def axial_ratio_to_inclination_with_intrinsic(ratio, intrinsic_ratio):
"""
This function ...
:param ratio:
:param intrinsic_ratio:
:return:
"""
# Check ratios
if ratio >= 1.: raise ValueError("Axial ratio must be ratio of minor axis to major axis length")
if intrinsic_ratio >= 1: raise ValueError("Intrinsic axial ratio must be ratio of scale height (smaller) to scale length (larger)")
# Calculate logq and logq0
logq = np.log10(ratio)
logq0 = np.log10(intrinsic_ratio)
# Calculate numerator and denominator of the formula
numerator = 1. - 10 ** (2. * logq)
denominator = 1. - 10 ** (2. * logq0)
# Calculate the inclination angle
sin2i = numerator / denominator
inclination_radians = np.arcsin(np.sqrt(sin2i))
inclination = Angle(inclination_radians, unit="rad")
return inclination.to("deg")
def deg_to_sex(ra, dec):
"""
Convert an Ra and Dec position in decimal degrees to hexadecimal
Parameters
----------
ra : float
Ra in decimal degrees
dec : float
Dec in decimal degrees
Returns
-------
ra : str
Ra in hexadecimal HH:MM:SS
dec : str
Dec in hexadecimal DD:MM:SS
"""
from astropy import units as u
from astropy.coordinates import Angle
rad = Angle(ra * u.deg)
decd = Angle(dec * u.deg)
ra = rad.to_string(unit=u.hour, sep=':')
dec = decd.to_string(unit=u.deg, sep=':')
return ra, dec
def test_3rd_body_Curtis(test_params):
# based on example 12.11 from Howard Curtis
body = test_params['body']
with solar_system_ephemeris.set('builtin'):
j_date = 2454283.0 * u.day
tof = (test_params['tof']).to(u.s).value
body_r = build_ephem_interpolant(body, test_params['period'], (j_date, j_date + test_params['tof']), rtol=1e-2)
epoch = Time(j_date, format='jd', scale='tdb')
initial = Orbit.from_classical(Earth, *test_params['orbit'], epoch=epoch)
r, v = cowell(initial, np.linspace(0, tof, 400), rtol=1e-10, ad=third_body,
k_third=body.k.to(u.km**3 / u.s**2).value, third_body=body_r)
incs, raans, argps = [], [], []
for ri, vi in zip(r, v):
angles = Angle(rv2coe(Earth.k.to(u.km**3 / u.s**2).value, ri, vi)[2:5] * u.rad) # inc, raan, argp
angles = angles.wrap_at(180 * u.deg)
incs.append(angles[0].value)
raans.append(angles[1].value)
argps.append(angles[2].value)
# averaging over 5 last values in the way Curtis does
inc_f, raan_f, argp_f = np.mean(incs[-5:]), np.mean(raans[-5:]), np.mean(argps[-5:])
assert_quantity_allclose([(raan_f * u.rad).to(u.deg) - test_params['orbit'][3],
(inc_f * u.rad).to(u.deg) - test_params['orbit'][2],
(argp_f * u.rad).to(u.deg) - test_params['orbit'][4]],
[test_params['raan'], test_params['inc'], test_params['argp']],
rtol=1e-1)
def __init__(
self, distance, inclination, azimuth, position_angle, pixels_x, pixels_y, center_x, center_y, field_x, field_y
):
"""
This function ...
:param distance:
:param inclination:
:param azimuth:
:param position_angle:
:param pixels_x:
:param pixels_y:
:param center_x:
:param center_y:
:param field_x:
:param field_y:
"""
self.distance = distance
self.inclination = Angle(inclination, "deg")
self.azimuth = Angle(azimuth, "deg")
self.position_angle = Angle(position_angle, "deg")
self.pixels_x = pixels_x
self.pixels_y = pixels_y
self.center_x = center_x
self.center_y = center_y
self.field_x_physical = field_x
self.field_y_physical = field_y
def reformat_catalog(coordinates=True, chi=True, sharp=True, bright=True):
"""Format RA, Dec as floats in the final catalog"""
t = Table.read('../data/extract.txt', format='ascii.commented_header')
if coordinates:
ra = Angle((t['rah'], t['ram'], t['ras']), unit=u.hour)
dec = Angle((t['decd'], t['decm'], t['decs']), unit=u.deg)
t.remove_columns(['rah', 'ram', 'ras', 'decd', 'decm', 'decs'])
add_npcolumn(t, 'ra', ra.to(u.deg), index=0)
add_npcolumn(t, 'dec', dec, index=1)
if bright:
ind = ((t['g']<18) | (t['i']<18)) & (t['ichi']<90)
t_bright = t[ind]
t = t[~ind]
if chi:
#ind = (t['gchi']<1.4) & (t['ichi']<1.4)
ind = (t['gchi']<1.5) & (t['ichi']<1.5)
t = t[ind]
if sharp:
#ind = (np.abs(t['gsharp'])<0.2) & (np.abs(t['isharp'])<0.2)
ind = (np.abs(t['gsharp'])<0.3) & (np.abs(t['isharp'])<0.3)
t = t[ind]
if bright:
t = astropy.table.vstack((t, t_bright))
t.pprint()
t.write('../data/ext_catalog.txt', format='ascii.commented_header')
def axial_ratio_to_inclination_mosenkov(ratio, hubble_stage):
"""
This function ...
:param ratio:
:param hubble_stage:
:return:
"""
# Check that axial ratio is smaller than one!
if ratio >= 1.: raise ValueError("Axial ratio must be ratio of minor axis to major axis length")
# From Mosenkov et al., 2017 (Appendix)
# Calculate logq and logq0
logq = np.log10(ratio)
logq0 = get_logq0_mosenkov(hubble_stage)
# Calculate numerator and denominator of the formula
numerator = 1. - 10**(2. * logq)
denominator = 1. - 10**(2. * logq0)
# Calculate the inclination angle
sin2i = numerator / denominator
inclination_radians = np.arcsin(np.sqrt(sin2i))
inclination = Angle(inclination_radians, unit="rad")
return inclination.to("deg")
def plot_vhs_tiles(table=None,ra=None, dec=None,
wrap_ra24hr=False, PA=90.0, overplot=True, savefig=True):
"""
table is astropy table
ra, dec in degrees
"""
plot_polygon=True
if wrap_ra24hr:
angle=Angle(xdata * u.deg)
angle.wrap_at('180d', inplace=True)
xdata=angle.degree
# assumes filename is in table.meta
print('plotting: ', table.meta['filename'])
filename = table.meta['filename']
if not plot_polygon:
plt.plot(xdata, ydata, 's', ms=7.0,
color='yellow', markeredgecolor='yellow',
alpha=0.1,
label='OB Progress (submitted)\n' + filename)
if plot_polygon:
plt.plot(xdata, ydata, '.', ms=7.0,
color='yellow', markeredgecolor='yellow',
alpha=0.1,
label='OB Progress (submitted):' + filename)
# filled polygon
ra=xdata
dec=ydata
for i in range(len(ra)):
if i==0 or i == len(ra): print('i: ', i)
ra_poly, dec_poly=plt_tile.mk_polytile(ra_cen=ra[i], dec_cen=dec[i],
coverage='twice', PA=PA)
xypolygon=np.column_stack((ra_poly, dec_poly))
if i==0 or i == len(ra):
print('xypolygon.shape: ', xypolygon.shape)
print('xypolygon: ',xypolygon)
polygon = Polygon(xypolygon, True, color='green', alpha=0.1)
plt.gca().add_patch(polygon)
#print(ob_table.meta)
plt.suptitle(dqcfile_vhs)
plt.legend(fontsize='small')
figname='vhs_des_check_progress_vhs_obprogress_'+ datestamp + '.png'
print('Saving: '+figname)
plt.savefig(plotdir+figname)
plt.suptitle('')
def deg_to_sex(ra, dec):
from astropy import units as u
from astropy.coordinates import Angle
rad = Angle(ra * u.deg)
decd = Angle(dec * u.deg)
ra = rad.to_string(unit=u.hour, sep=':')
dec = decd.to_string(unit=u.deg, sep=':')
return ra, dec
def add_object_pos_airmass(header, history=False):
"""
Add object information, such as RA/Dec and airmass.
Parameters
----------
header : astropy..io.fits.Header
FITS header to be modified.
history : bool
If `True`, write history for each keyword changed.
Notes
-----
Has side effect of setting feder site JD to JD-OBS, which means it
also assume JD.value has been set.
"""
# not sure why coverage is not picking up both branches, but it is not, so
# marking it no cover
if feder.JD_OBS.value is None: # pragma: no cover
raise ValueError('Need to set JD_OBS.value '
'before calling.')
try:
feder.RA.set_value_from_header(header)
except ValueError:
raise ValueError("No RA is present.")
feder.DEC.set_value_from_header(header)
feder.RA.value = feder.RA.value.replace(' ', ':')
feder.DEC.value = feder.DEC.value.replace(' ', ':')
obj_coord2 = SkyCoord(feder.RA.value, feder.DEC.value,
unit=(u.hour, u.degree), frame='fk5')
obstime = Time(feder.MJD_OBS.value, format='mjd')
alt_az = obj_coord2.transform_to(AltAz(obstime=obstime,
location=feder.site))
feder.ALT_OBJ.value = round(alt_az.alt.degree, 5)
feder.AZ_OBJ.value = round(alt_az.az.degree, 5)
feder.AIRMASS.value = round(1 / np.cos(np.pi / 2 - alt_az.alt.radian), 3)
# TODO: replace the LST calculation
LST = _lst_from_obstime(obstime)
HA = LST.hour - obj_coord2.ra.hour
HA = Angle(HA, unit=u.hour)
feder.HA.value = HA.to_string(unit=u.hour, sep=':')
for keyword in feder.keywords_for_light_files:
if keyword.value is not None:
keyword.add_to_header(header, history=history)
logger.info(keyword.history_comment())
def test_3rd_body_Curtis(test_params):
# based on example 12.11 from Howard Curtis
body = test_params["body"]
with solar_system_ephemeris.set("builtin"):
j_date = 2454283.0 * u.day
tof = (test_params["tof"]).to(u.s).value
body_r = build_ephem_interpolant(
body,
test_params["period"],
(j_date, j_date + test_params["tof"]),
rtol=1e-2,
)
epoch = Time(j_date, format="jd", scale="tdb")
initial = Orbit.from_classical(Earth, *test_params["orbit"], epoch=epoch)
rr, vv = cowell(
Earth.k,
initial.r,
initial.v,
np.linspace(0, tof, 400) * u.s,
rtol=1e-10,
ad=third_body,
k_third=body.k.to(u.km ** 3 / u.s ** 2).value,
third_body=body_r,
)
incs, raans, argps = [], [], []
for ri, vi in zip(rr.to(u.km).value, vv.to(u.km / u.s).value):
angles = Angle(
rv2coe(Earth.k.to(u.km ** 3 / u.s ** 2).value, ri, vi)[2:5] * u.rad
) # inc, raan, argp
angles = angles.wrap_at(180 * u.deg)
incs.append(angles[0].value)
raans.append(angles[1].value)
argps.append(angles[2].value)
# averaging over 5 last values in the way Curtis does
inc_f, raan_f, argp_f = (
np.mean(incs[-5:]),
np.mean(raans[-5:]),
np.mean(argps[-5:]),
)
assert_quantity_allclose(
[
(raan_f * u.rad).to(u.deg) - test_params["orbit"][3],
(inc_f * u.rad).to(u.deg) - test_params["orbit"][2],
(argp_f * u.rad).to(u.deg) - test_params["orbit"][4],
],
[test_params["raan"], test_params["inc"], test_params["argp"]],
rtol=1e-1,
)
def formatter(self, values, spacing):
if not isinstance(values, u.Quantity) and values is not None:
raise TypeError("values should be a Quantities array")
if len(values) > 0:
if self.format is None:
spacing = spacing.to(u.arcsec).value
if spacing > 3600:
fields = 1
precision = 0
elif spacing > 60:
fields = 2
precision = 0
elif spacing > 1:
fields = 3
precision = 0
else:
fields = 3
precision = -int(np.floor(np.log10(spacing)))
decimal = False
unit = u.degree
else:
fields = self._fields
precision = self._precision
decimal = self._decimal
unit = self._unit
if decimal:
sep = None
elif self._sep is not None:
sep = self._sep
else:
if unit == u.degree:
if rcParams['text.usetex']:
deg = r'$^\circ$'
else:
deg = six.u('\xb0')
sep = (deg, "'", '"')
else:
sep = ('h', 'm', 's')
angles = Angle(values)
string = angles.to_string(unit=unit,
precision=precision,
decimal=decimal,
fields=fields,
sep=sep).tolist()
return string
else:
return []
def __init__(self, skyevent_dict):
d = skyevent_dict
self.position = SkyCoord(d['ra'], d['dec'], unit='deg')
self.position_error = Angle(d['error'], unit='deg')
self.timestamp = None
if d.get('time'):
self.timestamp = iso8601.parse_date(d['time'])
def __init__(self, energy, offset, data=None, data_units=""):
self.energy = EnergyBounds(energy)
self.offset = Angle(offset)
if data is None:
self.data = Quantity(np.zeros((len(energy) - 1, len(offset) - 1)), data_units)
else:
self.data = Quantity(data, data_units)
def time_scatterplot(time, peakr, flux, proto, num, t_init, posx, posy):
#Convert the decimal degrees to HMS strings.
ra_deg = Angle(str(posx)+'d')
dec_deg = Angle(str(posy)+'d')
ra_hms = ra_deg.to_string(unit=units.hour, sep=':', precision=2)
dec_dms = dec_deg.to_string(unit=units.degree, sep=':', precision=2)
#Set the x-range.
x_hi = max(time)*1.1
x_lo = -max(time)*0.1
plt.xlim(x_lo, x_hi)
#Set the y-range.
y_hi = 1.25
y_lo = 0.75
if np.amax(peakr) > 1.25:
y_hi = np.amax(peakr)*1.5
##fi
if np.amax(peakr) < 0.75:
y_lo = min(peakr)*0.7
##fi
plt.ylim(y_lo, y_hi)
#Define the labels.
plt.xlabel('Time (days + '+t_init+')')
#Write important details about the source on the plot.
plt.annotate('Source Number: '+str(num), xy = (0.05,0.2),
xycoords = 'axes fraction', size = 'small')
plt.annotate('Peak Flux: '+'{:.4}'.format(flux)+' (Jy/Beam)',
xy = (0.05,0.15), xycoords = 'axes fraction', size = 'small')
plt.annotate('Class: '+proto, xy = (0.05,0.1), xycoords = 'axes fraction',
size = 'small')
plt.annotate('RA: '+ra_hms+', Dec: '+dec_dms, xy = (0.05,0.05),
xycoords = 'axes fraction', size = 'small')
#Plot the data.
plt.scatter(time, peakr, alpha = 0.6, s = 60, marker = 'o', facecolor = 'r')
#Plot reference lines.
plt.plot((x_lo,x_hi),(1.0,1.0), '-k')
plt.plot((x_lo,x_hi),(0.9,0.9), '--k')
plt.plot((x_lo,x_hi),(1.1,1.1), '--k')
def sky_time(coord, time, rise_set=False, limalt=0*u.deg, site=EarthLocation(0.0, 0.0, 0.0), fuse=TimeDelta(0, format='sec', scale='tai')):
"""
"""
if type(limalt) != u.quantity.Quantity:
limalt = limalt*u.deg
if time.isscalar == True:
time = Time([time.iso], format='iso', scale='utc')
coord = coord_pack(coord)
timeut = time - fuse
if len(time.shape) == 1:
timeut = Time([[i] for i in timeut.jd], format='jd', scale='utc')
timeut.delta_ut1_utc = 0
timeut.location = site
ra, ts = mesh_coord(coord, timeut[:,0])
dif_h_sid = Angle(ra-ts)
dif_h_sid.wrap_at('180d', inplace=True)
dif_h_sol = dif_h_sid * (23.0 + 56.0/60.0 + 4.0916/3600.0) / 24.0
dif = TimeDelta(dif_h_sol.hour*u.h, scale='tai')
culminacao = timeut + dif
culminacao.delta_ut1_utc = 0
culminacao.location = site
if (site.latitude > 0*u.deg):
alwaysup = np.where(coord.dec >= 90*u.deg - site.latitude + limalt)
neverup = np.where(coord.dec <= -(90*u.deg - site.latitude - limalt))
else:
alwaysup = np.where(coord.dec <= -(90*u.deg + site.latitude + limalt))
neverup = np.where(coord.dec >= 90*u.deg + site.latitude - limalt)
if rise_set == True:
hangle_lim = np.arccos((np.cos(90.0*u.deg-limalt) - np.sin(coord.dec)*np.sin(site.latitude)) / (np.cos(coord.dec)*np.cos(site.latitude)))
tsg_lim = Angle(ra + hangle_lim)
dtsg_lim = tsg_lim - culminacao.sidereal_time('mean')
dtsg_lim.wrap_at(360 * u.deg, inplace=True)
dtsg_lim_sol = dtsg_lim * (23.0 + 56.0/60.0 + 4.0916/3600.0) / 24.0
a = np.where(np.isnan(dtsg_lim_sol))
dtsg_lim_sol[a] = Angle([48.0]*len(a[0])*u.hour)
dtsg_np = TimeDelta((dtsg_lim_sol.hour*u.h))
sunrise = culminacao - dtsg_np
sunset = culminacao + dtsg_np
culminacao = culminacao + fuse
sunrise = sunrise + fuse
sunset = sunset + fuse
return culminacao, sunrise, sunset, alwaysup, neverup
culminacao = culminacao + fuse
return culminacao, alwaysup, neverup
def __init__(self, energy_lo, energy_hi, offset, rad_lo, rad_hi, psf_value, energy_thresh_lo=Quantity(0.1, 'TeV'),
energy_thresh_hi=Quantity(100, 'TeV')):
self.energy_lo = energy_lo.to('TeV')
self.energy_hi = energy_hi.to('TeV')
self.offset = Angle(offset)
self.rad_lo = Angle(rad_lo)
self.rad_hi = Angle(rad_hi)
self.psf_value = psf_value.to('sr^-1')
self.energy_thresh_lo = energy_thresh_lo.to('TeV')
self.energy_thresh_hi = energy_thresh_hi.to('TeV')
def averaging_params(max_decorr=0.1, frequency=(250. * 1e6 * units.Hz),
baseline=(870 * units.m), corr_FoV=Angle(90., units.degree),
latitude=hera_latitude, verbose=False, wrap_len=default_wrap_len):
wavelength = const.c / frequency.to(1 / units.s)
earth_rot_speed = (Angle(360, units.deg) / units.sday).to(units.arcminute / units.s)
params = {}
max_resolution = Angle(np.arcsin(wavelength / baseline), units.radian)
params['max_resolution'] = max_resolution
if verbose:
print_desc_val('Max resolution:', max_resolution.to(units.arcminute), wrap_len)
integration_time = (max_resolution * max_decorr).to(units.arcminute) / earth_rot_speed
params['integration_time'] = integration_time
if verbose:
print_desc_val('Max integration time required to keep the decorrelation '
'due to time integrating under max_decorr on the longest '
'baselines:', integration_time, wrap_len)
channel_width = ((const.c * max_decorr) / (baseline * np.sin(corr_FoV.to(units.rad)))).to(units.kHz)
params['channel_width'] = channel_width
if verbose:
print_desc_val('Max channel width to keep the decorrelation due to channel '
'width under max_decorr for a {fov} degree correlator '
'FoV on the longest baselines:'.format(fov=corr_FoV.degree),
channel_width, wrap_len)
# After fringe stopping, the rotation is from the sky rotating in the beam
# This is slower, so we can sum in time to longer integrations
# (this summing does cause more decorrelation but it's better than without
# fringe stopping and it decreases data rates)
fringe_stopped_int_time = ((max_resolution.to(units.arcminute) * max_decorr) /
(np.sin(corr_FoV.radian) * earth_rot_speed * abs(np.sin(hera_latitude))))
params['fringe_stopped_int_time'] = fringe_stopped_int_time
if verbose:
print_desc_val('Max integration time to keep the decorrelation due to time '
'integrating after fringe stopping under max_decorr for a {fov} '
'degree correlator FoV on the longest baselines:'.
format(fov=corr_FoV.degree), fringe_stopped_int_time, wrap_len)
return params
def formatter(self, values, spacing):
if len(values) > 0:
if self.format is None:
spacing = spacing.to(u.arcsec).value
if spacing > 3600:
fields = 1
precision = 0
elif spacing > 60:
fields = 2
precision = 0
elif spacing > 1:
fields = 3
precision = 0
else:
fields = 3
precision = -int(np.floor(np.log10(spacing)))
decimal = False
unit = u.degree
else:
fields = self._fields
precision = self._precision
decimal = self._decimal
unit = self._unit
if decimal:
sep = None
else:
if unit == u.degree:
sep=(six.u('\xb0'), "'", '"')[:fields]
else:
sep=('h', 'm', 's')[:fields]
angles = Angle(np.asarray(values), unit=u.deg)
string = angles.to_string(unit=unit,
precision=precision,
decimal=decimal,
fields=fields,
sep=sep).tolist()
return string
else:
return []
def get_dispersion_PA(self):
from astropy.coordinates import Angle
import astropy.units as u
### extra tilt of the 1st order grism spectra
x0 = self.conf.conf['BEAMA']
dy_trace, lam_trace = self.conf.get_beam_trace(x=507, y=507, dx=x0, beam='A')
extra = np.arctan2(dy_trace[1]-dy_trace[0], x0[1]-x0[0])/np.pi*180
# h = self.im_header
# pa = Angle((-np.arctan2(h['CD2_2'], h['CD2_1'])/np.pi*180-extra)*u.deg)
### Distorted WCS
crpix = self.flt_wcs.wcs.crpix
xref = [crpix[0], crpix[0]+1]
yref = [crpix[1], crpix[1]]
r, d = self.all_pix2world(xref, yref)
pa = Angle((extra+np.arctan2(np.diff(r), np.diff(d))[0]/np.pi*180)*u.deg)
self.dispersion_PA = pa.wrap_at(360*u.deg).value
def test_angle_arrays():
"""
Test arrays values with Angle objects.
"""
# Tests incomplete
a1 = Angle([0, 45, 90, 180, 270, 360, 720.], unit=u.degree)
npt.assert_almost_equal([0., 45., 90., 180., 270., 360., 720.], a1.value)
a2 = Angle(np.array([-90, -45, 0, 45, 90, 180, 270, 360]), unit=u.degree)
npt.assert_almost_equal([-90, -45, 0, 45, 90, 180, 270, 360],
a2.value)
a3 = Angle(["12 degrees", "3 hours", "5 deg", "4rad"])
npt.assert_almost_equal([12., 45., 5., 229.18311805],
a3.value)
assert a3.unit == u.degree
a4 = Angle(["12 degrees", "3 hours", "5 deg", "4rad"], u.radian)
npt.assert_almost_equal(a4.degree, a3.value)
assert a4.unit == u.radian
a5 = Angle([0, 45, 90, 180, 270, 360], unit=u.degree)
a6 = a5.sum()
npt.assert_almost_equal(a6.value, 945.0)
assert a6.unit is u.degree
with pytest.raises(TypeError):
# Arrays where the elements are Angle objects are not supported -- it's
# really tricky to do correctly, if at all, due to the possibility of
# nesting.
a7 = Angle([a1, a2, a3], unit=u.degree)
a8 = Angle(["04:02:02", "03:02:01", "06:02:01"], unit=u.degree)
npt.assert_almost_equal(a8.value, [4.03388889, 3.03361111, 6.03361111])
a9 = Angle(np.array(["04:02:02", "03:02:01", "06:02:01"]), unit=u.degree)
npt.assert_almost_equal(a9.value, a8.value)
with pytest.raises(u.UnitsError):
a10 = Angle(["04:02:02", "03:02:01", "06:02:01"])
def add_plugmap_info(plate_info, plugmap_keys):
import yanny
plate = plate_info['plate']
mjd = plate_info['mjd']
plan_name = os.path.join(plate_info['bossdir'], str(plate), 'spPlancomb-%s-%s.par' % (plate, mjd))
plan = yanny.yanny(plan_name)
# look up plugmap filename
unique_mapnames = set(zip(plan['SPEXP']['mjd'], plan['SPEXP']['mapname']))
(mapmjd, mapname) = unique_mapnames.pop()
plugmap_name = os.path.join(plate_info['speclog'], str(mapmjd), 'plPlugMapM-%s.par' % mapname)
plugmap = yanny.yanny(plugmap_name)
plate_info['mapmjd'] = mapmjd
plate_info['mapname'] = mapname
# Process plugmap header keywords
for keyword in plugmap_keys:
plate_info[keyword] = plugmap[keyword]
design_ra = Angle(float(plugmap['raCen']), unit=u.degree)
design_dec = Angle(float(plugmap['decCen']), unit=u.degree)
design_ha = Angle(float(plugmap['haMin']), unit=u.degree)
design_alt, design_az = equatorial_to_horizontal(design_ra, design_dec, apolat, design_ha)
plate_info['design_ra'] = design_ra.to(u.degree).value
plate_info['design_dec'] = design_dec.to(u.degree).value
plate_info['design_ha'] = design_ha.to(u.degree).value
plate_info['design_alt'] = design_alt.to(u.degree).value
plate_info['design_az'] = design_az.to(u.degree).value
def __init__(self, offset, dp_domega, spline_kwargs=DEFAULT_PSF_SPLINE_KWARGS):
self._offset = Angle(offset).to("radian")
self._dp_domega = Quantity(dp_domega).to("sr^-1")
assert self._offset.ndim == self._dp_domega.ndim == 1
assert self._offset.shape == self._dp_domega.shape
# Store input arrays as quantities in default internal units
self._dp_dtheta = (2 * np.pi * self._offset * self._dp_domega).to("radian^-1")
self._spline_kwargs = spline_kwargs
self._compute_splines(spline_kwargs)
def __init__(self, rad, dp_domega, spline_kwargs=DEFAULT_PSF_SPLINE_KWARGS):
self._rad = Angle(rad).to('radian')
self._dp_domega = Quantity(dp_domega).to('sr^-1')
assert self._rad.ndim == self._dp_domega.ndim == 1
assert self._rad.shape == self._dp_domega.shape
# Store input arrays as quantities in default internal units
self._dp_dr = (2 * np.pi * self._rad * self._dp_domega).to('radian^-1')
self._spline_kwargs = spline_kwargs
self._compute_splines(spline_kwargs)
def step(self, uniform_factor=1):
"""
Randomly steps all parameters with uncertainties in parifle.
"""
n_dim = len(self.fit_parameters)
for parameter in self.fit_parameters:
if (hasattr(self,parameter + 'err')):
value = getattr(self, parameter)
err = getattr(self, parameter + 'err') * uniform_factor / n_dim
if (err != 0.):
if (parameter == 'RAJ'):
ra = Angle(getattr(self, 'RAJ'), unit=u.hour)
err = getattr(self, 'RAJerr') / 3600
ra_new = ra.deg + err * np.random.uniform(-1., 1.)
ra_new = Angle(ra_new, unit=u.deg)
setattr(self, parameter, str(ra_new.to_string(unit=u.hour, sep=':', precision=10)))
elif (parameter == 'DECJ'):
dec = Angle(getattr(self, 'DECJ'), unit=u.deg)
err = getattr(self, 'DECJerr') / 3600
dec_new = dec.deg + err * np.random.uniform(-1., 1.)
dec_new = Angle(dec_new, unit=u.deg)
setattr(self, parameter, str(dec_new.to_string(unit=u.deg, sep=':', precision=10)))
elif (parameter == 'SINI'):
cosi = np.sqrt(1 - getattr(self, 'SINI')**2)
err = getattr(self, 'DECJerr') / 3600
cosi += err * np.random.uniform(-1., 1.)
setattr(self, parameter, np.sqrt(1.-cosi**2))
else:
setattr(self, parameter, value + err * np.random.uniform(-1., 1.))
setattr(self,parameter + 'flag', 0)
def fit_arrays(sci, wht, seg, psf, id=None, platescale=0.06, exptime=0, path='/tmp/', galfit_exec='galfit', gaussian_guess=False, components=[GalfitSersic()], recenter=True, psf_sample=1):
rms = 1/np.sqrt(wht)#*exptime
if exptime > 0:
rms = np.sqrt((rms*exptime)**2+sci*exptime*(sci > 0))/exptime
rms[wht == 0] = 1e30
if id is not None:
mask = ((seg > 0) & (seg != id)) | (wht == 0)
else:
mask = wht == 0
sh = sci.shape[0]
fp = open(path+'galfit.feedme','w')
fp.write(GALFIT_IMAGES.format(input=path+'gf_sci.fits', output=path+'gf_out.fits', sigma=path+'gf_rms.fits', psf=path+'gf_psf.fits', mask=path+'gf_mask.fits', xmax=sh, ymax=sh, sh=sh, ps=platescale, psf_sample=psf_sample))
if gaussian_guess:
fit, q, theta = fit_gauss(sci)
from astropy.coordinates import Angle
import astropy.units as u
for comp in components:
if recenter:
comp.set(pos=[sh/2., sh/2.])
if gaussian_guess:
comp.set(q=q, pa=Angle.wrap_at(theta*u.rad, 360*u.deg).to(u.deg).value)
fp.write(str(comp))
fp.close()
pyfits.writeto(path+'gf_sci.fits', data=sci, overwrite=True)
pyfits.writeto(path+'gf_rms.fits', data=rms, overwrite=True)
pyfits.writeto(path+'gf_mask.fits', data=mask*1, overwrite=True)
pyfits.writeto(path+'gf_psf.fits', data=psf, overwrite=True)
for ext in ['out', 'model']:
if os.path.exists(path+'gf_{0}.fits'.format(ext)):
os.remove(path+'gf_{0}.fits'.format(ext))
os.system('{0} {1}/galfit.feedme'.format(galfit_exec, path))
class SkyEvent(object):
"""
Represents universal attributes of an observed event on sky.
I.e. the most basic details that we expect to find in all packets
reporting events with sky-positions.
Attributes:
position (:class:`astropy.coordinates.SkyCoord`): Best-estimate
sky-coordinates of the event being reported.
position error (:class:`astropy.coordinates.Angle`): Error-cone on the
position estimate.
timestamp of event (:class:`datetime.datetime`):
Timestamp for the reported event (UTC timezone).
"""
def __init__(self, skyevent_dict):
d = skyevent_dict
self.position = SkyCoord(d['ra'], d['dec'], unit='deg')
self.position_error = Angle(d['error'], unit='deg')
self.timestamp = None
if d.get('time'):
self.timestamp = iso8601.parse_date(d['time'])
def __repr__(self):
return (str(self.position)
+ ' +/- ' + self.position_error.to_string(decimal=True)
+ ' @ ' + self.timestamp.isoformat())
@staticmethod
def _parse_coords_dict(coords_dict):
d = coords_dict
posn = SkyCoord(d['ra'], d['dec'], unit='deg')
posn_error = Angle(d['error'], unit='deg')
time = iso8601.parse_date(d['time'])
return SkyEvent(sky_position=posn,
sky_position_error=posn_error,
event_timestamp=time)
def __init__(
self,
energy_lo,
energy_hi,
offset,
rad_lo,
rad_hi,
psf_value,
energy_thresh_lo=u.Quantity(0.1, "TeV"),
energy_thresh_hi=u.Quantity(100, "TeV"),
interp_kwargs=None,
):
self.energy_lo = energy_lo.to("TeV")
self.energy_hi = energy_hi.to("TeV")
self.offset = Angle(offset)
self.rad_lo = Angle(rad_lo)
self.rad_hi = Angle(rad_hi)
self.psf_value = psf_value.to("sr^-1")
self.energy_thresh_lo = energy_thresh_lo.to("TeV")
self.energy_thresh_hi = energy_thresh_hi.to("TeV")
self._interp_kwargs = interp_kwargs or {}
from cdshealpix import cone_search
import astropy.units as u
ipix, depth, fully_covered = cone_search(lon=0 * u.deg,
lat=0 * u.deg,
radius=10 * u.deg,
depth=10)
from mocpy import MOC, WCS
from astropy.coordinates import SkyCoord, Angle
moc = MOC.from_healpix_cells(ipix, depth, fully_covered)
# Plot the MOC using matplotlib
import matplotlib.pyplot as plt
fig = plt.figure(111, figsize=(10, 10))
# Define a astropy WCS from the mocpy.WCS class
with WCS(fig,
fov=30 * u.deg,
center=SkyCoord(0, 0, unit='deg', frame='icrs'),
coordsys="icrs",
rotation=Angle(0, u.degree),
projection="AIT") as wcs:
ax = fig.add_subplot(1, 1, 1, projection=wcs)
# Call fill with a matplotlib axe and the `~astropy.wcs.WCS` wcs object.
moc.fill(ax=ax, wcs=wcs, alpha=0.5, fill=True, color="green")
# Draw the perimeter of the MOC in black
moc.border(ax=ax, wcs=wcs, alpha=0.5, color="black")
plt.xlabel('ra')
plt.ylabel('dec')
plt.title('Cone search')
plt.grid(color="black", linestyle="dotted")
plt.show()
def common_sky_region_select_test_routines(obs_table, selection):
"""Common routines for the tests of sky_box/sky_circle selection of obs tables."""
type = selection['type']
if type not in ['sky_box', 'sky_circle']:
raise ValueError("Invalid type: {}".format(type))
if type == 'sky_box':
lon_range_eff = (selection['lon'][0] - selection['border'],
selection['lon'][1] + selection['border'])
lat_range_eff = (selection['lat'][0] - selection['border'],
selection['lat'][1] + selection['border'])
elif type == 'sky_circle':
lon_cen = selection['lon']
lat_cen = selection['lat']
center = SkyCoord(lon_cen, lat_cen, frame=selection['frame'])
radius_eff = selection['radius'] + selection['border']
do_wrapping = False
# not needed in the case of sky_circle
if type == 'sky_box' and any(l < Angle(0., 'deg') for l in lon_range_eff):
do_wrapping = True
# observation table
skycoord = skycoord_from_table(obs_table)
# test on the selection
selected_obs_table = obs_table.select_observations(selection)
skycoord = skycoord_from_table(selected_obs_table)
if type == 'sky_box':
skycoord = skycoord.transform_to(selection['frame'])
lon = skycoord.data.lon
lat = skycoord.data.lat
if do_wrapping:
lon = lon.wrap_at(Angle(180, 'deg'))
assert ((lon_range_eff[0] < lon) & (lon < lon_range_eff[1]) &
(lat_range_eff[0] < lat) & (lat < lat_range_eff[1])).all()
elif type == 'sky_circle':
ang_distance = skycoord.separation(center)
assert (ang_distance < radius_eff).all()
# test on the inverted selection
selection['inverted'] = True
inv_selected_obs_table = obs_table.select_observations(selection)
skycoord = skycoord_from_table(inv_selected_obs_table)
if type == 'sky_box':
skycoord = skycoord.transform_to(selection['frame'])
lon = skycoord.data.lon
lat = skycoord.data.lat
if do_wrapping:
lon = lon.wrap_at(Angle(180, 'deg'))
assert ((lon_range_eff[0] >= lon) | (lon >= lon_range_eff[1]) |
(lat_range_eff[0] >= lat) | (lat >= lat_range_eff[1])).all()
elif type == 'sky_circle':
ang_distance = skycoord.separation(center)
assert (ang_distance >= radius_eff).all()
# the sum of number of entries in both selections should be the total
# number of entries
assert len(selected_obs_table) + len(inv_selected_obs_table) == len(
obs_table)
def test_conversion(self):
degrees = self.phase.to(u.degree)
assert_equal(degrees, Angle(self.phase1 + self.phase2, u.deg))
def db_ingest(filepath, filename, force=False):
'''Read an image header and add a row to the database'''
global telescopeids, instrumentids
if '-en0' in filename:
table = 'speclcoraw'
db_to_hdrkey = speclcoraw_to_hdrkey
else:
table = 'photlcoraw'
db_to_hdrkey = photlcoraw_to_hdrkey
fileindb = lsc.mysqldef.getfromdataraw(conn,
table,
'filename',
filename,
column2='filepath')
if fileindb:
filepath = fileindb[0]['filepath'] # could be marked as bad
if not fileindb or force:
if filename[-3:] == '.fz':
hdr = fits.getheader(filepath + filename, 1)
else:
hdr = fits.getheader(filepath + filename)
groupidcode, targetid = get_groupidcode(hdr)
dbdict = {
'filename': filename,
'filepath': filepath,
'groupidcode': groupidcode,
'targetid': targetid
}
for dbcol, hdrkey in db_to_hdrkey.items():
if hdrkey in hdr and hdr[hdrkey] not in [
'NaN', 'UNKNOWN', None, ''
]:
if hdrkey in ['RA', 'CAT-RA']:
dbdict[dbcol] = Angle(hdr[hdrkey],
u.hourangle).to_string(u.deg,
decimal=True,
precision=7)
elif hdrkey in ['DEC', 'CAT-DEC']:
dbdict[dbcol] = Angle(hdr[hdrkey],
u.deg).to_string(decimal=True,
precision=7)
else:
dbdict[dbcol] = hdr[hdrkey]
if hdr['TELESCOP'] not in telescopeids:
print hdr[
'TELESCOP'], 'not recognized. Adding to telescopes table.'
lsc.mysqldef.insert_values(conn, 'telescopes',
{'name': hdr['TELESCOP']})
telescopes = lsc.mysqldef.query(
['select id, name from telescopes'], conn)
telescopeids = {tel['name']: tel['id'] for tel in telescopes}
dbdict['telescopeid'] = telescopeids[hdr['TELESCOP']]
if hdr['INSTRUME'] not in instrumentids:
print hdr[
'INSTRUME'], 'not recognized. Adding to instruments table.'
lsc.mysqldef.insert_values(conn, 'instruments',
{'name': hdr['INSTRUME']})
instruments = lsc.mysqldef.query(
['select id, name from instruments'], conn)
instrumentids = {inst['name']: inst['id'] for inst in instruments}
dbdict['instrumentid'] = instrumentids[hdr['INSTRUME']]
if fileindb:
lsc.mysqldef.query([
"delete from " + table + " where filename='" + filename + "'"
], conn)
print 'ingesting', filename
lsc.mysqldef.insert_values(conn, table, dbdict)
else:
print filename, 'already ingested'
def on_region():
"""Example on_region for testing."""
pos = SkyCoord("08h35m20.65525s", "-45d10m35.1545s", frame="icrs")
radius = Angle(0.2, "deg")
return CircleSkyRegion(pos, radius)
def test_init_basics(self):
phase = Phase(1., 0.25)
assert isinstance(phase, Phase)
assert_equal(phase['int'], Angle(1. * u.cycle))
assert_equal(phase['frac'], FractionalPhase(0.25 * u.cycle))
assert_equal(phase.cycle, Angle(1.25 * u.cycle))
@pytest.fixture(scope="session")
def bkg_estimator(observations, exclusion_mask, on_region):
"""Example background estimator for testing."""
maker = ReflectedRegionsBackgroundEstimator(
observations=observations,
on_region=on_region,
exclusion_mask=exclusion_mask,
min_distance_input="0.2 deg",
)
maker.run()
return maker
region_finder_param = [
(SkyCoord(83.2, 22.5, unit="deg"), 15, Angle("82.592 deg"), 17, 17),
(SkyCoord(84.2, 22.5, unit="deg"), 17, Angle("83.636 deg"), 19, 19),
(SkyCoord(83.2, 21.5, unit="deg"), 15, Angle("83.672 deg"), 17, 17),
]
@requires_data()
@pytest.mark.parametrize("pointing_pos, nreg1, reg3_ra, nreg2, nreg3",
region_finder_param)
def test_find_reflected_regions(exclusion_mask, on_region, pointing_pos, nreg1,
reg3_ra, nreg2, nreg3):
pointing = pointing_pos
finder = ReflectedRegionsFinder(
center=pointing,
region=on_region,
exclusion_mask=exclusion_mask,
def on_region():
"""Example on_region for testing."""
pos = SkyCoord(83.63, 22.01, unit="deg", frame="icrs")
radius = Angle(0.11, "deg")
region = CircleSkyRegion(pos, radius)
return region
class EventListChecker(Checker):
"""Event list checker.
Data format specification: ref:`gadf:iact-events`
Parameters
----------
event_list : `~gammapy.data.EventList`
Event list
"""
CHECKS = {
"meta": "check_meta",
"columns": "check_columns",
"times": "check_times",
"coordinates_galactic": "check_coordinates_galactic",
"coordinates_altaz": "check_coordinates_altaz",
}
accuracy = {"angle": Angle("1 arcsec"), "time": Quantity(1, "microsecond")}
# https://gamma-astro-data-formats.readthedocs.io/en/latest/events/events.html#mandatory-header-keywords
meta_required = [
"HDUCLASS",
"HDUDOC",
"HDUVERS",
"HDUCLAS1",
"OBS_ID",
"TSTART",
"TSTOP",
"ONTIME",
"LIVETIME",
"DEADC",
"RA_PNT",
"DEC_PNT",
# TODO: what to do about these?
# They are currently listed as required in the spec,
# but I think we should just require ICRS and those
# are irrelevant, should not be used.
# 'RADECSYS',
# 'EQUINOX',
"ORIGIN",
"TELESCOP",
"INSTRUME",
"CREATOR",
# https://gamma-astro-data-formats.readthedocs.io/en/latest/general/time.html#time-formats
"MJDREFI",
"MJDREFF",
"TIMEUNIT",
"TIMESYS",
"TIMEREF",
# https://gamma-astro-data-formats.readthedocs.io/en/latest/general/coordinates.html#coords-location
"GEOLON",
"GEOLAT",
"ALTITUDE",
]
_col = collections.namedtuple("col", ["name", "unit"])
columns_required = [
_col(name="EVENT_ID", unit=""),
_col(name="TIME", unit="s"),
_col(name="RA", unit="deg"),
_col(name="DEC", unit="deg"),
_col(name="ENERGY", unit="TeV"),
]
def __init__(self, event_list):
self.event_list = event_list
def _record(self, level="info", msg=None):
obs_id = self.event_list.table.meta["OBS_ID"]
return {"level": level, "obs_id": obs_id, "msg": msg}
def check_meta(self):
meta_missing = sorted(
set(self.meta_required) - set(self.event_list.table.meta))
if meta_missing:
yield self._record(level="error",
msg=f"Missing meta keys: {meta_missing!r}")
def check_columns(self):
t = self.event_list.table
if len(t) == 0:
yield self._record(level="error", msg="Events table has zero rows")
for name, unit in self.columns_required:
if name not in t.colnames:
yield self._record(level="error",
msg=f"Missing table column: {name!r}")
else:
if Unit(unit) != (t[name].unit or ""):
yield self._record(
level="error",
msg=f"Invalid unit for column: {name!r}")
def check_times(self):
dt = (self.event_list.time -
self.event_list.observation_time_start).sec
if dt.min() < self.accuracy["time"].to_value("s"):
yield self._record(level="error",
msg="Event times before obs start time")
dt = (self.event_list.time - self.event_list.observation_time_end).sec
if dt.max() > self.accuracy["time"].to_value("s"):
yield self._record(level="error",
msg="Event times after the obs end time")
if np.min(np.diff(dt)) <= 0:
yield self._record(level="error",
msg="Events are not time-ordered.")
def check_coordinates_galactic(self):
"""Check if RA / DEC matches GLON / GLAT."""
t = self.event_list.table
if "GLON" not in t.colnames:
return
galactic = SkyCoord(t["GLON"], t["GLAT"], unit="deg", frame="galactic")
separation = self.event_list.radec.separation(galactic).to("arcsec")
if separation.max() > self.accuracy["angle"]:
yield self._record(level="error",
msg="GLON / GLAT not consistent with RA / DEC")
def check_coordinates_altaz(self):
"""Check if ALT / AZ matches RA / DEC."""
t = self.event_list.table
if "AZ" not in t.colnames:
return
altaz_astropy = self.event_list.altaz
separation = angular_separation(
altaz_astropy.data.lon,
altaz_astropy.data.lat,
t["AZ"].quantity,
t["ALT"].quantity,
)
if separation.max() > self.accuracy["angle"]:
yield self._record(level="error",
msg="ALT / AZ not consistent with RA / DEC")
def exclusion_mask():
"""Example mask for testing."""
pos = SkyCoord(83.63, 22.01, unit="deg", frame="icrs")
exclusion_region = CircleSkyRegion(pos, Angle(0.3, "deg"))
geom = WcsGeom.create(skydir=pos, binsz=0.02, width=10.0)
return ~geom.region_mask([exclusion_region])
def calculate_airmass(ctext):
"""
Calculates AIRMASS for the list of all FITS files and appends respective details in the headers.
Args:
ctext : Common text of all FITS files whose headers have to be edited
Returns:
None
"""
list_files = group_similar_files('', common_text=ctext)
for file_name in list_files:
hdulist = fits.open(file_name, mode='update')
file_header = hdulist[0].header
object_str = file_header[OBJECT_keyword]
date_obs = file_header[DATE_keyword]
time_start = file_header[TIMESTART_keyword]
object_ra = OBJECT_RA
object_dec = OBJECT_DEC
if object_str == 'Feige110':
object_ra = RA_Feige110
object_dec = DEC_Feige110
elif object_str == 'Feige34':
object_ra = RA_Feige34
object_dec = DEC_Feige34
elif object_str == 'Feige66':
object_ra = RA_Feige66
object_dec = DEC_Feige66
if RA_keyword in file_header:
file_header.set(RA_keyword, object_ra)
else:
file_header.append((RA_keyword, object_ra))
if DEC_keyword in file_header:
file_header.set(DEC_keyword, object_dec)
else:
file_header.append((DEC_keyword, object_dec))
time_obs = str(datetime.timedelta(seconds=int(time_start)))
time_utc = date_obs + ' ' + time_obs
julian_day = ephem.julian_date(time_utc)
telescope = ephem.Observer()
telescope.lon = OBS_LONG
telescope.lat = OBS_LAT
telescope.elevation = OBS_ALT
telescope.pressure = 0
telescope.epoch = ephem.J2000
telescope.date = time_utc
obj_pos = ephem.FixedBody()
obj_pos._ra = object_ra
obj_pos._dec = object_dec
obj_pos._epoch = ephem.J2000
obj_pos.compute(telescope)
time_sidereal = telescope.sidereal_time()
object_alt = Angle(str(obj_pos.alt) + ' degrees').degree
airmass = 1 / math.cos(math.radians(90 - object_alt))
list_keywords = [
'OBSERVAT', 'OBS_LAT', 'OBS_LONG', 'OBS_ALT', 'TIMEZONE',
'DATE_OBS', 'UT', 'JD', 'ST', 'RA', 'DEC', 'ALT', 'AZ', 'AIRMASS'
]
dict_header = {
'OBSERVAT': OBS_NAME,
'OBS_LAT': OBS_LAT,
'OBS_LONG': OBS_LONG,
'OBS_ALT': OBS_ALT,
'TIMEZONE': OBS_TIMEZONE,
'DATE_OBS': date_obs,
'UT': time_utc,
'JD': julian_day,
'ST': time_sidereal,
'RA': object_ra,
'DEC': object_dec,
'ALT': obj_pos.alt,
'AZ': obj_pos.az,
'AIRMASS': airmass
}
for keyword in list_keywords:
if keyword in file_header.keys():
file_header.remove(keyword, remove_all=True)
file_header.append(card=(keyword, str(dict_header[keyword])))
hdulist.flush()
hdulist.close()
def _info_map(self):
"""Print info from map analysis."""
d = self.data
ss = "\n*** Info from map analysis ***\n\n"
ra_str = Angle(d["RAJ2000"], "deg").to_string(unit="hour", precision=0)
dec_str = Angle(d["DEJ2000"], "deg").to_string(unit="deg", precision=0)
ss += "{:<20s} : {:8.3f} = {}\n".format("RA", d["RAJ2000"], ra_str)
ss += "{:<20s} : {:8.3f} = {}\n".format("DEC", d["DEJ2000"], dec_str)
ss += "{:<20s} : {:8.3f} +/- {:.3f} deg\n".format(
"GLON", d["GLON"].value, d["GLON_Err"].value
)
ss += "{:<20s} : {:8.3f} +/- {:.3f} deg\n".format(
"GLAT", d["GLAT"].value, d["GLAT_Err"].value
)
ss += "{:<20s} : {:.3f}\n".format("Position Error (68%)", d["Pos_Err_68"])
ss += "{:<20s} : {:.3f}\n".format("Position Error (95%)", d["Pos_Err_95"])
ss += "{:<20s} : {:.0f}\n".format("ROI number", d["ROI_Number"])
ss += "{:<20s} : {}\n".format("Spatial model", d["Spatial_Model"])
ss += "{:<20s} : {}\n".format("Spatial components", d["Components"])
ss += "{:<20s} : {:.1f}\n".format("TS", d["Sqrt_TS"] ** 2)
ss += "{:<20s} : {:.1f}\n".format("sqrt(TS)", d["Sqrt_TS"])
ss += "{:<20s} : {:.3f} +/- {:.3f} (UL: {:.3f}) deg\n".format(
"Size", d["Size"].value, d["Size_Err"].value, d["Size_UL"].value
)
ss += "{:<20s} : {:.3f}\n".format("R70", d["R70"])
ss += "{:<20s} : {:.3f}\n".format("RSpec", d["RSpec"])
ss += "{:<20s} : {:.1f}\n".format("Total model excess", d["Excess_Model_Total"])
ss += "{:<20s} : {:.1f}\n".format("Excess in RSpec", d["Excess_RSpec"])
ss += "{:<20s} : {:.1f}\n".format(
"Model Excess in RSpec", d["Excess_RSpec_Model"]
)
ss += "{:<20s} : {:.1f}\n".format("Background in RSpec", d["Background_RSpec"])
ss += "{:<20s} : {:.1f} hours\n".format("Livetime", d["Livetime"].value)
ss += "{:<20s} : {:.2f}\n".format("Energy threshold", d["Energy_Threshold"])
val, err = d["Flux_Map"].value, d["Flux_Map_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 cm^-2 s^-1 = ({:.2f} +/- {:.2f}) % Crab\n".format(
"Source flux (>1 TeV)",
val / FF,
err / FF,
val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB,
)
ss += "\nFluxes in RSpec (> 1 TeV):\n"
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Map measurement",
d["Flux_Map_RSpec_Data"].value / FF,
d["Flux_Map_RSpec_Data"].value * FLUX_TO_CRAB,
)
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Source model",
d["Flux_Map_RSpec_Source"].value / FF,
d["Flux_Map_RSpec_Source"].value * FLUX_TO_CRAB,
)
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Other component model",
d["Flux_Map_RSpec_Other"].value / FF,
d["Flux_Map_RSpec_Other"].value * FLUX_TO_CRAB,
)
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Large scale component model",
d["Flux_Map_RSpec_LS"].value / FF,
d["Flux_Map_RSpec_LS"].value * FLUX_TO_CRAB,
)
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Total model",
d["Flux_Map_RSpec_Total"].value / FF,
d["Flux_Map_RSpec_Total"].value * FLUX_TO_CRAB,
)
ss += "{:<35s} : {:5.1f} %\n".format(
"Containment in RSpec", 100 * d["Containment_RSpec"]
)
ss += "{:<35s} : {:5.1f} %\n".format(
"Contamination in RSpec", 100 * d["Contamination_RSpec"]
)
label, val = (
"Flux correction (RSpec -> Total)",
100 * d["Flux_Correction_RSpec_To_Total"],
)
ss += f"{label:<35s} : {val:5.1f} %\n"
label, val = (
"Flux correction (Total -> RSpec)",
100 * (1 / d["Flux_Correction_RSpec_To_Total"]),
)
ss += f"{label:<35s} : {val:5.1f} %\n"
return ss
def calcfaixa(vel, data, star, dist, ca, pa, tamanho, step, erro=None, ring=None, atm=None):
vec = np.arange(0, int(8000/(np.absolute(vel.value))), step)
g = np.sort(np.concatenate((vec,-vec[1:]), axis=0))
latlon = {'clat':{'lon':[], 'lat':[], 'lab': [], 'x': [], 'y': [], 'labx': []}, 'lats': {'lon':[], 'lat':[], 'lon2':[], 'lat2':[], 'x': [], 'y': [], 'x2':[], 'y2':[]}}
if not erro == None:
latlon['erro'] = {'lon': [], 'lat': [], 'lon2':[], 'lat2':[]}
err = erro*u.mas
errd = (dist.to(u.km)*err.to(u.rad)).value*u.km
if not ring == None:
latlon['ring'] = {'lon': [], 'lat': [], 'lon2':[], 'lat2':[]}
if not atm == None:
latlon['atm'] = {'lon': [], 'lat': [], 'lon2':[], 'lat2':[]}
pa = Angle(pa)
pa.wrap_at('180d', inplace=True)
if pa > 90*u.deg:
paplus = pa - 180*u.deg
elif pa < -90*u.deg:
paplus = pa + 180*u.deg
else:
paplus = pa
for delt in g:
deltatime = delt*u.s
datas1 = data + TimeDelta(deltatime)
datas1.delta_ut1_utc = 0
lon = star.ra - datas1.sidereal_time('mean', 'greenwich')
m = Basemap(projection='ortho',lat_0=star.dec.value,lon_0=lon.value,resolution=None)
a, b = m(lon.value, star.dec.value)
a = a*u.m
b = b*u.m
dista = (dist.to(u.km)*ca.to(u.rad)).value*u.km
ax = a + dista*np.sin(pa) + (deltatime*vel)*np.cos(paplus)
by = b + dista*np.cos(pa) - (deltatime*vel)*np.sin(paplus)
ax2 = ax - tamanho/2*np.sin(paplus)
by2 = by - tamanho/2*np.cos(paplus)
ax3 = ax + tamanho/2*np.sin(paplus)
by3 = by + tamanho/2*np.cos(paplus)
clon1, clat1 = m(ax.value, by.value, inverse=True)
if delt == 0:
latlon['clat']['cxy'] = [ax.value, by.value]
if clon1 < 1e+30:
latlon['clat']['lon'].append(clon1)
latlon['clat']['lat'].append(clat1)
latlon['clat']['lab'].append(datas1.iso)
else:
latlon['clat']['x'].append(ax.value)
latlon['clat']['y'].append(by.value)
latlon['clat']['labx'].append(datas1.iso)
lon1, lat1 = m(ax2.value, by2.value, inverse=True)
if lon1 < 1e+30:
latlon['lats']['lon'].append(lon1)
latlon['lats']['lat'].append(lat1)
else:
latlon['lats']['x'].append(ax2.value)
latlon['lats']['y'].append(by2.value)
lon2, lat2 = m(ax3.value, by3.value, inverse=True)
if lon2 < 1e+30:
latlon['lats']['lon2'].append(lon2)
latlon['lats']['lat2'].append(lat2)
else:
latlon['lats']['x2'].append(ax3.value)
latlon['lats']['y2'].append(by3.value)
if not erro == None:
ax2 = ax - errd*np.sin(paplus)
by2 = by - errd*np.cos(paplus)
ax3 = ax + errd*np.sin(paplus)
by3 = by + errd*np.cos(paplus)
lon1, lat1 = m(ax2.value, by2.value, inverse=True)
if lon1 < 1e+30:
latlon['erro']['lon'].append(lon1)
latlon['erro']['lat'].append(lat1)
lon2, lat2 = m(ax3.value, by3.value, inverse=True)
if lon2 < 1e+30:
latlon['erro']['lon2'].append(lon2)
latlon['erro']['lat2'].append(lat2)
if not ring == None:
rng = ring*u.km
ax2 = ax - rng*np.sin(paplus)
by2 = by - rng*np.cos(paplus)
ax3 = ax + rng*np.sin(paplus)
by3 = by + rng*np.cos(paplus)
lon1, lat1 = m(ax2.value, by2.value, inverse=True)
if lon1 < 1e+30:
latlon['ring']['lon'].append(lon1)
latlon['ring']['lat'].append(lat1)
lon2, lat2 = m(ax3.value, by3.value, inverse=True)
if lon2 < 1e+30:
latlon['ring']['lon2'].append(lon2)
latlon['ring']['lat2'].append(lat2)
if not atm == None:
atmo = atm*u.km
ax2 = ax - atmo*np.sin(paplus)
by2 = by - atmo*np.cos(paplus)
ax3 = ax + atmo*np.sin(paplus)
by3 = by + atmo*np.cos(paplus)
lon1, lat1 = m(ax2.value, by2.value, inverse=True)
if lon1 < 1e+30:
latlon['atm']['lon'].append(lon1)
latlon['atm']['lat'].append(lat1)
lon2, lat2 = m(ax3.value, by3.value, inverse=True)
if lon2 < 1e+30:
latlon['atm']['lon2'].append(lon2)
latlon['atm']['lat2'].append(lat2)
return latlon
def exclusion_mask(geom):
"""Example mask for testing."""
pos = SkyCoord(83.633, 22.014, unit="deg", frame="icrs")
region = CircleSkyRegion(pos, Angle(0.3, "deg"))
return ~geom.region_mask([region])
def _info_map(self):
"""Print info from map analysis."""
d = self.data
ss = '\n*** Info from map analysis ***\n\n'
ra_str = Angle(d['RAJ2000'], 'deg').to_string(unit='hour', precision=0)
dec_str = Angle(d['DEJ2000'], 'deg').to_string(unit='deg', precision=0)
ss += '{:<20s} : {:8.3f} = {}\n'.format('RA', d['RAJ2000'], ra_str)
ss += '{:<20s} : {:8.3f} = {}\n'.format('DEC', d['DEJ2000'], dec_str)
ss += '{:<20s} : {:8.3f} +/- {:.3f} deg\n'.format(
'GLON', d['GLON'].value, d['GLON_Err'].value)
ss += '{:<20s} : {:8.3f} +/- {:.3f} deg\n'.format(
'GLAT', d['GLAT'].value, d['GLAT_Err'].value)
ss += '{:<20s} : {:.3f}\n'.format('Position Error (68%)',
d['Pos_Err_68'])
ss += '{:<20s} : {:.3f}\n'.format('Position Error (95%)',
d['Pos_Err_95'])
ss += '{:<20s} : {:.0f}\n'.format('ROI number', d['ROI_Number'])
ss += '{:<20s} : {}\n'.format('Spatial model', d['Spatial_Model'])
ss += '{:<20s} : {}\n'.format('Spatial components', d['Components'])
ss += '{:<20s} : {:.1f}\n'.format('TS', d['Sqrt_TS']**2)
ss += '{:<20s} : {:.1f}\n'.format('sqrt(TS)', d['Sqrt_TS'])
ss += '{:<20s} : {:.3f} +/- {:.3f} (UL: {:.3f}) deg\n'.format(
'Size', d['Size'].value, d['Size_Err'].value, d['Size_UL'].value)
ss += '{:<20s} : {:.3f}\n'.format('R70', d['R70'])
ss += '{:<20s} : {:.3f}\n'.format('RSpec', d['RSpec'])
ss += '{:<20s} : {:.1f}\n'.format('Total model excess',
d['Excess_Model_Total'])
ss += '{:<20s} : {:.1f}\n'.format('Excess in RSpec', d['Excess_RSpec'])
ss += '{:<20s} : {:.1f}\n'.format('Model Excess in RSpec',
d['Excess_RSpec_Model'])
ss += '{:<20s} : {:.1f}\n'.format('Background in RSpec',
d['Background_RSpec'])
ss += '{:<20s} : {:.1f} hours\n'.format('Livetime',
d['Livetime'].value)
ss += '{:<20s} : {:.1f}\n'.format('Energy threshold',
d['Energy_Threshold'])
val, err = d['Flux_Map'].value, d['Flux_Map_Err'].value
ss += '{:<20s} : ({:.2f} +/- {:.2f}) x 10^-12 cm^-2 s^-1 = ({:.1f} +/- {:.1f}) % Crab\n'.format(
'Source flux (>1 TeV)', val / FF, err / FF, val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB)
ss += '\nFluxes in RSpec (> 1 TeV):\n'
ss += '{:<30s} : {:.2f} x 10^-12 cm^-2 s^-1 = {:5.1f} % Crab\n'.format(
'Map measurement', d['Flux_Map_RSpec_Data'].value / FF,
d['Flux_Map_RSpec_Data'].value * FLUX_TO_CRAB)
ss += '{:<30s} : {:.2f} x 10^-12 cm^-2 s^-1 = {:5.1f} % Crab\n'.format(
'Source model', d['Flux_Map_RSpec_Source'].value / FF,
d['Flux_Map_RSpec_Source'].value * FLUX_TO_CRAB)
ss += '{:<30s} : {:.2f} x 10^-12 cm^-2 s^-1 = {:5.1f} % Crab\n'.format(
'Other component model', d['Flux_Map_RSpec_Other'].value / FF,
d['Flux_Map_RSpec_Other'].value * FLUX_TO_CRAB)
ss += '{:<30s} : {:.2f} x 10^-12 cm^-2 s^-1 = {:5.1f} % Crab\n'.format(
'Large scale component model', d['Flux_Map_RSpec_LS'].value / FF,
d['Flux_Map_RSpec_LS'].value * FLUX_TO_CRAB)
ss += '{:<30s} : {:.2f} x 10^-12 cm^-2 s^-1 = {:5.1f} % Crab\n'.format(
'Total model', d['Flux_Map_RSpec_Total'].value / FF,
d['Flux_Map_RSpec_Total'].value * FLUX_TO_CRAB)
ss += '{:<35s} : {:5.1f} %\n'.format('Containment in RSpec',
100 * d['Containment_RSpec'])
ss += '{:<35s} : {:5.1f} %\n'.format('Contamination in RSpec',
100 * d['Contamination_RSpec'])
label, val = 'Flux correction (RSpec -> Total)', 100 * d[
'Flux_Correction_RSpec_To_Total']
ss += '{:<35s} : {:5.1f} %\n'.format(label, val)
label, val = 'Flux correction (Total -> RSpec)', 100 * (
1 / d['Flux_Correction_RSpec_To_Total'])
ss += '{:<35s} : {:5.1f} %\n'.format(label, val)
return ss
if metacat is not None: # Appends metadata sources[distbin][0].append(distbin+1) sources[distbin][1].append(cat[m,0]) sources[distbin][2].append(cat[m,1]) sources[distbin][4].append(z[infield][i]) # appends redshift if reliability > threshold if metacat[m,2] >= rel_thres: sources[distbin][3].append(metacat[m,3]) else: sources[distbin][3].append(-1) break #""" ## Random Sampling ## R_map_deg = Angle(R_map[i]).deg # converts to deg extract = rnd.randint(MC_repeat) # random simulation extraction for sim in range(MC_repeat): # loops MC_repeat times for the MC simulations while True: # random RA/Dec for random sampling (RA accounts for PBCs in ICRS frame) ra_rand = rnd.uniform(RA_min + R_map_deg, RA_max + 360 - R_map_deg) dec_rand = rnd.uniform(DEC_min + R_map_deg, DEC_max - R_map_deg) coords_rand = SkyCoord(ra_rand % 360, dec_rand, unit="deg") # cuts random sample try: # catches exception if cluster is at edge boxrand = Cutout2D(rebin, coords_rand, 2*R_map[i], wcs=w_new, mode="strict") cutrand = boxrand.data # image data of random cutout except (PartialOverlapError, NoOverlapError, ValueError): continue if np.count_nonzero(cutrand) == len(cutrand)**2:
def align_by_wcs(files, target=None, observer=None, time_key='DATE-OBS',
**kwargs):
"""Align a set of images using their world coordinate systems.
Parameters
----------
files : list
The list of FITS files to align.
target : SolarSysObject
Align in the reference frame of this object.
observer : SolarSysObject
Observe `target` with this observer.
time_key : string
The header keyword for the observation time.
**kwargs
Keyword arguments for `imshift`.
Results
-------
stack : ndarray
The aligned images.
dyx : ndarray
The offsets.
"""
import astropy.units as u
from astropy.io import fits
from astropy.wcs import WCS
from astropy.coordinates import Angle
im, h0 = fits.getdata(files[0], header=True)
wcs0 = WCS(h0)
stack = np.zeros((len(files), ) + im.shape)
stack[0] = im
y0, x0 = np.array(im.shape) / 2.0
if target is not None:
assert observer is not None, "observer required"
g0 = observer.observe(target, h0[time_key])
xt, yt = wcs0.wcs_world2pix(np.c_[g0.ra, g0.dec], 0)[0]
ra0, dec0 = Angle(wcs0.wcs_pix2world(np.c_[x0, y0], 0)[0] * u.deg)
dra = 0 * u.deg
ddec = 0 * u.deg
dyx = np.zeros((len(files), 2))
for i in range(1, len(files)):
im, h = fits.getdata(files[i], header=True)
wcs = WCS(h)
if target is not None:
g = observer.observe(target, h[time_key])
dra = g.ra - g0.ra
ddec = g.dec - g0.dec
x, y = wcs.wcs_world2pix(np.c_[ra0 + dra, dec0 + ddec], 0)[0]
dyx[i] = y0 - y, x0 - x
stack[i] = core.imshift(im, dyx[i], **kwargs)
if int(dyx[i, 0]) != 0:
if int(dyx[i, 0]) < 0:
stack[i, :, int(dyx[i, 0]):] = np.nan
else:
stack[i, :, :int(dyx[i, 0])] = np.nan
if int(dyx[i, 1]) != 0:
if int(dyx[i, 1]) < 0:
stack[i, int(dyx[i, 1]):] = np.nan
else:
stack[i, :int(dyx[i, 1])] = np.nan
return stack, dyx
def test_sample_sphere():
random_state = np.random.RandomState(seed=0)
# test general case
lon, lat = sample_sphere(size=2, random_state=random_state)
assert_quantity_allclose(lon, Angle([3.44829694, 4.49366732], "radian"))
assert_quantity_allclose(lat, Angle([0.20700192, 0.08988736], "radian"))
# test specify a limited range
lon_range = Angle([40.0, 45.0], "deg")
lat_range = Angle([10.0, 15.0], "deg")
lon, lat = sample_sphere(
size=10, lon_range=lon_range, lat_range=lat_range, random_state=random_state
)
assert ((lon_range[0] <= lon) & (lon < lon_range[1])).all()
assert ((lat_range[0] <= lat) & (lat < lat_range[1])).all()
# test lon within (-180, 180) deg range
lon_range = Angle([-40.0, 0.0], "deg")
lon, lat = sample_sphere(size=10, lon_range=lon_range, random_state=random_state)
assert ((lon_range[0] <= lon) & (lon < lon_range[1])).all()
lat_range = Angle([-90.0, 90.0], "deg")
assert ((lat_range[0] <= lat) & (lat < lat_range[1])).all()
# test lon range explicitly (0, 360) deg
lon_range = Angle([0.0, 360.0], "deg")
lon, lat = sample_sphere(size=100, lon_range=lon_range, random_state=random_state)
# test values in the desired range
lat_range = Angle([-90.0, 90.0], "deg")
assert ((lon_range[0] <= lon) & (lon < lon_range[1])).all()
assert ((lat_range[0] <= lat) & (lat < lat_range[1])).all()
# test if values are distributed along the whole range
nbins = 4
lon_delta = (lon_range[1] - lon_range[0]) / nbins
lat_delta = (lat_range[1] - lat_range[0]) / nbins
for i in np.arange(nbins):
assert (
(lon_range[0] + i * lon_delta <= lon)
& (lon < lon_range[0] + (i + 1) * lon_delta)
).any()
assert (
(lat_range[0] + i * lat_delta <= lat)
& (lat < lat_range[0] + (i + 1) * lat_delta)
).any()
# test lon range explicitly (-180, 180) deg
lon_range = Angle([-180.0, 180.0], "deg")
lon, lat = sample_sphere(size=100, lon_range=lon_range, random_state=random_state)
# test values in the desired range
lat_range = Angle([-90.0, 90.0], "deg")
assert ((lon_range[0] <= lon) & (lon < lon_range[1])).all()
assert ((lat_range[0] <= lat) & (lat < lat_range[1])).all()
# test if values are distributed along the whole range
nbins = 4
lon_delta = (lon_range[1] - lon_range[0]) / nbins
lat_delta = (lat_range[1] - lat_range[0]) / nbins
for i in np.arange(nbins):
assert (
(lon_range[0] + i * lon_delta <= lon)
& (lon < lon_range[0] + (i + 1) * lon_delta)
).any()
assert (
(lat_range[0] + i * lat_delta <= lat)
& (lat < lat_range[0] + (i + 1) * lat_delta)
).any()
# test box around Galactic center
lon_range = Angle([-5.0, 5.0], "deg")
lon, lat = sample_sphere(size=10, lon_range=lon_range, random_state=random_state)
# test if values are distributed along the whole range
nbins = 2
lon_delta = (lon_range[1] - lon_range[0]) / nbins
for i in np.arange(nbins):
assert (
(lon_range[0] + i * lon_delta <= lon)
& (lon < lon_range[0] + (i + 1) * lon_delta)
).any()
# test box around Galactic anticenter
lon_range = Angle([175.0, 185.0], "deg")
lon, lat = sample_sphere(size=10, lon_range=lon_range, random_state=random_state)
# test if values are distributed along the whole range
nbins = 2
lon_delta = (lon_range[1] - lon_range[0]) / nbins
for i in np.arange(nbins):
assert (
(lon_range[0] + i * lon_delta <= lon)
& (lon < lon_range[0] + (i + 1) * lon_delta)
).any()
def test_cube_pipe(tmpdir):
"""Example how to make a Cube analysis from a 2D background model."""
tmpdir = str(tmpdir)
outdir = tmpdir
outdir2 = outdir + '/background'
Path(outdir2).mkdir()
ds = DataStore.from_dir("$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2")
ds.copy_obs(ds.obs_table, tmpdir)
data_store = DataStore.from_dir(tmpdir)
# Create the background model from the 4 Crab observations
bgmaker = OffDataBackgroundMaker(data_store, outdir=outdir2)
bgmaker.select_observations(selection='all')
bgmaker.group_observations()
bgmaker.make_model("2D")
bgmaker.save_models("2D")
fn = outdir2 + '/group-def.fits'
# New hdu table that contains the link to the background model
hdu_index_table = bgmaker.make_total_index_table(
data_store=data_store,
modeltype='2D',
out_dir_background_model=outdir2,
filename_obs_group_table=fn)
fn = outdir + '/hdu-index.fits.gz'
hdu_index_table.write(fn, overwrite=True)
offset_band = Angle([0, 2.49], 'deg')
ref_cube_images = make_empty_cube(emin=0.5, emax=100, enumbins=5)
ref_cube_exposure = make_empty_cube(emin=0.1,
emax=120,
enumbins=80,
data_unit="m2 s")
ref_cube_skymask = make_empty_cube(emin=0.5, emax=100, enumbins=5)
data_store = DataStore.from_dir(tmpdir)
refheader = ref_cube_images.sky_image_ref.to_image_hdu().header
exclusion_mask = SkyImage.read(
'$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits')
exclusion_mask = exclusion_mask.reproject(reference=refheader)
ref_cube_skymask.data = np.tile(exclusion_mask.data, (5, 1, 1))
# TODO: Problem with the load psftable for one of the run that is not implemented yet...
data_store.hdu_table.remove_row(14)
# Cube Analysis
cube_maker = StackedObsCubeMaker(
empty_cube_images=ref_cube_images,
empty_exposure_cube=ref_cube_exposure,
offset_band=offset_band,
data_store=data_store,
obs_table=data_store.obs_table,
exclusion_mask=ref_cube_skymask,
save_bkg_scale=True,
)
cube_maker.make_cubes(make_background_image=True, radius=10.)
assert_allclose(cube_maker.counts_cube.data.sum(), 4898.0, atol=3)
assert_allclose(cube_maker.bkg_cube.data.sum(), 4260.120595293951, atol=3)
# Note: the tolerance in the following assert is low to pass here:
# https://travis-ci.org/gammapy/gammapy/jobs/234062946#L2112
cube_maker.significance_cube.data[np.where(
np.isinf(cube_maker.significance_cube.data))] = 0
actual = np.nansum(cube_maker.significance_cube.data)
assert_allclose(actual, 65777.69960178432, rtol=0.1)
actual = cube_maker.excess_cube.data.sum()
assert_allclose(actual, 637.8794047060486, rtol=1e-2)
actual = np.nansum(cube_maker.exposure_cube.data.to('m2 s').value)
assert_allclose(actual, 5399539029926424.0, rtol=1e-2)
assert_allclose(cube_maker.table_bkg_scale[0]["bkg_scale"],
0.8996676356375191,
rtol=0.03)
assert len(cube_maker.counts_cube.energies()) == 5
assert len(cube_maker.bkg_cube.energies()) == 5
assert len(cube_maker.significance_cube.energies()) == 5
assert len(cube_maker.excess_cube.energies()) == 5
assert len(cube_maker.exposure_cube.energies()) == 80
class TestPhaseInit:
def test_init_basics(self):
phase = Phase(1., 0.25)
assert isinstance(phase, Phase)
assert_equal(phase['int'], Angle(1. * u.cycle))
assert_equal(phase['frac'], FractionalPhase(0.25 * u.cycle))
assert_equal(phase.cycle, Angle(1.25 * u.cycle))
@pytest.mark.parametrize('phase1, phase2',
((1., 0.1), (1. * u.cycle, 0.125 * u.cycle),
(0., np.arange(-90., 170., 45.) * u.deg),
(Angle(720., 'deg'), FractionalPhase(0.25)),
('720d', '90d'), ('0h', '3h00m00s')))
def test_init(self, phase1, phase2):
phase = Phase(phase1, phase2)
assert isinstance(phase, Phase)
assert_equal(phase['int'], Angle(phase1, u.cycle))
assert_equal(phase['frac'], FractionalPhase(phase2, u.cycle))
expected_cycle = Angle(phase1, u.cycle) + Angle(phase2, u.cycle)
assert_equal(phase.cycle, expected_cycle)
def test_init_with_phase(self):
phase = Phase(1., 0.125)
phase2 = Phase(phase)
assert_equal(phase2, phase)
assert phase2 is not phase
assert not np.may_share_memory(phase2, phase)
phase3 = Phase(phase, copy=False)
assert phase3 is phase
phase4 = Phase(phase, 0., copy=False)
assert phase4 is not phase
assert_equal(phase4, phase)
phase5 = Phase(0., phase)
assert phase5 is not phase
assert_equal(phase5, phase)
phase6 = Phase(phase, phase)
assert_equal(phase6, Phase(2., 0.25))
def test_init_with_subclass(self):
class MyPhase(Phase):
pass
my_phase = MyPhase(1., 0.25)
assert type(my_phase) is MyPhase
phase2 = Phase(my_phase)
assert type(phase2) is Phase
phase3 = Phase(my_phase, subok=True)
assert type(phase3) is MyPhase
assert phase3 is not my_phase
assert not np.may_share_memory(phase3, my_phase)
phase4 = Phase(my_phase, copy=False)
assert type(phase4) is Phase
assert np.may_share_memory(phase4, my_phase)
phase5 = Phase(my_phase, copy=False, subok=True)
assert phase5 is my_phase
phase6 = Phase(my_phase, 0., copy=False, subok=True)
assert type(phase6) is MyPhase
assert not np.may_share_memory(phase6, my_phase)
phase7 = Phase(my_phase, phase2, copy=False, subok=True)
assert type(phase7) is MyPhase
phase8 = Phase(phase2, my_phase, copy=False, subok=True)
assert type(phase8) is MyPhase
def test_init_complex(self):
phase = Phase(1j)
assert isinstance(phase, Phase)
assert phase.imaginary
assert_equal(phase.int, Angle(1j, u.cycle))
assert_equal(phase.frac, Angle(0j, u.cycle))
assert_equal(phase.cycle, Angle(1j, u.cycle))
assert '1j cycle' in repr(phase)
phase2 = Phase(1 + 0j)
assert isinstance(phase2, Phase)
assert not phase2.imaginary
assert_equal(phase2, Phase(1))
assert '1j cycle' not in repr(phase2)
with pytest.raises(ValueError):
Phase(1., 0.0001j)
def degrees_to_ddmmss(dec):
return Angle(dec, u.degree).to_string(unit=u.degree,
fields=2)
for j in range(len(NUMBER_ID)):
DETECTION_ID[j]='CAHA_CAFOS_BBI_DR1_'+CAHA_ID[0]+'_'+'0'*(3-int(np.log10(1+j)))+NUMBER_ID[j]
c2 = fits.Column(name='Detection_ID', array=DETECTION_ID, format='50A')
MJD_array=np.zeros(len(final_objects[:,0]))
MJD_array[:]=MJD
c3 = fits.Column(name='MJD', array=MJD_array, format='D')
c4 = fits.Column(name='SNR_WIN',array=final_objects[:,SNR_WIN], format='E')
#c5 = fits.Column(name='RAJ2000', unit='deg',array=np.around(final_objects[:,ALPHA_J2000],5), format='E')
#c6 = fits.Column(name='DEJ2000', unit='deg',array=np.around(final_objects[:,DELTA_J2000],5), format='E')
#c7 = fits.Column(name='e_RAJ2000', unit='arcsec',array=np.around(3600*final_objects[:,ERRX2_WORLD]**0.5,5), format='E')
#c8 = fits.Column(name='e_DEJ2000', unit='arcsec',array=np.around(3600*final_objects[:,ERRY2_WORLD]**0.5,5), format='E')
c5 = fits.Column(name='RAJ2000', unit='deg',array=final_objects[:,ALPHA_J2000], format='E')
c6 = fits.Column(name='DEJ2000', unit='deg',array=final_objects[:,DELTA_J2000], format='E')
c7 = fits.Column(name='e_RAJ2000', unit='arcsec',array=3600*final_objects[:,ERRX2_WORLD]**0.5, format='E')
c8 = fits.Column(name='e_DEJ2000', unit='arcsec',array=3600*final_objects[:,ERRY2_WORLD]**0.5, format='E')
RA=Angle(final_objects[:,ALPHA_J2000]* u.deg)
DEC=Angle(final_objects[:,DELTA_J2000]* u.deg)
e_RA=Angle(final_objects[:,ERRX2_WORLD]**0.5* u.deg)
e_DEC=Angle(final_objects[:,ERRY2_WORLD]**0.5* u.deg)
c9 = fits.Column(name='RA_hms', unit='hh:mm:ss', array=RA.to_string(unit=u.hourangle, sep=(':',':')), format='20A')
c10 = fits.Column(name='DE_dms', unit='dd:mm:ss', array=DEC.to_string(unit=u.deg, sep=(':',':')), format='20A')
c11 = fits.Column(name='e_RA_hms', unit='hh:mm:ss', array=e_RA.to_string(unit=u.hourangle, sep=(':',':')), format='20A')
c12 = fits.Column(name='e_DE_dms', unit='dd:mm:ss', array=e_DEC.to_string(unit=u.deg, sep=(':',':')), format='20A')
c13 = fits.Column(name='MAG',array=np.around(calibration_mag,3), format='E')
c14 = fits.Column(name='e_MAG',array=np.around(calibration_mag_error,3), format='E')
c15 = fits.Column(name='MAG_sex',array=np.around(final_objects[:,MAG_PSF],3), format='E')
c16 = fits.Column(name='e_MAG_sex',array=np.around(final_objects[:,MAGERR_PSF],3), format='E')
c17 = fits.Column(name='cl_SDSS',array=cl_SDSS, format='E')
c18 = fits.Column(name='SPREAD_MODEL',array=np.around(SM_flag,2), format='E')
c19 = fits.Column(name='flag_calib',array=extrapolation_mag, format='3A')
c20 = fits.Column(name='Filter',array=np.array([name_filter]*len(final_objects[:,0])), format='10A')
def degrees_to_hhmmss(ra):
return Angle(ra, u.degree).to_string(unit=u.hour, fields=2)
def solve_file(self, fname, solve_params, downsample=2, search_rad=10):
"""
Plate solve the specified file using solve-field
:param str fname: Filename of the file to be solved.
:param PlateSolveParameters solve_params: Parameters for plate solver.
:param int downsample: Downsample factor for image.
:param float search_rad: Number of degrees to search.
:returns:
solved_position (SkyCoord)
The J2000 sky coordinate of the plate solve match, or None if no
match was found.
angle (Angle)
Position angle of Y axis expressed as East of North.
"""
# determine installed version of solve-field
rev = self.probe_solve_field_revision()
# example cmdline
# /usr/bin/solve-field -O --no-plots --no-verify --resort --no-fits2fits --do^Csample 2 -3 310.521 -4 45.3511 -5 10 --config /etc/astrometry.cfg -W /tmp/solution.wcs plate_solve_image.fits
# remove any solved files
# logging.debug(f'{filename} {extension} {solved_filename}')
# if os.path.isfile(solved_filename):
# logging.debug(f'Removing existing solved file {solved_filename}')
# os.remove(solved_filename)
cmd_line = self.exec_path
cmd_line += ' -O --no-plots --no-verify --resort'
cmd_line += f' --downsample {downsample}'
# this is only needed for rev of 0.67 or earlier
if rev <= 0.67:
cmd_line += ' --no-fits2fits'
cmd_line += f' -3 {solve_params.radec.ra.degree}'
cmd_line += f' -4 {solve_params.radec.dec.degree}'
# give guess of pixel scale unless given as 0
if solve_params.pixel_scale is not None and solve_params.pixel_scale > 0:
scale = solve_params.pixel_scale
cmd_line += f' -u arcsecperpix'
cmd_line += f' -L {0.9*scale} -H {1.1*scale}'
# search radius - default to 10 if not given
if search_rad is None:
search_rad = 10
cmd_line += f' -5 {search_rad}'
cmd_line += ' --config /etc/astrometry.cfg '
#cmd_line += ' -W /tmp/solution.wcs'
cmd_line += ' --crpix-center '
# disable most output files
# cmd_line += '-W none '
cmd_line += '-U none '
#cmd_line += '--axy none6 '
cmd_line += '-I none '
cmd_line += '-M none '
cmd_line += '-R none '
cmd_line += '-B none '
# cmd_line += f'{solve_params.fov_x.radian},'
# cmd_line += f'{solve_params.fov_y.radian},'
# cmd_line += fname + ','
# cmd_line += f'{wait}'
#/usr/bin/solve-field -O --no-plots --no-verify --resort --no-fits2fits --do^Csample 2 -3 310.521 -4 45.3511 -5 10 --config /etc/astrometry.cfg -W /tmp/solution.wcs plate_solve_image.fits
#tmpdirname = tempfile.TemporaryDirectory()
with tempfile.TemporaryDirectory() as tmpdirname:
logging.debug(f'Created temp dir {tmpdirname}')
# put solve-field files in this temp dir
new_fits_name = os.path.join(tmpdirname, "solved.fit")
solved_name = os.path.join(tmpdirname, "solved")
cmd_line += f'-D {tmpdirname} '
cmd_line += f'-N {new_fits_name} '
cmd_line += f'-S {solved_name} '
cmd_line += ' ' + fname
import shlex
cmd_args = shlex.split(cmd_line)
logging.debug(f'cmd_line for astrometry.net local = "{cmd_line}"')
logging.debug(f'cmd_args for astrometry.net local = "{cmd_args}"')
with subprocess.Popen(cmd_args,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True) as net_proc:
# poll_value = None
# while True:
# poll_value = net_proc.poll()
# if poll_value is not None:
# break
for l in net_proc.stdout:
logging.debug(f'astromentrynetlocal: {l.strip()}')
# see if solve succeeded
if os.path.isfile(solved_name):
logging.info('Solved file found!')
else:
logging.error('No solved file - solve failed!')
#import time
#time.sleep(5)
return None
# output
#Field center: (RA,Dec) = (2.101258, 29.091103) deg.
#Field center: (RA H:M:S, Dec D:M:S) = (00:08:24.302, +29:05:27.971).
#Field size: 76.07 x 57.4871 arcminutes
#Field rotation angle: up is 1.12149 degrees E of N
# for l in net_proc.stdout.readlines():
# ll = ''.join(filter(lambda x: x.isalnum() or x.isspace() or x == '.', l))
# print(ll)
# parse solution.wcs
from astropy import wcs
import astropy.io.fits as pyfits
#import time
#time.sleep(5)
wcs_hdulist = pyfits.open(new_fits_name)
#print(wcs_hdulist)
#print('wcs_hdulist: ', wcs_hdulist[0], vars(wcs_hdulist[0]))
w = wcs.WCS(wcs_hdulist[0].header)
#print(w.wcs.naxis)
wcs_hdulist.close()
#print('wcs.wcs=', wcs)
#print('vars(wcs.wcs): ',vars(wcs.wcs))
#wcs.wcs.print_contents()
# print('CRPIX = ', w.wcs.crpix)
# print('CD = ', w.wcs.cd)
# print('pixel scales = ', wcs.utils.proj_plane_pixel_scales(w))
solved_ra, solved_dec = w.wcs.crval
logging.info(f'solved_ra solved_dec = {solved_ra} {solved_dec}')
# solved_scale_x, solved_scale_y = wcs.utils.proj_plane_pixel_scales(w)
#FIXME just take X scale
# solved_scale = solved_scale_x
# convert CD matrix
cd_1_1 = w.wcs.cd[0][0]
cd_1_2 = w.wcs.cd[0][1]
cd_2_1 = w.wcs.cd[1][0]
cd_2_2 = w.wcs.cd[1][1]
cdelt1 = math.sqrt(cd_1_1**2 + cd_2_1**2)
cdelt2 = math.sqrt(cd_1_2**2 + cd_2_2**2)
# convention is to set cdelt1 negative if det of CD is negative
# if cd_1_1*cd_2_2-cd_1_2*cd_2_2 < 0:
# cdelt1 = -cdelt1
#
# logging.info(f'cdelt = {cdelt1*3600:5.2f} {cdelt2*3600:5.2f} arcsec/pixel')
#
# cdel = cdelt1 - cdelt2
#
# logging.debug(f'cd_1_1 cd_2_1 cd_1_2 cd_2_2 = {cd_1_1} {cd_2_1} {cd_1_2} {cd_2_2})')
# logging.debug(f'cdelt1 cdelt2 cdel = {cdelt1} {cdelt2} {cdel}')
# compute angle between North and the positive Y axis of sensor
# positive is CCW
crota = math.atan2(cd_2_1, cd_1_1)
crota_deg = np.rad2deg(crota)
logging.debug(
f'cdelt = {cdelt1*3600:5.2f} {cdelt2*3600:5.2f} arcsec/pixel')
logging.debug(f'crota/crota_deg = {crota} {crota_deg}')
# get roll angle
roll_angle_deg = -crota_deg
logging.info(f'roll_angle_deg = {roll_angle_deg:5.2f}')
solved_scale = cdelt1 * 3600
solved_angle = roll_angle_deg
# ra_str = None
# dec_str = None
# ang_str = None
# fov_x_str = None
# for l in net_proc.stdout.readlines():
# ll = ''.join(filter(lambda x: x.isalnum() or x.isspace() or x == '.', l))
# print(ll)
# fields = ll.split()
# # look for ra/dec in deg first
# if 'center' in ll and 'deg' in ll:
# # should look like:
# # Field center RADec 2.101258 29.091103 deg
# print(fields)
# ra_str = fields[3]
# dec_str = fields[4]
# elif 'angle' in ll:
# # should look like:
# # Field rotation angle up is 1.12149 degrees E of N.
# ang_str = fields[5]
# elif 'Field size' in ll:
# fov_x_str = fields[2]
#
# logging.info(f'{ra_str} {dec_str} {ang_str}')
#
# try:
# solved_ra = float(ra_str)
# solved_dec = float(dec_str)
# solved_angle = float(ang_str)
#
# # fov is given in arcmin so convert to arcsec
# fov_x = float(fov_x_str)*60.0
#
# logging.info(f'solved fov = {fov_x} arcsec')
# if solve_params.width is not None:
# solved_scale = fov_x / solve_params.width
# logging.info(f'using given width of {solve_params.width} pixel scale is {solved_scale} arcsec/pix')
# else:
# solved_scale = None
# logging.warning('No width given so pixel scale not computed!')
#
# except Exception as e:
# logging.exception('Failed to parse solution')
# return None
#
# logging.info(f'{solved_ra} {solved_dec} {solved_angle} {solved_scale}')
radec = SkyCoord(ra=solved_ra * u.degree,
dec=solved_dec * u.degree,
frame='fk5',
equinox='J2000')
logging.info(
f"AstrometryNetLocal solved coordinates: {radec.to_string('hmsdms', sep=':')}"
)
return PlateSolveSolution(radec,
pixel_scale=solved_scale,
angle=Angle(solved_angle * u.degree),
binning=solve_params.bin_x)
class SdssTranslator(FitsTranslator):
"""Metadata translator for SDSS standard headers.
NB: calibration data is not handled as calibration frames were
not available to me at time of writing.
"""
name = "SDSS"
"""Name of this translation class"""
supported_instrument = "Imager"
"""Supports the SDSS imager instrument."""
default_resource_root = posixpath.join(CORRECTIONS_RESOURCE_ROOT, "SDSS")
"""Default resource path root to use to locate header correction files."""
# SDSS has has a rotator, but in drift scan mode, the instrument
# angle on sky is set to +X=East, +Y=North which we define as a
# 0 degree rotation.
_const_map = {"boresight_rotation_angle": Angle(0*u.deg),
"boresight_rotation_coord": "sky",
"dark_time": 0.0*u.s, # Drift scan implies no dark time
"instrument": "Imager on SDSS 2.5m", # We only ever ingest data from the imager
"telescope": "SDSS 2.5m", # Value of TELESCOP in header is ambiguous
"relative_humidity": None,
"temperature": None,
"pressure": None,
"detector_serial": "UNKNOWN",
}
_trivial_map = {"exposure_time": ("EXPTIME", dict(unit=u.s)),
"object": "OBJECT",
"physical_filter": "FILTER",
"exposure_id": "RUN",
"visit_id": "RUN",
"science_program": "OBJECT", # This is the closest I can think of to a useful program
"detector_name": "CCDLOC", # This is a numeric incoding of the "slot", i.e. filter+camcol
}
# Need a mapping from unique name to index. The order is arbitrary.
detector_name_id_map = {"g1": 0, "z1": 1, "u1": 2, "i1": 3, "r1": 4, "g2": 5, "z2": 6, "u2": 7,
"i2": 8, "r2": 9, "g3": 10, "z3": 11, "u3": 12, "i3": 13, "r3": 14,
"g4": 15, "z4": 16, "u4": 17, "i4": 18, "r4": 19, "g5": 20, "z5": 21,
"u5": 22, "i5": 23, "r5": 24, "g6": 25, "z6": 26, "u6": 27, "i6": 28, "r6": 29}
@classmethod
def can_translate(cls, header, filename=None):
"""Indicate whether this translation class can translate the
supplied header.
Parameters
----------
header : `dict`-like
Header to convert to standardized form.
filename : `str`, optional
Name of file being translated.
Returns
-------
can : `bool`
`True` if the header is recognized by this class. `False`
otherwise.
"""
if (cls.is_keyword_defined(header, "ORIGIN") and cls.is_keyword_defined(header, "CCDMODE")
and cls.is_keyword_defined(header, "TELESCOP") and "2.5m" in header["TELESCOP"]
and "SDSS" in header["ORIGIN"] and "DRIFT" in header["CCDMODE"]):
return True
return False
@cache_translation
def to_detector_unique_name(self):
# Docstring will be inherited. Property defined in properties.py
if self.is_key_ok("CAMCOL"):
return self.to_physical_filter()+str(self._header["CAMCOL"])
else:
raise ValueError(f"{self._log_prefix}: CAMCOL key is not definded")
@cache_translation
def to_detector_num(self):
# Docstring will be inherited. Property defined in properties.py
return self.detector_name_id_map[self.to_detector_unique_name()]
@cache_translation
def to_observation_id(self):
"""Calculate the observation ID.
Returns
-------
observation_id : `str`
A string uniquely describing the observation.
This incorporates the run, camcol, filter and frame.
"""
return " ".join([str(self._header[el]) for el in
["RUN", "CAMCOL", "FILTER", "FRAME"]])
@cache_translation
def to_datetime_begin(self):
# Docstring will be inherited. Property defined in properties.py
# We know it is UTC
value = self._from_fits_date_string(self._header["DATE-OBS"],
time_str=self._header["TAIHMS"], scale="tai")
self._used_these_cards("DATE-OBS", "TAIHMS")
return value
@cache_translation
def to_datetime_end(self):
# Docstring will be inherited. Property defined in properties.py
return self.to_datetime_begin() + self.to_exposure_time()
@cache_translation
def to_location(self):
"""Calculate the observatory location.
Returns
-------
location : `astropy.coordinates.EarthLocation`
An object representing the location of the telescope.
"""
# Look up the value since files do not have location
value = EarthLocation.of_site("apo")
return value
@cache_translation
def to_observation_type(self):
"""Calculate the observation type.
Returns
-------
typ : `str`
Observation type. Normalized to standard set.
"""
obstype_key = "FLAVOR"
if not self.is_key_ok(obstype_key):
return "none"
obstype = self._header[obstype_key].strip().lower()
self._used_these_cards(obstype_key)
return obstype
@cache_translation
def to_tracking_radec(self):
# Docstring will be inherited. Property defined in properties.py
radecsys = ("RADECSYS",)
radecpairs = (("RA", "DEC"),)
return tracking_from_degree_headers(self, radecsys, radecpairs, unit=u.deg)
@cache_translation
def to_altaz_begin(self):
# Docstring will be inherited. Property defined in properties.py
try:
az = self._header["AZ"]
alt = self._header["ALT"]
# It appears SDSS defines azimuth as increasing
# from South through East. This translates to
# North through East
az = (-az + 180.)%360.
altaz = AltAz(az * u.deg, alt * u.deg,
obstime=self.to_datetime_begin(), location=self.to_location())
self._used_these_cards("AZ", "ALT")
return altaz
except Exception as e:
if self.to_observation_type() != "science":
return None # Allow Alt/Az not to be set for calibrations
raise(e)
@cache_translation
def to_boresight_airmass(self):
# Docstring will be inherited. Property defined in properties.py
altaz = self.to_altaz_begin()
if altaz is not None:
return altaz.secz.value # This is an estimate
@cache_translation
def to_detector_exposure_id(self):
# Docstring will be inherited. Property defined in properties.py
try:
frame_field_map = dict(r=0, i=2, u=4, z=6, g=8)
run = self._header["RUN"]
filt = self._header["FILTER"]
camcol = self._header["CAMCOL"]
field = self._header["FRAME"] - frame_field_map[filt]
self._used_these_cards("RUN", "FILTER", "CAMCOL", "FRAME")
except Exception as e:
if self.to_observation_type() != "science":
return None
raise(e)
filter_id_map = dict(u=0, g=1, r=2, i=3, z=4)
return ((int(run) * 10
+ filter_id_map[filt]) * 10
+ int(camcol)) * 10000 + int(field)
@cache_translation
def to_detector_group(self):
# Docstring will be inherited. Property defined in properties.py
if self.is_key_ok("CAMCOL"):
return str(self._header["CAMCOL"])
else:
raise ValueError(f"{self._log_prefix}: CAMCOL key is not definded")
def test_select_sky_regions():
# create random observation table with many entries
random_state = np.random.RandomState(seed=0)
obs_table = make_test_observation_table(n_obs=100,
random_state=random_state)
# test sky box selection in gal coordinates
lon_range = Angle([-100., 50.], 'deg')
lat_range = Angle([-25., 25.], 'deg')
frame = 'galactic'
border = Angle(2., 'deg')
selection = dict(type='sky_box',
frame=frame,
lon=lon_range,
lat=lat_range,
border=border)
common_sky_region_select_test_routines(obs_table, selection)
# test sky box selection in radec coordinates
lon_range = Angle([150., 300.], 'deg')
lat_range = Angle([-50., 0.], 'deg')
frame = 'icrs'
border = Angle(2., 'deg')
selection = dict(type='sky_box',
frame=frame,
lon=lon_range,
lat=lat_range,
border=border)
common_sky_region_select_test_routines(obs_table, selection)
# test sky circle selection in gal coordinates
lon_cen = Angle(0., 'deg')
lat_cen = Angle(0., 'deg')
radius = Angle(50., 'deg')
frame = 'galactic'
border = Angle(2., 'deg')
selection = dict(type='sky_circle',
frame=frame,
lon=lon_cen,
lat=lat_cen,
radius=radius,
border=border)
common_sky_region_select_test_routines(obs_table, selection)
# test sky circle selection in radec coordinates
lon_cen = Angle(130., 'deg')
lat_cen = Angle(-40., 'deg')
radius = Angle(50., 'deg')
frame = 'icrs'
border = Angle(2., 'deg')
selection = dict(type='sky_circle',
frame=frame,
lon=lon_cen,
lat=lat_cen,
radius=radius,
border=border)
common_sky_region_select_test_routines(obs_table, selection)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# make sure telescope coordinate is in range [-180°, 180°]
if isinstance(self._data, UnitSphericalRepresentation):
self._data.lon.wrap_angle = Angle(180, unit=u.deg)
def _generator(self):
with self.pyhessio.open_hessio(self.input_url) as file:
# the container is initialized once, and data is replaced within
# it after each yield
counter = 0
eventstream = file.move_to_next_event()
data = DataContainer()
data.meta['origin'] = "hessio"
# some hessio_event_source specific parameters
data.meta['input_url'] = self.input_url
data.meta['max_events'] = self.max_events
for event_id in eventstream:
if counter == 0:
# subarray info is only available when an event is loaded,
# so load it on the first event.
data.inst.subarray = self._build_subarray_info(file)
obs_id = file.get_run_number()
tels_with_data = set(file.get_teldata_list())
data.count = counter
data.r0.obs_id = obs_id
data.r0.event_id = event_id
data.r0.tels_with_data = tels_with_data
data.r1.obs_id = obs_id
data.r1.event_id = event_id
data.r1.tels_with_data = tels_with_data
data.dl0.obs_id = obs_id
data.dl0.event_id = event_id
data.dl0.tels_with_data = tels_with_data
# handle telescope filtering by taking the intersection of
# tels_with_data and allowed_tels
if len(self.allowed_tels) > 0:
selected = tels_with_data & self.allowed_tels
if len(selected) == 0:
continue # skip event
data.r0.tels_with_data = selected
data.r1.tels_with_data = selected
data.dl0.tels_with_data = selected
data.trig.tels_with_trigger = (
file.get_central_event_teltrg_list())
time_s, time_ns = file.get_central_event_gps_time()
data.trig.gps_time = Time(time_s * u.s,
time_ns * u.ns,
format='unix',
scale='utc')
data.mc.energy = file.get_mc_shower_energy() * u.TeV
data.mc.alt = Angle(file.get_mc_shower_altitude(), u.rad)
data.mc.az = Angle(file.get_mc_shower_azimuth(), u.rad)
data.mc.core_x = file.get_mc_event_xcore() * u.m
data.mc.core_y = file.get_mc_event_ycore() * u.m
first_int = file.get_mc_shower_h_first_int() * u.m
data.mc.h_first_int = first_int
data.mc.x_max = file.get_mc_shower_xmax() * u.g / (u.cm**2)
data.mc.shower_primary_id = file.get_mc_shower_primary_id()
# mc run header data
data.mcheader.run_array_direction = Angle(
file.get_mc_run_array_direction() * u.rad)
# this should be done in a nicer way to not re-allocate the
# data each time (right now it's just deleted and garbage
# collected)
data.r0.tel.clear()
data.r1.tel.clear()
data.dl0.tel.clear()
data.dl1.tel.clear()
data.mc.tel.clear() # clear the previous telescopes
for tel_id in tels_with_data:
mc = data.mc.tel[tel_id]
r0 = data.r0.tel[tel_id]
r1 = data.r1.tel[tel_id]
pointing = data.pointing[tel_id]
adc_samples = file.get_adc_sample(tel_id)
if adc_samples.size == 0:
adc_samples = file.get_adc_sum(tel_id)[..., None]
dc_to_pe = file.get_calibration(tel_id)
pedestal = file.get_pedestal(tel_id)
r0.waveform = adc_samples
r1.waveform = ((adc_samples - pedestal[..., np.newaxis]) *
dc_to_pe[..., np.newaxis])
mc.dc_to_pe = dc_to_pe
mc.pedestal = pedestal
r0.num_trig_pix = file.get_num_trig_pixels(tel_id)
r0.trig_pix_id = file.get_trig_pixels(tel_id)
mc.reference_pulse_shape = file.get_ref_shapes(tel_id)
# load the data per telescope/pixel
hessio_mc_npe = file.get_mc_number_photon_electron(tel_id)
mc.photo_electron_image = hessio_mc_npe
mc.meta['refstep'] = file.get_ref_step(tel_id)
mc.time_slice = file.get_time_slice(tel_id)
mc.azimuth_raw = file.get_azimuth_raw(tel_id)
mc.altitude_raw = file.get_altitude_raw(tel_id)
azimuth_cor = file.get_azimuth_cor(tel_id)
altitude_cor = file.get_altitude_cor(tel_id)
# hessioeventsource pass 0 if there is no altitude/azimuth correction
if azimuth_cor == 0 and mc.azimuth_raw != 0:
mc.azimuth_cor = np.nan
pointing.azimuth = u.Quantity(mc.azimuth_raw, u.rad)
else:
mc.azimuth_cor = azimuth_cor
pointing.azimuth = u.Quantity(azimuth_cor, u.rad)
if altitude_cor == 0 and mc.altitude_raw != 0:
mc.altitude_cor = np.nan
pointing.altitude = u.Quantity(mc.altitude_raw, u.rad)
else:
mc.altitude_cor = altitude_cor
pointing.altitude = u.Quantity(mc.altitude_cor, u.rad)
yield data
counter += 1
return
def make_theta_squared_table(observations,
theta_squared_axis,
position,
position_off=None):
"""Make theta squared distribution in the same FoV for a list of `Observation`
objects.
The ON theta2 profile is computed from a given distribution, on_position.
By default, the OFF theta2 profile is extracted from a mirror position
radially symmetric in the FOV to pos_on.
The ON and OFF regions are assumed to be of the same size, so the normalisation
factor between both region alpha = 1.
Parameters
----------
observations: `~gammapy.data.Observations`
List of observations
theta_squared_axis : `~gammapy.maps.geom.MapAxis`
Axis of edges of the theta2 bin used to compute the distribution
position : `~astropy.coordinates.SkyCoord`
Position from which the on theta^2 distribution is computed
position_off : `astropy.coordinates.SkyCoord`
Position from which the OFF theta^2 distribution is computed.
Default: reflected position w.r.t. to the pointing position
Returns
-------
table : `~astropy.table.Table`
Table containing the on counts, the off counts, acceptance, off acceptance and alpha
for each theta squared bin.
"""
if not theta_squared_axis.edges.unit.is_equivalent("deg2"):
raise ValueError("The theta2 axis should be equivalent to deg2")
table = Table()
table["theta2_min"] = theta_squared_axis.edges[:-1]
table["theta2_max"] = theta_squared_axis.edges[1:]
table["counts"] = 0
table["counts_off"] = 0
table["acceptance"] = 0.0
table["acceptance_off"] = 0.0
alpha_tot = np.zeros(len(table))
livetime_tot = 0
create_off = position_off is None
for observation in observations:
separation = position.separation(observation.events.radec)
counts, _ = np.histogram(separation**2, theta_squared_axis.edges)
table["counts"] += counts
if create_off:
# Estimate the position of the mirror position
pos_angle = observation.pointing_radec.position_angle(position)
sep_angle = observation.pointing_radec.separation(position)
position_off = observation.pointing_radec.directional_offset_by(
pos_angle + Angle(np.pi, "rad"), sep_angle)
# Angular distance of the events from the mirror position
separation_off = position_off.separation(observation.events.radec)
# Extract the ON and OFF theta2 distribution from the two positions.
counts_off, _ = np.histogram(separation_off**2,
theta_squared_axis.edges)
table["counts_off"] += counts_off
# Normalisation between ON and OFF is one
acceptance = np.ones(theta_squared_axis.nbin)
acceptance_off = np.ones(theta_squared_axis.nbin)
table["acceptance"] += acceptance
table["acceptance_off"] += acceptance_off
alpha = acceptance / acceptance_off
alpha_tot += alpha * observation.observation_live_time_duration.to_value(
"s")
livetime_tot += observation.observation_live_time_duration.to_value(
"s")
alpha_tot /= livetime_tot
table["alpha"] = alpha_tot
stat = WStatCountsStatistic(table["counts"], table["counts_off"],
table["alpha"])
table["excess"] = stat.n_sig
table["sqrt_ts"] = stat.sqrt_ts
table["excess_errn"] = stat.compute_errn()
table["excess_errp"] = stat.compute_errp()
table.meta["ON_RA"] = position.icrs.ra
table.meta["ON_DEC"] = position.icrs.dec
return table
def factorDirectionsWriter(table, fileName):
"""
Writes patches to a Factor directions file.
Note that Factor respects the order of patches and they are sorted here by
apparent flux from brightest to faintest.
Parameters
----------
table : astropy.table.Table object
Input sky model table; must have patches defined
fileName : str
Output file to which the sky model is written
"""
log = logging.getLogger('LSMTool.Write')
regionFile = open(fileName, 'w')
log.debug('Writing Factor directions file to {0}'.format(fileName))
outLines = []
outLines.append('# name position atrous_do mscale_field_do cal_imsize '
'solint_ph solint_amp dynamic_range region_selfcal '
'region_field peel_skymodel outlier_source cal_radius_deg cal_flux\n')
if 'History' in table.meta:
outLines.append('\n# LSMTool history:\n# ')
outLines.append('\n# '.join(table.meta['History']))
outLines.append('\n')
outLines.append('\n')
# Make sure all columns have the correct units
for colName in table.columns:
units = allowedColumnUnits[colName.lower()]
if units is not None:
table[colName].convert_unit_to(units)
table = table.group_by('Patch')
patchNames = table.groups.keys['Patch']
if 'patch_order' in table.meta:
indx = table.meta['patch_order']
else:
indx = range(len(table.groups))
if 'patch_size' in table.meta:
sizes = table.meta['patch_size']
else:
sizes = [''] * len(table.groups)
if 'patch_flux' in table.meta:
fluxes = table.meta['patch_flux']
else:
fluxes = [''] * len(table.groups)
for patchName, size, flux in zip(patchNames[indx], sizes[indx], fluxes[indx]):
if patchName in table.meta:
gRA, gDec = table.meta[patchName]
else:
gRA = Angle(0.0)
gDec = Angle(0.0)
outLines.append('{0} {1},{2} {3} {4} {5} {6} {7} {8} {9} {10} {11} {12} '
'{13} {14}\n'.format(patchName,
gRA.to_string(unit='hourangle', sep='hms'), gDec.to_string(sep='dms'),
'empty', 'empty', 0, 0, 0, 'LD', 'empty', 'empty', 'empty', False,
size, flux))
regionFile.writelines(outLines)
regionFile.close()
def validate(cls, v):
return Angle(v)
def create_bg_observation_list(indir, scheme, outdir, overwrite, test):
"""Make total observation list and filter the observations.
In a first version, all obs taken within 3 deg of a known source
will be rejected. If a source is extended, twice the extension is
added to the corresponding exclusion region radius of 3 deg.
Parameters
----------
indir : str
Input directory (that contains the event lists)
scheme : str
Scheme of file naming.
outdir : str
Dir path to store the results.
overwrite : bool
If true, run fast (not recommended for analysis).
test : bool
If true, run fast: skip many runs and catalog sources.
"""
log.info(' ')
log.info("#######################################")
log.info("# Starting create_bg_observation_list #")
log.info("#######################################")
# get full list of observations
data_store = DataStore(dir=indir, scheme=scheme)
observation_table = data_store.make_observation_table()
# for testing, only process a small subset of observations
if test and len(observation_table) > 100:
observation_table = observation_table.select_linspace_subset(num=100)
log.debug(' ')
log.debug("Full observation table:")
log.debug(observation_table)
# filter observations: load catalog and reject obs too close to sources
# load catalog: TeVCAT (no H.E.S.S. catalog)
catalog = load_catalog_tevcat()
# for testing, only process a small subset of sources
if test:
catalog = catalog[:5]
# sources coordinates
sources_coord = SkyCoord(catalog['coord_ra'], catalog['coord_dec'])
# sources sizes (x, y): radius
sources_size = Angle([catalog['size_x'], catalog['size_y']])
sources_size = sources_size.reshape(len(catalog), 2)
# substitute nan with 0
sources_size[np.isnan(sources_size)] = 0
# sources max size
sources_max_size = np.amax(sources_size, axis=1)
# sources exclusion radius = 2x max size + 3 deg (fov + 0.5 deg?)
sources_excl_radius = 2 * sources_max_size + Angle(3., 'deg')
# mask all obs taken within the excl radius of any of the sources
# loop over sources
obs_coords = SkyCoord(observation_table['RA'], observation_table['DEC'])
for i_source in range(len(catalog)):
selection = dict(type='sky_circle', frame='icrs',
lon=sources_coord[i_source].ra,
lat=sources_coord[i_source].dec,
radius=sources_excl_radius[i_source],
inverted=True,
border=Angle(0., 'deg'))
observation_table = observation_table.select_observations(selection)
# save the bg observation list to a fits file
outfile = Path(outdir) / 'bg_observation_table.fits.gz'
log.info("Writing {}".format(outfile))
observation_table.write(str(outfile), overwrite=overwrite)
def offset(self):
"""Event offset from the array pointing position (`~astropy.coordinates.Angle`)."""
position = self.radec
center = self.pointing_radec
offset = center.separation(position)
return Angle(offset, unit="deg")