bandpass = dorado.sensitivity.bandpasses.NUV_D
st.sidebar.markdown('## Background')
night = st.sidebar.checkbox('Orbit night (low airglow background)', True)
zodi = st.sidebar.radio('Zodiacal light background',
('Low', 'Medium', 'High', 'Specific time and place'))
if zodi == 'Specific time and place':
time = st.sidebar.text_input("""Time (e.g. '2025-03-01 12:00:00')""",
'2025-03-01 12:00:00')
try:
time = Time(time)
except ValueError:
st.sidebar.error('Did not understand time format')
st.stop()
st.sidebar.success(f'Resolved to ' + time.isot)
coord = st.sidebar.text_input(
"""Coordinates (e.g. 'NGC 4993', '13h09m47.706s -23d23'01.79"')""",
'NGC 4993')
try:
coord = SkyCoord.from_name(coord)
except NameResolveError:
try:
coord = SkyCoord(coord)
except ValueError:
st.sidebar.error('Did not understand coordinate format')
#corr[0] = XX
#corr[1] = XY
#corr[2] = YX
#corr[3] = XX
c = 299792458.0 #(m/s)
spw_table = '::'.join((myms, 'SPECTRAL_WINDOW'))
for a in xds_from_table(spw_table):
chan = a.CHAN_FREQ.data.squeeze(0).compute()
wavel = c / chan
#msdata = xms.xds_from_ms(myms, columns=['TIME', 'FLAG', 'FIELD_ID', 'UVW', 'CORRECTED_DATA', 'DATA'])
for i in datasets:
mjd = i.TIME.values / 86400
t = Time(mjd, format='mjd')
st_time = t.iso
v = i.UVW.values[:, 1]
u = i.UVW.values[:, 0]
phase = numpy.angle(i.DATA.values[:, 0]) #rads
amp = numpy.abs(i.DATA.values[:, 0])
real = numpy.real(i.DATA.values[:, 0])
imag = numpy.imag(i.DATA.values[:, 0])
#corr=p.POLARIZATION.CORR_PRODUCT.data.
#print (st_time)
#UV distance
uvdist = ((u**2.0) + (v**2.0))**0.5
#convert the xarray into panda dataframe to prepare it for plotting ...
#frame=i.to_dataframe()
def cone_search_async(self,
coo,
rad,
epoch,
location='500',
position_error=120,
find_planets=True,
find_asteroids=True,
find_comets=True,
get_query_payload=False,
get_raw_response=False,
cache=True):
"""
This method queries the IMCCE
`SkyBoT <http://vo.imcce.fr/webservices/skybot/?conesearch>`_
cone search service and produces a `~astropy.table.QTable` object
containing all Solar System bodies that might be in the cone
defined by the cone center coordinates and epoch provided.
Parameters
----------
coo : `~astropy.coordinates.SkyCoord` object or tuple
Center coordinates of the search cone in ICRS coordinates. If
provided as tuple, the input is excepted as (right ascension
in degrees, declination in degrees).
rad : `~astropy.units.Quantity` object or float
Radius of the search cone. If no units are provided (input as
float), degrees are assumed. The maximum search radius is 10
degrees; if this
maximum radius is exceeded, it will be clipped and a warning
will be provided to the user.
epoch : `~astropy.time.Time` object, float, or string
Epoch of search process in UT. If provided as float, it is
interpreted as Julian Date, if provided as string, it is
interpreted as date in the form ``'YYYY-MM-DD HH-MM-SS'``.
location : int or str, optional
Location of the observer on Earth as defined in the official
`list of IAU codes
<https://www.minorplanetcenter.net/iau/lists/ObsCodes.html>`_.
Default: geocentric location (``'500'``)
position_error : `~astropy.units.Quantity` or float, optional
Maximum positional error for targets to be queried. If no
unit is provided, arcseconds are assumed. Maximum positional
error is 120 arcseconds, larger values are clipped and warning
will be provided to the user. Default: 120 arcseconds
find_planets : boolean, optional
If ``True``, planets will be included in the search. Default:
``True``
find_asteroids : boolean, optional
If ``True``, asteroids will be included in the search. Default:
``True``
find_comets : boolean, optional
If ``True``, comets will be included in the search. Default:
``True``
get_query_payload : boolean, optional
Returns the query payload only and performs no query.
Default: ``False``
get_raw_response : boolean, optional
Returns the raw response as provided by the IMCCE server instead
of the parsed output. Default: ``False``
cache : boolean, optional
Cache this specfific query so it might be retrieved faster in
the future. Default: ``True``
Notes
-----
The following parameters are queried from the SkyBoT service:
+------------------+-----------------------------------------------+
| Column Name | Definition |
+==================+===============================================+
| ``'Number'`` | Target Number (``-1`` if none provided, int) |
+------------------+-----------------------------------------------+
| ``'Name'`` | Target Name (str) |
+------------------+-----------------------------------------------+
| ``'RA'`` | Target RA (J2000, deg, float) |
+------------------+-----------------------------------------------+
| ``'DEC'`` | Target declination (J2000, deg, float) |
+------------------+-----------------------------------------------+
| ``'Type'`` | Target dynamical/physical type (str) |
+------------------+-----------------------------------------------+
| ``'V'`` | Target apparent brightness (V-band, mag, |
| | float) |
+------------------+-----------------------------------------------+
| ``'posunc'`` | Positional uncertainty (arcsec, float) |
+------------------+-----------------------------------------------+
| ``'centerdist'`` | Angular distance of target from cone center |
| | (arcsec, float) |
+------------------+-----------------------------------------------+
| ``'RA_rate'`` | RA rate of motion (arcsec/hr, float) |
+------------------+-----------------------------------------------+
| ``'DEC_rate'`` | Declination rate of motion (arcsec/hr, float) |
+------------------+-----------------------------------------------+
| ``'geodist'`` | Geocentric distance of target (au, float) |
+------------------+-----------------------------------------------+
| ``'heliodist'`` | Heliocentric distance of target (au, float) |
+------------------+-----------------------------------------------+
| ``'alpha'`` | Solar phase angle (deg, float) |
+------------------+-----------------------------------------------+
| ``'elong'`` | Solar elongation angle (deg, float) |
+------------------+-----------------------------------------------+
| ``'x'`` | Target equatorial vector x (au, float) |
+------------------+-----------------------------------------------+
| ``'y'`` | Target equatorial vector y (au, float) |
+------------------+-----------------------------------------------+
| ``'z'`` | Target equatorial vector z (au, float) |
+------------------+-----------------------------------------------+
| ``'vx'`` | Target velocity vector x (au/d, float) |
+------------------+-----------------------------------------------+
| ``'vy'`` | Target velocity vector y (au/d, float) |
+------------------+-----------------------------------------------+
| ``'vz'`` | Target velocity vector z (au/d, float) |
+------------------+-----------------------------------------------+
| ``'epoch'`` | Ephemerides epoch (JD, float) |
+------------------+-----------------------------------------------+
Examples
--------
>>> from astroquery.imcce import Skybot
>>> from astropy.coordinates import SkyCoord
>>> from astropy.time import Time
>>> import astropy.units as u
>>> field = SkyCoord(1*u.deg, 1*u.deg)
>>> epoch = Time('2019-05-29 21:42', format='iso')
>>> Skybot.cone_search(field, 0.1*u.deg, epoch) # doctest: +SKIP
<QTable length=2>
Number Name RA ... vy vz epoch
deg ... AU / d AU / d d
int64 str9 float64 ... float64 float64 float64
------ --------- ------------------ ... ----------- ----------- ---------
180969 2005 MM39 1.0019566666666666 ... 0.00977568 0.003022634 2458630.0
107804 2001 FV58 1.0765258333333332 ... 0.006551369 0.003846177 2458630.0
"""
URL = conf.skybot_server
TIMEOUT = conf.timeout
# check for types and units
if not isinstance(coo, SkyCoord):
coo = SkyCoord(ra=coo[0]*u.degree,
dec=coo[1]*u.degree, frame='icrs')
if isinstance(rad, u.Quantity):
rad = Angle(rad.value, unit=rad.unit)
if not isinstance(rad, u.Quantity):
rad = Angle(rad, unit=u.degree)
if rad > Angle(10, unit=u.degree):
rad = Angle(10, unit=u.degree)
warnings.warn('search cone radius set to maximum: 10 deg',
UserWarning)
if isinstance(epoch, (int, float)):
epoch = Time(epoch, format='jd')
elif isinstance(epoch, str):
epoch = Time(epoch, format='iso')
if isinstance(position_error, u.Quantity):
position_error = Angle(position_error.value,
unit=position_error.unit)
if not isinstance(position_error, u.Quantity):
position_error = Angle(position_error, unit=u.arcsec)
if position_error > Angle(120, unit=u.arcsec):
position_error = Angle(120, unit=u.arcsec)
warnings.warn('positional error set to maximum: 120 arcsec',
UserWarning)
# assemble payload
request_payload = {'-ra': coo.ra.deg,
'-dec': coo.dec.deg,
'-rd': rad.deg,
'-ep': str(epoch.jd),
'-loc': str(location),
'-filter': position_error.arcsec,
'-objFilter':
str(int(find_asteroids)) +
str(int(find_planets)) +
str(int(find_comets)),
'-refsys': 'EQJ2000',
'-output': 'all',
'-mime': 'text'}
# check for diagnostic flags
if get_query_payload:
return request_payload
self._get_raw_response = get_raw_response
response = self._request(method='GET', url=URL,
params=request_payload,
timeout=TIMEOUT, cache=cache)
self._uri = response.url
return response
def readsdofile(datadir=None,
wavelength=None,
jdtime=None,
isexists=False,
timtol=1):
'''
read sdo file from local database
:param datadir:
:param wavelength:
:param jdtime: the timestamp or timerange in Julian days. if is timerange, return a list of files in the timerange
:param isexists: check if file exist. if files exist, return file name
:param timtol: time difference tolerance in days for considering data as the same timestamp
:return:
'''
from astropy.time import Time
import sunpy.map
from datetime import date
from datetime import timedelta as td
if timtol < 12. / 3600 / 24:
timtol = 12. / 3600 / 24
if isinstance(jdtime, list) or isinstance(
jdtime, tuple) or type(jdtime) == np.ndarray:
if len(jdtime) != 2:
raise ValueError(
'jdtime must be a number or a two elements array/list/tuple')
else:
if jdtime[1] < jdtime[0]:
raise ValueError(
'start time must be occur earlier than end time!')
else:
sdofitspath = []
jdtimestr = [Time(ll, format='jd').iso for ll in jdtime]
ymd = [ll.split(' ')[0].split('-') for ll in jdtimestr]
d1 = date(int(ymd[0][0]), int(ymd[0][1]), int(ymd[0][2]))
d2 = date(int(ymd[1][0]), int(ymd[1][1]), int(ymd[1][2]))
delta = d2 - d1
for i in xrange(delta.days + 1):
ymd = d1 + td(days=i)
sdofitspathtmp = glob.glob(
datadir +
'/{:04d}/{:02d}/{:02d}/aia.lev1_*Z.{}.image_lev1.fits'.
format(ymd.year, ymd.month, ymd.day, wavelength))
if len(sdofitspathtmp) > 0:
sdofitspath = sdofitspath + sdofitspathtmp
if len(sdofitspath) == 0:
if isexists:
return sdofitspath
else:
raise ValueError(
'No SDO file found under {} at the time range of {} to {}. Download the data with EvtBrowser first.'
.format(datadir, jdtimestr[0], jdtimestr[1]))
sdofits = [os.path.basename(ll) for ll in sdofitspath]
sdotimeline = Time([
insertchar(
insertchar(
ll.split('.')[2].replace('T', ' ').replace(
'Z', ''), ':', -4), ':', -2) for ll in sdofits
],
format='iso',
scale='utc')
sdofitspathnew = [
x for (y, x) in sorted(zip(sdotimeline.jd, sdofitspath))
]
sdofitsnew = [os.path.basename(ll) for ll in sdofitspathnew]
sdotimelinenew = Time([
insertchar(
insertchar(
ll.split('.')[2].replace('T', ' ').replace(
'Z', ''), ':', -4), ':', -2)
for ll in sdofitsnew
],
format='iso',
scale='utc')
sdofile = list(
np.array(sdofitspathnew)[np.where(
np.logical_and(jdtime[0] < sdotimelinenew.jd,
sdotimelinenew.jd < jdtime[1]))[0]])
return sdofile
else:
jdtimstr = Time(jdtime, format='jd').iso
ymd = jdtimstr.split(' ')[0].split('-')
sdofitspath = glob.glob(datadir +
'/{}/{}/{}/aia.lev1_*Z.{}.image_lev1.fits'.
format(ymd[0], ymd[1], ymd[2], wavelength))
if len(sdofitspath) == 0:
raise ValueError('No SDO file found under {}.'.format(datadir))
sdofits = [os.path.basename(ll) for ll in sdofitspath]
sdotimeline = Time([
insertchar(
insertchar(
ll.split('.')[2].replace('T', ' ').replace('Z', ''), ':',
-4), ':', -2) for ll in sdofits
],
format='iso',
scale='utc')
if timtol < np.min(np.abs(sdotimeline.jd - jdtime)):
raise ValueError(
'No SDO file found at the select timestamp. Download the data with EvtBrowser first.'
)
idxaia = np.argmin(np.abs(sdotimeline.jd - jdtime))
sdofile = sdofitspath[idxaia]
if isexists:
return sdofile
else:
try:
sdomap = sunpy.map.Map(sdofile)
return sdomap
except:
raise ValueError('File not found or invalid input')
if use_fnu:
output_filename = core_name + '_fnu_sensitivity_curve.txt'
else:
pass
obs_fits = fits.open(observed_file)
header = fits.getheader(observed_file)
obs_waves1 = obs_fits[0].data
obs_flux1 = obs_fits[1].data
obs_dlambda = obs_fits[4].data
airmass = header['AIRMASS']
obs_time = header['OPENTIME']
obs_date = header['OPENDATE']
obs_time = obs_date + 'T' + obs_time
obs_time = Time(obs_time, format='isot', scale='utc').mjd
exptime = header['EXPTIME']
obs_spec = np.vstack([obs_waves1, obs_flux1])
try:
dlambda = obs_fits[4].data
obs_spec = np.copy(spt.counts_to_flambda(obs_spec, dlambda))
print('Observed spectrum in units of erg/s/cm^2/angstrom')
except IndexError:
print(
'No dlambda extension in observed FITS file. You need to redo wave_cal.py to include that extension.'
)
print(
'This file is most likely generated before 2019-07-16 when this change was implemented'
)
sys.exit()
def inttag(tagfile,
output,
starttime=None,
increment=None,
rcount=1,
highres=False,
allevents=False,
verbose=True):
"""Convert an events table of TIMETAG into an integrated ACCUM image.
Parameters
----------
tagfile: str
input file that contains TIMETAG event stream. This is ordinarily a
FITS file containing two tables. The TIMETAG data are in the table
with EXTNAME = "EVENTS", and the "good time intervals" are in the
table with EXTNAME = "GTI". If the GTI table is missing or empty,
all times will be considered "good".
output: str
Name of the output FITS file.
starttime: float
Start time for integrating events, in units of seconds since the
beginning of the exposure. The default value of None means that
the start time will be set to the first START time in the GTI table.
increment: float
Time interval in seconds. The default value of None means integrate
to the last STOP time in the GTI table, divided by rcount.
rcount: int
Repeat count, the number of output image sets to create. If rcount is
greater than 1 and increment is not specified, will subdivide the total exposure time by rcount.
highres: bool
Create a high resolution output image? Default is False.
allevents: bool
If allevents is set to True, all events in the input EVENTS table will
be accumulated into the output image. The TIME column in the EVENTS
table will only be used to determine the exposure time, and the GTI
table will be ignored.
verbose: bool
Print additional info?
Returns
-------
"""
# Open Input File (_tag.fits)
with fits.open(tagfile) as tag_hdr:
# Read in TIME-TAG data (Events data and GTI data)
events_data = tag_hdr[1].data
if allevents: # If allevents, ignore GTI and generate gti_data based on the time of the first and last event
gti_data = np.rec.array(
[(events_data['TIME'][0], events_data['TIME'][-1])],
formats=">f8,>f8",
names='START, STOP')
else: # Otherwise, retrieve the GTIs from the GTI extension
gti_data = tag_hdr['GTI'].data
# Read in relevant header info
tag_pri_hdr = tag_hdr[0].header
cenx = tag_pri_hdr['CENTERA1'] # xcenter in c code
ceny = tag_pri_hdr['CENTERA2'] # ycenter in c code
siz_axx = tag_pri_hdr['SIZAXIS1'] # nx in c code
siz_axy = tag_pri_hdr['SIZAXIS2'] # ny in c code
tag_sci_hdr = tag_hdr[1].header
tzero_mjd = tag_sci_hdr['EXPSTART'] # MJD zero point
# Determine start and stop times for counting events
gti_start = gti_data['START'][0]
gti_stop = gti_data['STOP'][-1]
# Calculate corners from array size and centers
xcorner = ((cenx - siz_axx / 2.) - 1) * 2
ycorner = ((ceny - siz_axy / 2.) - 1) * 2
# Adjust axis sizes for highres, determine binning
bin_n = 2
if highres:
siz_axx *= 2
siz_axy *= 2
bin_n = 1
ltvx = ((bin_n - 2.) / 2. - xcorner) / bin_n
ltvy = ((bin_n - 2.) / 2. - ycorner) / bin_n
ltm = 2. / bin_n
# Read in start and stop time parameters
if starttime is None or starttime < gti_start:
starttime = gti_start # The first START time in the GTI (or first event)
if increment is None:
increment = (gti_stop - gti_start) / rcount
stoptime = starttime + increment
imset_hdr_ver = 0 # output header value corresponding to imset
texptime = 0 # total exposure time
hdu_list = []
for imset in range(rcount):
# Truncate stoptime at last available event time (GTI or allevents) if it exceeds that
if stoptime > gti_stop:
stoptime = gti_stop
# Get Exposure Times
exp_time, expstart, expstop, good_events = exp_range(
starttime, stoptime, events_data, gti_data, tzero_mjd)
if len(good_events) == 0:
if verbose:
print("Skipping imset, due to no overlap with GTI\n",
starttime, stoptime)
starttime = stoptime
stoptime += increment
continue
imset_hdr_ver += 1
if imset_hdr_ver == 1: # If first science header, texpstart keyword value is expstart
texpstart = expstart
texpend = expstop # texpend will be expstop of last imset
if verbose:
print("imset: {}, start: {}, stop: {}, exposure time: {}".format(
imset_hdr_ver, starttime, stoptime, exp_time))
# Convert events table to accum image
accum = events_to_accum(good_events, siz_axx, siz_axy, highres)
# Calculate errors from accum image
# Note: C version takes the square root of the counts, inttag.py uses a more robust confidence interval
conf_int = astropy.stats.poisson_conf_interval(
accum, interval='sherpagehrels', sigma=1)
err = conf_int[
1] - accum # error is the difference between upper confidence boundary and the data
# Copy EVENTS extension header to SCI, ERR, DQ extensions
sci_hdu = fits.ImageHDU(data=accum,
header=tag_sci_hdr.copy(),
name='SCI')
err_hdu = fits.ImageHDU(data=err,
header=tag_sci_hdr.copy(),
name='ERR')
dq_hdu = fits.ImageHDU(header=tag_sci_hdr.copy(), name='DQ')
# Generate datetime for 'DATE' header keyword
dtstr = str(dt.utcnow())
date, h, m, s = [
dtstr.split()[0],
dtstr.split()[1].split(':')[0],
dtstr.split()[1].split(':')[1],
str(round(float(dtstr.split()[1].split(':')[-1])))
]
if len(s) == 1:
s = '0' + s
dtval = date + 'T' + h + ':' + m + ':' + s
# Populate extensions
for idx, hdu in enumerate([sci_hdu, err_hdu, dq_hdu]):
hdu.header['EXPTIME'] = exp_time
hdu.header['EXPSTART'] = expstart
hdu.header['EXPEND'] = expstop
date_obs, time_obs = Time(float(expstart),
format='mjd').isot.split('T')
hdu.header['DATE-OBS'] = date_obs
hdu.header['TIME-OBS'] = time_obs
hdu.header['EXTVER'] = imset_hdr_ver
hdu.header['DATE'] = (dtval, "Date FITS file was generated")
hdu.header['ORIGIN'] = "stistools inttag.py"
# Check if image-specific WCS keywords already exist in the tag file (older tag files do)
keyword_list = list(hdu.header.keys())
if not any("CTYPE" in keyword for keyword in keyword_list):
n, k = [
keyword[-1] for keyword in keyword_list
if "TCTYP" in keyword
]
# Rename keywords
for val, i in zip([n, k], ['1', '2']):
hdu.header.rename_keyword('TCTYP' + val, 'CTYPE' + i)
hdu.header.rename_keyword('TCRPX' + val, 'CRPIX' + i)
hdu.header.rename_keyword('TCRVL' + val, 'CRVAL' + i)
hdu.header.rename_keyword('TCUNI' + val, 'CUNIT' + i)
hdu.header.rename_keyword('TC{}_{}'.format(n, n),
'CD{}_{}'.format(1, 1))
hdu.header.rename_keyword('TC{}_{}'.format(n, k),
'CD{}_{}'.format(1, 2))
hdu.header.rename_keyword('TC{}_{}'.format(k, n),
'CD{}_{}'.format(2, 1))
hdu.header.rename_keyword('TC{}_{}'.format(k, k),
'CD{}_{}'.format(2, 2))
# Time tag events table keywords
hdu.header['WCSAXES'] = 2
hdu.header['LTM1_1'] = ltm
hdu.header['LTM2_2'] = ltm
hdu.header['LTV1'] = ltvx
hdu.header['LTV2'] = ltvy
# Convert keyword values to lowres scale if not highres
if not highres:
hdu.header['CD1_1'] *= 2
hdu.header['CD1_2'] *= 2
hdu.header['CD2_1'] *= 2
hdu.header['CD2_2'] *= 2
hdu.header['CRPIX1'] = (hdu.header['CRPIX1'] + 0.5) / 2.
hdu.header['CRPIX2'] = (hdu.header['CRPIX2'] + 0.5) / 2.
# Populate DQ header with dq specific keywords
if idx == 2:
hdu.header['NPIX1'] = siz_axx
hdu.header['NPIX2'] = siz_axy
hdu.header[
'PIXVALUE'] = 0 # Fixes issue with calstis not running on raw output files
# Append imset extensions to header list
hdu_list.append(sci_hdu)
hdu_list.append(err_hdu)
hdu_list.append(dq_hdu)
# Prepare start and stop times for next image in imset
starttime = stoptime
stoptime += increment
texptime += exp_time
# Copy tag file primary header to output header
pri_hdu = fits.PrimaryHDU(header=tag_pri_hdr.copy())
# Add/Modify primary header keywords
pri_hdu.header[
'NEXTEND'] = imset_hdr_ver * 3 # Three extensions per imset (SCI, ERR, DQ)
pri_hdu.header['NRPTEXP'] = imset_hdr_ver
pri_hdu.header['TEXPSTRT'] = texpstart
pri_hdu.header['TEXPEND'] = texpend
pri_hdu.header['TEXPTIME'] = texptime
pri_hdu.header['BINAXIS1'] = bin_n
pri_hdu.header['BINAXIS2'] = bin_n
pri_hdu.header['FILENAME'] = output.split('/')[-1]
pri_hdu.header['DATE'] = (dtval, "Date FITS file was generated")
pri_hdu.header['ORIGIN'] = "stistools inttag.py"
if not highres:
pri_hdu.header[
'LORSCORR'] = "COMPLETE" # Corr flag detailing MAMA data conversion to low res
# Write output file
hdu_list = [pri_hdu] + hdu_list
out_hdul = fits.HDUList(hdu_list)
out_hdul.writeto(output, overwrite=True)
def sex_phot(zero, error, stackcat):
src = ascii.read(stackcat)
srccoords = SkyCoord(src["ALPHA_J2000"], src["DELTA_J2000"], unit="degree")
target = SkyCoord(287.63114, 7.8977429, unit="degree")
sample1 = SkyCoord(287.62955, 7.8983132, unit="degree")
sample2 = SkyCoord(287.65794, 7.8851428, unit="degree")
sample3 = SkyCoord(287.61966, 7.8913336, unit="degree")
real_ra = np.array([
target.ra.degree, sample1.ra.degree, sample2.ra.degree,
sample3.ra.degree
])
real_dec = np.array([
target.dec.degree, sample1.dec.degree, sample2.dec.degree,
sample3.dec.degree
])
real_wcs = np.array([real_ra, real_dec])
dist = target.separation(srccoords)
dist1 = sample1.separation(srccoords)
dist2 = sample2.separation(srccoords)
dist3 = sample3.separation(srccoords)
target_idx = src[np.argmin(dist)]
sample1_idx = src[np.argmin(dist1)]
sample2_idx = src[np.argmin(dist2)]
sample3_idx = src[np.argmin(dist3)]
data = [target_idx, sample1_idx, sample2_idx, sample3_idx]
target_mag = data[0]["MAG_AUTO"] + zero
sample1_mag = data[1]["MAG_AUTO"] + zero
sample2_mag = data[2]["MAG_AUTO"] + zero
sample3_mag = data[3]["MAG_AUTO"] + zero
mag = np.round(np.array(
[target_mag, sample1_mag, sample2_mag, sample3_mag]),
decimals=2)
target_magerr = data[0]["MAGERR_AUTO"]
sample1_magerr = data[1]["MAGERR_AUTO"]
sample2_magerr = data[2]["MAGERR_AUTO"]
sample3_magerr = data[3]["MAGERR_AUTO"]
magerr_ls = np.sqrt(
np.array([
target_magerr, sample1_magerr, sample2_magerr, sample3_magerr
])**2 + error**2)
magerr = np.round(magerr_ls, decimals=3)
target_FWHM = data[0]["FWHM_WORLD"] * 3600
sample1_FWHM = data[1]["FWHM_WORLD"] * 3600
sample2_FWHM = data[2]["FWHM_WORLD"] * 3600
sample3_FWHM = data[3]["FWHM_WORLD"] * 3600
FWHM_ls = np.array([target_FWHM, sample1_FWHM, sample2_FWHM, sample3_FWHM])
FWHM = np.round(FWHM_ls, 2)
sys_ra = np.array([
data[0]["ALPHA_J2000"], data[1]["ALPHA_J2000"], data[2]["ALPHA_J2000"],
data[3]["ALPHA_J2000"]
])
sys_dec = np.array([
data[0]["DELTA_J2000"], data[1]["DELTA_J2000"], data[2]["DELTA_J2000"],
data[3]["DELTA_J2000"]
])
sys_wcs = np.array([sys_ra, sys_dec])
factor_name = [
"object", "real_ra", "real_dec", "sys_ra", "sys_dec", "mag", "magerr",
"fwhm"
]
sokko_object = ["target", "comp1", "comp2", "comp3"]
ascii.write((sokko_object, real_wcs[0], real_wcs[1], sys_wcs[0],
sys_wcs[1], mag, magerr, FWHM),
"sample.dat",
names=factor_name)
g = pyfits.open("GL191032+075314_Hw.fits")
date = g[0].header["DATE-OBS"]
#astro_t = Time(date,format="isot",scale="utc")
#mjd = astro_t.mjd
mjd = np.array([Time(date, format="isot", scale="utc").mjd])
ascii.write((mjd), "mjd.dat", names=["mjd"])
def _parse_netcdf(filepath):
"""
Parses the netCDF GOES files to return the data, header and associated units.
Parameters
----------
filepath : `str`
The path of the file to parse
"""
with h5netcdf.File(filepath, mode="r",
**XRSTimeSeries._netcdf_read_kw) as h5nc:
header = MetaDict(OrderedDict(h5nc.attrs))
flux_name = h5nc.variables.get("a_flux") or h5nc.variables.get(
"xrsa_flux")
if flux_name is None:
raise ValueError(
f"No flux data (either a_flux or xrsa_flux) found in file: {filepath}"
)
flux_name_a = flux_name.name
flux_name_b = flux_name_a.replace("a", "b")
xrsa = np.array(h5nc[flux_name_a])
xrsb = np.array(h5nc[flux_name_b])
xrsa_quality = np.array(h5nc[flux_name_a.replace("flux", "flags")])
xrsb_quality = np.array(h5nc[flux_name_b.replace("flux", "flags")])
start_time_str = h5nc["time"].attrs["units"]
# h5netcdf < 0.14 return bytes instead of a str
if isinstance(start_time_str, bytes):
start_time_str = start_time_str.decode("utf-8")
start_time_str = start_time_str.lstrip("seconds since").rstrip(
"UTC").strip()
times = Time(parse_time(start_time_str).unix + h5nc["time"],
format="unix")
try:
times = times.datetime
except ValueError:
# We do not make the assumption that the leap second occurs at the end of the file.
# Therefore, we need to find it:
# To do so, we convert the times to isot strings, use numpy to find the the leap second string,
# then use that to workout the index of the leap timestamp.
idx = np.argwhere((np.char.find(times.isot, ":60.") != -1) == True)
# We only handle the case there is only 1 leap second in the file.
# I don't think there every would be a case where it would be more than 1.
if len(idx) != 1:
raise ValueError(
f"More than one leap second was found in: {Path(filepath).name}"
)
warn_user(
f"There is one leap second timestamp present in: {Path(filepath).name}, "
"This timestamp has been rounded to `:59.999` to allow its conversion into a Python datetime. "
f"The leap second timestamp was: {times.isot[idx]}")
times[idx] = Time(times[idx].isot.tolist()[0][0][:17] +
"59.999").unix
times = times.datetime
data = DataFrame(
{
"xrsa": xrsa,
"xrsb": xrsb,
"xrsa_quality": xrsa_quality,
"xrsb_quality": xrsb_quality
},
index=times)
data = data.replace(-9999, np.nan)
units = OrderedDict([
("xrsa", u.W / u.m**2),
("xrsb", u.W / u.m**2),
("xrsa_quality", int),
("xrsb_quality", int),
])
return data, header, units
#plt.show()
plt.savefig('uranus_skymodel/ortho_{}'.format(i))
plt.close()
if __name__ == "__main__":
#sbs = np.arange(76,198)[:78]
sbs = np.arange(76, 198)
freqs = sb_to_f(sbs, 3)
start = '2020-12-15T20:04:00' #isot format
duration = 176 * 60 * u.second # seconds
n_elem = 96
myobs = Observation('uranus', start, 'isot', duration.value, freqs)
t = Time('2020-12-15T20:04:00', format='isot')
c = coords.get_body('uranus', t)
ilofar = coords.EarthLocation(lat='53.095',
lon='-7.9218',
height=100 * u.m)
aa = coords.AltAz(location=ilofar, obstime=t)
altaz_coords = c.transform_to(aa) #at ilofar
gal_coords = c.transform_to(coords.Galactic())
print(myobs)
print('AltAz Coordinates\n' +
'alt: {}\taz: {}'.format(altaz_coords.alt, altaz_coords.az))
print('Galactic Coordinates\n' +
'l: {}\tb: {}\n'.format(gal_coords.l, gal_coords.b))
def telemetry():
path = '/Users/christoph/OneDrive - UNSW/telemetry/'
# # tau Ceti observation times
# jd_sep = readcol('/Volumes/BERGRAID/data/veloce/reduced/tauceti/tauceti_with_LFC/sep2018/' + 'tauceti_all_jds.dat')
# jd_nov = readcol('/Volumes/BERGRAID/data/veloce/reduced/tauceti/tauceti_with_LFC/nov2018/' + 'tauceti_all_jds.dat')
# jd = list(jd_sep) + list(jd_nov)
# # read detector temperature file(s)
# with open(path + 'arccamera.txt') as det_temp_file:
# for line in det_temp_file:
# if line[0] == '#':
# column_names = line.lstrip('#').split()
# else:
# timestamp, det_temp, cryohead_temp, perc_heater = line.rstrip('\n')
timestamp_cam, det_temp, cryohead_temp, perc_heater = readcol(
path + 'arccamera.txt', skipline=1, twod=False)
timestamp_cam_0, det_temp_0, cryohead_temp_0, perc_heater_0 = readcol(
path + 'arccamera.txt.0', skipline=1, twod=False)
timestamp_cam_1, det_temp_1, cryohead_temp_1, perc_heater_1 = readcol(
path + 'arccamera.txt.1', skipline=1, twod=False)
timestamp_cam_2, det_temp_2, cryohead_temp_2, perc_heater_2 = readcol(
path + 'arccamera.txt.2', skipline=1, twod=False)
cam_time = Time(timestamp_cam,
format='unix') # from 17/10/2018 - 27/11/2018
cam_time_0 = Time(timestamp_cam_0,
format='unix') # from 19/04/2018 - 17/10/2018
cam_time_1 = Time(timestamp_cam_1, format='unix')
cam_time_2 = Time(timestamp_cam_2, format='unix')
# combine all the subsets
t = np.array(
list(cam_time_2.jd) + list(cam_time_1.jd) + list(cam_time_0.jd) +
list(cam_time.jd)) - 2.458e6
tobj = Time(t + 2.458e6, format='jd')
dtemp = np.array(
list(det_temp_2) + list(det_temp_1) + list(det_temp_0) +
list(det_temp))
cryo = np.array(
list(cryohead_temp_2) + list(cryohead_temp_1) + list(cryohead_temp_0) +
list(cryohead_temp))
heater_load = np.array(
list(perc_heater_2) + list(perc_heater_1) + list(perc_heater_0) +
list(perc_heater))
# # for showing tau ceti obstimes
# ix = np.zeros(len(jd))
# # I checked, and the maximum diff in time is less than ~30s :)
# for i in range(len(jd)):
# ix[i] = find_nearest(t, jd[i] - 2.458e6, return_index=True)
# # now len(ix) = 138, whereas len(x_shift = 133, b/c the LFC peaks were not successfully measured for the first 5 tau ceti observations;
# # hence cut away first 5 entries
# ix = ix[5:]
# ix = ix.astype(int)
# observing runs were:
# 20180917 - 20180926
tstart_sep = 2458378.0
tend_sep = 2458388.0
# 20181115 - 20181127
tstart_nov = 2458437.0
tend_nov = 2458450.0
# 20190121 - 20190203
tstart_janfeb = 2458504.0
tend_janfeb = 2458518.0
# 20190408 - 20190415
tstart_apr = 2458581.0
tend_apr = 2458589.0
# 20190503 - 20190512
tstart_may01 = 2458606.0
tend_may01 = 2458616.0
# 20190517 - 20190528
tstart_may02 = 2458620.0
tend_may02 = 2458632.0
# 20190531 - 20190605
tstart_jun01 = 2458634.0
tend_jun01 = 2458640.0
# 20190619 - 20190625
tstart_jun02 = 2458653.0
tend_jun02 = 2458660.0
# 20190722 - 20190724
tstart_jul = 2458686.0
tend_jul = 2458689.0
starts = np.array([
tstart_sep, tstart_nov, tstart_janfeb, tstart_apr, tstart_may01,
tstart_may02, tstart_jun01, tstart_jun02, tstart_jul
]) - 2.458e6
ends = np.array([
tend_sep, tend_nov, tend_janfeb, tend_apr, tend_may01, tend_may02,
tend_jun01, tend_jun02, tend_jul
]) - 2.458e6
##### make a nice stacked plot #####
run = 'sep'
# runs = np.array(['all', 'sep', 'nov', 'jan', 'apr', 'may', 'jun', 'jul'])
# runix = np.argwhere(runs == run)[0]
# tlow, thigh = (starts[runix] - 3, ends[runix] + 3)
if run == 'all':
tlow, thigh = (np.min(starts) - 3, np.max(ends) + 3)
elif run == 'sep':
tlow, thigh = (tstart_sep - 2.458e6 - 3, tend_sep - 2.458e6 + 3)
elif run == 'nov':
tlow, thigh = (tstart_nov - 2.458e6 - 3, tend_nov - 2.458e6 + 3)
elif run == 'jan':
tlow, thigh = (tstart_janfeb - 2.458e6 - 3, tend_janfeb - 2.458e6 + 3)
elif run == 'apr':
tlow, thigh = (tstart_apr - 2.458e6 - 3, tend_apr - 2.458e6 + 3)
elif run == 'may':
tlow, thigh = (tstart_may01 - 2.458e6 - 3, tend_may01 - 2.458e6 + 3)
elif run == 'jun':
tlow, thigh = (tstart_may02 - 2.458e6 - 3, tend_jun02 - 2.458e6 + 3)
elif run == 'jul':
tlow, thigh = (tstart_jul - 2.458e6 - 3, tend_jul - 2.458e6 + 3)
# t_plot = tobj.datetime # for plotting nice calendar dates
t_plot = t.copy() #for plotting JD
fig, axarr = plt.subplots(3, sharex=True, figsize=(12, 6.75))
# plot 3 subplots
axarr[0].plot(t_plot, dtemp, 'b')
axarr[1].plot(t_plot, cryo, 'b')
axarr[2].plot(t_plot, heater_load, 'b')
# set (global) x-range
# axarr[0].set_xlim(370,455)
# axarr[0].set_xlim(500,692) # ~17 Jan - 28 July 2019
# axarr[0].set_xlim(375, 692) # ~14 Sep 2018 - 28 July 2019
axarr[0].set_xlim(tlow, thigh)
# set y-ranges
axarr[0].set_ylim(138, 150)
axarr[1].set_ylim(82, 88)
axarr[2].set_ylim(-5, 30)
# set titles
axarr[0].set_title('detector temp')
axarr[1].set_title('cryohead temp')
axarr[2].set_title('heater load')
# set x-axis label
axarr[2].set_xlabel('JD - 2458000.0')
# set y-axis labels
axarr[0].set_ylabel('T [K]')
axarr[1].set_ylabel('T [K]')
axarr[2].set_ylabel('[%]')
# indicate when Veloce was actually observing
for x1, x2 in zip(starts, ends):
axarr[0].axvspan(x1, x2, alpha=0.3, color='green')
axarr[1].axvspan(x1, x2, alpha=0.3, color='green')
axarr[2].axvspan(x1, x2, alpha=0.3, color='green')
# # indicate tau Ceti obstimes with dashed vertical lines
# for tobs in jd:
# axarr[0].axvline(tobs-2.458e6, color='gray', linestyle='--')
# axarr[1].axvline(tobs-2.458e6, color='gray', linestyle='--')
# axarr[2].axvline(tobs-2.458e6, color='gray', linestyle='--')
# save to file
# plt.savefig(...)
timestamp_mech, temp_internal, temp_external, temp_room, temp_elec_cabinet, p_internal, p_room, p_regulator, p_set_point, p_rosso_cryo, h_internal, h_external, h_room, focus = readcol(
path + 'velocemech.txt', skipline=1, twod=False)
timestamp_mech_0, temp_internal_0, temp_external_0, temp_room_0, temp_elec_cabinet_0, p_internal_0, p_room_0, p_regulator_0, p_set_point_0, p_rosso_cryo_0, h_internal_0, h_external_0, h_room_0, focus_0 = readcol(
path + 'velocemech.txt.0', skipline=1, twod=False)
timestamp_mech_1, temp_internal_1, temp_external_1, temp_room_1, temp_elec_cabinet_1, p_internal_1, p_room_1, p_regulator_1, p_set_point_1, p_rosso_cryo_1, h_internal_1, h_external_1, h_room_1, focus_1 = readcol(
path + 'velocemech.txt.1', skipline=1, twod=False)
mech_time = Time(timestamp_mech,
format='unix') # from 26/10/2018 - 27/11/2018
mech_time_0 = Time(timestamp_mech_0,
format='unix') # from 23/09/2018 - 26/10/2018
mech_time_1 = Time(timestamp_mech_1,
format='unix') # from 13/08/2018 - 23/09/2018
timestamp_therm, temp_mc_setpoint, temp_mc_int, temp_extencl, temp_extencl_setpoint, state_mc = readcol(
path + 'velocetherm.txt', skipline=1, twod=False)
therm_time = Time(timestamp_therm, format='unix')
timestamp_int_therm, temp_enc_setpoint, temp_enc_target, temp_cryo_setpoint, temp_sensor_1, temp_sensor_2, temp_sensor_3, temp_sensor_4, temp_sensor_5, temp_sensor_6, temp_sensor_7,\
pwm_1, pwm_2, pwm_3, pwm_4, pwm_5 = readcol(path + 'veloceinttherm.txt', skipline=1, twod=False)
timestamp_int_therm_0, temp_enc_setpoint_0, temp_enc_target_0, temp_cryo_setpoint_0, temp_sensor_1_0, temp_sensor_2_0, temp_sensor_3_0, temp_sensor_4_0, temp_sensor_5_0, temp_sensor_6_0, temp_sensor_7_0,\
pwm_1_0, pwm_2_0, pwm_3_0, pwm_4_0, pwm_5_0 = readcol(path + 'veloceinttherm.txt.0', skipline=1, twod=False)
timestamp_int_therm_1, temp_enc_setpoint_1, temp_enc_target_1, temp_cryo_setpoint_1, temp_sensor_1_1, temp_sensor_2_1, temp_sensor_3_1, temp_sensor_4_1, temp_sensor_5_1, temp_sensor_6_1, temp_sensor_7_1,\
pwm_1_1, pwm_2_1, pwm_3_1, pwm_4_1, pwm_5_0 = readcol(path + 'veloceinttherm.txt.1', skipline=1, twod=False)
int_therm_time = Time(timestamp_int_therm,
format='unix') # from 26/11/2018 - 27/11/2018
int_therm_time_0 = Time(timestamp_int_therm_0,
format='unix') # from 25/11/2018 - 26/11/2018
int_therm_time_1 = Time(timestamp_int_therm_1,
format='unix') # from 24/11/2018 - 25/11/2018
return
ephcoord = np.loadtxt(arq[4].strip(),
skiprows=3,
usecols=(2, 4, 5, 6, 7, 8, 9),
unpack=True,
dtype='S20',
ndmin=1)
coor = coords[1]
for i in np.arange(len(ephcoord))[2:]:
coor = np.core.defchararray.add(coor, ' ')
coor = np.core.defchararray.add(coor, ephcoord[i])
jpl = SkyCoord(coor, frame='icrs', unit=(u.hourangle, u.degree))
dalfa = jpl.ra * np.cos(jpl.dec) - lau.ra * np.cos(lau.dec)
ddelta = jpl.dec - lau.dec
a = np.where(tempo > Time('2015-01-01 00:00:00').jd)
print 'RA: ', np.absolute(dalfa[a].mas).max(), ', Dec: ', np.absolute(
ddelta[a].mas).max()
r = []
for i in np.arange(2015, 2019, 1):
r.append(Time('{}-01-01 00:00:00'.format(i), format='iso').jd - 2451544.5)
#print r
r = np.array(r)
############## Declinacao ############################################
plt.plot(tempo - 2451544.5, dalfa.mas, label=r'$\Delta\alpha\cos\delta$')
plt.plot(tempo - 2451544.5, ddelta.mas, '--', label=r'$\Delta\delta$')
def setup(self):
etrue = np.logspace(-1, 1, 10) * u.TeV
self.e_true = MapAxis.from_energy_edges(etrue, name="energy_true")
ereco = np.logspace(-1, 1, 5) * u.TeV
elo = ereco[:-1]
ehi = ereco[1:]
self.e_reco = MapAxis.from_energy_edges(ereco, name="energy")
start = u.Quantity([0], "s")
stop = u.Quantity([1000], "s")
time_ref = Time("2010-01-01 00:00:00.0")
self.gti = GTI.create(start, stop, time_ref)
self.livetime = self.gti.time_sum
self.on_region = make_region("icrs;circle(0.,1.,0.1)")
off_region = make_region("icrs;box(0.,1.,0.1, 0.2,30)")
self.off_region = off_region.union(
make_region("icrs;box(-1.,-1.,0.1, 0.2,150)")
)
self.wcs = WcsGeom.create(npix=300, binsz=0.01, frame="icrs").wcs
self.aeff = RegionNDMap.create(
region=self.on_region, wcs=self.wcs, axes=[self.e_true], unit="cm2"
)
self.aeff.data += 1
data = np.ones(elo.shape)
data[-1] = 0 # to test stats calculation with empty bins
axis = MapAxis.from_edges(ereco, name="energy", interp="log")
self.on_counts = RegionNDMap.create(
region=self.on_region,
wcs=self.wcs,
axes=[axis],
meta={"EXPOSURE": self.livetime.to_value("s")},
)
self.on_counts.data += 1
self.on_counts.data[-1] = 0
self.off_counts = RegionNDMap.create(
region=self.off_region, wcs=self.wcs, axes=[axis]
)
self.off_counts.data += 10
acceptance = RegionNDMap.from_geom(self.on_counts.geom)
acceptance.data += 1
data = np.ones(elo.shape)
data[-1] = 0
acceptance_off = RegionNDMap.from_geom(self.off_counts.geom)
acceptance_off.data += 10
self.edisp = EDispKernelMap.from_diagonal_response(
self.e_reco, self.e_true, self.on_counts.geom.to_image()
)
exposure = self.aeff * self.livetime
exposure.meta["livetime"] = self.livetime
mask_safe = RegionNDMap.from_geom(self.on_counts.geom, dtype=bool)
mask_safe.data += True
self.dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
exposure=exposure,
edisp=self.edisp,
acceptance=acceptance,
acceptance_off=acceptance_off,
name="test",
gti=self.gti,
mask_safe=mask_safe
)
def fullstack_times(request):
return Time(request.param)
def inverse(self, value):
return Time(value, scale=self.scale, format=self.format, copy=False)
def from_miriade(cls,
targetids,
objtype='asteroid',
epochs=None,
location='500',
**kwargs):
"""Load target ephemerides from
`IMCCE Miriade <http://vo.imcce.fr/webservices/miriade/>`_ using
`astroquery.imcce.MiriadeClass.get_ephemerides`
Parameters
----------
targetids : str or iterable of str
Target identifier, i.e., a number, name, designation, or JPL
Horizons record number, for one or more targets.
objtype : str, optional
The nature of ``targetids`` provided; possible values are
``'asteroid'``, ``'comet'``, ``'dwarf planet'``,
``'planet'``, or ``'satellite'``. Default: ``'asteroid'``
epochs : `~astropy.time.Time` object, or dictionary, optional
Epochs of elements to be queried; `~astropy.time.Time` objects
support single and multiple epochs;
a dictionary including keywords ``start`` and ``stop``, as well
as either ``step`` or ``number``, can be used to generate a range
of epochs. ``start`` and ``stop`` have to be
`~astropy.time.Time` objects (see :ref:`epochs`).
If ``step`` is provided, a range
of epochs will be queried starting at ``start`` and ending at
``stop`` in steps of ``step``; ``step`` has to be provided as
a `~astropy.units.Quantity` object with integer value and a
unit of either seconds, minutes, hours, or days. If
``number`` is provided as an integer, the
interval defined by
``start`` and ``stop`` is split into ``number`` equidistant
intervals. If ``None`` is
provided, current date and time are
used. All epochs should be provided in UTC; if not, they will be
converted to UTC and a `~sbpy.data.TimeScaleWarning` will be
raised. Default: ``None``
location : str or `~astropy.coordinates.EarthLocation`, optional
Location of the observer using IAU observatory codes
(see `IAU observatory codes
<https://www.minorplanetcenter.net/iau/lists/ObsCodesF.html>`__)
or as `~astropy.coordinates.EarthLocation`.
Default: ``'500'`` (geocentric)
**kwargs : optional
Arguments that will be provided to
`astroquery.imcce.MiriadeClass.get_ephemerides`.
Notes
-----
* For detailed explanations of the queried fields, refer to
`astroquery.imcce.MiriadeClass.get_ephemerides` and the
`Miriade documentation
<http://vo.imcce.fr/webservices/miriade/?documentation>`_.
* By default, all properties are provided in the J2000.0 reference
system. Different settings can be chosen using
additional keyword arguments as used by
`astroquery.imcce.MiriadeClass.get_ephemerides`.
Returns
-------
`~Ephem` object
The resulting object will be populated with columns as
defined in
`~astroquery.imcce.MiriadeClass.get_ephemerides`; refer
to that document on information on how to modify the list
of queried parameters.
Examples
--------
>>> from sbpy.data import Ephem
>>> from astropy.time import Time
>>> epoch = Time('2018-05-14', scale='utc')
>>> eph = Ephem.from_horizons('ceres', epochs=epoch) # doctest: +SKIP
"""
# modify epoch input to make it work with astroquery.imcce.Miriade
if epochs is None:
epochs = {'start': Time.now().utc.jd}
elif isinstance(epochs, Time):
if epochs.scale is not 'utc':
warn(('converting {} epochs to utc for use in '
'astroquery.imcce').format(epochs.scale),
TimeScaleWarning)
epochs = epochs.utc
epochs = {'start': epochs}
elif isinstance(epochs, dict):
if epochs['start'].scale is not 'utc':
warn(('converting {} start epoch to utc for use in '
'astroquery.imcce').format(epochs['start'].scale),
TimeScaleWarning)
epochs['start'] = epochs['start'].utc
if 'stop' in epochs and epochs['stop'].scale is not 'utc':
warn(('converting {} stop epoch to utc for use in '
'astroquery.imcce').format(epochs['stop'].scale),
TimeScaleWarning)
epochs['stop'] = epochs['stop'].utc
if 'number' in epochs:
# turn interval/number into step size based on full minutes
epochs['step'] = int(
(Time(epochs['stop']) - Time(epochs['start'])).jd * 86400 /
(epochs['number'] - 1)) * u.s
elif 'step' in epochs:
epochs['number'] = (
(Time(epochs['stop']) - Time(epochs['start'])).jd * 86400 /
epochs['step'].to('s').value) + 1
if 'step' in epochs:
epochs['step'] = '{:f}{:s}'.format(epochs['step'].value, {
u.s: 's',
u.minute: 'm',
u.hour: 'h',
u.day: 'd'
}[epochs['step'].unit])
# if targetids is a list, run separate Horizons queries and append
if not isinstance(targetids, (list, ndarray, tuple)):
targetids = [targetids]
# turn EarthLocation into dictionary of strings as used by
# astroquery.jplhorizons
if isinstance(location, EarthLocation):
location = '{:+f} {:+f} {:.1f}'.format(
location.lon.deg, location.lat.deg,
location.height.to('m').value)
# append ephemerides table for each targetid
all_eph = None
for targetid in targetids:
query = Miriade()
try:
if 'step' not in epochs and 'number' not in epochs:
if not iterable(epochs['start']):
# single epoch
eph = query.get_ephemerides(targetname=targetid,
objtype=objtype,
location=location,
epoch=epochs['start'],
**kwargs)
else:
# multiple epochs
eph = []
for i in range(len(epochs['start'])):
e = query.get_ephemerides(targetname=targetid,
objtype=objtype,
location=location,
epoch=epochs['start'][i],
**kwargs)
e['epoch'] = Time(e['epoch'],
format='jd',
scale='utc').iso
eph.append(e)
eph = vstack(eph)
eph['epoch'] = Time(eph['epoch'],
scale='utc',
format='iso')
else:
# dictionary
eph = query.get_ephemerides(targetname=targetid,
objtype=objtype,
location=location,
epoch=epochs['start'],
epoch_step=epochs['step'],
epoch_nsteps=epochs['number'],
**kwargs)
except RuntimeError as e:
raise QueryError(
('Error raised by astroquery.imcce: {:s}\n'
'The following query was attempted: {:s}').format(
str(e), query.uri))
if all_eph is None:
all_eph = eph
else:
all_eph = vstack([all_eph, eph])
self = cls.from_table(all_eph)
# turn epochs into astropy.time.Time and apply timescale
self.table['epoch'] = Time(self.table['epoch'],
format='jd',
scale='utc')
return self
usecols=[1, 2])
# In[36]:
# adjust data to plot:
starid = list([s.strip('fsubbfrmaster') for s in starid])
starid = list([s.replace('fsubbfrmaster', '') for s in starid])
starid = list([s.strip('.fits') for s in starid])
starid = list([s.replace('.fits', '') for s in starid])
statid = np.asarray([float(i) for i in starid])
x = data[:, 1]
magnitude = data[:, 0]
## need to convert observation dates from date to mjd
date = Time(dateobs, format='isot', scale='utc').mjd
time = []
for i in range(0, len(date)):
for j in range(0, 30):
time = np.append(time, x[(j + ((i) * 30))] + date[i])
# In[39]:
SI = np.tile(np.arange(30), 53)
plt.scatter(time, magnitude, s=1, c=SI)
plt.gca().invert_yaxis()
plt.title('Eclipsing Binary Star Magnitude versus Time')
plt.xlabel('Time (mjd)')
plt.ylabel('Magnitude')
# In[ ]:
def from_oo(self,
orbit,
epochs=None,
location='500',
scope='full',
dynmodel='N',
ephfile='de430'):
"""Uses pyoorb to derive ephemerides from an `~Orbit` object. For a
list of output parameters, please read the `pyoorb documentation
<https://github.com/oorb/oorb/tree/master/python>`_.
Parameters
----------
orbit : `~Orbit` object
Can contain any number of orbits, ephemerides will be calculated
for each orbit. Required fields are:
* target identifier (``'targetname'``)
* semi-major axis (``'a'``, for Keplerian orbit) or perihelion
distance (``'q'``, for cometary orbit), typically in au or
or x-component of state vector (``'x'``, for cartesian orbit),
typically in au
* eccentricity (``'e'``, for Keplerian or cometary orbit) or
y-component of state vector (``'y'``, for cartesian orbit) in
au
* inclination (``'i'``, for Keplerian or cometary orbit) in
degrees or z-component of state vector (``'z'``, for cartesian
orbit) in au
* longitude of the ascending node (``'Omega'``, for Keplerian or
cometary orbit) in degrees or x-component of velocity vector
(``'vx'``, for cartesian orbit), au/day
* argument of the periapsis (``'w'``, for Keplerian or cometary
orbit) in degrees or y-component of velocity vector (``'vy'``,
for cartesian orbit) in au/day
* mean anomaly (``'M'``, for Keplerian orbits) in degrees or
perihelion epoch (``'Tp_jd'``, for cometary orbits) in JD or
z-component of velocity vector (``'vz'``, for cartesian orbit)
in au/day
* epoch (``'epoch'``) as `~astropy.time.Time`
* absolute magnitude (``'H'``) in mag
* photometric phase slope (``'G'``)
epochs : `~astropy.time.Time` object, optional
Epochs of elements to be queried; must be a
`~astropy.time.Time` object holding a single or multiple epochs
(see :ref:`epochs`).
If ``None`` is provided, current date and
time are used. The same time scale that is used in ``epochs``
will be applied to the results. Default: ``None``
location : str, optional, default ``'500'`` (geocentric)
Location of the observer.
scope : str
Scope of data to be determined: ``'full'`` obtains all
available properties, ``'basic'`` obtains only a limited
amount of data. Default: ``'full'``
dynmodel : str, optional
The dynamical model to be used in the propagation: ``'N'``
for n-body simulation or ``'2'`` for a 2-body
simulation. Default: ``'N'``
ephfile : str, optional
Planet and Lunar ephemeris file version as provided by JPL
to be used in the propagation. Default: ``'de430'``
Returns
-------
`~Ephem` object
Examples
--------
Compute ephemerides for Ceres as seen from the Discovery Channel
Telescope for the next 10 days at 1hr intervals:
>>> import numpy as np
>>> from sbpy.data import Orbit, Ephem
>>> from astropy.time import Time
>>> epochs = Time(Time.now().jd + np.arange(0, 10, 1/24), format='jd')
>>> ceres = Orbit.from_horizons('1') # doctest: +REMOTE_DATA
>>> eph = Ephem.from_oo(ceres, epochs, 'G37') # doctest: +REMOTE_DATA
>>> eph # doctest: +SKIP
<QTable length=240>
targetname epoch ... obsz trueanom
d ... AU deg
str7 float64 ... float64 float64
---------- ------------------ ... ----------------------- -----------------
1 Ceres 2458519.316966272 ... 3.2083678848104924e-06 68.0863831954328
1 Ceres 2458519.3586329385 ... 2.7022422510736277e-07 68.09589266358881
1 Ceres 2458519.4002996054 ... -3.111046209036683e-06 68.10540191585879
1 Ceres 2458519.441966272 ... -6.700369254264427e-06 68.11491095202307
1 Ceres 2458519.4836329385 ... -1.0248419404668141e-05 68.12441977218093
1 Ceres 2458519.5252996054 ... -1.3508703580356052e-05 68.13392837643161
... ... ... ... ...
1 Ceres 2458529.066966272 ... 1.2522500440509399e-05 70.30569661787204
1 Ceres 2458529.1086329385 ... 1.4101698473351076e-05 70.31515536712485
1 Ceres 2458529.1502996054 ... 1.4771304981564537e-05 70.3246138990413
1 Ceres 2458529.191966272 ... 1.448582020449618e-05 70.33407221340468
1 Ceres 2458529.2336329385 ... 1.326517587380005e-05 70.34353031031534
1 Ceres 2458529.2752996054 ... 1.1193369555934085e-05 70.35298818987367 """
import pyoorb
# create a copy of orbit
from . import Orbit
orb = Orbit.from_table(orbit.table)
if epochs is None:
epochs = Time.now()
# extract time scale
timescale = epochs.scale.upper()
# initialize pyoorb
if os.getenv('OORB_DATA') is None:
# oorb installed using conda
pyoorb.pyoorb.oorb_init()
else:
ephfile = os.path.join(os.getenv('OORB_DATA'), ephfile + '.dat')
pyoorb.pyoorb.oorb_init(ephfile)
# identify orbit type based on available table columns
orbittype = None
for testtype in ['KEP', 'COM', 'CART']:
try:
orb._translate_columns(conf.oorb_orbit_fields[testtype][1:6])
orbittype = testtype
break
except KeyError:
pass
if orbittype is None:
raise OpenOrbError('orbit type cannot be determined from elements')
# add/update orbittype column
orb['orbittype'] = [orbittype] * len(orb)
# derive and apply default units
default_units = {}
for idx, field in enumerate(conf.oorb_orbit_fields[orbittype]):
try:
default_units[orb._translate_columns(field)
[0]] = conf.oorb_orbit_units[orbittype][idx]
except KeyError:
pass
for colname in orb.field_names:
if (colname in default_units.keys()
and not isinstance(orb[colname],
(u.Quantity, u.CompositeUnit, Time))):
orb[colname].unit = default_units[colname]
# convert epochs to TT
orb['epoch'] = orb['epoch'].tt
epochs = epochs.tt
try:
epochs = list(
zip(epochs.mjd, [conf.oorb_timeScales['TT']] * len(epochs)))
except TypeError:
epochs = [(epochs.mjd, conf.oorb_timeScales['TT'])]
if scope == 'full':
oo_eph, err = pyoorb.pyoorb.oorb_ephemeris_full(
orb._to_oo(), location, epochs, dynmodel)
elif scope == 'basic':
oo_eph, err = pyoorb.pyoorb.oorb_ephemeris_basic(
orb._to_oo(), location, epochs, dynmodel)
if err != 0:
OpenOrbError('pyoorb failed with error code {:d}'.format(err))
# reorder data on per-column basis and apply units
oo_eph_col = hstack([
oo_eph.transpose()[:, :, i] for i in range(oo_eph.shape[0])
]).tolist()
oo_eph_col_u = []
if scope == 'full':
for i, col in enumerate(oo_eph_col):
oo_eph_col_u.append(
Ephem._unit_apply(col, conf.oorb_ephem_full_units[i]))
ephem = self.from_columns(oo_eph_col_u,
names=conf.oorb_ephem_full_fields)
elif scope == 'basic':
for i, col in enumerate(oo_eph_col):
oo_eph_col_u.append(
Ephem._unit_apply(col, conf.oorb_ephem_basic_units[i]))
ephem = self.from_columns(oo_eph_col_u,
names=conf.oorb_ephem_basic_fields)
# add targetname column
ephem.table.add_column(Column(data=sum(
[[orb['targetname'][i]] * len(epochs)
for i in range(len(orb.table))], []),
name='targetname'),
index=0)
# convert MJD to astropy.time.TimeJulian Date
ephem.table['epoch'] = Time(Time(ephem['MJD'],
format='mjd',
scale=timescale.lower()),
format='jd')
ephem.table.remove_column('MJD')
return ephem
HD142527_sampler.run_sampler(total_orbits,
burn_steps=burn_steps,
thin=thin)
myResults = HD142527_sampler.results
# save posterior to disk
savefile = 'paperdraft_posterior.hdf5'
myResults.save_results(savefile)
print('Saved Posterior!')
plt.rcParams['font.family'] = 'monospace' # Fonts
plt.rcParams['font.monospace'] = 'DejaVu Sans Mono'
sns.set_context("talk")
starttime = Time(datetime.strptime('1990 January 1',
'%Y %B %d')).to_value('mjd', 'long')
# posterior plot
median_values = np.median(myResults.post,
axis=0) # Compute median of each parameter
range_values = np.ones_like(
median_values) * 0.95 # Plot only 95% range for each parameter
corner_figure_median_95 = myResults.plot_corner(range=range_values,
truths=median_values)
corner_figure_median_95.savefig('hd142_postplot.png')
print('Saved basic corner plot!')
# seaborn posterior plot
params = [
'a$_{1}$ [au]', 'e$_{1}$', 'i$_{1}$ [rad]', '$\\omega_{0}$ [rad]',
'$\\Omega_{0}$ [rad]', '$\\tau_{1}$', '$\\pi$ [mas]', '$\\mu_\\alpha$',
def get_from_flatdb(raw_file, mask=False):
'''
Retrieve a master flat and bpmap from the database. Uses the difference in time
to find the most suitable flat.
Params:
- raw_file: The file to be calibrated with the master flat
- mask: Indicates whether or not the program is searching for a mask flat
Return:
- masterFlat: The closest masterFlat
- bp_map: The corresponding bad pixel map
'''
# Establish database connection
connection = sql.MySQLConnection()
connection.connect(buffered=True, host=host, port=port, user=user, passwd=passwd)
cursor = connection.cursor()
cursor.execute('USE WIRC_POL')
masterFlat = ''
bp_map = ''
fore = fits.getheader(raw_file)['FORE']
cursor.execute('SELECT COUNT(*) FROM information_schema.tables WHERE table_name = \"master_flats\"')
if(cursor.fetchone()[0] > 0):
cursor.execute('SELECT COUNT(*) from master_flats where FORE = \"PG\"')
if (not mask) and fore == 'PG' and cursor.fetchone()[0] > 0:
cursor.execute('SELECT File_Path, BP_MAP, UTSHUT FROM master_flats WHERE FORE = \"PG\"')
else:
cursor.execute('SELECT File_Path, BP_MAP, UTSHUT FROM master_flats WHERE NOT FORE = \"PG\"')
files = cursor.fetchall()
if len(files) > 0:
# Retrieve the time in modified julian form for comparison
time_0 = Time([fits.getheader(raw_file)['UTSHUT']])
time_0 = time_0.mjd[0]
# If finding a mask flat, attempt to identify all of the mask flats, removing anything else
# If there are none, return empty strings
if mask:
files_temp = []
for file in files:
hdu = fits.open(file[0])
header = hdu[0].header
try:
if 'OUT' in header['MASKPOS']:
files_temp.append(file)
except KeyError:
pass
hdu.close()
if files_temp == []:
return '', ''
else:
files = files_temp
# Identify the times in modified julian form for comparison
# When attempting to match by filters as well, program was too often returning no results. Switched to only time
times = [x[2] for x in files]
times = Time(times)
times = times.mjd
# Create list of differences in time
diff = []
for time in times:
diff.append(abs(float(time) - float(time_0)))
diff = np.array(diff)
# Identify the closest master flat in time
ind = np.where(diff == min(diff))[0][0]
connection.commit()
connection.close()
# Retrieve the filepaths
masterFlat = files[ind][0]
bp_map = files[ind][1]
return masterFlat, bp_map
lat='25.995789d',
height=12 * u.m)
},
{
'name':
"Guillermo Haro",
'location':
EarthLocation.from_geodetic(lon='-110.384722d',
lat='31.052778d',
height=2480 * u.m)
},
]
from astropy.time import Time
# complete with Apr 10, 2019 and whatever time here
date_time = Time(...)
# Name, RA (in hour angle!!) and Dec (in degrees)
from astropy.io import ascii
target_list = ascii.read("""
PGC003183 0.90109 73.08478
UGC03858 7.51289 73.63019
UGC03859 7.51347 73.70633
UGC03889 7.56557 73.64353
PGC616899 14.55716 -37.83552
PGC021381 7.61031 74.44653
PGC021386 7.61209 74.45029
UGC03929 7.66532 75.42469
ESO336-006 18.60201 -37.94586
ESO336-001 18.54346 -39.45677
ESO327-012 14.71358 -40.60005
def photometry(image_paths, master_dark_path, master_flat_path, star_positions,
aperture_radii, centroid_stamp_half_width, psf_stddev_init,
aperture_annulus_radius):
"""
Parameters
----------
master_dark_path : str
Path to master dark frame
master_flat_path :str
Path to master flat field
target_centroid : `~numpy.ndarray`
position of centroid, with shape (2, 1)
comparison_flux_threshold : float
Minimum fraction of the target star flux required to accept for a
comparison star to be included
aperture_radii : `~numpy.ndarray`
Range of aperture radii to use
centroid_stamp_half_width : int
Centroiding is done within image stamps centered on the stars. This
parameter sets the half-width of the image stamps.
psf_stddev_init : float
Initial guess for the width of the PSF stddev parameter, used for
fitting 2D Gaussian kernels to the target star's PSF.
aperture_annulus_radius : int
For each aperture in ``aperture_radii``, measure the background in an
annulus ``aperture_annulus_radius`` pixels bigger than the aperture
radius
"""
master_dark = fits.getdata(master_dark_path)
master_flat = fits.getdata(master_flat_path)
star_positions = np.array(star_positions)#.T
# Initialize some empty arrays to fill with data:
times = np.zeros(len(image_paths))
fluxes = np.zeros((len(image_paths), len(star_positions),
len(aperture_radii)))
errors = np.zeros((len(image_paths), len(star_positions),
len(aperture_radii)))
xcentroids = np.zeros((len(image_paths), len(star_positions)))
ycentroids = np.zeros((len(image_paths), len(star_positions)))
airmass = np.zeros(len(image_paths))
psf_stddev = np.zeros(len(image_paths))
medians = np.zeros(len(image_paths))
with ProgressBar(len(image_paths)) as bar:
for i in range(len(image_paths)):
bar.update()
# Subtract image by the dark frame, normalize by flat field
imagedata = (fits.getdata(image_paths[i]) - master_dark) / master_flat
from scipy.ndimage import gaussian_filter
smoothed_image = gaussian_filter(imagedata, 3)
brightest_star_coords = np.unravel_index(np.argmax(smoothed_image),
smoothed_image.shape)
if i == 0:
brightest_start_coords_init = brightest_star_coords
offset = np.array(brightest_start_coords_init) - np.array(brightest_star_coords)
print('offset', offset)
# Collect information from the header
imageheader = fits.getheader(image_paths[i])
exposure_duration = imageheader['EXPTIME']
times[i] = Time(imageheader['DATE-OBS'], format='isot', scale=imageheader['TIMESYS'].lower()).jd
medians[i] = np.median(imagedata)
airmass[i] = imageheader['AIRMASS']
# Initial guess for each stellar centroid informed by previous centroid
for j in range(len(star_positions)):
init_x = star_positions[j][0] + offset[0]
init_y = star_positions[j][1] + offset[1]
# Cut out a stamp of the full image centered on the star
image_stamp = imagedata[int(init_y) - centroid_stamp_half_width:
int(init_y) + centroid_stamp_half_width,
int(init_x) - centroid_stamp_half_width:
int(init_x) + centroid_stamp_half_width]
x_stamp_centroid, y_stamp_centroid = np.unravel_index(np.argmax(image_stamp),
image_stamp.shape)
x_centroid = x_stamp_centroid + init_x - centroid_stamp_half_width
y_centroid = y_stamp_centroid + init_y - centroid_stamp_half_width
# plt.imshow(image_stamp, origin='lower')
# plt.scatter(y_stamp_centroid, x_stamp_centroid)
# plt.show()
# plt.imshow(imagedata, origin='lower')
# plt.scatter(x_centroid, y_centroid)
#
xcentroids[i, j] = x_centroid
ycentroids[i, j] = y_centroid
# For the target star, measure PSF:
if j == 0:
psf_model_init = models.Gaussian2D(amplitude=np.max(image_stamp),
x_mean=centroid_stamp_half_width,
y_mean=centroid_stamp_half_width,
x_stddev=psf_stddev_init,
y_stddev=psf_stddev_init)
fit_p = fitting.LevMarLSQFitter()
y, x = np.mgrid[:image_stamp.shape[0], :image_stamp.shape[1]]
best_psf_model = fit_p(psf_model_init, x, y, image_stamp -
np.median(image_stamp))
psf_stddev[i] = 0.5*(best_psf_model.x_stddev.value +
best_psf_model.y_stddev.value)
positions = np.vstack([ycentroids[i, :], xcentroids[i, :]]).T
for k, aperture_radius in enumerate(aperture_radii):
target_apertures = CircularAperture(positions, aperture_radius)
background_annuli = CircularAnnulus(positions,
r_in=aperture_radius +
aperture_annulus_radius,
r_out=aperture_radius +
2 * aperture_annulus_radius)
flux_in_annuli = aperture_photometry(imagedata,
background_annuli)['aperture_sum'].data
background = flux_in_annuli/background_annuli.area()
flux = aperture_photometry(imagedata,
target_apertures)['aperture_sum'].data
background_subtracted_flux = (flux - background *
target_apertures.area())
print(background, flux)
# plt.imshow(smoothed_image, origin='lower')
# target_apertures.plot()
# background_annuli.plot()
# plt.show()
fluxes[i, :, k] = background_subtracted_flux/exposure_duration
errors[i, :, k] = np.sqrt(flux)
# Save some values
results = PhotometryResults(times, fluxes, errors, xcentroids, ycentroids,
airmass, medians, psf_stddev, aperture_radii)
return results
def hdrcheck(imlist_name='a*.fit', camera='KCT_STX16803'):
"""
1. Description
: When performing observation, header input could be entered wrong. This issue can name the calibrated files inconsistent way for IMSNG survey. To resolve this problem, header ['OBJECT'] would be modified to the name in IMSNG target catalog (alltarget.dat) when the image center is < fov/2 of KCT STX16803. In addition, MJD will be entered, so even if input images are not IMSNG target, they should be processed by this function.
2. Usage
>>> hdrcheck()
3. History
2018. Firstly made.
2020.03.01 Edited for KL4040.
2020.03.06 Modified for KCT STX16803 process
"""
import os
import sys
import glob
import astropy.units as u
from astropy.time import Time
from astropy.io import ascii
from astropy.io import fits
from lgpy.hdrcheck import wcscenter
from astropy.coordinates import SkyCoord
KCT_fov = 49.4 # arcmin
all_catname = '/data1/code/alltarget.dat'
all_cat = ascii.read(all_catname)
ra, dec = all_cat['ra'], all_cat['dec']
radeg, decdeg = [], []
for i in range(len(all_cat)):
c = SkyCoord(str(ra[i]) + ' ' + str(dec[i]), unit=(u.hourangle, u.deg))
radeg.append(c.ra.deg)
decdeg.append(c.dec.deg)
all_cat['radeg'] = radeg
all_cat['decdeg'] = decdeg
coo_all = SkyCoord(radeg, decdeg, unit=(u.deg, u.deg))
imlist = glob.glob(imlist_name)
imlist.sort()
for i in range(len(imlist)):
inim = imlist[i]
print(inim)
data, hdr = fits.getdata(inim, header=True)
CRVAL1, CRVAL2 = wcscenter(inim)
#camera = inim.split('-')[1]
mjd0 = 2400000.5
if camera == 'MAO_SNUCAM':
t = Time(hdr['utdate'] + 'T' + hdr['utstart'],
format='isot',
scale='utc')
jd = t.jd
mjd = t.mjd
hdr['JD'] = round(jd, 5)
elif camera == 'KCT_STX16803':
t = Time(hdr['DATE-OBS'], format='isot', scale='utc')
jd = t.jd
mjd = t.mjd
else:
jd = hdr['jd']
mjd = jd - mjd0
hdr['MJD'] = round(mjd, 5)
coo_target = SkyCoord(CRVAL1, CRVAL2, unit=(u.deg, u.deg))
indx, d2d, d3d = coo_target.match_to_catalog_sky(coo_all)
if d2d.arcmin > KCT_fov / 2.:
print(
'Coordinates of the image are not in IMSNG catalog. No matching. Maybe you obtained wrong field. OR Non-IMSNG target.'
)
fits.writeto(inim, data, header=hdr, overwrite=True)
print('Only MJD is entered in image header.')
pass
elif d2d.arcmin < KCT_fov / 2.:
obj = all_cat[indx]['obj']
print('======================================')
print(obj + ' is matched.')
print(str(round(d2d.arcmin[0], 3)) + ' arcmin apart')
print('======================================')
hdr['object'] = obj
fits.writeto(inim, data, header=hdr, overwrite=True)
print('Header info inspection is finished.')
def t_DATE(self, t):
r'\d\d\d\d-\d\d-\d\d(T\d\d:\d\d(:\d\d(.\d+)?)?)?Z?'
from astropy.time import Time
t.value = Time(t.value, scale='utc')
return t
# units must simply all be the same. For proper motion (pmxi, pmxn) they
# must be [angular] per year.
# coords in radians
a = coords.ra.rad
d = coords.dec.rad
# parallax factors in xi and xn
Fxi = (R(t).dot(W(a)))
Fxn = (R(t).dot(N(a,d)))
# time difference in years
dt = t.jyear - t0.jyear
# coordinates at times in 't'
xix = xi0 + dt * pmxi + plx * Fxi
xnx = xn0 + dt * pmxn + plx * Fxn
# format output
if isinstance(xix, float):
return np.array([xix, xnx])
else:
return np.array(zip(xix, xnx))
if __name__=="__main__":
t0 = Time(2012.0, format='jyear')
t = Time([2010.0, 2010.5, 2011.3, 2015.0], format='jyear')
coord = SkyCoord(ra=180., dec=45., unit='deg', frame='icrs')
#print T_pos(t, t0, coord, 0, 0, 2, 1, 0.5)
val = float(s)
return True
except ValueError:
return False
if __name__ == "__main__":
sys.exit("Not prepared yet. Under construction!")
thetamin = 100.0 #mas
thetamax = 2000.0 #mas
maxalt = 30.0 #maximum altitude
lat_subaru = 19.828611
lon_subaru = 204.51945
height_subaru = 4139.0
utcoffset = 10.0 * u.hour
midlocal = Time('2019-7-16 00:00:00')
midnight = midlocal + utcoffset
print(midlocal.iso)
location_subaru = EarthLocation(lat=lat_subaru * u.deg,
lon=lon_subaru * u.deg,
height=height_subaru * u.m)
delta_midnight = np.linspace(-12, 12, 1000) * u.hour
times_obs = midnight + delta_midnight
frame = AltAz(obstime=midnight + delta_midnight, location=location_subaru)
sunaltazs = get_sun(midnight + delta_midnight).transform_to(frame)
nightmask = (sunaltazs.alt < -0 * u.deg)
dat = pd.read_csv("../database/sixth/sixth.dat",
delimiter="|",
comment="#")
def from_mpc(cls, targetids, epochs=None, location='500', **kwargs):
"""Load ephemerides from the
`Minor Planet Center <http://minorplanetcenter.net>`_.
Parameters
----------
targetids : str or iterable of str
Target identifier, resolvable by the Minor Planet
Ephemeris Service [MPES]_, e.g., 2P, C/1995 O1, P/Encke,
(1), 3200, Ceres, and packed designations, for one or more
targets.
epochs : `~astropy.time.Time` object, or dictionary, optional
Request ephemerides at these epochs. May be a single
epoch or multiple epochs as `~astropy.time.Time`
(see :ref:`epochs`)or a
dictionary describing a linearly-spaced array
of epochs. All epochs should be provided in UTC; if not,
they will be converted to UTC and a
`~sbpy.data.TimeScaleWarning` will be raised.
If ``None`` (default), the current date and
time will be used.
For the dictionary format, the keys ``start`` (start
epoch), ``step`` (step size), ``stop`` (end epoch), and/or
``number`` (number of epochs total) are used. Only one of
``stop`` and ``number`` may be specified at a time.
``step``, ``stop``, and ``number`` are optional. The values of
of ``start`` and ``stop`` must be `~astropy.time.Time` objects.
``number`` should be an integer value; ``step`` should be a
`~astropy.units.Quantity` with an integer value and units of
seconds, minutes, hours, or days.
All epochs should be provided in UTC; if not, they will be
converted to UTC and a `~sbpy.data.TimeScaleWarning` will be
raised.
location : various, optional
Location of the observer as an IAU observatory code
[OBSCODES]_ (string), a 3-element array of Earth
longitude, latitude, altitude, or an
`~astropy.coordinates.EarthLocation`. Longitude and
latitude should be parseable by
`~astropy.coordinates.Angle`, and altitude should be
parsable by `~astropy.units.Quantity` (with units of
length). If ``None``, then the geocenter (code 500) is
used.
**kwargs
Additional keyword arguments are passed to
`~astroquery.mpc.MPC.get_ephemerides`: ``eph_type``,
``ra_format``, ``dec_format``, ``proper_motion``,
``proper_motion_unit``, ``suppress_daytime``,
``suppress_set``, ``perturbed``, ``unc_links``, ``cache``.
Returns
-------
`~Ephem` object
The resulting object will be populated with columns as
defined in
`~astroquery.mpc.get_ephemerides`; refer
to that document on information on how to modify the list
of queried parameters.
Examples
--------
Query a single set of ephemerides of Ceres as observed from
Maunakea:
>>> from sbpy.data import Ephem
>>> from astropy.time import Time
>>> epoch = Time('2018-05-14', scale='utc')
>>> eph = Ephem.from_mpc('ceres', epoch, 568) # doctest: +REMOTE_DATA
Query a range of ephemerides of comet 2P/Encke as observed from
Maunakea:
>>> epochs = {'start': Time('2019-01-01'),
... 'step': 1*u.d, 'number': 365}
>>> eph = Ephem.from_mpc('2P', epochs, 568) # doctest: +REMOTE_DATA
Notes
-----
* All properties are provided in the J2000.0 reference system.
* See `astroquery.mpc.MPC.get_ephemerides` and the Minor
Planet Ephemeris Service user's guide [MPES]_ for details,
including accetable target names.
References
----------
.. [MPES] Wiliams, G. The Minor Planet Ephemeris Service.
https://minorplanetcenter.org/iau/info/MPES.pdf
.. [OBSCODES] IAU Minor Planet Center. List of observatory
codes. https://minorplanetcenter.org/iau/lists/ObsCodesF.html
"""
# parameter check
# if targetids is a list, run separate Horizons queries and append
if not isinstance(targetids, (list, ndarray, tuple)):
targetids = [targetids]
if isinstance(epochs, Time):
if epochs.scale is not 'utc':
warn(('converting {} epochs to utc for use in '
'astroquery.mpc').format(epochs.scale), TimeScaleWarning)
epochs = epochs.utc
start = None
elif isinstance(epochs, dict):
start = epochs['start'] # required
if start.scale is not 'utc':
warn(('converting {} start epoch to utc for use in '
'astroquery.mpc').format(start.scale), TimeScaleWarning)
start = start.utc
step = epochs.get('step')
stop = epochs.get('stop')
if stop is not None and stop.scale is not 'utc':
warn(('converting {} stop epoch to utc for use in '
'astroquery.mpc').format(stop.scale), TimeScaleWarning)
stop = stop.utc
number = epochs.get('number')
if step is not None and stop is None:
step = u.Quantity(step)
if step.unit not in (u.d, u.h, u.min, u.s):
raise QueryError(
'step must have units of days, hours, minutes,'
' or seconds')
if stop is not None:
if step is not None and number is None:
# start and stop both defined, estimate number of steps
dt = (Time(stop).jd - Time(start).jd) * u.d
number = int((dt / step).decompose()) + 1
elif step is None and number is not None:
step = int(
(stop - start).jd * 1440 / (number - 1)) * u.minute
else:
raise QueryError(
('epoch definition unclear; step xor number '
'must be provided with start and stop'))
else:
start = None
if epochs is None:
epochs = Time.now()
if not iterable(epochs):
epochs = [epochs]
# append ephemerides table for each targetid
all_eph = None
for targetid in targetids:
try:
# get ephemeris
if start is None:
eph = []
for i in range(len(epochs)):
e = MPC.get_ephemeris(targetid,
location=location,
start=Time(epochs[i],
scale='utc'),
number=1,
**kwargs)
e['Date'] = e['Date'].iso # for vstack to work
eph.append(e)
eph = vstack(eph)
eph['Date'] = Time(eph['Date'], scale='utc')
else:
eph = MPC.get_ephemeris(targetid,
location=location,
start=start,
step=step,
number=number,
**kwargs)
except InvalidQueryError as e:
raise QueryError('Error raised by astroquery.mpc: {:s}'.format(
str(e)))
# add targetname column
eph.add_column(Column([targetid] * len(eph), name='Targetname'),
index=0)
if all_eph is None:
all_eph = eph
else:
all_eph = vstack([all_eph, eph])
# if ra_format or dec_format is defined, then units must be
# dropped or else QTable will raise an exception because
# strings cannot have units
if 'ra_format' in kwargs:
all_eph['RA'].unit = None
if 'dec_format' in kwargs:
all_eph['Dec'].unit = None
return cls.from_table(all_eph)
def get_ephemerides_async(self, targetname, objtype='asteroid',
epoch=None, epoch_step='1d', epoch_nsteps=1,
location=500, coordtype=1,
timescale='UTC',
planetary_theory='INPOP',
ephtype=1,
refplane='equator',
elements='ASTORB',
radial_velocity=False,
get_query_payload=False,
get_raw_response=False, cache=True):
"""
Query the
`IMCCE Miriade <http://vo.imcce.fr/webservices/miriade/>`_
`ephemcc <http://vo.imcce.fr/webservices/miriade/?ephemcc>`_
service.
Parameters
----------
targetname : str
Name of the target to be queried.
objtype : str, optional
Type of the object to be queried. Available are: ``'asteroid'``,
``'comet'``, ``'dwarf planet'``, ``'planet'``, ``'satellite'``.
Default: ``'asteroid'``
epoch : `~astropy.time.Time` object, float, str,``None``, optional
Start epoch of the query. If a float is provided, it is
expected to be a Julian Date; if a str is provided, it is
expected to be an iso date of the form
``'YYYY-MM-DD HH-MM-SS'``. If ``None`` is provided, the
current date and time are used as epoch. Default: ``None``
epoch_step : str, optional
Step size for ephemerides calculation. Must consist of a decimal
number followed by a single character: (d)ays, (h)ours,
(m)inutes or (s)econds. Default: ``'1d'``
epoch_nsteps : int, optional
Number of increments of ``epoch_step`` starting from ``epoch``
for which ephemerides are calculated. Maximum number of steps
is 5000. Default: 1
location : str, optional
Location of the observer on Earth as a code or a set of
coordinates. See the
`Miriade manual <http://vo.imcce.fr/webservices/miriade/?documentation#field_7>`_
for details. Default: geocentric location (``'500'``)
coordtype : int, optional
Type of coordinates to be calculated: ``1``: spherical, ``2``:
rectangular, ``3``: local coordinates (azimuth and elevation),
``4``: hour angle coordinates, ``5``: dedicated to observation,
``6``: dedicated to AO observation. Default: ``1``
timescale : str, optional
The time scale used in the computation of the ephemerides:
``'UTC'`` or ``'TT'``. Default: ``'UTC'``
planetary_theory : str, optional
Planetary ephemerides set to be utilized in the calculations:
``'INPOP'``, ``'DE405'``, ``'DE406'``. Default: ``'INPOP'``
ephtype : int, optional
Type of ephemerides to be calculated: ``1``: astrometric J2000,
``2``: apparent of the date, ``3``: mean of the date,
``4``: mean J2000, Default: ``1``
refplane : str, optional
Reference plane: ``'equator'`` or ``'ecliptic'``. Default:
``'equator'``
elements : str, optional
Set of osculating elements to be used in the calculations:
``'ASTORB'`` or ``'MPCORB'``. Default: ``'ASTORB'``
radial_velocity : bool, optional
Calculate additional information on the target's radial velocity.
Default: ``False``
get_query_payload : bool, optional
When set to ``True`` the method returns the HTTP request
parameters as a dict, default: ``False``
get_raw_response : bool, optional
Return raw data as obtained by Miriade without parsing the
data into a table, default: ``False``
cache : bool, optional
If ``True`` the query will be cached. Default: ``True``
Notes
-----
The following parameters can be queried using this function. Note
that different ``coordtype`` setting provide different sets of
parameters; number in parentheses denote which ``coordtype``
settings include the parameters.
+------------------+-----------------------------------------------+
| Column Name | Definition |
+==================+===============================================+
| ``target`` | Target name (str, 1, 2, 3, 4, 5, 6 ) |
+------------------+-----------------------------------------------+
| ``epoch`` | Ephemerides epoch (JD, float, 1, 2, 3, 4, 5, |
| | 6) |
+------------------+-----------------------------------------------+
| ``RA`` | Target RA at ``ephtype`` (deg, float, 1) |
+------------------+-----------------------------------------------+
| ``DEC`` | Target declination at ``ephtype`` (deg, |
| | float, 1, 4, 5) |
+------------------+-----------------------------------------------+
| ``RAJ2000`` | Target RA at J2000 (deg, float, 5, 6) |
+------------------+-----------------------------------------------+
| ``DECJ2000`` | Target declination at J2000 (deg, float, 5, 6)|
+------------------+-----------------------------------------------+
| ``AZ`` | Target azimuth (deg, float, 3, 5) |
+------------------+-----------------------------------------------+
| ``EL`` | Target elevation (deg, float, 3, 5) |
+------------------+-----------------------------------------------+
| ``delta`` | Distance from observer (au, float, 1, 2, 3, |
| | 4, 5, 6) |
+------------------+-----------------------------------------------+
| ``delta_rate`` | Rate in observer distance (km/s, float, |
| | 1, 5, 6) |
+------------------+-----------------------------------------------+
| ``V`` | Apparent visual magnitude (mag, float, 1, 2, |
| | 3, 4, 5, 6) |
+------------------+-----------------------------------------------+
| ``alpha`` | Solar phase angle (deg, 1, 2, 3, 4, 5, 6) |
+------------------+-----------------------------------------------+
| ``elong`` | Solar elongation angle (deg, 1, 2, 3, 4, 5, 6)|
+------------------+-----------------------------------------------+
| ``RAcosD_rate`` | Rate of motion in RA * cos(DEC) (arcsec/min, |
| | float, 1, 5, 6) |
+------------------+-----------------------------------------------+
| ``DEC_rate`` | Rate of motion in DEC (arcsec/min, float, 1, |
| | 5, 6) |
+------------------+-----------------------------------------------+
| ``x`` | X position state vector (au, float, 2) |
+------------------+-----------------------------------------------+
| ``y`` | Y position state vector (au, float, 2) |
+------------------+-----------------------------------------------+
| ``z`` | Z position state vector (au, float, 2) |
+------------------+-----------------------------------------------+
| ``vx`` | X velocity state vector (au/d, float, 2) |
+------------------+-----------------------------------------------+
| ``vy`` | Y velocity state vector (au/d, float, 2) |
+------------------+-----------------------------------------------+
| ``vz`` | Z velocity state vector (au/d, float, 2) |
+------------------+-----------------------------------------------+
| ``rv`` | Radial velocity (km/s, float, 2) |
+------------------+-----------------------------------------------+
| ``heldist`` | Target heliocentric distance (au, float, 2, |
| | 5, 6) |
+------------------+-----------------------------------------------+
| ``x_h`` | X heliocentric position vector (au, float, 2) |
+------------------+-----------------------------------------------+
| ``y_h`` | Y heliocentric position vector (au, float, 2) |
+------------------+-----------------------------------------------+
| ``z_h`` | Z heliocentric position vector (au, float, 2) |
+------------------+-----------------------------------------------+
| ``vx_h`` | X heliocentric vel. vector (au/d, float, 2) |
+------------------+-----------------------------------------------+
| ``vy_h`` | Y heliocentric vel. vector (au/d, float, 2) |
+------------------+-----------------------------------------------+
| ``vz_h`` | Z heliocentric vel. vector (au/d, float, 2) |
+------------------+-----------------------------------------------+
| ``hourangle`` | Target hour angle (deg, float, 4, 5) |
+------------------+-----------------------------------------------+
| ``siderealtime`` | Local sidereal time (hr, float, 5, 6) |
+------------------+-----------------------------------------------+
| ``refraction`` | Atmospheric refraction (arcsec, float, 5, 6) |
+------------------+-----------------------------------------------+
| ``airmass`` | Target airmass (float, 5, 6) |
+------------------+-----------------------------------------------+
| ``posunc`` | Positional uncertainty (arcsec, float, 5, 6) |
+------------------+-----------------------------------------------+
Examples
--------
>>> from astroquery.imcce import Miriade
>>> from astropy.time import Time
>>> epoch = Time('2019-01-01', format='iso')
>>> Miriade.get_ephemerides('3552', epoch=epoch) # doctest: +SKIP
<Table masked=True length=1>
target epoch RA ... DEC_rate delta_rate
d deg ... arcs / min km / s
bytes20 float64 float64 ... float64 float64
----------- -------------------- ------------------ ... ---------- ------------
Don Quixote 2458484.5 16.105294999999998 ... -0.25244 31.4752734
"""
URL = conf.ephemcc_server
TIMEOUT = conf.timeout
if isinstance(epoch, (int, float)):
epoch = Time(epoch, format='jd')
elif isinstance(epoch, str):
epoch = Time(epoch, format='iso')
elif epoch is None:
epoch = Time.now()
request_payload = OrderedDict([
('-name', targetname),
('-type', objtype[0].upper()+objtype[1:]),
('-ep', str(epoch.jd)),
('-step', epoch_step),
('-nbd', epoch_nsteps),
('-observer', location),
('-output', '--jul'),
('-tscale', timescale),
('-theory', planetary_theory),
('-teph', ephtype),
('-tcoor', coordtype),
('-rplane', {'equator': 1, 'ecliptic': 2}[refplane]),
('-oscelem', elements),
('-mime', 'votable')])
if radial_velocity:
request_payload['-output'] += ',--rv'
if get_query_payload:
return request_payload
# query and parse
response = self._request('GET', URL, params=request_payload,
timeout=TIMEOUT, cache=cache)
self._query_uri = response.url
self._get_raw_response = get_raw_response
return response
def from_horizons(cls,
targetids,
id_type='smallbody',
epochs=None,
location='500',
**kwargs):
"""Load target ephemerides from
`JPL Horizons <https://ssd.jpl.nasa.gov/horizons.cgi>`_ using
`astroquery.jplhorizons.HorizonsClass.ephemerides`
Parameters
----------
targetids : str or iterable of str
Target identifier, i.e., a number, name, designation, or JPL
Horizons record number, for one or more targets.
id_type : str, optional
The nature of ``targetids`` provided; possible values are
``'smallbody'`` (asteroid or comet), ``'majorbody'`` (planet or
satellite), ``'designation'`` (asteroid or comet designation),
``'name'`` (asteroid or comet name), ``'asteroid_name'``,
``'comet_name'``, ``'id'`` (Horizons id).
Default: ``'smallbody'``
epochs : `~astropy.time.Time` object, or dictionary, optional
Epochs of elements to be queried; `~astropy.time.Time` objects
support single and multiple epochs;
a dictionary including keywords ``start`` and ``stop``, as well
as either ``step`` or ``number``, can be used to generate a range
of epochs. ``start`` and ``stop`` have to be
`~astropy.time.Time` objects (see :ref:`epochs`).
If ``step`` is provided, a range
of epochs will be queries starting at ``start`` and ending at
``stop`` in steps of ``step``; ``step`` has to be provided as
a `~astropy.units.Quantity` object with integer value and a
unit of either minutes, hours, days, or years. If
``number`` is provided as an integer, the
interval defined by
``start`` and ``stop`` is split into ``number`` equidistant
intervals. If ``None`` is
provided, current date and time are
used. All epochs should be provided in UTC; if not, they will be
converted to UTC and a `~sbpy.data.TimeScaleWarning` will be
raised. Default: ``None``
location : str or `~astropy.coordinates.EarthLocation`, optional
Location of the observer using IAU observatory codes
(see `IAU observatory codes
<https://www.minorplanetcenter.net/iau/lists/ObsCodesF.html>`__)
or as `~astropy.coordinates.EarthLocation`.
Default: ``'500'`` (geocentric)
**kwargs : optional
Arguments that will be provided to
`astroquery.jplhorizons.HorizonsClass.ephemerides`.
Notes
-----
* For detailed explanations of the queried fields, refer to
`astroquery.jplhorizons.HorizonsClass.ephemerides` and the
`JPL Horizons documentation <https://ssd.jpl.nasa.gov/?horizons_doc>`_.
* By default, all properties are provided in the J2000.0 reference
system. Different settings can be chosen using
additional keyword arguments as used by
`astroquery.jplhorizons.HorizonsClass.ephemerides`.
Returns
-------
`~Ephem` object
The resulting object will be populated with columns as
defined in
`~astroquery.jplhorizons.HorizonsClass.ephemerides`; refer
to that document on information on how to modify the list
of queried parameters.
Examples
--------
>>> from sbpy.data import Ephem
>>> from astropy.time import Time
>>> epoch = Time('2018-05-14', scale='utc')
>>> eph = Ephem.from_horizons('ceres', epochs=epoch) # doctest: +SKIP
"""
# modify epoch input to make it work with astroquery.jplhorizons
# maybe this stuff should really go into that module....
if epochs is None:
epochs = [Time.now().utc.jd]
elif isinstance(epochs, Time):
if epochs.scale is not 'utc':
warn(('converting {} epochs to utc for use in '
'astroquery.jplhorizons').format(epochs.scale),
TimeScaleWarning)
epochs = epochs.utc.jd
elif isinstance(epochs, dict):
if 'start' in epochs and 'stop' in epochs and 'number' in epochs:
epochs['step'] = epochs['number'] * u.dimensionless_unscaled
# convert to utc and iso for astroquery.jplhorizons
epochs['start'] = epochs['start'].utc.iso
epochs['stop'] = epochs['stop'].utc.iso
if 'step' in epochs:
if epochs['step'].unit is not u.dimensionless_unscaled:
epochs['step'] = '{:d}{:s}'.format(
int(epochs['step'].value), {
u.minute: 'm',
u.hour: 'h',
u.d: 'd',
u.year: 'y'
}[epochs['step'].unit])
else:
epochs['step'] = '{:d}'.format(
int(epochs['step'].value - 1))
# if targetids is a list, run separate Horizons queries and append
if not isinstance(targetids, (list, ndarray, tuple)):
targetids = [targetids]
# turn EarthLocation into dictionary of strings as used by
# astroquery.jplhorizons
if isinstance(location, EarthLocation):
location = {
'lon': location.lon.deg,
'lat': location.lat.deg,
'elevation': location.height.to('km')
}
# append ephemerides table for each targetid
all_eph = None
for targetid in targetids:
# load ephemerides using astroquery.jplhorizons
obj = Horizons(id=targetid,
id_type=id_type,
location=location,
epochs=epochs)
try:
eph = obj.ephemerides(**kwargs)
except ValueError as e:
raise QueryError(
('Error raised by astroquery.jplhorizons: {:s}\n'
'The following query was attempted: {:s}').format(
str(e), obj.uri))
# workaround for current version of astroquery to make
# column units compatible with astropy.table.QTable
# should really change '---' units to None in
# astroquery.jplhorizons.__init__.py
for column_name in eph.columns:
if eph[column_name].unit == '---':
eph[column_name].unit = None
# workaround for astroquery 0.3.9.dev5056 and earlier,
# Horizons column named RA_rate always includes the
# cos(Dec) term:
if 'RA_rate' in eph.colnames:
eph['RA_rate'].name = 'RA*cos(Dec)_rate'
if all_eph is None:
all_eph = eph
else:
all_eph = vstack([all_eph, eph])
# turn epochs into astropy.time.Time and apply timescale
# convert ut1 epochs to utc
# https://ssd.jpl.nasa.gov/?horizons_doc
if any(all_eph['datetime_jd'] < 2437665.5):
all_eph['datetime_jd'][all_eph['datetime_jd'] < 2437665.5] = Time(
all_eph['datetime_jd'][all_eph['datetime_jd'] < 2437665.5],
scale='ut1',
format='jd').utc.jd
all_eph['epoch'] = Time(all_eph['datetime_jd'],
format='jd',
scale='utc')
all_eph['siderealtime'].unit = u.Unit('hour')
all_eph.remove_column('datetime_jd')
all_eph.remove_column('datetime_str')
return cls.from_table(all_eph)
def from_hdu(self, hdu=None, module=None, output=None, **kwargs):
"""Class method to instantiate a KeplerCotrendingBasisVectors object
from a CBV FITS HDU.
Kepler/K2 CBVs are all in the same FITS file for each quarter/campaign,
so, when instantiating the CBV object we must specify which module and
output we desire. Only Single-Scale CBVs are stored for Kepler.
Parameters
----------
hdu : astropy.io.fits.hdu.hdulist.HDUList
A pyfits opened FITS file containing the CBVs
module : int
Kepler CCD module 2 - 84
output : int
Kepler CCD output 1 - 4
**kwargs : Optional arguments
Passed to the TimeSeries superclass
"""
assert module > 1 and module < 85, 'Invalid module number'
assert output > 0 and output < 5, 'Invalid output number'
# Get the mission: Kepler or K2
# Sadly, the HDU does not explicitly say if this is Kepler or K2 CBVs.
if 'QUARTER' in hdu['PRIMARY'].header:
mission = 'Kepler'
elif 'CAMPAIGN' in hdu['PRIMARY'].header:
mission = 'K2'
else:
raise Exception(
'This does not appear to be a Kepler or K2 FITS HDU')
extName = 'MODOUT_{0}_{1}'.format(module, output)
try:
# Read the columns and meta data
dataTbl = Table.read(hdu[extName], format="fits")
dataTbl.meta.update(hdu[0].header)
dataTbl.meta.update(hdu[extName].header)
# TimeSeries-based objects require a dedicated time column
# Replace NaNs with default time '2000-01-01', otherwise,
# astropy.time.Time complains
nanHere = np.nonzero(np.isnan(dataTbl['TIME_MJD'].data))[0]
timeData = dataTbl['TIME_MJD'].data
timeData[nanHere] = Time(['2000-01-01'], scale='utc').mjd
cbvTime = Time(timeData, format='mjd')
dataTbl.remove_column('TIME_MJD')
# Gaps are labelled as 'GAPFLAG' so rename!
dataTbl['GAP'] = dataTbl['GAPFLAG']
dataTbl.remove_column('GAPFLAG')
dataTbl.meta['MISSION'] = mission
dataTbl.meta['CBV_TYPE'] = 'SingleScale'
except:
dataTbl = None
cbvTime = None
# Here we instantiate the actual object
return self(data=dataTbl, time=cbvTime, **kwargs)
def test_time2et(self):
from astropy.time import Time
core.time2et(Time('2000-1-1', scale='utc'))
