def prepare_for_model_step(self, sc, time_step, model_time_datetime):
"""
Call base class method using super
Also updates windage for this timestep
:param sc: an instance of gnome.spill_container.SpillContainer class
:param time_step: time step in seconds
:param model_time_datetime: current time of model as a date time object
"""
# not sure if we need to redefine this or what we want to do here
super(ShipDriftMover,
self).prepare_for_model_step(sc, time_step, model_time_datetime)
# if no particles released, then no need for windage
# TODO: revisit this since sc.num_released shouldn't be None
if sc.num_released is None or sc.num_released == 0:
return
self.grid.prepare_for_model_step(model_time_datetime)
# here we might put in drift angle stuff ?
if self.active:
rand.random_with_persistance(sc['windage_range'][:, 0],
sc['windage_range'][:, 1],
sc['windages'], sc['windage_persist'],
time_step)
def test_exceptions(low, high):
"""
Test exceptions
"""
arr = np.zeros((len(low),), dtype=np.float64)
with pytest.raises(ValueError):
random_with_persistance(low, high, arr)
def prepare_for_model_step(self, sc, time_step, model_time_datetime):
"""
Call base class method using super
Also updates windage for this timestep
:param sc: an instance of gnome.spill_container.SpillContainer class
:param time_step: time step in seconds
:param model_time_datetime: current time of model as a date time object
"""
# not sure if we need to redefine this or what we want to do here
super(ShipDriftMover, self).prepare_for_model_step(sc, time_step,
model_time_datetime)
# if no particles released, then no need for windage
# TODO: revisit this since sc.num_released shouldn't be None
if sc.num_released is None or sc.num_released == 0:
return
self.grid.prepare_for_model_step(model_time_datetime)
# here we might put in drift angle stuff ?
rand.random_with_persistance(sc['windage_range'][:, 0],
sc['windage_range'][:, 1],
sc['windages'],
sc['windage_persist'],
time_step)
def test_random_with_persistance(low, high, array, persist, timestep):
"""
Since numbers are randomly generated, can only test
array length
"""
if array is None:
array = random_with_persistance(low, high, array, persist,
time_step=timestep)
else:
random_with_persistance(low, high, array, persist, time_step=timestep)
# persist set to timestep
if persist is None:
assert np.all(array >= low) and np.all(array <= high)
else:
# do not update values if persist < 0
low = np.asarray(low)
high = np.asarray(high)
persist = np.asarray(persist)
assert array[persist <= 0] == 0
# for persist == timestep, output is within [low, high] bounds
eq_mask = [persist == timestep]
if np.any(eq_mask):
assert (array[eq_mask] >= low[eq_mask] and
array[eq_mask] <= high[eq_mask])
def test_random_with_persistance(low, high, array, persist, timestep):
"""
Since numbers are randomly generated, can only test
array length
"""
if array is None:
array = random_with_persistance(low, high, array, persist,
time_step=timestep)
else:
random_with_persistance(low, high, array, persist, time_step=timestep)
# persist set to timestep
if persist is None:
assert np.all(array >= low) and np.all(array <= high)
else:
# do not update values if persist < 0
low = np.asarray(low)
high = np.asarray(high)
persist = np.asarray(persist)
assert array[persist <= 0] == 0
# for persist == timestep, output is within [low, high] bounds
eq_mask = persist == timestep
if np.any(eq_mask):
assert (array[eq_mask] >= low[eq_mask] and
array[eq_mask] <= high[eq_mask])
def test_exceptions(low, high):
"""
Test exceptions
"""
arr = np.zeros((len(low),), dtype=np.float64)
with pytest.raises(ValueError):
random_with_persistance(low, high, arr)
def prepare_for_model_step(
self,
sc,
time_step,
model_time_datetime,
):
"""
Call base class method using super
Also updates windage for this timestep
:param sc: an instance of gnome.spill_container.SpillContainer class
:param time_step: time step in seconds
:param model_time_datetime: current time of model as a date time object
"""
super(WindMoversBase, self).prepare_for_model_step(sc, time_step,
model_time_datetime)
# if no particles released, then no need for windage
# todo: revisit this since sc.num_released shouldn't be None
if sc.num_released is None or sc.num_released == 0:
return
for spill in sc.spills:
spill_mask = sc.get_spill_mask(spill)
if np.any(spill_mask):
rand.random_with_persistance(
sc['windage_range'][spill_mask, 0],
sc['windage_range'][spill_mask, 1],
sc['windages'][spill_mask],
sc['windage_persist'][spill_mask],
time_step)
def initialize(self, num_new_particles, data_arrays, substance):
"""
Since windages exists in data_arrays, so must windage_range and
windage_persist if this initializer is used/called
"""
if any([k not in data_arrays for k in self.array_types.keys()]):
return
sl = slice(-num_new_particles, None, 1)
data_arrays['windage_range'][sl] = self.windage_range
data_arrays['windage_persist'][sl] = self.windage_persist
random_with_persistance(
data_arrays['windage_range'][-num_new_particles:, 0],
data_arrays['windage_range'][-num_new_particles:, 1],
data_arrays['windages'][-num_new_particles:]
)
def initialize(self, num_new_particles, spill, data_arrays):
"""
Since windages exists in data_arrays, so must windage_range and
windage_persist if this initializer is used/called
"""
data_arrays['windage_range'][-num_new_particles:, 0] = \
self.windage_range[0]
data_arrays['windage_range'][-num_new_particles:, 1] = \
self.windage_range[1]
data_arrays['windage_persist'][-num_new_particles:] = \
self.windage_persist
# initialize all windages - ignore persistence during initialization
# if we have infinite persistence, these values are never updated
random_with_persistance(
data_arrays['windage_range'][-num_new_particles:][:, 0],
data_arrays['windage_range'][-num_new_particles:][:, 1],
data_arrays['windages'][-num_new_particles:])
def prepare_for_model_step(self, sc, time_step, model_time_datetime):
"""
Call base class method using super
Also updates windage for this timestep
:param sc: an instance of gnome.spill_container.SpillContainer class
:param time_step: time step in seconds
:param model_time_datetime: current time of model as a date time object
"""
super(PyWindMover, self).prepare_for_model_step(sc, time_step, model_time_datetime)
# if no particles released, then no need for windage
# TODO: revisit this since sc.num_released shouldn't be None
if sc.num_released is None or sc.num_released == 0:
return
rand.random_with_persistance(
sc["windage_range"][:, 0], sc["windage_range"][:, 1], sc["windages"], sc["windage_persist"], time_step
)
def prepare_for_model_step(self, sc, time_step, model_time_datetime):
"""
Call base class method using super
Also updates windage for this timestep
:param sc: an instance of gnome.spill_container.SpillContainer class
:param time_step: time step in seconds
:param model_time_datetime: current time of model as a date time object
"""
super(WindMoversBase,
self).prepare_for_model_step(sc, time_step, model_time_datetime)
# if no particles released, then no need for windage
# TODO: revisit this since sc.num_released shouldn't be None
if sc.num_released is None or sc.num_released == 0:
return
rand.random_with_persistance(sc['windage_range'][:, 0],
sc['windage_range'][:, 1], sc['windages'],
sc['windage_persist'], time_step)
def initialize(self,
num_new_particles,
spill,
data_arrays,
substance=None):
"""
Since windages exists in data_arrays, so must windage_range and
windage_persist if this initializer is used/called
"""
(data_arrays['windage_range'][-num_new_particles:, 0],
data_arrays['windage_range'][-num_new_particles:, 1],
data_arrays['windage_persist'][-num_new_particles:]) = \
(self.windage_range[0],
self.windage_range[1],
self.windage_persist)
# initialize all windages - ignore persistence during initialization
# if we have infinite persistence, these values are never updated
random_with_persistance(
data_arrays['windage_range'][-num_new_particles:][:, 0],
data_arrays['windage_range'][-num_new_particles:][:, 1],
data_arrays['windages'][-num_new_particles:])
def test_random_with_persistance_low_equals_high(low, high):
"""
if low==high, then return low - deterministic output
"""
x = random_with_persistance(low, high)
assert np.all(x == low)
def test_random_with_persistance_low_equals_high(low, high):
"""
if low==high, then return low - deterministic output
"""
x = random_with_persistance(low, high)
assert np.all(x == low)