\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": [
" \n",
"\n",
"## Query observations covering a frequency range\n",
"\n",
" \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": [
" \n",
"\n",
"## Plot all frequency ranges covered by one source\n",
"\n",
" \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": {
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\n",
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