{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6097307c",
   "metadata": {},
   "source": [
    "# In this example we select a list of science window and submit it to ODA to obtain an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dfc5c155",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with no surprise, we do it for the Crab\n",
    "source_name='Crab'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4468a42c",
   "metadata": {},
   "source": [
    "## Astroquery can be used to retrieve the list\n",
    "\n",
    "We build a convenience function.\n",
    "\n",
    "For the scope of this socumentation, we make sure the `astroquery` library version, is within a specific version range. For newer (or older) version, please consult the offical documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a016c3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example of functions to select a science window list\n",
    "import time\n",
    "\n",
    "import astroquery.heasarc\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy import units as u\n",
    "\n",
    "Heasarc = astroquery.heasarc.Heasarc()\n",
    "\n",
    "\n",
    "def get_scw_list(ra_obj, dec_obj,radius,start_date,end_date ):\n",
    "    R = Heasarc.query_region(\n",
    "            position = SkyCoord(ra_obj, dec_obj, unit='deg'),\n",
    "            radius = f\"{radius} deg\",\n",
    "            mission = 'intscw',\n",
    "            time = start_date + \" .. \" + end_date,\n",
    "            good_isgri = \">1000\",\n",
    "        )\n",
    "    R.sort('SCW_ID')\n",
    "\n",
    "    return R['SCW_ID'], R['SCW_VER']\n",
    "\n",
    "\n",
    "assert astroquery.__version__ >= '0.4.2.dev6611' and astroquery.__version__ <= '0.4.7'\n",
    "\n",
    "# it means it's our fork\n",
    "assert 'isdc' in astroquery.heasarc.Conf.server.cfgtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc0b1d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astroquery.simbad import Simbad\n",
    "from astropy import coordinates as coord\n",
    "\n",
    "import json\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import random\n",
    "\n",
    "from astropy.io import fits\n",
    "\n",
    "from scipy import stats\n",
    "result_table = Simbad.query_object(source_name)\n",
    "source_coord = coord.SkyCoord(result_table['RA'][0], result_table['DEC'][0], unit=(\"hourangle\", \"deg\"))\n",
    "ra=source_coord.ra.deg\n",
    "dec=source_coord.dec.deg\n",
    "radius=8."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e42cd47b",
   "metadata": {},
   "source": [
    "- We get science windows within 10 degrees from the Crab position \n",
    "- between the given dates expressed in the ISOT format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4924358e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: InputWarning: Coordinate string is being interpreted as an ICRS coordinate provided in degrees. [astroquery.utils.commons]\n",
      "WARNING: UnitsWarning: 'mjd' did not parse as fits unit: At col 0, Unit 'mjd' not supported by the FITS standard. Did you mean MJ or mJ? If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'IJD' did not parse as fits unit: At col 0, Unit 'IJD' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'mjd' did not parse as fits unit: At col 0, Unit 'mjd' not supported by the FITS standard. Did you mean MJ or mJ? If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "scwlist,version = get_scw_list(ra,dec, 10, '2020-01-01T00:00:00', '2020-06-01T00:00:00')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be05a50e",
   "metadata": {},
   "source": [
    "- We pick at random nscw = 10 science window from the list (this is as ODA backend does)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa71e299",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "nscw = 10\n",
    "if len(scwlist) == 0:\n",
    "    raise Exception('No science windows')\n",
    "\n",
    "scw_pick = [(s+\".\"+v).strip() for s,v in zip(scwlist,version)]\n",
    "\n",
    "if nscw >0 and len(scw_pick)>=nscw:\n",
    "    random.seed(0)\n",
    "    scw_pick = random.sample(scw_pick, nscw)\n",
    "scw_pick = sorted(scw_pick)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81cacabf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['219400710010.001',\n",
       " '221000540010.001',\n",
       " '221200100010.001',\n",
       " '221400280010.001',\n",
       " '221400420010.001',\n",
       " '221400520010.001',\n",
       " '221500030010.001',\n",
       " '221500320010.001',\n",
       " '221500360010.001',\n",
       " '221500490010.001']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scw_pick"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b12cad",
   "metadata": {},
   "source": [
    "- Token for a given user "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be5db1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "token=''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4920c68e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insert the token········\n"
     ]
    }
   ],
   "source": [
    "#You can provide a valid token as explained in the 'Authentication' example or skip this cell\n",
    "import getpass\n",
    "token = getpass.getpass('Insert the token')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aafe37a",
   "metadata": {},
   "source": [
    "## Let's get some logging\n",
    "\n",
    "This is to help visualizing the progress.\n",
    "\n",
    "- WANRING is the default level\n",
    "- INFO writes some more information\n",
    "- DEBUG is maily for developers and issue tracking\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62842f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "#default\n",
    "#logging.getLogger().setLevel(logging.WARNING)\n",
    "#slightly more verbose\n",
    "logging.getLogger().setLevel(logging.INFO)\n",
    "#all messages\n",
    "#logging.getLogger().setLevel(logging.DEBUG)\n",
    "\n",
    "logging.getLogger('oda_api').addHandler(logging.StreamHandler()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a76dfcad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "found token in TokenLocation.FILE_CUR_DIR your token payload: {\n",
      "    \"email\": \"Gabriele.Barni@unige.ch\",\n",
      "    \"exp\": 1748257223,\n",
      "    \"msdone\": false,\n",
      "    \"msfail\": false,\n",
      "    \"mssub\": false,\n",
      "    \"name\": \"gbarni\",\n",
      "    \"roles\": \"authenticated user, administrator, user manager, content manager, general, integral-private-qla, magic, unige-hpc-full, public-pool-hpc, antares, sdss, apc, bitp, renku contributor, gallery contributor, job manager, developer, oda workflow developer, refresh-tokens\",\n",
      "    \"sub\": \"Gabriele.Barni@unige.ch\"\n",
      "}\n",
      "token expires in 1220.8 h\n",
      "discovered token in environment\n",
      "please beware that by default, in a typical setup, oda_api will not output much. To learn how to increase the verbosity, please refer to the documentation: https://oda-api.readthedocs.io/en/latest/user_guide/ScienceWindowList.html?highlight=logging#Let's-get-some-logging . \n",
      "To disable this message you can pass `.get_product(..., silent=True)`\n",
      "/home/gabriele/Workspace/oda_api/venv/lib/python3.8/site-packages/oda_api/api.py:1102: UserWarning: \n",
      "----------------------------------------------------------------------------\n",
      "the parameter: oda_api_version   is not among valid ones:\n",
      "['src_name', 'RA', 'DEC', 'T1', 'T_format', 'T2', 'token', 'scw_list', 'selected_catalog', 'radius', 'max_pointings', 'osa_version', 'integral_data_rights', 'E1_keV', 'E2_keV', 'detection_threshold', 'image_scale_min', 'image_scale_max']\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "  warnings.warn(msg)\n",
      "- waiting for remote response (since 2025-04-05 16:13:13), please wait for https://www.astro.unige.ch/mmoda/dispatch-data/run_analysis\n",
      "session: T9SH9V8N9S2FYUPS job: f807231b8c529ad2\n",
      "\n",
      "... query status \u001b[35mprepared\u001b[0m => \u001b[35mdone\u001b[0m\n",
      "... assigned job id: \u001b[33mf807231b8c529ad2\u001b[0m\n",
      "\u001b[32mquery COMPLETED SUCCESSFULLY (state done)\u001b[0m\n",
      "query complete: terminating\n"
     ]
    }
   ],
   "source": [
    "from oda_api.api import DispatcherAPI\n",
    "from astroquery.simbad import Simbad\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "disp = DispatcherAPI(url=\"https://www.astro.unige.ch/mmoda/dispatch-data\", instrument=\"mock\")\n",
    "\n",
    "# par_dict = {\n",
    "# \"E1_keV\": \"28\",\n",
    "# \"E2_keV\": \"40\",\n",
    "# \"detection_threshold\": \"7\",\n",
    "# \"instrument\": \"isgri\",\n",
    "# \"osa_version\": \"OSA11.2\",\n",
    "# \"product\": \"isgri_image\",\n",
    "# \"product_type\": \"Real\",\n",
    "# \"scw_list\": scw_pick,\n",
    "# \"integral_data_rights\": \"all-private\"\n",
    "# }\n",
    "\n",
    "source_name=\"GX 1+4\"\n",
    "simbad = Simbad.query_object(source_name)\n",
    "coord = SkyCoord(simbad['RA'], simbad['DEC'], unit=[u.hour, u.deg])\n",
    "\n",
    "ra = coord.ra.deg\n",
    "dec = coord.dec.deg\n",
    "\n",
    "par_dict = {\n",
    "\"DEC\": \"%f\" % dec,\n",
    "\"E1_keV\": 28,\n",
    "\"E2_keV\": 50,\n",
    "\"RA\": \"%f\" % ra,\n",
    "\"T1\": '2019-01-01T00:00:00',\n",
    "\"T2\": '2019-03-31T23:59:59',\n",
    "\"radius\": 8,\n",
    "\"src_name\": source_name,\n",
    "\"max_pointings\": 10,\n",
    "\"detection_threshold\": \"7.0\",\n",
    "\"instrument\": \"isgri\",\n",
    "\"integral_data_rights\": \"public\",\n",
    "\"oda_api_version\": \"1.1.22\",\n",
    "\"off_line\": \"False\",\n",
    "\"osa_version\": \"OSA11.2\",\n",
    "\"product\": \"isgri_image\",\n",
    "\"product_type\": \"Real\",\n",
    "}\n",
    "\n",
    "if token != '':\n",
    "    par_dict.update({'token': token})\n",
    "\n",
    "data_collection = disp.get_product(**par_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "10244e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "name: mosaic_image\n",
      "meta_data dict_keys(['product', 'instrument', 'src_name', 'query_parameters'])\n",
      "number of data units 6\n",
      "------------------------------\n",
      "data uniti 0 ,name: PRIMARY\n",
      "data uniti 1 ,name: GROUPING\n",
      "data uniti 2 ,name: ISGR-MOSA-IMA\n",
      "data uniti 3 ,name: ISGR-MOSA-IMA\n",
      "data uniti 4 ,name: ISGR-MOSA-IMA\n",
      "data uniti 5 ,name: ISGR-MOSA-IMA\n"
     ]
    }
   ],
   "source": [
    "data_collection.mosaic_image_0_mosaic.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0134b4a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "product : mosaic\n",
      "instrument : isgri\n",
      "src_name : \n",
      "query_parameters : None\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "data_collection.mosaic_image_0_mosaic.show_meta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f52bfdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: FITSFixedWarning: RADECSYS= 'FK5 ' \n",
      "the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]\n",
      "WARNING: FITSFixedWarning: 'datfix' made the change 'Set DATEREF to '2000-01-01' from MJDREF.\n",
      "Set MJD-OBS to 58526.447905 from DATE-OBS.\n",
      "Set MJD-END to 58557.991262 from DATE-END'. [astropy.wcs.wcs]\n",
      "/home/gabriele/Workspace/oda_api/venv/lib/python3.8/site-packages/oda_api/plot_tools.py:104: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  plt.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x355.556 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from oda_api.plot_tools import OdaImage\n",
    "#interactive\n",
    "%matplotlib inline\n",
    "\n",
    "im=OdaImage(data_collection)\n",
    "im.show(unit_ID=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83e19ad1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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