skipping set_427 because only one date.\n",
      "Skipping set_428 because only one date.\n",
      "Skipping set_429 because only one date.\n",
      "Skipping set_430 because only one date.\n",
      "Skipping set_431 because only one date.\n",
      "Skipping set_432 because only one date.\n",
      "Skipping set_433 because only one date.\n",
      "Skipping set_434 because only one date.\n",
      "Skipping set_435 because only one date.\n",
      "Skipping set_436 because only one date.\n",
      "Skipping set_437 because only one date.\n",
      "Skipping set_438 because only one date.\n",
      "Skipping set_439 because only one date.\n",
      "Skipping set_440 because only one date.\n",
      "Skipping set_441 because only one date.\n",
      "Skipping set_442 because only one date.\n",
      "Skipping set_443 because only one date.\n",
      "Skipping set_444 because only one date.\n",
      "Skipping set_445 because only one date.\n",
      "Skipping set_446 because only one date.\n",
      "Skipping set_447 because only one date.\n",
      "Skipping set_448 because only one date.\n",
      "Skipping set_449 because only one date.\n",
      "Skipping set_450 because only one date.\n",
      "Skipping set_451 because only one date.\n",
      "Skipping set_452 because only one date.\n",
      "Skipping set_453 because only one date.\n",
      "Skipping set_454 because only one date.\n",
      "Skipping set_455 because only one date.\n",
      "Skipping set_456 because only one date.\n",
      "Skipping set_457 because only one date.\n",
      "Skipping set_458 because only one date.\n",
      "Skipping set_459 because only one date.\n",
      "Skipping set_460 because only one date.\n",
      "Skipping set_461 because only one date.\n",
      "Skipping set_462 because only one date.\n",
      "Skipping set_463 because only one date.\n",
      "Skipping set_464 because only one date.\n",
      "Skipping set_465 because only one date.\n",
      "Skipping set_466 because only one date.\n",
      "Skipping set_467 because only one date.\n",
      "Skipping set_468 because only one date.\n",
      "Skipping set_469 because only one date.\n",
      "Skipping set_470 because only one date.\n",
      "Skipping set_471 because only one date.\n",
      "Skipping set_472 because only one date.\n",
      "Skipping set_473 because only one date.\n",
      "Skipping set_474 because only one date.\n",
      "Skipping set_475 because only one date.\n",
      "Skipping set_476 because only one date.\n",
      "Skipping set_477 because only one date.\n",
      "Skipping set_478 because only one date.\n",
      "Skipping set_479 because only one date.\n",
      "Skipping set_480 because only one date.\n",
      "Skipping set_481 because only one date.\n",
      "Skipping set_482 because only one date.\n",
      "Skipping set_483 because only one date.\n",
      "Skipping set_484 because only one date.\n",
      "Skipping set_485 because only one date.\n",
      "Skipping set_486 because only one date.\n",
      "Skipping set_487 because only one date.\n",
      "Skipping set_488 because only one date.\n"
     ]
    }
   ],
   "source": [
    "# Exploring some timespan considerations 4/18/2024\n",
    "#\n",
    "# Note: in this repository (\"Hayden DEEP\") there are no multi-date overlaps.\n",
    "# ... thus, we will need to revisit this with the broader \"Steven DEEP\" repository. 4/23/2024 COC\n",
    "overlapping_sets\n",
    "for overlapping_set in overlapping_sets:\n",
    "    dates = [id_to_date[dataId_to_dataIdStr(i)] for i in overlapping_sets[overlapping_set]]\n",
    "    unique_dates = list(set(dates))\n",
    "    unique_dates.sort()\n",
    "    if len(unique_dates) < 2:\n",
    "        print(f\"Skipping {overlapping_set} because only one date.\")\n",
    "        continue\n",
    "    counts = [dates.count(i) for i in unique_dates]\n",
    "    plt.plot_date(unique_dates, counts)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e81bd6",
   "metadata": {},
   "source": [
    "### (RA, Dec) searching of image sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f3417ffd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"set_1\" is the first set name in overlapping_sets.\n",
      "Its first record dataId: {instrument: 'DECam', detector: 1, visit: 898287}.\n",
      "It has an (RA, Dec) coordinate of (351.0695696334572, -4.336293374423113).\n"
     ]
    }
   ],
   "source": [
    "# Fetch an (RA, Dec) pair from an image set.\n",
    "first_set_name = list(overlapping_sets.keys())[0]\n",
    "first_sets_data_id = overlapping_sets[first_set_name][0]\n",
    "searching_ra_dec = df.set_index(\"data_id_str\")[\"center_coord\"].to_dict()[\n",
    "    dataId_to_dataIdStr(first_sets_data_id)\n",
    "]\n",
    "\n",
    "print(f'\"{first_set_name}\" is the first set name in overlapping_sets.')\n",
    "print(f\"Its first record dataId: {first_sets_data_id}.\")\n",
    "print(f\"It has an (RA, Dec) coordinate of {searching_ra_dec}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bfb23aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ra_dec_search_overlapping_sets(df, overlapping_sets, ra_dec, search_radius, verbose=False):\n",
    "    \"\"\"\n",
    "    2/6/2024 COC\n",
    "    Implementing an extremely basic (RA, Dec) query functionality.\n",
    "    This will work within the overlapping_sets framework.\n",
    "    \"\"\"\n",
    "    ra_dec_coord = SkyCoord(ra=ra_dec[0] * u.degree, dec=ra_dec[1] * u.degree)\n",
    "\n",
    "    all_centers = []\n",
    "    all_labels = []\n",
    "\n",
    "    # id_to_coord = df.set_index(\"data_id_str\")[\"center_coord\"].to_dict() # note this was generated already but outside the function\n",
    "\n",
    "    for i, set_name in enumerate(list(overlapping_sets.keys())):\n",
    "        all_labels.append(set_name)\n",
    "        center_data_id = overlapping_sets[set_name][0]\n",
    "        center_coord = id_to_coord[dataId_to_dataIdStr(center_data_id)]\n",
    "        all_centers.append(center_coord)\n",
    "\n",
    "    all_centers = SkyCoord(\n",
    "        ra=[x[0] for x in all_centers] * u.degree,\n",
    "        dec=[x[1] for x in all_centers] * u.degree,\n",
    "    )\n",
    "\n",
    "    distances = ra_dec_coord.separation(all_centers)\n",
    "    within_radius = (distances <= search_radius) & (\n",
    "        distances >= 0\n",
    "    )  # ≥ (not >) because we could supply the an exact match\n",
    "\n",
    "    results = []\n",
    "    for i, item in enumerate(within_radius):\n",
    "        if item == True:\n",
    "            results.append(all_labels[i])\n",
    "    if verbose:\n",
    "        print(f\"We found {len(results)} sets that are within {search_radius} of {ra_dec}.\")\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0b98b0e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 4 sets that are within 14.0 arcmin of (351.0695696334572, -4.336293374423113).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['set_1', 'set_3', 'set_4', 'set_68']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find matching image sets.\n",
    "# With 1*u.arcminute we would only find set_1 (where we got searching_ra_dec).\n",
    "# With 15*u.arcminute, we find 3 others (for a total of 4).\n",
    "search_radius = 14 * u.arcminute\n",
    "ra_dec_search_results = ra_dec_search_overlapping_sets(\n",
    "    df=df,\n",
    "    overlapping_sets=overlapping_sets,\n",
    "    ra_dec=searching_ra_dec,\n",
    "    search_radius=search_radius,\n",
    "    verbose=True,\n",
    ")\n",
    "ra_dec_search_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "447b9b70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(350.9194972863569, -4.410893881734765), (350.9194972863569, -4.261692867111463), (351.2196419805575, -4.410893881734765), (351.2196419805575, -4.261692867111463)], [(350.7636367448476, -4.57504777569875), (350.7636367448476, -4.425558984666654), (351.06385253666514, -4.57504777569875), (351.06385253666514, -4.425558984666654)], [(351.07489718950103, -4.574714132229647), (351.07489718950103, -4.425184883190398), (351.3752749096747, -4.574714132229647), (351.3752749096747, -4.425184883190398)], [(351.1044786505892, -4.535445547532834), (351.1044786505892, -4.385657669534001), (351.40443040247294, -4.535445547532834), (351.40443040247294, -4.385657669534001)]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matching_regions = []\n",
    "id_to_region = df.set_index(\"data_id_str\")[\"region\"].to_dict()\n",
    "\n",
    "for set_name in ra_dec_search_results:\n",
    "    matching_regions.append(id_to_region[dataId_to_dataIdStr(overlapping_sets[set_name][0])])\n",
    "\n",
    "all_corners = [getRegionCorners(region) for region in matching_regions]\n",
    "print(all_corners)\n",
    "\n",
    "for i, quad in enumerate(all_corners):\n",
    "    ra_bounds, dec_bounds = getMinMaxRaDec(quad)\n",
    "    #\n",
    "    coords = []\n",
    "    coords.append((ra_bounds[0], dec_bounds[0]))\n",
    "    coords.append((ra_bounds[0], dec_bounds[1]))\n",
    "    coords.append((ra_bounds[1], dec_bounds[1]))\n",
    "    coords.append((ra_bounds[1], dec_bounds[0]))\n",
    "    coords.append((ra_bounds[0], dec_bounds[0]))  # close the \"shape\"\n",
    "    #\n",
    "    plt.plot([x[0] for x in coords], [x[1] for x in coords], label=ra_dec_search_results[i])\n",
    "\n",
    "plt.scatter(\n",
    "    searching_ra_dec[0],\n",
    "    searching_ra_dec[1],\n",
    "    s=25,\n",
    "    marker=\"+\",\n",
    "    alpha=0.75,\n",
    "    label=f\"search center\",\n",
    "    color=\"red\",\n",
    ")\n",
    "circle = plt.Circle(\n",
    "    (searching_ra_dec[0], searching_ra_dec[1]),\n",
    "    search_radius.to(u.degree).value,\n",
    "    color=\"black\",\n",
    "    alpha=0.75,\n",
    "    fill=False,\n",
    "    label=\"search area\",\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "plt.legend()\n",
    "plt.xlabel(f\"RA\")\n",
    "plt.ylabel(\"Dec\")\n",
    "ax = plt.gca()\n",
    "ax.add_patch(circle)\n",
    "ax.set_aspect(\"equal\", adjustable=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ccf409",
   "metadata": {},
   "source": [
    "### (RA, Dec) searching of our DataFrame (more general case)\n",
    "It is far faster to search overlapping_sets as there are few of these to inspect.\n",
    "A more general case, however, would be to simply find all images within a radius of a sky coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b636fef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ra_dec_search_df(df, ra_dec, search_radius, verbose=False):\n",
    "    \"\"\"\n",
    "    2/6/2024 COC\n",
    "    Implementing a basic (RA, Dec) query function for our derived Pandas dataframe.\n",
    "    \"\"\"\n",
    "\n",
    "    ra_dec_coord = SkyCoord(ra=ra_dec[0] * u.degree, dec=ra_dec[1] * u.degree)\n",
    "\n",
    "    all_centers = SkyCoord(\n",
    "        ra=[x[0] for x in df[\"center_coord\"].iloc()] * u.degree,\n",
    "        dec=[x[1] for x in df[\"center_coord\"].iloc()] * u.degree,\n",
    "    )\n",
    "\n",
    "    distances = ra_dec_coord.separation(all_centers)\n",
    "    within_radius = (distances <= search_radius) & (\n",
    "        distances >= 0\n",
    "    )  # ≥ (not >) because we could supply the an exact match\n",
    "\n",
    "    results = df[within_radius]\n",
    "\n",
    "    if verbose:\n",
    "        print(f\"We found {len(results.index)} matches within {search_radius} of {ra_dec}.\")\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "90b6aa45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 95 matches within 3.0 arcsec of (351.0695696334572, -4.336293374423113).\n",
      "CPU times: user 2.88 s, sys: 23.5 ms, total: 2.9 s\n",
      "Wall time: 2.9 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_id</th>\n",
       "      <th>region</th>\n",
       "      <th>detector</th>\n",
       "      <th>data_id_str</th>\n",
       "      <th>uri</th>\n",
       "      <th>ut</th>\n",
       "      <th>ut_datetime</th>\n",
       "      <th>center_coord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847372525065534...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898286</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:20:22.932</td>\n",
       "      <td>2019-09-27 00:20:22.932</td>\n",
       "      <td>(351.0694028401149, -4.336598368890197)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847381014554984...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898287</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:22:51.015</td>\n",
       "      <td>2019-09-27 00:22:51.015</td>\n",
       "      <td>(351.0695696334572, -4.336293374423113)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847383417970056...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898288</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:25:19.136</td>\n",
       "      <td>2019-09-27 00:25:19.136</td>\n",
       "      <td>(351.0696571334576, -4.336293374415341)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847382159041213...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898289</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:27:47.118</td>\n",
       "      <td>2019-09-27 00:27:47.118</td>\n",
       "      <td>(351.069611300124, -4.336293374419411)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847381374341414...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898290</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:30:15.537</td>\n",
       "      <td>2019-09-27 00:30:15.537</td>\n",
       "      <td>(351.0695696451088, -4.336265319377865)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847391753851408...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898376</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:02:58.424</td>\n",
       "      <td>2019-09-27 04:02:58.424</td>\n",
       "      <td>(351.06998627728365, -4.3363483733857136)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847400909178194...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898377</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:05:27.654</td>\n",
       "      <td>2019-09-27 04:05:27.654</td>\n",
       "      <td>(351.0703196106188, -4.336348373356122)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847397935953284...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898378</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:07:56.159</td>\n",
       "      <td>2019-09-27 04:07:56.159</td>\n",
       "      <td>(351.07019878847524, -4.336321429412502)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847392459182945...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898379</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:10:25.141</td>\n",
       "      <td>2019-09-27 04:10:25.141</td>\n",
       "      <td>(351.069986300126, -4.336293374386118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847397036888316...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898380</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:12:52.877</td>\n",
       "      <td>2019-09-27 04:12:52.877</td>\n",
       "      <td>(351.07015296679367, -4.336293374371324)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              data_id   \n",
       "0   {'instrument': 'DECam', 'detector': 1, 'visit'...  \\\n",
       "1   {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "2   {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "3   {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "4   {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "..                                                ...   \n",
       "90  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "91  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "92  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "93  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "94  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "\n",
       "                                               region  detector   \n",
       "0   ConvexPolygon([UnitVector3d(0.9847372525065534...         1  \\\n",
       "1   ConvexPolygon([UnitVector3d(0.9847381014554984...         1   \n",
       "2   ConvexPolygon([UnitVector3d(0.9847383417970056...         1   \n",
       "3   ConvexPolygon([UnitVector3d(0.9847382159041213...         1   \n",
       "4   ConvexPolygon([UnitVector3d(0.9847381374341414...         1   \n",
       "..                                                ...       ...   \n",
       "90  ConvexPolygon([UnitVector3d(0.9847391753851408...         1   \n",
       "91  ConvexPolygon([UnitVector3d(0.9847400909178194...         1   \n",
       "92  ConvexPolygon([UnitVector3d(0.9847397935953284...         1   \n",
       "93  ConvexPolygon([UnitVector3d(0.9847392459182945...         1   \n",
       "94  ConvexPolygon([UnitVector3d(0.9847397036888316...         1   \n",
       "\n",
       "       data_id_str                                                uri   \n",
       "0   DECam_1_898286  file:///epyc/users/smotherh/DEEP/PointingGroup...  \\\n",
       "1   DECam_1_898287  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "2   DECam_1_898288  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "3   DECam_1_898289  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "4   DECam_1_898290  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "..             ...                                                ...   \n",
       "90  DECam_1_898376  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "91  DECam_1_898377  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "92  DECam_1_898378  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "93  DECam_1_898379  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "94  DECam_1_898380  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "\n",
       "                         ut             ut_datetime   \n",
       "0   2019-09-27T00:20:22.932 2019-09-27 00:20:22.932  \\\n",
       "1   2019-09-27T00:22:51.015 2019-09-27 00:22:51.015   \n",
       "2   2019-09-27T00:25:19.136 2019-09-27 00:25:19.136   \n",
       "3   2019-09-27T00:27:47.118 2019-09-27 00:27:47.118   \n",
       "4   2019-09-27T00:30:15.537 2019-09-27 00:30:15.537   \n",
       "..                      ...                     ...   \n",
       "90  2019-09-27T04:02:58.424 2019-09-27 04:02:58.424   \n",
       "91  2019-09-27T04:05:27.654 2019-09-27 04:05:27.654   \n",
       "92  2019-09-27T04:07:56.159 2019-09-27 04:07:56.159   \n",
       "93  2019-09-27T04:10:25.141 2019-09-27 04:10:25.141   \n",
       "94  2019-09-27T04:12:52.877 2019-09-27 04:12:52.877   \n",
       "\n",
       "                                 center_coord  \n",
       "0     (351.0694028401149, -4.336598368890197)  \n",
       "1     (351.0695696334572, -4.336293374423113)  \n",
       "2     (351.0696571334576, -4.336293374415341)  \n",
       "3      (351.069611300124, -4.336293374419411)  \n",
       "4     (351.0695696451088, -4.336265319377865)  \n",
       "..                                        ...  \n",
       "90  (351.06998627728365, -4.3363483733857136)  \n",
       "91    (351.0703196106188, -4.336348373356122)  \n",
       "92   (351.07019878847524, -4.336321429412502)  \n",
       "93     (351.069986300126, -4.336293374386118)  \n",
       "94   (351.07015296679367, -4.336293374371324)  \n",
       "\n",
       "[95 rows x 8 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ra_dec_search_radius = 3 * u.arcsec\n",
    "ra_dec_search_results_df = ra_dec_search_df(\n",
    "    df=df, ra_dec=searching_ra_dec, search_radius=ra_dec_search_radius, verbose=True\n",
    ")\n",
    "ra_dec_search_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1874ddaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the centers\n",
    "plt.scatter(\n",
    "    [x[0] for x in ra_dec_search_results_df[\"center_coord\"]],\n",
    "    [x[1] for x in ra_dec_search_results_df[\"center_coord\"]],\n",
    "    s=2,\n",
    "    alpha=0.5,\n",
    "    label=\"matches\",\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    searching_ra_dec[0], searching_ra_dec[1], color=\"red\", s=20, alpha=0.5, marker=\"+\", label=\"search center\"\n",
    ")\n",
    "\n",
    "circle = plt.Circle(\n",
    "    (searching_ra_dec[0], searching_ra_dec[1]),\n",
    "    ra_dec_search_radius.to(u.degree).value,\n",
    "    color=\"black\",\n",
    "    alpha=0.75,\n",
    "    fill=False,\n",
    "    label=\"search area\",\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "\n",
    "plt.xlabel(f\"RA\")\n",
    "plt.ylabel(\"Dec\")\n",
    "ax = plt.gca()\n",
    "ax.add_patch(circle)\n",
    "plt.legend()\n",
    "ax.set_aspect(\"equal\", adjustable=\"box\")\n",
    "\n",
    "# TODO consider making this dRA, dDec from minimum (RA, Dec) values 4/23/2024 COC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805d6699",
   "metadata": {},
   "source": [
    "These are the 95 matching center coordinates.\\\n",
    "These may look far off, but this is just the scale.\\\n",
    "We can plot the entire chip area on-sky, as we did earlier overlapping_sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3db99133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the centers\n",
    "# plt.scatter([x[0] for x in ra_dec_search_results_df[\"center_coord\"]], [x[1] for x in ra_dec_search_results_df[\"center_coord\"]], s=2, alpha=0.5, label=\"matches\")\n",
    "plt.scatter(\n",
    "    searching_ra_dec[0], searching_ra_dec[1], color=\"red\", s=20, alpha=0.5, marker=\"+\", label=\"search center\"\n",
    ")\n",
    "\n",
    "for quad in [getRegionCorners(x) for x in ra_dec_search_results_df[\"region\"]]:\n",
    "    ra_bounds, dec_bounds = getMinMaxRaDec(quad)\n",
    "    #\n",
    "    coords = []\n",
    "    coords.append((ra_bounds[0], dec_bounds[0]))\n",
    "    coords.append((ra_bounds[0], dec_bounds[1]))\n",
    "    coords.append((ra_bounds[1], dec_bounds[1]))\n",
    "    coords.append((ra_bounds[1], dec_bounds[0]))\n",
    "    coords.append((ra_bounds[0], dec_bounds[0]))  # close the \"shape\"\n",
    "    #\n",
    "    plt.plot([x[0] for x in coords], [x[1] for x in coords]),  # label=ra_dec_search_results[i])\n",
    "\n",
    "circle = plt.Circle(\n",
    "    (searching_ra_dec[0], searching_ra_dec[1]),\n",
    "    ra_dec_search_radius.to(u.degree).value,\n",
    "    color=\"black\",\n",
    "    alpha=0.75,\n",
    "    fill=False,\n",
    "    label=\"search area\",\n",
    "    linestyle=\"--\",\n",
    "    linewidth=0.1,\n",
    ")\n",
    "\n",
    "plt.xlabel(f\"RA\")\n",
    "plt.ylabel(\"Dec\")\n",
    "ax = plt.gca()\n",
    "ax.add_patch(circle)\n",
    "plt.legend()\n",
    "ax.set_aspect(\"equal\", adjustable=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337ceedc",
   "metadata": {},
   "source": [
    "# Recap and Master Functions\n",
    "Here is a master function that takes a repo_path and returns \\\n",
    "(1) a Pandas dataframe with needed info, and\\\n",
    "(2) a dictionary with the images in discrete piles (sets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fccdee6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_image_sets(\n",
    "    repo_path,\n",
    "    basedir=\"default\",\n",
    "    desired_datasetTypes=[\"deepDiff_differenceExp\"],\n",
    "    overwrite=False,\n",
    "    overlap_uncertainty_radius_arcsec=30,\n",
    "):\n",
    "    \"\"\"\n",
    "    2/6/2024 COC\n",
    "    \"\"\"\n",
    "    if basedir == \"default\":\n",
    "        basedir = f'{os.environ[\"HOME\"]}/kbmod_tmp'\n",
    "        print(f'Changing \"default\" basedir to {basedir} now.')\n",
    "\n",
    "    os.makedirs(basedir, exist_ok=True)\n",
    "\n",
    "    #     repo_path = f\"/epyc/users/smotherh/DEEP/PointingGroups/butler-repo\"\n",
    "    butler = dafButler.Butler(repo_path)\n",
    "\n",
    "    all_collection_names = get_collection_names(butler=butler, basedir=basedir, verbose=True, export=True)\n",
    "    desired_collections = get_desired_collections(all_collections_list=all_collection_names)\n",
    "    #     datasetTypes = getDatasetTypeStats(butler=butler, overwrite=False) # not used 2/6/2024 COC\n",
    "    #     desired_datasetTypes = [\"deepDiff_differenceExp\"]\n",
    "    df, example_vdr_ref = get_vdr_data(\n",
    "        butler=butler, desired_collections=desired_collections, desired_datasetTypes=desired_datasetTypes\n",
    "    )\n",
    "    desired_instruments = getInstruments(butler=butler, vdr_ids=df[\"data_id\"])\n",
    "    df[\"center_coord\"] = [getCenterRaDec(i) for i in df[\"region\"]]\n",
    "    df[\"uri\"] = getURIs(\n",
    "        butler=butler,\n",
    "        dataIds=df[\"data_id\"],\n",
    "        repo_path=repo_path,\n",
    "        desired_datasetTypes=desired_datasetTypes,\n",
    "        desired_collections=desired_collections,\n",
    "        overwrite=overwrite,\n",
    "    )\n",
    "\n",
    "    df[\"ut\"] = getTimestamps(dataIds=df[\"data_id\"], overwrite=overwrite)\n",
    "    df[\"ut_datetime\"] = pd.to_datetime(df[\"ut\"])\n",
    "    overlapping_sets = find_overlapping_coords(\n",
    "        df=df, uncertainty_radius=overlap_uncertainty_radius_arcsec, overwrite=overwrite\n",
    "    )\n",
    "    return df, overlapping_sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "cf221f77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changing \"default\" basedir to /astro/users/coc123/kbmod_tmp now.\n",
      "Found 1292 collections in the Butler. Wrote to \"/astro/users/coc123/kbmod_tmp/all_collection_names.lst\".\n",
      "Found DECam. Adding to \"desired_instruments\" now.\n",
      "WARNING: we are not iterating over all rows to find instruments, just taking the first one.\n",
      "Recycled 47383 paths from /astro/users/coc123/kbmod_tmp/uri_cache.lst as overwrite was False.\n",
      "Overwrite is False, so we will read the timestamps from file now...\n",
      "Recycled 47383 from /astro/users/coc123/kbmod_tmp/vdr_timestamps.lst.\n",
      "Recycling /astro/users/coc123/kbmod_tmp/overlapping_sets.pickle as overwrite=False.\n",
      "CPU times: user 4.84 s, sys: 443 ms, total: 5.28 s\n",
      "Wall time: 6.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Example\n",
    "# TIMING NOTE: this requires about 7 seconds to run ***with everything already cached***.\n",
    "df1, overlapping_sets1 = retrieve_image_sets(\n",
    "    repo_path=f\"/epyc/users/smotherh/DEEP/PointingGroups/butler-repo\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38d90f55",
   "metadata": {},
   "source": [
    "The other high-level function is searching the overlapping sets for a specific (RA, Dec) coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "45f09318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 4 sets that are within 15.0 arcmin of (351.0695696334572, -4.336293374423113).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['set_1', 'set_3', 'set_4', 'set_68']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_dec_search_results1 = ra_dec_search_overlapping_sets(\n",
    "    df=df1,\n",
    "    overlapping_sets=overlapping_sets1,\n",
    "    ra_dec=(351.0695696334572, -4.336293374423113),\n",
    "    search_radius=15 * u.arcminute,\n",
    "    verbose=True,\n",
    ")\n",
    "ra_dec_search_results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8a6c4def",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 95 matches within 3.0 arcsec of (351.0695696334572, -4.336293374423113).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_id</th>\n",
       "      <th>region</th>\n",
       "      <th>detector</th>\n",
       "      <th>data_id_str</th>\n",
       "      <th>uri</th>\n",
       "      <th>ut</th>\n",
       "      <th>ut_datetime</th>\n",
       "      <th>center_coord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847372525065534...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898286</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:20:22.932</td>\n",
       "      <td>2019-09-27 00:20:22.932</td>\n",
       "      <td>(351.0694028401149, -4.336598368890197)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847381014554984...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898287</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:22:51.015</td>\n",
       "      <td>2019-09-27 00:22:51.015</td>\n",
       "      <td>(351.0695696334572, -4.336293374423113)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847383417970056...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898288</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:25:19.136</td>\n",
       "      <td>2019-09-27 00:25:19.136</td>\n",
       "      <td>(351.0696571334576, -4.336293374415341)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847382159041213...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898289</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:27:47.118</td>\n",
       "      <td>2019-09-27 00:27:47.118</td>\n",
       "      <td>(351.069611300124, -4.336293374419411)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847381374341414...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898290</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T00:30:15.537</td>\n",
       "      <td>2019-09-27 00:30:15.537</td>\n",
       "      <td>(351.0695696451088, -4.336265319377865)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847391753851408...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898376</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:02:58.424</td>\n",
       "      <td>2019-09-27 04:02:58.424</td>\n",
       "      <td>(351.06998627728365, -4.3363483733857136)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847400909178194...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898377</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:05:27.654</td>\n",
       "      <td>2019-09-27 04:05:27.654</td>\n",
       "      <td>(351.0703196106188, -4.336348373356122)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847397935953284...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898378</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:07:56.159</td>\n",
       "      <td>2019-09-27 04:07:56.159</td>\n",
       "      <td>(351.07019878847524, -4.336321429412502)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847392459182945...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898379</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:10:25.141</td>\n",
       "      <td>2019-09-27 04:10:25.141</td>\n",
       "      <td>(351.069986300126, -4.336293374386118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>{'instrument': 'DECam', 'detector': 1, 'visit'...</td>\n",
       "      <td>ConvexPolygon([UnitVector3d(0.9847397036888316...</td>\n",
       "      <td>1</td>\n",
       "      <td>DECam_1_898380</td>\n",
       "      <td>file:///epyc/users/smotherh/DEEP/PointingGroup...</td>\n",
       "      <td>2019-09-27T04:12:52.877</td>\n",
       "      <td>2019-09-27 04:12:52.877</td>\n",
       "      <td>(351.07015296679367, -4.336293374371324)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              data_id   \n",
       "0   {'instrument': 'DECam', 'detector': 1, 'visit'...  \\\n",
       "1   {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "2   {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "3   {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "4   {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "..                                                ...   \n",
       "90  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "91  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "92  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "93  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "94  {'instrument': 'DECam', 'detector': 1, 'visit'...   \n",
       "\n",
       "                                               region  detector   \n",
       "0   ConvexPolygon([UnitVector3d(0.9847372525065534...         1  \\\n",
       "1   ConvexPolygon([UnitVector3d(0.9847381014554984...         1   \n",
       "2   ConvexPolygon([UnitVector3d(0.9847383417970056...         1   \n",
       "3   ConvexPolygon([UnitVector3d(0.9847382159041213...         1   \n",
       "4   ConvexPolygon([UnitVector3d(0.9847381374341414...         1   \n",
       "..                                                ...       ...   \n",
       "90  ConvexPolygon([UnitVector3d(0.9847391753851408...         1   \n",
       "91  ConvexPolygon([UnitVector3d(0.9847400909178194...         1   \n",
       "92  ConvexPolygon([UnitVector3d(0.9847397935953284...         1   \n",
       "93  ConvexPolygon([UnitVector3d(0.9847392459182945...         1   \n",
       "94  ConvexPolygon([UnitVector3d(0.9847397036888316...         1   \n",
       "\n",
       "       data_id_str                                                uri   \n",
       "0   DECam_1_898286  file:///epyc/users/smotherh/DEEP/PointingGroup...  \\\n",
       "1   DECam_1_898287  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "2   DECam_1_898288  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "3   DECam_1_898289  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "4   DECam_1_898290  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "..             ...                                                ...   \n",
       "90  DECam_1_898376  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "91  DECam_1_898377  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "92  DECam_1_898378  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "93  DECam_1_898379  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "94  DECam_1_898380  file:///epyc/users/smotherh/DEEP/PointingGroup...   \n",
       "\n",
       "                         ut             ut_datetime   \n",
       "0   2019-09-27T00:20:22.932 2019-09-27 00:20:22.932  \\\n",
       "1   2019-09-27T00:22:51.015 2019-09-27 00:22:51.015   \n",
       "2   2019-09-27T00:25:19.136 2019-09-27 00:25:19.136   \n",
       "3   2019-09-27T00:27:47.118 2019-09-27 00:27:47.118   \n",
       "4   2019-09-27T00:30:15.537 2019-09-27 00:30:15.537   \n",
       "..                      ...                     ...   \n",
       "90  2019-09-27T04:02:58.424 2019-09-27 04:02:58.424   \n",
       "91  2019-09-27T04:05:27.654 2019-09-27 04:05:27.654   \n",
       "92  2019-09-27T04:07:56.159 2019-09-27 04:07:56.159   \n",
       "93  2019-09-27T04:10:25.141 2019-09-27 04:10:25.141   \n",
       "94  2019-09-27T04:12:52.877 2019-09-27 04:12:52.877   \n",
       "\n",
       "                                 center_coord  \n",
       "0     (351.0694028401149, -4.336598368890197)  \n",
       "1     (351.0695696334572, -4.336293374423113)  \n",
       "2     (351.0696571334576, -4.336293374415341)  \n",
       "3      (351.069611300124, -4.336293374419411)  \n",
       "4     (351.0695696451088, -4.336265319377865)  \n",
       "..                                        ...  \n",
       "90  (351.06998627728365, -4.3363483733857136)  \n",
       "91    (351.0703196106188, -4.336348373356122)  \n",
       "92   (351.07019878847524, -4.336321429412502)  \n",
       "93     (351.069986300126, -4.336293374386118)  \n",
       "94   (351.07015296679367, -4.336293374371324)  \n",
       "\n",
       "[95 rows x 8 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_dec_search_results2 = ra_dec_search_df(\n",
    "    df=df, ra_dec=(351.0695696334572, -4.336293374423113), search_radius=3 * u.arcsec, verbose=True\n",
    ")\n",
    "ra_dec_search_results2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f34362e",
   "metadata": {},
   "source": [
    "### Sky Patches\n",
    "For datasets spanning large spatial areas, such as LSST, we need a different approach.\\\n",
    "We will uniformly divide the sky into \"patches\" of the sky.\\\n",
    "We will also give an option to have the patches overlap by some percentage.\\\n",
    "Patches can primarily be used in three ways:\\\n",
    "1. Their center (RA, Dec) coordinate.\n",
    "2. The \"corner\" (RA, Dec) coordinates. Corners are really vertices of quadrilateral patches.\n",
    "3. A LSST Sphgeom region, which may be hashed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "49bc209f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we introduce our patch generator.\n",
    "\n",
    "\n",
    "def generate_patches(arcminutes, overlap_percentage, verbose=True, decRange=[-90, 90], export=False):\n",
    "    \"\"\"Given a \"rectangle\" in (RA, Dec) touple (probably based on some chip size),\n",
    "    produce a list of bounded regions on the sky,\n",
    "    with user-supplied edge overlap percentage (overlap_percentage).\n",
    "    The list is to be iterated over when matching against actual observations,\n",
    "    i.e., for later shift-and-stack.\n",
    "    v0: 12/11/2023 COC\n",
    "    Note: this all assumes small angle approximation is OK. 1/9/2024 COC\n",
    "    TODO: something to limit Dec range (min or max).\n",
    "        E.g., at LSST (30.241° S) they cannot see anything above 59.759° N.\n",
    "    \"\"\"\n",
    "    import numpy as np\n",
    "\n",
    "    def checkDec(n):\n",
    "        if n > 90:\n",
    "            n -= 180\n",
    "        elif n < -90:\n",
    "            n += 180\n",
    "        return n\n",
    "\n",
    "    def checkRA(n):\n",
    "        if n > 360:\n",
    "            n -= 360\n",
    "        if n < 0:\n",
    "            n += 360\n",
    "        return n\n",
    "\n",
    "    # Convert arcminutes to degrees (work base unit)\n",
    "    arcdegrees = np.array(arcminutes) / 60.0\n",
    "\n",
    "    # Calculate overlap in degrees\n",
    "    overlap = arcdegrees * (overlap_percentage / 100.0)\n",
    "\n",
    "    # Number of patches needed in RA, Dec space\n",
    "    # TODO: consider these aren't decimal; so should be ceil for bounds 1/9/2024 COC\n",
    "    num_patches_ra = int(360 / (arcdegrees[0] - overlap[0]))\n",
    "    num_patches_dec = int(180 / (arcdegrees[1] - overlap[1]))\n",
    "    if verbose:\n",
    "        print(\n",
    "            f\"Number of patches in (RA, Dec): ({num_patches_ra},{num_patches_dec}).\"\n",
    "        )  # Recall (RA, Dec) ranges are (0-360,0-180), so square inputs result in (n*2, n) ranges.\n",
    "\n",
    "    # Generate patches\n",
    "    patches = []\n",
    "    centers = []  # 1/15/2024 COC\n",
    "    skippedBecauseOfDec = 0\n",
    "    for ra_index in range(num_patches_ra):\n",
    "        # Calculate corner RA coordinates; moved out of dec loop 1/11/2024 COC\n",
    "        ra_start = checkRA(ra_index * (arcdegrees[0] - overlap[0]))\n",
    "        center_ra = checkRA(ra_start + arcdegrees[0] / 2)  # 1/15/2024 COC\n",
    "        ra_end = checkRA(ra_start + arcdegrees[0])\n",
    "        #\n",
    "        for dec_index in range(num_patches_dec):\n",
    "            # Calculate corner Dec coordinates\n",
    "            dec_start = checkDec(dec_index * (arcdegrees[1] - overlap[1]) - 90)\n",
    "            center_dec = checkDec(dec_start + arcdegrees[1] / 2)  # 1/15/2024 COC\n",
    "            dec_end = checkDec(dec_start + arcdegrees[1])\n",
    "            #\n",
    "            # Make sure Dec is in allowed range; KLUDGE 1/9/2024 COC\n",
    "            OK = True\n",
    "            for d in [dec_start, dec_end]:\n",
    "                if d < decRange[0] or d > decRange[1]:\n",
    "                    OK = False\n",
    "                    break\n",
    "            if OK == False:\n",
    "                skippedBecauseOfDec += 1\n",
    "                #                 print(f'Something is outside of valid Dec range: dec_start={dec_start}, dec_end={dec_end}')\n",
    "                continue\n",
    "            #\n",
    "            # Append patch coordinates to the list\n",
    "            patches.append(((ra_start, dec_start), (ra_end, dec_end)))\n",
    "            centers.append((center_ra, center_dec))  # 1/15/2024 COC\n",
    "\n",
    "    #\n",
    "    npatches = len(patches)\n",
    "    info = {\"npatches\": npatches, \"arcminutes\": arcminutes, \"overlap\": overlap_percentage}\n",
    "    if verbose:\n",
    "        print(\n",
    "            f\"There were {npatches} produced, skipping {skippedBecauseOfDec} because Dec was outside {decRange}. Info: {info}.\"\n",
    "        )\n",
    "    #\n",
    "    # produce CSV if desired\n",
    "    if export == True:\n",
    "        outfile = f\"patches_{arcminutes[0]}x{arcminutes[1]}arcmin_{overlap_percentage}pctOverlap\"\n",
    "        if decRange != None:\n",
    "            outfile += f\"_Dec{decRange[0]}to{decRange[1]}\"\n",
    "        outfile += \".csv\"\n",
    "        with open(outfile, \"w\") as f:\n",
    "            print(f\"ra0,dec0,ra1,dec1\", file=f)\n",
    "            for quad in patches:\n",
    "                print(f\"{quad[0][0]},{quad[0][1]},{quad[1][0]},{quad[1][1]}\", file=f)\n",
    "            print(f\"Wrote {len(patches)} patch rows to {outfile}.\")\n",
    "    #\n",
    "    return patches, centers, info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fd12f4a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patches in (RA, Dec): (360,180).\n",
      "There were 64800 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 64800, 'arcminutes': (60, 60), 'overlap': 0}.\n"
     ]
    }
   ],
   "source": [
    "# Let's start by generating 1°X1° (one degree square) patches, with no overlap.\n",
    "# This should be the number of degrees in the range of RA (0 to 360 = 360) * the range in Dec (-90 to 90 = 180.)\n",
    "# 360 * 180 = 64,800 square degrees.\n",
    "# (We know a priori that this should be around 41,253 square degrees,\n",
    "# but our grid is of small angles and does not take into account spherical geometry.)\n",
    "\n",
    "tmp = generate_patches(arcminutes=(1 * 60, 1 * 60), overlap_percentage=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6004da63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patches in (RA, Dec): (400,200).\n",
      "There were 80000 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 80000, 'arcminutes': (60, 60), 'overlap': 10}.\n"
     ]
    }
   ],
   "source": [
    "# The numbers go up dramatically when we introduce even a small-ish overlap. Let's try 10%.\n",
    "tmp = generate_patches(arcminutes=(1 * 60, 1 * 60), overlap_percentage=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0dc3d71d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patches in (RA, Dec): (1440,720).\n",
      "There were 1440 produced, skipping 1035360 because Dec was outside [0, 0.25]. Info: {'npatches': 1440, 'arcminutes': (15, 15), 'overlap': 0}.\n"
     ]
    }
   ],
   "source": [
    "# Let's consider a single slice along Right Ascension, keeping Declination constant at 0.25° = 15'.\n",
    "# This is roughly one LSST chip FOV.\n",
    "tmp = generate_patches(arcminutes=(15, 15), overlap_percentage=0, decRange=[0, 0.25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c3f1fd78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2.95 times more chip fields in one Dec slice than all of DEEP.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1440"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many is this, compared to the Hayden DEEP set?\n",
    "print(\n",
    "    f'There are {round(tmp[2][\"npatches\"]/len(overlapping_sets),2)} times more chip fields in one Dec slice than all of DEEP.'\n",
    ")\n",
    "tmp[2][\"npatches\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d018cb09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ground-based observatories cannot see all of the Celestial Sphere because Earth gets in the way.\n",
    "# The farthest hypothetical dec observable from a given latitude is:\n",
    "#     (observer latitude) - 90° (in the North)\n",
    "#     (observer latitude) + 90° (in the South)\n",
    "# The key fun fact is that observer latitude = elevation of the corresponding (N or S) celestial pole.\n",
    "#\n",
    "# Examples:\n",
    "# North Pole, latitude +90°: +90° - 90° = 0°\n",
    "# South Pole, latitude -90°: -90° + 90° = 0°\n",
    "# Equator, latitude ±0°: 0° ±90° = ±90°.\n",
    "# LSST, latitude -30.1732°: -30.1732° + 90° = 59.8268°.\n",
    "#\n",
    "# Note: this is the geometric limit, but in practice\n",
    "# (a) the horizon is rarely unobstructed/flat\n",
    "# (b) telescopes cannot (or will not) point anywhere near the horizon\n",
    "#\n",
    "# Given (b) above, it would be optimal to further offset the dec range for a telescope's stated lower elevation (altitude) limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "db66acde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we have a function to visualize the patches we are generating.\n",
    "def plot_patches(\n",
    "    patches, limit=None, xrange=[0, 360], yrange=[-90, 90], title=None, subfolder=\".\", export=False\n",
    "):\n",
    "    \"\"\"\n",
    "    Plot the patches provided by generate_patches(), with options for visualization.\n",
    "    2/15/2024 COC updated\n",
    "    \"\"\"\n",
    "    import os\n",
    "\n",
    "    os.makedirs(subfolder, exist_ok=True)\n",
    "    import matplotlib.pyplot as plt\n",
    "    from matplotlib.patches import Rectangle\n",
    "\n",
    "    patch_count = len(patches)\n",
    "    print(f\"There are {patch_count} to plot.\")\n",
    "    fig, ax = plt.subplots()\n",
    "    colors = [\"blue\", \"orange\", \"green\", \"red\"]\n",
    "    linestyles = [\"solid\", \"dashed\"]\n",
    "    c = 0\n",
    "    for i, patch in enumerate(patches):\n",
    "        if limit != None and i > limit:\n",
    "            print(f\"Breaking at limit {limit} passed to function.\")\n",
    "            break\n",
    "        if c >= 10000:\n",
    "            print(f\"Row {i!s:6} of {patch_count}\")\n",
    "            c = 0\n",
    "        (ra_start, dec_start), (ra_end, dec_end) = patch\n",
    "        width = ra_end - ra_start\n",
    "        height = dec_end - dec_start\n",
    "\n",
    "        # Alternating fill color and line style\n",
    "        color = colors[i % len(colors)]\n",
    "        linestyle = linestyles[i % len(linestyles)]\n",
    "\n",
    "        # Plot rectangle\n",
    "        rect = Rectangle(\n",
    "            (ra_start, dec_start),\n",
    "            width,\n",
    "            height,\n",
    "            edgecolor=color,\n",
    "            facecolor=\"none\",\n",
    "            linestyle=linestyle,\n",
    "            alpha=0.5,\n",
    "        )\n",
    "        ax.add_patch(rect)\n",
    "        #\n",
    "        c += 1\n",
    "    # Set axis labels and limits\n",
    "    ax.set_xlabel(\"Right Ascension (degrees)\")\n",
    "    ax.set_ylabel(\"Declination (degrees)\")\n",
    "    #    ax.set_xlim(0, 360)\n",
    "    #    ax.set_ylim(-90, 90)\n",
    "    ax.set_xlim(xrange[0], xrange[1])\n",
    "    ax.set_ylim(yrange[0], yrange[1])\n",
    "\n",
    "    plt.grid(True)\n",
    "\n",
    "    outfile_base = f\"{subfolder}/patches\"\n",
    "    if title != None:\n",
    "        plt.title(title)\n",
    "        outfile_base += f'_{info[\"arcminutes\"][0]}_{info[\"arcminutes\"][1]}_{info[\"overlap\"]}'\n",
    "\n",
    "    if export == True:\n",
    "        for ext in [\"png\", \"pdf\"]:\n",
    "            plt.savefig(f\"{outfile_base}.{ext}\")\n",
    "        print(f'Wrote \"{outfile_base} (prefix) plots to disk.')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6e70866c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patches in (RA, Dec): (45,45).\n",
      "There were 2025 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 2025, 'arcminutes': (480, 240), 'overlap': 0}.\n",
      "There are 2025 to plot.\n",
      "Breaking at limit 3 passed to function.\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": [
      "Number of patches in (RA, Dec): (50,50).\n",
      "There were 2500 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 2500, 'arcminutes': (480, 240), 'overlap': 10}.\n",
      "There are 2500 to plot.\n",
      "Breaking at limit 3 passed to function.\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": [
      "Number of patches in (RA, Dec): (56,56).\n",
      "There were 3136 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 3136, 'arcminutes': (480, 240), 'overlap': 20}.\n",
      "There are 3136 to plot.\n",
      "Breaking at limit 3 passed to function.\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": [
      "Number of patches in (RA, Dec): (64,64).\n",
      "There were 4096 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 4096, 'arcminutes': (480, 240), 'overlap': 30}.\n",
      "There are 4096 to plot.\n",
      "Breaking at limit 3 passed to function.\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": [
      "Number of patches in (RA, Dec): (75,75).\n",
      "There were 5625 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 5625, 'arcminutes': (480, 240), 'overlap': 40}.\n",
      "There are 5625 to plot.\n",
      "Breaking at limit 3 passed to function.\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": [
      "Number of patches in (RA, Dec): (90,90).\n",
      "There were 8100 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 8100, 'arcminutes': (480, 240), 'overlap': 50}.\n",
      "There are 8100 to plot.\n",
      "Breaking at limit 3 passed to function.\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": [
      "CPU times: user 1.9 s, sys: 1.4 s, total: 3.3 s\n",
      "Wall time: 1.68 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# TIMING NOTE: takes < 5 seconds to run 2/15/2024 COC\n",
    "\n",
    "for i in [0, 10, 20, 30, 40, 50]:\n",
    "    arcminutes_input = (8 * 60, 4 * 60)  # 8° X 4°\n",
    "    patches_result, patches_centers, info = generate_patches(\n",
    "        arcminutes_input,\n",
    "        i,\n",
    "        # decRange=[-90,61],\n",
    "        export=False,\n",
    "    )\n",
    "    plot_patches(\n",
    "        patches_result,\n",
    "        xrange=[0, 20],\n",
    "        yrange=[-90, -70],\n",
    "        title=f\"size: {info['arcminutes']}', overlap: {info['overlap']}% ({info['npatches']} patches)\",\n",
    "        subfolder=\"generic\",\n",
    "        limit=3,  # Just show the first 3 patches generated, otherwise results are visually overwhelming.\n",
    "    )  # , limit=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a039bdb4",
   "metadata": {},
   "source": [
    "You can see the first set has no overlap at all, and each subsequent plot has further overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5f4490c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rather than copying and pasting numbers, we make a dictionary to store parameters going forward.\n",
    "chipDict = {}\n",
    "\n",
    "chipDict[\"DECam\"] = {\"chipsize_arcmin\": [9, 18]}  # 0.263\"/pixel, (2048,4096) pix/chip = (8.98,17.95')/chip\n",
    "chipDict[\"DECam\"][\"matches_per_sec\"] = 20000  # sphgeom region matching; guess, TODO benchmark me\n",
    "chipDict[\"DECam\"][\"chips_per_mo\"] = 62 * 1900  # estimate; 1900/mo MAX 12/20/2023 COC (mean 708)\n",
    "chipDict[\"DECam\"][\"dec_range\"] = [-90, 90 - 30.1732]\n",
    "\n",
    "chipDict[\"LSST\"] = {\"chipsize_arcmin\": [14, 14]}  # 0.2\"/pixel, (4096,4096) pix/chip = (13.65',13.65')/chip\n",
    "chipDict[\"LSST\"][\"matches_per_sec\"] = 20000  # sphgeom region matching; benchmarked; on RSP or local\n",
    "chipDict[\"LSST\"][\"chips_per_mo\"] = 6000000  # 2000/night * 30 nights/month\n",
    "chipDict[\"LSST\"][\"dec_range\"] = [-90, 30.1732]\n",
    "# chipDict['LMI'] = [13,13] # 0.120\"/pixel, (6144,6150) pix/chip = (12.288',12.3')/chip # just use LSST\n",
    "\n",
    "chipDict[\"TEST\"] = {\"chipsize_arcmin\": [3 * 60, 3 * 60]}  # 3 degree squares; 12/19/2023 COC\n",
    "chipDict[\"TEST\"][\"matches_per_sec\"] = 20000  # sphgeom region matching; reasonable estimate for testing\n",
    "chipDict[\"TEST\"][\"chips_per_mo\"] = 6000\n",
    "chipDict[\"TEST\"][\"dec_range\"] = [-90, 90]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d486c8c",
   "metadata": {},
   "source": [
    "This would be a good place to simulate benchmarks for region-region matching (visa lsst.sphgeom .intersect).\\\n",
    "The approach (demonstrated elsewhere) checks for any overlapping regions of two lsst.sphgeom regions.\\\n",
    "This is especially useful for handling arbitrarility rotated chips/images.\\\n",
    "In an early design, this was the approach to do our searching, but it was far too slow.\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "0a0e7f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: consider how we link this spot in the notebook to Region demo, etc. 2/15/2024 COC\n",
    "# benchmarking, prettiertime, etc. would need to be brought here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ba72d59",
   "metadata": {},
   "source": [
    "### Regions from Patches\n",
    "Here we work on producing lsst.sphgeom regions (objects).\\\n",
    "The synthetic patch regions can be used as an interim step to filter out images (with 0 overlap).\\\n",
    "This is helpful to reduce clutter sent to Reproject, for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e2f4536b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def latLonFromRaDecDeg(ra, dec, verbose=False):\n",
    "    \"\"\"\n",
    "    Return a sphgeom LonLat object given an input RA, Dec (in degrees).\n",
    "    We correct Dec values outside of ±90° (e.g., subtract 180 from Dec=90.1).\n",
    "    \"\"\"\n",
    "    if dec > 90:\n",
    "        print(f\"WARNING: Dec > 90° ({dec}°) so subtracting 180°.\")\n",
    "        dec -= 180\n",
    "    elif dec < -90:\n",
    "        print(f\"WARNING: Dec < -90° ({dec}°) so adding 180°.\")\n",
    "        dec += 180\n",
    "    if ra > 360:\n",
    "        print(f\"WARNING: RA > 360° ({ra}°) so subtracting 360°.\")\n",
    "        ra -= 360\n",
    "    elif ra < 0:  # just in case 1/9/2024 COC\n",
    "        print(f\"WARNING: RA < 0° ({ra}°) so adding 360°.\")\n",
    "        dec += 180\n",
    "    t = lsst.sphgeom.LonLat(\n",
    "        lsst.sphgeom._sphgeom.NormalizedAngle(np.deg2rad(ra)), lsst.sphgeom.Angle(np.deg2rad(dec))\n",
    "    )\n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "122c1379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7853981633974483, 0.7853981633974483]\n",
      "WARNING: Dec > 90° (91°) so subtracting 180°.\n",
      "WARNING: RA > 360° (361°) so subtracting 360°.\n",
      "[0.017453292519943295, -1.5533430342749532]\n"
     ]
    }
   ],
   "source": [
    "# Testing cases of invalid RA and Dec values.\n",
    "print(latLonFromRaDecDeg(ra=45, dec=45))\n",
    "print(latLonFromRaDecDeg(ra=361, dec=91))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6e03ae98",
   "metadata": {},
   "outputs": [],
   "source": [
    "def patches_to_sphgeom(input_data):\n",
    "    \"\"\"\n",
    "    Convert a list of patches or FITS files to sphgeom regions.\n",
    "\n",
    "    Parameters:\n",
    "    - input_data: List of patches or FITS file paths.\n",
    "\n",
    "    Returns:\n",
    "    - List of sphgeom.SphericalPolygon objects.\n",
    "    \"\"\"\n",
    "\n",
    "    def read_patch_fits(fits_path):\n",
    "        # Read FITS file and extract WCS information\n",
    "        hdul = fits.open(fits_path)\n",
    "        wcs = astropy.wcs.WCS(hdul[0].header)\n",
    "\n",
    "        # Extract coordinates from the WCS\n",
    "        ra, dec = wcs.all_pix2world(np.array([0, 0]), np.array([0, 0]), 0)\n",
    "        ra_start, dec_start = ra[0], dec[0]\n",
    "\n",
    "        ra, dec = wcs.all_pix2world(\n",
    "            np.array([hdul[0].data.shape[1], hdul[0].data.shape[0]]), np.array([1, 1]), 0\n",
    "        )\n",
    "        ra_end, dec_end = ra[0], dec[0]\n",
    "\n",
    "    #         return sphgeom.ConvexPolygon.from_radec_sequence(\n",
    "    #             [(ra_start, dec_start), (ra_start, dec_end), (ra_end, dec_end), (ra_end, dec_start)]\n",
    "    #         )\n",
    "\n",
    "    sphgeom_regions = []\n",
    "\n",
    "    for item in input_data:\n",
    "        if isinstance(item, tuple):\n",
    "            # If it's a patch tuple, convert to sphgeom.SphericalPolygon\n",
    "            ra_start, dec_start = item[0]\n",
    "            ra_end, dec_end = item[1]\n",
    "            box = lsst.sphgeom.Box(\n",
    "                latLonFromRaDecDeg(ra_start, dec_start), latLonFromRaDecDeg(ra_end, dec_end)\n",
    "            )\n",
    "            sphgeom_regions.append(box)\n",
    "        elif isinstance(item, str):\n",
    "            # NOTE: untested 2/15/2024 COC TODO\n",
    "            # If it's a FITS file path, read the file and convert to sphgeom.SphericalPolygon\n",
    "            sphgeom_regions.append(read_patch_fits(item))\n",
    "        else:\n",
    "            raise ValueError(\n",
    "                \"Unsupported input type. Supported types are tuple (patch) or str (FITS file path).\"\n",
    "            )\n",
    "\n",
    "    return sphgeom_regions\n",
    "\n",
    "\n",
    "# Example usage:\n",
    "# Assuming you have a list of patches or FITS file paths\n",
    "# patches_or_fits = generate_patches(arcminutes_input, overlap_percentage_input)\n",
    "# sphgeom_regions_result = patches_to_sphgeom(patches_or_fits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "4b8e1b38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patches in (RA, Dec): (150,75).\n",
      "There were 11250 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 11250, 'arcminutes': [180, 180], 'overlap': 20}.\n",
      "CPU times: user 320 ms, sys: 15.5 ms, total: 335 ms\n",
      "Wall time: 334 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "test_patches_results, test_patches_centers, test_patches_info = generate_patches(\n",
    "    chipDict[\"TEST\"][\"chipsize_arcmin\"], 20\n",
    ")\n",
    "\n",
    "sphgeom_regions = patches_to_sphgeom(test_patches_results)\n",
    "\n",
    "# Now we have a list of sphgeom.SphericalPolygon objects in sphgeom_regions_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7d297ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Box(NormalizedAngleInterval.fromRadians(0.0, 0.05235987755982989), AngleInterval.fromRadians(-1.5707963267948966, -1.5184364492350666))"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first one of our example regions\n",
    "sphgeom_regions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "31068315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'b\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xd6\\xeb{\\xf3\\xe9\\xce\\xaa?\\x18-DT\\xfb!\\xf9\\xbf\\xb9M\\xa8\\x04\\x84K\\xf8\\xbf'"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sphgeom regions can be encoded for portability; these are \"region hashes\"\n",
    "encoded = sphgeom_regions[0].encode()\n",
    "encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "70fb95ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Box(NormalizedAngleInterval.fromRadians(0.0, 0.05235987755982989), AngleInterval.fromRadians(-1.5707963267948966, -1.5184364492350666))"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Come back to an object\n",
    "r = lsst.sphgeom.Region.decode(encoded)\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a26fc2a",
   "metadata": {},
   "source": [
    "### Working with the Butler + Regions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "9431fdb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>visit_detector_region:\n",
       "  instrument: 'DECam'\n",
       "  detector: 62\n",
       "  visit: 946176\n",
       "  region: ConvexPolygon([UnitVector3d(0.9876086828694174, -0.13336028508776862, -0.08272922024438323), UnitVector3d(0.9873378171284917, -0.13332652431396907, -0.08595389916869185), UnitVector3d(0.9881047366097594, -0.12752395595185462, -0.08594573955553172), UnitVector3d(0.9883760335240734, -0.12755303452468866, -0.0827226676235914)])<pre>"
      ],
      "text/plain": [
       "visit_detector_region.RecordClass(instrument='DECam', detector=62, visit=946176, region=ConvexPolygon([UnitVector3d(0.9876086828694174, -0.13336028508776862, -0.08272922024438323), UnitVector3d(0.9873378171284917, -0.13332652431396907, -0.08595389916869185), UnitVector3d(0.9881047366097594, -0.12752395595185462, -0.08594573955553172), UnitVector3d(0.9883760335240734, -0.12755303452468866, -0.0827226676235914)]))"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We should still have our example VisitInfo object from the Butler.\n",
    "example_vdr_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8f92b9b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some example comparisons, using regions in our DataFrame\n",
    "reg1 = df[\"region\"].iloc()[0]\n",
    "reg2 = df[\"region\"].iloc()[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "5976ab67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reg1.contains(reg1) =  True\n",
      "reg1.contains(reg2) = False\n"
     ]
    }
   ],
   "source": [
    "# Basic reality testing first\n",
    "print(f\"reg1.contains(reg1) =  {reg1.contains(reg1)}\")  # should be True\n",
    "print(f\"reg1.contains(reg2) = {reg1.contains(reg2)}\")  # should be False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "06edb382",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking overlap matches of reg1 against all records region for matchces...\n",
      "There were 1 overlap matches:\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898286}\n"
     ]
    }
   ],
   "source": [
    "# First we will look for the \"contains\" case, where one region completely contains another.\n",
    "# We do not expect this for DEEP necessarily, but a region must contain itself, so we should see 1 match.\n",
    "\n",
    "print(f\"Checking overlap matches of reg1 against all records region for matchces...\")\n",
    "matches = []\n",
    "for i, reg in enumerate(df[\"region\"].iloc()):\n",
    "    if reg.contains(reg1):\n",
    "        matches.append(df[\"data_id\"].iloc()[i])\n",
    "print(f\"There were {len(matches)} overlap matches:\")\n",
    "for i in matches:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "1f8a61e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking intersect matches of reg1 against all records region for matchces...\n",
      "There were 347 intersection matches:\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898286}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898287}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898288}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898289}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898290}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898291}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898292}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898293}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898294}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898295}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898296}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898297}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898298}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898299}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898300}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898301}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898302}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898303}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898304}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898305}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898306}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898307}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898308}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898309}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898310}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898311}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898312}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898313}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898314}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898315}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898316}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898317}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898318}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898319}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898320}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898321}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898322}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898323}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898324}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898325}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898326}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898327}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898328}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898329}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898330}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898331}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898332}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898333}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898334}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898335}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898336}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898337}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898338}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898339}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898340}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898341}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898342}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898343}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898344}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898345}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898346}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898347}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898348}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898349}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898350}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898351}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898352}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898353}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898354}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898355}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898356}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898357}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898358}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898359}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898360}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898361}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898362}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898363}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898364}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898365}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898366}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898367}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898368}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898369}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898370}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898371}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898372}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898373}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898374}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898375}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898376}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898377}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898378}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898379}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 898380}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891455}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891456}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891457}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891458}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891459}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891460}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891461}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891462}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891463}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891464}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891465}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891466}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891467}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891468}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891469}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891470}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891471}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891472}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891473}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891474}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891475}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891476}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891477}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891478}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891479}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891480}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891481}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891482}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891483}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891484}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891485}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891486}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891487}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891488}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891489}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891490}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891491}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891492}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891493}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891494}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891495}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891496}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891497}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891498}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891499}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891500}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891501}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891502}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891503}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891504}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891505}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891506}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891507}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891508}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891509}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891510}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891511}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891512}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891513}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891514}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891515}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891516}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891517}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891518}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891519}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891520}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891521}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891522}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891523}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891524}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891525}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891526}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891527}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891528}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891529}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891530}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891531}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891532}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891533}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891534}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891535}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891536}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891537}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891538}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891539}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891540}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891541}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891542}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891543}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891544}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891545}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891546}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891547}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891548}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891549}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891550}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891551}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891552}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891553}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891554}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891555}\n",
      "{'instrument': 'DECam', 'detector': 8, 'visit': 891556}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946689}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946690}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946691}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946692}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946693}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946694}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946695}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946696}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946697}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946698}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946699}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946700}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946701}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946702}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946703}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946704}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946705}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946706}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946707}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946708}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946709}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946710}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946711}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946712}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946713}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946714}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946715}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946716}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946717}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946718}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946719}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946720}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946721}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946722}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946723}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946724}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946725}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946726}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946727}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946728}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946729}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946730}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946731}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946732}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946733}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946734}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946735}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946736}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946737}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946738}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946739}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946740}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946741}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946742}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946743}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946744}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946745}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946746}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946747}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946748}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946749}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946750}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946751}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946752}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946753}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946754}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946755}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946756}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946757}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946758}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946759}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946760}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946761}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946762}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946763}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946764}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946765}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946766}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946767}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946768}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946769}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946770}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946771}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946772}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946773}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946774}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946775}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946776}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946777}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946778}\n",
      "{'instrument': 'DECam', 'detector': 1, 'visit': 946779}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946081}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946082}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946083}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946084}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946085}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946086}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946087}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946088}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946089}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946090}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946091}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946092}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946093}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946094}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946095}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946096}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946098}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946099}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946102}\n",
      "{'instrument': 'DECam', 'detector': 39, 'visit': 946103}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946081}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946082}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946083}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946084}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946085}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946086}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946087}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946088}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946089}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946090}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946091}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946092}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946093}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946094}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946095}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946096}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946097}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946098}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946099}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946100}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946101}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946102}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946103}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946104}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946105}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946106}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946107}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946108}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946109}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946110}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946112}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946113}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946114}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946115}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946116}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946117}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946118}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946121}\n",
      "{'instrument': 'DECam', 'detector': 45, 'visit': 946122}\n"
     ]
    }
   ],
   "source": [
    "# Next we check \"intersect\" -- meaning any overlap of region area.\n",
    "\n",
    "print(f\"Checking intersect matches of reg1 against all records region for matchces...\")\n",
    "matches = []\n",
    "for i, reg in enumerate(df[\"region\"].iloc()):\n",
    "    if reg.intersects(reg1):\n",
    "        matches.append(df[\"data_id\"].iloc()[i])\n",
    "print(f\"There were {len(matches)} intersection matches:\")\n",
    "for i in matches:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "aa1e3080",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saw minDec=-6.406214566625444 and maxDec=-2.719246815973079.\n",
      "Will use minDec=-8.906214566625444 and maxDec=-0.21924681597307893.\n"
     ]
    }
   ],
   "source": [
    "# lazily grabbing min and max Decs to limit number of patches we will produce for the dataset\n",
    "ras = []\n",
    "decs = []\n",
    "for i in df[\"center_coord\"]:\n",
    "    ra = i[0]\n",
    "    dec = i[1]\n",
    "    ras.append(ra)\n",
    "    decs.append(dec)\n",
    "# deep_min_dec = min([df['center_coord'].iloc()[0][i])\n",
    "minDec = min(decs)\n",
    "maxDec = max(decs)\n",
    "print(f\"Saw minDec={minDec} and maxDec={maxDec}.\")\n",
    "\n",
    "# add a buffer of about a field radius\n",
    "minDec -= 2.5\n",
    "maxDec += 2.5\n",
    "print(f\"Will use minDec={minDec} and maxDec={maxDec}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2d4bde26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patches in (RA, Dec): (1542,771).\n",
      "There were 55512 produced, skipping 1133370 because Dec was outside [-8.906214566625444, -0.21924681597307893]. Info: {'npatches': 55512, 'arcminutes': [14, 14], 'overlap': 0}.\n",
      "CPU times: user 6.48 s, sys: 70.5 ms, total: 6.55 s\n",
      "Wall time: 6.56 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Timing note: takes < 7 seconds 2/15/2024 COC\n",
    "\n",
    "# First we want a set of sky patches. We are going to use 14' X 14' patches (LSST-size) as a test.\n",
    "deep_patches_results, deep_patches_centers, deep_patches_info = generate_patches(\n",
    "    arcminutes=chipDict[\"LSST\"][\"chipsize_arcmin\"],\n",
    "    overlap_percentage=0,\n",
    "    verbose=True,\n",
    "    decRange=[minDec, maxDec],\n",
    "    export=False,\n",
    ")\n",
    "# convert the patches to sphgeom regions\n",
    "sphgeom_regions_deep_limited = patches_to_sphgeom(deep_patches_results)\n",
    "\n",
    "# Now you have a list of sphgeom.SphericalPolygon objects in sphgeom_regions_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7ecd13b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the generalized Region Search, sky patches, we need a function for matching.\n",
    "# making a function to test iterating and matching 1/12/2024 COC\n",
    "# non-vectorized wanted > 1 hr to do this 2/15/2024 COC\n",
    "\n",
    "\n",
    "def checkMatches(regions1, regions2):\n",
    "    \"\"\"\n",
    "    regions1 will be our generated patches (sphgeom_regions)\n",
    "    regions2 will be actual images (pointing_regions)\n",
    "    updated 2/15/2024 COC\n",
    "    \"\"\"\n",
    "    print(f\"Starting region matching. Lens: region1 = {len(regions1)}, region2 = {len(regions2)}\")\n",
    "    startTime = time.time()\n",
    "    matches = {}\n",
    "    matches_attempted = 0\n",
    "    progress_c = 0\n",
    "    progress_interval = 10  # len(regions1)//200\n",
    "    with progressbar.ProgressBar(max_value=len(regions1)) as bar:\n",
    "        for i, patch in enumerate(regions1):\n",
    "            matches[i] = []\n",
    "            for j, pointing in enumerate(regions2):\n",
    "                matches_attempted += 1\n",
    "                if pointing.intersects(patch):\n",
    "                    matches[i].append(j)\n",
    "            progress_c += 1\n",
    "            if progress_c >= progress_interval:\n",
    "                progress_c = 0\n",
    "                bar.update(i)\n",
    "\n",
    "    elapsed = round(time.time() - startTime, 3)\n",
    "    print(\n",
    "        f\"There were {len(matches)} matches in {matches_attempted} matches attempted. {len(matches_i)} region1s (patches?) and {len(matches_j)} region2s (pointings?) were involved. [{elapsed} s elapsed.]\"\n",
    "    )\n",
    "    return matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "94956cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import multiprocessing\n",
    "# import time\n",
    "# from progressbar import ProgressBar\n",
    "\n",
    "\n",
    "def worker(patch_and_regions2):\n",
    "    patch, regions2 = patch_and_regions2\n",
    "    return [j for j, pointing in enumerate(regions2) if pointing.intersects(patch)]\n",
    "\n",
    "\n",
    "def checkMatches(regions1, regions2):\n",
    "    print(f\"Starting region matching. Lens: region1 = {len(regions1)}, region2 = {len(regions2)}\")\n",
    "    startTime = time.time()\n",
    "\n",
    "    # Prepare input for worker function\n",
    "    input_for_worker = [(patch, regions2) for patch in regions1]\n",
    "\n",
    "    # Use multiprocessing Pool\n",
    "    with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:\n",
    "        results = pool.map(worker, input_for_worker)\n",
    "\n",
    "    # Aggregate results\n",
    "    matches = {i: result for i, result in enumerate(results)}\n",
    "\n",
    "    elapsed = round(time.time() - startTime, 3)\n",
    "    print(\n",
    "        f\"There were {sum(len(m) for m in matches.values())} matches in the {len(regions1)*len(regions2)} matches executed. [Elapsed time: {elapsed} s]\"\n",
    "    )\n",
    "\n",
    "    return matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f8955306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting region matching. Lens: region1 = 55512, region2 = 47383\n",
      "There were 192160 matches in the 2630325096 matches executed. [Elapsed time: 97.388 s]\n",
      "CPU times: user 52.8 s, sys: 11.8 s, total: 1min 4s\n",
      "Wall time: 1min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Timing note: this takes about 2 minutes 2/15/2024 COC\n",
    "match_d = checkMatches(regions1=sphgeom_regions_deep_limited, regions2=df[\"region\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "809add85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.7 ms, sys: 4.38 ms, total: 17.1 ms\n",
      "Wall time: 15.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "matching_regions = []\n",
    "matching_counts = []\n",
    "for r in match_d:\n",
    "    c = len(match_d[r])\n",
    "    if c > 0:\n",
    "        matching_regions.append(r)\n",
    "        matching_counts.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "36b31a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many sky patches had images in them?\n",
    "len(matching_regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4d2452f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_45553/2117250381.py:37: MatplotlibDeprecationWarning: Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n",
      "  plt.colorbar(sm, label=\"Matching Images\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us produce a heat map of how many images are in each sky patch.\n",
    "\n",
    "# Assuming sphgeom_regions_deep_limited, matching_regions, and matching_counts are defined\n",
    "# all_corners as defined in your original code\n",
    "all_corners = [getRegionCorners(region) for region in sphgeom_regions_deep_limited]\n",
    "\n",
    "# Define a colormap\n",
    "cmap = plt.cm.plasma  # You can choose any available colormap\n",
    "\n",
    "# Normalize your matching_counts to the colormap range (0, 1)\n",
    "# This step is important for mapping your counts to colors\n",
    "norm = mcolors.Normalize(vmin=min(matching_counts), vmax=max(matching_counts))\n",
    "\n",
    "# Create a scalar mappable for coloring and creating the colorbar later\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
    "sm.set_array([])  # Only needed for the colorbar\n",
    "\n",
    "# Plotting each quadrilateral with color based on matching_counts\n",
    "for matching_ix, count in zip(matching_regions, matching_counts):\n",
    "    quad = all_corners[matching_ix]\n",
    "    ra_bounds, dec_bounds = getMinMaxRaDec(quad)\n",
    "\n",
    "    coords = [\n",
    "        (ra_bounds[0], dec_bounds[0]),\n",
    "        (ra_bounds[0], dec_bounds[1]),\n",
    "        (ra_bounds[1], dec_bounds[1]),\n",
    "        (ra_bounds[1], dec_bounds[0]),\n",
    "    ]\n",
    "\n",
    "    # Use the normalized count to get the corresponding color\n",
    "    color = cmap(norm(count))\n",
    "\n",
    "    # Fill the quadrilateral with the selected color\n",
    "    plt.fill(*zip(*coords), color=color)\n",
    "\n",
    "# Add the colorbar to the plot\n",
    "plt.colorbar(sm, label=\"Matching Images\")\n",
    "\n",
    "# Setting labels for clarity\n",
    "plt.xlabel(\"RA\")\n",
    "plt.ylabel(\"Dec\")\n",
    "# plt.title('Heat Map by Counts')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aebe1fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3df17bc4",
   "metadata": {},
   "source": [
    "# Next Steps\n",
    "\n",
    "In no particular order:\n",
    "\n",
    "1. User-specified (RA, Dec) pair: [DONE]\n",
    "- from overlapping_sets (DONE 2/7/2024 COC)\n",
    "- from our extracted Butler data (DONE 2/7/2024 COC)\n",
    "2. Heat map / histogrammed results. [DONE 2/15/2024 COC]\n",
    "- nice way to filter by top matches or the like\n",
    "3. Sky patches approach. [DONE 2/14/2024 COC]\n",
    "- matching before passing to reprojection / image collection\n",
    "4. Handling time (manually)\n",
    "- options to restrict date range\n",
    "- options to restrict time delta\n",
    "- visualize time information\n",
    "5. Reflex correction.\n",
    "6. Deduplicate based on URI? 2/26/2024 COC/WB (or maybe UT + RA + Dec)\n",
    "7. DP 0.2 testing\n",
    "- RA, Dec search\n",
    "- Pulling out all of our metadata\n",
    "8. Storing results\n",
    "- what other metadata do we need\n",
    "9. Region-region intersection function\n",
    "10. Tracts-and-patches approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "120f67b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "LSST w_2022_06",
   "language": "python",
   "name": "opt_lsst_w_2022_06"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}