{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Observing Run Preparation Module\n",
"\n",
"**Lecturer:** Robert Quimby
\n",
"**Jupyter Notebook Author:** Shubham Srivastav, Cameron Hummels & Robert Quimby\n",
"\n",
"This is a Jupyter notebook lesson taken from the GROWTH Summer School 2019. 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": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: AstropyDeprecationWarning: astropy.extern.six will be removed in 4.0, use the six module directly if it is still needed [astropy.extern.six]\n"
]
}
],
"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": [
"2019-07-09 09:28:32.298729\n",
"2458673.8948182724\n",
"58673.394818272325\n",
"2019.5188899130749\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 2019-07-09 11:13:32.298729 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, ATST, Anglo-Australian Observatory, Apache Point, Apache Point Observatory, Atacama Large Millimeter Array, BAO, BBSO, Beijing XingLong Observatory, Black Moshannon Observatory, CHARA, Canada-France-Hawaii Telescope, Catalina Observatory, Cerro Pachon, Cerro Paranal, Cerro Tololo, Cerro Tololo Interamerican Observatory, DCT, DKIST, 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, NST, National Observatory of Venezuela, Noto, Observatorio Astronomico Nacional, San Pedro Martir, Observatorio Astronomico Nacional, Tonantzintla, Palomar, Paranal Observatory, Roque de los Muchachos, SAAO, SALT, SPO, SRT, Sac Peak, Sacramento Peak, Siding Spring Observatory, Southern African Large Telescope, Subaru, Subaru Telescope, Sunspot, Sutherland, TUG, UKIRT, United Kingdom Infrared Telescope, Vainu Bappu Observatory, Very Large Array, W. M. Keck Observatory, Whipple, Whipple Observatory, aao, alma, apo, bbso, bmo, cfht, ctio, dao, dct, dkist, 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, spo, 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": [
"# Mount Laguna Observatory is not listed in the database, so let's define the location\n",
"latitude = 32.842167 * u.deg\n",
"longitude = -116.426938 * u.deg\n",
"elevation = 1860 * u.m\n",
"location = EarthLocation.from_geodetic(longitude, latitude, elevation)\n",
"mlo = Observer(location = location, timezone = 'America/Los_Angeles',\n",
" name = \"MLO\", description = \"MLO 1.0-m telescope\")\n",
"mlo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sunset, Sunrise, Midnight"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# ##### just for testing...REMOVE! #####\n",
"now = Time('2019-08-04 21:17:59')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sunset at MLO will be at 2019-08-05 02:37:31.424 UTC\n",
"Astronomical evening twilight at MLO will be at 2019-08-05 04:14:21.609 UTC\n",
"Midnight at MLO will be at 2019-08-05 07:51:56.122 UTC\n",
"Astronomical morning twilight at MLO will be at 2019-08-05 11:29:30.653 UTC\n",
"Sunrise at MLO will be at 2019-08-05 13:06:21.359 UTC\n"
]
}
],
"source": [
"# Calculating the sunset, midnight and sunrise times for our observatory \n",
"# What is astronomical twilight?\n",
"sunset_mlo = mlo.sun_set_time(now, which='nearest')\n",
"eve_twil_mlo = mlo.twilight_evening_astronomical(now, which='nearest')\n",
"midnight_mlo = mlo.midnight(now, which='next')\n",
"morn_twil_mlo = mlo.twilight_morning_astronomical(now, which='next')\n",
"sunrise_mlo = mlo.sun_rise_time(now, which='next')\n",
"\n",
"print(\"Sunset at MLO will be at {0.iso} UTC\".format(sunset_mlo))\n",
"print(\"Astronomical evening twilight at MLO will be at {0.iso} UTC\".format(eve_twil_mlo))\n",
"print(\"Midnight at MLO will be at {0.iso} UTC\".format(midnight_mlo))\n",
"print(\"Astronomical morning twilight at MLO will be at {0.iso} UTC\".format(morn_twil_mlo))\n",
"print(\"Sunrise at MLO will be at {0.iso} UTC\".format(sunrise_mlo))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise \n",
"Find the effective length of time (in hours) available for optical astronomical observations at MLO tonight"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You can observe for 7.3 h at MLO tonight\n"
]
}
],
"source": [
"observing_time = (morn_twil_mlo - eve_twil_mlo).to(u.hour)\n",
"print(\"You can observe for {0:.1f} at MLO tonight\".format(observing_time))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Local Sidereal Time (LST)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LST at MLO now is 10.41 hourangle\n",
"LST at MLO at local midnight will be 21.01 hourangle\n"
]
}
],
"source": [
"#What is the LST now at MLO?\n",
"#What would the LST be at MLO at local midnight?\n",
"lst_now = mlo.local_sidereal_time(now)\n",
"lst_mid = mlo.local_sidereal_time(midnight_mlo)\n",
"print(\"LST at MLO now is {0:.2f}\".format(lst_now))\n",
"print(\"LST at MLO 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": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"hms_tuple(h=18.0, m=53.0, s=35.09699999999441)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# using coordinates\n",
"coords = SkyCoord('18h53m35.097s +33d01m44.8831s', frame='icrs') # coordinates of the Ring Nebula (M57) \n",
"m57 = FixedTarget(name = 'M57', coord=coords)\n",
"m57.ra.hms"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# by name\n",
"target = FixedTarget.from_name('m57') # Messier 57\n",
"target.coord"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check to see if target is \"up\" at evening twilight (assume \"up\" means more than 30 degrees above the horizon). Also check if target is available at midnight and morning twilight."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"True\n",
"False\n"
]
}
],
"source": [
"# check if the target is up\n",
"print(mlo.target_is_up(eve_twil_mlo, m57, horizon=30*u.deg))\n",
"print(mlo.target_is_up(midnight_mlo, m57, horizon=30*u.deg))\n",
"print(mlo.target_is_up(morn_twil_mlo, m57, horizon=30*u.deg))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(70.74681163016001, 83.05825913756574)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Altitude and Azimuth of target at evening twilight\n",
"aa = mlo.altaz(eve_twil_mlo, m57)\n",
"aa.alt.degree, aa.az.degree"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Determine the time at which the target rises"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019-08-04 22:07:56.216\n"
]
}
],
"source": [
"m57rise = mlo.target_rise_time(now, m57, which = 'next', horizon=0*u.deg)\n",
"print(m57rise.iso) #default format is JD"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dealing with moving targets"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_body('jupiter', now)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get moon position at midnight \n",
"get_moon(midnight_mlo)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.24118319358448292"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How bright is the moon at midnight?\n",
"moon_illumination(midnight_mlo)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 20,
"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_mlo))\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 M57 at midnight at MLO"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Is the target up at MLO at midnight?\n",
"mlo.target_is_up(midnight_mlo, target)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#lets check the alt and az of the target at midnight\n",
"target_altaz = mlo.altaz(midnight_mlo, target)\n",
"target_altaz.altaz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That is high over head; ideal for observing."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1.1162226 \\; \\mathrm{}$"
],
"text/plain": [
""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Find the airmass\n",
"target_altaz.secz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plots to help planning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can visualize what we have done so far using some plots"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from astroplan.plots import plot_sky, plot_airmass"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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