{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table width=\"100%\" style=\"padding:0; margin-left:-6px;\">\n",
    "    <tr style=\"padding=0;\" width=\"100%\">\n",
    "        <td width=\"auto\">\n",
    "           <hr style=\"border:2px solid darkblue\">\n",
    "            <h1> nb7. Query by sensitivity</h1>\n",
    "           <hr style=\"border:2px solid darkblue\">\n",
    "        </td>\n",
    "        <td width=\"60px\">\n",
    "           <img src=\"ALMAsmall.png\" align=\"right\"/> \n",
    "        </td>\n",
    "    </tr>\n",
    "</table>  \n",
    "\n",
    "In this notebook we query the observations of higher sensitivity at a given resolution.\n",
    "\n",
    "The relevant columns in the ALMA Archive datasets are \n",
    "* *sensitivity_10kms* \n",
    "* *cont_sensitivity_bandwidth*. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import all necessary modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:15.134879Z",
     "iopub.status.busy": "2021-11-16T10:27:15.134179Z",
     "iopub.status.idle": "2021-11-16T10:27:16.297538Z",
     "shell.execute_reply": "2021-11-16T10:27:16.296356Z"
    }
   },
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import numpy as np\n",
    "import pyvo\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "service = pyvo.dal.TAPService(\"https://almascience.eso.org/tap\")      # for the EU ALMA TAP service\n",
    "\n",
    "# service = pyvo.dal.TAPService(\"https://almascience.nao.ac.jp/tap\")  # for the EA ALMA TAP service\n",
    "# service = pyvo.dal.TAPService(\"https://almascience.nrao.edu/tap\")   # for the NA ALMA TAP service"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"border:2px solid darkblue\"> </hr>\n",
    "\n",
    "\n",
    "## Query spectra by sensitivity \n",
    "<hr style=\"border:2px solid darkblue\"> </hr>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function queries the (spectroscopic) observations with a given science keyword, which have ALMA observations at sensitivities smaller than the given value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:16.305065Z",
     "iopub.status.busy": "2021-11-16T10:27:16.304156Z",
     "iopub.status.idle": "2021-11-16T10:27:16.307521Z",
     "shell.execute_reply": "2021-11-16T10:27:16.306751Z"
    }
   },
   "outputs": [],
   "source": [
    "def query_line_sensitivity(service, science_keyword, sensitivity_10kms_max):\n",
    "    \"\"\"Queries the science observations with the given keyword for sensitivities smaller than the given sensitivity\n",
    "    \n",
    "       service                     pyvo TAPService instance\n",
    "       science_keyword             one of the science keywords (see Notebook 4, Example 4a or the ALMA Science Archive query interface)\n",
    "       sensitivity_10kms_max       maximum sensitivity in a 10km/s bandwidth bin in mJy/beam (see Notebook 0 for all columns and units)\n",
    "              \n",
    "       returns                     pandas table    \n",
    "    \"\"\"\n",
    "\n",
    "    query = f\"\"\"  \n",
    "            SELECT * \n",
    "            FROM ivoa.obscore  \n",
    "            WHERE science_keyword like '%{science_keyword}%'  \n",
    "            AND science_observation = 'T'  \n",
    "            AND sensitivity_10kms < {sensitivity_10kms_max}  \n",
    "            \"\"\"\n",
    "\n",
    "    return service.search(query).to_table().to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"border:2px solid darkblue\"> </hr>\n",
    "\n",
    "\n",
    "## Query  by continuum sensitivity\n",
    "<hr style=\"border:2px solid darkblue\"> </hr>\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function queries the (continuum) observations with a given science keyword, which have ALMA observations at sensitivities smaller than the given value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:16.313751Z",
     "iopub.status.busy": "2021-11-16T10:27:16.313043Z",
     "iopub.status.idle": "2021-11-16T10:27:16.316797Z",
     "shell.execute_reply": "2021-11-16T10:27:16.316075Z"
    }
   },
   "outputs": [],
   "source": [
    "def query_continuum_sensitivity(service, science_keyword, continuum_sensitivity_max):\n",
    "    \"\"\"Queries the observations of a given scientific keyword for their aggregate continuum sensitivity\n",
    "    \n",
    "       service                     pyvo TAPService instance\n",
    "       science_keyword             one of the science keywords (see Notebook 4, Example 4a or the ALMA Science Archive query interface)\n",
    "       continuum_sensitivity_max   maximum sensitivity in the aggregate bandwidth in mJy/beam (see Notebook 0 for all columns and units)\n",
    "              \n",
    "       returns                     pandas table    \n",
    "    \"\"\"\n",
    "\n",
    "    query = f\"\"\"  \n",
    "            SELECT *  \n",
    "            FROM ivoa.obscore  \n",
    "            WHERE science_keyword like '%{science_keyword}%'  \n",
    "            AND science_observation = 'T'   \n",
    "            AND cont_sensitivity_bandwidth < {continuum_sensitivity_max}   \n",
    "            \"\"\"\n",
    "\n",
    "    return service.search(query).to_table().to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------\n",
    "\n",
    "## Example 7a.  Where are the most sensitive 'starburst galaxies' (science keyword) continuum observations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:16.322771Z",
     "iopub.status.busy": "2021-11-16T10:27:16.322000Z",
     "iopub.status.idle": "2021-11-16T10:27:21.650100Z",
     "shell.execute_reply": "2021-11-16T10:27:21.650609Z"
    }
   },
   "outputs": [],
   "source": [
    "output = query_continuum_sensitivity(service, 'Starburst galaxies', 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:21.655883Z",
     "iopub.status.busy": "2021-11-16T10:27:21.655305Z",
     "iopub.status.idle": "2021-11-16T10:27:22.117340Z",
     "shell.execute_reply": "2021-11-16T10:27:22.116823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='s_ra', ylabel='s_dec'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
    "\n",
    "output.plot(kind='scatter', x='s_ra', y='s_dec', c='cont_sensitivity_bandwidth', s=15, alpha=0.3, colormap='viridis')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}