def _propagate_states(self, state_vectors, propagation_params, opm_params_templ):
"""Propagate states using many initial state vectors.
Args:
state_vectors (list of lists):
list of lists with 6 elements [rx, ry, rz, vx, vy, vz] [km, km/s]
propagation_params (PropagationParams):
propagation-related parameters to be used for all propagations
opm_params_templ (OpmParams):
opm-related parameters to be used for all propagations, once with each
of the given state vectors.
Returns:
end_state_vectors (list of lists):
states at end of integration [rx, ry, rz, vx, vy, vz] [km, km/s]
"""
# Create batches from state vectors
batches = []
for state_vector in state_vectors:
opm_params = deepcopy(opm_params_templ)
opm_params.set_state_vector(state_vector)
batches.append(Batch(propagation_params, opm_params))
# submit batches and wait till they finish running
runner = BatchRunManager(self.batches_module, batches)
runner.run()
# Get final states
end_state_vectors = []
for batch in batches:
end_state_vectors.append(batch.get_results().get_end_state_vector())
return end_state_vectors
def test_small_batch(self):
num_batches = 3
duration = 365 # 1 year
batches = [self.new_dummy_batch(duration) for i in range(num_batches)]
runner = BatchRunManager(self.service.get_batches_module(), batches)
runner.run()
statuses = runner.get_latest_statuses()
self.assertEqual(0, len(statuses['PENDING']))
self.assertEqual(0, len(statuses['RUNNING']))
self.assertEqual(num_batches, len(statuses['COMPLETED']))
self.assertEqual(0, len(statuses['FAILED']))
end_state_vec = [-37535741.96415495,
492953227.1713997,
204483503.94517875,
-11.337510170756701,
7.185009462698965,
3.3597614338244766]
for batch in batches:
npt.assert_allclose(end_state_vec,
batch.get_results().get_end_state_vector(),
rtol=1e-3,
atol=0)
def test_corners(self, service, working_project):
batch = self._new_hypercube_batch('CORNERS', working_project)
runner = BatchRunManager(service.get_batches_module(), [batch])
runner.run()
'COMPLETED' == batch.get_calc_state()
parts = batch.get_results().get_parts()
assert len(parts) == 65
def test_corners(self):
batch = self.new_hypercube_batch('CORNERS')
runner = BatchRunManager(self.service.get_batches_module(), [batch])
runner.run()
self.assertEqual('COMPLETED', batch.get_calc_state())
parts = batch.get_results().get_parts()
self.assertEqual(65, len(parts))
def test_sun_ememe(self, service, working_project):
start_time_str = "2000-01-01T11:58:55.816Z"
end_time_str = "2009-07-21T21:55:08.813Z"
sun_ememe_state_vec = [
-306536346.18024945, -120966638.54521248, -12981.069369263947,
15.759854830195243, -11.539570959741736, 0.0005481049628786039
]
propagation_params = PropagationParams({
'start_time':
start_time_str,
'end_time':
end_time_str,
'step_size':
86400,
'project_uuid':
working_project.get_uuid(),
'description':
'Created by test at ' + start_time_str
})
opm_params = OpmParams({
'epoch': start_time_str,
'state_vector': sun_ememe_state_vec,
'center_name': 'SUN',
'ref_frame': 'EMEME2000',
})
batch = Batch(propagation_params, opm_params)
runner = BatchRunManager(service.get_batches_module(), [batch])
runner.run()
end_state = batch.get_results().get_end_state_vector()
# The output state is expected to be in ICRF.
expected_end_state = [
73978163.61069362, -121822760.05571477, -52811158.83249758,
31.71000343989318, 29.9657246374751, .6754531613947713
]
# These values are in EMEME. The resulting ephemeris is not expected to match these values.
# expected_end_state = [73978158.47632701, -132777272.5255892, 5015.073123970032,
# 31.710003506237434, 27.761693311026138, -11.299967713192564]
difference = np.subtract(expected_end_state, end_state)
print("Difference is %s" % difference)
print("End state: %s" % end_state)
npt.assert_allclose(difference[0:3], [0, 0, 0], rtol=0, atol=.02)
npt.assert_allclose(difference[3:6], [0, 0, 0], rtol=0, atol=.00002)
# The returned ephemeris will be in Sun-centered ICRF, not EMEME. My best guess is that
# the ephemeris file doesn't support all reference frames, so if it encounters one that
# isn't supported, it'll choose a similar one.
ephem = batch.get_results().get_parts()[-1].get_ephemeris()
assert "ICRF" in ephem
assert "EMEME" not in ephem
def test_backwards_and_forwards(self):
now = datetime.datetime.now()
later = now + datetime.timedelta(10 * 365) # 10 years
state_vec = [
130347560.13690618, -74407287.6018632, -35247598.541470632,
23.935241263310683, 27.146279819258538, 10.346605942591514
]
print("Starting at %s" % (state_vec))
print("Propagating forward from %s to %s" % (now, later))
batch = self.make_batch(state_vec, now, later)
runner = BatchRunManager(self.service.get_batches_module(), [batch])
runner.run()
forward_end_state = batch.get_results().get_end_state_vector()
print("Final state at %s" % forward_end_state)
print("Propagating backward from %s to %s" % (later, now))
batch = self.make_batch(forward_end_state, later, now)
runner = BatchRunManager(self.service.get_batches_module(), [batch])
runner.run()
backwards_end_state = batch.get_results().get_end_state_vector()
print("Final state at %s" % backwards_end_state)
difference = np.subtract(state_vec, backwards_end_state)
print("Difference is %s" % difference)
npt.assert_allclose(difference[0:3], [0, 0, 0], rtol=0, atol=1e-3)
npt.assert_allclose(difference[3:6], [0, 0, 0], rtol=0, atol=1e-10)
def test_icrf(self, service, working_project):
start_time_str = "2000-01-01T11:58:55.816Z"
end_time_str = "2009-07-21T21:55:08.813Z"
sun_icrf_state_vec = [
-306536341.5010222, -110979556.84640282, -48129706.42252728,
15.75985527640906, -10.587567329195842, -4.589673432886975
]
propagation_params = PropagationParams({
'start_time':
start_time_str,
'end_time':
end_time_str,
'step_size':
86400,
'project_uuid':
working_project.get_uuid(),
'description':
'Created by test at ' + start_time_str
})
opm_params = OpmParams({
'epoch': start_time_str,
'state_vector': sun_icrf_state_vec,
'center_name': 'SUN',
'ref_frame': 'ICRF',
})
batch = Batch(propagation_params, opm_params)
runner = BatchRunManager(service.get_batches_module(), [batch])
runner.run()
end_state = batch.get_results().get_end_state_vector()
expected_end_state = [
73978163.61069362, -121822760.05571477, -52811158.83249758,
31.71000343989318, 29.9657246374751, .6754531613947713
]
difference = np.subtract(expected_end_state, end_state)
print("Difference is %s" % difference)
print("End state: %s" % end_state)
npt.assert_allclose(difference[0:3], [0, 0, 0], rtol=0, atol=.02)
npt.assert_allclose(difference[3:6], [0, 0, 0], rtol=0, atol=.00002)
ephem = batch.get_results().get_parts()[-1].get_ephemeris()
assert "ICRF" in ephem
def propagate_states(state_vectors, epoch_time, end_time):
"""Propagate states from one time to another
Assume state epoch is the same as integration start time
Args:
sate_vectors (list of lists) - list of lists with 6 elements
[rx, ry, rz, vx, vy, vz] [km, km/s]
epoch_time (datetime.datetime) - epoch of state (UTC datetime)
end_time (datetime.datetime) - time at which to end the simulation
(UTC datetime)
Returns:
end_state_vectors (list of lists) - states at end of integration
[rx, ry, rz, vx, vy, vz] [km, km/s]
"""
# Convert times to strings
epoch_time_str = batch_time_string_from_datetime(epoch_time)
start_time_str = epoch_time_str
end_time_str = batch_time_string_from_datetime(end_time)
print("Propagating %i states to propagate from %s to %s" %
(len(state_vectors), start_time_str, end_time_str))
url = "https://pro-equinox-162418.appspot.com/_ah/api/adam/v1"
rest = RestRequests(url)
batches_module = Batches(rest)
# Create batches from statevectors
batches = []
propagation_params = PropagationParams({
'start_time':
start_time_str,
'end_time':
end_time_str,
'project_uuid':
'ffffffff-ffff-ffff-ffff-ffffffffffff'
})
for state_vector in state_vectors:
opm_params = OpmParams({
'epoch': start_time_str,
'state_vector': state_vector
})
batches.append(Batch(propagation_params, opm_params))
# submit batches and wait till they finish running
BatchRunManager(batches_module, batches).run()
# Get final states
end_state_vectors = []
for batch in batches:
end_state_vectors.append(batch.get_results().get_end_state_vector())
return end_state_vectors
def test_config_in_use_pins_project(self):
# Config management isn't very common, doesn't merit direct addition to service.
configs = PropagatorConfigs(self.service.rest)
projects = self.service.get_projects_module()
project = self.service.new_working_project()
project1 = projects.new_project(project.get_uuid(), "", "")
self.assertIsNotNone(project1)
project2 = projects.new_project(project.get_uuid(), "", "")
self.assertIsNotNone(project2)
print("Added child projects to working project: " + "[" +
project1.get_uuid() + ", " + project2.get_uuid() + "]")
config = configs.new_config({
'project': project1.get_uuid(),
'description': 'test config'
})
self.assertEqual(project1.get_uuid(), config.get_project())
batch = Batch(
PropagationParams({
'start_time': '2017-10-04T00:00:00Z',
'end_time': '2017-10-05T00:00:00Z',
'project_uuid': project2.get_uuid(),
'propagator_uuid': config.get_uuid()
}),
OpmParams({
'epoch':
'2017-10-04T00:00:00Z',
'state_vector': [
130347560.13690618, -74407287.6018632, -35247598.541470632,
23.935241263310683, 27.146279819258538, 10.346605942591514
]
}))
BatchRunManager(self.service.get_batches_module(), [batch]).run()
# Attempt to delete the project with the config in it. It should refuse because the
# config is still in use by the batch.
with self.assertRaises(RuntimeError):
projects.delete_project(project1.get_uuid())
# Then delete the batch. After that, the project with the config in it should
# delete no problem.
self.service.batches.delete_batch(batch.get_uuid())
projects.delete_project(project1.get_uuid())
# Clean up the batch holder project.
projects.delete_project(project2.get_uuid())
def test_keplerian_and_cartesian(self):
start = datetime.datetime.now()
end = start + datetime.timedelta(10) # 10 days
start_time_str = start.isoformat() + 'Z'
end_time_str = end.isoformat() + 'Z'
cartesian, keplerian = self.make_cartesian_and_keplerian_batches(
start_time_str, end_time_str)
BatchRunManager(self.service.get_batches_module(),
[cartesian, keplerian]).run()
cartesian_end_state = cartesian.get_results().get_end_state_vector()
keplerian_end_state = keplerian.get_results().get_end_state_vector()
difference = np.subtract(cartesian_end_state, keplerian_end_state)
npt.assert_allclose(difference[0:3], [0, 0, 0], rtol=0, atol=1e-5)
npt.assert_allclose(difference[3:6], [0, 0, 0], rtol=0, atol=1e-10)
def test_backwards_and_forwards(self, service, working_project):
now = datetime.datetime.now()
later = now + datetime.timedelta(10 * 365) # 10 years
state_vec = [
130347560.13690618, -74407287.6018632, -35247598.541470632,
23.935241263310683, 27.146279819258538, 10.346605942591514
]
print("Starting at %s" % (state_vec))
print("Propagating forward from %s to %s" % (now, later))
batch = self._make_batch(state_vec, now, later, working_project)
runner = BatchRunManager(service.get_batches_module(), [batch])
runner.run()
forward_end_state = batch.get_results().get_end_state_vector()
print("Final state at %s" % forward_end_state)
print("Propagating backward from %s to %s" % (later, now))
batch = self._make_batch(forward_end_state, later, now,
working_project)
runner = BatchRunManager(service.get_batches_module(), [batch])
runner.run()
backwards_end_state = batch.get_results().get_end_state_vector()
print("Final state at %s" % backwards_end_state)
difference = np.subtract(state_vec, backwards_end_state)
print("Difference is %s" % difference)
# from test:
# Difference is [8.20617378e-03 9.39679146e-03 3.57937068e-03
# -1.62931713e-09 9.38538136e-10 4.41515269e-10]
# Seems like when the test was written (and presumably was working), the tolerances
# were small (the commented-out lines below). This was back in April 2018, so probably
# ADAM was also using STK 2017r(something) and not 2018r3, which is the version ADAM
# is currently using. Could the same propagation be calculated with slight differences?
# npt.assert_allclose(difference[0:3], [0, 0, 0], rtol=0, atol=1e-3)
# npt.assert_allclose(difference[3:6], [0, 0, 0], rtol=0, atol=1e-10)
npt.assert_allclose(difference[0:3], [0, 0, 0], rtol=0, atol=1e-2)
npt.assert_allclose(difference[3:6], [0, 0, 0], rtol=0, atol=1e-8)
def test_backwards_and_forwards(self, service, working_project):
now = datetime.datetime.now()
later = now + datetime.timedelta(10 * 365) # 10 years
state_vec = [
130347560.13690618, -74407287.6018632, -35247598.541470632,
23.935241263310683, 27.146279819258538, 10.346605942591514
]
print("Starting at %s" % (state_vec))
print("Propagating forward from %s to %s" % (now, later))
batch = self._make_batch(state_vec, now, later, working_project)
runner = BatchRunManager(service.get_batches_module(), [batch])
runner.run()
forward_end_state = batch.get_results().get_end_state_vector()
print("Final state at %s" % forward_end_state)
print("Propagating backward from %s to %s" % (later, now))
batch = self._make_batch(forward_end_state, later, now,
working_project)
runner = BatchRunManager(service.get_batches_module(), [batch])
runner.run()
backwards_end_state = batch.get_results().get_end_state_vector()
print("Final state at %s" % backwards_end_state)
difference = np.subtract(state_vec, backwards_end_state)
print("Difference is %s" % difference)
# This test appears to be flaky. Usually it fails on the first of the following 2 asserts,
# but sometimes, very rarely, it will pass on the first assert and fail on the second.
# Example differences (subtracting backwards end state from initial state vector):
# [ 4.60617244e-03 5.23680449e-03 1.98936462e-03
# -9.19317955e-10 5.18536325e-10 2.51514365e-10] <-- fails on 1st assert
# [ 2.29061693e-02 2.58967876e-02 9.86936688e-03
# -4.50932092e-09 2.57853827e-09 1.22151356e-09] <-- fails on 1st assert
# [ 6.06164336e-04 7.16790557e-04 2.69368291e-04
# -1.19317889e-10 7.85398413e-11 3.15143467e-11] <-- fails on 2nd assert
npt.assert_allclose(difference[0:3], [0, 0, 0], rtol=0, atol=1e-3)
npt.assert_allclose(difference[3:6], [0, 0, 0], rtol=0, atol=1e-10)
# 'covariance': covariance, # object covariance
# 'perturbation': 3, # sigma perturbation on state vector
# 'hypercube': 'FACES', # hypercube propagation type
# 'originator': 'Robot', # originator of run
# 'object_name': 'TestObj', # object name
# 'object_id': 'test1234', # object ID
})
batch = Batch(propagation_params, opm_params)
print("Submitting OPM:")
print(batch.get_opm_params().generate_opm())
# Submit and wait until batch run is ready
batches_module = Batches(auth_rest)
BatchRunManager(batches_module, [batch]).run()
# Get final parts count
parts_count = batch.get_state_summary().get_parts_count()
print("Final state: %s, part count %s\n" % (batch.get_calc_state(), parts_count))
# Get ephemeris of specified part
part_to_get = 0
eph = batch.get_results().get_parts()[part_to_get].get_ephemeris()
print("Ephemeris:")
print(eph)
# Get the end state vector (uncomment to use)
# end_state_vector = batch.get_results().get_end_state_vector()
# print("State vector at the end of propagation:")
# print(end_state_vector)
