{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick Start (oda api v1.3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from oda_api.api import DispatcherAPI\n",
    "from oda_api.plot_tools import OdaImage,OdaLightCurve\n",
    "from oda_api.data_products import BinaryData\n",
    "import os\n",
    "#%matplotlib notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connection to the dispatcher "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### build the dispatcher object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "disp=DispatcherAPI(url='https://www.astro.unige.ch/mmoda/dispatch-data',instrument='mock')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "magic\n",
      "isgri\n",
      "jemx\n",
      "polar\n",
      "antares\n",
      "spi_acs\n"
     ]
    }
   ],
   "source": [
    "instr_list=disp.get_instruments_list()\n",
    "for i in instr_list:\n",
    "    print (i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get the description of the instrument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'instrumet': 'isgri'},\n",
       "  {'prod_dict': {'isgri_image': 'isgri_image_query',\n",
       "    'isgri_lc': 'isgri_lc_query',\n",
       "    'isgri_spectrum': 'isgri_spectrum_query',\n",
       "    'spectral_fit': 'spectral_fit_query'}},\n",
       "  '[{\"query_name\": \"src_query\"}, {\"name\": \"src_name\", \"units\": \"str\", \"value\": \"test\"}, {\"name\": \"RA\", \"units\": \"deg\", \"value\": 0.0}, {\"name\": \"DEC\", \"units\": \"deg\", \"value\": 0.0}, {\"name\": \"T1\", \"units\": None, \"value\": \"2001-12-11T00:00:00.000\"}, {\"name\": \"T2\", \"units\": None, \"value\": \"2001-12-11T00:00:00.000\"}, {\"name\": \"token\", \"units\": \"str\", \"value\": None}]',\n",
       "  '[{\"query_name\": \"isgri_parameters\"}, {\"name\": \"user_catalog\", \"units\": \"str\", \"value\": None}, {\"name\": \"scw_list\", \"units\": \"names_list\", \"value\": []}, {\"name\": \"selected_catalog\", \"units\": \"str\", \"value\": None}, {\"name\": \"radius\", \"units\": \"deg\", \"value\": 5.0}, {\"name\": \"max_pointings\", \"units\": None, \"value\": 50}, {\"name\": \"osa_version\", \"units\": \"str\", \"value\": None}, {\"name\": \"integral_data_rights\", \"units\": \"str\", \"value\": \"public\"}, {\"name\": \"E1_keV\", \"units\": \"keV\", \"value\": 15.0}, {\"name\": \"E2_keV\", \"units\": \"keV\", \"value\": 40.0}]',\n",
       "  '[{\"query_name\": \"isgri_image_query\"}, {\"product_name\": \"isgri_image\"}, {\"name\": \"detection_threshold\", \"units\": \"sigma\", \"value\": 0.0}, {\"name\": \"image_scale_min\", \"units\": None, \"value\": None}, {\"name\": \"image_scale_max\", \"units\": None, \"value\": None}]',\n",
       "  '[{\"query_name\": \"isgri_spectrum_query\"}, {\"product_name\": \"isgri_spectrum\"}]',\n",
       "  '[{\"query_name\": \"isgri_lc_query\"}, {\"product_name\": \"isgri_lc\"}, {\"name\": \"time_bin\", \"units\": \"sec\", \"value\": 1000.0}]',\n",
       "  '[{\"query_name\": \"spectral_fit_query\"}, {\"product_name\": \"spectral_fit\"}, {\"name\": \"xspec_model\", \"units\": \"str\", \"value\": \"powerlaw\"}, {\"name\": \"ph_file_name\", \"units\": \"str\", \"value\": \"\"}, {\"name\": \"arf_file_name\", \"units\": \"str\", \"value\": \"\"}, {\"name\": \"rmf_file_name\", \"units\": \"str\", \"value\": \"\"}]']]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disp.get_instrument_description('isgri')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get the description of the product\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'instrumet': 'isgri'},\n",
       "  {'prod_dict': {'isgri_image': 'isgri_image_query',\n",
       "    'isgri_lc': 'isgri_lc_query',\n",
       "    'isgri_spectrum': 'isgri_spectrum_query',\n",
       "    'spectral_fit': 'spectral_fit_query'}},\n",
       "  '[{\"query_name\": \"src_query\"}, {\"name\": \"src_name\", \"units\": \"str\", \"value\": \"test\"}, {\"name\": \"RA\", \"units\": \"deg\", \"value\": 0.0}, {\"name\": \"DEC\", \"units\": \"deg\", \"value\": 0.0}, {\"name\": \"T1\", \"units\": None, \"value\": \"2001-12-11T00:00:00.000\"}, {\"name\": \"T2\", \"units\": None, \"value\": \"2001-12-11T00:00:00.000\"}, {\"name\": \"token\", \"units\": \"str\", \"value\": None}]',\n",
       "  '[{\"query_name\": \"isgri_parameters\"}, {\"name\": \"user_catalog\", \"units\": \"str\", \"value\": None}, {\"name\": \"scw_list\", \"units\": \"names_list\", \"value\": []}, {\"name\": \"selected_catalog\", \"units\": \"str\", \"value\": None}, {\"name\": \"radius\", \"units\": \"deg\", \"value\": 5.0}, {\"name\": \"max_pointings\", \"units\": None, \"value\": 50}, {\"name\": \"osa_version\", \"units\": \"str\", \"value\": None}, {\"name\": \"integral_data_rights\", \"units\": \"str\", \"value\": \"public\"}, {\"name\": \"E1_keV\", \"units\": \"keV\", \"value\": 15.0}, {\"name\": \"E2_keV\", \"units\": \"keV\", \"value\": 40.0}]',\n",
       "  '[{\"query_name\": \"isgri_image_query\"}, {\"product_name\": \"isgri_image\"}, {\"name\": \"detection_threshold\", \"units\": \"sigma\", \"value\": 0.0}, {\"name\": \"image_scale_min\", \"units\": None, \"value\": None}, {\"name\": \"image_scale_max\", \"units\": None, \"value\": None}]']]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disp.get_product_description(instrument='isgri',product_name='isgri_image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check query before submission\n",
    "in case of error in the parameters, an error is issued"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "RequestNotUnderstood",
     "evalue": "[ RequestNotUnderstood: RequestNotUnderstood():[ InstrumentQueryBackEnd : isgri ] constructor failed: Invalid character at col 0 in angle 'a' ]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRequestNotUnderstood\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_11979/2086683828.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m data_collection=disp.get_product(instrument='isgri',\n\u001b[0m\u001b[1;32m      2\u001b[0m                                  \u001b[0mproduct\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'isgri_image'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                                  \u001b[0mT1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2003-03-15T23:27:40.0'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                                  \u001b[0mT2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2003-03-16T00:03:12.0'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                                  \u001b[0mosa_version\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'OSA10.2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py\u001b[0m in \u001b[0;36mget_product\u001b[0;34m(self, product, instrument, verbose, dry_run, product_type, **kwargs)\u001b[0m\n\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0;31m# >\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 888\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_failed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py\u001b[0m in \u001b[0;36mfunc_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mUserError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"user error: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, parameters_dict, handle, url, wait, quiet)\u001b[0m\n\u001b[1;32m    661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py\u001b[0m in \u001b[0;36mfunc_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mUserError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"user error: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, verbose, silent)\u001b[0m\n\u001b[1;32m    558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0;31m# >\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_to_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    561\u001b[0m         \u001b[0;31m# <\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.venv/myVE/lib/python3.8/site-packages/oda_api/api.py\u001b[0m in \u001b[0;36mrequest_to_json\u001b[0;34m(self, verbose)\u001b[0m\n\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m                 raise RequestNotUnderstood(\n\u001b[0m\u001b[1;32m    395\u001b[0m                     response.json())\n\u001b[1;32m    396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRequestNotUnderstood\u001b[0m: [ RequestNotUnderstood: RequestNotUnderstood():[ InstrumentQueryBackEnd : isgri ] constructor failed: Invalid character at col 0 in angle 'a' ]"
     ]
    }
   ],
   "source": [
    "data_collection=disp.get_product(instrument='isgri',\n",
    "                                 product='isgri_image',\n",
    "                                 T1='2003-03-15T23:27:40.0',\n",
    "                                 T2='2003-03-16T00:03:12.0',\n",
    "                                 osa_version='OSA10.2',\n",
    "                                 RA='a',\n",
    "                                 DEC=-37.844167,\n",
    "                                 detection_threshold=5.0,\n",
    "                                 radius=15.,\n",
    "                                 product_type='Real')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get ODA products\n",
    "now we actually get the products with the right parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_collection=disp.get_product(instrument='isgri',\n",
    "                      product='isgri_image',\n",
    "                      T1='2003-03-15T23:27:40.0',\n",
    "                      T2='2003-03-16T00:03:15.0',\n",
    "                      E1_keV=20.0,\n",
    "                      E2_keV=40.0,\n",
    "                      osa_version='OSA10.2',\n",
    "                      RA=257.815417,\n",
    "                      DEC=-41.593417,\n",
    "                      detection_threshold=5.0,\n",
    "                      radius=15.,\n",
    "                      product_type='Real')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the ODA data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID=0 prod_name=mosaic_image_0_mosaic  meta_data: {'product': 'mosaic', 'instrument': 'isgri', 'src_name': '', 'query_parameters': '[{\"query_name\": \"isgri_image_query\"}, {\"product_name\": \"isgri_image_query\"}, {\"name\": \"detection_threshold\", \"units\": \"sigma\", \"value\": \"5.0\"}, {\"name\": \"image_scale_min\", \"units\": None, \"value\": None}, {\"name\": \"image_scale_max\", \"units\": None, \"value\": None}]'}\n",
      "\n",
      "ID=1 prod_name=dispatcher_catalog_1  meta_data: \n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can acess memeber by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<oda_api.data_products.NumpyDataProduct at 0x7f54f77a95e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collection.mosaic_image_0_mosaic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or by position in the data list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<oda_api.data_products.NumpyDataProduct at 0x7f54f77a95e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collection._p_list[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the ODA catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=4</i>\n",
       "<table id=\"table140002906006576\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>meta_ID</th><th>src_names</th><th>significance</th><th>ra</th><th>dec</th><th>NEW_SOURCE</th><th>ISGRI_FLAG</th><th>FLAG</th><th>ERR_RAD</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>str12</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th></tr></thead>\n",
       "<tr><td>68</td><td>OAO 1657-415</td><td>17.827661514282227</td><td>255.19740295410156</td><td>-41.653175354003906</td><td>-32768</td><td>2</td><td>0</td><td>0.00014000000373926014</td></tr>\n",
       "<tr><td>74</td><td>4U 1700-377</td><td>21.19563102722168</td><td>255.9771270751953</td><td>-37.83724594116211</td><td>-32768</td><td>2</td><td>0</td><td>0.0002800000074785203</td></tr>\n",
       "<tr><td>77</td><td>GX 349+2</td><td>13.917550086975098</td><td>256.42901611328125</td><td>-36.41566467285156</td><td>-32768</td><td>2</td><td>0</td><td>0.0002800000074785203</td></tr>\n",
       "<tr><td>130</td><td>GX 354-0</td><td>8.97690486907959</td><td>262.9885559082031</td><td>-33.82877731323242</td><td>-32768</td><td>2</td><td>0</td><td>0.0002800000074785203</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=4>\n",
       "meta_ID  src_names      significance    ...  FLAG        ERR_RAD        \n",
       "                                        ...                             \n",
       " int64     str12          float64       ... int64        float64        \n",
       "------- ------------ ------------------ ... ----- ----------------------\n",
       "     68 OAO 1657-415 17.827661514282227 ...     0 0.00014000000373926014\n",
       "     74  4U 1700-377  21.19563102722168 ...     0  0.0002800000074785203\n",
       "     77     GX 349+2 13.917550086975098 ...     0  0.0002800000074785203\n",
       "    130     GX 354-0   8.97690486907959 ...     0  0.0002800000074785203"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collection.dispatcher_catalog_1.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can use astropy.table commands to modify the table of the catatlog http://docs.astropy.org/en/stable/table/modify_table.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to generate a catalog to pass to the dispatcher api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_cat=data_collection.dispatcher_catalog_1.get_api_dictionary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"cat_frame\": \"fk5\", \"cat_coord_units\": \"deg\", \"cat_column_list\": [[68, 74, 77, 130], [\"OAO 1657-415\", \"4U 1700-377\", \"GX 349+2\", \"GX 354-0\"], [17.827661514282227, 21.19563102722168, 13.917550086975098, 8.97690486907959], [255.19740295410156, 255.9771270751953, 256.42901611328125, 262.9885559082031], [-41.653175354003906, -37.83724594116211, -36.41566467285156, -33.82877731323242], [-32768, -32768, -32768, -32768], [2, 2, 2, 2], [0, 0, 0, 0], [0.00014000000373926014, 0.0002800000074785203, 0.0002800000074785203, 0.0002800000074785203]], \"cat_column_names\": [\"meta_ID\", \"src_names\", \"significance\", \"ra\", \"dec\", \"NEW_SOURCE\", \"ISGRI_FLAG\", \"FLAG\", \"ERR_RAD\"], \"cat_column_descr\": [[\"meta_ID\", \"<i8\"], [\"src_names\", \"<U12\"], [\"significance\", \"<f8\"], [\"ra\", \"<f8\"], [\"dec\", \"<f8\"], [\"NEW_SOURCE\", \"<i8\"], [\"ISGRI_FLAG\", \"<i8\"], [\"FLAG\", \"<i8\"], [\"ERR_RAD\", \"<f8\"]], \"cat_lat_name\": \"dec\", \"cat_lon_name\": \"ra\"}'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collection=disp.get_product(instrument='isgri',\n",
    "                              product='isgri_image',\n",
    "                              T1='2003-03-15T23:27:40.0',\n",
    "                              T2='2003-03-16T00:03:15.0',\n",
    "                              E1_keV=20.0,\n",
    "                              E2_keV=40.0,\n",
    "                              osa_version='OSA10.2',\n",
    "                              RA=255.986542,\n",
    "                              DEC=-37.844167,\n",
    "                              detection_threshold=5.0,\n",
    "                              radius=15.,\n",
    "                              product_type='Real',\n",
    "                              selected_catalog=api_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can explore the image with the following command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "product : mosaic\n",
      "instrument : isgri\n",
      "src_name : \n",
      "query_parameters : [{\"query_name\": \"isgri_image_query\"}, {\"product_name\": \"isgri_image_query\"}, {\"name\": \"detection_threshold\", \"units\": \"sigma\", \"value\": \"5.0\"}, {\"name\": \"image_scale_min\", \"units\": None, \"value\": None}, {\"name\": \"image_scale_max\", \"units\": None, \"value\": None}]\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "data_collection.mosaic_image_0_mosaic.show_meta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FITS_rec([('IMAGE', 'ISGR-MOSA-IMA', 1, 3, '', '', 'INTENSITY',     20,     40, 20., 30., 40., 1169.97884473, 1169.99724527, 1589., 1587.05859375),\n",
       "          ('IMAGE', 'ISGR-MOSA-IMA', 2, 4, '', '', 'VARIANCE',     20,     40, 20., 30., 40., 1169.97884473, 1169.99724527, 1589., 1587.05859375),\n",
       "          ('IMAGE', 'ISGR-MOSA-IMA', 3, 5, '', '', 'SIGNIFICANCE',     20,     40, 20., 30., 40., 1169.97884473, 1169.99724527, 1589., 1587.05859375),\n",
       "          ('IMAGE', 'ISGR-MOSA-IMA', 4, 6, '', '', 'EXPOSURE',     20,     40, 20., 30., 40., 1169.97884473, 1169.99724527, 1589., 1587.05859375)],\n",
       "         dtype=(numpy.record, [('MEMBER_XTENSION', 'S8'), ('MEMBER_NAME', 'S32'), ('MEMBER_VERSION', '>i4'), ('MEMBER_POSITION', '>i4'), ('MEMBER_LOCATION', 'S256'), ('MEMBER_URI_TYPE', 'S3'), ('IMATYPE', 'S32'), ('CHANMIN', '>i2'), ('CHANMAX', '>i2'), ('E_MIN', '>f4'), ('E_MEAN', '>f4'), ('E_MAX', '>f4'), ('TFIRST', '>f8'), ('TLAST', '>f8'), ('TELAPSE', '>f8'), ('ONTIME', '>f8')]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collection.mosaic_image_0_mosaic.data_unit[1].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdu=data_collection.mosaic_image_0_mosaic.to_fits_hdu_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collection.mosaic_image_0_mosaic.data_unit[1].data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collection.mosaic_image_0_mosaic.write_fits_file('test.fits',overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the ODA Image   plotting tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#interactive\n",
    "#%matplotlib notebook\n",
    "\n",
    "%matplotlib inline\n",
    "im=OdaImage(data_collection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im.show(unit_ID=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BASETYPE': 'DAL_GROUP',\n",
       " 'BITPIX': 8,\n",
       " 'CHECKSUM': '59K3A7H157H1A7H1',\n",
       " 'COMMENT': '  on the next keyword which has the name CONTINUE.',\n",
       " 'CONFIGUR': 'osa_2019-07-29T14:07:33',\n",
       " 'CREATOR': 'ii_skyimage 5.4.4',\n",
       " 'DATASUM': '910507346',\n",
       " 'DATE': '2021-09-01T13:14:08',\n",
       " 'DATE-END': '2003-03-15T23:57:39',\n",
       " 'DATE-OBS': '2003-03-15T23:27:53',\n",
       " 'DETNAM': 'ISGRI',\n",
       " 'EXTNAME': 'GROUPING',\n",
       " 'EXTREL': '7.2',\n",
       " 'EXTVER': 1,\n",
       " 'GCOUNT': 1,\n",
       " 'GRPID1': -1,\n",
       " 'GRPLC1': 'ogg.fits',\n",
       " 'GRPNAME': 'ISGR-MOSA-IMA-IDX',\n",
       " 'IDXMEMBR': 'ISGR-MOSA-IMA',\n",
       " 'INSTRUME': 'IBIS',\n",
       " 'ISDCLEVL': 'IMA',\n",
       " 'LONGSTRN': 'OGIP 1.0',\n",
       " 'MJDREF': 51544.0,\n",
       " 'NAXIS': 2,\n",
       " 'NAXIS1': 387,\n",
       " 'NAXIS2': 4,\n",
       " 'ORIGIN': 'ISDC',\n",
       " 'PCOUNT': 0,\n",
       " 'STAMP': '2021-09-01T13:14:08 ii_skyimage 5.4.4',\n",
       " 'TELESCOP': 'INTEGRAL',\n",
       " 'TFIELDS': 16,\n",
       " 'TFORM1': '8A',\n",
       " 'TFORM10': '1E',\n",
       " 'TFORM11': '1E',\n",
       " 'TFORM12': '1E',\n",
       " 'TFORM13': '1D',\n",
       " 'TFORM14': '1D',\n",
       " 'TFORM15': '1D',\n",
       " 'TFORM16': '1D',\n",
       " 'TFORM2': '32A',\n",
       " 'TFORM3': '1J',\n",
       " 'TFORM4': '1J',\n",
       " 'TFORM5': '256A',\n",
       " 'TFORM6': '3A',\n",
       " 'TFORM7': '32A',\n",
       " 'TFORM8': '1I',\n",
       " 'TFORM9': '1I',\n",
       " 'TIMEREF': 'LOCAL',\n",
       " 'TIMESYS': 'TT',\n",
       " 'TIMEUNIT': 'd',\n",
       " 'TNULL3': 0,\n",
       " 'TNULL4': 0,\n",
       " 'TSCAL8': 1,\n",
       " 'TSCAL9': 1,\n",
       " 'TSTART': 1169.97844975867,\n",
       " 'TSTOP': 1169.99912106495,\n",
       " 'TTYPE1': 'MEMBER_XTENSION',\n",
       " 'TTYPE10': 'E_MIN',\n",
       " 'TTYPE11': 'E_MEAN',\n",
       " 'TTYPE12': 'E_MAX',\n",
       " 'TTYPE13': 'TFIRST',\n",
       " 'TTYPE14': 'TLAST',\n",
       " 'TTYPE15': 'TELAPSE',\n",
       " 'TTYPE16': 'ONTIME',\n",
       " 'TTYPE2': 'MEMBER_NAME',\n",
       " 'TTYPE3': 'MEMBER_VERSION',\n",
       " 'TTYPE4': 'MEMBER_POSITION',\n",
       " 'TTYPE5': 'MEMBER_LOCATION',\n",
       " 'TTYPE6': 'MEMBER_URI_TYPE',\n",
       " 'TTYPE7': 'IMATYPE',\n",
       " 'TTYPE8': 'CHANMIN',\n",
       " 'TTYPE9': 'CHANMAX',\n",
       " 'TUNIT10': 'keV',\n",
       " 'TUNIT11': 'keV',\n",
       " 'TUNIT12': 'keV',\n",
       " 'TUNIT13': 'd',\n",
       " 'TUNIT14': 'd',\n",
       " 'TUNIT15': 's',\n",
       " 'TUNIT16': 's',\n",
       " 'TZERO8': 32768,\n",
       " 'TZERO9': 32768,\n",
       " 'XTENSION': 'BINTABLE'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collection.mosaic_image_0_mosaic.data_unit[1].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the ODA LC  plotting tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_collection=disp.get_product(instrument='isgri',\n",
    "                      product='isgri_lc',\n",
    "                      T1='2003-03-15T23:27:40.0',\n",
    "                      T2='2003-03-16T00:03:12.0',\n",
    "                      time_bin=70,\n",
    "                      osa_version='OSA10.2',\n",
    "                      RA=255.986542,\n",
    "                      DEC=-37.844167,\n",
    "                      radius=15.,\n",
    "                      product_type='Real')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### explore LC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID=0 prod_name=isgri_lc_0_OAO1657m415  meta_data: {'src_name': 'OAO 1657-415', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}\n",
      "\n",
      "ID=1 prod_name=isgri_lc_1_4U1700m377  meta_data: {'src_name': '4U 1700-377', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}\n",
      "\n",
      "ID=2 prod_name=isgri_lc_2_GX349p2  meta_data: {'src_name': 'GX 349+2', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}\n",
      "\n",
      "ID=3 prod_name=isgri_lc_3_H1705m440  meta_data: {'src_name': 'H 1705-440', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}\n",
      "\n",
      "ID=4 prod_name=isgri_lc_4_GX354m0  meta_data: {'src_name': 'GX 354-0', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}\n",
      "\n",
      "ID=5 prod_name=isgri_lc_5_GX1p4  meta_data: {'src_name': 'GX 1+4', 'time_bin': 0.000810143479094966, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "name: isgri_lc\n",
      "meta_data dict_keys(['src_name', 'time_bin', 'time', 'rate', 'rate_err'])\n",
      "number of data units 2\n",
      "------------------------------\n",
      "data uniti 0 ,name: PRIMARY\n",
      "data uniti 1 ,name: ISGR-SRC.-LCR\n"
     ]
    }
   ],
   "source": [
    "data_collection.isgri_lc_2_GX349p2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 OAO 1657-415\n",
      "1 4U 1700-377\n",
      "2 GX 349+2\n",
      "3 H 1705-440\n",
      "4 GX 354-0\n",
      "5 GX 1+4\n"
     ]
    }
   ],
   "source": [
    "for ID,s in enumerate(data_collection._p_list):\n",
    "    print (ID,s.meta_data['src_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1169.97924981, 189.06061, 208.61382 , 3.0928352, 36.32327 , 8.367494 , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98006   , 184.00623, 128.52094 , 3.0739527, 44.749542, 8.25954  , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98087017, 156.61322, 107.2609  , 2.7437818, 19.096039, 7.5178704, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98168037, 160.68495, 113.83268 , 2.8039339, 19.251225, 7.650763 , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98249057, 169.17253, 113.41755 , 2.8749352, 25.947037, 7.839797 , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98330074, 161.08427, 107.967094, 2.8938215, 23.221224, 7.6593533, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98411092, 168.77126, 117.82072 , 2.8845947, 31.26426 , 7.8425374, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98492112, 160.38077, 115.27191 , 2.942873 , 25.611507, 7.637102 , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.9857313 , 159.43456, 114.650406, 2.8541234, 25.057646, 7.6175227, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98654149, 154.47977, 101.08613 , 2.791371 , 32.808315, 7.4923277, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98735168, 150.6925 , 102.68129 , 2.76442  , 22.8526  , 7.3896174, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98816182, 146.80734, 100.66747 , 2.6543057, 46.189266, 7.3128138, 0.9980645, 0.00040507, 0.00081014),\n",
       "       (1169.98897204, 148.31848, 105.12519 , 2.8063016, 30.23531 , 7.36563  , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.98978224, 151.88261, 106.98407 , 2.8313267, 20.044998, 7.464697 , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.9905924 , 193.02788, 135.57555 , 3.1867957, 18.478785, 8.478564 , 0.9987744, 0.00040507, 0.00081014),\n",
       "       (1169.9914026 , 176.79617, 122.84473 , 3.1522508, 30.608587, 8.218244 , 0.9656138, 0.00040507, 0.00081014),\n",
       "       (1169.99221276, 165.54268, 118.42887 , 2.8857293, 35.064762, 7.792433 , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.99302296, 155.6777 , 107.725555, 2.7923737, 30.44729 , 7.5125117, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.99383313, 153.74835, 109.006096, 2.8350844, 41.316975, 7.4837537, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.99464334, 152.44218, 100.14908 , 2.7715   , 36.908443, 7.4560814, 0.9983044, 0.00040507, 0.00081014),\n",
       "       (1169.99545352, 152.91246, 107.17616 , 2.7963147, 35.36147 , 7.4534373, 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.99626372, 154.3732 , 103.014656, 2.7716458, 27.397203, 7.483038 , 0.9999995, 0.00040507, 0.00081014),\n",
       "       (1169.99695709, 157.10977, 109.46534 , 3.3767562, 40.66689 , 8.954308 , 0.9999995, 0.0002883 , 0.00057659)],\n",
       "      dtype=[('TIME', '>f8'), ('TOT_COUNTS', '>f4'), ('BACKV', '>f4'), ('BACKE', '>f4'), ('RATE', '>f4'), ('ERROR', '>f4'), ('FRACEXP', '>f4'), ('XAX_E', '>f4'), ('TIMEDEL', '<f8')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc=data_collection._p_list[0]\n",
    "lc.data_unit[1].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "name: isgri_lc\n",
      "meta_data dict_keys(['src_name', 'time_bin', 'time', 'rate', 'rate_err'])\n",
      "number of data units 2\n",
      "------------------------------\n",
      "data uniti 0 ,name: PRIMARY\n",
      "data uniti 1 ,name: ISGR-SRC.-LCR\n"
     ]
    }
   ],
   "source": [
    "lc.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'src_name': 'OAO 1657-415',\n",
       " 'time_bin': 0.000810143479094966,\n",
       " 'time': 'TIME',\n",
       " 'rate': 'RATE',\n",
       " 'rate_err': 'ERROR'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "OdaLightCurve(lc).show(unit_ID=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BITPIX': 8, 'EXTEND': True, 'NAXIS': 0, 'SIMPLE': True}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.data_unit[0].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polar LC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collection=disp.get_product(instrument='polar',product='polar_lc',T1='2016-12-18T08:32:21.000',T2='2016-12-18T08:34:01.000',time_bin=0.5,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID=0 prod_name=polar_lc_0_lc  meta_data: {'src_name': 'lc', 'time_bin': 0.5, 'time': 'time', 'rate': 'rate', 'rate_err': 'rate_err'}\n",
      "\n",
      "ID=1 prod_name=polar_lc_1  meta_data: \n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'src_name': 'lc',\n",
       " 'time_bin': 0.5,\n",
       " 'time': 'time',\n",
       " 'rate': 'rate',\n",
       " 'rate_err': 'rate_err'}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collection._p_list[0].meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5022"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc=data_collection._p_list[0]\n",
    "root=data_collection._p_list[1]\n",
    "open('lc.root', \"wb\").write(root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5022"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " open('lc.root', \"wb\").write(root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "OdaLightCurve(lc).show(unit_ID=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SPIACS LC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'instrumet': 'spi_acs'},\n",
       "  {'prod_dict': {'spi_acs_lc': 'spi_acs_lc_query'}},\n",
       "  '[{\"query_name\": \"src_query\"}, {\"name\": \"src_name\", \"units\": \"str\", \"value\": \"test\"}, {\"name\": \"RA\", \"units\": \"deg\", \"value\": 0.0}, {\"name\": \"DEC\", \"units\": \"deg\", \"value\": 0.0}, {\"name\": \"T1\", \"units\": None, \"value\": \"2001-12-11T00:00:00.000\"}, {\"name\": \"T2\", \"units\": None, \"value\": \"2001-12-11T00:00:00.000\"}, {\"name\": \"token\", \"units\": \"str\", \"value\": None}]',\n",
       "  '[{\"query_name\": \"spiacs_parameters\"}, {\"name\": \"user_catalog\", \"units\": \"str\", \"value\": None}, {\"name\": None, \"units\": \"names_list\", \"value\": []}, {\"name\": \"selected_catalog\", \"units\": \"str\", \"value\": None}]',\n",
       "  '[{\"query_name\": \"spi_acs_lc_query\"}, {\"product_name\": \"spi_acs_lc\"}, {\"name\": \"time_bin\", \"units\": \"sec\", \"value\": 1000.0}]']]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disp.get_instrument_description('spi_acs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collection=disp.get_product(instrument='spi_acs',\n",
    "                      product='spi_acs_lc',\n",
    "                      T1='2003-03-15T23:27:40.0',\n",
    "                      T2='2003-03-15T23:57:12.0',\n",
    "                      time_bin=2,\n",
    "                      RA=255.986542,\n",
    "                      DEC=-37.844167,\n",
    "                      product_type='Real')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID=0 prod_name=spi_acs_lc_0_query  meta_data: {'src_name': 'query', 'time_bin': 2.0, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc=data_collection._p_list[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'src_name': 'query',\n",
       " 'time_bin': 2.0,\n",
       " 'time': 'TIME',\n",
       " 'rate': 'RATE',\n",
       " 'rate_err': 'ERROR'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "name: \n",
      "meta_data dict_keys(['src_name', 'time_bin', 'time', 'rate', 'rate_err'])\n",
      "number of data units 2\n",
      "------------------------------\n",
      "data uniti 0 ,name: Primary\n",
      "data uniti 1 ,name: RATE\n"
     ]
    }
   ],
   "source": [
    "lc.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BITPIX': 8,\n",
       " 'DATE-END': '2003-03-15T23:28:44.216',\n",
       " 'DATE-OBS': '2003-03-15T23:28:44.216',\n",
       " 'EXTNAME': 'RATE',\n",
       " 'GCOUNT': 1,\n",
       " 'INSTRUME': 'SPI-ACS',\n",
       " 'MJDREF': 51544.0,\n",
       " 'NAXIS': 2,\n",
       " 'NAXIS1': 24,\n",
       " 'NAXIS2': 886,\n",
       " 'ONTIME': 1772.0008558046072,\n",
       " 'PCOUNT': 0,\n",
       " 'TASSIGN': 'SATELLITE',\n",
       " 'TELESCOP': 'INTEGRAL',\n",
       " 'TFIELDS': 3,\n",
       " 'TFORM1': 'D',\n",
       " 'TFORM2': 'D',\n",
       " 'TFORM3': 'D',\n",
       " 'TIMEDEL': 2.0,\n",
       " 'TIMEREF': 'LOCAL',\n",
       " 'TIMESYS': 'TT',\n",
       " 'TIMEUNIT': 's',\n",
       " 'TIMEZERO': 101087010.19105513,\n",
       " 'TSTART': 101086124.21562724,\n",
       " 'TSTOP': 101087896.21648304,\n",
       " 'TTYPE1': 'TIME',\n",
       " 'TTYPE2': 'RATE',\n",
       " 'TTYPE3': 'ERROR',\n",
       " 'XTENSION': 'BINTABLE'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.data_unit[1].header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(-885.00042667, 1515580., 870.51134398),\n",
       "       (-883.00042573, 1509680., 868.81528532),\n",
       "       (-881.00042474, 1523800., 872.86883322),\n",
       "       (-879.00042396, 1513890., 870.02586168),\n",
       "       (-877.00042318, 1506440., 867.88248052),\n",
       "       (-875.00042222, 1520350., 871.88015231),\n",
       "       (-873.0004213 , 1510580., 869.07422008),\n",
       "       (-871.00042036, 1516300., 870.71809445),\n",
       "       (-869.00041942, 1526480., 873.63607984),\n",
       "       (-867.00041848, 1508970., 868.6109601 )],\n",
       "      dtype=[('TIME', '<f8'), ('RATE', '<f8'), ('ERROR', '<f8')])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.data_unit[1].data[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "OdaLightCurve(lc).show(unit_ID=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the ODA  and spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_collection=disp.get_product(instrument='isgri',\n",
    "                      product='isgri_spectrum',\n",
    "                      T1='2003-03-15T23:27:40.0',\n",
    "                      T2='2003-03-16T00:03:12.0',\n",
    "                      osa_version='OSA10.2',\n",
    "                      RA=255.986542,\n",
    "                      DEC=-37.844167,\n",
    "                      radius=15.,\n",
    "                      product_type='Real')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### explore spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID=0 prod_name=isgri_spectrum_0_OAO1657m415_isgri_spectrum  meta_data: {'src_name': 'OAO 1657-415', 'product': 'isgri_spectrum'}\n",
      "\n",
      "ID=1 prod_name=isgri_spectrum_1_OAO1657m415_isgri_arf  meta_data: {'src_name': 'OAO 1657-415', 'product': 'isgri_arf'}\n",
      "\n",
      "ID=2 prod_name=isgri_spectrum_2_OAO1657m415_isgri_rmf  meta_data: {'src_name': 'OAO 1657-415', 'product': 'isgri_rmf'}\n",
      "\n",
      "ID=3 prod_name=isgri_spectrum_3_4U1700m377_isgri_spectrum  meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_spectrum'}\n",
      "\n",
      "ID=4 prod_name=isgri_spectrum_4_4U1700m377_isgri_arf  meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_arf'}\n",
      "\n",
      "ID=5 prod_name=isgri_spectrum_5_4U1700m377_isgri_rmf  meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_rmf'}\n",
      "\n",
      "ID=6 prod_name=isgri_spectrum_6_GX349p2_isgri_spectrum  meta_data: {'src_name': 'GX 349+2', 'product': 'isgri_spectrum'}\n",
      "\n",
      "ID=7 prod_name=isgri_spectrum_7_GX349p2_isgri_arf  meta_data: {'src_name': 'GX 349+2', 'product': 'isgri_arf'}\n",
      "\n",
      "ID=8 prod_name=isgri_spectrum_8_GX349p2_isgri_rmf  meta_data: {'src_name': 'GX 349+2', 'product': 'isgri_rmf'}\n",
      "\n",
      "ID=9 prod_name=isgri_spectrum_9_H1705m440_isgri_spectrum  meta_data: {'src_name': 'H 1705-440', 'product': 'isgri_spectrum'}\n",
      "\n",
      "ID=10 prod_name=isgri_spectrum_10_H1705m440_isgri_arf  meta_data: {'src_name': 'H 1705-440', 'product': 'isgri_arf'}\n",
      "\n",
      "ID=11 prod_name=isgri_spectrum_11_H1705m440_isgri_rmf  meta_data: {'src_name': 'H 1705-440', 'product': 'isgri_rmf'}\n",
      "\n",
      "ID=12 prod_name=isgri_spectrum_12_GX354m0_isgri_spectrum  meta_data: {'src_name': 'GX 354-0', 'product': 'isgri_spectrum'}\n",
      "\n",
      "ID=13 prod_name=isgri_spectrum_13_GX354m0_isgri_arf  meta_data: {'src_name': 'GX 354-0', 'product': 'isgri_arf'}\n",
      "\n",
      "ID=14 prod_name=isgri_spectrum_14_GX354m0_isgri_rmf  meta_data: {'src_name': 'GX 354-0', 'product': 'isgri_rmf'}\n",
      "\n",
      "ID=15 prod_name=isgri_spectrum_15_GX1p4_isgri_spectrum  meta_data: {'src_name': 'GX 1+4', 'product': 'isgri_spectrum'}\n",
      "\n",
      "ID=16 prod_name=isgri_spectrum_16_GX1p4_isgri_arf  meta_data: {'src_name': 'GX 1+4', 'product': 'isgri_arf'}\n",
      "\n",
      "ID=17 prod_name=isgri_spectrum_17_GX1p4_isgri_rmf  meta_data: {'src_name': 'GX 1+4', 'product': 'isgri_rmf'}\n",
      "\n",
      "ID=18 prod_name=isgri_spectrum_18_Background_isgri_spectrum  meta_data: {'src_name': 'Background', 'product': 'isgri_spectrum'}\n",
      "\n",
      "ID=19 prod_name=isgri_spectrum_19_Background_isgri_arf  meta_data: {'src_name': 'Background', 'product': 'isgri_arf'}\n",
      "\n",
      "ID=20 prod_name=isgri_spectrum_20_Background_isgri_rmf  meta_data: {'src_name': 'Background', 'product': 'isgri_rmf'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "d=data_collection._p_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'src_name': 'OAO 1657-415', 'product': 'isgri_spectrum'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.meta_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can select all the products for the same soruce name and build a new data collection on-the-fly with a single instruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_sel=data_collection.new_from_metadata('src_name','4U 1700-377')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID=0 prod_name=prod_0_4U1700m377_isgri_spectrum  meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_spectrum'}\n",
      "\n",
      "ID=1 prod_name=prod_1_4U1700m377_isgri_arf  meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_arf'}\n",
      "\n",
      "ID=2 prod_name=prod_2_4U1700m377_isgri_rmf  meta_data: {'src_name': '4U 1700-377', 'product': 'isgri_rmf'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_sel.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can save all these products "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sel.save_all_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=data_collection._p_list[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ANCRFILE': 'NONE',\n",
       " 'AREASCAL': 1,\n",
       " 'BACKFILE': 'NONE',\n",
       " 'BACKSCAL': 1,\n",
       " 'BASETYPE': 'DAL_TABLE',\n",
       " 'BITPIX': 8,\n",
       " 'BKGPARAM': 'rebinned_back_spe.fits',\n",
       " 'CHANTYPE': 'PI',\n",
       " 'CHECKSUM': '9kKSIjKQ9jKQGjKQ',\n",
       " 'COMMENT': '  on the next keyword which has the name CONTINUE.',\n",
       " 'CONFIGUR': 'osa_2019-07-29T14:07:33',\n",
       " 'CORRFILE': 'NONE',\n",
       " 'CORRSCAL': 0,\n",
       " 'CREATOR': 'ISGRISpectraSum.v5.4.2.3.extractall',\n",
       " 'DATASUM': '2445425117',\n",
       " 'DATE': '2021-09-01T13:26:15.942451',\n",
       " 'DEADC': 0.775885283090927,\n",
       " 'DEC_OBJ': -37.8394165039062,\n",
       " 'DETCHANS': 62,\n",
       " 'DETNAM': 'ISGRI',\n",
       " 'EQUINOX': 2000.0,\n",
       " 'EXPOSURE': 1198.97207125461,\n",
       " 'EXP_SRC': 610.283081054688,\n",
       " 'EXTNAME': 'ISGR-EVTS-SPE',\n",
       " 'EXTREL': '10.4',\n",
       " 'EXTVER': 2,\n",
       " 'FILTER': 'none',\n",
       " 'FITTYPE': 6,\n",
       " 'GCOUNT': 1,\n",
       " 'GRPID1': 1,\n",
       " 'HDUCLAS1': 'SPECTRUM',\n",
       " 'HDUCLAS2': 'TOTAL',\n",
       " 'HDUCLAS3': 'RATE',\n",
       " 'HDUCLASS': 'OGIP',\n",
       " 'HDUVERS': '1.2.42',\n",
       " 'INSTRUME': 'IBIS',\n",
       " 'ISDCLEVL': 'SPE',\n",
       " 'LONGSTRN': 'OGIP 1.0',\n",
       " 'MJDREF': 51544.0,\n",
       " 'NAME': '4U 1700-377',\n",
       " 'NAXIS': 2,\n",
       " 'NAXIS1': 18,\n",
       " 'NAXIS2': 62,\n",
       " 'OFFCORR': 'rebinned_corr_spe.fits',\n",
       " 'ONTIME': 1587.05859375,\n",
       " 'ORIGIN': 'ISDC',\n",
       " 'PCOUNT': 0,\n",
       " 'RADECSYS': 'FK5',\n",
       " 'RA_OBJ': 255.984405517578,\n",
       " 'RESPFILE': 'NONE',\n",
       " 'REVOL': 51,\n",
       " 'SOURCEID': 'J170356.8-375039',\n",
       " 'STAMP': '',\n",
       " 'SW_TYPE': 'POINTING',\n",
       " 'TELAPSE': 1589.0,\n",
       " 'TELESCOP': 'INTEGRAL',\n",
       " 'TFIELDS': 6,\n",
       " 'TFORM1': '1I',\n",
       " 'TFORM2': '1E',\n",
       " 'TFORM3': '1E',\n",
       " 'TFORM4': '1E',\n",
       " 'TFORM5': '1I',\n",
       " 'TFORM6': '1I',\n",
       " 'TIMEREF': 'LOCAL',\n",
       " 'TIMESYS': 'TT',\n",
       " 'TIMEUNIT': 'd',\n",
       " 'TLMAX1': 61,\n",
       " 'TLMIN1': 0,\n",
       " 'TSTART': 1169.97844975867,\n",
       " 'TSTOP': 1169.99912106495,\n",
       " 'TTYPE1': 'CHANNEL',\n",
       " 'TTYPE2': 'RATE',\n",
       " 'TTYPE3': 'SYS_ERR',\n",
       " 'TTYPE4': 'STAT_ERR',\n",
       " 'TTYPE5': 'QUALITY',\n",
       " 'TTYPE6': 'GROUPING',\n",
       " 'TUNIT2': 'count/s',\n",
       " 'TUNIT3': '',\n",
       " 'TUNIT4': 'count/s',\n",
       " 'XTENSION': 'BINTABLE'}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.data_unit[1].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JEM-X test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'instrumet': 'jemx'},\n",
       "  {'prod_dict': {'jemx_image': 'jemx_image_query',\n",
       "    'jemx_lc': 'jemx_lc_query',\n",
       "    'jemx_spectrum': 'jemx_spectrum_query',\n",
       "    'spectral_fit': 'spectral_fit_query'}},\n",
       "  '[{\"query_name\": \"src_query\"}, {\"name\": \"src_name\", \"units\": \"str\", \"value\": \"test\"}, {\"name\": \"RA\", \"units\": \"deg\", \"value\": 0.0}, {\"name\": \"DEC\", \"units\": \"deg\", \"value\": 0.0}, {\"name\": \"T1\", \"units\": None, \"value\": \"2001-12-11T00:00:00.000\"}, {\"name\": \"T2\", \"units\": None, \"value\": \"2001-12-11T00:00:00.000\"}, {\"name\": \"token\", \"units\": \"str\", \"value\": None}]',\n",
       "  '[{\"query_name\": \"jemx_parameters\"}, {\"name\": \"user_catalog\", \"units\": \"str\", \"value\": None}, {\"name\": \"scw_list\", \"units\": \"names_list\", \"value\": []}, {\"name\": \"selected_catalog\", \"units\": \"str\", \"value\": None}, {\"name\": \"radius\", \"units\": \"deg\", \"value\": 5.0}, {\"name\": \"max_pointings\", \"units\": None, \"value\": 50}, {\"name\": \"osa_version\", \"units\": \"str\", \"value\": None}, {\"name\": \"integral_data_rights\", \"units\": \"str\", \"value\": \"public\"}, {\"name\": \"jemx_num\", \"units\": None, \"value\": 2}, {\"name\": \"E1_keV\", \"units\": \"keV\", \"value\": 3.0}, {\"name\": \"E2_keV\", \"units\": \"keV\", \"value\": 35.0}]',\n",
       "  '[{\"query_name\": \"jemx_image_query\"}, {\"product_name\": \"jemx_image\"}, {\"name\": \"detection_threshold\", \"units\": \"sigma\", \"value\": 0.0}, {\"name\": \"image_scale_min\", \"units\": None, \"value\": None}, {\"name\": \"image_scale_max\", \"units\": None, \"value\": None}]',\n",
       "  '[{\"query_name\": \"jemx_spectrum_query\"}, {\"product_name\": \"jemx_spectrum\"}]',\n",
       "  '[{\"query_name\": \"spectral_fit_query\"}, {\"product_name\": \"spectral_fit\"}, {\"name\": \"xspec_model\", \"units\": \"str\", \"value\": \"powerlaw\"}, {\"name\": \"ph_file_name\", \"units\": \"str\", \"value\": \"\"}, {\"name\": \"arf_file_name\", \"units\": \"str\", \"value\": \"\"}, {\"name\": \"rmf_file_name\", \"units\": \"str\", \"value\": \"\"}]',\n",
       "  '[{\"query_name\": \"jemx_lc_query\"}, {\"product_name\": \"jemx_lc\"}, {\"name\": \"time_bin\", \"units\": \"sec\", \"value\": 1000.0}]']]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disp.get_instrument_description('jemx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collection=disp.get_product(instrument='jemx',\n",
    "                      jemx_num='2',\n",
    "                      product='jemx_lc',\n",
    "                      scw_list=['010200230010.001'],\n",
    "                      osa_version='OSA10.2',\n",
    "                      radius=15.,\n",
    "                      product_type='Real')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID=0 prod_name=jemx_lc_0_String  meta_data: {'src_name': 'String', 'time_bin': 0.01157407, 'time': 'TIME', 'rate': 'RATE', 'rate_err': 'ERROR'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collection=disp.get_product(instrument='jemx',\n",
    "                      jemx_num='2',\n",
    "                      product='jemx_spectrum',\n",
    "                      scw_list=['010200230010.001'],\n",
    "                      osa_version='OSA10.2',\n",
    "                      radius=15.,\n",
    "                      product_type='Real')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID=0 prod_name=jemx_spectrum_0_Crab_jemx_spectrum  meta_data: {'src_name': 'Crab', 'product': 'jemx_spectrum'}\n",
      "\n",
      "ID=1 prod_name=jemx_spectrum_1_Crab_jemx_arf  meta_data: {'src_name': 'Crab', 'product': 'jemx_arf'}\n",
      "\n",
      "ID=2 prod_name=jemx_spectrum_2_Crab_jemx_rmf  meta_data: {'src_name': 'Crab', 'product': 'jemx_rmf'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is an example of spectral analysis with threeML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m The naima package is not available. Models that depend on it will not be available\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it will not be available.\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m The ebltable package is not available. Models that depend on it will not be available\u001b[0m\n",
      "[\u001b[32mINFO    \u001b[0m]\u001b[32m Starting 3ML!\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m ROOT minimizer not available\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m PyGMO is not available\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m The cthreeML package is not installed. You will not be able to use plugins which require the C/C++ interface (currently HAWC)\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m Could not import plugin FermiLATLike.py. Do you have the relative instrument software installed and configured?\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m Could not import plugin HAWCLike.py. Do you have the relative instrument software installed and configured?\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from threeML.plugins.OGIPLike import  OGIPLike\n",
    "from threeML.io.package_data import get_path_of_data_file\n",
    "from threeML import *\n",
    "#warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m unable to find SPECTRUM extension: not OGIP PHA!\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m File has no SPECTRUM extension, but found a spectrum in extension ISGR-EVTS-SPE\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m Found TSTOP and TELAPSE. This file is invalid. Using TSTOP.\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m POISSERR is not set. Assuming non-poisson errors as given in the STAT_ERR column\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m The default choice for MATRIX extension failed:KeyError(\"Extension ('MATRIX', 1) not found.\")available: 'PRIMARY' 'GROUPING' 'SPECRESP MATRIX' 'EBOUNDS'\u001b[0m\n",
      "[\u001b[32mINFO    \u001b[0m]\u001b[32m Auto-probed noise models:\u001b[0m\n",
      "[\u001b[32mINFO    \u001b[0m]\u001b[32m - observation: gaussian\u001b[0m\n",
      "[\u001b[32mINFO    \u001b[0m]\u001b[32m - background: None\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "ogip_data = OGIPLike('ogip',\n",
    "                     observation='prod_0_4U1700m377_isgri_spectrum.fits',\n",
    "                     arf_file= 'prod_1_4U1700m377_isgri_arf.fits' ,\n",
    "                     response= 'prod_2_4U1700m377_isgri_rmf.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[32mINFO    \u001b[0m]\u001b[32m Range 20-60 translates to channels 7-24\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "ogip_data.set_active_measurements('20-60')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[32mINFO    \u001b[0m]\u001b[32m bad channels shown in red hatching\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING RuntimeWarning: invalid value encountered in sqrt\n",
      "\n",
      "\n",
      "WARNING UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1e-05, 10)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ogip_data.view_count_spectrum()\n",
    "plt.ylim(1E-5,10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[32mINFO    \u001b[0m]\u001b[32m set the minimizer to minuit\u001b[0m\n",
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m 4.22 percent of samples have been thrown away because they failed the constraints on the parameters. This results might not be suitable for error propagation. Enlarge the boundaries until you loose less than 1 percent of the samples.\u001b[0m\n",
      "Best fit values:\n",
      "\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>result</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parameter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.K</th>\n",
       "      <td>0.005 -0.005 +4</td>\n",
       "      <td>1 / (cm2 keV s)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.index</th>\n",
       "      <td>0.2 +/- 2.8</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.xc</th>\n",
       "      <td>(1.1 -0.7 +1.7) x 10</td>\n",
       "      <td>keV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      result             unit\n",
       "parameter                                                                    \n",
       "ps.spectrum.main.Cutoff_powerlaw.K           0.005 -0.005 +4  1 / (cm2 keV s)\n",
       "ps.spectrum.main.Cutoff_powerlaw.index           0.2 +/- 2.8                 \n",
       "ps.spectrum.main.Cutoff_powerlaw.xc     (1.1 -0.7 +1.7) x 10              keV"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Correlation matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><table id=\"table140002367086496\">\n",
       "<tr><td>1.00</td><td>-1.00</td><td>0.99</td></tr>\n",
       "<tr><td>-1.00</td><td>1.00</td><td>-1.00</td></tr>\n",
       "<tr><td>0.99</td><td>-1.00</td><td>1.00</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       " 1.00 -1.00  0.99\n",
       "-1.00  1.00 -1.00\n",
       " 0.99 -1.00  1.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Values of -log(likelihood) at the minimum:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>-log(likelihood)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ogip</th>\n",
       "      <td>4.390866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>4.390866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       -log(likelihood)\n",
       "ogip           4.390866\n",
       "total          4.390866"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Values of statistical measures:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>statistical measures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>16.496017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIC</th>\n",
       "      <td>17.452847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     statistical measures\n",
       "AIC             16.496017\n",
       "BIC             17.452847"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_function = Cutoff_powerlaw()\n",
    "\n",
    "# define the point source\n",
    "point_source = PointSource('ps', 0, 0, spectral_shape=fit_function)\n",
    "\n",
    "#define the model\n",
    "model = Model(point_source)\n",
    "\n",
    "# create a data list\n",
    "datalist = DataList(ogip_data)\n",
    "\n",
    "# make the joint likelihood\n",
    "jl = JointLikelihood(model, datalist)\n",
    "\n",
    "#fit\n",
    "jl.fit();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING MatplotlibDeprecationWarning: The 'nonposy' parameter of __init__() has been renamed 'nonpositive' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_spectrum_model_counts(jl, step=True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[31mERROR   \u001b[0m]\u001b[31m plot_point_source_spectra() has been replaced by plot_spectra().\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9cde05b33a44910b16cb3dee73a3075",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "processing MLE analyses:   0%|                            | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f37aff3e774477ea98cb1f17f7631ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Propagating errors:   0%|                               | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_point_source_spectra(jl.results, ene_min=20, ene_max=60, num_ene=100,\n",
    "                          flux_unit='erg / (cm2 s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Model summary:<br><br><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>N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Point sources</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extended sources</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Particle sources</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><br>Free parameters (3):<br><br><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>value</th>\n",
       "      <th>min_value</th>\n",
       "      <th>max_value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.K</th>\n",
       "      <td>0.005445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>keV-1 s-1 cm-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.index</th>\n",
       "      <td>0.188702</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.xc</th>\n",
       "      <td>10.82301</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>keV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><br>Fixed parameters (4):<br>(abridged. Use complete=True to see all fixed parameters)<br><br><br>Linked parameters (0):<br><br>(none)<br><br>Independent variables:<br><br>(none)<br>"
      ],
      "text/plain": [
       "Model summary:\n",
       "==============\n",
       "\n",
       "                  N\n",
       "Point sources     1\n",
       "Extended sources  0\n",
       "Particle sources  0\n",
       "\n",
       "Free parameters (3):\n",
       "--------------------\n",
       "\n",
       "                                           value min_value max_value  \\\n",
       "ps.spectrum.main.Cutoff_powerlaw.K      0.005445       0.0    1000.0   \n",
       "ps.spectrum.main.Cutoff_powerlaw.index  0.188702     -10.0      10.0   \n",
       "ps.spectrum.main.Cutoff_powerlaw.xc     10.82301       1.0      None   \n",
       "\n",
       "                                                  unit  \n",
       "ps.spectrum.main.Cutoff_powerlaw.K      keV-1 s-1 cm-2  \n",
       "ps.spectrum.main.Cutoff_powerlaw.index                  \n",
       "ps.spectrum.main.Cutoff_powerlaw.xc                keV  \n",
       "\n",
       "Fixed parameters (4):\n",
       "(abridged. Use complete=True to see all fixed parameters)\n",
       "\n",
       "\n",
       "Linked parameters (0):\n",
       "----------------------\n",
       "\n",
       "(none)\n",
       "\n",
       "Independent variables:\n",
       "----------------------\n",
       "\n",
       "(none)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# A uniform prior can be defined directly, like:\n",
    "model.ps.spectrum.main.Cutoff_powerlaw.index.prior = Uniform_prior(lower_bound=-2.,\n",
    "                                                                     upper_bound=2.)\n",
    "\n",
    "# or it can be set using the currently defined boundaries\n",
    "model.ps.spectrum.main.Cutoff_powerlaw.xc.prior=  Log_uniform_prior(lower_bound=1,\n",
    "                                                                     upper_bound=100)\n",
    "\n",
    "\n",
    "# The same for the Log_uniform prior\n",
    "model.ps.spectrum.main.Cutoff_powerlaw.K.prior = Log_uniform_prior(lower_bound=1e-3,\n",
    "                                                                     upper_bound=100)\n",
    "\n",
    "\n",
    "model.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[35mWARNING \u001b[0m]\u001b[35m External parameter cons_ogip already exist in the model. Overwriting it...\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "bs = BayesianAnalysis(model, datalist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[32mINFO    \u001b[0m]\u001b[32m sampler set to multinest\u001b[0m\n",
      " *****************************************************\n",
      " MultiNest v3.10\n",
      " Copyright Farhan Feroz & Mike Hobson\n",
      " Release Jul 2015\n",
      "\n",
      " no. of live points =  500\n",
      " dimensionality =    3\n",
      " *****************************************************\n",
      "  analysing data from chains/fit-.txt ln(ev)=  -11.862533639413844      +/-  0.11122341917410335     \n",
      " Total Likelihood Evaluations:         7493\n",
      " Sampling finished. Exiting MultiNest\n",
      "\n",
      "Maximum a posteriori probability (MAP) point:\n",
      "\n"
     ]
    },
    {
     "data": {
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       "        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>result</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parameter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.K</th>\n",
       "      <td>(1.7 -1.7 +2.2) x 10^-1</td>\n",
       "      <td>1 / (cm2 keV s)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.index</th>\n",
       "      <td>(-6 +/- 10) x 10^-1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ps.spectrum.main.Cutoff_powerlaw.xc</th>\n",
       "      <td>(1.9 +/- 0.9) x 10</td>\n",
       "      <td>keV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         result  \\\n",
       "parameter                                                         \n",
       "ps.spectrum.main.Cutoff_powerlaw.K      (1.7 -1.7 +2.2) x 10^-1   \n",
       "ps.spectrum.main.Cutoff_powerlaw.index      (-6 +/- 10) x 10^-1   \n",
       "ps.spectrum.main.Cutoff_powerlaw.xc          (1.9 +/- 0.9) x 10   \n",
       "\n",
       "                                                   unit  \n",
       "parameter                                                \n",
       "ps.spectrum.main.Cutoff_powerlaw.K      1 / (cm2 keV s)  \n",
       "ps.spectrum.main.Cutoff_powerlaw.index                   \n",
       "ps.spectrum.main.Cutoff_powerlaw.xc                 keV  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Values of -log(posterior) at the minimum:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>-log(posterior)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ogip</th>\n",
       "      <td>-2.457204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>-2.457204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       -log(posterior)\n",
       "ogip         -2.457204\n",
       "total        -2.457204"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Values of statistical measures:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>statistical measures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>12.628695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIC</th>\n",
       "      <td>13.585524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIC</th>\n",
       "      <td>-10870.282000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDIC</th>\n",
       "      <td>-10881.214804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log(Z)</th>\n",
       "      <td>-5.151833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        statistical measures\n",
       "AIC                12.628695\n",
       "BIC                13.585524\n",
       "DIC            -10870.282000\n",
       "PDIC           -10881.214804\n",
       "log(Z)             -5.151833"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bs.set_sampler(\"multinest\")\n",
    "bs.sampler.setup(n_live_points=500)\n",
    "bs.sample()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap(\"viridis\").copy()\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 547.2x547.2 with 9 Axes>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 547.2x547.2 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bs.results.corner_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[31mERROR   \u001b[0m]\u001b[31m plot_point_source_spectra() has been replaced by plot_spectra().\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7478d4127764fe3a558f796ff25be20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "processing Bayesian analyses:   0%|                       | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0676378f9f814734a9e495d12415929a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Propagating errors:   0%|                               | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_point_source_spectra(bs.results, ene_min=20, ene_max=60, num_ene=100,\n",
    "                          flux_unit='erg / (cm2 s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\u001b[31mERROR   \u001b[0m]\u001b[31m get_point_source_flux() has been replaced by get_flux()\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf1ddafb506a455ca6037e4bd48d12db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "processing Bayesian analyses:   0%|                       | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fluxes_bs = bs.results.get_point_source_flux(100 * u.keV, 1 * u.MeV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>flux</th>\n",
       "      <th>low bound</th>\n",
       "      <th>hi bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ps: total</th>\n",
       "      <td>7.98682085812617e-12 erg / (cm2 s)</td>\n",
       "      <td>2.2496983896203776e-12 erg / (cm2 s)</td>\n",
       "      <td>3.2021187085022926e-11 erg / (cm2 s)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         flux  \\\n",
       "ps: total  7.98682085812617e-12 erg / (cm2 s)   \n",
       "\n",
       "                                      low bound  \\\n",
       "ps: total  2.2496983896203776e-12 erg / (cm2 s)   \n",
       "\n",
       "                                       hi bound  \n",
       "ps: total  3.2021187085022926e-11 erg / (cm2 s)  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxes_bs"
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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