def _find_crossing_times(finding_function: Callable, t_min: Time,
duration: TimeDelta,
precision: TimeDelta) -> np.ndarray:
""" """
# Set t_min to a higher dimension in preparation for multiple matches
t_min = t_min.reshape((1, ))
# At each iteration, dt will be reduced by down_factor
down_factor = 5
dt = duration / down_factor
# If duration is too big, intial dt is quite big as well
# therefore, we set the max dt to 6h
max_dt = TimeDelta(6 * 3600, format="sec")
if dt > max_dt:
down_factor = int(np.ceil(duration / max_dt))
dt = duration / down_factor
# Loop until the precision is reached
while dt * down_factor > precision:
# Prepare the time array upon which the coordinates are computed
n_steps = np.ceil(duration / dt)
times = t_min[:, None] + np.arange(n_steps + 1) * dt
# Find the indices depending on the function to apply
transit_indices = finding_function(times)
# Update t_min at the spots where the transit(s) have been found
t_min = times[transit_indices]
if t_min.size == 0:
# Nothing has been found
return Time([], format='jd')
elif t_min.isscalar:
t_min = t_min.reshape((1, ))
# Next loop will occur on the last time step only
duration = dt
dt /= down_factor
return times[transit_indices] + dt / 2.
def _get_time(cls, control, num_energies, packets, pad_after):
times = []
durations = []
start = 0
for i, (ns, it) in enumerate(control['num_samples',
'integration_time']):
off_sets = np.array(packets.get('NIX00485')[start:start + ns]) * it
base_time = Time(
scet_to_datetime(
f'{control["scet_coarse"][i]}:{control["scet_fine"][i]}'))
start_times = base_time + off_sets
end_times = base_time + off_sets + it
cur_time = start_times + (end_times - start_times) / 2
times.extend(cur_time)
durations.extend([it] * ns)
start += ns
time = Time(times)
time = Time(
np.pad(time.datetime64, (0, pad_after),
constant_values=time[-1].datetime64))
time = time.reshape(-1, num_energies)
duration = np.pad(np.hstack(durations),
(0, pad_after)).reshape(-1, num_energies) * it.unit
return duration, time
def _make_schedule(self, blocks):
pre_filled = np.array([[block.start_time, block.end_time] for
block in self.schedule.scheduled_blocks])
if len(pre_filled) == 0:
a = self.schedule.start_time
filled_times = Time([a - 1*u.hour, a - 1*u.hour,
a - 1*u.minute, a - 1*u.minute])
pre_filled = filled_times.reshape((2, 2))
else:
filled_times = Time(pre_filled.flatten())
pre_filled = filled_times.reshape((int(len(filled_times)/2), 2))
for b in blocks:
if b.constraints is None:
b._all_constraints = self.constraints
else:
b._all_constraints = self.constraints + b.constraints
# to make sure the scheduler has some constraint to work off of
# and to prevent scheduling of targets below the horizon
# TODO : change default constraints to [] and switch to append
if b._all_constraints is None:
b._all_constraints = [AltitudeConstraint(min=0 * u.deg)]
b.constraints = [AltitudeConstraint(min=0 * u.deg)]
elif not any(isinstance(c, AltitudeConstraint) for c in b._all_constraints):
b._all_constraints.append(AltitudeConstraint(min=0 * u.deg))
if b.constraints is None:
b.constraints = [AltitudeConstraint(min=0 * u.deg)]
else:
b.constraints.append(AltitudeConstraint(min=0 * u.deg))
b._duration_offsets = u.Quantity([0*u.second, b.duration/2,
b.duration])
b.observer = self.observer
current_time = self.schedule.start_time
while (len(blocks) > 0) and (current_time < self.schedule.end_time):
# first compute the value of all the constraints for each block
# given the current starting time
block_transitions = []
block_constraint_results = []
for b in blocks:
# first figure out the transition
if len(self.schedule.observing_blocks) > 0:
trans = self.transitioner(
self.schedule.observing_blocks[-1], b, current_time, self.observer)
else:
trans = None
block_transitions.append(trans)
transition_time = 0*u.second if trans is None else trans.duration
times = current_time + transition_time + b._duration_offsets
# make sure it isn't in a pre-filled slot
if (any((current_time < filled_times) & (filled_times < times[2])) or
any(abs(pre_filled.T[0]-current_time) < 1*u.second)):
block_constraint_results.append(0)
else:
constraint_res = []
for constraint in b._all_constraints:
constraint_res.append(constraint(
self.observer, b.target, times))
# take the product over all the constraints *and* times
block_constraint_results.append(np.prod(constraint_res))
# now identify the block that's the best
bestblock_idx = np.argmax(block_constraint_results)
if block_constraint_results[bestblock_idx] == 0.:
# if even the best is unobservable, we need a gap
current_time += self.gap_time
else:
# If there's a best one that's observable, first get its transition
trans = block_transitions.pop(bestblock_idx)
if trans is not None:
self.schedule.insert_slot(trans.start_time, trans)
current_time += trans.duration
# now assign the block itself times and add it to the schedule
newb = blocks.pop(bestblock_idx)
newb.start_time = current_time
current_time += newb.duration
newb.end_time = current_time
newb.constraints_value = block_constraint_results[bestblock_idx]
self.schedule.insert_slot(newb.start_time, newb)
return self.schedule
def __getitem__(self, time: Time):
""" """
starts = (self.time).jd
stops = (self.time + self.duration).jd
if time.isscalar:
time = time.reshape(1,)
if self.coordinates.isscalar:
# coordinates = self.coordinates.reshape(1,)
coordinates = SkyCoord(
np.repeat(self.coordinates.ra.deg, self.time.size),
np.repeat(self.coordinates.dec.deg, self.time.size),
unit="deg"
)
else:
coordinates = self.coordinates
ras = coordinates.ra.deg
decs = coordinates.dec.deg
ra = []
dec = []
custom_az = []
custom_el = []
for t in time.jd:
# Find the corresponding RA/Dec
mask = (t >= starts) & (t < stops)
if np.all(~mask):
# No match
t = Time(t, format="jd")
log.warning(
f"Default zenith pointing at {t.isot}."
)
zenith = SkyCoord(
0*u.deg,
90*u.deg,
frame=AltAz(
obstime=t,
location=self.observer
)
).transform_to(ICRS)
ra.append(zenith.ra)
dec.append(zenith.dec)
if hasattr(self, "custom_ho_coordinates"):
custom_az.append(0*u.deg)
custom_el.append(90*u.deg)
else:
# there is a match
ra.append(ras[mask][0])
dec.append(decs[mask][0])
if hasattr(self, "custom_ho_coordinates"):
custom_az.append(self.custom_ho_coordinates[mask].az[0])
custom_el.append(self.custom_ho_coordinates[mask].alt[0])
pointing = Pointing(
coordinates=SkyCoord(
ra,
dec,
unit='deg'
),
time=time,
)
if hasattr(self, "custom_ho_coordinates"):
#pointing.custom_ho_coordinates = self.custom_ho_coordinates
pointing.custom_ho_coordinates = SkyCoord(
custom_az,
custom_el,
frame=AltAz(
obstime=time,#.reshape(time.size, 1),
location=nenufar_position
)
)
return pointing
def _make_schedule(self, blocks):
pre_filled = np.array([[block.start_time, block.end_time]
for block in self.schedule.scheduled_blocks])
if len(pre_filled) == 0:
a = self.schedule.start_time
filled_times = Time([
a - 1 * u.hour, a - 1 * u.hour, a - 1 * u.minute,
a - 1 * u.minute
])
pre_filled = filled_times.reshape((2, 2))
else:
filled_times = Time(pre_filled.flatten())
pre_filled = filled_times.reshape((int(len(filled_times) / 2), 2))
for b in blocks:
if b.constraints is None:
b._all_constraints = self.constraints
else:
b._all_constraints = self.constraints + b.constraints
if b._all_constraints is None:
b._all_constraints = [AltitudeConstraint(min=0 * u.deg)]
b.constraints = [AltitudeConstraint(min=0 * u.deg)]
elif not any(
isinstance(c, AltitudeConstraint)
for c in b._all_constraints):
b._all_constraints.append(AltitudeConstraint(min=0 * u.deg))
if b.constraints is None:
b.constraints = [AltitudeConstraint(min=0 * u.deg)]
else:
b.constraints.append(AltitudeConstraint(min=0 * u.deg))
b._duration_offsets = u.Quantity(
[0 * u.second, b.duration / 2, b.duration])
b.observer = self.observer
current_time = self.schedule.start_time
while (len(blocks) > 0) and (current_time < self.schedule.end_time):
block_transitions = []
block_constraint_results = []
for b in blocks:
if len(self.schedule.observing_blocks) > 0:
trans = self.transitioner(
self.schedule.observing_blocks[-1], b, current_time,
self.observer)
else:
trans = None
block_transitions.append(trans)
transition_time = 0 * u.second if trans is None else trans.duration
times = current_time + transition_time + b._duration_offsets
if (any((current_time < filled_times)
& (filled_times < times[2])) or any(
abs(pre_filled.T[0] - current_time) < 1 *
u.second)):
block_constraint_results.append(0)
else:
constraint_res = []
for constraint in b._all_constraints:
constraint_res.append(
constraint(self.observer, [b.target], times))
block_constraint_results.append(np.prod(constraint_res))
bestblock_idx = np.argmax(block_constraint_results)
if block_constraint_results[bestblock_idx] == 0.:
current_time += self.gap_time
else:
trans = block_transitions.pop(bestblock_idx)
if trans is not None:
self.schedule.insert_slot(trans.start_time, trans)
current_time += trans.duration
newb = blocks.pop(bestblock_idx)
newb.start_time = current_time
current_time += newb.duration
newb.end_time = current_time
newb.constraints_value = block_constraint_results[
bestblock_idx]
self.schedule.insert_slot(newb.start_time, newb)
return self.schedule
class TestManipulation:
"""Manipulation of Time objects, ensuring attributes are done correctly."""
def setup(self):
mjd = np.arange(50000, 50010)
frac = np.arange(0., 0.999, 0.2)
if use_masked_data:
frac = np.ma.array(frac)
frac[1] = np.ma.masked
self.t0 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc')
self.t1 = Time(mjd[:, np.newaxis] + frac,
format='mjd',
scale='utc',
location=('45d', '50d'))
self.t2 = Time(mjd[:, np.newaxis] + frac,
format='mjd',
scale='utc',
location=(np.arange(len(frac)), np.arange(len(frac))))
# Note: location is along last axis only.
self.t2 = Time(mjd[:, np.newaxis] + frac,
format='mjd',
scale='utc',
location=(np.arange(len(frac)), np.arange(len(frac))))
def test_ravel(self, masked):
t0_ravel = self.t0.ravel()
assert t0_ravel.shape == (self.t0.size, )
assert np.all(t0_ravel.jd1 == self.t0.jd1.ravel())
assert np.may_share_memory(t0_ravel.jd1, self.t0.jd1)
assert t0_ravel.location is None
t1_ravel = self.t1.ravel()
assert t1_ravel.shape == (self.t1.size, )
assert np.all(t1_ravel.jd1 == self.t1.jd1.ravel())
assert np.may_share_memory(t1_ravel.jd1, self.t1.jd1)
assert t1_ravel.location is self.t1.location
t2_ravel = self.t2.ravel()
assert t2_ravel.shape == (self.t2.size, )
assert np.all(t2_ravel.jd1 == self.t2.jd1.ravel())
assert np.may_share_memory(t2_ravel.jd1, self.t2.jd1)
assert t2_ravel.location.shape == t2_ravel.shape
# Broadcasting and ravelling cannot be done without a copy.
assert not np.may_share_memory(t2_ravel.location, self.t2.location)
def test_flatten(self, masked):
t0_flatten = self.t0.flatten()
assert t0_flatten.shape == (self.t0.size, )
assert t0_flatten.location is None
# Flatten always makes a copy.
assert not np.may_share_memory(t0_flatten.jd1, self.t0.jd1)
t1_flatten = self.t1.flatten()
assert t1_flatten.shape == (self.t1.size, )
assert not np.may_share_memory(t1_flatten.jd1, self.t1.jd1)
assert t1_flatten.location is not self.t1.location
assert t1_flatten.location == self.t1.location
t2_flatten = self.t2.flatten()
assert t2_flatten.shape == (self.t2.size, )
assert not np.may_share_memory(t2_flatten.jd1, self.t2.jd1)
assert t2_flatten.location.shape == t2_flatten.shape
assert not np.may_share_memory(t2_flatten.location, self.t2.location)
def test_transpose(self, masked):
t0_transpose = self.t0.transpose()
assert t0_transpose.shape == (5, 10)
assert np.all(t0_transpose.jd1 == self.t0.jd1.transpose())
assert np.may_share_memory(t0_transpose.jd1, self.t0.jd1)
assert t0_transpose.location is None
t1_transpose = self.t1.transpose()
assert t1_transpose.shape == (5, 10)
assert np.all(t1_transpose.jd1 == self.t1.jd1.transpose())
assert np.may_share_memory(t1_transpose.jd1, self.t1.jd1)
assert t1_transpose.location is self.t1.location
t2_transpose = self.t2.transpose()
assert t2_transpose.shape == (5, 10)
assert np.all(t2_transpose.jd1 == self.t2.jd1.transpose())
assert np.may_share_memory(t2_transpose.jd1, self.t2.jd1)
assert t2_transpose.location.shape == t2_transpose.shape
assert np.may_share_memory(t2_transpose.location, self.t2.location)
# Only one check on T, since it just calls transpose anyway.
t2_T = self.t2.T
assert t2_T.shape == (5, 10)
assert np.all(t2_T.jd1 == self.t2.jd1.T)
assert np.may_share_memory(t2_T.jd1, self.t2.jd1)
assert t2_T.location.shape == t2_T.location.shape
assert np.may_share_memory(t2_T.location, self.t2.location)
def test_diagonal(self, masked):
t0_diagonal = self.t0.diagonal()
assert t0_diagonal.shape == (5, )
assert np.all(t0_diagonal.jd1 == self.t0.jd1.diagonal())
assert t0_diagonal.location is None
assert np.may_share_memory(t0_diagonal.jd1, self.t0.jd1)
t1_diagonal = self.t1.diagonal()
assert t1_diagonal.shape == (5, )
assert np.all(t1_diagonal.jd1 == self.t1.jd1.diagonal())
assert t1_diagonal.location is self.t1.location
assert np.may_share_memory(t1_diagonal.jd1, self.t1.jd1)
t2_diagonal = self.t2.diagonal()
assert t2_diagonal.shape == (5, )
assert np.all(t2_diagonal.jd1 == self.t2.jd1.diagonal())
assert t2_diagonal.location.shape == t2_diagonal.shape
assert np.may_share_memory(t2_diagonal.jd1, self.t2.jd1)
assert np.may_share_memory(t2_diagonal.location, self.t2.location)
def test_swapaxes(self, masked):
t0_swapaxes = self.t0.swapaxes(0, 1)
assert t0_swapaxes.shape == (5, 10)
assert np.all(t0_swapaxes.jd1 == self.t0.jd1.swapaxes(0, 1))
assert np.may_share_memory(t0_swapaxes.jd1, self.t0.jd1)
assert t0_swapaxes.location is None
t1_swapaxes = self.t1.swapaxes(0, 1)
assert t1_swapaxes.shape == (5, 10)
assert np.all(t1_swapaxes.jd1 == self.t1.jd1.swapaxes(0, 1))
assert np.may_share_memory(t1_swapaxes.jd1, self.t1.jd1)
assert t1_swapaxes.location is self.t1.location
t2_swapaxes = self.t2.swapaxes(0, 1)
assert t2_swapaxes.shape == (5, 10)
assert np.all(t2_swapaxes.jd1 == self.t2.jd1.swapaxes(0, 1))
assert np.may_share_memory(t2_swapaxes.jd1, self.t2.jd1)
assert t2_swapaxes.location.shape == t2_swapaxes.shape
assert np.may_share_memory(t2_swapaxes.location, self.t2.location)
def test_reshape(self, masked):
t0_reshape = self.t0.reshape(5, 2, 5)
assert t0_reshape.shape == (5, 2, 5)
assert np.all(t0_reshape.jd1 == self.t0._time.jd1.reshape(5, 2, 5))
assert np.all(t0_reshape.jd2 == self.t0._time.jd2.reshape(5, 2, 5))
assert np.may_share_memory(t0_reshape.jd1, self.t0.jd1)
assert np.may_share_memory(t0_reshape.jd2, self.t0.jd2)
assert t0_reshape.location is None
t1_reshape = self.t1.reshape(2, 5, 5)
assert t1_reshape.shape == (2, 5, 5)
assert np.all(t1_reshape.jd1 == self.t1.jd1.reshape(2, 5, 5))
assert np.may_share_memory(t1_reshape.jd1, self.t1.jd1)
assert t1_reshape.location is self.t1.location
# For reshape(5, 2, 5), the location array can remain the same.
t2_reshape = self.t2.reshape(5, 2, 5)
assert t2_reshape.shape == (5, 2, 5)
assert np.all(t2_reshape.jd1 == self.t2.jd1.reshape(5, 2, 5))
assert np.may_share_memory(t2_reshape.jd1, self.t2.jd1)
assert t2_reshape.location.shape == t2_reshape.shape
assert np.may_share_memory(t2_reshape.location, self.t2.location)
# But for reshape(5, 5, 2), location has to be broadcast and copied.
t2_reshape2 = self.t2.reshape(5, 5, 2)
assert t2_reshape2.shape == (5, 5, 2)
assert np.all(t2_reshape2.jd1 == self.t2.jd1.reshape(5, 5, 2))
assert np.may_share_memory(t2_reshape2.jd1, self.t2.jd1)
assert t2_reshape2.location.shape == t2_reshape2.shape
assert not np.may_share_memory(t2_reshape2.location, self.t2.location)
t2_reshape_t = self.t2.reshape(10, 5).T
assert t2_reshape_t.shape == (5, 10)
assert np.may_share_memory(t2_reshape_t.jd1, self.t2.jd1)
assert t2_reshape_t.location.shape == t2_reshape_t.shape
assert np.may_share_memory(t2_reshape_t.location, self.t2.location)
# Finally, reshape in a way that cannot be a view.
t2_reshape_t_reshape = t2_reshape_t.reshape(10, 5)
assert t2_reshape_t_reshape.shape == (10, 5)
assert not np.may_share_memory(t2_reshape_t_reshape.jd1, self.t2.jd1)
assert (
t2_reshape_t_reshape.location.shape == t2_reshape_t_reshape.shape)
assert not np.may_share_memory(t2_reshape_t_reshape.location,
t2_reshape_t.location)
def test_shape_setting(self, masked):
t0_reshape = self.t0.copy()
mjd = t0_reshape.mjd # Creates a cache of the mjd attribute
t0_reshape.shape = (5, 2, 5)
assert t0_reshape.shape == (5, 2, 5)
assert mjd.shape != t0_reshape.mjd.shape # Cache got cleared
assert np.all(t0_reshape.jd1 == self.t0._time.jd1.reshape(5, 2, 5))
assert np.all(t0_reshape.jd2 == self.t0._time.jd2.reshape(5, 2, 5))
assert t0_reshape.location is None
# But if the shape doesn't work, one should get an error.
t0_reshape_t = t0_reshape.T
with pytest.raises(AttributeError):
t0_reshape_t.shape = (10, 5)
# check no shape was changed.
assert t0_reshape_t.shape == t0_reshape.T.shape
assert t0_reshape_t.jd1.shape == t0_reshape.T.shape
assert t0_reshape_t.jd2.shape == t0_reshape.T.shape
t1_reshape = self.t1.copy()
t1_reshape.shape = (2, 5, 5)
assert t1_reshape.shape == (2, 5, 5)
assert np.all(t1_reshape.jd1 == self.t1.jd1.reshape(2, 5, 5))
# location is a single element, so its shape should not change.
assert t1_reshape.location.shape == ()
# For reshape(5, 2, 5), the location array can remain the same.
# Note that we need to work directly on self.t2 here, since any
# copy would cause location to have the full shape.
self.t2.shape = (5, 2, 5)
assert self.t2.shape == (5, 2, 5)
assert self.t2.jd1.shape == (5, 2, 5)
assert self.t2.jd2.shape == (5, 2, 5)
assert self.t2.location.shape == (5, 2, 5)
assert self.t2.location.strides == (0, 0, 24)
# But for reshape(50), location would need to be copied, so this
# should fail.
oldshape = self.t2.shape
with pytest.raises(AttributeError):
self.t2.shape = (50, )
# check no shape was changed.
assert self.t2.jd1.shape == oldshape
assert self.t2.jd2.shape == oldshape
assert self.t2.location.shape == oldshape
# reset t2 to its original.
self.setup()
def test_squeeze(self, masked):
t0_squeeze = self.t0.reshape(5, 1, 2, 1, 5).squeeze()
assert t0_squeeze.shape == (5, 2, 5)
assert np.all(t0_squeeze.jd1 == self.t0.jd1.reshape(5, 2, 5))
assert np.may_share_memory(t0_squeeze.jd1, self.t0.jd1)
assert t0_squeeze.location is None
t1_squeeze = self.t1.reshape(1, 5, 1, 2, 5).squeeze()
assert t1_squeeze.shape == (5, 2, 5)
assert np.all(t1_squeeze.jd1 == self.t1.jd1.reshape(5, 2, 5))
assert np.may_share_memory(t1_squeeze.jd1, self.t1.jd1)
assert t1_squeeze.location is self.t1.location
t2_squeeze = self.t2.reshape(1, 1, 5, 2, 5, 1, 1).squeeze()
assert t2_squeeze.shape == (5, 2, 5)
assert np.all(t2_squeeze.jd1 == self.t2.jd1.reshape(5, 2, 5))
assert np.may_share_memory(t2_squeeze.jd1, self.t2.jd1)
assert t2_squeeze.location.shape == t2_squeeze.shape
assert np.may_share_memory(t2_squeeze.location, self.t2.location)
def test_add_dimension(self, masked):
t0_adddim = self.t0[:, np.newaxis, :]
assert t0_adddim.shape == (10, 1, 5)
assert np.all(t0_adddim.jd1 == self.t0.jd1[:, np.newaxis, :])
assert np.may_share_memory(t0_adddim.jd1, self.t0.jd1)
assert t0_adddim.location is None
t1_adddim = self.t1[:, :, np.newaxis]
assert t1_adddim.shape == (10, 5, 1)
assert np.all(t1_adddim.jd1 == self.t1.jd1[:, :, np.newaxis])
assert np.may_share_memory(t1_adddim.jd1, self.t1.jd1)
assert t1_adddim.location is self.t1.location
t2_adddim = self.t2[:, :, np.newaxis]
assert t2_adddim.shape == (10, 5, 1)
assert np.all(t2_adddim.jd1 == self.t2.jd1[:, :, np.newaxis])
assert np.may_share_memory(t2_adddim.jd1, self.t2.jd1)
assert t2_adddim.location.shape == t2_adddim.shape
assert np.may_share_memory(t2_adddim.location, self.t2.location)
def test_take(self, masked):
t0_take = self.t0.take((5, 2))
assert t0_take.shape == (2, )
assert np.all(t0_take.jd1 == self.t0._time.jd1.take((5, 2)))
assert t0_take.location is None
t1_take = self.t1.take((2, 4), axis=1)
assert t1_take.shape == (10, 2)
assert np.all(t1_take.jd1 == self.t1.jd1.take((2, 4), axis=1))
assert t1_take.location is self.t1.location
t2_take = self.t2.take((1, 3, 7), axis=0)
assert t2_take.shape == (3, 5)
assert np.all(t2_take.jd1 == self.t2.jd1.take((1, 3, 7), axis=0))
assert t2_take.location.shape == t2_take.shape
t2_take2 = self.t2.take((5, 15))
assert t2_take2.shape == (2, )
assert np.all(t2_take2.jd1 == self.t2.jd1.take((5, 15)))
assert t2_take2.location.shape == t2_take2.shape
def test_broadcast(self, masked):
"""Test using a callable method."""
t0_broadcast = self.t0._apply(np.broadcast_to, shape=(3, 10, 5))
assert t0_broadcast.shape == (3, 10, 5)
assert np.all(t0_broadcast.jd1 == self.t0.jd1)
assert np.may_share_memory(t0_broadcast.jd1, self.t0.jd1)
assert t0_broadcast.location is None
t1_broadcast = self.t1._apply(np.broadcast_to, shape=(3, 10, 5))
assert t1_broadcast.shape == (3, 10, 5)
assert np.all(t1_broadcast.jd1 == self.t1.jd1)
assert np.may_share_memory(t1_broadcast.jd1, self.t1.jd1)
assert t1_broadcast.location is self.t1.location
t2_broadcast = self.t2._apply(np.broadcast_to, shape=(3, 10, 5))
assert t2_broadcast.shape == (3, 10, 5)
assert np.all(t2_broadcast.jd1 == self.t2.jd1)
assert np.may_share_memory(t2_broadcast.jd1, self.t2.jd1)
assert t2_broadcast.location.shape == t2_broadcast.shape
assert np.may_share_memory(t2_broadcast.location, self.t2.location)
def _make_schedule(self, blocks):
pre_filled = np.array([[block.start_time, block.end_time] for
block in self.schedule.scheduled_blocks])
if len(pre_filled) == 0:
a = self.schedule.start_time
filled_times = Time([a - 1*u.hour, a - 1*u.hour,
a - 1*u.minute, a - 1*u.minute])
pre_filled = filled_times.reshape((2, 2))
else:
filled_times = Time(pre_filled.flatten())
pre_filled = filled_times.reshape((int(len(filled_times)/2), 2))
for b in blocks:
if b.constraints is None:
b._all_constraints = self.constraints
else:
b._all_constraints = self.constraints + b.constraints
# to make sure the scheduler has some constraint to work off of
# and to prevent scheduling of targets below the horizon
# TODO : change default constraints to [] and switch to append
if b._all_constraints is None:
b._all_constraints = [AltitudeConstraint(min=0 * u.deg)]
b.constraints = [AltitudeConstraint(min=0 * u.deg)]
elif not any(isinstance(c, AltitudeConstraint) for c in b._all_constraints):
b._all_constraints.append(AltitudeConstraint(min=0 * u.deg))
if b.constraints is None:
b.constraints = [AltitudeConstraint(min=0 * u.deg)]
else:
b.constraints.append(AltitudeConstraint(min=0 * u.deg))
b._duration_offsets = u.Quantity([0*u.second, b.duration/2,
b.duration])
b.observer = self.observer
current_time = self.schedule.start_time
while (len(blocks) > 0) and (current_time < self.schedule.end_time):
# first compute the value of all the constraints for each block
# given the current starting time
block_transitions = []
block_constraint_results = []
for b in blocks:
# first figure out the transition
if len(self.schedule.observing_blocks) > 0:
trans = self.transitioner(
self.schedule.observing_blocks[-1], b, current_time, self.observer)
else:
trans = None
block_transitions.append(trans)
transition_time = 0*u.second if trans is None else trans.duration
times = current_time + transition_time + b._duration_offsets
# make sure it isn't in a pre-filled slot
if (any((current_time < filled_times) & (filled_times < times[2])) or
any(abs(pre_filled.T[0]-current_time) < 1*u.second)):
block_constraint_results.append(0)
else:
constraint_res = []
for constraint in b._all_constraints:
constraint_res.append(constraint(
self.observer, b.target, times))
# take the product over all the constraints *and* times
block_constraint_results.append(np.prod(constraint_res))
# now identify the block that's the best
bestblock_idx = np.argmax(block_constraint_results)
if block_constraint_results[bestblock_idx] == 0.:
# if even the best is unobservable, we need a gap
current_time += self.gap_time
else:
# If there's a best one that's observable, first get its transition
trans = block_transitions.pop(bestblock_idx)
if trans is not None:
self.schedule.insert_slot(trans.start_time, trans)
current_time += trans.duration
# now assign the block itself times and add it to the schedule
newb = blocks.pop(bestblock_idx)
newb.start_time = current_time
current_time += newb.duration
newb.end_time = current_time
newb.constraints_value = block_constraint_results[bestblock_idx]
self.schedule.insert_slot(newb.start_time, newb)
return self.schedule
def attenuation_from_zenith(self,
coordinates,
time: Time = Time.now(),
frequency: u.Quantity = 50 * u.MHz,
polarization: Polarization = Polarization.NW):
""" Returns the attenuation factor evaluated at given ``coordinates``
compared to the zenithal Mini-Array beam gain.
:param coordinates:
Sky positions equatorial coordinates.
:type coordinates:
:class:`~astropy.coordinates.SkyCoord`
:param time:
UTC time at which the attenuation is evaluated. Default is ``now``.
:type time:
:class:`~astropy.time.Time`
:param frequency:
Frequency at which the attenuation is evaluated. Default is ``50 MHz``.
:type frquency:
:class:`~astropy.units.Quantity`
:param polarization:
NenuFAR antenna polarization. Default is ``Polarization.NW``.
:type polarization:
:class:`~nenupy.instru.nenufar.Polarization`
:returns:
Attenuation factor shaped as ``(time, frequency, polarization, coordinates)``.
``NaN`` is returned for any ``coordinates`` that is below the horizon.
:rtype:
:class:`~numpy.ndarray`
:Example:
>>> from nenupy.instru.nenufar import MiniArray
>>> from astropy.coordinates import SkyCoord
>>> ma = MiniArray(index=0)
>>> attenuation = ma.attenuation_from_zenith(
coordinates=SkyCoord.from_name("Cyg A")
)
>>> from nenupy.instru.nenufar import MiniArray
>>> from astropy.coordinates import SkyCoord
>>> import astropy.units as u
>>> ma = MiniArray(index=0)
>>> attenuation = ma.attenuation_from_zenith(
coordinates=SkyCoord.from_name("Cyg A"),
frequency=np.linspace(20, 80, 10)*u.MHz
)
.. versionadded:: 2.0.0
"""
# Define the pointing towards the zenith
pointing = Pointing.zenith_tracking(time=time.reshape((1, )),
duration=TimeDelta(10,
format="sec"))
# Compute the local zenith in equatorial coordinates
local_zenith = SkyCoord(
180,
90,
unit="deg",
frame=AltAz(obstime=time, location=nenufar_position)).transform_to(
coordinates.frame)
# Find the coordinates below the horizon and compute a mask
input_coord_altaz = radec_to_altaz(radec=coordinates, time=time)
invisible_mask = input_coord_altaz.alt.deg <= 0
# Concatenate local_zenith and coordinates
if coordinates.obstime is None:
coordinates.obstime = local_zenith.obstime
if coordinates.location is None:
coordinates.location = local_zenith.location
if coordinates.isscalar:
coordinates = coordinates.reshape((1, ))
coordinates = coordinates.insert(0, local_zenith)
# Prepare a Sky instance for the beam simulation
sky = Sky(coordinates=coordinates,
frequency=frequency,
time=time,
polarization=polarization)
# Compute the beam
beam = self.beam(sky=sky, pointing=pointing)
# Compute the attenuation factor relative to the zenith (first member)
values = beam.value.compute()
output_values = values[..., 1:] / np.expand_dims(values[..., 0], 3)
output_values[..., invisible_mask] = np.nan
return output_values
class TestManipulation():
"""Manipulation of Time objects, ensuring attributes are done correctly."""
def setup(self):
mjd = np.arange(50000, 50010)
frac = np.arange(0., 0.999, 0.2)
if use_masked_data:
frac = np.ma.array(frac)
frac[1] = np.ma.masked
self.t0 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc')
self.t1 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc',
location=('45d', '50d'))
self.t2 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc',
location=(np.arange(len(frac)), np.arange(len(frac))))
# Note: location is along last axis only.
self.t2 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc',
location=(np.arange(len(frac)), np.arange(len(frac))))
def test_ravel(self, masked):
t0_ravel = self.t0.ravel()
assert t0_ravel.shape == (self.t0.size,)
assert np.all(t0_ravel.jd1 == self.t0.jd1.ravel())
assert np.may_share_memory(t0_ravel.jd1, self.t0.jd1)
assert t0_ravel.location is None
t1_ravel = self.t1.ravel()
assert t1_ravel.shape == (self.t1.size,)
assert np.all(t1_ravel.jd1 == self.t1.jd1.ravel())
assert np.may_share_memory(t1_ravel.jd1, self.t1.jd1)
assert t1_ravel.location is self.t1.location
t2_ravel = self.t2.ravel()
assert t2_ravel.shape == (self.t2.size,)
assert np.all(t2_ravel.jd1 == self.t2.jd1.ravel())
assert np.may_share_memory(t2_ravel.jd1, self.t2.jd1)
assert t2_ravel.location.shape == t2_ravel.shape
# Broadcasting and ravelling cannot be done without a copy.
assert not np.may_share_memory(t2_ravel.location, self.t2.location)
def test_flatten(self, masked):
t0_flatten = self.t0.flatten()
assert t0_flatten.shape == (self.t0.size,)
assert t0_flatten.location is None
# Flatten always makes a copy.
assert not np.may_share_memory(t0_flatten.jd1, self.t0.jd1)
t1_flatten = self.t1.flatten()
assert t1_flatten.shape == (self.t1.size,)
assert not np.may_share_memory(t1_flatten.jd1, self.t1.jd1)
assert t1_flatten.location is not self.t1.location
assert t1_flatten.location == self.t1.location
t2_flatten = self.t2.flatten()
assert t2_flatten.shape == (self.t2.size,)
assert not np.may_share_memory(t2_flatten.jd1, self.t2.jd1)
assert t2_flatten.location.shape == t2_flatten.shape
assert not np.may_share_memory(t2_flatten.location, self.t2.location)
def test_transpose(self, masked):
t0_transpose = self.t0.transpose()
assert t0_transpose.shape == (5, 10)
assert np.all(t0_transpose.jd1 == self.t0.jd1.transpose())
assert np.may_share_memory(t0_transpose.jd1, self.t0.jd1)
assert t0_transpose.location is None
t1_transpose = self.t1.transpose()
assert t1_transpose.shape == (5, 10)
assert np.all(t1_transpose.jd1 == self.t1.jd1.transpose())
assert np.may_share_memory(t1_transpose.jd1, self.t1.jd1)
assert t1_transpose.location is self.t1.location
t2_transpose = self.t2.transpose()
assert t2_transpose.shape == (5, 10)
assert np.all(t2_transpose.jd1 == self.t2.jd1.transpose())
assert np.may_share_memory(t2_transpose.jd1, self.t2.jd1)
assert t2_transpose.location.shape == t2_transpose.shape
assert np.may_share_memory(t2_transpose.location, self.t2.location)
# Only one check on T, since it just calls transpose anyway.
t2_T = self.t2.T
assert t2_T.shape == (5, 10)
assert np.all(t2_T.jd1 == self.t2.jd1.T)
assert np.may_share_memory(t2_T.jd1, self.t2.jd1)
assert t2_T.location.shape == t2_T.location.shape
assert np.may_share_memory(t2_T.location, self.t2.location)
def test_diagonal(self, masked):
t0_diagonal = self.t0.diagonal()
assert t0_diagonal.shape == (5,)
assert np.all(t0_diagonal.jd1 == self.t0.jd1.diagonal())
assert t0_diagonal.location is None
assert np.may_share_memory(t0_diagonal.jd1, self.t0.jd1)
t1_diagonal = self.t1.diagonal()
assert t1_diagonal.shape == (5,)
assert np.all(t1_diagonal.jd1 == self.t1.jd1.diagonal())
assert t1_diagonal.location is self.t1.location
assert np.may_share_memory(t1_diagonal.jd1, self.t1.jd1)
t2_diagonal = self.t2.diagonal()
assert t2_diagonal.shape == (5,)
assert np.all(t2_diagonal.jd1 == self.t2.jd1.diagonal())
assert t2_diagonal.location.shape == t2_diagonal.shape
assert np.may_share_memory(t2_diagonal.jd1, self.t2.jd1)
assert np.may_share_memory(t2_diagonal.location, self.t2.location)
def test_swapaxes(self, masked):
t0_swapaxes = self.t0.swapaxes(0, 1)
assert t0_swapaxes.shape == (5, 10)
assert np.all(t0_swapaxes.jd1 == self.t0.jd1.swapaxes(0, 1))
assert np.may_share_memory(t0_swapaxes.jd1, self.t0.jd1)
assert t0_swapaxes.location is None
t1_swapaxes = self.t1.swapaxes(0, 1)
assert t1_swapaxes.shape == (5, 10)
assert np.all(t1_swapaxes.jd1 == self.t1.jd1.swapaxes(0, 1))
assert np.may_share_memory(t1_swapaxes.jd1, self.t1.jd1)
assert t1_swapaxes.location is self.t1.location
t2_swapaxes = self.t2.swapaxes(0, 1)
assert t2_swapaxes.shape == (5, 10)
assert np.all(t2_swapaxes.jd1 == self.t2.jd1.swapaxes(0, 1))
assert np.may_share_memory(t2_swapaxes.jd1, self.t2.jd1)
assert t2_swapaxes.location.shape == t2_swapaxes.shape
assert np.may_share_memory(t2_swapaxes.location, self.t2.location)
def test_reshape(self, masked):
t0_reshape = self.t0.reshape(5, 2, 5)
assert t0_reshape.shape == (5, 2, 5)
assert np.all(t0_reshape.jd1 == self.t0._time.jd1.reshape(5, 2, 5))
assert np.all(t0_reshape.jd2 == self.t0._time.jd2.reshape(5, 2, 5))
assert np.may_share_memory(t0_reshape.jd1, self.t0.jd1)
assert np.may_share_memory(t0_reshape.jd2, self.t0.jd2)
assert t0_reshape.location is None
t1_reshape = self.t1.reshape(2, 5, 5)
assert t1_reshape.shape == (2, 5, 5)
assert np.all(t1_reshape.jd1 == self.t1.jd1.reshape(2, 5, 5))
assert np.may_share_memory(t1_reshape.jd1, self.t1.jd1)
assert t1_reshape.location is self.t1.location
# For reshape(5, 2, 5), the location array can remain the same.
t2_reshape = self.t2.reshape(5, 2, 5)
assert t2_reshape.shape == (5, 2, 5)
assert np.all(t2_reshape.jd1 == self.t2.jd1.reshape(5, 2, 5))
assert np.may_share_memory(t2_reshape.jd1, self.t2.jd1)
assert t2_reshape.location.shape == t2_reshape.shape
assert np.may_share_memory(t2_reshape.location, self.t2.location)
# But for reshape(5, 5, 2), location has to be broadcast and copied.
t2_reshape2 = self.t2.reshape(5, 5, 2)
assert t2_reshape2.shape == (5, 5, 2)
assert np.all(t2_reshape2.jd1 == self.t2.jd1.reshape(5, 5, 2))
assert np.may_share_memory(t2_reshape2.jd1, self.t2.jd1)
assert t2_reshape2.location.shape == t2_reshape2.shape
assert not np.may_share_memory(t2_reshape2.location, self.t2.location)
t2_reshape_t = self.t2.reshape(10, 5).T
assert t2_reshape_t.shape == (5, 10)
assert np.may_share_memory(t2_reshape_t.jd1, self.t2.jd1)
assert t2_reshape_t.location.shape == t2_reshape_t.shape
assert np.may_share_memory(t2_reshape_t.location, self.t2.location)
# Finally, reshape in a way that cannot be a view.
t2_reshape_t_reshape = t2_reshape_t.reshape(10, 5)
assert t2_reshape_t_reshape.shape == (10, 5)
assert not np.may_share_memory(t2_reshape_t_reshape.jd1, self.t2.jd1)
assert (t2_reshape_t_reshape.location.shape ==
t2_reshape_t_reshape.shape)
assert not np.may_share_memory(t2_reshape_t_reshape.location,
t2_reshape_t.location)
def test_shape_setting(self, masked):
t0_reshape = self.t0.copy()
mjd = t0_reshape.mjd # Creates a cache of the mjd attribute
t0_reshape.shape = (5, 2, 5)
assert t0_reshape.shape == (5, 2, 5)
assert mjd.shape != t0_reshape.mjd.shape # Cache got cleared
assert np.all(t0_reshape.jd1 == self.t0._time.jd1.reshape(5, 2, 5))
assert np.all(t0_reshape.jd2 == self.t0._time.jd2.reshape(5, 2, 5))
assert t0_reshape.location is None
# But if the shape doesn't work, one should get an error.
t0_reshape_t = t0_reshape.T
with pytest.raises(AttributeError):
t0_reshape_t.shape = (10, 5)
# check no shape was changed.
assert t0_reshape_t.shape == t0_reshape.T.shape
assert t0_reshape_t.jd1.shape == t0_reshape.T.shape
assert t0_reshape_t.jd2.shape == t0_reshape.T.shape
t1_reshape = self.t1.copy()
t1_reshape.shape = (2, 5, 5)
assert t1_reshape.shape == (2, 5, 5)
assert np.all(t1_reshape.jd1 == self.t1.jd1.reshape(2, 5, 5))
# location is a single element, so its shape should not change.
assert t1_reshape.location.shape == ()
# For reshape(5, 2, 5), the location array can remain the same.
# Note that we need to work directly on self.t2 here, since any
# copy would cause location to have the full shape.
self.t2.shape = (5, 2, 5)
assert self.t2.shape == (5, 2, 5)
assert self.t2.jd1.shape == (5, 2, 5)
assert self.t2.jd2.shape == (5, 2, 5)
assert self.t2.location.shape == (5, 2, 5)
assert self.t2.location.strides == (0, 0, 24)
# But for reshape(50), location would need to be copied, so this
# should fail.
oldshape = self.t2.shape
with pytest.raises(AttributeError):
self.t2.shape = (50,)
# check no shape was changed.
assert self.t2.jd1.shape == oldshape
assert self.t2.jd2.shape == oldshape
assert self.t2.location.shape == oldshape
# reset t2 to its original.
self.setup()
def test_squeeze(self, masked):
t0_squeeze = self.t0.reshape(5, 1, 2, 1, 5).squeeze()
assert t0_squeeze.shape == (5, 2, 5)
assert np.all(t0_squeeze.jd1 == self.t0.jd1.reshape(5, 2, 5))
assert np.may_share_memory(t0_squeeze.jd1, self.t0.jd1)
assert t0_squeeze.location is None
t1_squeeze = self.t1.reshape(1, 5, 1, 2, 5).squeeze()
assert t1_squeeze.shape == (5, 2, 5)
assert np.all(t1_squeeze.jd1 == self.t1.jd1.reshape(5, 2, 5))
assert np.may_share_memory(t1_squeeze.jd1, self.t1.jd1)
assert t1_squeeze.location is self.t1.location
t2_squeeze = self.t2.reshape(1, 1, 5, 2, 5, 1, 1).squeeze()
assert t2_squeeze.shape == (5, 2, 5)
assert np.all(t2_squeeze.jd1 == self.t2.jd1.reshape(5, 2, 5))
assert np.may_share_memory(t2_squeeze.jd1, self.t2.jd1)
assert t2_squeeze.location.shape == t2_squeeze.shape
assert np.may_share_memory(t2_squeeze.location, self.t2.location)
def test_add_dimension(self, masked):
t0_adddim = self.t0[:, np.newaxis, :]
assert t0_adddim.shape == (10, 1, 5)
assert np.all(t0_adddim.jd1 == self.t0.jd1[:, np.newaxis, :])
assert np.may_share_memory(t0_adddim.jd1, self.t0.jd1)
assert t0_adddim.location is None
t1_adddim = self.t1[:, :, np.newaxis]
assert t1_adddim.shape == (10, 5, 1)
assert np.all(t1_adddim.jd1 == self.t1.jd1[:, :, np.newaxis])
assert np.may_share_memory(t1_adddim.jd1, self.t1.jd1)
assert t1_adddim.location is self.t1.location
t2_adddim = self.t2[:, :, np.newaxis]
assert t2_adddim.shape == (10, 5, 1)
assert np.all(t2_adddim.jd1 == self.t2.jd1[:, :, np.newaxis])
assert np.may_share_memory(t2_adddim.jd1, self.t2.jd1)
assert t2_adddim.location.shape == t2_adddim.shape
assert np.may_share_memory(t2_adddim.location, self.t2.location)
def test_take(self, masked):
t0_take = self.t0.take((5, 2))
assert t0_take.shape == (2,)
assert np.all(t0_take.jd1 == self.t0._time.jd1.take((5, 2)))
assert t0_take.location is None
t1_take = self.t1.take((2, 4), axis=1)
assert t1_take.shape == (10, 2)
assert np.all(t1_take.jd1 == self.t1.jd1.take((2, 4), axis=1))
assert t1_take.location is self.t1.location
t2_take = self.t2.take((1, 3, 7), axis=0)
assert t2_take.shape == (3, 5)
assert np.all(t2_take.jd1 == self.t2.jd1.take((1, 3, 7), axis=0))
assert t2_take.location.shape == t2_take.shape
t2_take2 = self.t2.take((5, 15))
assert t2_take2.shape == (2,)
assert np.all(t2_take2.jd1 == self.t2.jd1.take((5, 15)))
assert t2_take2.location.shape == t2_take2.shape
def test_broadcast(self, masked):
"""Test using a callable method."""
t0_broadcast = self.t0._apply(np.broadcast_to, shape=(3, 10, 5))
assert t0_broadcast.shape == (3, 10, 5)
assert np.all(t0_broadcast.jd1 == self.t0.jd1)
assert np.may_share_memory(t0_broadcast.jd1, self.t0.jd1)
assert t0_broadcast.location is None
t1_broadcast = self.t1._apply(np.broadcast_to, shape=(3, 10, 5))
assert t1_broadcast.shape == (3, 10, 5)
assert np.all(t1_broadcast.jd1 == self.t1.jd1)
assert np.may_share_memory(t1_broadcast.jd1, self.t1.jd1)
assert t1_broadcast.location is self.t1.location
t2_broadcast = self.t2._apply(np.broadcast_to, shape=(3, 10, 5))
assert t2_broadcast.shape == (3, 10, 5)
assert np.all(t2_broadcast.jd1 == self.t2.jd1)
assert np.may_share_memory(t2_broadcast.jd1, self.t2.jd1)
assert t2_broadcast.location.shape == t2_broadcast.shape
assert np.may_share_memory(t2_broadcast.location, self.t2.location)
def radec_to_altaz(radec: SkyCoord,
time: Time,
observer: EarthLocation = nenufar_position,
fast_compute: bool = True) -> SkyCoord:
r""" Converts a celestial object equatorial coordinates
to horizontal coordinates.
If ``fast_compute=True`` is selected, the computation is
accelerated using Local Sidereal Time approximation
(see :func:`~nenupy.astro.astro_tools.local_sidereal_time`).
The altitude :math:`\theta` and azimuth :math:`\varphi` are computed
as follows:
.. math::
\cases{
\sin(\theta) = \sin(\delta) \sin(l) + \cos(\delta) \cos(l) \cos(h)\\
\cos(\varphi) = \frac{\sin(\delta) - \sin(l) \sin(\theta)}{\cos(l)\cos(\varphi)}
}
with :math:`\delta` the object's declination, :math:`l`
the ``observer``'s latitude and :math:`h` the Local Hour Angle
(see :func:`~nenupy.astro.astro_tools.hour_angle`).
If :math:`\sin(h) \geq 0`, then :math:`\varphi = 2 \pi - \varphi`.
Otherwise, :meth:`~astropy.coordinates.SkyCoord.transform_to`
is used.
:param radec:
Celestial object equatorial coordinates.
:type radec:
:class:`~astropy.coordinates.SkyCoord`
:param time:
Coordinated universal time.
:type time:
:class:`~astropy.time.Time`
:param observer:
Earth location where the observer is at.
Default is NenuFAR's position.
:type observer:
:class:`~astropy.coordinates.EarthLocation`
:param fast_compute:
If set to ``True``, it enables faster computation time for the
conversion, mainly relying on an approximation of the
local sidereal time. All other values would lead to
accurate coordinates computation. Differences in
coordinates values are of the order of :math:`10^{-2}`
degrees or less.
:type fast_compute:
`bool`
:returns:
Celestial object's horizontal coordinates.
If either ``radec`` or ``time`` are 1D arrays, the
resulting object will be of shape ``(time, positions)``.
:rtype:
:class:`~astropy.coordinates.SkyCoord`
:Example:
.. code-block:: python
from nenupy.astro import radec_to_altaz
from astropy.time import Time
from astropy.coordinates import SkyCoord
altaz = radec_to_altaz(
radec=SkyCoord([300, 200], [45, 45], unit="deg"),
time=Time("2022-01-01T12:00:00"),
fast_compute=True
)
"""
if not time.isscalar:
time = time.reshape((time.size, 1))
radec = radec.reshape((1, radec.size))
if fast_compute:
radec = radec.transform_to(FK5(equinox=time))
ha = hour_angle(radec=radec,
time=time,
observer=observer,
fast_compute=fast_compute)
two_pi = Angle(360.0, unit='deg')
ha = hour_angle(radec=radec,
time=time,
observer=observer,
fast_compute=fast_compute)
sin_dec = np.sin(radec.dec.rad)
cos_dec = np.cos(radec.dec.rad)
sin_lat = np.sin(observer.lat.rad)
cos_lat = np.cos(observer.lat.rad)
# Compute elevation
sin_elevation = sin_dec * sin_lat + cos_dec * cos_lat * np.cos(ha.rad)
elevation = Latitude(np.arcsin(sin_elevation), unit="rad")
# Compute azimuth
cos_azimuth = (sin_dec - np.sin(elevation.rad)*sin_lat)/\
(np.cos(elevation.rad)*cos_lat)
azimuth = Longitude(np.arccos(cos_azimuth), unit="rad")
if azimuth.isscalar:
if np.sin(ha.rad) > 0:
azimuth *= -1
azimuth += two_pi
else:
posMask = np.sin(ha.rad) > 0
azimuth[posMask] *= -1
azimuth[posMask] += two_pi
return SkyCoord(azimuth,
elevation,
frame=AltAz(obstime=time, location=observer))
else:
return radec.transform_to(AltAz(obstime=time, location=observer))
