def test_to_dict(self):
c_spill = [point_line_release_spill(self.num_elements,
self.start_position, self.start_time) for i in
range(2)]
u_spill = [point_line_release_spill(self.num_elements,
self.start_position2, self.start_time2) for i in
range(2)]
scp = SpillContainerPair(True)
for sp_tuple in zip(c_spill, u_spill):
scp += sp_tuple
dict_ = scp.to_dict()
for key in dict_.keys():
if key == 'certain_spills':
enum_spill = c_spill
elif key == 'uncertain_spills':
enum_spill = u_spill
for (i, spill) in enumerate(enum_spill):
assert dict_[key]['items'][i][0] \
== '{0}.{1}'.format(spill.__module__,
spill.__class__.__name__)
assert dict_[key]['items'][i][1] == i
def test_to_dict(self, json_):
c_spill = [point_line_release_spill(self.num_elements,
self.start_position, self.start_time) for i in
range(2)]
u_spill = [point_line_release_spill(self.num_elements,
self.start_position2, self.start_time2) for i in
range(2)]
scp = SpillContainerPair(True)
for sp_tuple in zip(c_spill, u_spill):
scp += sp_tuple
toserial = scp.to_dict()
assert 'spills' in toserial
assert 'uncertain_spills' in toserial
for key in ('spills', 'uncertain_spills'):
if key == 'spills':
check = c_spill
else:
check = u_spill
alltrue = [check[ix].id == spill['id'] \
for ix, spill in enumerate(toserial[key])]
assert all(alltrue)
alltrue = [check[ix].obj_type == spill['obj_type'] \
for ix, spill in enumerate(toserial[key])]
assert all(alltrue)
def test_SpillContainerPair_uncertainty(self):
'test uncertainty property'
u_scp = SpillContainerPair(True)
u_scp.uncertain = False
assert not u_scp.uncertain
assert not hasattr(u_scp, '_u_spill_container')
u_scp.uncertain = True
assert u_scp.uncertain
assert hasattr(u_scp, '_u_spill_container')
def test_spill_by_index(self):
'test spill_by_index returns the correct spill object'
spill = [point_line_release_spill(self.num_elements,
self.start_position, self.start_time),
point_line_release_spill(self.num_elements,
self.start_position, self.start_time)]
scp = SpillContainerPair(True)
scp += spill[0]
scp += spill[1]
for ix in range(2):
assert scp.spill_by_index(ix) is spill[ix]
u_spill = scp.spill_by_index(ix, uncertain=True)
assert u_spill is not spill[ix]
assert scp.items()[1].spills[ix] is u_spill
def __restore__(self, time_step, start_time, duration, weathering_substeps, map, uncertain, cache_enabled):
"""
Take out initialization that does not register the callback here.
This is because new_from_dict will use this to restore the model _state
when doing a midrun persistence.
"""
# making sure basic stuff is in place before properties are set
self.environment = OrderedCollection(dtype=Environment)
self.movers = OrderedCollection(dtype=Mover)
self.weatherers = OrderedCollection(dtype=Weatherer)
# contains both certain/uncertain spills
self.spills = SpillContainerPair(uncertain)
self._cache = gnome.utilities.cache.ElementCache()
self._cache.enabled = cache_enabled
# list of output objects
self.outputters = OrderedCollection(dtype=Outputter)
# default to now, rounded to the nearest hour
self._start_time = start_time
self._duration = duration
self.weathering_substeps = weathering_substeps
self._map = map
self.time_step = time_step # this calls rewind() !
def test_release_particles(self):
'''test that the 'id' for uncertain spill container's data is
starting from 0'''
spill = [point_line_release_spill(self.num_elements,
self.start_position, self.start_time) for i in
range(2)]
scp = SpillContainerPair(True)
scp += spill[0]
scp += spill[1]
for sc in scp.items():
sc.prepare_for_model_run(windage_at)
# model sets this for each step
sc.current_time_stamp = self.start_time
sc.release_elements(100, self.start_time)
for key in ['id', 'spill_num', 'age']:
c_val = scp.LE(key)
u_val = scp.LE(key, 'uncertain')
assert np.all(c_val == u_val)
def test_exception_tuple(self):
"""
tests that spills can be added to SpillContainerPair object
"""
spill = point_line_release_spill(self.num_elements,
self.start_position, self.start_time)
sp2 = point_line_release_spill(self.num_elements, self.start_position2,
self.start_time2)
scp = SpillContainerPair(True)
with raises(ValueError):
scp += (spill, sp2, spill)
def test_add_spill(self):
spill = [
point_line_release_spill(self.num_elements, self.start_position,
self.start_time) for i in range(2)
]
scp = SpillContainerPair(False)
scp += (spill[0], )
scp += spill[1]
for sp_ix in zip(scp._spill_container.spills, range(len(spill))):
spill_ = sp_ix[0]
index = sp_ix[1]
assert spill_.id == spill[index].id
def test_exceptions(output_filename):
spill_pair = SpillContainerPair()
print "output_filename:", output_filename
# begin tests
netcdf = NetCDFOutput(output_filename, which_data='all')
netcdf.rewind() # delete temporary files
with raises(TypeError):
# need to pass in model start time
netcdf.prepare_for_model_run(num_time_steps=4)
with raises(TypeError):
# need to pass in model start time and spills
netcdf.prepare_for_model_run()
with raises(ValueError):
# need a cache object
netcdf.write_output(0)
with raises(ValueError):
netcdf.which_data = 'some random string'
# changed renderer and netcdf ouputter to delete old files in
# prepare_for_model_run() rather than rewind()
# -- rewind() was getting called a lot
# -- before there was time to change the ouput file names, etc.
# So for this unit test, there should be no exception if we do it twice.
netcdf.prepare_for_model_run(model_start_time=datetime.now(),
spills=spill_pair,
num_time_steps=4)
netcdf.prepare_for_model_run(model_start_time=datetime.now(),
spills=spill_pair,
num_time_steps=4)
with raises(AttributeError):
'cannot change after prepare_for_model_run has been called'
netcdf.prepare_for_model_run(model_start_time=datetime.now(),
spills=spill_pair,
num_time_steps=4)
netcdf.which_data = 'most'
def test_add_spillpair(self):
c_spill = [
point_line_release_spill(self.num_elements, self.start_position,
self.start_time) for i in range(2)
]
u_spill = [
point_line_release_spill(self.num_elements, self.start_position2,
self.start_time2) for i in range(2)
]
scp = SpillContainerPair(True)
for sp_tuple in zip(c_spill, u_spill):
scp += sp_tuple
for sp, idx in zip(scp._spill_container.spills, range(len(c_spill))):
assert sp.id == c_spill[idx].id
for sp, idx in zip(scp._u_spill_container.spills, range(len(c_spill))):
assert sp.id == u_spill[idx].id
def test_rewind_change_spill_attribute(self):
'''
check that if an attribute of forcast spillcontainer is updated,
the uncertain spill container creates a new copy of uncertain spills
'''
num_elements = 100
release_time = datetime(2012, 1, 1, 12)
start_position = (23.0, -78.5, 0.0)
scp = SpillContainerPair(uncertain=True)
scp += point_line_release_spill(num_elements, start_position,
release_time)
(forecast_sc, uncertain_sc) = scp.items()
assert forecast_sc.spills == uncertain_sc.spills
forecast_sc.spills[0].release_time = release_time + timedelta(hours=1)
(forecast_sc, uncertain_sc) = scp.items()
assert forecast_sc.spills != uncertain_sc.spills
scp.rewind()
(forecast_sc, uncertain_sc) = scp.items()
assert forecast_sc.spills == uncertain_sc.spills
def test_rewind_change_spill_attribute(self):
'''
check that if an attribute of forcast spillcontainer is updated,
the uncertain spill container creates a new copy of uncertain spills
'''
num_elements = 100
release_time = datetime(2012, 1, 1, 12)
start_position = (23.0, -78.5, 0.0)
scp = SpillContainerPair(uncertain=True)
scp += point_line_release_spill(num_elements, start_position,
release_time)
(forecast_sc, uncertain_sc) = scp.items()
assert forecast_sc.spills == uncertain_sc.spills
forecast_sc.spills[0].release_time = release_time + timedelta(hours=1)
(forecast_sc, uncertain_sc) = scp.items()
assert forecast_sc.spills != uncertain_sc.spills
scp.rewind()
(forecast_sc, uncertain_sc) = scp.items()
assert forecast_sc.spills == uncertain_sc.spills
class Model(serializable.Serializable):
"""
PyGNOME Model Class
"""
_update = [
'time_step',
'start_time',
'duration',
'uncertain',
'movers',
'environment',
'spills',
'map',
'outputters',
'cache_enabled',
]
_create = []
_create.extend(_update)
state = copy.deepcopy(serializable.Serializable.state)
state.add(create=_create, update=_update) # no need to copy parent's state in tis case
@classmethod
def new_from_dict(cls, dict_):
"""
Restore model from previously persisted state
"""
l_env = dict_.pop('environment')
l_out = dict_.pop('outputters')
l_movers = dict_.pop('movers')
c_spills = dict_.pop('certain_spills')
if 'uncertain_spills' in dict_.keys():
u_spills = dict_.pop('uncertain_spills')
l_spills = zip(c_spills, u_spills)
else:
l_spills = c_spills
model = object.__new__(cls)
model.__restore__(**dict_)
[model.environment.add(obj) for obj in l_env]
[model.outputters.add(obj) for obj in l_out]
[model.spills.add(obj) for obj in l_spills]
[model.movers.add(obj) for obj in l_movers]
# register callback with OrderedCollection
model.movers.register_callback(model._callback_add_mover, ('add'
, 'replace'))
return model
def __init__(
self,
time_step=timedelta(minutes=15),
start_time=round_time(datetime.now(), 3600),
duration=timedelta(days=1),
map=gnome.map.GnomeMap(),
uncertain=False,
cache_enabled=False,
id=None,
):
"""
Initializes a model. All arguments have a default.
:param time_step=timedelta(minutes=15): model time step in seconds or as a timedelta object
:param start_time=datetime.now(): start time of model, datetime object. default to now, rounded to the nearest hour
:param duration=timedelta(days=1): how long to run the model, a timedelta object
:param map=gnome.map.GnomeMap(): the land-water map, default is a map with no land-water
:param uncertain=False: flag for setting uncertainty
:param cache_enabled=False: flag for setting whether the mocel should cache results to disk.
:param id: Unique Id identifying the newly created mover (a UUID as a string).
This is used when loading an object from a persisted model
"""
self.__restore__(
time_step,
start_time,
duration,
map,
uncertain,
cache_enabled,
id,
)
# register callback with OrderedCollection
self.movers.register_callback(self._callback_add_mover, ('add',
'replace'))
def __restore__(
self,
time_step,
start_time,
duration,
map,
uncertain,
cache_enabled,
id,
):
"""
Take out initialization that does not register the callback here.
This is because new_from_dict will use this to restore the model state
when doing a midrun persistence.
"""
# making sure basic stuff is in place before properties are set
self.environment = OrderedCollection(dtype=Environment)
self.movers = OrderedCollection(dtype=Mover)
self.spills = SpillContainerPair(uncertain) # contains both certain/uncertain spills
self._cache = gnome.utilities.cache.ElementCache()
self._cache.enabled = cache_enabled
self.outputters = \
OrderedCollection(dtype=gnome.outputter.Outputter) # list of output objects
self._start_time = start_time # default to now, rounded to the nearest hour
self._duration = duration
self._map = map
self.time_step = time_step # this calls rewind() !
self._gnome_id = gnome.GnomeId(id)
def reset(self, **kwargs):
"""
Resets model to defaults -- Caution -- clears all movers, spills, etc.
Takes same keyword arguments as __init__
"""
self.__init__(**kwargs)
def rewind(self):
"""
Rewinds the model to the beginning (start_time)
"""
# # fixme: do the movers need re-setting? -- or wait for prepare_for_model_run?
self.current_time_step = -1 # start at -1
self.model_time = self._start_time
# # note: this may be redundant -- they will get reset in setup_model_run() anyway..
self.spills.rewind()
gnome.utilities.rand.seed(1) # set rand before each call so windages are set correctly
# clear the cache:
self._cache.rewind()
for outputter in self.outputters:
outputter.rewind()
# def write_from_cache(self, filetype='netcdf', time_step='all'):
# """
# write the already-cached data to an output files.
# """
# ## Assorted properties
@property
def uncertain(self):
return self.spills.uncertain
@uncertain.setter
def uncertain(self, uncertain_value):
"""
only if uncertainty switch is toggled, then restart model
"""
if self.spills.uncertain != uncertain_value:
self.spills.uncertain = uncertain_value # update uncertainty
self.rewind()
@property
def cache_enabled(self):
return self._cache.enabled
@cache_enabled.setter
def cache_enabled(self, enabled):
self._cache.enabled = enabled
@property
def id(self):
return self._gnome_id.id
@property
def start_time(self):
return self._start_time
@start_time.setter
def start_time(self, start_time):
self._start_time = start_time
self.rewind()
@property
def time_step(self):
return self._time_step
@time_step.setter
def time_step(self, time_step):
"""
sets the time step, and rewinds the model
:param time_step: the timestep as a timedelta object or integer seconds.
"""
try:
self._time_step = time_step.total_seconds()
except AttributeError:
# not a timedelta object -- assume it's in seconds.
self._time_step = int(time_step)
# there is a zeroth time step
self._num_time_steps = int(self._duration.total_seconds()
// self._time_step) + 1
self.rewind()
@property
def current_time_step(self):
return self._current_time_step
@current_time_step.setter
def current_time_step(self, step):
self.model_time = self._start_time + timedelta(seconds=step
* self.time_step)
self._current_time_step = step
@property
def duration(self):
return self._duration
@duration.setter
def duration(self, duration):
if duration < self._duration: # only need to rewind if shorter than it was...
# # fixme: actually, only need to rewide is current model time is byond new time...
self.rewind()
self._duration = duration
self._num_time_steps = int(self._duration.total_seconds()
// self.time_step) + 1 # there is a zeroth time step
@property
def map(self):
return self._map
@map.setter
def map(self, map_in):
self._map = map_in
self.rewind()
@property
def num_time_steps(self):
return self._num_time_steps
def setup_model_run(self):
"""
Sets up each mover for the model run
"""
self.spills.rewind() # why is rewind for spills here?
for outputter in self.outputters:
outputter.prepare_for_model_run(model_start_time=self.start_time,
cache=self._cache,
uncertain=self.uncertain,
spills=self.spills)
array_types = {}
for mover in self.movers:
mover.prepare_for_model_run()
array_types.update(mover.array_types)
for sc in self.spills.items():
sc.prepare_for_model_run(array_types)
def setup_time_step(self):
"""
sets up everything for the current time_step:
right now only prepares the movers -- maybe more later?.
"""
# initialize movers differently if model uncertainty is on
for mover in self.movers:
for sc in self.spills.items():
mover.prepare_for_model_step(sc, self.time_step,
self.model_time)
for outputter in self.outputters:
outputter.prepare_for_model_step(self.time_step, self.model_time)
def move_elements(self):
"""
Moves elements:
- loops through all the movers. and moves the elements
- sets new_position array for each spill
- calls the beaching code to beach the elements that need beaching.
- sets the new position
"""
# # if there are no spills, there is nothing to do:
if len(self.spills) > 0: # can this check be removed?
for sc in self.spills.items():
if sc.num_released > 0: # can this check be removed?
# possibly refloat elements
self.map.refloat_elements(sc, self.time_step)
# reset next_positions
(sc['next_positions'])[:] = sc['positions']
# loop through the movers
for mover in self.movers:
delta = mover.get_move(sc, self.time_step,
self.model_time)
sc['next_positions'] += delta
self.map.beach_elements(sc)
# the final move to the new positions
(sc['positions'])[:] = sc['next_positions']
def step_is_done(self):
"""
Loop through movers and call model_step_is_done
"""
for mover in self.movers:
for sc in self.spills.items():
mover.model_step_is_done(sc)
for sc in self.spills.items():
sc.model_step_is_done()
for outputter in self.outputters:
outputter.model_step_is_done()
def write_output(self):
output_info = {'step_num': self.current_time_step}
for outputter in self.outputters:
if self.current_time_step == self.num_time_steps - 1:
output = outputter.write_output(self.current_time_step, True)
else:
output = outputter.write_output(self.current_time_step)
if output is not None:
output_info.update(output)
return output_info
def step(self):
"""
Steps the model forward (or backward) in time. Needs testing for
hind casting.
"""
for sc in self.spills.items():
# set the current time stamp only after current_time_step is
# incremented and before the output is written. Set it to None here
# just so we're not carrying around the old time_stamp
sc.current_time_stamp = None
# it gets incremented after this check
if self.current_time_step >= self._num_time_steps - 1:
raise StopIteration
if self.current_time_step == -1:
# that's all we need to do for the zeroth time step
self.setup_model_run()
else:
self.setup_time_step()
self.move_elements()
self.step_is_done()
self.current_time_step += 1
# this is where the new step begins!
# the elements released are during the time period:
# self.model_time + self.time_step
# The else part of the loop computes values for data_arrays that
# correspond with time_stamp:
# self.model_time + self.time_step
# This is the current_time_stamp attribute of the SpillContainer
# [sc.current_time_stamp for sc in self.spills.items()]
for sc in self.spills.items():
sc.current_time_stamp = self.model_time
sc.release_elements(self.time_step, self.model_time)
# cache the results - current_time_step is incremented but the
# current_time_stamp in spill_containers (self.spills) is not updated
# till we go through the prepare_for_model_step
self._cache.save_timestep(self.current_time_step, self.spills)
output_info = self.write_output()
return output_info
def __iter__(self):
"""
for compatibility with Python's iterator protocol
rewinds the model and returns itself so it can be iterated over.
"""
self.rewind()
return self
def next(self):
"""
(This method here to satisfy Python's iterator and generator protocols)
Simply calls model.step()
:return: the step number
"""
return self.step()
def full_run(self, rewind=True, log=False):
"""
Do a full run of the model.
:param rewind=True: whether to rewind the model first -- defaults to True
if set to false, model will be run from the current
step to the end
:returns: list of outputter info dicts
"""
if rewind:
self.rewind()
# run the model
output_data = []
while True:
try:
results = self.step()
if log:
print results
output_data.append(results)
except StopIteration:
print 'Done with the model run'
break
return output_data
def movers_to_dict(self):
"""
call to_dict method of OrderedCollection object
"""
return self.movers.to_dict()
def environment_to_dict(self):
"""
call to_dict method of OrderedCollection object
"""
return self.environment.to_dict()
def spills_to_dict(self):
return self.spills.to_dict()
def outputters_to_dict(self):
"""
call to_dict method of OrderedCollection object
"""
return self.outputters.to_dict()
def map_to_dict(self):
"""
create a tuple that contains: (type, object.id)
"""
# dict_ = {'map': ("{0}.{1}".format(self.map.__module__, self.map.__class__.__name__), self.map.id)}
return ('{0}.{1}'.format(self.map.__module__,
self.map.__class__.__name__), self.map.id)
# if self.output_map is not None:
# dict_.update({'output_map': ("{0}.{1}".format(self.output_map.__module__, self.output_map.__class__.__name__), self.output_map.id)})
def _callback_add_mover(self, obj_added):
""" callback after mover has been added """
if isinstance(obj_added, WindMover):
if obj_added.wind.id not in self.environment:
self.environment += obj_added.wind
if isinstance(obj_added, CatsMover):
if obj_added.tide is not None and obj_added.tide.id \
not in self.environment:
self.environment += obj_added.tide
self.rewind() # rewind model if a new mover is added
def __eq__(self, other):
check = super(Model, self).__eq__(other)
if check:
# also check the data in spill_container object
if type(self.spills) != type(other.spills):
return False
if self.spills != other.spills:
return False
return check
def __ne__(self, other):
"""
Compare inequality (!=) of two objects
"""
if self == other:
return False
else:
return True
def test_init_SpillContainerPair(self):
'All this does is test that it can be initialized'
SpillContainerPair()
SpillContainerPair(True)
assert True
def init_scp(self):
scp = SpillContainerPair(uncertain=True)
for s in self.s0:
scp += s
return scp
class Model(Serializable):
"PyGNOME Model Class"
_update = [
"time_step",
"weathering_substeps",
"start_time",
"duration",
"uncertain",
"movers",
"weatherers",
"environment",
"spills",
"map",
"outputters",
"cache_enabled",
]
_create = []
_create.extend(_update)
_state = copy.deepcopy(Serializable._state)
# no need to copy parent's _state in this case
_state.add(create=_create, update=_update)
@classmethod
def new_from_dict(cls, dict_):
"Restore model from previously persisted _state"
l_env = dict_.pop("environment")
l_out = dict_.pop("outputters")
l_movers = dict_.pop("movers")
l_weatherers = dict_.pop("weatherers")
c_spills = dict_.pop("certain_spills")
if "uncertain_spills" in dict_:
u_spills = dict_.pop("uncertain_spills")
l_spills = zip(c_spills, u_spills)
else:
l_spills = c_spills
model = object.__new__(cls)
model.__restore__(**dict_)
[model.environment.add(obj) for obj in l_env]
[model.outputters.add(obj) for obj in l_out]
[model.spills.add(obj) for obj in l_spills]
[model.movers.add(obj) for obj in l_movers]
[model.weatherers.add(obj) for obj in l_weatherers]
# register callback with OrderedCollection
model.movers.register_callback(model._callback_add_mover, ("add", "replace"))
model.weatherers.register_callback(model._callback_add_weatherer, ("add", "replace"))
return model
def __init__(
self,
time_step=timedelta(minutes=15),
start_time=round_time(datetime.now(), 3600),
duration=timedelta(days=1),
weathering_substeps=1,
map=gnome.map.GnomeMap(),
uncertain=False,
cache_enabled=False,
id=None,
):
"""
Initializes a model. All arguments have a default.
:param time_step=timedelta(minutes=15): model time step in seconds
or as a timedelta object
:param start_time=datetime.now(): start time of model, datetime
object. Rounded to the nearest hour.
:param duration=timedelta(days=1): How long to run the model,
a timedelta object.
:param int weathering_substeps=1: How many weathering substeps to
run inside a single model time step.
:param map=gnome.map.GnomeMap(): The land-water map.
:param uncertain=False: Flag for setting uncertainty.
:param cache_enabled=False: Flag for setting whether the model should
cache results to disk.
:param id: Unique Id identifying the newly created mover (a UUID as a
string). This is used when loading an object from a
persisted model
"""
self.__restore__(time_step, start_time, duration, weathering_substeps, map, uncertain, cache_enabled)
self._gnome_id = gnome.GnomeId(id)
# register callback with OrderedCollection
self.movers.register_callback(self._callback_add_mover, ("add", "replace"))
self.weatherers.register_callback(self._callback_add_weatherer, ("add", "replace"))
def __restore__(self, time_step, start_time, duration, weathering_substeps, map, uncertain, cache_enabled):
"""
Take out initialization that does not register the callback here.
This is because new_from_dict will use this to restore the model _state
when doing a midrun persistence.
"""
# making sure basic stuff is in place before properties are set
self.environment = OrderedCollection(dtype=Environment)
self.movers = OrderedCollection(dtype=Mover)
self.weatherers = OrderedCollection(dtype=Weatherer)
# contains both certain/uncertain spills
self.spills = SpillContainerPair(uncertain)
self._cache = gnome.utilities.cache.ElementCache()
self._cache.enabled = cache_enabled
# list of output objects
self.outputters = OrderedCollection(dtype=Outputter)
# default to now, rounded to the nearest hour
self._start_time = start_time
self._duration = duration
self.weathering_substeps = weathering_substeps
self._map = map
self.time_step = time_step # this calls rewind() !
def reset(self, **kwargs):
"""
Resets model to defaults -- Caution -- clears all movers, spills, etc.
Takes same keyword arguments as __init__
"""
self.__init__(**kwargs)
def rewind(self):
"""
Rewinds the model to the beginning (start_time)
"""
# fixme: do the movers need re-setting? -- or wait for
# prepare_for_model_run?
self.current_time_step = -1
self.model_time = self._start_time
# note: This may be redundant. They will get reset in
# setup_model_run() anyway..
self.spills.rewind()
# set rand before each call so windages are set correctly
gnome.utilities.rand.seed(1)
# clear the cache:
self._cache.rewind()
for outputter in self.outputters:
outputter.rewind()
# def write_from_cache(self, filetype='netcdf', time_step='all'):
# """
# write the already-cached data to an output files.
# """
@property
def uncertain(self):
return self.spills.uncertain
@uncertain.setter
def uncertain(self, uncertain_value):
"""
only if uncertainty switch is toggled, then restart model
"""
if self.spills.uncertain != uncertain_value:
self.spills.uncertain = uncertain_value # update uncertainty
self.rewind()
@property
def cache_enabled(self):
return self._cache.enabled
@cache_enabled.setter
def cache_enabled(self, enabled):
self._cache.enabled = enabled
@property
def id(self):
return self._gnome_id.id
@property
def start_time(self):
return self._start_time
@start_time.setter
def start_time(self, start_time):
self._start_time = start_time
self.rewind()
@property
def time_step(self):
return self._time_step
@time_step.setter
def time_step(self, time_step):
"""
Sets the time step, and rewinds the model
:param time_step: The timestep can be a timedelta object
or integer seconds.
"""
try:
self._time_step = time_step.total_seconds()
except AttributeError:
self._time_step = int(time_step)
# there is a zeroth time step
self._num_time_steps = int(self._duration.total_seconds() // self._time_step) + 1
self.rewind()
@property
def current_time_step(self):
return self._current_time_step
@current_time_step.setter
def current_time_step(self, step):
self.model_time = self._start_time + timedelta(seconds=step * self.time_step)
self._current_time_step = step
@property
def duration(self):
return self._duration
@duration.setter
def duration(self, duration):
if duration < self._duration:
# only need to rewind if shorter than it was...
# fixme: actually, only need to rewind if current model time
# is beyond new time...
self.rewind()
self._duration = duration
# there is a zeroth time step
self._num_time_steps = int(self._duration.total_seconds() // self.time_step) + 1
@property
def map(self):
return self._map
@map.setter
def map(self, map_in):
self._map = map_in
self.rewind()
@property
def num_time_steps(self):
return self._num_time_steps
def setup_model_run(self):
"""
Sets up each mover for the model run
"""
self.spills.rewind() # why is rewind for spills here?
array_types = {}
for mover in self.movers:
mover.prepare_for_model_run()
array_types.update(mover.array_types)
for w in self.weatherers:
w.prepare_for_model_run()
array_types.update(w.array_types)
for sc in self.spills.items():
sc.prepare_for_model_run(array_types)
# outputters need array_types, so this needs to come after those
# have been updated.
for outputter in self.outputters:
outputter.prepare_for_model_run(
model_start_time=self.start_time, cache=self._cache, uncertain=self.uncertain, spills=self.spills
)
def setup_time_step(self):
"""
sets up everything for the current time_step:
"""
# initialize movers differently if model uncertainty is on
for m in self.movers:
for sc in self.spills.items():
m.prepare_for_model_step(sc, self.time_step, self.model_time)
for w in self.weatherers:
for sc in self.spills.items():
# maybe we will setup a super-sampling step here???
w.prepare_for_model_step(sc, self.time_step, self.model_time)
for outputter in self.outputters:
outputter.prepare_for_model_step(self.time_step, self.model_time)
def move_elements(self):
"""
Moves elements:
- loops through all the movers. and moves the elements
- sets new_position array for each spill
- calls the beaching code to beach the elements that need beaching.
- sets the new position
"""
for sc in self.spills.items():
if sc.num_released > 0: # can this check be removed?
# possibly refloat elements
self.map.refloat_elements(sc, self.time_step)
# reset next_positions
(sc["next_positions"])[:] = sc["positions"]
# loop through the movers
for m in self.movers:
delta = m.get_move(sc, self.time_step, self.model_time)
sc["next_positions"] += delta
self.map.beach_elements(sc)
# the final move to the new positions
(sc["positions"])[:] = sc["next_positions"]
def weather_elements(self):
"""
Weathers elements:
- loops through all the weatherers, passing in the spill_container
and the time range
- a weatherer modifies the data arrays in the spill container, so a
particular time range should not be run multiple times. It is
expected that we are processing a sequence of contiguous time ranges.
- Note: If there are multiple sequential weathering processes, some
inaccuracy could occur. A proposed solution is to
'super-sample' the model time step so that it will be replaced
with many smaller time steps. We'll have to see if this pans
out in practice.
"""
for sc in self.spills.items():
for w in self.weatherers:
for model_time, time_step in self._split_into_substeps():
w.weather_elements(sc, time_step, model_time)
def _split_into_substeps(self):
"""
:return: sequence of (datetime, timestep)
(Note: we divide evenly on second boundaries.
Thus, there will likely be a remainder
that needs to be included. We include
this remainder, which results in
1 more sub-step than we requested.)
"""
time_step = int(self._time_step)
sub_step = time_step / self.weathering_substeps
indexes = [idx for idx in range(0, time_step + 1, sub_step)]
res = [(idx, next_idx - idx) for idx, next_idx in zip(indexes, indexes[1:])]
if sum(res[-1]) < time_step:
# collect the remaining slice
res.append((sum(res[-1]), time_step % sub_step))
res = [(self.model_time + timedelta(seconds=idx), delta) for idx, delta in res]
return res
def step_is_done(self):
"""
Loop through movers and call model_step_is_done
"""
for mover in self.movers:
for sc in self.spills.items():
mover.model_step_is_done(sc)
for w in self.weatherers:
w.model_step_is_done()
for sc in self.spills.items():
"removes elements with oil_status.to_be_removed"
sc.model_step_is_done()
# age remaining particles
sc["age"][:] = sc["age"][:] + self.time_step
for outputter in self.outputters:
outputter.model_step_is_done()
def write_output(self):
output_info = {"step_num": self.current_time_step}
for outputter in self.outputters:
if self.current_time_step == self.num_time_steps - 1:
output = outputter.write_output(self.current_time_step, True)
else:
output = outputter.write_output(self.current_time_step)
if output is not None:
output_info.update(output)
return output_info
def step(self):
"""
Steps the model forward (or backward) in time. Needs testing for
hind casting.
"""
for sc in self.spills.items():
# Set the current time stamp only after current_time_step is
# incremented and before the output is written. Set it to None here
# just so we're not carrying around the old time_stamp
sc.current_time_stamp = None
# it gets incremented after this check
if self.current_time_step >= self._num_time_steps - 1:
raise StopIteration
if self.current_time_step == -1:
# that's all we need to do for the zeroth time step
self.setup_model_run()
else:
self.setup_time_step()
self.move_elements()
self.weather_elements()
self.step_is_done()
self.current_time_step += 1
# this is where the new step begins!
# the elements released are during the time period:
# self.model_time + self.time_step
# The else part of the loop computes values for data_arrays that
# correspond with time_stamp:
# self.model_time + self.time_step
# This is the current_time_stamp attribute of the SpillContainer
# [sc.current_time_stamp for sc in self.spills.items()]
for sc in self.spills.items():
sc.current_time_stamp = self.model_time
# release particles for next step - these particles will be aged
# in the next step
sc.release_elements(self.time_step, self.model_time)
# cache the results - current_time_step is incremented but the
# current_time_stamp in spill_containers (self.spills) is not updated
# till we go through the prepare_for_model_step
self._cache.save_timestep(self.current_time_step, self.spills)
output_info = self.write_output()
return output_info
def __iter__(self):
"""
Rewinds the model and returns itself so it can be iterated over.
"""
self.rewind()
return self
def next(self):
"""
(This method satisfies Python's iterator and generator protocols)
:return: the step number
"""
return self.step()
def full_run(self, rewind=True, log=False):
"""
Do a full run of the model.
:param rewind=True: whether to rewind the model first
-- if set to false, model will be run from the
current step to the end
:returns: list of outputter info dicts
"""
if rewind:
self.rewind()
# run the model
output_data = []
while True:
try:
results = self.step()
if log:
print results
output_data.append(results)
except StopIteration:
print "Done with the model run"
break
return output_data
def movers_to_dict(self):
"""
Call to_dict method of OrderedCollection object
"""
return self.movers.to_dict()
def weatherers_to_dict(self):
"""
Call to_dict method of OrderedCollection object
"""
return self.weatherers.to_dict()
def environment_to_dict(self):
"""
Call to_dict method of OrderedCollection object
"""
return self.environment.to_dict()
def spills_to_dict(self):
return self.spills.to_dict()
def outputters_to_dict(self):
"""
Call to_dict method of OrderedCollection object
"""
return self.outputters.to_dict()
def map_to_dict(self):
"""
returns the gnome object type as a string
"""
return "{0}.{1}".format(self.map.__module__, self.map.__class__.__name__)
def _callback_add_mover(self, obj_added):
"Callback after mover has been added"
if isinstance(obj_added, WindMover):
if obj_added.wind.id not in self.environment:
self.environment += obj_added.wind
if isinstance(obj_added, CatsMover):
if obj_added.tide is not None and obj_added.tide.id not in self.environment:
self.environment += obj_added.tide
self.rewind() # rewind model if a new mover is added
def _callback_add_weatherer(self, obj_added):
"Callback after weatherer has been added"
if isinstance(obj_added, Weatherer):
# not sure what kind of dependencies we have just yet.
pass
self.rewind() # rewind model if a new weatherer is added
def __eq__(self, other):
check = super(Model, self).__eq__(other)
if check:
# also check the data in spill_container object
if type(self.spills) != type(other.spills):
return False
if self.spills != other.spills:
return False
return check
def __ne__(self, other):
"Compare inequality (!=) of two objects"
if self == other:
return False
else:
return True
"""
Following methods are for saving a Model instance or creating a new
model instance from a saved location
"""
def save(self, saveloc):
"""
save model in json format to user specified saveloc
:param saveloc: A valid directory. Model files are either persisted
here or a new model is re-created from the files
stored here. The files are clobbered when save() is
called.
:type saveloc: A path as a string or unicode
"""
path_, savedir = os.path.split(saveloc)
if path_ == "":
path_ = "."
if not os.path.exists(path_):
raise ValueError('"{0}" does not exist. \nCannot create "{1}"'.format(path_, savedir))
if not os.path.exists(saveloc):
os.mkdir(saveloc)
self._empty_save_dir(saveloc)
json_ = self.serialize("create")
self._save_json_to_file(saveloc, json_, "{0}.json".format(self.__class__.__name__))
json_ = self.map.serialize("create")
self._save_json_to_file(saveloc, json_, "{0}.json".format(self.map.__class__.__name__))
self._save_collection(saveloc, self.movers)
self._save_collection(saveloc, self.weatherers)
self._save_collection(saveloc, self.environment)
self._save_collection(saveloc, self.outputters)
for sc in self.spills.items():
self._save_collection(saveloc, sc.spills)
# persist model _state since middle of run
if self.current_time_step > -1:
self._save_spill_data(os.path.join(saveloc, "spills_data_arrays.nc"))
def _save_collection(self, saveloc, coll_):
"""
Function loops over an orderedcollection or any other iterable
containing a list of objects. It calls the to_dict method for each
object, then converts it o valid json (dict_to_json),
and finally saves it to file (_save_json_to_file)
:param OrderedCollection coll_: ordered collection to be saved
Note: The movers and weatherer objects reference the environment
collection. If a field is saved as reference (field.save_reference is
True), then this function adds json_[field.name] = index where
index is the index into the environment array for the reference
object. Currently, only objects in the environment collection are
referenced by movers.
"""
for count, obj in enumerate(coll_):
json_ = obj.serialize("create")
for field in obj._state:
if field.save_reference:
"attribute is stored as a reference to environment list"
if getattr(obj, field.name) is not None:
obj_id = getattr(obj, field.name).id
index = self.environment.index(obj_id)
json_[field.name] = index
self._save_json_to_file(saveloc, json_, "{0}_{1}.json".format(obj.__class__.__name__, count))
def _save_json_to_file(self, saveloc, data, name):
"""
write json data to file
:param dict data: JSON data to be saved
:param obj: gnome object corresponding w/ data
"""
fname = os.path.join(saveloc, name)
data = self._move_data_file(saveloc, data) # if there is a
with open(fname, "w") as outfile:
json.dump(data, outfile, indent=True)
def _move_data_file(self, saveloc, json_):
"""
Look at _state attribute of object. Find all fields with 'isdatafile'
attribute as True. If there is a key in to_json corresponding with
'name' of the fields with True 'isdatafile' attribute then move that
datafile and update the key in the to_json to point to new location
todo: maybe this belongs in serializable base class? Revisit this
"""
_state = eval("{0}._state".format(json_["obj_type"]))
fields = _state.get_field_by_attribute("isdatafile")
for field in fields:
if field.name not in json_:
continue
value = json_[field.name]
if os.path.exists(value) and os.path.isfile(value):
shutil.copy(value, saveloc)
json_[field.name] = os.path.split(json_[field.name])[1]
return json_
def _save_spill_data(self, datafile):
""" save the data arrays for current timestep to NetCDF """
nc_out = NetCDFOutput(datafile, which_data="all", cache=self._cache)
nc_out.prepare_for_model_run(model_start_time=self.start_time, uncertain=self.uncertain, spills=self.spills)
nc_out.write_output(self.current_time_step)
def _empty_save_dir(self, saveloc):
"""
Remove all files, directories under saveloc
First clean out directory, then add new save files
This should only be called by self.save()
"""
(dirpath, dirnames, filenames) = os.walk(saveloc).next()
if dirnames:
for dir_ in dirnames:
shutil.rmtree(os.path.join(dirpath, dir_))
if filenames:
for file_ in filenames:
os.remove(os.path.join(dirpath, file_))
def test_eq_spill_container_pair(uncertain):
"""
SpillContainerPair inherits from SpillContainer so it should
compute __eq__ and __ne__ in the same way - test it here
Incomplete - this doesn't currently work!
Test fails if uncertainty is on whether particles are released or not
This is because 'id' of uncertain spills do not match and one should not
expect them to match.
todo: remove 'id' property as a check for equality. This requires changes
in persisting logic. Update persistence then revisit this test
and simplify it
"""
(sp1, sp2) = get_eq_spills()
# windages array will not match after elements are released so lets not
# add any more types to data_arrays for this test. Just look at base
# array_types for SpillContainer's and ensure the data matches for them
#sp1.element_type = ElementType()
#sp2.element_type = ElementType()
scp1 = SpillContainerPair(uncertain) # uncertainty is on
scp1.add(sp1)
scp2 = SpillContainerPair(uncertain)
if uncertain:
u_sp1 = [
scp1.items()[1].spills[spill.id]
for spill in scp1.items()[1].spills
][0]
u_sp2 = copy.deepcopy(u_sp1)
# deepcopy does not match ids!
# for test, we need these to match so force them to be equal here
u_sp2._id = u_sp1.id
scp2.add((sp2, u_sp2))
else:
scp2.add(sp2)
for sc in zip(scp1.items(), scp2.items()):
sc[0].prepare_for_model_run()
sc[0].release_elements(360, sp1.release.release_time)
sc[1].prepare_for_model_run()
sc[1].release_elements(360, sp2.release.release_time)
assert scp1 == scp2
assert scp2 == scp1
assert not (scp1 != scp2)
assert not (scp2 != scp1)
def test_eq_spill_container_pair(uncertain):
"""
SpillContainerPair inherits from SpillContainer so it should
compute __eq__ and __ne__ in the same way - test it here
Incomplete - this doesn't currently work!
Test fails if uncertainty is on whether particles are released or not
This is because 'id' of uncertain spills do not match and one should not
expect them to match.
todo: remove 'id' property as a check for equality. This requires changes
in persisting logic. Update persistence then revisit this test
and simplify it
"""
(sp1, sp2) = get_eq_spills()
# windages array will not match after elements are released so lets not
# add any more types to data_arrays for this test. Just look at base
# array_types for SpillContainer's and ensure the data matches for them
#sp1.element_type = ElementType()
#sp2.element_type = ElementType()
scp1 = SpillContainerPair(uncertain) # uncertainty is on
scp1.add(sp1)
scp2 = SpillContainerPair(uncertain)
if uncertain:
u_sp1 = [scp1.items()[1].spills[spill.id] for spill in
scp1.items()[1].spills][0]
u_sp2 = copy.deepcopy(u_sp1)
# deepcopy does not match ids!
# for test, we need these to match so force them to be equal here
u_sp2._id = u_sp1.id
scp2.add((sp2, u_sp2))
else:
scp2.add(sp2)
for sc in zip(scp1.items(), scp2.items()):
sc[0].prepare_for_model_run()
sc[0].release_elements(360, sp1.release.release_time)
sc[1].prepare_for_model_run()
sc[1].release_elements(360, sp2.release.release_time)
assert scp1 == scp2
assert scp2 == scp1
assert not (scp1 != scp2)
assert not (scp2 != scp1)
class Model(Serializable):
'''
PyGnome Model Class
'''
_update = ['time_step',
'weathering_substeps',
'start_time',
'duration',
'time_step',
'uncertain',
'cache_enabled',
'weathering_substeps',
'map',
'movers',
'weatherers',
'environment',
'outputters'
]
_create = []
_create.extend(_update)
_state = copy.deepcopy(Serializable._state)
_schema = ModelSchema
# no need to copy parent's _state in this case
_state.add(save=_create, update=_update)
# override __eq__ since 'spills' and 'uncertain_spills' need to be checked
# They both have _to_dict() methods to return underlying ordered
# collections and that would not be the correct way to check equality
_state += [Field('spills', save=True, update=True, test_for_eq=False),
Field('uncertain_spills', save=True, test_for_eq=False)]
# list of OrderedCollections
_oc_list = ['movers', 'weatherers', 'environment', 'outputters']
@classmethod
def new_from_dict(cls, dict_):
'Restore model from previously persisted _state'
json_ = dict_.pop('json_')
l_env = dict_.pop('environment', [])
l_out = dict_.pop('outputters', [])
l_movers = dict_.pop('movers', [])
l_weatherers = dict_.pop('weatherers', [])
c_spills = dict_.pop('spills', [])
if 'uncertain_spills' in dict_:
u_spills = dict_.pop('uncertain_spills')
l_spills = zip(c_spills, u_spills)
else:
l_spills = c_spills
# define defaults for properties that a location file may not contain
kwargs = inspect.getargspec(cls.__init__)
default_restore = dict(zip(kwargs[0][1:], kwargs[3]))
if json_ == 'webapi':
# default is to enable cache
default_restore['cache_enabled'] = True
for key in default_restore:
default_restore[key] = dict_.pop(key, default_restore[key])
model = object.__new__(cls)
model.__restore__(**default_restore)
# if there are other values in dict_, setattr
if json_ == 'webapi':
model.update_from_dict(dict_)
else:
cls._restore_attr_from_save(model, dict_)
[model.environment.add(obj) for obj in l_env]
[model.outputters.add(obj) for obj in l_out]
[model.spills.add(obj) for obj in l_spills]
[model.movers.add(obj) for obj in l_movers]
[model.weatherers.add(obj) for obj in l_weatherers]
# register callback with OrderedCollection
model.movers.register_callback(model._callback_add_mover,
('add', 'replace'))
model.weatherers.register_callback(model._callback_add_weatherer,
('add', 'replace'))
# restore the spill data outside this method - let's not try to find
# the saveloc here
return model
def __init__(self,
time_step=timedelta(minutes=15),
start_time=round_time(datetime.now(), 3600),
duration=timedelta(days=1),
weathering_substeps=1,
map=None,
uncertain=False,
cache_enabled=False,
name=None):
'''
Initializes a model.
All arguments have a default.
:param time_step=timedelta(minutes=15): model time step in seconds
or as a timedelta object
:param start_time=datetime.now(): start time of model, datetime
object. Rounded to the nearest hour.
:param duration=timedelta(days=1): How long to run the model,
a timedelta object.
:param int weathering_substeps=1: How many weathering substeps to
run inside a single model time step.
:param map=gnome.map.GnomeMap(): The land-water map.
:param uncertain=False: Flag for setting uncertainty.
:param cache_enabled=False: Flag for setting whether the model should
cache results to disk.
'''
self.__restore__(time_step, start_time, duration,
weathering_substeps,
uncertain, cache_enabled, map, name)
# register callback with OrderedCollection
self.movers.register_callback(self._callback_add_mover,
('add', 'replace'))
self.weatherers.register_callback(self._callback_add_weatherer,
('add', 'replace'))
def __restore__(self, time_step, start_time, duration,
weathering_substeps, uncertain, cache_enabled, map, name):
'''
Take out initialization that does not register the callback here.
This is because new_from_dict will use this to restore the model _state
when doing a midrun persistence.
'''
# making sure basic stuff is in place before properties are set
self.environment = OrderedCollection(dtype=Environment)
self.movers = OrderedCollection(dtype=Mover)
self.weatherers = OrderedCollection(dtype=Weatherer)
# contains both certain/uncertain spills
self.spills = SpillContainerPair(uncertain)
self._cache = gnome.utilities.cache.ElementCache()
self._cache.enabled = cache_enabled
# list of output objects
self.outputters = OrderedCollection(dtype=Outputter)
# default to now, rounded to the nearest hour
self._start_time = start_time
self._duration = duration
self.weathering_substeps = weathering_substeps
if not map:
map = gnome.map.GnomeMap()
if name:
self.name = name
self._map = map
self.time_step = time_step # this calls rewind() !
def reset(self, **kwargs):
'''
Resets model to defaults -- Caution -- clears all movers, spills, etc.
Takes same keyword arguments as :meth:`__init__()`
'''
self.__init__(**kwargs)
def rewind(self):
'''
Rewinds the model to the beginning (start_time)
'''
# fixme: do the movers need re-setting? -- or wait for
# prepare_for_model_run?
self.current_time_step = -1
self.model_time = self._start_time
# note: This may be redundant. They will get reset in
# setup_model_run() anyway..
self.spills.rewind()
# set rand before each call so windages are set correctly
gnome.utilities.rand.seed(1)
# clear the cache:
self._cache.rewind()
for outputter in self.outputters:
outputter.rewind()
# def write_from_cache(self, filetype='netcdf', time_step='all'):
# """
# write the already-cached data to an output files.
# """
@property
def uncertain(self):
'''
Uncertainty attribute of the model. If flag is toggled, rewind model
'''
return self.spills.uncertain
@uncertain.setter
def uncertain(self, uncertain_value):
'''
Uncertainty attribute of the model
'''
if self.spills.uncertain != uncertain_value:
self.spills.uncertain = uncertain_value # update uncertainty
self.rewind()
@property
def cache_enabled(self):
'''
If True, then generated data is cached
'''
return self._cache.enabled
@cache_enabled.setter
def cache_enabled(self, enabled):
self._cache.enabled = enabled
@property
def start_time(self):
'''
Start time of the simulation
'''
return self._start_time
@start_time.setter
def start_time(self, start_time):
self._start_time = start_time
self.rewind()
@property
def time_step(self):
'''
time step over which the dynamics is computed
'''
return self._time_step
@time_step.setter
def time_step(self, time_step):
'''
Sets the time step, and rewinds the model
:param time_step: The timestep can be a timedelta object
or integer seconds.
'''
try:
self._time_step = time_step.total_seconds()
except AttributeError:
self._time_step = int(time_step)
# there is a zeroth time step
self._num_time_steps = int(self._duration.total_seconds()
// self._time_step) + 1
self.rewind()
@property
def current_time_step(self):
'''
Current timestep of the simulation
'''
return self._current_time_step
@current_time_step.setter
def current_time_step(self, step):
self.model_time = self._start_time + timedelta(seconds=step *
self.time_step)
self._current_time_step = step
@property
def duration(self):
'''
total duration of the model run
'''
return self._duration
@duration.setter
def duration(self, duration):
if duration < self._duration:
# only need to rewind if shorter than it was...
# fixme: actually, only need to rewind if current model time
# is beyond new time...
self.rewind()
self._duration = duration
# there is a zeroth time step
self._num_time_steps = int(self._duration.total_seconds()
// self.time_step) + 1
@property
def map(self):
'''
land water map used for simulation
'''
return self._map
@map.setter
def map(self, map_in):
self._map = map_in
self.rewind()
@property
def num_time_steps(self):
'''
Read only attribute
computed number of timesteps based on py:attribute:`duration` and
py:attribute:`time_step`
'''
return self._num_time_steps
def setup_model_run(self):
'''
Sets up each mover for the model run
'''
self.spills.rewind() # why is rewind for spills here?
# remake orderedcollections defined by model
for oc in [self.movers, self.weatherers,
self.outputters, self.environment]:
oc.remake()
array_types = {}
for mover in self.movers:
mover.prepare_for_model_run()
array_types.update(mover.array_types)
for w in self.weatherers:
w.prepare_for_model_run()
array_types.update(w.array_types)
for sc in self.spills.items():
sc.prepare_for_model_run(array_types)
# outputters need array_types, so this needs to come after those
# have been updated.
for outputter in self.outputters:
outputter.prepare_for_model_run(model_start_time=self.start_time,
cache=self._cache,
uncertain=self.uncertain,
spills=self.spills)
def setup_time_step(self):
'''
sets up everything for the current time_step:
'''
# initialize movers differently if model uncertainty is on
for m in self.movers:
for sc in self.spills.items():
m.prepare_for_model_step(sc, self.time_step, self.model_time)
for w in self.weatherers:
for sc in self.spills.items():
# maybe we will setup a super-sampling step here???
w.prepare_for_model_step(sc, self.time_step, self.model_time)
for outputter in self.outputters:
outputter.prepare_for_model_step(self.time_step, self.model_time)
def move_elements(self):
'''
Moves elements:
- loops through all the movers. and moves the elements
- sets new_position array for each spill
- calls the beaching code to beach the elements that need beaching.
- sets the new position
'''
for sc in self.spills.items():
if sc.num_released > 0: # can this check be removed?
# possibly refloat elements
self.map.refloat_elements(sc, self.time_step)
# reset next_positions
(sc['next_positions'])[:] = sc['positions']
# loop through the movers
for m in self.movers:
delta = m.get_move(sc, self.time_step, self.model_time)
sc['next_positions'] += delta
self.map.beach_elements(sc)
# the final move to the new positions
(sc['positions'])[:] = sc['next_positions']
def weather_elements(self):
'''
Weathers elements:
- loops through all the weatherers, passing in the spill_container
and the time range
- a weatherer modifies the data arrays in the spill container, so a
particular time range should not be run multiple times. It is
expected that we are processing a sequence of contiguous time ranges.
- Note: If there are multiple sequential weathering processes, some
inaccuracy could occur. A proposed solution is to
'super-sample' the model time step so that it will be replaced
with many smaller time steps. We'll have to see if this pans
out in practice.
'''
for sc in self.spills.items():
for w in self.weatherers:
for model_time, time_step in self._split_into_substeps():
w.weather_elements(sc, time_step, model_time)
def _split_into_substeps(self):
'''
:return: sequence of (datetime, timestep)
(Note: we divide evenly on second boundaries.
Thus, there will likely be a remainder
that needs to be included. We include
this remainder, which results in
1 more sub-step than we requested.)
'''
time_step = int(self._time_step)
sub_step = time_step / self.weathering_substeps
indexes = [idx for idx in range(0, time_step + 1, sub_step)]
res = [(idx, next_idx - idx)
for idx, next_idx in zip(indexes, indexes[1:])]
if sum(res[-1]) < time_step:
# collect the remaining slice
res.append((sum(res[-1]), time_step % sub_step))
res = [(self.model_time + timedelta(seconds=idx), delta)
for idx, delta in res]
return res
def step_is_done(self):
'''
Loop through movers and call model_step_is_done
'''
for mover in self.movers:
for sc in self.spills.items():
mover.model_step_is_done(sc)
for w in self.weatherers:
w.model_step_is_done()
for sc in self.spills.items():
'removes elements with oil_status.to_be_removed'
sc.model_step_is_done()
# age remaining particles
sc['age'][:] = sc['age'][:] + self.time_step
for outputter in self.outputters:
outputter.model_step_is_done()
def write_output(self):
output_info = {}
for outputter in self.outputters:
if self.current_time_step == self.num_time_steps - 1:
output = outputter.write_output(self.current_time_step, True)
else:
output = outputter.write_output(self.current_time_step)
if output is not None:
output_info.update(output)
if not output_info:
return {'step_num': self.current_time_step}
return output_info
def step(self):
'''
Steps the model forward (or backward) in time. Needs testing for
hind casting.
'''
for sc in self.spills.items():
# Set the current time stamp only after current_time_step is
# incremented and before the output is written. Set it to None here
# just so we're not carrying around the old time_stamp
sc.current_time_stamp = None
# it gets incremented after this check
if self.current_time_step >= self._num_time_steps - 1:
raise StopIteration
if self.current_time_step == -1:
# that's all we need to do for the zeroth time step
self.setup_model_run()
else:
self.setup_time_step()
self.move_elements()
self.weather_elements()
self.step_is_done()
self.current_time_step += 1
# this is where the new step begins!
# the elements released are during the time period:
# self.model_time + self.time_step
# The else part of the loop computes values for data_arrays that
# correspond with time_stamp:
# self.model_time + self.time_step
# This is the current_time_stamp attribute of the SpillContainer
# [sc.current_time_stamp for sc in self.spills.items()]
for sc in self.spills.items():
sc.current_time_stamp = self.model_time
# release particles for next step - these particles will be aged
# in the next step
sc.release_elements(self.time_step, self.model_time)
# cache the results - current_time_step is incremented but the
# current_time_stamp in spill_containers (self.spills) is not updated
# till we go through the prepare_for_model_step
self._cache.save_timestep(self.current_time_step, self.spills)
output_info = self.write_output()
return output_info
def __iter__(self):
'''
Rewinds the model and returns itself so it can be iterated over.
'''
self.rewind()
return self
def next(self):
'''
(This method satisfies Python's iterator and generator protocols)
:return: the step number
'''
return self.step()
def full_run(self, rewind=True, log=False):
'''
Do a full run of the model.
:param rewind=True: whether to rewind the model first -- if set to
false, model will be run from the current step to the end
:returns: list of outputter info dicts
'''
if rewind:
self.rewind()
# run the model
output_data = []
while True:
try:
results = self.step()
if log:
print results
output_data.append(results)
except StopIteration:
print 'Done with the model run'
break
return output_data
def _callback_add_mover(self, obj_added):
'Callback after mover has been added'
if hasattr(obj_added, 'wind'):
if obj_added.wind.id not in self.environment:
self.environment += obj_added.wind
if hasattr(obj_added, 'tide') and obj_added.tide is not None:
if obj_added.tide.id not in self.environment:
self.environment += obj_added.tide
self.rewind() # rewind model if a new mover is added
def _callback_add_weatherer(self, obj_added):
'Callback after weatherer has been added'
if isinstance(obj_added, Weatherer):
# not sure what kind of dependencies we have just yet.
pass
self.rewind() # rewind model if a new weatherer is added
def __eq__(self, other):
check = super(Model, self).__eq__(other)
print 'Model.__eq__(): super check =', check
if check:
# also check the data in ordered collections
if type(self.spills) != type(other.spills):
print 'Model.__eq__(): spill types:', (type(self.spills),
type(other.spills))
return False
if self.spills != other.spills:
print 'Model.__eq__(): spills:'
pp.pprint((self.spills, other.spills))
return False
return check
def __ne__(self, other):
return not self == other
'''
Following methods are for saving a Model instance or creating a new
model instance from a saved location
'''
def spills_to_dict(self):
'''
return the spills ordered collection for serialization
'''
return self.spills.to_dict()['spills']
def uncertain_spills_to_dict(self):
'''
return the uncertain_spills ordered collection for serialization/save
files
'''
if self.uncertain:
dict_ = self.spills.to_dict()
return dict_['uncertain_spills']
return None
def save(self, saveloc, references=None, name=None):
# Note: Defining references=References() in the function definition
# keeps the references object in memory between tests - it changes the
# scope of Referneces() to be outside the Model() instance. We don't
# want this
references = (references, References())[references is None]
self._make_saveloc(saveloc)
self._empty_save_dir(saveloc)
json_ = self.serialize('save')
# map is the only nested structure - let's manually call
# _move_data_file on it
self.map._move_data_file(saveloc, json_['map'])
for oc in self._oc_list:
coll_ = getattr(self, oc)
self._save_collection(saveloc, coll_, references, json_[oc])
for sc in self.spills.items():
if sc.uncertain:
key = 'uncertain_spills'
else:
key = 'spills'
self._save_collection(saveloc, sc.spills, references, json_[key])
if self.current_time_step > -1:
'''
hard code the filename - can make this an attribute if user wants
to change it - but not sure if that will ever be needed?
'''
self._save_spill_data(os.path.join(saveloc,
'spills_data_arrays.nc'))
# there should be no more references
self._json_to_saveloc(json_, saveloc, references, name)
if name and references.reference(self) != name:
# todo: want a warning here instead of an exception
raise Exception("{0} already exists, cannot name "
"the model's json file: {0}".format(name))
pass
return references
def _save_collection(self, saveloc, coll_, refs, coll_json):
"""
Reference objects inside OrderedCollections. Since the OC itself
isn't a reference but the objects in the list are a reference, do
something a little differently here
:param OrderedCollection coll_: ordered collection to be saved
"""
for count, obj in enumerate(coll_):
json_ = obj.serialize('save')
for field in obj._state:
if field.save_reference:
'attribute is stored as a reference to environment list'
if getattr(obj, field.name) is not None:
ref_obj = getattr(obj, field.name)
index = self.environment.index(ref_obj)
json_[field.name] = index
obj_ref = refs.get_reference(obj)
if obj_ref is None:
# try following name - if 'fname' already exists in references,
# then obj.save() assigns a different name to file
fname = '{0.__class__.__name__}_{1}.json'.format(obj, count)
obj.save(saveloc, refs, fname)
coll_json[count]['id'] = refs.reference(obj)
else:
coll_json[count]['id'] = obj_ref
def _save_spill_data(self, datafile):
""" save the data arrays for current timestep to NetCDF """
nc_out = NetCDFOutput(datafile, which_data='all', cache=self._cache)
nc_out.prepare_for_model_run(model_start_time=self.start_time,
uncertain=self.uncertain,
spills=self.spills)
nc_out.write_output(self.current_time_step)
def _load_spill_data(self, spill_data):
"""
load NetCDF file and add spill data back in - designed for savefiles
"""
if not os.path.exists(spill_data):
return
if self.uncertain:
saveloc, spill_data_fname = os.path.split(spill_data)
spill_data_fname, ext = os.path.splitext(spill_data_fname)
u_spill_data = os.path.join(saveloc,
'{0}_uncertain{1}'.format(spill_data_fname, ext))
array_types = {}
for m in self.movers:
array_types.update(m.array_types)
for w in self.weatherers:
array_types.update(w.array_types)
for sc in self.spills.items():
if sc.uncertain:
data = NetCDFOutput.read_data(u_spill_data, time=None,
which_data='all')
else:
data = NetCDFOutput.read_data(spill_data, time=None,
which_data='all')
sc.current_time_stamp = data.pop('current_time_stamp').item()
sc._data_arrays = data
sc._array_types.update(array_types)
def _empty_save_dir(self, saveloc):
'''
Remove all files, directories under saveloc
First clean out directory, then add new save files
This should only be called by self.save()
'''
(dirpath, dirnames, filenames) = os.walk(saveloc).next()
if dirnames:
for dir_ in dirnames:
shutil.rmtree(os.path.join(dirpath, dir_))
if filenames:
for file_ in filenames:
os.remove(os.path.join(dirpath, file_))
def serialize(self, json_='webapi'):
'''
Serialize Model object
treat special-case attributes of Model.
'''
toserial = self.to_serialize(json_)
schema = self.__class__._schema()
o_json_ = schema.serialize(toserial)
o_json_['map'] = self.map.serialize(json_)
if json_ == 'webapi':
for attr in ('environment', 'outputters', 'weatherers', 'movers',
'spills'):
o_json_[attr] = self.serialize_oc(attr, json_)
return o_json_
def serialize_oc(self, attr, json_='webapi'):
'''
Serialize Model attributes of type ordered collection
'''
json_out = []
attr = getattr(self, attr)
if isinstance(attr, (OrderedCollection, SpillContainerPair)):
for item in attr:
json_out.append(item.serialize(json_))
return json_out
@classmethod
def deserialize(cls, json_):
'''
treat special-case attributes of Model.
'''
deserial = cls._schema().deserialize(json_)
if 'map' in json_ and json_['map']:
deserial['map'] = json_['map']
if json_['json_'] == 'webapi':
for attr in ('environment', 'outputters', 'weatherers', 'movers',
'spills'):
if attr in json_ and json_[attr]:
deserial[attr] = cls.deserialize_oc(json_[attr])
return deserial
@classmethod
def deserialize_oc(cls, json_):
'''
check contents of orderered collections to figure out what schema to
use.
Basically, the json serialized ordered collection looks like a regular
list.
'''
deserial = []
for item in json_:
fqn = item['obj_type']
name, scope = (list(reversed(fqn.rsplit('.', 1)))
if fqn.find('.') >= 0
else [fqn, ''])
my_module = __import__(scope, globals(), locals(), [str(name)], -1)
py_class = getattr(my_module, name)
deserial.append(py_class.deserialize(item))
return deserial
@classmethod
def load(cls, saveloc, json_data, references=None):
'''
'''
references = (references, References())[references is None]
# model has no datafiles or 'save_reference' attributes so no need to
# do anything special for it. But let's add this as a check anyway
datafiles = cls._state.get_field_by_attribute('isdatafile')
ref_fields = cls._state.get_field_by_attribute('save_reference')
if (datafiles or ref_fields):
raise Exception("Model.load() assumes none of the attributes "
"defining the state 'isdatafile' or is 'save_reference'. "
"If this changes, then we need to make this more robust.")
# deserialize after removing references
_to_dict = cls.deserialize(json_data)
# load nested map object and add it - currently, 'load' is only used
# for laoding save files/location files, so it assumes:
# json_data['json_'] == 'save'
if ('map' in json_data):
map_obj = eval(json_data['map']['obj_type']).load(saveloc,
json_data['map'], references)
_to_dict['map'] = map_obj
# load collections
for oc in cls._oc_list:
if oc in _to_dict:
_to_dict[oc] = cls._load_collection(saveloc, _to_dict[oc],
references)
for spill in ['spills', 'uncertain_spills']:
if spill in _to_dict:
_to_dict[spill] = cls._load_collection(saveloc,
_to_dict[spill],
references)
# also need to load spill data for mid-run save!
model = cls.new_from_dict(_to_dict)
model._load_spill_data(os.path.join(saveloc, 'spills_data_arrays.nc'))
return model
@classmethod
def _load_collection(cls, saveloc, l_coll_dict, refs):
'''
doesn't need to be classmethod of the Model, but its only used by
Model at present
'''
l_coll = []
for item in l_coll_dict:
i_ref = item['id']
if refs.retrieve(i_ref):
l_coll.append(refs.retrieve(i_ref))
else:
f_name = os.path.join(saveloc, item['id'])
obj = load(f_name, refs) # will add obj to refs
l_coll.append(obj)
return (l_coll)