def start_tasks(self, **kwargs):
try:
logger.info('Adding %i particles as tasks' % self.total_particle_count())
tasks = []
for part in self.particles:
forcer = BaseForcer(self.hydrodataset,
particle=part,
common_variables=self.common_variables,
timevar=self.timevar,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
shoreline_index_buffer=self.shoreline_index_buffer)
tasks.append(forcer)
logger.progress((5, 'Running model'))
return self.pool.map_async(Runner(), tasks)
except Exception:
logger.exception("Something didn't start correctly!")
raise
def run(self):
while True:
try:
next_task = self.task_queue.get(True, 10)
except queue.Empty:
logger.info("No tasks left to complete, closing %s" % self.name)
break
else:
answer = (None, None)
try:
answer = (1, next_task(self.active))
except Exception:
logger.exception("Disabling Error")
if isinstance(next_task, CachingDataController):
answer = (-2, "CachingDataController")
# Tell the particles that the CachingDataController is releasing file
self.get_data.value = False
# The data controller has died, so don't process any more tasks
self.active.value = False
elif isinstance(next_task, BaseForcer):
answer = (-1, next_task.particle)
else:
logger.warn("Strange task raised an exception: %s" % str(next_task.__class__))
answer = (None, None)
finally:
self.result_queue.put(answer)
self.nproc_lock.acquire()
self.n_run.value = self.n_run.value - 1
self.nproc_lock.release()
self.task_queue.task_done()
def listen_for_results(self, output_h5_file, total_particles):
logger.info("Waiting for %i particle results" % total_particles)
particles = []
retrieved = 0
timeout = 200
while retrieved < total_particles:
try:
# self.result is an iterator that can timeout on next()
particle = self.result.next(timeout)
retrieved += 1
particles.append(particle)
except StopIteration:
assert retrieved >= total_particles
break
except:
logger.exception("Particle has FAILED!!")
continue
# We multiply by 90 here to save 10% for the exporting
logger.progress((round((float(retrieved) / total_particles) * 90.,
1), "%s Particle(s) complete" % retrieved))
logger.info(particles)
results = ex.ResultsPyTable(output_h5_file)
for p in particles:
for x in range(len(p.locations)):
results.write(p.timestep_index_dump(x))
results.compute()
results.close()
return
def run(self, **kwargs):
logger.progress((4, "Starting tasks"))
self.result = self.start_tasks(**kwargs)
if self.result is None:
raise BaseDataControllerError("Not all tasks started! Exiting.")
# Store results in hdf5 file for processing later
output_h5_file = None
if kwargs.get('output_path') is not None:
output_h5_file = os.path.join(kwargs.get('output_path'),
'results.h5')
if self.thread_result_listener is True:
rl = threading.Thread(name="ResultListener",
target=self.listen_for_results,
args=(output_h5_file,
self.total_particle_count()))
rl.daemon = True
rl.start()
rl.join() # This blocks until the tasks are all done.
else:
self.listen_for_results(output_h5_file, self.total_particle_count(
)) # This blocks until the tasks are all done.
logger.info('Tasks are all finished... Cleaning up!!')
self.cleanup()
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
logger.progress((96, "Exporting results"))
# Make sure output_path is also included
if kwargs.get("output_path", None) is not None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for fmt in formats:
logger.info("Exporting to: %s" % fmt)
try:
# Calls the export function
fmt.export(output_path, output_h5_file)
except:
logger.exception("Failed to export to: %s" % fmt)
else:
logger.warn(
'The output_formats parameter should be a list, not saving any output!'
)
else:
logger.warn('No output path defined, not saving any output!')
else:
logger.warn(
'No output_formats parameter was defined, not saving any output!'
)
logger.progress((97, "Model Run Complete"))
return
def listen_for_results(self, output_h5_file, total_particles):
logger.info("Waiting for %i particle results" % total_particles)
particles = []
retrieved = 0
timeout = 200
while retrieved < total_particles:
try:
# self.result is an iterator that can timeout on next()
particle = self.result.next(timeout)
retrieved += 1
particles.append(particle)
except StopIteration:
assert retrieved >= total_particles
break
except:
logger.exception("Particle has FAILED!!")
continue
# We multiply by 90 here to save 10% for the exporting
logger.progress((round((float(retrieved) / total_particles) * 90., 1), "%s Particle(s) complete" % retrieved))
logger.info(particles)
results = ex.ResultsPyTable(output_h5_file)
for p in particles:
for x in range(len(p.locations)):
results.write(p.timestep_index_dump(x))
results.compute()
results.close()
return
def start_tasks(self):
try:
logger.info('Starting CachingDataController')
# Add data controller to the queue first so that it
# can get the initial data and is not blocked
data_controller = CachingDataController(self.hydrodataset, self.common_variables, self.n_run, self.get_data, self.write_lock, self.has_write_lock, self.read_lock, self.read_count,
self.time_chunk, self.horiz_chunk, self.times, self.start, self.point_get, self.reference_location, cache_path=self.cache_path)
self.tasks.put(data_controller)
# Create CachingDataController worker
self.data_controller_process = Consumer(self.tasks, self.results, self.n_run, self.nproc_lock, self.active, self.get_data, name="CachingDataController")
self.data_controller_process.start()
logger.info('Adding %i particles as tasks' % len(self.particles))
for part in self.particles:
forcer = CachingForcer(self.cache_path,
particle=part,
common_variables=self.common_variables,
timevar=self.timevar,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
redis_url=self.redis_url,
redis_results_channel=self.redis_results_channel,
shoreline_index_buffer=self.shoreline_index_buffer,
get_data=self.get_data,
read_lock=self.read_lock,
has_read_lock=self.has_read_lock,
read_count=self.read_count,
point_get=self.point_get,
data_request_lock=self.data_request_lock,
has_data_request_lock=self.has_data_request_lock
)
self.tasks.put(forcer)
# Create workers for the particles.
self.procs = [Consumer(self.tasks, self.results, self.n_run, self.nproc_lock, self.active, self.get_data, name="CachingForcer-%d" % i)
for i in xrange(self.nproc - 1) ]
for w in self.procs:
w.start()
logger.info('Started %s' % w.name)
return True
except Exception:
logger.exception("Something didn't start correctly!")
return False
def run(self, hydrodataset, **kwargs):
self.hydrodataset = hydrodataset
self.setup_run(**kwargs)
logger.progress((4, "Starting tasks"))
self.result = self.start_tasks()
if self.result is None:
raise BaseDataControllerError("Not all tasks started! Exiting.")
# This blocks until the tasks are all done.
self.particles = self.listen_for_results()
logger.info('Consumers are all finished!')
logger.info('Cleaning up')
self.cleanup()
if len(self.particles) > 0:
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
logger.progress((96, "Exporting results"))
# Make sure output_path is also included
if kwargs.get("output_path", None) is not None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for format in formats:
logger.info("Exporting to: %s" % format)
try:
self.export(output_path, format=format)
except:
logger.exception("Failed to export to: %s" % format)
else:
logger.warn('The output_formats parameter should be a list, not saving any output!')
else:
logger.warn('No output path defined, not saving any output!')
else:
logger.warn('No output format defined, not saving any output!')
else:
logger.warn("Model didn't actually do anything, check the log.")
if self.error_code == -2:
raise BaseDataControllerError("Error in the BaseDataController")
else:
raise ModelError("Error in the model")
logger.progress((97, "Model Run Complete"))
return self.particles
def run(self, **kwargs):
logger.progress((4, "Starting tasks"))
self.result = self.start_tasks(**kwargs)
if self.result is None:
raise BaseDataControllerError("Not all tasks started! Exiting.")
# Store results in hdf5 file for processing later
output_h5_file = None
if kwargs.get('output_path') is not None:
output_h5_file = os.path.join(kwargs.get('output_path'), 'results.h5')
if self.thread_result_listener is True:
rl = threading.Thread(name="ResultListener", target=self.listen_for_results, args=(output_h5_file, self.total_particle_count()))
rl.daemon = True
rl.start()
rl.join() # This blocks until the tasks are all done.
else:
self.listen_for_results(output_h5_file, self.total_particle_count()) # This blocks until the tasks are all done.
logger.info('Tasks are all finished... Cleaning up!!')
self.cleanup()
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
logger.progress((96, "Exporting results"))
# Make sure output_path is also included
if kwargs.get("output_path", None) is not None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for fmt in formats:
logger.info("Exporting to: %s" % fmt)
try:
# Calls the export function
fmt.export(output_path, output_h5_file)
except:
logger.exception("Failed to export to: %s" % fmt)
else:
logger.warn('The output_formats parameter should be a list, not saving any output!')
else:
logger.warn('No output path defined, not saving any output!')
else:
logger.warn('No output_formats parameter was defined, not saving any output!')
logger.progress((97, "Model Run Complete"))
return
def get_nearest_data(self, i):
""" Note: self.dataset.opennc() must be called before calling this function.
This is because the caching forcer must close it everytime, while a non caching
forcer can leave the dataset open.
"""
try:
# Grab data at time index closest to particle location
u = np.mean(np.mean(self.dataset.get_values('u', timeinds=[np.asarray([i])], point=self.particle.location )))
v = np.mean(np.mean(self.dataset.get_values('v', timeinds=[np.asarray([i])], point=self.particle.location )))
# if there is vertical velocity inthe dataset, get it
if 'w' in self.dataset.nc.variables:
w = np.mean(np.mean(self.dataset.get_values('w', timeindsf=[np.asarray([i])], point=self.particle.location )))
else:
w = 0.0
# If there is salt and temp in the dataset, get it
if self.temp_name is not None and self.salt_name is not None:
temp = np.mean(np.mean(self.dataset.get_values('temp', timeinds=[np.asarray([i])], point=self.particle.location )))
salt = np.mean(np.mean(self.dataset.get_values('salt', timeinds=[np.asarray([i])], point=self.particle.location )))
# Check for nans that occur in the ocean (happens because
# of model and coastline resolution mismatches)
if np.isnan(u).any() or np.isnan(v).any() or np.isnan(w).any():
# Take the mean of the closest 4 points
# If this includes nan which it will, result is nan
uarray1 = self.dataset.get_values('u', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
varray1 = self.dataset.get_values('v', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
if 'w' in self.dataset.nc.variables:
warray1 = self.dataset.get_values('w', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
w = warray1.mean()
else:
w = 0.0
if self.temp_name is not None and self.salt_name is not None:
temparray1 = self.dataset.get_values('temp', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
saltarray1 = self.dataset.get_values('salt', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
temp = temparray1.mean()
salt = saltarray1.mean()
u = uarray1.mean()
v = varray1.mean()
if self.temp_name is None:
temp = np.nan
if self.salt_name is None:
salt = np.nan
except Exception:
logger.exception("Could not retrieve data.")
raise
return u, v, w, temp, salt
def particle_runner(part, model):
from paegan.logger import logger
logger.setLevel(logging.PROGRESS)
from paegan.logger.redis_handler import RedisHandler
rhandler = RedisHandler(model.redis_log_channel, model.redis_url)
rhandler.setLevel(logging.PROGRESS)
logger.addHandler(rhandler)
try:
redis_connection = redis.from_url(model.redis_url)
forcer = BaseForcer(
model.hydrodataset,
particle=part,
common_variables=model.common_variables,
times=model.times,
start_time=model.start,
models=model._models,
release_location_centroid=model.reference_location.point,
usebathy=model._use_bathymetry,
useshore=model._use_shoreline,
usesurface=model._use_seasurface,
reverse_distance=model.reverse_distance,
bathy_path=model.bathy_path,
shoreline_path=model.shoreline_path,
shoreline_feature=model.shoreline_feature,
time_method=model.time_method,
redis_url=model.redis_url,
redis_results_channel=model.redis_results_channel,
shoreline_index_buffer=model.shoreline_index_buffer)
forcer.run()
except Exception:
logger.exception(traceback.format_exc())
redis_connection.publish(
model.redis_results_channel,
json.dumps({
"status": "FAILED",
"uid": part.uid
}))
else:
redis_connection.publish(
model.redis_results_channel,
json.dumps({
"status": "COMPLETED",
"uid": part.uid
}))
def get_linterp_data(self, i, currenttime):
self.fill_cache_with_linterp_data(i, currenttime)
# Now that the cache is filled, get the actual data and return
with self.read_lock:
self.read_count.value += 1
self.has_read_lock.append(os.getpid())
try:
self.dataset.opennc()
return super(CachingForcer, self).get_linterp_data(i, currenttime)
except Exception:
logger.exception("Could not retrieve data")
finally:
self.dataset.closenc()
with self.read_lock:
self.read_count.value -= 1
self.has_read_lock.remove(os.getpid())
def get_linterp_data(self, i, currenttime):
self.fill_cache_with_linterp_data(i, currenttime)
# Now that the cache is filled, get the actual data and return
with self.read_lock:
self.read_count.value += 1
self.has_read_lock.append(os.getpid())
try:
self.dataset.opennc()
return super(CachingForcer, self).get_linterp_data(i, currenttime)
except Exception:
logger.exception("Could not retrieve data")
finally:
self.dataset.closenc()
with self.read_lock:
self.read_count.value -= 1
self.has_read_lock.remove(os.getpid())
def start_tasks(self, **kwargs):
logger.progress((5, 'Running model'))
rc = redis.from_url(self.redis_url)
if kwargs.get('task_queue_call'):
for p in self.particles:
try:
kwargs.get('task_queue_call')(func=particle_runner,
args=(
p,
self,
))
except Exception:
logger.exception(traceback.format_exc())
rc.publish(self.redis_results_channel,
json.dumps({
"status": "FAILED",
"uid": p.uid
}))
return True
else:
tasks = []
for p in self.particles:
f = BaseForcer(
self.hydrodataset,
particle=p,
common_variables=self.common_variables,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
redis_url=self.redis_url,
redis_results_channel=self.redis_results_channel,
shoreline_index_buffer=self.shoreline_index_buffer)
tasks.append(f)
self.pool = multiprocessing.Pool()
return self.pool.imap(Runner(), tasks)
return False
def start_tasks(self):
# @TODO: this is more initialization, but need to prevent derived classes from doing this
if self.pool is None:
self.pool = multiprocessing.Pool()
try:
logger.info('Adding %i particles as tasks' % len(self.particles))
tasks = []
for part in self.particles:
forcer = BaseForcer(self.hydrodataset,
particle=part,
common_variables=self.common_variables,
timevar=self.timevar,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
redis_url=self.redis_url,
redis_results_channel=self.redis_results_channel,
shoreline_index_buffer=self.shoreline_index_buffer)
tasks.append(forcer)
# @TODO: better mechanism than switching on type
if isinstance(self.pool, multiprocessing.pool.Pool):
aiter = self.pool.imap(Runner(), tasks)
else:
# IPython parallel View
aiter = self.pool.map_async(Runner(), tasks)
return aiter
except Exception:
logger.exception("Something didn't start correctly!")
raise
def listen_for_results(self):
logger.info("Waiting for %i particle results" % len(self.particles))
logger.progress((5, "Running model"))
particles = []
retrieved = 0
timeout = 200
while retrieved < len(self.particles):
try:
# @TODO: better mechanism than switching on type
if isinstance(self.pool, multiprocessing.pool.Pool):
# self.result is an iterator that can timeout on next()
particle = self.result.next(timeout)
retrieved += 1
particles.append(particle)
else:
# IPython parallel View
# self.result is an AsyncMapResult
from IPython.parallel import TimeoutError
try:
new_particles = self.result.get(timeout=1)
except TimeoutError:
pass # this is fine, get incremental progress below
else:
particles = new_particles
# progress is absolute, not incremental
retrieved = self.result.progress
except StopIteration:
assert retrieved >= len(self.particles)
break
except:
logger.exception("Particle has FAILED!!")
#logger.warn("Particle %s has FAILED!!" % particle.uid)
continue
# We multiply by 95 here to save 5% for the exporting
logger.progress((round((float(retrieved) / self.number_of_tasks) * 90., 1), "%s Particle(s) complete" % retrieved))
return particles
def nc_object(ncfile, tname='time'):
if isinstance(ncfile, str):
try:
return netCDF4.Dataset(ncfile)
except (IOError, RuntimeError, IndexError):
# Are we a set of files?
try:
return netCDF4.MFDataset(ncfile)
except (IOError, RuntimeError, IndexError):
try:
return netCDF4.MFDataset(ncfile, aggdim=tname)
except (IOError, RuntimeError, IndexError):
try:
# Unicode isn't working sometimes?
return netCDF4.MFDataset(str(ncfile), aggdim=tname)
except Exception:
logger.exception("Can not open %s" % ncfile)
raise
except ValueError:
# Probably a DAP endpoint
logger.exception("Can not open %s" % ncfile)
raise
except Exception:
logger.exception("Can not open %s" % ncfile)
raise
elif isinstance(ncfile, Dataset):
# Passed in paegan Dataset object
return ncfile.nc
elif isinstance(ncfile, netCDF4.Dataset) or isinstance(ncfile, netCDF4.MFDataset):
# Passed in a netCDF4 Dataset object
return ncfile
def start_tasks(self):
try:
logger.info('Adding %i particles as tasks' % len(self.particles))
for part in self.particles:
forcer = BaseForcer(self.hydrodataset,
particle=part,
common_variables=self.common_variables,
timevar=self.timevar,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
redis_url=self.redis_url,
redis_results_channel=self.redis_results_channel,
shoreline_index_buffer=self.shoreline_index_buffer
)
self.tasks.put(forcer)
# Create workers for the particles.
self.procs = [ Consumer(self.tasks, self.results, self.n_run, self.nproc_lock, self.active, None, name="BaseForcer-%d" % i)
for i in xrange(self.nproc - 1) ]
for w in self.procs:
w.start()
logger.info('Started %s' % w.name)
return True
except Exception:
logger.exception("Something didn't start correctly!")
return False
def listen_for_results(self, output_h5_file, total_particles):
logger.info("Waiting for %i particle results" % total_particles)
particles = []
retrieved = 0
while retrieved < total_particles:
try:
# IPython parallel View
# self.result is an AsyncMapResult
from IPython.parallel import TimeoutError
try:
new_particles = self.result.get(timeout=1)
except TimeoutError:
pass # this is fine, get incremental progress below
else:
particles = new_particles
# progress is absolute, not incremental
retrieved = self.result.progress
except StopIteration:
assert retrieved >= total_particles
break
except:
logger.exception("Particle has FAILED!!")
continue
# We multiply by 90 here to save 10% for the exporting
logger.progress((round((float(retrieved) / total_particles) * 90., 1), "%s Particle(s) complete" % retrieved))
results = ex.ResultsPyTable(output_h5_file)
for p in particles:
for x in range(len(p.locations)):
results.write(p.timestep_index_dump(x))
results.compute()
results.close()
return
def run(self):
while True:
try:
next_task = self.task_queue.get(True, 10)
except queue.Empty:
logger.info("No tasks left to complete, closing %s" %
self.name)
break
else:
answer = (None, None)
try:
answer = (1, next_task(self.active))
except Exception:
logger.exception("Disabling Error")
if isinstance(next_task, CachingDataController):
answer = (-2, "CachingDataController")
# Tell the particles that the CachingDataController is releasing file
self.get_data.value = False
# The data controller has died, so don't process any more tasks
self.active.value = False
elif isinstance(next_task, BaseForcer):
answer = (-1, next_task.particle)
else:
logger.warn("Strange task raised an exception: %s" %
str(next_task.__class__))
answer = (None, None)
finally:
self.result_queue.put(answer)
self.nproc_lock.acquire()
self.n_run.value = self.n_run.value - 1
self.nproc_lock.release()
self.task_queue.task_done()
def __call__(self, active):
c = 0
self.dataset = CommonDataset.open(self.hydrodataset)
self.remote = self.dataset.nc
# Calculate the datetimes of the model timesteps like
# the particle objects do, so we can figure out unique
# time indices
modelTimestep, newtimes = AsaTransport.get_time_objects_from_model_timesteps(
self.times, start=self.start_time)
timevar = self.dataset.gettimevar(self.uname)
# Don't need to grab the last datetime, as it is not needed for forcing, only
# for setting the time of the final particle forcing
time_indexs = timevar.nearest_index(newtimes[0:-1], select='before')
# Have to make sure that we get the plus 1 for the
# linear interpolation of u,v,w,temp,salt
self.inds = np.unique(time_indexs)
self.inds = np.append(self.inds, self.inds.max() + 1)
# While there is at least 1 particle still running,
# stay alive, if not break
while self.n_run.value > 1:
if self.caching is False:
logger.debug(
"Caching is False, not doing much. Just hanging out until all of the particles finish."
)
timer.sleep(10)
continue
# If particle asks for data, do the following
if self.get_data.value is True:
logger.debug("Particle asked for data!")
# Wait for particles to get out
while True:
self.read_lock.acquire()
logger.debug("Read count: %d" % self.read_count.value)
if self.read_count.value > 0:
logger.debug(
"Waiting for write lock on cache file (particles must stop reading)..."
)
self.read_lock.release()
timer.sleep(2)
else:
break
# Get write lock on the file. Already have read lock.
self.write_lock.acquire()
self.has_write_lock.value = os.getpid()
if c == 0:
logger.debug("Creating cache file")
try:
# Open local cache for writing, overwrites
# existing file with same name
self.local = netCDF4.Dataset(self.cache_path, 'w')
indices = self.dataset.get_indices(
self.uname,
timeinds=[np.asarray([0])],
point=self.start)
self.point_get.value = [
self.inds[0], indices[-2], indices[-1]
]
# Create dimensions for u and v variables
self.local.createDimension('time', None)
self.local.createDimension('level', None)
self.local.createDimension('x', None)
self.local.createDimension('y', None)
# Create 3d or 4d u and v variables
if self.remote.variables[self.uname].ndim == 4:
self.ndim = 4
dimensions = ('time', 'level', 'y', 'x')
coordinates = "time z lon lat"
elif self.remote.variables[self.uname].ndim == 3:
self.ndim = 3
dimensions = ('time', 'y', 'x')
coordinates = "time lon lat"
shape = self.remote.variables[self.uname].shape
# If there is no FillValue defined in the dataset, use np.nan.
# Sometimes it will work out correctly and other times we will
# have a huge cache file.
try:
fill = self.remote.variables[
self.uname].missing_value
except Exception:
fill = np.nan
# Create domain variable that specifies
# where there is data geographically/by time
# and where there is not data,
# Used for testing if particle needs to
# ask cache to update
domain = self.local.createVariable('domain',
'i',
dimensions,
zlib=False,
fill_value=0)
domain.coordinates = coordinates
# Create local u and v variables
u = self.local.createVariable('u',
'f',
dimensions,
zlib=False,
fill_value=fill)
v = self.local.createVariable('v',
'f',
dimensions,
zlib=False,
fill_value=fill)
v.coordinates = coordinates
u.coordinates = coordinates
localvars = [
u,
v,
]
remotevars = [
self.remote.variables[self.uname],
self.remote.variables[self.vname]
]
# Create local w variable
if self.wname is not None:
w = self.local.createVariable('w',
'f',
dimensions,
zlib=False,
fill_value=fill)
w.coordinates = coordinates
localvars.append(w)
remotevars.append(
self.remote.variables[self.wname])
if self.temp_name is not None and self.salt_name is not None:
# Create local temp and salt vars
temp = self.local.createVariable('temp',
'f',
dimensions,
zlib=False,
fill_value=fill)
salt = self.local.createVariable('salt',
'f',
dimensions,
zlib=False,
fill_value=fill)
temp.coordinates = coordinates
salt.coordinates = coordinates
localvars.append(temp)
localvars.append(salt)
remotevars.append(
self.remote.variables[self.temp_name])
remotevars.append(
self.remote.variables[self.salt_name])
# Create local lat/lon coordinate variables
if self.remote.variables[self.xname].ndim == 2:
lon = self.local.createVariable('lon',
'f', ("y", "x"),
zlib=False)
lon[:] = self.remote.variables[self.xname][:, :]
lat = self.local.createVariable('lat',
'f', ("y", "x"),
zlib=False)
lat[:] = self.remote.variables[self.yname][:, :]
if self.remote.variables[self.xname].ndim == 1:
lon = self.local.createVariable('lon',
'f', ("x"),
zlib=False)
lon[:] = self.remote.variables[self.xname][:]
lat = self.local.createVariable('lat',
'f', ("y"),
zlib=False)
lat[:] = self.remote.variables[self.yname][:]
# Create local z variable
if self.zname is not None:
if self.remote.variables[self.zname].ndim == 4:
z = self.local.createVariable(
'z',
'f', ("time", "level", "y", "x"),
zlib=False)
remotez = self.remote.variables[self.zname]
localvars.append(z)
remotevars.append(remotez)
elif self.remote.variables[self.zname].ndim == 3:
z = self.local.createVariable(
'z', 'f', ("level", "y", "x"), zlib=False)
z[:] = self.remote.variables[
self.zname][:, :, :]
elif self.remote.variables[self.zname].ndim == 1:
z = self.local.createVariable('z',
'f', ("level", ),
zlib=False)
z[:] = self.remote.variables[self.zname][:]
# Create local time variable
time = self.local.createVariable('time',
'f8', ("time", ),
zlib=False)
if self.tname is not None:
time[:] = self.remote.variables[self.tname][
self.inds]
if self.point_get.value[0] + self.time_size > np.max(
self.inds):
current_inds = np.arange(self.point_get.value[0],
np.max(self.inds) + 1)
else:
current_inds = np.arange(
self.point_get.value[0],
self.point_get.value[0] + self.time_size)
# Get data from remote dataset and add
# to local cache.
# Try 20 times on the first attempt
current_attempt = 1
max_attempts = 20
while True:
try:
assert current_attempt <= max_attempts
self.get_remote_data(localvars, remotevars,
current_inds, shape)
except AssertionError:
raise
except:
logger.warn(
"CachingDataController failed to get remote data. Trying again in 20 seconds. %s attempts left."
% str(max_attempts - current_attempt))
logger.exception("Data Access Error")
timer.sleep(20)
current_attempt += 1
else:
break
c += 1
except (Exception, AssertionError):
logger.error(
"CachingDataController failed to get data (first request)"
)
raise
finally:
self.local.sync()
self.local.close()
self.has_write_lock.value = -1
self.write_lock.release()
self.get_data.value = False
self.read_lock.release()
logger.debug(
"Done updating cache file, closing file, and releasing locks"
)
else:
logger.debug("Updating cache file")
try:
# Open local cache dataset for appending
self.local = netCDF4.Dataset(self.cache_path, 'a')
# Create local and remote variable objects
# for the variables of interest
u = self.local.variables['u']
v = self.local.variables['v']
time = self.local.variables['time']
remoteu = self.remote.variables[self.uname]
remotev = self.remote.variables[self.vname]
# Create lists of variable objects for
# the data updater
localvars = [
u,
v,
]
remotevars = [
remoteu,
remotev,
]
if self.salt_name is not None and self.temp_name is not None:
salt = self.local.variables['salt']
temp = self.local.variables['temp']
remotesalt = self.remote.variables[self.salt_name]
remotetemp = self.remote.variables[self.temp_name]
localvars.append(salt)
localvars.append(temp)
remotevars.append(remotesalt)
remotevars.append(remotetemp)
if self.wname is not None:
w = self.local.variables['w']
remotew = self.remote.variables[self.wname]
localvars.append(w)
remotevars.append(remotew)
if self.zname is not None:
remotez = self.remote.variables[self.zname]
if remotez.ndim == 4:
z = self.local.variables['z']
localvars.append(z)
remotevars.append(remotez)
if self.tname is not None:
# remotetime = self.remote.variables[self.tname]
time[self.inds] = self.remote.variables[self.inds]
if self.point_get.value[0] + self.time_size > np.max(
self.inds):
current_inds = np.arange(self.point_get.value[0],
np.max(self.inds) + 1)
else:
current_inds = np.arange(
self.point_get.value[0],
self.point_get.value[0] + self.time_size)
# Get data from remote dataset and add
# to local cache
while True:
try:
self.get_remote_data(localvars, remotevars,
current_inds, shape)
except:
logger.warn(
"CachingDataController failed to get remote data. Trying again in 30 seconds"
)
timer.sleep(30)
else:
break
c += 1
except Exception:
logger.error(
"CachingDataController failed to get data (not first request)"
)
raise
finally:
self.local.sync()
self.local.close()
self.has_write_lock.value = -1
self.write_lock.release()
self.get_data.value = False
self.read_lock.release()
logger.debug(
"Done updating cache file, closing file, and releasing locks"
)
else:
logger.debug(
"Particles are still running, waiting for them to request data..."
)
timer.sleep(2)
self.dataset.closenc()
return "CachingDataController"
def get_linterp_data(self, i, currenttime):
""" Note: self.dataset.opennc() must be called before calling this function.
This is because the caching forcer must close it everytime, while a non caching
forcer can leave the dataset open.
"""
try:
# Grab data at time index closest to particle location
u = [
np.mean(
np.mean(
self.dataset.get_values(
'u',
timeinds=[np.asarray([i])],
point=self.particle.location))),
np.mean(
np.mean(
self.dataset.get_values('u',
timeinds=[np.asarray([i + 1])],
point=self.particle.location)))
]
v = [
np.mean(
np.mean(
self.dataset.get_values(
'v',
timeinds=[np.asarray([i])],
point=self.particle.location))),
np.mean(
np.mean(
self.dataset.get_values('v',
timeinds=[np.asarray([i + 1])],
point=self.particle.location)))
]
# if there is vertical velocity inthe dataset, get it
if 'w' in self.dataset.nc.variables:
w = [
np.mean(
np.mean(
self.dataset.get_values(
'w',
timeinds=[np.asarray([i])],
point=self.particle.location))),
np.mean(
np.mean(
self.dataset.get_values(
'w',
timeinds=[np.asarray([i + 1])],
point=self.particle.location)))
]
else:
w = [0.0, 0.0]
# If there is salt and temp in the dataset, get it
if self.temp_name is not None and self.salt_name is not None:
temp = [
np.mean(
np.mean(
self.dataset.get_values(
'temp',
timeinds=[np.asarray([i])],
point=self.particle.location))),
np.mean(
np.mean(
self.dataset.get_values(
'temp',
timeinds=[np.asarray([i + 1])],
point=self.particle.location)))
]
salt = [
np.mean(
np.mean(
self.dataset.get_values(
'salt',
timeinds=[np.asarray([i])],
point=self.particle.location))),
np.mean(
np.mean(
self.dataset.get_values(
'salt',
timeinds=[np.asarray([i + 1])],
point=self.particle.location)))
]
# Check for nans that occur in the ocean (happens because
# of model and coastline resolution mismatches)
if np.isnan(u).any() or np.isnan(v).any() or np.isnan(w).any():
# Take the mean of the closest 4 points
# If this includes nan which it will, result is nan
uarray1 = self.dataset.get_values('u',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
varray1 = self.dataset.get_values('v',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
uarray2 = self.dataset.get_values(
'u',
timeinds=[np.asarray([i + 1])],
point=self.particle.location,
num=2)
varray2 = self.dataset.get_values(
'v',
timeinds=[np.asarray([i + 1])],
point=self.particle.location,
num=2)
if 'w' in self.dataset.nc.variables:
warray1 = self.dataset.get_values(
'w',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
warray2 = self.dataset.get_values(
'w',
timeinds=[np.asarray([i + 1])],
point=self.particle.location,
num=2)
w = [warray1.mean(), warray2.mean()]
else:
w = [0.0, 0.0]
if self.temp_name is not None and self.salt_name is not None:
temparray1 = self.dataset.get_values(
'temp',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
saltarray1 = self.dataset.get_values(
'salt',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
temparray2 = self.dataset.get_values(
'temp',
timeinds=[np.asarray([i + 1])],
point=self.particle.location,
num=2)
saltarray2 = self.dataset.get_values(
'salt',
timeinds=[np.asarray([i + 1])],
point=self.particle.location,
num=2)
temp = [temparray1.mean(), temparray2.mean()]
salt = [saltarray1.mean(), saltarray2.mean()]
u = [uarray1.mean(), uarray2.mean()]
v = [varray1.mean(), varray2.mean()]
# Linear interp of data between timesteps
currenttime = date2num(currenttime)
timevar = self.timevar.datenum
u = self.linterp(timevar[i:i + 2], u, currenttime)
v = self.linterp(timevar[i:i + 2], v, currenttime)
w = self.linterp(timevar[i:i + 2], w, currenttime)
if self.temp_name is not None and self.salt_name is not None:
temp = self.linterp(timevar[i:i + 2], temp, currenttime)
salt = self.linterp(timevar[i:i + 2], salt, currenttime)
if self.temp_name is None:
temp = np.nan
if self.salt_name is None:
salt = np.nan
except Exception:
logger.exception("Could not retrieve data.")
raise
return u, v, w, temp, salt
def run(self):
self.load_initial_dataset()
redis_connection = None
if self.redis_url is not None and self.redis_results_channel is not None:
import redis
redis_connection = redis.from_url(self.redis_url)
# Setup shoreline
self._shoreline = None
if self.useshore is True:
self._shoreline = Shoreline(path=self.shoreline_path, feature_name=self.shoreline_feature, point=self.release_location_centroid, spatialbuffer=self.shoreline_index_buffer)
# Make sure we are not starting on land. Raises exception if we are.
self._shoreline.intersect(start_point=self.release_location_centroid, end_point=self.release_location_centroid)
# Setup Bathymetry
if self.usebathy is True:
try:
self._bathymetry = Bathymetry(file=self.bathy_path)
except Exception:
logger.exception("Could not load Bathymetry file: %s, using no Bathymetry for this run!" % self.bathy_path)
self.usebathy = False
# Calculate datetime at every timestep
modelTimestep, newtimes = AsaTransport.get_time_objects_from_model_timesteps(self.times, start=self.start_time)
if self.time_method == 'interp':
time_indexs = self.timevar.nearest_index(newtimes, select='before')
elif self.time_method == 'nearest':
time_indexs = self.timevar.nearest_index(newtimes)
else:
logger.warn("Method for computing u,v,w,temp,salt not supported!")
try:
assert len(newtimes) == len(time_indexs)
except AssertionError:
logger.exception("Time indexes are messed up. Need to have equal datetime and time indexes")
raise
# Keep track of how much time we spend in each area.
tot_boundary_time = 0.
tot_model_time = {}
tot_read_data = 0.
for m in self.models:
tot_model_time[m.name] = 0.
# Set the base conditions
# If using Redis, send the results
if redis_connection is not None:
redis_connection.publish(self.redis_results_channel, json.dumps(self.particle.timestep_dump()))
# loop over timesteps
# We don't loop over the last time_index because
# we need to query in the time_index and set the particle's
# location as the 'newtime' object.
for loop_i, i in enumerate(time_indexs[0:-1]):
if self.active and self.active.value is False:
raise ValueError("Particle exiting due to Failure.")
newloc = None
st = time.clock()
# Get the variable data required by the models
if self.time_method == 'nearest':
u, v, w, temp, salt = self.get_nearest_data(i)
elif self.time_method == 'interp':
u, v, w, temp, salt = self.get_linterp_data(i, newtimes[loop_i])
else:
logger.warn("Method for computing u,v,w,temp,salt is unknown. Only 'nearest' and 'interp' are supported.")
tot_read_data += (time.clock() - st)
# Get the bathy value at the particles location
if self.usebathy is True:
bathymetry_value = self._bathymetry.get_depth(self.particle.location)
else:
bathymetry_value = -999999999999999
# Age the particle by the modelTimestep (seconds)
# 'Age' meaning the amount of time it has been forced.
self.particle.age(seconds=modelTimestep[loop_i])
# loop over models - sort these in the order you want them to run
for model in self.models:
st = time.clock()
movement = model.move(self.particle, u, v, w, modelTimestep[loop_i], temperature=temp, salinity=salt, bathymetry_value=bathymetry_value)
newloc = Location4D(latitude=movement['latitude'], longitude=movement['longitude'], depth=movement['depth'], time=newtimes[loop_i+1])
tot_model_time[m.name] += (time.clock() - st)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("%s - moved %.3f meters (horizontally) and %.3f meters (vertically) by %s with data from %s" % (self.particle.logstring(), movement['distance'], movement['vertical_distance'], model.__class__.__name__, newtimes[loop_i].isoformat()))
if newloc:
st = time.clock()
self.boundary_interaction(particle=self.particle, starting=self.particle.location, ending=newloc,
distance=movement['distance'], angle=movement['angle'],
azimuth=movement['azimuth'], reverse_azimuth=movement['reverse_azimuth'],
vertical_distance=movement['vertical_distance'], vertical_angle=movement['vertical_angle'])
tot_boundary_time += (time.clock() - st)
if logger.isEnabledFor(logging.DEBUG):
logger.debug("%s - was forced by %s and is now at %s" % (self.particle.logstring(), model.__class__.__name__, self.particle.location.logstring()))
self.particle.note = self.particle.outputstring()
# Each timestep, save the particles status and environmental variables.
# This keep fields such as temp, salt, halted, settled, and dead matched up with the number of timesteps
self.particle.save()
# If using Redis, send the results
if redis_connection is not None:
redis_connection.publish(self.redis_results_channel, json.dumps(self.particle.timestep_dump()))
self.dataset.closenc()
# We won't pull data for the last entry in locations, but we need to populate it with fill data.
self.particle.fill_gap()
if self.usebathy is True:
self._bathymetry.close()
if self.useshore is True:
self._shoreline.close()
logger.info(textwrap.dedent('''Particle %i Stats:
Data read: %f seconds
Model forcing: %s seconds
Boundary intersection: %f seconds''' % (self.particle.uid, tot_read_data, { s: '{:g} seconds'.format(f) for s, f in list(tot_model_time.items()) }, tot_boundary_time)))
return self.particle
def get_linterp_data(self, i, currenttime):
""" Note: self.dataset.opennc() must be called before calling this function.
This is because the caching forcer must close it everytime, while a non caching
forcer can leave the dataset open.
"""
try:
# Grab data at time index closest to particle location
u = [np.mean(np.mean(self.dataset.get_values('u', timeinds=[np.asarray([i])], point=self.particle.location ))),
np.mean(np.mean(self.dataset.get_values('u', timeinds=[np.asarray([i+1])], point=self.particle.location )))]
v = [np.mean(np.mean(self.dataset.get_values('v', timeinds=[np.asarray([i])], point=self.particle.location ))),
np.mean(np.mean(self.dataset.get_values('v', timeinds=[np.asarray([i+1])], point=self.particle.location )))]
# if there is vertical velocity inthe dataset, get it
if 'w' in self.dataset.nc.variables:
w = [np.mean(np.mean(self.dataset.get_values('w', timeinds=[np.asarray([i])], point=self.particle.location ))),
np.mean(np.mean(self.dataset.get_values('w', timeinds=[np.asarray([i+1])], point=self.particle.location )))]
else:
w = [0.0, 0.0]
# If there is salt and temp in the dataset, get it
if self.temp_name is not None and self.salt_name is not None:
temp = [np.mean(np.mean(self.dataset.get_values('temp', timeinds=[np.asarray([i])], point=self.particle.location ))),
np.mean(np.mean(self.dataset.get_values('temp', timeinds=[np.asarray([i+1])], point=self.particle.location )))]
salt = [np.mean(np.mean(self.dataset.get_values('salt', timeinds=[np.asarray([i])], point=self.particle.location ))),
np.mean(np.mean(self.dataset.get_values('salt', timeinds=[np.asarray([i+1])], point=self.particle.location )))]
# Check for nans that occur in the ocean (happens because
# of model and coastline resolution mismatches)
if np.isnan(u).any() or np.isnan(v).any() or np.isnan(w).any():
# Take the mean of the closest 4 points
# If this includes nan which it will, result is nan
uarray1 = self.dataset.get_values('u', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
varray1 = self.dataset.get_values('v', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
uarray2 = self.dataset.get_values('u', timeinds=[np.asarray([i+1])], point=self.particle.location, num=2)
varray2 = self.dataset.get_values('v', timeinds=[np.asarray([i+1])], point=self.particle.location, num=2)
if 'w' in self.dataset.nc.variables:
warray1 = self.dataset.get_values('w', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
warray2 = self.dataset.get_values('w', timeinds=[np.asarray([i+1])], point=self.particle.location, num=2)
w = [warray1.mean(), warray2.mean()]
else:
w = [0.0, 0.0]
if self.temp_name is not None and self.salt_name is not None:
temparray1 = self.dataset.get_values('temp', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
saltarray1 = self.dataset.get_values('salt', timeinds=[np.asarray([i])], point=self.particle.location, num=2)
temparray2 = self.dataset.get_values('temp', timeinds=[np.asarray([i+1])], point=self.particle.location, num=2)
saltarray2 = self.dataset.get_values('salt', timeinds=[np.asarray([i+1])], point=self.particle.location, num=2)
temp = [temparray1.mean(), temparray2.mean()]
salt = [saltarray1.mean(), saltarray2.mean()]
u = [uarray1.mean(), uarray2.mean()]
v = [varray1.mean(), varray2.mean()]
# Linear interp of data between timesteps
currenttime = date2num(currenttime)
timevar = self.timevar.datenum
u = self.linterp(timevar[i:i+2], u, currenttime)
v = self.linterp(timevar[i:i+2], v, currenttime)
w = self.linterp(timevar[i:i+2], w, currenttime)
if self.temp_name is not None and self.salt_name is not None:
temp = self.linterp(timevar[i:i+2], temp, currenttime)
salt = self.linterp(timevar[i:i+2], salt, currenttime)
if self.temp_name is None:
temp = np.nan
if self.salt_name is None:
salt = np.nan
except Exception:
logger.exception("Could not retrieve data.")
raise
return u, v, w, temp, salt
def run(self):
self.load_initial_dataset()
redis_connection = None
if self.redis_url is not None and self.redis_results_channel is not None:
import redis
redis_connection = redis.from_url(self.redis_url)
# Setup shoreline
self._shoreline = None
if self.useshore is True:
self._shoreline = Shoreline(
path=self.shoreline_path,
feature_name=self.shoreline_feature,
point=self.release_location_centroid,
spatialbuffer=self.shoreline_index_buffer)
# Make sure we are not starting on land. Raises exception if we are.
self._shoreline.intersect(
start_point=self.release_location_centroid,
end_point=self.release_location_centroid)
# Setup Bathymetry
if self.usebathy is True:
try:
self._bathymetry = Bathymetry(file=self.bathy_path)
except Exception:
logger.exception(
"Could not load Bathymetry file: %s, using no Bathymetry for this run!"
% self.bathy_path)
self.usebathy = False
# Calculate datetime at every timestep
modelTimestep, newtimes = AsaTransport.get_time_objects_from_model_timesteps(
self.times, start=self.start_time)
if self.time_method == 'interp':
time_indexs = self.timevar.nearest_index(newtimes, select='before')
elif self.time_method == 'nearest':
time_indexs = self.timevar.nearest_index(newtimes)
else:
logger.warn("Method for computing u,v,w,temp,salt not supported!")
try:
assert len(newtimes) == len(time_indexs)
except AssertionError:
logger.exception(
"Time indexes are messed up. Need to have equal datetime and time indexes"
)
raise
# Keep track of how much time we spend in each area.
tot_boundary_time = 0.
tot_model_time = {}
tot_read_data = 0.
for m in self.models:
tot_model_time[m.name] = 0.
# Set the base conditions
# If using Redis, send the results
if redis_connection is not None:
redis_connection.publish(self.redis_results_channel,
json.dumps(self.particle.timestep_dump()))
# loop over timesteps
# We don't loop over the last time_index because
# we need to query in the time_index and set the particle's
# location as the 'newtime' object.
for loop_i, i in enumerate(time_indexs[0:-1]):
if self.active and self.active.value is False:
raise ValueError("Particle exiting due to Failure.")
newloc = None
st = time.clock()
# Get the variable data required by the models
if self.time_method == 'nearest':
u, v, w, temp, salt = self.get_nearest_data(i)
elif self.time_method == 'interp':
u, v, w, temp, salt = self.get_linterp_data(
i, newtimes[loop_i])
else:
logger.warn(
"Method for computing u,v,w,temp,salt is unknown. Only 'nearest' and 'interp' are supported."
)
tot_read_data += (time.clock() - st)
# Get the bathy value at the particles location
if self.usebathy is True:
bathymetry_value = self._bathymetry.get_depth(
self.particle.location)
else:
bathymetry_value = -999999999999999
# Age the particle by the modelTimestep (seconds)
# 'Age' meaning the amount of time it has been forced.
self.particle.age(seconds=modelTimestep[loop_i])
# loop over models - sort these in the order you want them to run
for model in self.models:
st = time.clock()
movement = model.move(self.particle,
u,
v,
w,
modelTimestep[loop_i],
temperature=temp,
salinity=salt,
bathymetry_value=bathymetry_value)
newloc = Location4D(latitude=movement['latitude'],
longitude=movement['longitude'],
depth=movement['depth'],
time=newtimes[loop_i + 1])
tot_model_time[m.name] += (time.clock() - st)
if logger.isEnabledFor(logging.DEBUG):
logger.debug(
"%s - moved %.3f meters (horizontally) and %.3f meters (vertically) by %s with data from %s"
% (self.particle.logstring(), movement['distance'],
movement['vertical_distance'],
model.__class__.__name__,
newtimes[loop_i].isoformat()))
if newloc:
st = time.clock()
self.boundary_interaction(
particle=self.particle,
starting=self.particle.location,
ending=newloc,
distance=movement['distance'],
angle=movement['angle'],
azimuth=movement['azimuth'],
reverse_azimuth=movement['reverse_azimuth'],
vertical_distance=movement['vertical_distance'],
vertical_angle=movement['vertical_angle'])
tot_boundary_time += (time.clock() - st)
if logger.isEnabledFor(logging.DEBUG):
logger.debug(
"%s - was forced by %s and is now at %s" %
(self.particle.logstring(), model.__class__.__name__,
self.particle.location.logstring()))
self.particle.note = self.particle.outputstring()
# Each timestep, save the particles status and environmental variables.
# This keep fields such as temp, salt, halted, settled, and dead matched up with the number of timesteps
self.particle.save()
# If using Redis, send the results
if redis_connection is not None:
redis_connection.publish(
self.redis_results_channel,
json.dumps(self.particle.timestep_dump()))
self.dataset.closenc()
# We won't pull data for the last entry in locations, but we need to populate it with fill data.
self.particle.fill_gap()
if self.usebathy is True:
self._bathymetry.close()
if self.useshore is True:
self._shoreline.close()
logger.info(
textwrap.dedent('''Particle %i Stats:
Data read: %f seconds
Model forcing: %s seconds
Boundary intersection: %f seconds''' %
(self.particle.uid, tot_read_data, {
s: '{:g} seconds'.format(f)
for s, f in list(tot_model_time.items())
}, tot_boundary_time)))
return self.particle
def setup_run(self, **kwargs):
logger.setLevel(logging.PROGRESS)
self.redis_url = None
self.redis_log_channel = None
self.redis_results_channel = None
if "redis" in kwargs.get("output_formats", []):
from paegan.logger.redis_handler import RedisHandler
self.redis_url = kwargs.get("redis_url")
self.redis_log_channel = kwargs.get("redis_log_channel")
self.redis_results_channel = kwargs.get("redis_results_channel")
rhandler = RedisHandler(self.redis_log_channel, self.redis_url)
rhandler.setLevel(logging.PROGRESS)
logger.addHandler(rhandler)
# Relax.
time.sleep(0.5)
# Add ModelController description to logfile
logger.info(unicode(self))
# Add the model descriptions to logfile
for m in self._models:
logger.info(unicode(m))
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
self.times = range(0, (self._step*self._nstep)+1, self._step)
# Calculate a datetime object for each model timestep
# This method is duplicated in CachingDataController and CachingForcer
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
self.modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(self.times, start=self.start)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(self.geometry, MultiPolygon):
point_locations = [Location4D(latitude=loc.y, longitude=loc.x, depth=self._depth, time=self.start) for loc in AsaTransport.fill_polygon_with_points(goal=self._npart, polygon=self.geometry)]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in xrange(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
if kwargs.get("manager", True):
# Get the number of cores (may take some tuning) and create that
# many workers then pass particles into the queue for the workers
self.mgr = multiprocessing.Manager()
# This tracks if the system is 'alive'. Most looping whiles will check this
# and break out if it is False. This is True until something goes very wrong.
self.active = self.mgr.Value('bool', True)
# Each particle is a task, plus the CachingDataController
self.number_of_tasks = self.get_number_of_tasks()
# Either spin up the number of cores, or the number of tasks
self.nproc = min(multiprocessing.cpu_count() - 1, self.number_of_tasks)
# Number of tasks that we need to run. This is decremented everytime something exits.
self.n_run = self.mgr.Value('int', self.number_of_tasks)
# The lock that controls access to the 'n_run' variable
self.nproc_lock = self.mgr.Lock()
# Create the task queue for all of the particles and the CachingDataController
self.tasks = multiprocessing.JoinableQueue(self.number_of_tasks)
# Create the result queue for all of the particles and the CachingDataController
self.results = self.mgr.Queue(self.number_of_tasks)
logger.progress((3, "Initializing and caching hydro model's grid"))
try:
ds = CommonDataset.open(self.hydrodataset)
except Exception:
logger.exception("Failed to access dataset %s" % self.hydrodataset)
raise BaseDataControllerError("Inaccessible Dataset: %s" % self.hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
self.common_variables = self.get_common_variables_from_dataset(ds)
self.timevar = None
try:
assert self.common_variables.get("u") in ds._current_variables
assert self.common_variables.get("v") in ds._current_variables
assert self.common_variables.get("x") in ds._current_variables
assert self.common_variables.get("y") in ds._current_variables
self.timevar = ds.gettimevar(self.common_variables.get("u"))
except AssertionError:
logger.exception("Could not locate variables needed to run model: %s" % unicode(self.common_variables))
raise BaseDataControllerError("A required data variable was not found in %s" % self.hydrodataset)
model_start = self.timevar.get_dates()[0]
model_end = self.timevar.get_dates()[-1]
try:
assert self.start > model_start
assert self.start < model_end
except AssertionError:
raise BaseDataControllerError("Start time for model (%s) is not available in source dataset (%s/%s)" % (self.datetimes[0], model_start, model_end))
try:
assert self.datetimes[-1] > model_start
assert self.datetimes[-1] < model_end
except AssertionError:
raise BaseDataControllerError("End time for model (%s) is not available in source dataset (%s/%s)" % (self.datetimes[-1], model_start, model_end))
ds.closenc()
def __init__(self, **kwargs):
"""
Mandatory named arguments:
* geometry (Shapely Geometry Object) no default
* depth (meters) default 0
* start (DateTime Object) none
* step (seconds) default 3600
* npart (number of particles) default 1
* nstep (number of steps) no default
* models (list object) no default, so far there is a transport model and a behavior model
geometry is interchangeable (if it is a point release) with:
* latitude (DD) no default
* longitude (DD) no default
* depth (meters) default 0
"""
# Shoreline
self._use_shoreline = kwargs.pop('use_shoreline', True)
self.shoreline_path = kwargs.get("shoreline_path", None)
self.shoreline_feature = kwargs.get("shoreline_feature", None)
self.shoreline_index_buffer = kwargs.get("shoreline_index_buffer", 0.1)
self.reverse_distance = kwargs.get("reverse_distance", 100)
# Bathy
self._use_bathymetry = kwargs.pop('use_bathymetry', True)
self.bathy_path = kwargs.get("bathy_path", None)
# SeaSurface
self._use_seasurface = kwargs.pop('use_seasurface', True)
self._depth = kwargs.pop('depth', 0)
self._npart = kwargs.pop('npart', 1)
self._step = kwargs.pop('step', 3600)
self.start = kwargs.get('start', None)
if self.start is None:
raise TypeError("must provide a start time to run the model")
# Always convert to UTC
if self.start.tzinfo is None:
self.start = self.start.replace(tzinfo=pytz.utc)
self.start = self.start.astimezone(pytz.utc)
self._models = kwargs.pop('models', None)
self._dirty = True
self.particles = []
self.time_method = kwargs.get('time_method', 'interp').lower()
try:
assert "interp" == self.time_method or "nearest" == self.time_method
except:
raise TypeError("Not a recognized 'time_method' parameter. Only 'nearest' or 'interp' are allowed.")
# The model timesteps in datetime objects
self.datetimes = []
# Interchangeables
if "geometry" in kwargs:
self.geometry = kwargs.pop('geometry')
if not isinstance(self.geometry, Point) and not isinstance(self.geometry, Polygon) and not isinstance(self.geometry, MultiPolygon):
raise TypeError("The geometry attribute must be a shapely Point or Polygon")
elif "latitude" and "longitude" in kwargs:
self.geometry = Point(kwargs.pop('longitude'), kwargs.pop('latitude'))
else:
raise TypeError("must provide a shapely geometry object (point or polygon) or a latitude and a longitude")
# Errors
try:
self._nstep = kwargs.pop('nstep')
except StandardError:
logger.exception("Must provide the number of timesteps to the ModelController")
raise
def run(self, hydrodataset, **kwargs):
# Relax.
time.sleep(2)
# Add ModelController description to logfile
logger.info(self)
# Add the model descriptions to logfile
for m in self._models:
logger.info(m)
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
times = range(0, (self._step*self._nstep)+1, self._step)
# Calculate a datetime object for each model timestep
# This method is duplicated in DataController and ForceParticle
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(times, start=self.start)
time_chunk = self._time_chunk
horiz_chunk = self._horiz_chunk
caching = kwargs.get("caching", True)
if caching is True:
# Should we remove the cache file at the end of the run?
remove_cache = kwargs.get("remove_cache", True)
self.cache_path = kwargs.get("cache", None)
# Create a temp file for the cache if nothing was passed in
if self.cache_path is None:
default_cache_dir = os.path.join(os.path.dirname(__file__), "_cache")
temp_name = AsaRandom.filename(prefix=str(datetime.now().microsecond), suffix=".nc")
self.cache_path = os.path.join(default_cache_dir, temp_name)
# Be sure the cache directory exists
if not os.path.exists(os.path.dirname(self.cache_path)):
logger.info("Creating cache directory: %s" % self.cache_path)
os.makedirs(os.path.dirname(self.cache_path))
else:
# Don't remove cache if we are not caching, because the cache path is set to the dataset path!
# DONT SET THIS TO TRUE
remove_cache = False
# Use the hydrodataset as the cache
self.cache_path = hydrodataset
self.bathy_path = kwargs.get("bathy", None)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(self.geometry, MultiPolygon):
point_locations = [Location4D(latitude=loc.y, longitude=loc.x, depth=self._depth, time=self.start) for loc in AsaTransport.fill_polygon_with_points(goal=self._npart, polygon=self.geometry)]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in xrange(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
# This is where it makes sense to implement the multiprocessing
# looping for particles and models. Can handle each particle in
# parallel probably.
#
# Get the number of cores (may take some tuning) and create that
# many workers then pass particles into the queue for the workers
mgr = multiprocessing.Manager()
nproc = multiprocessing.cpu_count() - 1
if nproc <= 0:
raise ValueError("Model does not run using less than two CPU cores")
# Each particle is a task, plus the DataController
number_of_tasks = len(self.particles) + 1
# We need a process for each particle and one for the data controller
nproc = min(number_of_tasks, nproc)
# Create the task queue for all of the particles and the DataController
tasks = multiprocessing.JoinableQueue(number_of_tasks)
# Create the result queue for all of the particles and the DataController
results = mgr.Queue(number_of_tasks)
# Number of tasks that we need to run. This is decremented everytime something
# completes.
n_run = mgr.Value('int', number_of_tasks)
# The lock that controls access to the 'n_run' variable
nproc_lock = mgr.Lock()
# Create the shared state objects
# This tracks if the system is 'alive'. Most looping whiles will check this
# and break out if it is False. This is True until something goes very wrong.
active = mgr.Value('bool', True)
# Particles use this to tell the Data Controller to "get_data".
# The DataController sets this to False when it is done writing to the cache file.
# Particles will wait for this to be False before reading from the cache file.
# If we are caching, this starts as True so the Particles don't take off. If we
# are not caching, this is False so the Particles can start immediatly.
get_data = mgr.Value('bool', caching)
# Particles use this to tell the DataContoller which indices to 'get_data' for
point_get = mgr.Value('list', [0, 0, 0])
# This locks access to the 'has_data_request_lock' value
data_request_lock = mgr.Lock()
# This tracks which Particle PID is asking the DataController for data
has_data_request_lock = mgr.Value('int', -1)
# The lock that controls access to modifying 'has_read_lock' and 'read_count'
read_lock = mgr.Lock()
# List of Particle PIDs that are reading from the cache
has_read_lock = mgr.list()
# The number of Particles that are reading from the cache
read_count = mgr.Value('int', 0)
# When something is writing to the cache file
write_lock = mgr.Lock()
# PID of process with lock
has_write_lock = mgr.Value('int', -1)
logger.progress((3, "Initializing and caching hydro model's grid"))
try:
ds = CommonDataset.open(hydrodataset)
except Exception:
logger.exception("Failed to access dataset %s" % hydrodataset)
raise DataControllerError("Inaccessible Dataset: %s" % hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
common_variables = self.get_common_variables_from_dataset(ds)
timevar = None
try:
assert common_variables.get("u") in ds._current_variables
assert common_variables.get("v") in ds._current_variables
assert common_variables.get("x") in ds._current_variables
assert common_variables.get("y") in ds._current_variables
timevar = ds.gettimevar(common_variables.get("u"))
except AssertionError:
logger.exception("Could not locate variables needed to run model: %s" % unicode(common_variables))
raise DataControllerError("A required data variable was not found in %s" % hydrodataset)
# Add data controller to the queue first so that it
# can get the initial data and is not blocked
logger.debug('Starting DataController')
logger.progress((4, "Starting processes"))
data_controller = parallel.DataController(hydrodataset, common_variables, n_run, get_data, write_lock, has_write_lock, read_lock, read_count,
time_chunk, horiz_chunk, times,
self.start, point_get, self.reference_location,
caching=caching, cache_path=self.cache_path)
tasks.put(data_controller)
# Create DataController worker
data_controller_process = parallel.Consumer(tasks, results, n_run, nproc_lock, active, get_data, name="DataController")
data_controller_process.start()
logger.debug('Adding %i particles as tasks' % len(self.particles))
for part in self.particles:
forcing = parallel.ForceParticle(self.cache_path,
part,
common_variables,
timevar,
times,
self.start,
self._models,
self.reference_location.point,
self._use_bathymetry,
self._use_shoreline,
self._use_seasurface,
get_data,
n_run,
read_lock,
has_read_lock,
read_count,
point_get,
data_request_lock,
has_data_request_lock,
reverse_distance=self.reverse_distance,
bathy=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
caching=caching)
tasks.put(forcing)
# Create workers for the particles.
procs = [ parallel.Consumer(tasks, results, n_run, nproc_lock, active, get_data, name="ForceParticle-%d" % i)
for i in xrange(nproc - 1) ]
for w in procs:
w.start()
logger.debug('Started %s' % w.name)
# Get results back from queue, test for failed particles
return_particles = []
retrieved = 0.
error_code = 0
logger.info("Waiting for %i particle results" % len(self.particles))
logger.progress((5, "Running model"))
while retrieved < number_of_tasks:
try:
# Returns a tuple of code, result
code, tempres = results.get(timeout=240)
except Queue.Empty:
# Poll the active processes to make sure they are all alive and then continue with loop
if not data_controller_process.is_alive() and data_controller_process.exitcode != 0:
# Data controller is zombied, kill off other processes.
get_data.value is False
results.put((-2, "DataController"))
new_procs = []
old_procs = []
for p in procs:
if not p.is_alive() and p.exitcode != 0:
# Do what the Consumer would do if something finished.
# Add something to results queue
results.put((-3, "ZombieParticle"))
# Decrement nproc (DataController exits when this is 0)
with nproc_lock:
n_run.value = n_run.value - 1
# Remove task from queue (so they can be joined later on)
tasks.task_done()
# Start a new Consumer. It will exit if there are no tasks available.
np = parallel.Consumer(tasks, results, n_run, nproc_lock, active, get_data, name=p.name)
new_procs.append(np)
old_procs.append(p)
# Release any locks the PID had
if p.pid in has_read_lock:
with read_lock:
read_count.value -= 1
has_read_lock.remove(p.pid)
if has_data_request_lock.value == p.pid:
has_data_request_lock.value = -1
try:
data_request_lock.release()
except:
pass
if has_write_lock.value == p.pid:
has_write_lock.value = -1
try:
write_lock.release()
except:
pass
for p in old_procs:
try:
procs.remove(p)
except ValueError:
logger.warn("Did not find %s in the list of processes. Continuing on." % p.name)
for p in new_procs:
procs.append(p)
logger.warn("Started a new consumer (%s) to replace a zombie consumer" % p.name)
p.start()
else:
# We got one.
retrieved += 1
if code is None:
logger.warn("Got an unrecognized response from a task.")
elif code == -1:
logger.warn("Particle %s has FAILED!!" % tempres.uid)
elif code == -2:
error_code = code
logger.warn("DataController has FAILED!! Removing cache file so the particles fail.")
try:
os.remove(self.cache_path)
except OSError:
logger.debug("Could not remove cache file, it probably never existed")
pass
elif code == -3:
error_code = code
logger.info("A zombie process was caught and task was removed from queue")
elif isinstance(tempres, Particle):
logger.info("Particle %d finished" % tempres.uid)
return_particles.append(tempres)
# We mulitply by 95 here to save 5% for the exporting
logger.progress((round((retrieved / number_of_tasks) * 90., 1), "Particle %d finished" % tempres.uid))
elif tempres == "DataController":
logger.info("DataController finished")
logger.progress((round((retrieved / number_of_tasks) * 90., 1), "DataController finished"))
else:
logger.info("Got a strange result on results queue")
logger.info(str(tempres))
logger.info("Retrieved %i/%i results" % (int(retrieved), number_of_tasks))
if len(return_particles) != len(self.particles):
logger.warn("Some particles failed and are not included in the output")
# The results queue should be empty at this point
assert results.empty() is True
# Should be good to join on the tasks now that the queue is empty
logger.info("Joining the task queue")
tasks.join()
# Join all processes
logger.info("Joining the processes")
for w in procs + [data_controller_process]:
# Wait 10 seconds
w.join(10.)
if w.is_alive():
# Process is hanging, kill it.
logger.info("Terminating %s forcefully. This should have exited itself." % w.name)
w.terminate()
logger.info('Workers complete')
self.particles = return_particles
# Remove Manager so it shuts down
del mgr
# Remove timevar
del timevar
# Remove the cache file
if remove_cache is True:
try:
os.remove(self.cache_path)
except OSError:
logger.debug("Could not remove cache file, it probably never existed")
logger.progress((96, "Exporting results"))
if len(self.particles) > 0:
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
# Make sure output_path is also included
if kwargs.get("output_path", None) is not None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for format in formats:
logger.info("Exporting to: %s" % format)
try:
self.export(output_path, format=format)
except:
logger.exception("Failed to export to: %s" % format)
else:
logger.warn('The output_formats parameter should be a list, not saving any output!')
else:
logger.warn('No output path defined, not saving any output!')
else:
logger.warn('No output format defined, not saving any output!')
else:
logger.warn("Model didn't actually do anything, check the log.")
if error_code == -2:
raise DataControllerError("Error in the DataController")
else:
raise ModelError("Error in the model")
logger.progress((99, "Model Run Complete"))
return
def __init__(self, **kwargs):
"""
Mandatory named arguments:
* geometry (Shapely Geometry Object) no default
* depth (meters) default 0
* start (DateTime Object) none
* step (seconds) default 3600
* npart (number of particles) default 1
* nstep (number of steps) no default
* models (list object) no default, so far there is a transport model and a behavior model
geometry is interchangeable (if it is a point release) with:
* latitude (DD) no default
* longitude (DD) no default
* depth (meters) default 0
Non-mandatory named arguments:
* pool (task pool) - defaults to multiprocessing.pool, inject your own for cluster ability
"""
# Should we thread the result listener? Defined in subclasses that need it.
self.thread_result_listener = False
# Shoreline
self._use_shoreline = kwargs.pop('use_shoreline', True)
self.shoreline_path = kwargs.get("shoreline_path", None)
self.shoreline_feature = kwargs.get("shoreline_feature", None)
self.shoreline_index_buffer = kwargs.get("shoreline_index_buffer", 0.1)
self.reverse_distance = kwargs.get("reverse_distance", 100)
# Bathy
self._use_bathymetry = kwargs.pop('use_bathymetry', True)
self.bathy_path = kwargs.get("bathy_path", None)
# SeaSurface
self._use_seasurface = kwargs.pop('use_seasurface', True)
self._depth = kwargs.pop('depth', 0)
self._npart = kwargs.pop('npart', 1)
self._step = kwargs.pop('step', 3600)
self.start = kwargs.get('start', None)
if self.start is None:
raise TypeError("must provide a start time to run the model")
# Always convert to UTC
if self.start.tzinfo is None:
self.start = self.start.replace(tzinfo=pytz.utc)
self.start = self.start.astimezone(pytz.utc)
self._models = kwargs.pop('models', None)
self._dirty = True
self.particles = []
self.time_method = kwargs.get('time_method', 'interp').lower()
try:
assert "interp" == self.time_method or "nearest" == self.time_method
except:
raise TypeError(
"Not a recognized 'time_method' parameter. Only 'nearest' or 'interp' are allowed."
)
# The model timesteps in datetime objects
self.datetimes = []
# Interchangeables
if "geometry" in kwargs:
self.geometry = kwargs.pop('geometry')
if not isinstance(self.geometry, Point) and not isinstance(
self.geometry, Polygon) and not isinstance(
self.geometry, MultiPolygon):
raise TypeError(
"The geometry attribute must be a shapely Point or Polygon"
)
elif "latitude" and "longitude" in kwargs:
self.geometry = Point(kwargs.pop('longitude'),
kwargs.pop('latitude'))
else:
raise TypeError(
"must provide a shapely geometry object (point or polygon) or a latitude and a longitude"
)
# Errors
try:
self._nstep = kwargs.pop('nstep')
except Exception:
logger.exception(
"Must provide the number of timesteps to the ModelController")
raise
self.pool = kwargs.get('pool', None)
def setup_run(self, hydrodataset, **kwargs):
self.hydrodataset = hydrodataset
logger.setLevel(logging.PROGRESS)
# Relax.
time.sleep(0.5)
# Add ModelController description to logfile
logger.info(str(self))
# Add the model descriptions to logfile
for m in self._models:
logger.info(str(m))
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
self.times = list(range(0, (self._step * self._nstep) + 1, self._step))
# Calculate a datetime object for each model timestep
# This method is duplicated in CachingDataController and CachingForcer
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
self.modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(
self.times, start=self.start)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(
self.geometry, MultiPolygon):
point_locations = [
Location4D(latitude=loc.y,
longitude=loc.x,
depth=self._depth,
time=self.start)
for loc in AsaTransport.fill_polygon_with_points(
goal=self._npart, polygon=self.geometry)
]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in range(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
logger.progress((3, "Initializing and caching hydro model's grid %s" %
self.hydrodataset))
try:
ds = CommonDataset.open(self.hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
self.common_variables = self.get_common_variables_from_dataset(ds)
except Exception:
logger.exception("Failed to access dataset %s" % self.hydrodataset)
raise BaseDataControllerError("Inaccessible Dataset: %s" %
self.hydrodataset)
self.timevar = None
try:
assert self.common_variables.get("u") in ds._current_variables
assert self.common_variables.get("v") in ds._current_variables
assert self.common_variables.get("x") in ds._current_variables
assert self.common_variables.get("y") in ds._current_variables
self.timevar = ds.gettimevar(self.common_variables.get("u"))
model_start = self.timevar.get_dates()[0]
model_end = self.timevar.get_dates()[-1]
except AssertionError:
logger.exception(
"Could not locate variables needed to run model: %s" %
str(self.common_variables))
raise BaseDataControllerError(
"A required data variable was not found in %s" %
self.hydrodataset)
finally:
ds.closenc()
try:
assert self.start > model_start
assert self.start < model_end
except AssertionError:
raise BaseDataControllerError(
"Start time for model (%s) is not available in source dataset (%s/%s)"
% (self.datetimes[0], model_start, model_end))
try:
assert self.datetimes[-1] > model_start
assert self.datetimes[-1] < model_end
except AssertionError:
raise BaseDataControllerError(
"End time for model (%s) is not available in source dataset (%s/%s)"
% (self.datetimes[-1], model_start, model_end))
def setup_run(self, hydrodataset, **kwargs):
self.hydrodataset = hydrodataset
logger.setLevel(logging.PROGRESS)
# Relax.
time.sleep(0.5)
# Add ModelController description to logfile
logger.info(str(self))
# Add the model descriptions to logfile
for m in self._models:
logger.info(str(m))
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
self.times = list(range(0, (self._step*self._nstep)+1, self._step))
# Calculate a datetime object for each model timestep
# This method is duplicated in CachingDataController and CachingForcer
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
self.modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(self.times, start=self.start)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(self.geometry, MultiPolygon):
point_locations = [Location4D(latitude=loc.y, longitude=loc.x, depth=self._depth, time=self.start) for loc in AsaTransport.fill_polygon_with_points(goal=self._npart, polygon=self.geometry)]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in range(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
logger.progress((3, "Initializing and caching hydro model's grid %s" % self.hydrodataset))
try:
ds = CommonDataset.open(self.hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
self.common_variables = self.get_common_variables_from_dataset(ds)
except Exception:
logger.exception("Failed to access dataset %s" % self.hydrodataset)
raise BaseDataControllerError("Inaccessible Dataset: %s" % self.hydrodataset)
self.timevar = None
try:
assert self.common_variables.get("u") in ds._current_variables
assert self.common_variables.get("v") in ds._current_variables
assert self.common_variables.get("x") in ds._current_variables
assert self.common_variables.get("y") in ds._current_variables
self.timevar = ds.gettimevar(self.common_variables.get("u"))
model_start = self.timevar.get_dates()[0]
model_end = self.timevar.get_dates()[-1]
except AssertionError:
logger.exception("Could not locate variables needed to run model: %s" % str(self.common_variables))
raise BaseDataControllerError("A required data variable was not found in %s" % self.hydrodataset)
finally:
ds.closenc()
try:
assert self.start > model_start
assert self.start < model_end
except AssertionError:
raise BaseDataControllerError("Start time for model (%s) is not available in source dataset (%s/%s)" % (self.datetimes[0], model_start, model_end))
try:
assert self.datetimes[-1] > model_start
assert self.datetimes[-1] < model_end
except AssertionError:
raise BaseDataControllerError("End time for model (%s) is not available in source dataset (%s/%s)" % (self.datetimes[-1], model_start, model_end))
def get_nearest_data(self, i):
""" Note: self.dataset.opennc() must be called before calling this function.
This is because the caching forcer must close it everytime, while a non caching
forcer can leave the dataset open.
"""
try:
# Grab data at time index closest to particle location
u = np.mean(
np.mean(
self.dataset.get_values('u',
timeinds=[np.asarray([i])],
point=self.particle.location)))
v = np.mean(
np.mean(
self.dataset.get_values('v',
timeinds=[np.asarray([i])],
point=self.particle.location)))
# if there is vertical velocity inthe dataset, get it
if 'w' in self.dataset.nc.variables:
w = np.mean(
np.mean(
self.dataset.get_values('w',
timeindsf=[np.asarray([i])],
point=self.particle.location)))
else:
w = 0.0
# If there is salt and temp in the dataset, get it
if self.temp_name is not None and self.salt_name is not None:
temp = np.mean(
np.mean(
self.dataset.get_values('temp',
timeinds=[np.asarray([i])],
point=self.particle.location)))
salt = np.mean(
np.mean(
self.dataset.get_values('salt',
timeinds=[np.asarray([i])],
point=self.particle.location)))
# Check for nans that occur in the ocean (happens because
# of model and coastline resolution mismatches)
if np.isnan(u).any() or np.isnan(v).any() or np.isnan(w).any():
# Take the mean of the closest 4 points
# If this includes nan which it will, result is nan
uarray1 = self.dataset.get_values('u',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
varray1 = self.dataset.get_values('v',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
if 'w' in self.dataset.nc.variables:
warray1 = self.dataset.get_values(
'w',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
w = warray1.mean()
else:
w = 0.0
if self.temp_name is not None and self.salt_name is not None:
temparray1 = self.dataset.get_values(
'temp',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
saltarray1 = self.dataset.get_values(
'salt',
timeinds=[np.asarray([i])],
point=self.particle.location,
num=2)
temp = temparray1.mean()
salt = saltarray1.mean()
u = uarray1.mean()
v = varray1.mean()
if self.temp_name is None:
temp = np.nan
if self.salt_name is None:
salt = np.nan
except Exception:
logger.exception("Could not retrieve data.")
raise
return u, v, w, temp, salt
def start_tasks(self, **kwargs):
try:
logger.info('Starting CachingDataController')
# Add data controller to the queue first so that it
# can get the initial data and is not blocked
data_controller = CachingDataController(self.hydrodataset,
self.common_variables,
self.n_run,
self.get_data,
self.write_lock,
self.has_write_lock,
self.read_lock,
self.read_count,
self.time_chunk,
self.horiz_chunk,
self.times,
self.start,
self.point_get,
self.reference_location,
cache_path=self.cache_path)
self.tasks.put(data_controller)
# Create CachingDataController worker
self.data_controller_process = Consumer(
self.tasks,
self.results,
self.n_run,
self.nproc_lock,
self.active,
self.get_data,
name="CachingDataController")
self.data_controller_process.start()
logger.info('Adding %i particles as tasks' %
self.total_particle_count())
for part in self.particles:
forcer = CachingForcer(
self.cache_path,
particle=part,
common_variables=self.common_variables,
timevar=self.timevar,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
shoreline_index_buffer=self.shoreline_index_buffer,
get_data=self.get_data,
read_lock=self.read_lock,
has_read_lock=self.has_read_lock,
read_count=self.read_count,
point_get=self.point_get,
data_request_lock=self.data_request_lock,
has_data_request_lock=self.has_data_request_lock)
self.tasks.put(forcer)
# Create workers for the particles.
self.procs = [
Consumer(self.tasks,
self.results,
self.n_run,
self.nproc_lock,
self.active,
self.get_data,
name="CachingForcer-%d" % i)
for i in range(self.nproc - 1)
]
logger.progress((5, 'Running model'))
for w in self.procs:
w.start()
logger.info('Started %s' % w.name)
return True
except Exception:
logger.exception("Something didn't start correctly!")
return False
def __call__(self, active):
c = 0
self.dataset = CommonDataset.open(self.hydrodataset)
self.remote = self.dataset.nc
# Calculate the datetimes of the model timesteps like
# the particle objects do, so we can figure out unique
# time indices
modelTimestep, newtimes = AsaTransport.get_time_objects_from_model_timesteps(self.times, start=self.start_time)
timevar = self.dataset.gettimevar(self.uname)
# Don't need to grab the last datetime, as it is not needed for forcing, only
# for setting the time of the final particle forcing
time_indexs = timevar.nearest_index(newtimes[0:-1], select='before')
# Have to make sure that we get the plus 1 for the
# linear interpolation of u,v,w,temp,salt
self.inds = np.unique(time_indexs)
self.inds = np.append(self.inds, self.inds.max()+1)
# While there is at least 1 particle still running,
# stay alive, if not break
while self.n_run.value > 1:
if self.caching is False:
logger.debug("Caching is False, not doing much. Just hanging out until all of the particles finish.")
timer.sleep(10)
continue
# If particle asks for data, do the following
if self.get_data.value is True:
logger.debug("Particle asked for data!")
# Wait for particles to get out
while True:
self.read_lock.acquire()
logger.debug("Read count: %d" % self.read_count.value)
if self.read_count.value > 0:
logger.debug("Waiting for write lock on cache file (particles must stop reading)...")
self.read_lock.release()
timer.sleep(2)
else:
break
# Get write lock on the file. Already have read lock.
self.write_lock.acquire()
self.has_write_lock.value = os.getpid()
if c == 0:
logger.debug("Creating cache file")
try:
# Open local cache for writing, overwrites
# existing file with same name
self.local = netCDF4.Dataset(self.cache_path, 'w')
indices = self.dataset.get_indices(self.uname, timeinds=[np.asarray([0])], point=self.start)
self.point_get.value = [self.inds[0], indices[-2], indices[-1]]
# Create dimensions for u and v variables
self.local.createDimension('time', None)
self.local.createDimension('level', None)
self.local.createDimension('x', None)
self.local.createDimension('y', None)
# Create 3d or 4d u and v variables
if self.remote.variables[self.uname].ndim == 4:
self.ndim = 4
dimensions = ('time', 'level', 'y', 'x')
coordinates = "time z lon lat"
elif self.remote.variables[self.uname].ndim == 3:
self.ndim = 3
dimensions = ('time', 'y', 'x')
coordinates = "time lon lat"
shape = self.remote.variables[self.uname].shape
# If there is no FillValue defined in the dataset, use np.nan.
# Sometimes it will work out correctly and other times we will
# have a huge cache file.
try:
fill = self.remote.variables[self.uname].missing_value
except Exception:
fill = np.nan
# Create domain variable that specifies
# where there is data geographically/by time
# and where there is not data,
# Used for testing if particle needs to
# ask cache to update
domain = self.local.createVariable('domain', 'i', dimensions, zlib=False, fill_value=0)
domain.coordinates = coordinates
# Create local u and v variables
u = self.local.createVariable('u', 'f', dimensions, zlib=False, fill_value=fill)
v = self.local.createVariable('v', 'f', dimensions, zlib=False, fill_value=fill)
v.coordinates = coordinates
u.coordinates = coordinates
localvars = [u, v, ]
remotevars = [self.remote.variables[self.uname], self.remote.variables[self.vname]]
# Create local w variable
if self.wname is not None:
w = self.local.createVariable('w', 'f', dimensions, zlib=False, fill_value=fill)
w.coordinates = coordinates
localvars.append(w)
remotevars.append(self.remote.variables[self.wname])
if self.temp_name is not None and self.salt_name is not None:
# Create local temp and salt vars
temp = self.local.createVariable('temp', 'f', dimensions, zlib=False, fill_value=fill)
salt = self.local.createVariable('salt', 'f', dimensions, zlib=False, fill_value=fill)
temp.coordinates = coordinates
salt.coordinates = coordinates
localvars.append(temp)
localvars.append(salt)
remotevars.append(self.remote.variables[self.temp_name])
remotevars.append(self.remote.variables[self.salt_name])
# Create local lat/lon coordinate variables
if self.remote.variables[self.xname].ndim == 2:
lon = self.local.createVariable('lon', 'f', ("y", "x"), zlib=False)
lon[:] = self.remote.variables[self.xname][:, :]
lat = self.local.createVariable('lat', 'f', ("y", "x"), zlib=False)
lat[:] = self.remote.variables[self.yname][:, :]
if self.remote.variables[self.xname].ndim == 1:
lon = self.local.createVariable('lon', 'f', ("x"), zlib=False)
lon[:] = self.remote.variables[self.xname][:]
lat = self.local.createVariable('lat', 'f', ("y"), zlib=False)
lat[:] = self.remote.variables[self.yname][:]
# Create local z variable
if self.zname is not None:
if self.remote.variables[self.zname].ndim == 4:
z = self.local.createVariable('z', 'f', ("time", "level", "y", "x"), zlib=False)
remotez = self.remote.variables[self.zname]
localvars.append(z)
remotevars.append(remotez)
elif self.remote.variables[self.zname].ndim == 3:
z = self.local.createVariable('z', 'f', ("level", "y", "x"), zlib=False)
z[:] = self.remote.variables[self.zname][:, :, :]
elif self.remote.variables[self.zname].ndim == 1:
z = self.local.createVariable('z', 'f', ("level",), zlib=False)
z[:] = self.remote.variables[self.zname][:]
# Create local time variable
time = self.local.createVariable('time', 'f8', ("time",), zlib=False)
if self.tname is not None:
time[:] = self.remote.variables[self.tname][self.inds]
if self.point_get.value[0]+self.time_size > np.max(self.inds):
current_inds = np.arange(self.point_get.value[0], np.max(self.inds)+1)
else:
current_inds = np.arange(self.point_get.value[0], self.point_get.value[0] + self.time_size)
# Get data from remote dataset and add
# to local cache.
# Try 20 times on the first attempt
current_attempt = 1
max_attempts = 20
while True:
try:
assert current_attempt <= max_attempts
self.get_remote_data(localvars, remotevars, current_inds, shape)
except AssertionError:
raise
except:
logger.warn("CachingDataController failed to get remote data. Trying again in 20 seconds. %s attempts left." % str(max_attempts-current_attempt))
logger.exception("Data Access Error")
timer.sleep(20)
current_attempt += 1
else:
break
c += 1
except (Exception, AssertionError):
logger.error("CachingDataController failed to get data (first request)")
raise
finally:
self.local.sync()
self.local.close()
self.has_write_lock.value = -1
self.write_lock.release()
self.get_data.value = False
self.read_lock.release()
logger.debug("Done updating cache file, closing file, and releasing locks")
else:
logger.debug("Updating cache file")
try:
# Open local cache dataset for appending
self.local = netCDF4.Dataset(self.cache_path, 'a')
# Create local and remote variable objects
# for the variables of interest
u = self.local.variables['u']
v = self.local.variables['v']
time = self.local.variables['time']
remoteu = self.remote.variables[self.uname]
remotev = self.remote.variables[self.vname]
# Create lists of variable objects for
# the data updater
localvars = [u, v, ]
remotevars = [remoteu, remotev, ]
if self.salt_name is not None and self.temp_name is not None:
salt = self.local.variables['salt']
temp = self.local.variables['temp']
remotesalt = self.remote.variables[self.salt_name]
remotetemp = self.remote.variables[self.temp_name]
localvars.append(salt)
localvars.append(temp)
remotevars.append(remotesalt)
remotevars.append(remotetemp)
if self.wname is not None:
w = self.local.variables['w']
remotew = self.remote.variables[self.wname]
localvars.append(w)
remotevars.append(remotew)
if self.zname is not None:
remotez = self.remote.variables[self.zname]
if remotez.ndim == 4:
z = self.local.variables['z']
localvars.append(z)
remotevars.append(remotez)
if self.tname is not None:
# remotetime = self.remote.variables[self.tname]
time[self.inds] = self.remote.variables[self.inds]
if self.point_get.value[0]+self.time_size > np.max(self.inds):
current_inds = np.arange(self.point_get.value[0], np.max(self.inds)+1)
else:
current_inds = np.arange(self.point_get.value[0], self.point_get.value[0] + self.time_size)
# Get data from remote dataset and add
# to local cache
while True:
try:
self.get_remote_data(localvars, remotevars, current_inds, shape)
except:
logger.warn("CachingDataController failed to get remote data. Trying again in 30 seconds")
timer.sleep(30)
else:
break
c += 1
except Exception:
logger.error("CachingDataController failed to get data (not first request)")
raise
finally:
self.local.sync()
self.local.close()
self.has_write_lock.value = -1
self.write_lock.release()
self.get_data.value = False
self.read_lock.release()
logger.debug("Done updating cache file, closing file, and releasing locks")
else:
logger.debug("Particles are still running, waiting for them to request data...")
timer.sleep(2)
self.dataset.closenc()
return "CachingDataController"
def __call__(self, proc, active):
self.active = active
if self.usebathy is True:
try:
self._bathymetry = Bathymetry(file=self.bathy)
except Exception:
logger.exception("Could not load Bathymetry file: %s, using no Bathymetry for this run!" % self.bathy)
self.usebathy = False
self._shoreline = None
if self.useshore is True:
self._shoreline = Shoreline(path=self.shoreline_path, feature_name=self.shoreline_feature, point=self.release_location_centroid, spatialbuffer=0.25)
# Make sure we are not starting on land. Raises exception if we are.
self._shoreline.intersect(start_point=self.release_location_centroid, end_point=self.release_location_centroid)
if self.active.value is True:
while self.get_data.value is True:
logger.info("Waiting for DataController to start...")
timer.sleep(5)
pass
# Initialize commondataset of local cache, then
# close the related netcdf file
try:
if self.caching is True:
with self.read_lock:
self.read_count.value += 1
self.has_read_lock.append(os.getpid())
self.dataset = CommonDataset.open(self.hydrodataset)
self.dataset.closenc()
except StandardError:
logger.warn("No source dataset: %s. Particle exiting" % self.hydrodataset)
raise
finally:
if self.caching is True:
with self.read_lock:
self.read_count.value -= 1
self.has_read_lock.remove(os.getpid())
# Calculate datetime at every timestep
modelTimestep, newtimes = AsaTransport.get_time_objects_from_model_timesteps(self.times, start=self.start_time)
if self.time_method == 'interp':
time_indexs = self.timevar.nearest_index(newtimes, select='before')
elif self.time_method == 'nearest':
time_indexs = self.timevar.nearest_index(newtimes)
else:
logger.warn("Method for computing u,v,w,temp,salt not supported!")
try:
assert len(newtimes) == len(time_indexs)
except AssertionError:
logger.error("Time indexes are messed up. Need to have equal datetime and time indexes")
raise
# loop over timesteps
# We don't loop over the last time_index because
# we need to query in the time_index and set the particle's
# location as the 'newtime' object.
for loop_i, i in enumerate(time_indexs[0:-1]):
if self.active.value is False:
raise ValueError("Particle exiting due to Failure.")
newloc = None
# Get the variable data required by the models
if self.time_method == 'nearest':
u, v, w, temp, salt = self.data_nearest(i, newtimes[loop_i])
elif self.time_method == 'interp':
u, v, w, temp, salt = self.data_interp(i, newtimes[loop_i])
else:
logger.warn("Method for computing u,v,w,temp,salt is unknown. Only 'nearest' and 'interp' are supported.")
# Get the bathy value at the particles location
if self.usebathy is True:
bathymetry_value = self._bathymetry.get_depth(self.part.location)
else:
bathymetry_value = -999999999999999
# Age the particle by the modelTimestep (seconds)
# 'Age' meaning the amount of time it has been forced.
self.part.age(seconds=modelTimestep[loop_i])
# loop over models - sort these in the order you want them to run
for model in self.models:
movement = model.move(self.part, u, v, w, modelTimestep[loop_i], temperature=temp, salinity=salt, bathymetry_value=bathymetry_value)
newloc = Location4D(latitude=movement['latitude'], longitude=movement['longitude'], depth=movement['depth'], time=newtimes[loop_i+1])
logger.debug("%s - moved %.3f meters (horizontally) and %.3f meters (vertically) by %s with data from %s" % (self.part.logstring(), movement['distance'], movement['vertical_distance'], model.__class__.__name__, newtimes[loop_i].isoformat()))
if newloc:
self.boundary_interaction(particle=self.part, starting=self.part.location, ending=newloc,
distance=movement['distance'], angle=movement['angle'],
azimuth=movement['azimuth'], reverse_azimuth=movement['reverse_azimuth'],
vertical_distance=movement['vertical_distance'], vertical_angle=movement['vertical_angle'])
logger.debug("%s - was forced by %s and is now at %s" % (self.part.logstring(), model.__class__.__name__, self.part.location.logstring()))
self.part.note = self.part.outputstring()
# Each timestep, save the particles status and environmental variables.
# This keep fields such as temp, salt, halted, settled, and dead matched up with the number of timesteps
self.part.save()
self.dataset.closenc()
# We won't pull data for the last entry in locations, but we need to populate it with fill data.
self.part.fill_environment_gap()
if self.usebathy is True:
self._bathymetry.close()
if self.useshore is True:
self._shoreline.close()
return self.part
