{
 "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> nb6. Query by frequency</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",
    "\n",
    "The relevant columns of the ALMA TAP service are:\n",
    "* *frequency*  : central frequency of the observation\n",
    "* *bandwidth*   : bandwidth of the observation/Spectral Window\n",
    "\n",
    "\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import all necessary modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:03.292871Z",
     "iopub.status.busy": "2021-11-16T10:27:03.291943Z",
     "iopub.status.idle": "2021-11-16T10:27:04.461966Z",
     "shell.execute_reply": "2021-11-16T10:27:04.461366Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import astropy\n",
    "import pyvo\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\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",
    "## Query observations covering a frequency range\n",
    "\n",
    "<hr style=\"border:2px solid darkblue\"> </hr>\n",
    "\n",
    "The ALMA archive metadata contain the central frequency of the observations in the *frequency* column and width of the spectral window in the *bandwidth* column of each observation. In this function we see which observations do overlap with the frequency range given by the user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:04.467663Z",
     "iopub.status.busy": "2021-11-16T10:27:04.467033Z",
     "iopub.status.idle": "2021-11-16T10:27:04.469530Z",
     "shell.execute_reply": "2021-11-16T10:27:04.469017Z"
    }
   },
   "outputs": [],
   "source": [
    "def query_spectral_range(service, science_category, freq_min, freq_max):\n",
    "    \"\"\"Returns all observations that have (a portion of) their spectral coverage overlapping the range freq_min to freq_max\n",
    "        \n",
    "       service               pyvo TAPService instance\n",
    "       science_category      (substring of) one of the ALMA science science categories (see +Notebook 4)\n",
    "       freq_min              minimum frequency of the requested coverage (GHz)\n",
    "       freq_max              maximum frequency of the requested coverage (GHz)\n",
    "              \n",
    "       returns               pandas table with frequency (GHz) and bandwidth (GHz)\n",
    "    \"\"\"\n",
    "\n",
    "    query = f\"\"\"   \n",
    "            SELECT member_ous_uid, target_name, frequency, bandwidth\n",
    "            FROM ivoa.obscore   \n",
    "            WHERE (frequency - 0.5 * bandwidth/1e9) < {freq_max} AND (frequency + 0.5 * bandwidth/1e9) > {freq_min} AND scientific_category like '%{science_category}%'\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",
    "## Plot all frequency ranges covered by one source\n",
    "\n",
    "<hr style=\"border:2px solid darkblue\"> </hr>\n",
    "\n",
    "This function allows the user to read out all frequency ranges covered for one source. To achieve this we need to use the *frequency_support column*. This column contains strings including the frequency limits of each spectral window within each measurent set, sensivity and polarization information. We will show how to apply this function for a source in example 6b."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:04.478273Z",
     "iopub.status.busy": "2021-11-16T10:27:04.477472Z",
     "iopub.status.idle": "2021-11-16T10:27:04.491228Z",
     "shell.execute_reply": "2021-11-16T10:27:04.490659Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_all_frequencies(output_table):\n",
    "    \"\"\"Plots the frequencies and bandwidths for all of the observations in the output table\n",
    "    \n",
    "    output_table      pandas output table with frequency (GHz) and bandwidth (Hz) column\n",
    "    \n",
    "    returns           None   \n",
    "    \"\"\"\n",
    "\n",
    "    plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot([0,len(output_table['frequency'])],[84,480], alpha = 0)\n",
    "    \n",
    "    for i in range(len(output_table['frequency'])):\n",
    "        freq_min = output_table['frequency'][i] - output_table['bandwidth'][i]/1e9/2\n",
    "        freq_max = output_table['frequency'][i] + output_table['bandwidth'][i]/1e9/2\n",
    "        left, bottom, width, height = (i, freq_min, 0.5,  freq_max - freq_min )\n",
    "        ax.add_patch(Rectangle((left,bottom), width, height))\n",
    "\n",
    "    ### Code each band\n",
    "    ax.add_patch(Rectangle((-3,84),2,116-84,   facecolor = 'blue'))\n",
    "    ax.add_patch(Rectangle((-3,125),2,163-125, facecolor = 'red'))\n",
    "    ax.add_patch(Rectangle((-3,163),2,211-163, facecolor = 'green'))\n",
    "    ax.add_patch(Rectangle((-3,211),2,275-211, facecolor = 'orange'))\n",
    "    ax.add_patch(Rectangle((-3,275),2,373-275, facecolor = 'yellow'))\n",
    "    ax.add_patch(Rectangle((-3,385),2,500-385, facecolor = 'magenta'))\n",
    "\n",
    "    plt.title('Frequency coverage for \"SPT0311-58\"',fontsize=15)\n",
    "    plt.xlabel('Observations',fontsize=15)\n",
    "    plt.ylabel('Frequency [GHz]',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------\n",
    "\n",
    "## Example 6a: Query all 'active galaxies' (science category) with frequencies between 200-400 GHz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:04.496233Z",
     "iopub.status.busy": "2021-11-16T10:27:04.495642Z",
     "iopub.status.idle": "2021-11-16T10:27:08.567618Z",
     "shell.execute_reply": "2021-11-16T10:27:08.568135Z"
    }
   },
   "outputs": [],
   "source": [
    "output = query_spectral_range(service, 'Active galaxies', 200, 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:08.576679Z",
     "iopub.status.busy": "2021-11-16T10:27:08.575905Z",
     "iopub.status.idle": "2021-11-16T10:27:12.986590Z",
     "shell.execute_reply": "2021-11-16T10:27:12.985820Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Objects')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.errorbar(np.arange(len(output['frequency'])), output['frequency'], yerr=output['bandwidth']/1e9/2, \n",
    "    elinewidth = 10,marker = '',ls = '')\n",
    "plt.ylabel('Frequency (GHz)')\n",
    "plt.xlabel('Objects')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "-------------------------------------------------------------\n",
    "\n",
    "## Example 6b: Display all frequencies covered for one source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first need to query all observations available for one specific source. In this example we use the sub-mm galaxy SPT0311-58. This galaxy is not in Sesame (Simbad, NED or VizieR) but has been observed by ALMA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:12.995503Z",
     "iopub.status.busy": "2021-11-16T10:27:12.994934Z",
     "iopub.status.idle": "2021-11-16T10:27:13.184533Z",
     "shell.execute_reply": "2021-11-16T10:27:13.185116Z"
    }
   },
   "outputs": [],
   "source": [
    "query = f\"\"\"SELECT * FROM ivoa.obscore WHERE target_name = 'SPT0311-58'\"\"\"\n",
    "result = service.search(query)\n",
    "output = result.to_table().to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use the function *read_band_freq()* to visualize the frequency coverage of each spectral window observed for this source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T10:27:13.207212Z",
     "iopub.status.busy": "2021-11-16T10:27:13.206586Z",
     "iopub.status.idle": "2021-11-16T10:27:13.582681Z",
     "shell.execute_reply": "2021-11-16T10:27:13.581488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_frequencies(output)"
   ]
  }
 ],
 "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
}