{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Observing Run Preparation Module\n",
"\n",
"**Lecturer:** Robert Quimby
\n",
"**Jupyter Notebook Author:** Shubham Srivastav & Cameron Hummels\n",
"\n",
"This is a Jupyter notebook lesson taken from the GROWTH Winter School 2018. For other lessons and their accompanying lectures, please see: http://growth.caltech.edu/growth-astro-school-2018-resources.html\n",
"\n",
"## Objective\n",
"Demonstrate how to plan observations prior to an observing run.\n",
"\n",
"## Key steps\n",
"- Select targets\n",
"- Get visibility and airmass plots\n",
"- Get moon separation angles\n",
"- Calculate exposure times for targets\n",
"\n",
"## Required dependencies\n",
"\n",
"See GROWTH school webpage for detailed instructions on how to install these modules and packages. Nominally, you should be able to install the python modules with `pip install `. The external astromatic packages are easiest installed using package managers (e.g., `rpm`, `apt-get`).\n",
"\n",
"### Python modules\n",
"* python 3\n",
"* astropy\n",
"* numpy\n",
"* matplotlib\n",
"* astroplan\n",
"* pytz\n",
"\n",
"### External packages\n",
"None"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from astropy import units as u\n",
"from astropy.time import Time\n",
"from astropy.coordinates import SkyCoord\n",
"from astropy.coordinates import EarthLocation\n",
"import pytz\n",
"%matplotlib inline\n",
"from astroplan import Observer, FixedTarget\n",
"from astropy.utils.iers import conf\n",
"conf.auto_max_age = None\n",
"from astroplan import download_IERS_A \n",
"from astropy.coordinates import get_sun, get_moon, get_body\n",
"from astroplan import moon_illumination"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Date and Time\n",
"- Dates and times are in UTC\n",
"- Default time is 00:00:00 UTC (verify this)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-12-03 00:00:00.000\n"
]
}
],
"source": [
"date = Time(\"2018-12-03\", format='iso')\n",
"print(date)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What is the current UTC?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019-04-02 05:53:06.289935\n",
"2458575.7452116893\n",
"58575.24521168906\n",
"2019.24998688134\n"
]
}
],
"source": [
"now = Time.now()\n",
"print(now)\n",
"print(now.jd)\n",
"print(now.mjd)\n",
"print(now.decimalyear)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"What time will it be (in UTC) after 1 hour 45 minutes from now? Complete the line below to print it out."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In 1 hour and 45 minutes, the time will be 2019-04-02 07:38:06.289935 UTC\n"
]
}
],
"source": [
"print(\"In 1 hour and 45 minutes, the time will be {0} UTC\".format(now + 1*u.h + 45*u.min))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using UT1\n",
"- To keep accurate time, the changes in earth's rotation period have to be taken into account.\n",
"- AstroPy does this using a convention called UT1, that is tied to the rotation of earth with respect to the position of distant quasars. IERS - International Earth Rotation and Reference Systems Service keeps continuous tabs on the orientation of the earth and updates the data in the IERS bulletin.\n",
"Update the bulletin:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"download_IERS_A()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check to see what observatories are available in the database."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available observatories: \n",
", , , ALMA, Anglo-Australian Observatory, Apache Point, Apache Point Observatory, Atacama Large Millimeter Array, BAO, Beijing XingLong Observatory, Black Moshannon Observatory, CHARA, Canada-France-Hawaii Telescope, Catalina Observatory, Cerro Pachon, Cerro Paranal, Cerro Tololo, Cerro Tololo Interamerican Observatory, DCT, Discovery Channel Telescope, Dominion Astrophysical Observatory, GBT, Gemini South, Green Bank Telescope, Hale Telescope, Haleakala Observatories, Happy Jack, IAO, JCMT, James Clerk Maxwell Telescope, Jansky Very Large Array, Keck Observatory, Kitt Peak, Kitt Peak National Observatory, La Silla Observatory, Large Binocular Telescope, Las Campanas Observatory, Lick Observatory, Lowell Observatory, MWA, Manastash Ridge Observatory, McDonald Observatory, Medicina, Medicina Dish, Michigan-Dartmouth-MIT Observatory, Mount Graham International Observatory, Mt Graham, Mt. Ekar 182 cm. Telescope, Mt. Stromlo Observatory, Multiple Mirror Telescope, Murchison Widefield Array, NOV, National Observatory of Venezuela, Noto, Observatorio Astronomico Nacional, San Pedro Martir, Observatorio Astronomico Nacional, Tonantzintla, Palomar, Paranal Observatory, Roque de los Muchachos, SAAO, SALT, SRT, Siding Spring Observatory, Southern African Large Telescope, Subaru, Subaru Telescope, Sutherland, TUG, UKIRT, United Kingdom Infrared Telescope, Vainu Bappu Observatory, Very Large Array, W. M. Keck Observatory, Whipple, Whipple Observatory, aao, alma, apo, bmo, cfht, ctio, dao, dct, ekar, example_site, flwo, gbt, gemini_north, gemini_south, gemn, gems, greenwich, haleakala, iao, irtf, jcmt, keck, kpno, lapalma, lasilla, lbt, lco, lick, lowell, mcdonald, mdm, medicina, mmt, mro, mso, mtbigelow, mwa, mwo, noto, ohp, paranal, salt, sirene, spm, srt, sso, tona, tug, ukirt, vbo, vla\n"
]
}
],
"source": [
"print(\"Available observatories: \\n{0}\"\n",
" .format(', '.join(EarthLocation.get_site_names())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setting up observatory location"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
">"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Indian Astronomical Observatory is not listed in the database, so let's define the location\n",
"longitude = '78d57m53s'\n",
"latitude = '32d46m44s'\n",
"elevation = 4500 * u.m\n",
"location = EarthLocation.from_geodetic(longitude, latitude, elevation)\n",
"iaohanle = Observer(location = location, timezone = 'Asia/Kolkata',\n",
" name = \"IAO\", description = \"GROWTH-India 70cm telescope\")\n",
"iaohanle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sunset, Sunrise, Midnight"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sunset at IAO will be at 2019-04-02 13:00:32.540 UTC\n",
"Astronomical evening twilight at IAO will be at 2019-04-02 14:28:19.957 UTC\n",
"Midnight at IAO will be at 2019-04-02 18:47:32.580 UTC\n",
"Astronomical morning twilight at IAO will be at 2019-04-02 23:06:42.282 UTC\n",
"Sunrise at IAO will be at 2019-04-03 00:34:25.511 UTC\n"
]
}
],
"source": [
"#Calculating the sunset, midnight and sunrise times for our observatory \n",
"#What is astronomical twilight?\n",
"sunset_iao = iaohanle.sun_set_time(now, which='nearest')\n",
"eve_twil_iao = iaohanle.twilight_evening_astronomical(now, which='nearest')\n",
"midnight_iao = iaohanle.midnight(now, which='next')\n",
"morn_twil_iao = iaohanle.twilight_morning_astronomical(now, which='next')\n",
"sunrise_iao = iaohanle.sun_rise_time(now, which='next')\n",
"\n",
"print(\"Sunset at IAO will be at {0.iso} UTC\".format(sunset_iao))\n",
"print(\"Astronomical evening twilight at IAO will be at {0.iso} UTC\".format(eve_twil_iao))\n",
"print(\"Midnight at IAO will be at {0.iso} UTC\".format(midnight_iao))\n",
"print(\"Astronomical morning twilight at IAO will be at {0.iso} UTC\".format(morn_twil_iao))\n",
"print(\"Sunrise at IAO will be at {0.iso} UTC\".format(sunrise_iao))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise \n",
"Find the effective length of time (in hours) available for astronomical observations at IAO tonight"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You can observe for 8.6 h at IAO tonight\n"
]
}
],
"source": [
"observing_time = (morn_twil_iao - eve_twil_iao).to(u.h)\n",
"print(\"You can observe for {0:.1f} at IAO tonight\".format(observing_time))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Local Sidereal Time (LST)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LST at IAO now is 23.84 hourangle\n",
"LST at IAO at local midnight will be 12.78 hourangle\n"
]
}
],
"source": [
"#What is the LST now at IAO Hanle?\n",
"#What would the LST be at IAO at local midnight?\n",
"lst_now = iaohanle.local_sidereal_time(now)\n",
"lst_mid = iaohanle.local_sidereal_time(midnight_iao)\n",
"print(\"LST at IAO now is {0:.2f}\".format(lst_now))\n",
"print(\"LST at IAO at local midnight will be {0:.2f}\".format(lst_mid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Choosing targets for observations\n",
"Targets can be defined by name or coordinates.\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"hms_tuple(h=0.0, m=42.0, s=41.799999999999926)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coords = SkyCoord('00h42m41.8s', '+40d51m55.0s', frame='icrs') # coordinates of Andromeda Galaxy (M32) \n",
"m32 = FixedTarget(name = 'M32', coord=coords)\n",
"m32.ra.hms"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"False\n",
"True\n"
]
}
],
"source": [
"#Check to see if target is up at evening twilight.\n",
"#Also check if target is available at midnight and morning twilight.\n",
"\n",
"print(iaohanle.target_is_up(eve_twil_iao, m32))\n",
"print(iaohanle.target_is_up(midnight_iao, m32))\n",
"print(iaohanle.target_is_up(morn_twil_iao, m32))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4.53096054709229, 316.98764800592306)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Altitude and Azimuth of target\n",
"aa = iaohanle.altaz(eve_twil_iao, m32)\n",
"aa.alt.degree, aa.az.degree"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking rise times of targets"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019-04-02 22:27:58.640\n"
]
}
],
"source": [
"m32rise = iaohanle.target_rise_time(now, m32, which = 'next', horizon = 0 * u.deg)\n",
"print(m32rise.iso) #default format is JD"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Defining targets by name"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target = FixedTarget.from_name('m51') #Messier 51\n",
"target.coord"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dealing with moving targets"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_body('jupiter', now)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get moon position at midnight \n",
"get_moon(midnight_iao)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0635173895581661"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#How bright is the moon at midnight?\n",
"moon_illumination(midnight_iao)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#We can turn solar system objects into 'pseudo-fixed' targets to plan observations\n",
"saturn_midnight = FixedTarget(name = 'Saturn', coord = get_body('saturn', midnight_iao))\n",
"saturn_midnight.coord"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Airmass\n",
"- Ideally, targets should be observed when they have the least airmass. Airmass ranges from 1 (zenith) to ~38 at the horizon.\n",
"- Airmass is 2.0 at alt=30, 2.9 at alt=20 and 3.9 at alt=15 degrees\n",
"- As a general rule of thumb, try observing targets when airmass > 2\n",
"- Let us find the airmass of M33 at midnight at IAO"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Is the target up at IAO at midnight?\n",
"iaohanle.target_is_up(midnight_iao, target)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#lets check the alt and az of the target at midnight\n",
"target_altaz = iaohanle.altaz(midnight_iao, target)\n",
"target_altaz.altaz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's a good enough elevation to observe the target."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1.0433854 \\; \\mathrm{}$"
],
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Find the airmass\n",
"target_altaz.secz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can visualize what we have done so far using some plots"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from astroplan.plots import plot_sky, plot_airmass"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/chummels/miniconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: The frac parameter was deprecated in version 2.1. Use tick padding via Axes.tick_params instead.\n",
" warnings.warn(message, mplDeprecation, stacklevel=1)\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"