def azel2radec(az_deg, el_deg, lat_deg, lon_deg, dtime):
obs = EarthLocation(lat=lat_deg * u.deg, lon=lon_deg * u.deg)
direc = AltAz(location=obs, obstime=Time(dtime),
az=az_deg * u.deg, alt=el_deg * u.deg)
sky = SkyCoord(direc.transform_to(ICRS()))
return sky.ra.deg, sky.dec.deg
def RunAstrometryCalibration(InputImage,Outputfile=None):
""" Does Astrometry calibration of input fits image InputImage.
Returns True if calibration succeded."""
# Following lines are TIRSPEC specific for formating the time from fits header
obs_timehdr = fits.getval(InputImage,'OBSTIME')
obs_datehdr = fits.getval(InputImage,'OBSDATE')
time_str = '{0} {1}'.format('-'.join(obs_datehdr.split('.')),obs_timehdr)
time = Time(time_str,format='iso',scale='utc')
hct_hanle = EarthLocation(lat=32.77944*u.deg, lon=78.9641*u.deg, height=4500*u.m)
zenith = AltAz(location=hct_hanle, obstime=time, az=0*u.deg, alt=90*u.deg)
ZenithRaDec = zenith.transform_to(ICRS)
# Finally call astrometry.net software to calibrate fits image
Z_ra = '{0.ra}'.format(ZenithRaDec).split()[0]
Z_dec = '{0.dec}'.format(ZenithRaDec).split()[0]
ret = subprocess.call(['solve-field','--no-plots', '--ra',Z_ra, '--dec',Z_dec, '--radius','85',
'--scale-units','arcsecperpix', '--scale-low','0.28', '--scale-high','0.32',
InputImage])
print(ret)
if ret == 0:
if Outputfile is not None:
print('Copying {0} to {1}'.format(os.path.splitext(InputImage)[0]+'.new',Outputfile))
shutil.copy(os.path.splitext(InputImage)[0]+'.new',Outputfile)
return True
else:
return False
def azel2radec(az_deg, el_deg, lat_deg, lon_deg, dtime):
if usevallado:
ra_deg, dec_deg = azel2radecvallado(
az_deg,el_deg,lat_deg,lon_deg,dtime)
else: #use astropy v1.0 +
obs = EarthLocation(lat=lat_deg*u.deg, lon=lon_deg*u.deg)
direc = AltAz(location=obs, obstime=Time(dtime),
az=az_deg*u.deg, alt=el_deg*u.deg)
sky = SkyCoord(direc.transform_to(ICRS()))
return sky.ra.deg, sky.dec.deg
def azel2radec(az_deg: float, el_deg: float,
lat_deg: float, lon_deg: float,
time: datetime, usevallado: bool = False) -> Tuple[float, float]:
"""
viewing angle (az, el) to sky coordinates (ra, dec)
Parameters
----------
az_deg : float or numpy.ndarray of float
azimuth [degrees clockwize from North]
el_deg : float or numpy.ndarray of float
elevation [degrees above horizon (neglecting aberration)]
lat_deg : float
observer latitude [-90, 90]
lon_deg : float
observer longitude [-180, 180] (degrees)
time : datetime.datetime or str
time of observation
usevallado : bool, optional
default use astropy. If true, use Vallado algorithm
Returns
-------
ra_deg : float or numpy.ndarray of float
ecliptic right ascension (degress)
dec_deg : float or numpy.ndarray of float
ecliptic declination (degrees)
"""
if usevallado or Time is None: # non-AstroPy method, less accurate
return vazel2radec(az_deg, el_deg, lat_deg, lon_deg, time)
obs = EarthLocation(lat=lat_deg * u.deg, lon=lon_deg * u.deg)
direc = AltAz(location=obs, obstime=Time(str2dt(time)),
az=az_deg * u.deg, alt=el_deg * u.deg)
sky = SkyCoord(direc.transform_to(ICRS()))
return sky.ra.deg, sky.dec.deg
def altaz_to_radec(alt=35, az=90, location=None, obstime=None, *args, **kwargs):
""" Convert alt/az degrees to RA/Dec SkyCoord
Args:
alt (int, optional): Altitude, defaults to 35
az (int, optional): Azimute, defaults to 90 (east)
location (None, required): A ~astropy.coordinates.EarthLocation
location must be passed.
obstime (None, optional): Time for object, defaults to `current_time`
Returns:
`astropy.coordinates.SkyCoord: FK5 SkyCoord
"""
assert location is not None
if obstime is None:
obstime = current_time()
verbose = kwargs.get('verbose', False)
if verbose:
print("Getting coordinates for Alt {} Az {}, from {} at {}".format(alt, az, location, obstime))
altaz = AltAz(obstime=obstime, location=location, alt=alt * u.deg, az=az * u.deg)
return SkyCoord(altaz.transform_to(ICRS))
def SUN_POS():
"""
Finds sun position in RA and DEC at sunrise and sunset to find
which parts of the sky are viewable
"""
LIST = []
loc = coord.EarthLocation(lon=28.7134 * u.deg, lat=17.9058 * u.deg)
start = Time(datetime.utcnow(), format='datetime',
scale='utc') - TimeDelta(1, format='jd')
for i in range(0, 384):
T = start + TimeDelta(i * 225, format='sec')
LST = T.sidereal_time('apparent', '28.7134d')
TMP1 = str(LST).split('h')
TMP2 = TMP1[1].split('m')
TMP3 = TMP2[1].split('s')
LST = (15 * float(TMP1[0]) + 0.0166667 * float(TMP2[0]) +
0.000277778 * float(TMP3[0]))
altazframe = AltAz(obstime=T, location=loc)
sunaltaz = coord.get_sun(T).transform_to(altazframe)
if sunaltaz.alt.value <= -47 and len(LIST) == 0:
LIST.append(str(coord.get_sun(T).ra).split('d')[0])
print('Sunset')
print(T)
elif sunaltaz.alt.value >= 48 and len(LIST) == 1:
LIST.append(str(coord.get_sun(T).ra).split('d')[0])
print('Sunrise')
print(T)
break
UP = 30 + (float(LIST[0]) + float(LIST[1])) / 2
LOW = (float(LIST[0]) + float(LIST[1])) / 2 - 30
return ([UP, LOW])
def test_gcrs_altaz():
"""
Check GCRS<->AltAz transforms for round-tripping. Has multiple paths
"""
from astropy.coordinates import EarthLocation
ra, dec, _ = randomly_sample_sphere(1)
gcrs = GCRS(ra=ra[0], dec=dec[0], obstime='J2000')
# check array times sure N-d arrays work
times = Time(np.linspace(2456293.25, 2456657.25, 51) * u.day, format='jd')
loc = EarthLocation(lon=10 * u.deg, lat=80. * u.deg)
aaframe = AltAz(obstime=times, location=loc)
aa1 = gcrs.transform_to(aaframe)
aa2 = gcrs.transform_to(ICRS()).transform_to(CIRS()).transform_to(aaframe)
aa3 = gcrs.transform_to(ITRS()).transform_to(CIRS()).transform_to(aaframe)
# make sure they're all consistent
assert_allclose(aa1.alt, aa2.alt)
assert_allclose(aa1.az, aa2.az)
assert_allclose(aa1.alt, aa3.alt)
assert_allclose(aa1.az, aa3.az)
def airmass(self, ra, dec, lat_input, lon_input, height_input, time_input,
AIRMASS_MIN, AIRMASS_MAX):
""" Airmass calculation at a given time in a particular site."""
# Geodetic coordinates of observatory
observatory = astropy.coordinates.EarthLocation(lat=lat_input * u.deg,
lon=lon_input * u.deg,
height=height_input *
u.m)
max_prob_p = SkyCoord(ra=ra * u.deg, dec=dec * u.deg,
frame='icrs') # ipix sky coordinates
time = Time(time_input) # Time object
altaz = max_prob_p.transform_to(
AltAz(obstime=time,
location=observatory)) # Altitude-Azimuth system
#--> Horizontal coordinates
airmass_value = altaz.secz # 1/cos(altaz)
if airmass_value <= AIRMASS_MIN or airmass_value >= AIRMASS_MAX:
airmass_value = "nan"
else:
airmass_value = round(airmass_value, 2)
return airmass_value
def put_radec(fitsfile,skycoo):
"""Put RA,DEC coordinates into a fits file.
Parameters
----------
fitsfile : string
Fits file name to update.
skycoo : astropy.coordinates.SkyCoord Object
Sky coordinates of the object.
"""
comm = {'OBJCTRA':'Nominal Right Ascension of center of image',
'OBJCTDEC':'Nominal Declination of center of image'}
hdulist = fits.open(fitsfile)
hdu = hdulist[0]
data = hdu.data
try:
target_name = hdu.header['OBJECT'].strip()
except:
msg = '*** FATAL ERROR: target '+target_name+' not found'
t120.log.error(msg)
raiseValueError(msg)
hdu.header['OBJECT'] = (target_name.upper().replace(' ',''),'Target name')
t120.log.info('target_name='+target_name)
time = Time(hdu.header['DATE-OBS'])
site = EarthLocation(lat=hdu.header['SITELAT'], lon=hdu.header['SITELONG'])
altazframe = AltAz(obstime=time, location=site)
altaz = skycoo.transform_to(altazframe)
hdu.header['OBJCTRA'] = (skycoo.ra.to('deg').value,comm['OBJCTRA'])
hdu.header['OBJCTDEC'] = (skycoo.dec.to('deg').value,comm['OBJCTDEC'])
hdu.header['CRVAL1'] = (skycoo.ra.to('deg').value ,'Reference Right ascencion in decimal deg')
hdu.header['CRVAL2'] = (skycoo.dec.to('deg').value,'Reference Declination in decimal deg')
hdu.header['HISTORY'] = 'RA,DEC values inserted with py routine.'
hdu.writeto(fitsfile,overwrite=True)
t120.log.info('File '+fitsfile+' successfully updated with RADEC info')
return
def get_pointing_meridian_scan(time, alt, az, location, x_position, y_position,
altaz=False):
'''
estimate the pointing RA Dec accoriding obs time and init Dec
for drift scan pointing at meridian
time: obs time
'''
c0 = SkyCoord(alt=alt, az=az, frame='altaz', location=location, obstime=time)
c0 = c0.transform_to('icrs')
separation = np.sqrt(x_position ** 2 + y_position ** 2) * u.arcmin
position_angle = np.arctan2(x_position, -y_position) * u.rad
_c = offset_by(c0[:, None], position_angle[None, :], separation[None, :])
#return _c.ra.deg, _c.dec.deg
if altaz:
_altaz = _c.transform_to(AltAz(obstime=time, location=location)).altaz
return _altaz.alt.deg, _altaz.az.deg
else:
return _c.ra.deg, _c.dec.deg
def hx_aer_astropy(x_gcrs, time, observer_lla=None, trans_matrix=None, observer_itrs=None):
"""
desc: measurement function - convert state into a measurement where measurements are [azimuth, elevation]
:param x_gcrs:
:param time:
:param observer_lla:
:param trans_matrix:
:param observer_itrs: Observer coordinates in itrs (meters)
:return: array[azimuth, elevation, slant]
"""
object = SkyCoord(x=x_gcrs[0] * u.m, y=x_gcrs[1] * u.m, z=x_gcrs[2] * u.m, frame='gcrs',
representation_type='cartesian', obstime=time)
if observer_itrs is None:
obs = EarthLocation.from_geodetic(lat=observer_lla[0] * u.rad, lon=observer_lla[1] * u.rad, height=observer_lla[2] * u.m)
else:
obs = EarthLocation.from_geocentric(x=observer_itrs[0] * u.m, y=observer_itrs[1] * u.m, z=observer_itrs[2] * u.m)
AltAz_frame = AltAz(obstime=time, location=obs)
results = object.transform_to(AltAz_frame)
az = results.az.to_value(u.rad)
alt = results.alt.to_value(u.rad)
sr = results.distance.to_value(u.m)
aer = np.array([az, alt, sr])
return aer
def plot2D_runs(runs, names, source, threshold, ax=None):
for i, df in enumerate(runs):
ax = ax or plt.gca()
df_selected = df.query(f'gammaness > {threshold}')
crab = SkyCoord.from_name(source)
altaz = AltAz(
location = EarthLocation.of_site('Roque de los Muchachos'),
obstime = Time(df_selected.dragon_time, format='unix')
)
telescope_pointing = SkyCoord(
alt = u.Quantity(df_selected.alt_tel.to_numpy(), u.rad, copy=False),
az = u.Quantity(df_selected.az_tel.to_numpy(), u.rad, copy=False),
frame = altaz
)
camera_frame = CameraFrame(
focal_length = u.Quantity(df_selected.focal_length.to_numpy(), u.m, copy=False),
telescope_pointing = telescope_pointing,
location = EarthLocation.of_site('Roque de los Muchachos'),
obstime = Time(df_selected.dragon_time, format='unix')
)
crab_cf = crab.transform_to(camera_frame)
crab_tf = crab_cf.transform_to(TelescopeFrame())
ax.scatter(
crab_tf.fov_lon.to_value(u.deg),
crab_tf.fov_lat.to_value(u.deg),
color = f'C{i}',
marker = ',',
label = names[i]
)
ax.legend()
ax.set_xlabel(r'fov_lon$ \,/\, \mathrm{deg}$')
ax.set_ylabel(r'fov_lat$ \,/\, \mathrm{deg}$')
return ax
def hx2(x,
t,
obs_lat=observer_lat,
obs_lon=observer_lon,
obs_height=observer_alt):
# measurement function - convert state into a measurement
# where measurements are [azimuth, elevation]
object = SkyCoord(x=x[0] * u.km,
y=x[1] * u.km,
z=x[2] * u.km,
frame='gcrs',
representation_type='cartesian',
obstime=t)
obs = EarthLocation.from_geodetic(lon=obs_lon * u.rad,
lat=obs_lat * u.rad,
height=obs_height * u.m)
AltAz_frame = AltAz(obstime=t, location=obs)
results = object.transform_to(AltAz_frame)
az = results.az.to_value(u.rad)
alt = results.alt.to_value(u.rad)
sr = results.distance.to_value(u.km)
aer = np.array([az, alt, sr])
return aer
def horizontal_to_camera_cta_simtel(alt, az, alt_pointing, az_pointing,
focal_length):
with warnings.catch_warnings():
warnings.simplefilter('ignore', MissingFrameAttributeWarning)
altaz = AltAz()
source_altaz = SkyCoord(
alt=u.Quantity(alt, u.deg, copy=False),
az=u.Quantity(az, u.deg, copy=False),
frame=altaz,
)
tel_pointing = SkyCoord(
alt=u.Quantity(alt_pointing, u.deg, copy=False),
az=u.Quantity(az_pointing, u.deg, copy=False),
frame=altaz,
)
camera_frame = CameraFrame(
focal_length=u.Quantity(focal_length, u.m, copy=False),
telescope_pointing=tel_pointing,
)
cam_coords = source_altaz.transform_to(camera_frame)
return cam_coords.x.to_value(u.m), cam_coords.y.to_value(u.m)
def makeTableElevConstrains(year, month):
day = 1
t0 = Time("%d-%02d-%02d 00:00:00" % (year, month, day), format = 'iso',
scale = 'utc')
titer = t0
while titer.datetime.month == month:
moonpos = astropy.coordinates.get_moon(titer, location=IAR)
moonHA = observer.target_hour_angle(target = moonpos, time = titer)
moonHA = moonHA.deg
moonAltAz = moonpos.transform_to(AltAz(obstime = titer, location = IAR))
if 180 < moonHA < 360:
moonHA = moonHA - 360.
if (-29 <= moonHA <= -27) or (27 <= moonHA <= 29):
print "%s,%02.02f,%0.03f,%0.02f,%0.02f" % (titer.datetime,
moonHA,
moonpos.dec.deg,
moonAltAz.az.deg,
moonAltAz.alt.deg)
titer += fiveMins
def itrs2horizon(station, ts, positions, coord_type):
"""
Convert cartesian coordinates of targets in ITRF to spherical coordinates in topocentric reference frame for a specific station.
Usage:
az,alt,rho = itrs2horizon(station,ts,ts_quasi_mjd,positions,coord_type)
Inputs:
station -> [numercial array or list with 3 elements] coordinates of station. It can either be geocentric(x, y, z) coordinates or geodetic(lon, lat, height) coordinates.
Unit for (x, y, z) are meter, and for (lon, lat, height) are degree and meter.
ts -> [str array] isot-formatted UTC for interpolated prediction
positions -> [2d float array] target positions in cartesian coordinates in meters w.r.t. ITRF for interpolated prediction.
coord_type -> [str] coordinates type for coordinates of station; it can either be 'geocentric' or 'geodetic'.
Outputs:
az -> [float array] Azimuth for interpolated prediction in degrees
alt -> [float array] Altitude for interpolated prediction in degrees
rho -> [float array] Range for interpolated prediction in meters
"""
if coord_type == 'geocentric':
x, y, z = station
site = EarthLocation.from_geocentric(x, y, z, unit='m')
elif coord_type == 'geodetic':
lat, lon, height = station
site = EarthLocation.from_geodetic(lon, lat, height)
coords = SkyCoord(positions,
unit='m',
representation_type='cartesian',
frame='itrs',
obstime=Time(ts))
horizon = coords.transform_to(AltAz(obstime=Time(ts), location=site))
az, alt, rho = horizon.az.deg, horizon.alt.deg, horizon.distance.m
return az, alt, rho
def get_altaz_in_rotated_frame(
self,
time: Time,
location: EarthLocation,
altaz: SkyCoord,
) -> SkyCoord:
"""Rotates the given coordinates to the frame defined by given the altitude and
azimuth offsets for the given time and observing_location.
Parameters
----------
time : `Time`
The Time for which the AltAz coordinates are valid
location : `EarthLocation`
The observing_location for which the AltAz coordinates are valid
altaz : `SkyCoord`
The altitude and azimuth to rotate.
Returns
-------
telescope_altaz: `SkyCoord`
The altitude and azimuth rotated to the new frame.
"""
telescope_altaz = AltAz(alt=self.delta_alt,
az=self.delta_az,
obstime=time,
location=location)
telescope_frame = altaz.transform_to(
SkyOffsetFrame(origin=telescope_altaz))
return SkyCoord(
alt=Angle(telescope_frame.lon.deg * u.deg),
az=Angle(telescope_frame.lat.deg * u.deg),
frame="altaz",
obstime=time,
location=location,
)
def test_separation_is_the_same():
from ctapipe.coordinates import TelescopeFrame
obstime = Time('2013-11-01T03:00')
location = EarthLocation.of_site('Roque de los Muchachos')
horizon_frame = AltAz(location=location, obstime=obstime)
crab = SkyCoord(ra='05h34m31.94s', dec='22d00m52.2s')
ceta_tauri = SkyCoord(ra='5h37m38.6854231s', dec='21d08m33.158804s')
# simulate crab "on" observations
telescope_pointing = crab.transform_to(horizon_frame)
telescope_frame = TelescopeFrame(
telescope_pointing=telescope_pointing,
location=location,
obstime=obstime,
)
ceta_tauri_telescope = ceta_tauri.transform_to(telescope_frame)
crab_telescope = crab.transform_to(telescope_frame)
sep = ceta_tauri_telescope.separation(crab_telescope).to_value(u.deg)
assert ceta_tauri.separation(crab).to_value(u.deg) == approx(sep, rel=1e-4)
def _orientation(location, time='now'):
"""
Return the orientation angle for the Sun from a specified Earth location and time. The
orientation angle is the angle between local zenith and solar north, measured eastward from
local zenith.
Parameters
----------
location : `~astropy.coordinates.EarthLocation`
Observer location on Earth
time : {parse_time_types}
Time to use in a parse_time-compatible format
Returns
-------
out : `~astropy.coordinates.Angle`
The orientation of the Sun
"""
obstime = parse_time(time)
# Define the frame where its Z axis is aligned with local zenith
local_frame = AltAz(obstime=obstime, location=location)
return _sun_north_angle_to_z(local_frame)
def test_regression_5133():
N = 1000
np.random.seed(12345)
lon = np.random.uniform(-10, 10, N) * u.deg
lat = np.random.uniform(50, 52, N) * u.deg
alt = np.random.uniform(0, 10., N) * u.km
time = Time('2010-1-1')
objects = EarthLocation.from_geodetic(lon, lat, height=alt)
itrs_coo = objects.get_itrs(time)
homes = [
EarthLocation.from_geodetic(lon=-1 * u.deg, lat=52 * u.deg, height=h)
for h in (0, 1000, 10000) * u.km
]
altaz_frames = [AltAz(obstime=time, location=h) for h in homes]
altaz_coos = [itrs_coo.transform_to(f) for f in altaz_frames]
# they should all be different
for coo in altaz_coos[1:]:
assert not quantity_allclose(coo.az, coo.az[0])
assert not quantity_allclose(coo.alt, coo.alt[0])
def astropy_parallactic_angles(times, antenna_positions, field_centre):
"""
Computes parallactic angles per timestep for the given
reference antenna position and field centre.
"""
ap = antenna_positions
fc = field_centre
# Convert from MJD second to MJD
times = Time(times / 86400.00, format='mjd', scale='utc')
ap = EarthLocation.from_geocentric(
ap[:, 0], ap[:, 1], ap[:, 2], unit='m')
fc = SkyCoord(ra=fc[0], dec=fc[1], unit=units.rad, frame='fk5')
pole = SkyCoord(ra=0, dec=90, unit=units.deg, frame='fk5')
cirs_frame = CIRS(obstime=times)
pole_cirs = pole.transform_to(cirs_frame)
fc_cirs = fc.transform_to(cirs_frame)
altaz_frame = AltAz(location=ap[None, :], obstime=times[:, None])
pole_altaz = pole_cirs[:, None].transform_to(altaz_frame)
fc_altaz = fc_cirs[:, None].transform_to(altaz_frame)
return fc_altaz.position_angle(pole_altaz)
def save_png(self, figname: str, beam_contours: bool = True, show_sources: bool = True, **kwargs):
""" """
image_center = altaz_to_radec(
SkyCoord(
self.analog_pointing.az,
self.analog_pointing.alt,
frame=AltAz(
obstime=self.tv_image.time[0],
location=nenufar_position
)
)
)
#kwargs = {}
if show_sources:
src_names = []
src_position = []
with open(join(dirname(__file__), "nenufar_tv_sources.json")) as src_file:
sources = json.load(src_file)
for name in sources["FixedSources"]:
src = FixedTarget.from_name(name, time=self.tv_image.time[0])
if src.coordinates.separation(image_center) <= 0.8*self.fov_radius:
src_names.append(name)
src_position.append(src.coordinates)
for name in sources["SolarSystemSources"]:
src = SolarSystemTarget.from_name(name, time=self.tv_image.time[0])
if src.coordinates.separation(image_center) <= 0.8*self.fov_radius:
src_names.append(name)
src_position.append(src.coordinates)
if len(src_position) != 0:
kwargs["text"] = (SkyCoord(src_position), src_names, "white")
if beam_contours:
# Simulate the array factor
ma = MiniArray()
af_sky = ma.array_factor(
sky=HpxSky(
resolution=0.2*u.deg,
time=self.tv_image.time[0],
frequency=self.tv_image.frequency[0]
),
pointing=Pointing(
coordinates=image_center,
time=self.tv_image.time[0]
)
)
# Normalize the array factor
af = af_sky[0, 0, 0].compute()
af_normalized = af/af.max()
kwargs["contour"] = (af_normalized, np.arange(0.5, 1, 0.2), "copper")
# Plot
self.tv_image[0, 0, 0].plot(
center=image_center,
radius=self.fov_radius - 2.5*u.deg,
figname=figname,
colorbar_label=f"Stokes {self.tv_image.polarization[0]}",
**kwargs
)
return
def rephase_visibilities(self, phase_center, uvw):
""" """
# Compute the zenith original phase center
zenith = SkyCoord(
np.zeros(self.time.size),
np.ones(self.time.size)*90,
unit="deg",
frame=AltAz(
obstime=self.time,
location=nenufar_position
)
)
zenith_phase_center = altaz_to_radec(zenith)
# Define the rotation matrix
def rotation_matrix(skycoord):
"""
"""
ra_rad = skycoord.ra.rad
dec_rad = skycoord.dec.rad
if np.isscalar(ra_rad):
ra_rad = np.array([ra_rad])
dec_rad = np.array([dec_rad])
cos_ra = np.cos(ra_rad)
sin_ra = np.sin(ra_rad)
cos_dec = np.cos(dec_rad)
sin_dec = np.sin(dec_rad)
return np.array([
[cos_ra, -sin_ra, np.zeros(ra_rad.size)],
[-sin_ra*sin_dec, -cos_ra*sin_dec, cos_dec],
[sin_ra*cos_dec, cos_ra*cos_dec, sin_dec],
])
# Transformation matrices
to_origin = rotation_matrix(zenith_phase_center) # (3, 3, ntimes)
to_new_center = rotation_matrix(phase_center) # (3, 3, 1)
total_transformation = np.matmul(
np.transpose(
to_new_center,
(2, 0, 1)
),
to_origin
) # (3, 3, ntimes)
rotUVW = np.matmul(
np.expand_dims(
(to_origin[2, :] - to_new_center[2, :]).T,
axis=1
),
np.transpose(
to_origin,
(2, 1, 0)
)
) # (ntimes, 1, 3)
phase = np.matmul(
rotUVW,
np.transpose(uvw, (0, 2, 1))
) # (ntimes, 1, nvis)
rotate_visibilities = np.exp(
2.j*np.pi*phase/wavelength(self.frequency).to(u.m).value[None, :, None]
) # (ntimes, nfreqs, nvis)
new_uvw = np.matmul(
uvw, # (ntimes, nvis, 3)
np.transpose(total_transformation, (2, 0, 1))
)
return rotate_visibilities, new_uvw
def coordinate_calc(self,
x_list,
y_list,
time_list,
coord,
off_az,
off_el,
hosei,
lamda,
press,
temp,
humi,
limit=True,
rotation=True):
"""
x_list and y_list == [deg]
"""
if coord == "planet":
planet_flag = True
coord = "altaz"
else:
planet_flag = False
pass
# coordinate check len(x_list)=len(y_list)=len(time_list)
frame = self.coord_list[coord.lower()]
az_list = []
el_list = []
if frame == "altaz":
az_list = list(map(lambda k: k + off_az, x_list))
el_list = list(map(lambda k: k + off_el, y_list))
if planet_flag == True:
tmp_az_list = []
tmp_el_list = []
for i in range(len(az_list)):
azel_list = self.kisa_calc2(az_list[i], el_list[i], hosei)
tmp_az_list.append(azel_list[0] + off_az)
tmp_el_list.append(azel_list[1] + off_el)
az_list = tmp_az_list
el_list = tmp_el_list
else:
pass
else:
on_coord = SkyCoord(
x_list,
y_list,
frame=frame,
unit='arcsec',
)
# convert_azel
nanten2 = EarthLocation(lat=self.latitude * u.deg,
lon=self.longitude * u.deg,
height=self.height * u.m)
on_coord.location = nanten2
on_coord.pressure = press * u.hPa #weather correction
on_coord.temperature = temp * u.deg_C #weather correction
on_coord.relative_humidity = humi #weather correction
on_coord.obswl = lamda * u.um #weather correction
altaz_list = on_coord.transform_to(AltAz(obstime=time_list))
for i in range(len(altaz_list)):
azel_list = self.kisa_calc(altaz_list[i], hosei)
az_list.append(azel_list[0] + off_az)
el_list.append(azel_list[1] + off_el)
#az_list.append(altaz_list[i].az.arcsec+off_az) #no_kisa
#el_list.append(altaz_list[i].alt.arcsec+off_el) #no_kisa
pass
if rotation == False:
az_list = [
i - 360 * 3600. if i > 180 * 3600. else i for i in az_list
]
az_list = [
i + 360 * 3600. if i < -180 * 3600. else i for i in az_list
]
#print("az :",az_list[0]/3600.,"el :", el_list[0]/3600.)
return [az_list, el_list]
def analyze_muon_event(event):
"""
Generic muon event analyzer.
Parameters
----------
event : ctapipe dl1 event container
Returns
-------
muonringparam, muonintensityparam : MuonRingParameter
and MuonIntensityParameter container event
"""
names = [
'LST_LST_LSTCam', 'MST_MST_NectarCam', 'MST_MST_FlashCam',
'MST_SCT_SCTCam', 'SST_1M_DigiCam', 'SST_GCT_CHEC',
'SST_ASTRI_ASTRICam', 'SST_ASTRI_CHEC'
]
tail_cuts = [(5, 7), (5, 7), (10, 12), (5, 7), (5, 7), (5, 7), (5, 7),
(5, 7)] # 10, 12?
impact = [(0.2, 0.9), (0.1, 0.95), (0.2, 0.9), (0.2, 0.9), (0.1, 0.95),
(0.1, 0.95), (0.1, 0.95), (0.1, 0.95)] * u.m
ringwidth = [(0.04, 0.08), (0.02, 0.1), (0.01, 0.1), (0.02, 0.1),
(0.01, 0.5), (0.02, 0.2), (0.02, 0.2), (0.02, 0.2)] * u.deg
total_pix = [1855., 1855., 1764., 11328., 1296., 2048., 2368., 2048]
# 8% (or 6%) as limit
min_pix = [148., 148., 141., 680., 104., 164., 142., 164]
# Need to either convert from the pixel area in m^2 or check the camera specs
ang_pixel_width = [0.1, 0.2, 0.18, 0.067, 0.24, 0.2, 0.17, 0.2, 0.163
] * u.deg
# Found from TDRs (or the pixel area)
hole_rad = [
0.308 * u.m, 0.244 * u.m, 0.244 * u.m, 4.3866 * u.m, 0.160 * u.m,
0.130 * u.m, 0.171 * u.m, 0.171 * u.m
] # Assuming approximately spherical hole
cam_rad = [2.26, 3.96, 3.87, 4., 4.45, 2.86, 5.25, 2.86] * u.deg
# Above found from the field of view calculation
sec_rad = [
0. * u.m, 0. * u.m, 0. * u.m, 2.7 * u.m, 0. * u.m, 1. * u.m, 1.8 * u.m,
1.8 * u.m
]
sct = [False, False, False, True, False, True, True, True]
# Added cleaning here. All these options should go to an input card
cleaning = True
muon_cuts = {
'Name': names,
'tail_cuts': tail_cuts,
'Impact': impact,
'RingWidth': ringwidth,
'total_pix': total_pix,
'min_pix': min_pix,
'CamRad': cam_rad,
'SecRad': sec_rad,
'SCT': sct,
'AngPixW': ang_pixel_width,
'HoleRad': hole_rad
}
logger.debug(muon_cuts)
muonringlist = [] # [None] * len(event.dl0.tels_with_data)
muonintensitylist = [] # [None] * len(event.dl0.tels_with_data)
tellist = []
muon_event_param = {
'TelIds': tellist,
'MuonRingParams': muonringlist,
'MuonIntensityParams': muonintensitylist
}
for telid in event.dl0.tels_with_data:
logger.debug("Analysing muon event for tel %d", telid)
image = event.dl1.tel[telid].image
# Get geometry
teldes = event.inst.subarray.tel[telid]
geom = teldes.camera
x, y = geom.pix_x, geom.pix_y
dict_index = muon_cuts['Name'].index(str(teldes))
logger.debug('found an index of %d for camera %d', dict_index,
geom.cam_id)
tailcuts = muon_cuts['tail_cuts'][dict_index]
logger.debug("Tailcuts are %s", tailcuts)
clean_mask = tailcuts_clean(geom,
image,
picture_thresh=tailcuts[0],
boundary_thresh=tailcuts[1])
# TODO: correct this hack for values over 90
altval = event.mcheader.run_array_direction[1]
if altval > Angle(90, unit=u.deg):
warnings.warn('Altitude over 90 degrees')
altval = Angle(90, unit=u.deg)
telescope_pointing = SkyCoord(alt=altval,
az=event.mcheader.run_array_direction[0],
frame=AltAz())
camera_coord = SkyCoord(
x=x,
y=y,
frame=CameraFrame(
focal_length=teldes.optics.equivalent_focal_length,
rotation=geom.pix_rotation,
telescope_pointing=telescope_pointing,
))
nom_coord = camera_coord.transform_to(
NominalFrame(origin=telescope_pointing))
x = nom_coord.delta_az.to(u.deg)
y = nom_coord.delta_alt.to(u.deg)
if (cleaning):
img = image * clean_mask
else:
img = image
muonring = ChaudhuriKunduRingFitter(None)
logger.debug("img: %s mask: %s, x=%s y= %s", np.sum(image),
np.sum(clean_mask), x, y)
if not sum(img): # Nothing left after tail cuts
continue
muonringparam = muonring.fit(x, y, image * clean_mask)
dist = np.sqrt(
np.power(x - muonringparam.ring_center_x, 2) +
np.power(y - muonringparam.ring_center_y, 2))
ring_dist = np.abs(dist - muonringparam.ring_radius)
muonringparam = muonring.fit(
x, y, img * (ring_dist < muonringparam.ring_radius * 0.4))
dist = np.sqrt(
np.power(x - muonringparam.ring_center_x, 2) +
np.power(y - muonringparam.ring_center_y, 2))
ring_dist = np.abs(dist - muonringparam.ring_radius)
muonringparam = muonring.fit(
x, y, img * (ring_dist < muonringparam.ring_radius * 0.4))
muonringparam.tel_id = telid
muonringparam.obs_id = event.dl0.obs_id
muonringparam.event_id = event.dl0.event_id
dist_mask = np.abs(
dist - muonringparam.ring_radius) < muonringparam.ring_radius * 0.4
pix_im = image * dist_mask
nom_dist = np.sqrt(
np.power(muonringparam.ring_center_x, 2) +
np.power(muonringparam.ring_center_y, 2))
minpix = muon_cuts['min_pix'][dict_index] # 0.06*numpix #or 8%
mir_rad = np.sqrt(teldes.optics.mirror_area.to("m2") / np.pi)
# Camera containment radius - better than nothing - guess pixel
# diameter of 0.11, all cameras are perfectly circular cam_rad =
# np.sqrt(numpix*0.11/(2.*np.pi))
if (npix_above_threshold(pix_im, tailcuts[0]) > 0.1 * minpix
and npix_composing_ring(pix_im) > minpix
and nom_dist < muon_cuts['CamRad'][dict_index]
and muonringparam.ring_radius < 1.5 * u.deg
and muonringparam.ring_radius > 1. * u.deg):
muonringparam.ring_containment = ring_containment(
muonringparam.ring_radius, muon_cuts['CamRad'][dict_index],
muonringparam.ring_center_x, muonringparam.ring_center_y)
# Guess HESS is 0.16
# sec_rad = 0.*u.m
# sct = False
# if numpix == 2048 and mir_rad > 2.*u.m and mir_rad < 2.1*u.m:
# sec_rad = 1.*u.m
# sct = True
#
# Store muon ring parameters (passing cuts stage 1)
# muonringlist[idx] = muonringparam
tellist.append(telid)
muonringlist.append(muonringparam)
muonintensitylist.append(None)
ctel = MuonLineIntegrate(
mir_rad,
hole_radius=muon_cuts['HoleRad'][dict_index],
pixel_width=muon_cuts['AngPixW'][dict_index],
sct_flag=muon_cuts['SCT'][dict_index],
secondary_radius=muon_cuts['SecRad'][dict_index])
if image.shape[0] == muon_cuts['total_pix'][dict_index]:
muonintensityoutput = ctel.fit_muon(
muonringparam.ring_center_x, muonringparam.ring_center_y,
muonringparam.ring_radius, x[dist_mask], y[dist_mask],
image[dist_mask])
muonintensityoutput.tel_id = telid
muonintensityoutput.obs_id = event.dl0.obs_id
muonintensityoutput.event_id = event.dl0.event_id
muonintensityoutput.mask = dist_mask
idx_ring = np.nonzero(pix_im)
muonintensityoutput.ring_completeness = ring_completeness(
x[idx_ring],
y[idx_ring],
pix_im[idx_ring],
muonringparam.ring_radius,
muonringparam.ring_center_x,
muonringparam.ring_center_y,
threshold=30,
bins=30)
muonintensityoutput.ring_size = np.sum(pix_im)
dist_ringwidth_mask = np.abs(
dist - muonringparam.ring_radius) < (
muonintensityoutput.ring_width)
pix_ringwidth_im = image * dist_ringwidth_mask
idx_ringwidth = np.nonzero(pix_ringwidth_im)
muonintensityoutput.ring_pix_completeness = npix_above_threshold(
pix_ringwidth_im[idx_ringwidth], tailcuts[0]) / len(
pix_im[idx_ringwidth])
logger.debug(
"Tel %d Impact parameter = %s mir_rad=%s "
"ring_width=%s", telid,
muonintensityoutput.impact_parameter, mir_rad,
muonintensityoutput.ring_width)
conditions = [
muonintensityoutput.impact_parameter * u.m <
muon_cuts['Impact'][dict_index][1] * mir_rad,
muonintensityoutput.impact_parameter >
muon_cuts['Impact'][dict_index][0],
muonintensityoutput.ring_width <
muon_cuts['RingWidth'][dict_index][1],
muonintensityoutput.ring_width >
muon_cuts['RingWidth'][dict_index][0]
]
if all(conditions):
muonintensityparam = muonintensityoutput
idx = tellist.index(telid)
muonintensitylist[idx] = muonintensityparam
logger.debug("Muon found in tel %d, tels in event=%d",
telid, len(event.dl0.tels_with_data))
else:
continue
return muon_event_param
def pwv(location,
Ra=None,
Dec=None,
P_min=300,
P_max=750,
line_of_site='zenith',
plot=False):
"""
Compute the precipitable water vapor at ``location`` in direction of
``line_of_site``.
Parameters
----------
location : `~fyodor.Location`
Location of observatory.
Ra : float
Right ascension of target
Dec : float
Declination of target
P_min : float
Minimum pressure (hPa)
P_max : float
Maximum pressure (hPa)
line_of_site : str, {"target", "zenith"}
Either compute line of sight to the target or to the zenith.
plot : bool
Generate a plot of the PWV at each time.
Returns
-------
dates : list
PWV : `~numpy.ndarray`
"""
params = load_files()
times, day, epoch, date, nc_filesT, nc_filesM, h, e, r_pol = params[:9]
r_eq, P, H, lon_origin, g16_data_file = params[9:]
# Pressure level boundaries
P_min = np.abs(P - P_min).argmin()
P_max = np.abs(P - P_max).argmin()
# Convert from radian to degrees:
Ra = float(Ra)
Dec = float(Dec)
Sky = SkyCoord(ra=Ra * u.degree, dec=Dec * u.degree)
Aa = Sky.transform_to(
AltAz(obstime=times, location=location.to_EarthLocation()))
latt = Dec
lont = Ra
# Computes PWV along line of sight
if line_of_site == 'target':
INDEX = np.ravel(np.where(Aa.alt.degree < 30))
INDEXP = np.ravel(np.where(Aa.alt.degree > 30))
# Keep time values corresponding to Alt above 30 degrees
EPOCH = epoch
for index in sorted(INDEX, reverse=True):
del EPOCH[index]
DATE = []
for i in range(0, len(epoch)):
DATEt = time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(EPOCH[i]))
DATE.append(DATEt)
Alt = Aa.alt.rad
Az = Aa.az.rad
# Compute distance from location to projection point
delta_x = []
d_lat = []
d_lon = []
for i in range(0, len(Alt)):
delta_xi = []
for j in h:
delta_xt = j / np.tan(Alt[i])
delta_xt = delta_xt * u.m**-1
delta_xi.append(delta_xt)
delta_x.append(delta_xi)
delta_x = np.array(delta_x)
for i in range(0, len(Az)):
d_latt = delta_x[i, ] * np.cos(Az[i])
d_lont = delta_x[i, ] * np.sin(Az[i])
d_lat.append(d_latt)
d_lon.append(d_lont)
d_lat = np.array(d_lat)
d_lon = np.array(d_lon)
# Compute latitude and longitude of projection points
lat_proj = []
lon_proj = []
for i in range(0, len(Alt)):
obs_latt = (
location.latdeg +
np.degrees(np.arctan(d_lat[i, ] * u.m / R_earth)).value)
obs_lont = (
location.londeg +
np.degrees(np.arctan(d_lon[i, ] * u.m / R_earth)).value)
lat_proj.append(obs_latt)
lon_proj.append(obs_lont)
lat_proj = np.array(lat_proj)
lon_proj = np.array(lon_proj)
lat_proj_rad = np.radians(lat_proj)
lon_proj_rad = np.radians(lon_proj)
lambda_0 = np.radians(lon_origin)
# Transform into scan angles
lat_origin = np.arctan(r_pol**2 / r_eq**2 * np.tan(lat_proj_rad))
r_c = r_pol / np.sqrt(1 - (e**2) * np.cos(lat_origin)**2)
x, y = geodetic_to_cartesian(lat_origin, lon_proj_rad, lambda_0, r_c,
H)
g16_data_fileT = []
xscanT = []
yscanT = []
XT = []
YT = []
LVT = []
# Retrieve Temperature data
for i in INDEXP:
g16_data = nc_filesT[i]
g16_data_fileT.append(g16_data)
g16 = Dataset(nc_filesT[i], 'r')
xtemp = g16.variables['x'][:]
xscanT.append(xtemp)
ytemp = g16.variables['y'][:]
yscanT.append(ytemp)
LVTi = []
Xi = []
Yi = []
for j in range(P_min, P_max + 1):
Xtemp = np.abs(xtemp - x[i, j]).argmin()
Xi.append(Xtemp)
Ytemp = np.abs(ytemp - y[i, j]).argmin()
Yi.append(Ytemp)
LVTtemp = g16.variables['LVT'][Ytemp, Xtemp, j]
LVTi.append(LVTtemp)
LVT.append(LVTi)
XT.append(Xi)
YT.append(Yi)
LVT = np.array(LVT)
# Retrieve Relative humidity data
g16_data_fileM = []
xscanM = []
yscanM = []
XM = []
YM = []
LVM = []
for i in INDEXP:
g16_dataM = nc_filesM[i]
g16_data_fileM.append(g16_dataM)
g16M = Dataset(nc_filesM[i], 'r')
xtempM = g16M.variables['x'][:]
xscanM.append(xtempM)
ytempM = g16M.variables['y'][:]
yscanM.append(ytempM)
LVMi = []
Xi = []
Yi = []
for j in range(P_min, P_max + 1):
XtempM = np.abs(xtempM - x[i, j]).argmin()
Xi.append(XtempM)
YtempM = np.abs(ytempM - y[i, j]).argmin()
Yi.append(YtempM)
LVMtemp = g16M.variables['LVM'][YtempM, XtempM, j]
LVMi.append(LVMtemp)
LVM.append(LVMi)
XM.append(Xi)
YM.append(Yi)
LVM = np.array(LVM)
P = 100 * P
LVT = LVT - 273.15
Pi = P[P_min:P_max + 1]
PWV = pwv_integral(LVT, LVM, Pi)
if plot:
# Plot and save data
fig = plt.figure(figsize=(8, 5))
ax = fig.add_subplot(111)
ax.plot(DATE, PWV, 'bo', ms=4)
plt.title('Precipitable Water Vapor along line of sight, {} on {}'.
format(location.site, day[0]),
fontsize=20)
plt.xticks(rotation='vertical', fontsize=16)
plt.yticks(fontsize=16)
ax.set_xlabel("Date", color="C0", fontsize=18)
ax.set_ylabel("PWV (mm)", color="C0", fontsize=18)
RA_patch = mpatches.Patch(color='white',
label='RA: {} degrees'.format(Ra))
Dec_patch = mpatches.Patch(color='white',
label='Dec: {} degrees'.format(Dec))
every_nth = 4
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
for n, label in enumerate(ax.xaxis.get_ticklines()):
if n % every_nth != 0:
label.set_visible(False)
plt.tight_layout()
plt.legend(handles=[RA_patch, Dec_patch],
loc='lower right',
fontsize=18)
out_date = DATE
# Computes PWV at zenith
elif line_of_site == 'zenith':
# Transform latitude and longitude into scan angles
lambda_0 = np.radians(lon_origin)
obs_lat_rad = np.radians(latt)
obs_lon_rad = np.radians(lont)
lat_origin = np.arctan(r_pol**2 / r_eq**2 * np.tan(obs_lat_rad))
r_c = r_pol / (np.sqrt(1 - (e**2) * (np.cos(lat_origin))**2))
x, y = geodetic_to_cartesian(lat_origin, obs_lon_rad, lambda_0, r_c, H)
xscanT = []
yscanT = []
# Retrieve Temperature data
LVT = []
for i in range(0, len(nc_filesT)):
g16_data = nc_filesT[i]
g16_data_file.append(g16_data)
g16 = Dataset(g16_data_file[i], 'r')
xtemp = g16.variables['x'][:]
xscanT.append(xtemp)
ytemp = g16.variables['y'][:]
yscanT.append(ytemp)
XT = []
YT = []
LVTi = []
for j in range(0, len(P)):
Xtemp = np.abs(xtemp - x).argmin()
XT.append(Xtemp)
Ytemp = np.abs(ytemp - y).argmin()
YT.append(Ytemp)
LVTtemp = g16.variables['LVT'][Ytemp, Xtemp, j]
LVTi.append(LVTtemp)
LVT.append(LVTi)
LVT = np.array(LVT)
# Retrieve Relative humidity data
g16_data_fileM = []
xscanM = []
yscanM = []
LVM = []
for i in range(0, len(nc_filesM)):
g16_dataM = nc_filesM[i]
g16_data_fileM.append(g16_dataM)
g16M = Dataset(g16_data_fileM[i], 'r')
xtempM = g16M.variables['x'][:]
xscanM.append(xtempM)
ytempM = g16M.variables['y'][:]
yscanM.append(ytempM)
XM = []
YM = []
LVMi = []
for j in range(0, len(P)):
XtempM = np.abs(xtempM - x).argmin()
XM.append(XtempM)
YtempM = np.abs(ytempM - y).argmin()
YM.append(YtempM)
LVMtemp = g16M.variables['LVM'][YtempM, XtempM, j]
LVMi.append(LVMtemp)
LVM.append(LVMi)
LVM = np.array(LVM)
# Change pressure units to Pa and Temperature to K
P = 100 * P
LVT = LVT - 273.15
PWV = pwv_integral(LVT, LVM, P)
if plot:
fig = plt.figure(figsize=(8, 5))
ax = fig.add_subplot(111)
ax.plot(date, PWV, 'bo', ms=4)
plt.title('Precipitable Water Vapor at zenith, {} on {}'.format(
location.site, day[0]),
fontsize=20)
plt.xticks(rotation='vertical', fontsize=16)
plt.yticks(fontsize=16)
ax.set_xlabel("Date", color="C0", fontsize=18)
ax.set_ylabel("PWV (mm)", color="C0", fontsize=18)
every_nth = 4
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
for n, label in enumerate(ax.xaxis.get_ticklines()):
if n % every_nth != 0:
label.set_visible(False)
plt.tight_layout()
out_date = date
return out_date, PWV
def showPfsDesign(pfsDesigns,
date=None,
timerange=8,
showTime=None,
showDesignId=True):
"""Show the altitude at Subaru of the available pfsDesigns as a function of time.
pfsDesigns: array of `pfs.datamodel.PfsDesign`
The available designs
date: `str` or ``None``
The desired date; if None use today (in HST)
If None, also show the current time unless showTime is False
timerange: `int`
Total number of hours to show, centred at midnight
showTime: `bool` or `None`
If False don't show the current time even if known
showDesignId: `bool`
Include the pfsDesignId in the labels
"""
if date is None:
now = datetime.datetime.now(pytz.timezone('US/Hawaii'))
date = now.date()
now += datetime.timedelta(utcoffset /
24) # all times are actually kept in UTC
else:
now = None
midnight = Time(f'{date} 00:00:00') # UTC, damn astropy
time = midnight + np.linspace(24 - timerange / 2, 24 + timerange / 2,
100) * u.hour
global subaru
if subaru is None:
subaru = EarthLocation.of_site('Subaru')
telstate = AltAz(obstime=time - utcoffset * u.hour,
location=subaru) # AltAz needs UTC
xformatter = mdates.DateFormatter('%H:%M')
plt.gca().xaxis.set_major_formatter(xformatter)
for pfsDesign in pfsDesigns:
boresight = SkyCoord(ra=pfsDesign.raBoresight * u.degree,
dec=pfsDesign.decBoresight * u.degree,
frame='icrs')
label = showDesignId
plt.plot(time.datetime,
boresight.transform_to(telstate).alt,
label=f"0x{pfsDesign.pfsDesignId:x} {pfsDesign.designName}"
if showDesignId else pfsDesign.designName)
plt.gcf().autofmt_xdate()
plt.legend(prop={"family": "monospace"} if showDesignId else {})
if now is not None and showTime is not False:
plt.axvline(now, ls='-', color='black', alpha=0.5)
plt.ylim(max([-1, plt.ylim()[0]]), None)
plt.xlabel("Local Time")
plt.ylabel("Altitude (deg)")
plt.title(f"{date}")
def altaz2radec(altaz, location, obstime=None, epoch_RA=2000.0, time_type=None):
"""
----------------------------------------------------------------------------
Convert Alt-Az to RA-Dec with accurate ephemeris
Inputs:
altaz [numpy array] Altitude and Azimuth as a Nx2 numpy array. All units
in degrees
location
[instance of class astropy.coordinates.EarthLocation] Location of
the observer provided as an instance of class
astropy.coordinates.EarthLocation
obstime [scalar, string, or instance of class astropy.time.Time] The time
or epoch which applies to input altaz. It can be a scalar (in JD
or JYear), string (JYear string prefixed with 'J' or ISO or ISOT),
or an instance of class astropy.time.Time. The appropriate format
must be specified in input time_type. If set to None (default), it
will be set equal to epoch_RA.
epoch_RA
[scalar, string, or instance of class astropy.time.Time] The time
or epoch which applies to output radec. It can be a scalar (in JD
or JYear), string (JYear string prefixed with 'J' or ISO or ISOT),
or an instance of class astropy.time.Time. The appropriate format
must be specified in input time_type. It must be in the same format
as the one obstime is specified in. If set to 2000.0 (default), it
is assumed to be in 'jyear' format. If set to None, it will be set
equal to default of 2000.0 in 'jyear' format.
time_type
[string] Specifies the format in which obstime and epoch_RA are
provided. Accepted values are 'jd' (Julian Day), 'jyear' (Julian
year), 'iso' or 'isot'. If set to None (default) and if obstime
and/or epoch_RA is a scalar, the corresponding scalar entries are
assumed to be in Julian Year.
Output:
The output radec as a numpy array of shape (N,2) in units of degrees is
returned at the epoch specified in epoch_RA.
----------------------------------------------------------------------------
"""
if isinstance(altaz, NP.ndarray):
if altaz.size == 2:
altaz = altaz.reshape(1,-1)
if altaz.ndim != 2:
raise ValueError('Input altaz must be a numpy array of shape (N,2)')
if altaz.shape[1] != 2:
raise ValueError('Input altaz must be a numpy array of shape (N,2)')
elif not isinstance(altaz, AltAz):
raise TypeError('Input altaz must be a numpy array or an instance of class astropy.coordinates.AltAz')
if not isinstance(location, EarthLocation):
raise TypeError('Input location must be an instance of class astropy.coordinates.EarthLocation')
if epoch_RA is not None:
if isinstance(epoch_RA, (int,float)):
if (time_type is None) or (time_type.lower() == 'jyear'):
equinox_RA = Time(epoch_RA, scale='utc', format='jyear')
elif time_type.lower() == 'jd':
equinox_RA = Time(epoch_RA, scale='utc', format='jd')
elif isinstance(epoch_RA, str):
if time_type.lower() == 'jyear':
equinox_RA = Time('J{0:.9f}'.format(epoch_RA), scale='utc', format='jyear_str')
elif (time_type.lower() == 'iso') or (time_type.lower() == 'isot'):
equinox_RA = Time(epoch_RA, scale='utc', format=time_type.lower())
elif isinstance(epoch_RA, Time):
equinox_RA = copy.copy(epoch_RA)
else:
raise TypeError('Input epoch_RA is invalid or currently not accepted')
else:
equinox_RA = Time(2000.0, format='jyear', scale='utc')
warnings.warn('No epoch_RA provided. Setting epoch to {0}'.format(equinox_RA.jyear_str))
if obstime is not None:
if isinstance(obstime, (int,float)):
if (time_type is None) or (time_type.lower() == 'jyear'):
equinox_altaz = Time(obstime, scale='utc', format='jyear')
elif time_type.lower() == 'jd':
equinox_altaz = Time(obstime, scale='utc', format='jd')
elif isinstance(obstime, str):
if time_type.lower() == 'jyear':
equinox_altaz = Time('J{0:.9f}'.format(obstime), scale='utc', format='jyear_str')
if (time_type.lower() == 'iso') or (time_type.lower() == 'isot'):
equinox_altaz = Time(obstime, scale='utc', format=time_type.lower())
elif isinstance(obstime, Time):
equinox_altaz = copy.copy(obstime)
else:
raise TypeError('Input obstime is invalid or currently not accepted')
else:
if isinstance(altaz, AltAz):
equinox_altaz = copy.deepcopy(altaz.obstime)
else:
equinox_altaz = copy.copy(equinox_RA)
warnings.warn('No obstime provided. Setting obstime to {0}'.format(equinox_altaz.jyear_str))
if isinstance(altaz, AltAz):
elaz = copy.deepcopy(altaz)
else:
elaz = AltAz(alt=altaz[:,0]*U.deg, az=altaz[:,1]*U.deg, obstime=equinox_altaz, location=location)
coords_radec = elaz.transform_to(FK5(equinox=equinox_RA))
radec = NP.hstack((coords_radec.ra.deg.reshape(-1,1), coords_radec.dec.deg.reshape(-1,1)))
return radec
observation_time = ephem.Date('2018/3/30 03:00:00.00')
for i in xrange(len(data)):
observer = ephem.Observer()
observer.lon = np.radians(mandi_lon)
observer.lat = np.radians(mandi_lat)
observer.elevation = 1000
observer.date = observation_time
output_alt, output_az = [], []
for l in xrange(5):
names = ['Star1', 'Star2', 'Star3', 'Star4', 'Star5']
ra, dec = observer.radec_of(np.radians(stars_az[l]),
np.radians(stars_alt[l]))
ab = data[i]
lato, lono = ab['latitude'], ab['longitude']
new_location = EarthLocation(lat=lato, lon=lono, height=100 * u.m)
new_time = Time('2018-3-30 12:00:00')
aa = AltAz(location=new_location, obstime=new_time)
coord = SkyCoord(str(ra), str(dec), unit='deg')
g = coord.transform_to(aa)
az, alt = boring_function(str(g.az)), boring_function(str(g.alt))
output_az.append(az)
output_alt.append(alt)
wb = load_workbook('output.xlsx')
sheet = wb.get_sheet_by_name('N-W')
sheet[putter_string[l] +
str(i + 2)] = "alt=" + str(alt) + ", az=" + str(az)
wb.save(filename='output.xlsx')
def run(obs,
method,
h5parmFilename,
maxdtec=0.5,
maxvtec=50,
alphaIon=11 / 3,
hIon=250e3,
vIon=20,
seed=0,
fitsFilename=None,
stepname='tec',
angRes=60,
expfolder='',
ncpu=0):
"""
Simulate TEC values and store them to a h5parm.
Parameters
----------
method : str
Method to use:
"fits": read TEC values from the FITS cube specified by fitsFilename
"tid": generate a traveling ionospheric disturbance (TID) wave
"turbulence": generate a turbulent ionosphere
h5parmFilename : str
Filename of output h5parm file.
maxdtec : float, optional. Default = 0.5
Maximum screen dTEC per timestep in TECU.
maxvtec: float, optional. Default = 50.
Highest vTEC in daily modulation in TECU.
alphaIon: float, optional. Default = 11/3.
Ionosphere power spectrum exponent. Is 11/3 for Kolmogorov,
de Gasperin and Mevius found 3.89 for LOFAR.
hIon : float, optional. Default = 250 km
Height of thin layer ionoshpere.
vIono : float, optional. Default = 20 m/s
Velocity of tecscreen. This controls the tec variation frequency.
seed: int, optional.
Random screen seed. Use for reproducibility.
fitsFilename : str, optional
Filename of input FITS cube with dTEC solutions.
stepname _ str, optional
Name of step to use in DPPP parset
angRes : float, optional. Default = 60.
Angular resolution of the screen [arcsec]. Only for turbulent model.
expfolder : str, optional. Default = None
Export the tecscreen data to this folder for plotting. Depending on
system memory, this will not work for very large/highres screens.
Only for 'turbulence' method.
ncpu : int, optional
Number of cores to use, by default all available.
"""
# TODO : Test TID and polynomial method for multi-ms usage
method = method.lower()
if ncpu == 0:
ncpu = mp.cpu_count()
# Get sky model properties
ras, decs = obs.get_patch_coords()
source_names = obs.get_patch_names()
ants = obs.stations
sp = obs.stationpositions
times = obs.get_times()
tecvals = np.zeros((len(times), len(ants), len(ras)))
weights = np.ones_like(tecvals)
if method == 'turbulence':
directions = np.array([ras, decs]).T
tecvals = comoving_tecscreen(sp,
directions,
times,
alpha=alphaIon,
angRes=angRes,
hIon=hIon,
vIon=vIon,
maxvtec=maxvtec,
maxdtec=maxdtec,
ncpu=ncpu,
expfolder=expfolder,
seed=seed)
elif method == 'fits':
# Load solutions from FITS cube
hdu = pyfits.open(fitsFilename, memmap=False)
data = hdu[0].data
header = hdu[0].header
w = wcs.WCS(header)
ntimes, _, nstations, ny, nx = data.shape
# Check that number of stations in input FITS cube matches MS
if nstations != len(ants):
logger.error('Number of stations in input FITS cube does not '
'match that in the input MS')
return 1
# Get solutions at the source coords
for d, (ra_deg, dec_deg) in enumerate(zip(ras, decs)):
ra_dec = np.array([[ra_deg, dec_deg, 0, 0, 0]])
x = int(w.wcs_world2pix(ra_dec, 0)[0][0])
y = int(w.wcs_world2pix(ra_dec, 0)[0][1])
if x < 0 or x > nx or y < 0 or y > ny:
tecvals[:, :, d, :] = 0.0
weights[:, :, d, :] = 0.0
continue
for t in range(ntimes):
for s in range(nstations):
tecvals[t, s, d] = data[t, 0, s, y, x]
tecvals = daytime_tec_modulation(times)[:, np.newaxis, np.newaxis] * (
tecvals + maxvtec)
elif method == 'tid':
# Properties of TID wave
tidLen = 200e3
tidVel = 500e3 / 3600,
tid_prop = [maxdtec, tidLen, tidVel]
# Generate solutions for TID wave
A12 = EarthLocation(lat=52.91 * u.deg,
lon=6.87 * u.deg,
height=1 * u.m)
mjd = Time(times / (3600.0 * 24.0), format="mjd")
aa = AltAz(location=A12, obstime=mjd)
altazcoord = []
pool = mp.Pool(processes=ncpu)
radec = [(r, d, aa) for r, d in zip(ras, decs)]
altazcoord = pool.map(_getaltaz, radec)
gettec_args = [(a, sp, A12, times, *tid_prop) for a in altazcoord]
alltec = pool.map(_gettec, gettec_args)
pool.close()
pool.join()
alltec = np.array(alltec)
# Fill the axis arrays
tecvals = alltec[:, :, 0, :].transpose([2, 1, 0]) # [:,:,:,0]
# convert to vTEC
tecvals = daytime_tec_modulation(times)[:, np.newaxis, np.newaxis] * (
tecvals + maxvtec)
else:
logger.error('method "{}" not understood'.format(method))
return 1
# Write tec values to h5parm file as DPPP input
ho = h5parm(h5parmFilename, readonly=False)
if 'sol000' in ho.getSolsetNames():
solset = ho.getSolset('sol000')
else:
solset = ho.makeSolset(solsetName='sol000')
if 'tec000' in solset.getSoltabNames():
logger.info('''Solution-table tec000 is already present in
{}. It will be overwritten.'''.format(h5parmFilename +
'/sol000'))
solset.getSoltab('tec000').delete()
st = solset.makeSoltab('tec',
'tec000',
axesNames=['time', 'ant', 'dir'],
axesVals=[times, ants, source_names],
vals=tecvals,
weights=weights)
antennaTable = solset.obj._f_get_child('antenna')
antennaTable.append(list(zip(*(ants, sp))))
sourceTable = solset.obj._f_get_child('source')
vals = [[ra, dec] for ra, dec in zip(ras, decs)]
sourceTable.append(list(zip(*(source_names, vals))))
# Add CREATE entry to history
soltabs = solset.getSoltabs()
for st in soltabs:
st.addHistory('CREATE (by TEC operation of LoSiTo from obs {0} '
'and method="{{1}}")'.format(h5parmFilename, method))
ho.close()
# Update predict parset parameters for the obs
obs.add_to_parset(stepname, 'tec000', h5parmFilename)
return 0
def get_observation_site_location():
current_time = get_current_time()
observation_site = get_current_location()
observation_site_frame = AltAz(obstime=current_time,
location=observation_site)
return observation_site_frame
Greenland = EarthLocation( lat=Angle(72.5796, 'deg'),
lon=Angle(-38.4592, 'deg'),
height=3200 * u.m),
Tibet = EarthLocation( lat=Angle(32.3166667, 'deg'),
lon=Angle(80.0166666667, 'deg'),
height=5100 * u.m),
)
location = locations[LOCATION]
start_time = Time(datetime.datetime(2015, 8, 1), scale='ut1')
sampling_interval = TimeDelta(SAMPLING, format="sec")
time = start_time + sampling_interval * np.arange(0., HOURS*3600/SAMPLING)
# zenith
altaz = AltAz(az=Angle(0., unit=u.degree), alt=ELEVATION, obstime=time, location=location)
radec = altaz.transform_to(ICRS)
ra_zenith = radec.ra.radian
dec_zenith = radec.dec.radian
# local north
altaz = AltAz(az=Angle(0., unit=u.degree), alt=Angle(0., unit=u.degree), obstime=time, location=location)
radec = altaz.transform_to(ICRS)
ra_north = radec.ra.radian
dec_north = radec.dec.radian
import pandas as pd
pd.DataFrame({"ra_zenith_rad":ra_zenith, "dec_zenith_rad":dec_zenith,
"ra_north_rad":ra_north, "dec_north_rad":dec_north}, index=time.datetime).to_hdf("zenith_pointing_" + LOCATION.lower() + ".h5", "data")
def execute(self, command):
t = Time(Time.now(), location=self.location)
altaz = SkyCoord(self.STATE['az'],
self.STATE['alt'],
unit=(u.hour, u.deg),
frame=AltAz(obstime=t, location=self.location))
# Apply state changes based on the command.
if command == 'Abort':
self.STATE['status'].slewing = 0
self.STATE['status'].tracking = 0
elif command == 'Park':
self.STATE['alt'] = 50
self.STATE['az'] = 90
self.STATE['status'].parked = 1
elif command == 'UnPark':
self.STATE['status'].parked = 0
elif command.startswith('GoToAltAz'):
command, az, alt = command.split(' ')
altaz = SkyCoord(az,
alt,
unit=u.deg,
frame=AltAz(obstime=t, location=self.location))
self.STATE['destination']['alt'] = altaz.alt.deg
self.STATE['destination']['az'] = altaz.az.deg
# Slew to the given alt/az, updating current alt and az as we go.
# TODO: threading.Thread(target=self._slew_altaz).start()
self.STATE['alt'] = altaz.alt.deg
self.STATE['az'] = altaz.az.deg
elif command.startswith('GoTo'):
command, ra, dec, equinox = command.split(' ')
radec = SkyCoord(ra,
dec,
unit=(u.hour, u.deg),
equinox='J2000',
obstime=t,
location=self.location)
self.STATE['destination']['ra'] = radec.ra.hour
self.STATE['destination']['dec'] = radec.dec.deg
self.STATE['destination']['equinox'] = str(equinox)
self.STATE['destination']['alt'] = radec.altaz.alt.deg
self.STATE['destination']['az'] = radec.altaz.az.deg
# Slew to the given ra/dec, updating current ra and dec as we go.
# TODO: threading.Thread(target=self._slew_radec).start()
self.STATE['ra'] = radec.ra.hour
self.STATE['dec'] = radec.dec.deg
self.STATE['status'].tracking = 1
elif command.startswith('SetTrackMode'):
command, tracking, track_type, ra_rate, dec_rate = command.split(
' ')
self.STATE['status'].tracking = int(tracking)
self.STATE['status'].non_sidereal_tracking = int(track_type)
self.STATE.update({
'tracking': {
'enabled': int(tracking),
'ra_rate': float(ra_rate),
'dec_rate': float(dec_rate),
},
})
elif command.startswith('SyncToAltAz'):
command, az, alt = command.split(' ')
self.STATE['alt'] = float(alt)
self.STATE['az'] = float(az)
elif command.startswith('Sync'):
command, ra, dec, type_, equinox = command.split(' ')
self.STATE['ra'] = float(ra)
self.STATE['dec'] = float(dec)
self.STATE['equinox'] = equinox
elif command.startswith('PulseGuide'):
pass # TODO
elif command == 'MotorsToBlinky':
self.STATE['status'].manual = 1
elif command == 'MotorsToAuto':
self.STATE['status'].manual = 0
elif command.startswith('CookCoordinates'):
pass # TODO
elif command.startswith('UnCookCoordinates'):
pass # TODO
elif command.startswith('JogArcSeconds'):
command, direction, arcsec = command.split(' ')
arcsec = float(arcsec) * u.arcsec
if direction == 'N':
self.STATE['dec'] = (Angle(self.STATE['dec'] * u.deg) +
arcsec).value
elif direction == 'S':
self.STATE['dec'] = (Angle(self.STATE['dec'] * u.deg) -
arcsec).value
elif direction == 'E':
self.STATE['ra'] = (Angle(self.STATE['ra'] * u.hour) +
arcsec).value
elif direction == 'W':
self.STATE['ra'] = (Angle(self.STATE['ra'] * u.hour) -
arcsec).value
# Construct a response based on the issued command.
if command == 'ReadScopeDestination':
response = '{};{};{};{};{};{};{};{};{};{};{};_'.format(
self.STATE['status'].value,
self.STATE['destination']['ra'],
self.STATE['destination']['dec'],
self.STATE['destination']['alt'],
self.STATE['destination']['az'],
self.STATE['secondary_axis_angle'],
self.STATE['primary_axis_angle'],
t.sidereal_time(kind='apparent').hour,
t.jd,
self.STATE['time'],
altaz.secz,
)
elif command == 'RotatorComms':
response = '{};{};{};{};{};{};{};{};{};{};{};_'.format(
self.STATE['status'].value,
self.STATE['ra'],
self.STATE['dec'],
self.STATE['alt'],
self.STATE['az'],
self.STATE['derotator'][
self.STATE['instrument']]['parallactic_angle'],
self.STATE['derotator'][
self.STATE['instrument']]['parallactic_rate'],
self.STATE['derotator'][
self.STATE['instrument']]['camera_solved_angle'],
self.STATE['derotator'][self.STATE['instrument']]
['destination_position'],
self.STATE['time'],
altaz.secz,
)
elif command == 'SiteLocations':
response = '{};{};{};_SiteLocations'.format(
self.location.lat.value, self.location.lon.value,
self.location.height.value)
else:
response = '{};{};{};{};{};{};{};{};{};{};{};_'.format(
self.STATE['status'].value,
self.STATE['ra'],
self.STATE['dec'],
self.STATE['alt'],
self.STATE['az'],
self.STATE['secondary_axis_angle'],
self.STATE['primary_axis_angle'],
t.sidereal_time(kind='apparent').hour,
t.jd,
self.STATE['time'],
altaz.secz,
)
return response + Driver.TERMINATOR
def get_instrumental_mags(data, coords, obsname, ext):
'''
Takes a hipercam data object, and exctracts the instrumental magnitude of each aperture in each CCD
If Coords and an observatory are supplied, also correct for airmass, using supplied extinction coeffiecients
Arguments:
----------
data: hipercam.Hlog
The data to analyse. tseries will be extracted from here.
coords: str
Ra, Dec of the data. Must be readable by Astropy.
obsname: str
Observing location name of the data.
ext: iterable
Extinction corrections to apply, given in CCD order. i.e. [<CCD1 ext>, <CCD2 ext>, ...]
Returns:
--------
all_mags: dict
Dict, with the keys corresponding to the CCD numbers. Each entry is a numpy array of the
instrumental magnitudes, in the order they're found in the aperture list.
'''
logger.printer("------- Getting instrumental magnitude -------")
logger.printer(" I'm correcting for airmass, using the following:")
logger.printer(" Extinction: {} mags/airmass".format(ext))
logger.printer(" Ra, Dec: {}".format(coords))
logger.printer(" Observatory: {}".format(obsname))
# Where are we?
try:
observatory = coord.EarthLocation.of_site(obsname)
logger.printer("Observatory successfully retrieved from site name")
except:
obsname = obsname.split(',')
if len(obsname) != 2:
logger.printer(" The (lat, lon) MUST!!! be comma separated!")
exit()
lat, lon = obsname
logger.printer(
" Earth location from latitude, longitude: {}, {}".format(
lat, lon))
observatory = coord.EarthLocation.from_geodetic(lat=lat, lon=lon)
star_loc = coord.SkyCoord(coords, unit=(u.hourangle, u.deg))
# I want altitude converted to zenith angle.
obs_T = data.tseries('1', '1').t
obs_T = time.Time(obs_T, format='mjd')
# Define the altAz frame, containing the time and location
altAz_frame = AltAz(obstime=obs_T, location=observatory)
star_loc_AltAz = star_loc.transform_to(altAz_frame)
# Compute the airmass, at the time of the first frame
zenith_angle = 90 - star_loc_AltAz.alt.deg
zenith_angle_rad = np.deg2rad(zenith_angle)
airmass = 1. / np.cos(zenith_angle_rad)
logger.printer(
" For the observations starting at {} and ending at {}...".format(
obs_T[0].iso, obs_T[-1].iso))
logger.printer(
" -> Zenith angle starts at {:.3f}, and ends at {:.3f}".format(
zenith_angle[0], zenith_angle[-1]))
logger.printer(" -> Airmass starts at {:.3f}, ends at {:.3f}".format(
airmass[0], airmass[-1]))
# I'll use the mean for reporting.
mean_airmass = np.mean(airmass)
logger.printer("Mean airmass: {:.3f}".format(mean_airmass))
if np.any(airmass) <= 0:
logger.printer("Airmass is negative!! We have a problem there!")
logger.printer("EarthLocation (constructed from {}):".format(obsname))
logger.printer(str(observatory))
logger.printer(str(observatory.lat), str(observatory.lon))
logger.printer("Star location:")
logger.printer(str(star_loc))
input("Hit enter to continue... ")
logger.printer(
"\nGetting the INSTRUMENTAL (electron flux) magnitudes for the log file"
)
all_mags = {}
aps = data.apnames
CCDs = [str(i + 1) for i, key in enumerate(aps)]
if ext is None:
logger.printer("NOT applying extinction corrections!!!")
ext = [0.0 for i in CCDs]
ext = np.array(ext)
# Data masking stuff
FLAG = np.uint32(0)
for f in FLAGS_TO_IGNORE:
FLAG = FLAG | f
if FLAG:
logger.printer(" Ignoring bad data flags: {}".format(FLAGS_TO_IGNORE))
logger.printer("List of keys:")
logger.printer(hcam.FLAGS)
for CCD in CCDs:
logger.printer("\n---> Doing CCD {} <---".format(CCD))
# information gathering
ap = sorted(aps[CCD])
ex = ext[int(CCD) - 1]
exptime = data[CCD]['Exptim']
# star magnitudes
mags = []
for comp in ap:
logger.printer("")
star = data.tseries(CCD, comp)
### AIRMASSES ###
# I want altitude converted to zenith angle.
obs_T = star.t
obs_T = time.Time(obs_T, format='mjd')
# Define the altAz frame, containing the time and location
altAz_frame = AltAz(obstime=obs_T, location=observatory)
star_loc_AltAz = star_loc.transform_to(altAz_frame)
# Compute the airmass, at the time of the first frame
zenith_angle = 90 - star_loc_AltAz.alt.deg
zenith_angle_rad = np.deg2rad(zenith_angle)
airmass = 1. / np.cos(zenith_angle_rad)
# Mask out data that has flags
# Filter out flags I don't care about.
star.mask = star.mask & (~FLAG)
mask = star.mask != 0
# First and last data are never good
mask[0] = True
mask[-1] = True
if np.any(star.mask):
removed = np.sum(star.mask != 0)
logger.printer("Bad data detected!")
logger.printer(
"The mask will remove {}/{} data points.".format(
removed, len(star.y)))
# star counts/s
fl = star.y / exptime
# Get magnitudes, and apply atmospheric extinction correction to each frame.
aperture_mags = -2.5 * np.log10(fl)
aperture_mags -= (ex * airmass)
# Take a clipped mean
mag, mag_median, mag_sigma = sigma_clipped_stats(aperture_mags,
mask=mask,
sigma=3.0)
# Just for reporting
counts_per_frame = np.mean(star.y[np.where(mask == False)])
counts_per_frame_err = np.std(star.y[np.where(mask == False)])
logger.printer(
"Aperture {} had a clipped mean counts per frame of {:.2f}+/-{:.2f}"
.format(comp, counts_per_frame, counts_per_frame_err))
logger.printer(" and a mean exposure time of {:.3f}s".format(
np.mean(exptime)))
# logger.printer(" Pre-ext correct: CCD {}, Ap {}, mag: {:.3f}+/-{:.3f}".format(CCD, comp, -2.5*np.log10(fl), mag_sigma))
mags.append(mag)
mags = np.array(mags)
logger.printer(" CCD {} extinction: {:.3f} mags".format(
CCD, ex * mean_airmass))
logger.printer(" Post-ext correct:")
for i, mag in enumerate(mags):
logger.printer(" Ap {}: {:.3f} mags".format(i, mag))
logger.printer("\n\n")
all_mags[CCD] = np.array(mags)
return all_mags
def enu_to_enu(from_coo, to_frame):
# for now we just implement this through AltAz to make sure we get everything
# covered
return from_coo.transform_to(
AltAz(obstime=from_coo.obstime)).transform_to(to_frame)
def altaz_frame(self):
"""ALT / AZ frame (`~astropy.coordinates.AltAz`)."""
return AltAz(obstime=self.time, location=self.location)
def plot(self, date):
"""
Produce plot of the object visability for date
Modified from https://docs.astropy.org/en/stable/generated/examples/coordinates/plot_obs-planning.html
"""
import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
midnight = self.observer.midnight(date, which='next')
delta_midnight = np.linspace(-2, 10, 100) * u.hour
frame_night = AltAz(obstime=midnight + delta_midnight,
location=self.location)
targetaltazs_night = self.target.transform_to(frame_night)
# Use `~astropy.coordinates.get_sun` to find the location of the Sun at 1000
from astropy.coordinates import get_sun
delta_midnight = np.linspace(-12, 12, 1000) * u.hour
times = midnight + delta_midnight
frame = AltAz(obstime=times, location=self.location)
sunaltazs = get_sun(times).transform_to(frame)
# Do the same with `~astropy.coordinates.get_moon` to find when the moon is
# up. Be aware that this will need to download a 10MB file from the internet
# to get a precise location of the moon.
from astropy.coordinates import get_moon
moon = get_moon(times)
moonaltazs = moon.transform_to(frame)
##############################################################################
# Find the alt,az coordinates of M33 at those same times:
targetaltazs = self.target.transform_to(frame)
##############################################################################
# Make a beautiful figure illustrating nighttime and the altitudes of M33 and
# the Sun over that time:
plt.plot(delta_midnight, sunaltazs.alt, color='r', label='Sun')
plt.plot(delta_midnight,
moonaltazs.alt,
color=[0.75] * 3,
ls='--',
label='Moon')
plt.scatter(delta_midnight,
targetaltazs.alt,
c=targetaltazs.az,
label='Target',
lw=0,
s=8,
cmap='viridis')
plt.fill_between(delta_midnight.to('hr').value,
0,
90,
sunaltazs.alt < -0 * u.deg,
color='0.5',
zorder=0)
plt.fill_between(delta_midnight.to('hr').value,
0,
90,
sunaltazs.alt < -18 * u.deg,
color='k',
zorder=0)
plt.hlines(self.altConstraint,
-12,
12,
colors='red',
linestyles='dotted',
lw=1)
plt.colorbar().set_label('Azimuth [deg]')
plt.legend(loc='upper left')
plt.xlim(-12, 12)
plt.xticks(np.arange(13) * 2 - 12)
plt.ylim(0, 90)
plt.xlabel('Hours from Local Midnight')
plt.ylabel('Altitude [deg]')
plt.show()
def make_test_observation_table(observatory_name='HESS', n_obs=10,
datestart=None, dateend=None,
use_abs_time=False,
random_state='random-seed'):
"""Make a test observation table.
For the moment, only random observation tables are created.
If `datestart` and `dateend` are specified, the starting time
of the observations will be restricted to the specified interval.
These parameters are interpreted as date, the precise hour of the
day is ignored, unless the end date is closer than 1 day to the
starting date, in which case, the precise time of the day is also
considered.
Parameters
----------
observatory_name : str
Name of the observatory; a list of choices is given in
`~gammapy.obs.observatory_locations`.
n_obs : int
Number of observations for the obs table.
datestart : `~astropy.time.Time`, optional
Starting date for random generation of observation start time.
dateend : `~astropy.time.Time`, optional
Ending date for random generation of observation start time.
use_abs_time : bool, optional
Use absolute UTC times instead of [MET]_ seconds after the reference.
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}, optional
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
Returns
-------
obs_table : `~gammapy.obs.ObservationTable`
Observation table.
"""
random_state = get_random_state(random_state)
n_obs_start = 1
obs_table = ObservationTable()
# build a time reference as the start of 2010
dateref = Time('2010-01-01T00:00:00', format='isot', scale='utc')
dateref_mjd_fra, dateref_mjd_int = np.modf(dateref.mjd)
# define table header
obs_table.meta['OBSERVATORY_NAME'] = observatory_name
obs_table.meta['MJDREFI'] = dateref_mjd_int
obs_table.meta['MJDREFF'] = dateref_mjd_fra
if use_abs_time:
# show the observation times in UTC
obs_table.meta['TIME_FORMAT'] = 'absolute'
else:
# show the observation times in seconds after the reference
obs_table.meta['TIME_FORMAT'] = 'relative'
header = obs_table.meta
# obs id
obs_id = np.arange(n_obs_start, n_obs_start + n_obs)
obs_table['OBS_ID'] = obs_id
# obs time: 30 min
time_observation = Quantity(30. * np.ones_like(obs_id), 'minute').to('second')
obs_table['TIME_OBSERVATION'] = time_observation
# livetime: 25 min
time_live = Quantity(25. * np.ones_like(obs_id), 'minute').to('second')
obs_table['TIME_LIVE'] = time_live
# start time
# - random points between the start of 2010 and the end of 2014 (unless
# otherwise specified)
# - using the start of 2010 as a reference time for the header of the table
# - observations restrict to night time (only if specified time interval is
# more than 1 day)
# - considering start of astronomical day at midday: implicit in setting
# the start of the night, when generating random night hours
if datestart == None:
datestart = Time('2010-01-01T00:00:00', format='isot', scale='utc')
if dateend == None:
dateend = Time('2015-01-01T00:00:00', format='isot', scale='utc')
time_start = random_state.uniform(datestart.mjd, dateend.mjd, len(obs_id))
time_start = Time(time_start, format='mjd', scale='utc')
# check if time interval selected is more than 1 day
if (dateend - datestart).jd > 1.:
# keep only the integer part (i.e. the day, not the fraction of the day)
time_start_f, time_start_i = np.modf(time_start.mjd)
time_start = Time(time_start_i, format='mjd', scale='utc')
# random generation of night hours: 6 h (from 22 h to 4 h), leaving 1/2 h
# time for the last run to finish
night_start = Quantity(22., 'hour')
night_duration = Quantity(5.5, 'hour')
hour_start = random_state.uniform(night_start.value,
night_start.value + night_duration.value,
len(obs_id))
hour_start = Quantity(hour_start, 'hour')
# add night hour to integer part of MJD
time_start += hour_start
if use_abs_time:
# show the observation times in UTC
time_start = Time(time_start.isot)
else:
# show the observation times in seconds after the reference
time_start = time_relative_to_ref(time_start, header)
# converting to quantity (better treatment of units)
time_start = Quantity(time_start.sec, 'second')
obs_table['TIME_START'] = time_start
# stop time
# calculated as TIME_START + TIME_OBSERVATION
if use_abs_time:
time_stop = Time(obs_table['TIME_START'])
time_stop += TimeDelta(obs_table['TIME_OBSERVATION'])
else:
time_stop = TimeDelta(obs_table['TIME_START'])
time_stop += TimeDelta(obs_table['TIME_OBSERVATION'])
# converting to quantity (better treatment of units)
time_stop = Quantity(time_stop.sec, 'second')
obs_table['TIME_STOP'] = time_stop
# az, alt
# random points in a sphere above 45 deg altitude
az, alt = sample_sphere(size=len(obs_id),
lon_range=Angle([0, 360], 'degree'),
lat_range=Angle([45, 90], 'degree'),
random_state=random_state)
az = Angle(az, 'degree')
alt = Angle(alt, 'degree')
obs_table['AZ'] = az
obs_table['ALT'] = alt
# RA, dec
# derive from az, alt taking into account that alt, az represent the values
# at the middle of the observation, i.e. at time_ref + (TIME_START + TIME_STOP)/2
# (or better: time_ref + TIME_START + (TIME_OBSERVATION/2))
# in use_abs_time mode, the time_ref should not be added, since it's already included
# in TIME_START and TIME_STOP
az = Angle(obs_table['AZ'])
alt = Angle(obs_table['ALT'])
if use_abs_time:
obstime = Time(obs_table['TIME_START'])
obstime += TimeDelta(obs_table['TIME_OBSERVATION']) / 2.
else:
obstime = time_ref_from_dict(obs_table.meta)
obstime += TimeDelta(obs_table['TIME_START'])
obstime += TimeDelta(obs_table['TIME_OBSERVATION']) / 2.
location = observatory_locations[observatory_name]
alt_az_coord = AltAz(az=az, alt=alt, obstime=obstime, location=location)
sky_coord = alt_az_coord.transform_to(FK5)
obs_table['RA'] = sky_coord.ra
obs_table['DEC'] = sky_coord.dec
# positions
# number of telescopes
# random integers between 3 and 4
n_tels_min = 3
n_tels_max = 4
n_tels = random_state.randint(n_tels_min, n_tels_max + 1, len(obs_id))
obs_table['N_TELS'] = n_tels
# muon efficiency
# random between 0.6 and 1.0
muon_efficiency = random_state.uniform(low=0.6, high=1.0, size=len(obs_id))
obs_table['MUON_EFFICIENCY'] = muon_efficiency
return obs_table
