Herschel_PACS_Cube.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-weight: bold;text-align: center;font-size: 2em;color: #00338D\">Herschel PACS IFU Data Visualization and Analysis</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Authors:** Elena Puga, Henrik Norman\n",
    "**Creation:** 05.08.2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context\n",
    "This notebook is a worked example on how to download, manipulate and perform a basic analysis on [__Herschel PACS__](https://www.cosmos.esa.int/web/herschel/legacy-documentation-pacs) data cubes, as the PACS instrument was the first Integral Field Unit on space.\n",
    "The original analysis capabilities were a functionality provided by the Herschel Interactive Pipeline Environment (HIPE), which had the Spectrum explorer and contained dedicated toolboxes like: a) Spectrum Toolbox, b) SpectrumFitterGUI, and c) the Cube Toolbox.\n",
    "\n",
    "New Python manipulation (`specutils`, `spectral-cube`) and visualization tools (`SpecViz`, `CubeViz`) have been developed in the context of future observatories ([__JWST__](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis#JWSTPost-PipelineDataAnalysis-JWSTJupyterNotebooks), Roman ST), and in this notebook we showcase the usability of these tools with existing calibrated and consolidated ESA data products. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Science case\n",
    "\n",
    "Observation of the bipolar nebula IRAS 19312+1950 from the OT2_mcordine_2 program that exhibits both features of a massive oxigen-rich AGB star, and a young stellar object ([Cordiner et al. 2016](https://ui.adsabs.harvard.edu/#abs/2016ApJ...828...51C)). \n",
    "\n",
    "**Note:** These are the only chopNod SED oversampled raster observations that PACS performed during its mission, displaying a plethora of spectral features, mostly CO, H$_2$O, OH and O lines \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook Structure\n",
    "- [Data access](#data_access)\n",
    "- [Data manipulation and launching of visualization tool](#data_manipulation)\n",
    "- [Data analysis](#data_analysis):\n",
    "    1. Create 'sci' spectral cube, check dimensions  \n",
    "    2. Create a Boolean Mask from the 'sci' finite data\n",
    "    3. Extract the object 1D spectrum of the central spaxel and cast it in specutils.spectra.Spectrum1D\n",
    "    4. 1D Model fitting:\n",
    "       1. Examine with Spectrum Viewer User Interface, select spectral range and test the line+continuum model fit\n",
    "       2. Automated cluster line + local continuum fit class\n",
    "    5. Extraction of cube spectral slab\n",
    "    6. Cube fitting (pixel-by-pixel) using the backend of the app model fitting plugin\n",
    "    7. Continuum model evaluation and subtraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import os\n",
    "import requests\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v20200428.1) - Connection:\n",
      "\tHost: sky.esa.int\n",
      "\tUse HTTPS: True\n",
      "\tPort: 443\n",
      "\tSSL Port: 443\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'jdaviz'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-9ff54af1e382>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mastroquery\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mesasky\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mESASky\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mastropy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwcs\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mWCS\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mjdaviz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapp\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mApplication\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mjdaviz\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpecViz\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mspcv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mspectral_cube\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpectralCube\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mLazyMask\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'jdaviz'"
     ]
    }
   ],
   "source": [
    "from astropy.io import fits\n",
    "from astropy import units as u\n",
    "from astropy.nddata import StdDevUncertainty\n",
    "import astropy.modeling.models as models\n",
    "from astropy.table import Table\n",
    "from astroquery.esasky import ESASky\n",
    "from astropy.wcs import WCS\n",
    "from jdaviz.app import Application\n",
    "from jdaviz import SpecViz as spcv\n",
    "from spectral_cube import SpectralCube, LazyMask\n",
    "from specutils.fitting import fit_lines\n",
    "from specutils.spectra import Spectrum1D, SpectralRegion\n",
    "from specutils.manipulation import extract_region\n",
    "\n",
    "from utils import LineFitterMult, fit_model_to_spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"data_access\"></a>\n",
    "## Data Identification and Access \n",
    "We make use of [`astroquery.esasky`](https://astroquery.readthedocs.io/en/latest/esasky/esasky.html), the python interface for querying the [ESASky](https://www.cosmos.esa.int/web/esdc/esasky-help) web service. This module allows querying an object as well as querying a region around the target. Its latest development supports the query and download of spectral data.\n",
    "Alternatively, we may retrieve the cube using the `TapPlus` in [astroquery.utils.tap](https://astroquery.readthedocs.io/en/latest/utils/tap.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['XMM-NEWTON', 'Chandra', 'IUE', 'HST-UV', 'HST-OPTICAL', 'HST-IR', 'ISO-IR', 'Herschel', 'LAMOST']\n"
     ]
    }
   ],
   "source": [
    "# Available missions with spectra in ESASky\n",
    "spectra_list = ESASky.list_spectra()\n",
    "print(spectra_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: W50: None:43:0: W50: Invalid unit string 'seconds' [astropy.io.votable.tree]\n",
      "WARNING:astropy:W50: None:43:0: W50: Invalid unit string 'seconds'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Table length=12>\n",
      "        name         dtype    unit                         description                       \n",
      "------------------- ------- ------- ---------------------------------------------------------\n",
      "       postcard_url  object                          The URL to download the postcard preview\n",
      "        product_url  object                                   The URL to download the product\n",
      "     observation_id  object                          The identifier of a Herschel observation\n",
      "             ra_deg float64     deg                        Actual right ascension of pointing\n",
      "            dec_deg float64     deg                            Actual declination of pointing\n",
      "        target_name  object                                            The name of the target\n",
      "         instrument  object                                           The Herschel instrument\n",
      "observing_mode_name  object                               Name of the Herschel observing mode\n",
      "               band  object         Frequency or wavelength band in which the data were taken\n",
      "         start_time  object                        The start date and time of the observation\n",
      "           duration float64 seconds                    Duration of the observation in seconds\n",
      "              stc_s  object                                                                  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Search Herschel mission spectroscopic observations for our target\n",
    "target_name = 'IRAS 19312+1950'\n",
    "radius = '1arcmin'\n",
    "result = ESASky.query_region_spectra(position=target_name, radius=radius, missions=['HERSCHEL'])\n",
    "\n",
    "# Print table data model\n",
    "print(result['HERSCHEL'].info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observation_id   target_name   instrument observing_mode_name         band        duration\n",
      "                                                                                  seconds \n",
      "-------------- --------------- ---------- ------------------- ------------------- --------\n",
      "    1342245380 IRAS 19312+1950       HIFI    HifiPointModeDBS   543.78-559.90 GHz  758.028\n",
      "    1342245378 IRAS 19312+1950       HIFI    HifiPointModeDBS 1094.85-1110.97 GHz 2028.414\n",
      "    1342255789 IRAS 19312+1950       HIFI    HifiPointModeDBS   606.39-622.52 GHz  739.043\n",
      "    1342245227 IRAS 19312+1950       PACS        PacsLineSpec 53.74-110.25 micron   1869.0\n",
      "    1342245230 IRAS 19312+1950       PACS       PacsRangeSpec 50.65-146.52 micron   4616.0\n",
      "    1342252110 IRAS 19312+1950       HIFI    HifiPointModeDBS   645.33-661.45 GHz 2448.249\n",
      "    1342252111 IRAS 19312+1950       HIFI    HifiPointModeDBS   677.29-693.42 GHz 1548.428\n",
      "    1342255788 IRAS 19312+1950       HIFI    HifiPointModeDBS   574.26-590.38 GHz  894.281\n",
      "    1342245228 IRAS 19312+1950       PACS        PacsLineSpec 58.68-186.99 micron   3011.0\n",
      "    1342245377 IRAS 19312+1950       HIFI    HifiPointModeDBS 1099.90-1116.02 GHz 3285.736\n",
      "    1342245229 IRAS 19312+1950       PACS       PacsRangeSpec 69.81-210.30 micron   9134.0\n",
      "    1342245231 IRAS 19312+1950       PACS        PacsLineSpec 53.40-186.51 micron   3105.0\n"
     ]
    }
   ],
   "source": [
    "# This is the list of observations\n",
    "result['HERSCHEL']['observation_id', 'target_name', 'instrument', 'observing_mode_name', 'band', 'duration'].pprint_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observation_id   target_name   instrument observing_mode_name         band        duration\n",
      "                                                                                  seconds \n",
      "-------------- --------------- ---------- ------------------- ------------------- --------\n",
      "    1342245229 IRAS 19312+1950       PACS       PacsRangeSpec 69.81-210.30 micron   9134.0\n"
     ]
    }
   ],
   "source": [
    "obs_by_sel = result['HERSCHEL']\n",
    "mask = obs_by_sel['observation_id'] == '1342245229'\n",
    "obs_by_sel = obs_by_sel[mask]\n",
    "obs_by_sel = Table(obs_by_sel, masked=True, copy=False)\n",
    "obs_by_sel['observation_id', 'target_name', 'instrument', 'observing_mode_name', 'band', 'duration'].pprint_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postcard_url = obs_by_sel['postcard_url'][0]\n",
    "Image(requests.get(postcard_url).content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download spectral data from Herschel Archive \n",
    "\n",
    "We download our _Herschel_ _PACS_ Standalone Browse Products, which are a bundle of recommended observations tailored for the specific observing mode in which they were obtained. Our `astroquery.esasky` function sorts them out by band, but we also organize them by product. A detailed explanation about PACS Standalone Browse Products is provided [here](http://herschel.esac.esa.int/hcss-doc-14.0/print/pacs-ppe/pacs-ppe.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:astropy:Starting download of HERSCHEL data. (1 files)\n",
      "INFO:astropy:Downloading Observation ID: 1342245229 from http://archives.esac.esa.int/hsa/whsa-tap-server/data?RETRIEVAL_TYPE=STANDALONE&observation_oid=8620309&DATA_RETRIEVAL_ORIGIN=UI\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Starting download of HERSCHEL data. (1 files) [astroquery.esasky.core]\n",
      "INFO: Downloading Observation ID: 1342245229 from http://archives.esac.esa.int/hsa/whsa-tap-server/data?RETRIEVAL_TYPE=STANDALONE&observation_oid=8620309&DATA_RETRIEVAL_ORIGIN=UI [astroquery.esasky.core]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:astropy:[Done]\n",
      "INFO:astropy:Downloading of HERSCHEL data complete.\n",
      "INFO:astropy:Spectra available at /Users/epuga/ESDC/pyesasky_demo/myDemo/Spectra.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: [Done] [astroquery.esasky.core]\n",
      "INFO: Downloading of HERSCHEL data complete. [astroquery.esasky.core]\n",
      "INFO: Spectra available at /Users/epuga/ESDC/pyesasky_demo/myDemo/Spectra. [astroquery.esasky.core]\n",
      "OBSID: 1342245229\n",
      "HPS3DEQIBS:\n",
      "Filename: Spectra/HERSCHEL/anonymous1604664129/hpacs1342245229_20hps3deqibs_00_1469612618651.fits.gz\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     337   ()      \n",
      "  1  image         1 ImageHDU        62   (25, 23, 8325)   float64   \n",
      "  2  error         1 ImageHDU        62   (25, 23, 8325)   float64   \n",
      "  3  wcs-tab       1 BinTableHDU     37   1R x 2C   [8325D, 8325J]   \n",
      "  4  flag          1 ImageHDU        41   (25, 23, 8325)   int16   \n",
      "  5  History       1 ImageHDU        23   ()      \n",
      "  6  HistoryScript    1 BinTableHDU     39   68R x 1C   [185A]   \n",
      "  7  HistoryTasks    1 BinTableHDU     46   37R x 4C   [1K, 31A, 1K, 9A]   \n",
      "  8  HistoryParameters    1 BinTableHDU     74   360R x 10C   [1K, 27A, 13A, 20A, 1L, 1K, 1L, 58A, 11A, 42A]   \n",
      "HPSTBRBS:\n",
      "Filename: Spectra/HERSCHEL/anonymous1604664129/hpacs1342245229_20hpstbrbs_00_1469612627214.fits.gz\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     311   ()      \n",
      "  1  Spectra       1 BinTableHDU     78   266000R x 11C   [1J, 1J, 3A, 1J, 1J, 1J, 1D, 1D, 1D, 1D, 1D]   \n",
      "  2  History       1 ImageHDU        23   ()      \n",
      "  3  HistoryScript    1 BinTableHDU     39   63R x 1C   [185A]   \n",
      "  4  HistoryTasks    1 BinTableHDU     46   33R x 4C   [1K, 31A, 1K, 9A]   \n",
      "  5  HistoryParameters    1 BinTableHDU     74   314R x 10C   [1K, 23A, 13A, 20A, 1L, 1K, 1L, 58A, 11A, 42A]   \n",
      "HPSTBRRS:\n",
      "Filename: Spectra/HERSCHEL/anonymous1604664129/hpacs1342245229_20hpstbrrs_00_1469612628686.fits.gz\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     314   ()      \n",
      "  1  Spectra       1 BinTableHDU     78   163000R x 11C   [1J, 1J, 2A, 1J, 1J, 1J, 1D, 1D, 1D, 1D, 1D]   \n",
      "  2  History       1 ImageHDU        23   ()      \n",
      "  3  HistoryScript    1 BinTableHDU     39   63R x 1C   [185A]   \n",
      "  4  HistoryTasks    1 BinTableHDU     46   33R x 4C   [1K, 31A, 1K, 9A]   \n",
      "  5  HistoryParameters    1 BinTableHDU     74   314R x 10C   [1K, 23A, 13A, 20A, 1L, 1K, 1L, 58A, 11A, 42A]   \n",
      "HPS3DEQPRS:\n",
      "Filename: Spectra/HERSCHEL/anonymous1604664129/hpacs1342245229_20hps3deqprs_00_1469612623359.fits.gz\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     338   ()      \n",
      "  1  image         1 ImageHDU        62   (25, 24, 4915)   float64   \n",
      "  2  error         1 ImageHDU        62   (25, 24, 4915)   float64   \n",
      "  3  coverage      1 ImageHDU        62   (25, 24, 4915)   float64   \n",
      "  4  wcs-tab       1 BinTableHDU     37   1R x 2C   [4915D, 4915J]   \n",
      "  5  flag          1 ImageHDU        38   (25, 24, 4915)   int16   \n",
      "  6  History       1 ImageHDU        23   ()      \n",
      "  7  HistoryScript    1 BinTableHDU     39   67R x 1C   [185A]   \n",
      "  8  HistoryTasks    1 BinTableHDU     46   36R x 4C   [1K, 31A, 1K, 9A]   \n",
      "  9  HistoryParameters    1 BinTableHDU     74   357R x 10C   [1K, 27A, 13A, 20A, 1L, 1K, 1L, 58A, 11A, 42A]   \n"
     ]
    }
   ],
   "source": [
    "spectra = ESASky.get_spectra_from_table(query_table_list=[('HERSCHEL', obs_by_sel)])\n",
    "\n",
    "sabps = {}\n",
    "for idx, obs in enumerate(spectra['HERSCHEL']):\n",
    "    if (obs_by_sel['instrument'][idx] == 'PACS'):\n",
    "        obsid = obs_by_sel['observation_id'][idx]\n",
    "        print('OBSID: {}'.format(obsid))\n",
    "        band = obs.keys()\n",
    "        first_level = []\n",
    "        for key, lst in obs.items():\n",
    "            second_level = {}\n",
    "            for hduList in lst:\n",
    "                print('{}:'.format(hduList[0].header['TYPE']))\n",
    "                hduList.info()\n",
    "                second_level[hduList[0].header['TYPE']] = hduList\n",
    "            first_level.append(second_level)\n",
    "        sabps[obsid] = dict(zip(band, first_level))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Product selection\n",
    "\n",
    "PACS has several Level 2 cubes for chopNod observations, depending on the spatial sampling in the raster maps: projected, drizzled, and interpolated. For this Nyquist sampled rastered map observation of a semi-extended source, we use the projected blue cube **HPS3DEQP[R|B]** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pacs_product = 'HPS3DEQPRS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_hduList = sabps['1342245229']['red'][pacs_product]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WCS modification to comply with CubeViz cube standards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modification of CTYPE3, to be compatible with standard jdaviz .fits parser\n",
    "extnames = {'image': 'sci', 'error': 'err', 'flag': 'dq'} \n",
    "for ext in ['image', 'error', 'flag']:\n",
    "    obs_hduList[ext].header['CTYPE3'] = 'WAVE'\n",
    "    obs_hduList[ext].header['EXTNAME'] = extnames[ext]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_hduList.writeto(os.path.join(os.getcwd(), '{}_spectralCube.fits'.format(pacs_product)), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"data_manipulation\"></a>\n",
    "## JDAVIZ tool - CubeViz embedded   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed89dc956f064cccad63d3c79dc8d5b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Application(components={'g-viewer-tab': '<template>\\n  <component :is=\"stack.container\">\\n    <g-viewer-tab\\n …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app = Application(configuration='cubeviz')\n",
    "app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: No spectral axis found; header may be non-compliant. [spectral_cube.cube_utils]\n",
      "WARNING:astropy:No spectral axis found; header may be non-compliant.\n",
      "WARNING:root:'NoneType' object has no attribute 'to_header'\n",
      "WARNING:root:'NoneType' object has no attribute 'to_header'\n",
      "WARNING:root:'NoneType' object has no attribute 'to_header'\n",
      "WARNING:root:'NoneType' object has no attribute 'to_header'\n",
      "WARNING:root:'NoneType' object has no attribute 'to_header'\n",
      "WARNING:root:'NoneType' object has no attribute 'to_header'\n"
     ]
    }
   ],
   "source": [
    "#test 2 - PACS parsed as hdu object\n",
    "#pacs_path = os.path.join(os.getcwd(), '{}_spectralCube.fits'.format(pacs_product))\n",
    "pacs_path = '/Users/epuga/ESDC/pyesasky_demo/myDemo/HPS3DEQPRS_spectralCube.fits'\n",
    "app.load_data(pacs_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"data_analysis\"></a>\n",
    "## Data Analysis Workflow\n",
    "1. Create 'sci' spectral cube, check dimensions  \n",
    "2. Create a Boolean Mask from the 'sci' finite data\n",
    "3. Extract the object 1D spectrum of the central spaxel and cast it in specutils.spectra.Spectrum1D\n",
    "4. 1D Model fitting:\n",
    "   1. Examine with Spectrum Viewer User Interface, select spectral range and test the line+continuum model fit\n",
    "   2. Automated cluster line + local continuum fit class\n",
    "5. Extraction of cube spectral slab\n",
    "6. Cube fitting (pixel-by-pixel) using the backend of the app model fitting plugin\n",
    "7. Continuum model evaluation and subtraction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the FITS file for reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pacs_projected = fits.open(pacs_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WCS Keywords\n",
      "\n",
      "Number of WCS axes: 3\n",
      "CTYPE : 'RA---TAN'  'DEC--TAN'  'WAVE'  \n",
      "CRVAL : 293.3620179989573  19.938972915404335  0.00013954963947469156  \n",
      "CRPIX : 1.0  1.0  1.0  \n",
      "PC1_1 PC1_2 PC1_3  : 1.0  0.0  0.0  \n",
      "PC2_1 PC2_2 PC2_3  : 0.0  1.0  0.0  \n",
      "PC3_1 PC3_2 PC3_3  : 0.0  0.0  1.0  \n",
      "CDELT : -0.0008333333333333  0.0008333333333333334  1.0272446775701382e-08  \n",
      "NAXIS : 25  24  4915\n",
      "SpectralCube with shape=(4915, 24, 25) and unit=Jy:\n",
      " n_x:     25  type_x: RA---TAN  unit_x: deg    range:   293.340740 deg:  293.362018 deg\n",
      " n_y:     24  type_y: DEC--TAN  unit_y: deg    range:    19.938972 deg:   19.958140 deg\n",
      " n_s:   4915  type_s: WAVE      unit_s: um     range:      139.550 um:     190.028 um\n",
      "Note: cube shape convention: (n_s, n_y, n_x)\n",
      "The cube has flux units of Jy\n",
      "The cube has spectral_axis units of um\n"
     ]
    }
   ],
   "source": [
    "#1. Create sci spectral cube with units, check dimensions\n",
    "funit = u.Jy\n",
    "wunit = u.um\n",
    "flux = pacs_projected['sci'].data * funit\n",
    "err = pacs_projected['err'].data * funit\n",
    "\n",
    "wcs = WCS(pacs_projected['sci'])\n",
    "sc = SpectralCube(data=flux, wcs=wcs)\n",
    "mask = LazyMask(np.isfinite, cube=sc)\n",
    "sc = sc.with_mask(mask)\n",
    "sc_um = sc.with_spectral_unit(wunit)\n",
    "\n",
    "print(wcs)\n",
    "print(sc_um)\n",
    "print('Note: {}'.format('cube shape convention: (n_s, n_y, n_x)'))\n",
    "print('The cube has flux units of {}'.format(sc_um.unit))\n",
    "print('The cube has spectral_axis units of {}'.format(sc_um.spectral_axis.unit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Unit(\"um\"), Unit(\"Jy\"))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3. Extract and cast object spectrum1D of central spaxel\n",
    "_, ny, nx = sc_um.shape\n",
    "flux_cnt = sc_um[:, ny//2, nx//2] \n",
    "err_cnt = err[:, ny//2, nx//2] \n",
    "wave = sc_um.spectral_axis\n",
    "wave.unit, flux_cnt.unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "spectrum_cnt = Spectrum1D(spectral_axis=wave, flux=flux_cnt, uncertainty=StdDevUncertainty(err_cnt))\n",
    "#Spectrum viewer loading\n",
    "app.add_data(spectrum_cnt, data_label='Central pixel ({},{}) spectrum'.format(nx, ny))\n",
    "app.add_data_to_viewer('spectrum-viewer', 'Central pixel ({},{}) spectrum'.format(nx, ny), clear_other_data=True)\n",
    "\n",
    "#Because this is Glue, you can programatically configure viewers\n",
    "#Modify settings for the (only) layer shown\n",
    "v = app.get_viewer('spectrum-viewer')\n",
    "v.state.layers[0].color = 'blue'\n",
    "v.state.layers[0].linewidth = 2\n",
    "\n",
    "#Retrieving data product to save as .fits file\n",
    "spec_data = app.get_data_from_viewer('spectrum-viewer')\n",
    "#spec_data\n",
    "spec = spec_data['Central pixel ({},{}) spectrum'.format(nx, ny)]\n",
    "spec.write('{}_cntr_spaxel.fits'.format(os.path.splitext(os.path.basename(pacs_path))[0]), format='tabular-fits', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify viewer-level options: zoom\n",
    "v.state.x_min = 145.\n",
    "v.state.x_max = 155.\n",
    "v.state.y_min = 6.\n",
    "v.state.y_max = 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spectral Region, 1 sub-regions:\n",
       "  (152.22508241417373 um, 154.05421713487473 um) "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.A Examine with Spectrum Viewer and test the continuum fitting parameters\n",
    "wlines = {'CO14-13': [185.999], 'CO15-14': [173.631]}\n",
    "\n",
    "#Select spectral region in the UI creating a Subset 1\n",
    "subset = app.get_subsets_from_viewer('spectrum-viewer')['Subset 1']\n",
    "unit = subset.meta.get('spectral_axis_unit', u.Unit('micron'))\n",
    "spec_reg_ui = SpectralRegion.from_center(subset.center.x * unit, subset.width * unit)\n",
    "spec_reg_ui\n",
    "\n",
    "#A spectral region extraction may be done programatically with a combination of \n",
    "#specutils.spectra.SpectralRegion and specutils.manipulation.extract_region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the UI application model fitting plugin in the Spectrum Viewer and generate a compound Model of a line+continuum region.<br>\n",
    "PACS continuum has an in-band uncertainty of XX, and that is the reason why it is recommended a local fit of lines+continuum.<br>\n",
    "If you want to fit only the continuum, you may either:\n",
    "- mask the line and fit only the continuum\n",
    "- include the line and increase the relative contribution of the continuum fitting spectral range. This is not always possible with numerous lines "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since efficiently masking lines involves as much work as fitting them, a simple approach is to fit simultaneously line(s) and continuum. <br> \n",
    "The class `LineFitterMult` uses the PACS spectral resolution calibration file to estimate the width of the spectral region to fit and uses a multiple line model using 1D Gaussians and a Polynomial of order 1 to represent the local continuum.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centre: [186.02895352], Peak: [3.709409], FWHM: [0.11296768]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa2180e6860>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4.B Use the local class 'LineFitterMult'\n",
    "wline = wlines['CO14-13']\n",
    "band = 'R1'\n",
    "\n",
    "line = LineFitterMult(spectrum_cnt, wline, band, polyOrder=1, widthFactor=10, verbose=0)\n",
    "\n",
    "print('Centre: {}, Peak: {}, FWHM: {}'.format(line.get_line_centre(), line.get_line_peak(), line.get_line_fwhm()))\n",
    "plt.plot(spectrum_cnt.spectral_axis, spectrum_cnt.flux, label='spectrum', color='k')\n",
    "plt.plot(spectrum_cnt.spectral_axis, line.get_line_spec().flux, label='continuum subtracted', color='r')  # with units\n",
    "plt.plot(spectrum_cnt.spectral_axis, line.continuumModel(spectrum_cnt.spectral_axis.value), label='continuum model', color='g')\n",
    "plt.xlim((line.get_wmin(), line.get_wmax()))\n",
    "plt.xlabel('Spectral Axis ({})'.format(spectrum_cnt.spectral_axis.unit)) \n",
    "plt.ylabel('Flux Axis({})'.format(spectrum_cnt.flux.unit)) \n",
    "plt.grid(True) \n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpectralCube with shape=(234, 24, 25) and unit=Jy:\n",
      " n_x:     25  type_x: RA---TAN  unit_x: deg    range:   293.340740 deg:  293.362018 deg\n",
      " n_y:     24  type_y: DEC--TAN  unit_y: deg    range:    19.938972 deg:   19.958140 deg\n",
      " n_s:    234  type_s: WAVE      unit_s: um     range:      184.800 um:     187.193 um\n"
     ]
    }
   ],
   "source": [
    "#5. Cube extraction for cube fitting efficiency\n",
    "# Extracting a cube spectral slab between two spectral coordinates\n",
    "sc_slab = sc_um.spectral_slab(line.get_wmin() * u.um, line.get_wmax() * u.um)  \n",
    "print(sc_slab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<QuantityModel CompoundModel(c0_0=24.56368156, c1_0=-0.11077251, amplitude_1=3.709409, mean_1=186.02895352, stddev_1=0.04797296), input_units=um, return_units=Jy>\n"
     ]
    }
   ],
   "source": [
    "#6. Pixel by pixel cube fitting using the initialized model (only valid for the spectral region)\n",
    "# This cell takes the compoundModel fitted to the central spaxel spectrum\n",
    "model_init = line.get_line_model()            \n",
    "print(model_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decomposing the compoundModel prescription into: 1) model components in a list \n",
    "# and 2) the model arithmetic expression. The model prescription changes to do cube \n",
    "# fitting using backend     \n",
    "component_list = model_init._leaflist\n",
    "expression = model_init._format_expression()\n",
    "\n",
    "names = model_init.submodel_names\n",
    "n_submodels = model_init.n_submodels\n",
    "for i in range(n_submodels):\n",
    "    expression = expression.replace('[{}]'.format(i), names[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n",
      "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n",
      "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n",
      "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n",
      "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n",
      "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n",
      "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n",
      "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n",
      "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n",
      "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n",
      "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n",
      "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n",
      "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n",
      "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter callback\n",
      "Enter callback\n",
      "Enter callback\n",
      "Enter callback\n",
      "Enter callback\n",
      "Enter callback\n",
      "Enter callback\n"
     ]
    }
   ],
   "source": [
    "# Casting cube into a Spectrum1D to use the backend fitting generalized for a cube \n",
    "nan_mask = ~sc_slab.get_mask_array()\n",
    "nan_mask = nan_mask.transpose(1, 2, 0)\n",
    "flux_data = sc_slab.unmasked_data[:].value.transpose(1, 2, 0)\n",
    "\n",
    "cube_spec = Spectrum1D(spectral_axis=sc_slab.spectral_axis, flux=flux_data * u.um, \n",
    "                       mask=nan_mask)\n",
    "\n",
    "fitted_pars, fitted_spectrum = fit_model_to_spectrum(cube_spec, component_list,\n",
    "                                                     expression, run_fitter=True)\n",
    "# I/O: Outputs are a dictionary of 2D parameters and a 3D Spectrum1D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- Returns\n",
    "-------\n",
    ":list: a list that stores 2D arrays. Each array contains one\n",
    "    parameter from `astropy.modeling.CompoundModel` instances\n",
    "    fitted to every spaxel in the input cube.\n",
    ":class:`specutils.spectrum.Spectrum1D`\n",
    "    The spectrum that stores the fitted model values in its 'flux'\n",
    "    attribute.\n",
    "-->    \n",
    "cube_from_model taking advantage that the model parameters can be of any size, provided they are broadcastable (like numpy arrays)\n",
    "It would be good to display the fitted model values in one of the cube uncert-viewer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: PossiblySlowWarning: This function (<function BaseSpectralCube._cube_on_cube_operation at 0x7fa2311d4620>) requires loading the entire cube into memory and may therefore be slow. [spectral_cube.utils]\n",
      "WARNING:astropy:PossiblySlowWarning: This function (<function BaseSpectralCube._cube_on_cube_operation at 0x7fa2311d4620>) requires loading the entire cube into memory and may therefore be slow.\n",
      "WARNING: PossiblySlowWarning: This function (<function BaseSpectralCube.to at 0x7fa2311d77b8>) requires loading the entire cube into memory and may therefore be slow. [spectral_cube.utils]\n",
      "WARNING:astropy:PossiblySlowWarning: This function (<function BaseSpectralCube.to at 0x7fa2311d77b8>) requires loading the entire cube into memory and may therefore be slow.\n",
      "WARNING: PossiblySlowWarning: This function (<function BaseSpectralCube._cube_on_cube_operation at 0x7fa2311d4620>) requires loading the entire cube into memory and may therefore be slow. [spectral_cube.utils]\n",
      "WARNING:astropy:PossiblySlowWarning: This function (<function BaseSpectralCube._cube_on_cube_operation at 0x7fa2311d4620>) requires loading the entire cube into memory and may therefore be slow.\n",
      "WARNING: PossiblySlowWarning: This function (<function BaseSpectralCube.to at 0x7fa2311d77b8>) requires loading the entire cube into memory and may therefore be slow. [spectral_cube.utils]\n",
      "WARNING:astropy:PossiblySlowWarning: This function (<function BaseSpectralCube.to at 0x7fa2311d77b8>) requires loading the entire cube into memory and may therefore be slow.\n",
      "WARNING: PossiblySlowWarning: This function (<function BaseSpectralCube._cube_on_cube_operation at 0x7fa2311d4620>) requires loading the entire cube into memory and may therefore be slow. [spectral_cube.utils]\n",
      "WARNING:astropy:PossiblySlowWarning: This function (<function BaseSpectralCube._cube_on_cube_operation at 0x7fa2311d4620>) requires loading the entire cube into memory and may therefore be slow.\n",
      "WARNING: PossiblySlowWarning: This function (<function BaseSpectralCube.to at 0x7fa2311d77b8>) requires loading the entire cube into memory and may therefore be slow. [spectral_cube.utils]\n",
      "WARNING:astropy:PossiblySlowWarning: This function (<function BaseSpectralCube.to at 0x7fa2311d77b8>) requires loading the entire cube into memory and may therefore be slow.\n"
     ]
    }
   ],
   "source": [
    "# 7. Continuum model evaluation and subtraction\n",
    "p_names = list(fitted_pars.keys())\n",
    "nx, ny = fitted_pars[p_names[0]].shape\n",
    "nl = sc_slab.spectral_axis.size\n",
    "continuum_model = models.Polynomial1D(degree=1, c0=np.repeat(fitted_pars[p_names[0]].value[:,:,np.newaxis], nl, axis=2),\n",
    "                                      c1=np.repeat(fitted_pars[p_names[1]].value[:,:,np.newaxis], nl, axis=2), name='cont_cube')\n",
    "continuum_cube = continuum_model(sc_slab.spectral_axis.value) * u.Jy\n",
    "\n",
    "# Back to the SpectralCube domain\n",
    "sc_cont = SpectralCube(data=continuum_cube.transpose(2,0,1), wcs=sc_slab.wcs)\n",
    "sc_fit = SpectralCube(data=fitted_spectrum.flux.value.transpose(2,0,1) * u.Jy, wcs=sc_slab.wcs)\n",
    "sc_line = sc_slab - sc_cont\n",
    "\n",
    "# Cube arithmetic to subtract continuum to original cube\n",
    "sc_line = sc_slab - sc_cont\n",
    "sc_line_model = sc_fit - sc_cont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Visualize result in UI\n",
    "app.add_data(sc_line, data_label='Line (cube - cont)')\n",
    "app.add_data_to_viewer('mask-viewer', 'Line (cube - cont)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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