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 run(run_id):
with app.app_context():
job = get_current_job(connection=redis_connection)
output_path = os.path.join(current_app.config['OUTPUT_PATH'], run_id)
shutil.rmtree(output_path, ignore_errors=True)
os.makedirs(output_path)
cache_path = os.path.join(current_app.config['CACHE_PATH'], run_id)
shutil.rmtree(cache_path, ignore_errors=True)
os.makedirs(cache_path)
f, log_file = tempfile.mkstemp(dir=cache_path, prefix=run_id, suffix=".log")
os.close(f)
os.chmod(log_file, 0644)
run = db.Run.find_one( { '_id' : ObjectId(run_id) } )
if run is None:
return "Failed to locate run %s. May have been deleted while task was in the queue?" % run_id
# Wait for PubSub listening to begin
time.sleep(2)
model = None
try:
hydropath = run['hydro_path']
geometry = loads(run['geometry'])
start_depth = run['release_depth']
num_particles = run['particles']
time_step = run['timestep']
num_steps = int(math.ceil((run['duration'] * 24 * 60 * 60) / time_step))
start_time = run['start'].replace(tzinfo = pytz.utc)
shoreline_path = run['shoreline_path'] or app.config.get("SHORE_PATH")
shoreline_feat = run['shoreline_feature']
# Setup Models
models = []
if run['cached_behavior'] is not None and run['cached_behavior'].get('results', None) is not None:
behavior_data = run['cached_behavior']['results'][0]
l = LarvaBehavior(data=behavior_data)
models.append(l)
models.append(Transport(horizDisp=run['horiz_dispersion'], vertDisp=run['vert_dispersion']))
output_formats = [
ex.H5Trackline,
ex.H5TracklineWithPoints,
ex.H5ParticleTracklines,
ex.H5ParticleMultiPoint,
ex.H5GDALShapefile
]
def save_progress(stop, progress_deque, logger):
while True:
if stop():
break
try:
record = progress_deque.pop()
if record == StopIteration:
break
job.meta["updated"] = record[0]
if record is not None and record[1] >= 0:
job.meta["progress"] = record[1]
if isinstance(record[2], unicode) or isinstance(record[2], str):
job.meta["message"] = record[2]
job.save()
except IndexError:
pass
except Exception:
raise
# Relax
time.sleep(0.01)
# Set up Logger
from paegan.logger.progress_handler import ProgressHandler
from paegan.logger.multi_process_logging import MultiProcessingLogHandler
from paegan.logger import logger
# Logging handler
queue = multiprocessing.Queue(-1)
logger.setLevel(logging.PROGRESS)
if app.config.get("DEBUG") is True:
mphandler = MultiProcessingLogHandler(log_file, queue, stream=True)
else:
mphandler = MultiProcessingLogHandler(log_file, queue)
mphandler.setLevel(logging.PROGRESS)
formatter = logging.Formatter('[%(asctime)s] - %(levelname)s - %(name)s - %(processName)s - %(message)s')
mphandler.setFormatter(formatter)
logger.addHandler(mphandler)
# Progress handler
progress_deque = collections.deque(maxlen=1)
progress_handler = ProgressHandler(progress_deque)
progress_handler.setLevel(logging.PROGRESS)
logger.addHandler(progress_handler)
stop_log_listener = False
pl = threading.Thread(name="ProgressUpdater", target=save_progress, args=(lambda: stop_log_listener, progress_deque, logger,))
pl.start()
model = CachingModelController(
geometry=geometry,
depth=start_depth,
start=start_time,
step=time_step,
nstep=num_steps,
npart=num_particles,
models=models,
use_bathymetry=True,
bathy_path=current_app.config['BATHY_PATH'],
use_shoreline=True,
time_method=run['time_method'],
time_chunk=run['time_chunk'],
horiz_chunk=run['horiz_chunk'],
shoreline_path=shoreline_path,
shoreline_feature=shoreline_feat,
shoreline_index_buffer=0.05)
cache_file = os.path.join(cache_path, run_id + ".nc.cache")
model.setup_run(hydropath, cache_path=cache_file, remove_cache=False)
run.started = datetime.utcnow()
model.run(output_formats=output_formats, output_path=output_path)
except Exception as e:
# Stop progress log handler
stop_log_listener = True
# Wait for log listener to exit
pl.join()
app.logger.exception("Failed to run model")
job.meta["message"] = e.message
job.meta["outcome"] = "failed"
else:
# Stop progress log handler
stop_log_listener = True
# Wait for log listener to exit
pl.join()
job.meta["message"] = "Complete"
job.meta["outcome"] = "success"
job.meta["progress"] = 100
finally:
# Close and remove the handlers so we can use the log file without a file lock
for hand in list(logger.handlers):
logger.removeHandler(hand)
hand.flush()
hand.close()
queue.close()
queue.join_thread()
time.sleep(1)
# Move logfile to output directory
shutil.move(log_file, os.path.join(output_path, 'model.log'))
# Move cachefile to output directory if we made one
shutil.move(cache_file, os.path.join(output_path, 'hydro_cache.nc'))
output_files = []
for filename in os.listdir(output_path):
outfile = os.path.join(output_path, filename)
output_files.append(outfile)
result_files = []
# Handle results and cleanup
if current_app.config['USE_S3'] is True:
base_access_url = urljoin("http://%s.s3.amazonaws.com/output/" % current_app.config['S3_BUCKET'], run_id)
# Upload results to S3 and remove the local copies
conn = S3Connection()
bucket = conn.get_bucket(current_app.config['S3_BUCKET'])
for outfile in output_files:
# Upload the outfile with the same as the run name
_, ext = os.path.splitext(outfile)
new_filename = slugify(unicode(run['name'])) + ext
k = Key(bucket)
k.key = "output/%s/%s" % (run_id, new_filename)
k.set_contents_from_filename(outfile)
k.set_acl('public-read')
result_files.append(base_access_url + "/" + new_filename)
os.remove(outfile)
shutil.rmtree(output_path, ignore_errors=True)
else:
result_files = output_files
job.meta["updated"] = datetime.utcnow().replace(tzinfo=pytz.utc).astimezone(pytz.utc)
job.save()
# Set output fields
run.output = result_files
run.ended = datetime.utcnow()
run.compute()
run.save()
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 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 run(run_id):
# Sleep to give the Run object enough time to save
time.sleep(10)
with app.app_context():
from paegan.logger import logger
job = get_current_job()
output_path = os.path.join(current_app.config['OUTPUT_PATH'], run_id)
shutil.rmtree(output_path, ignore_errors=True)
os.makedirs(output_path)
cache_path = os.path.join(current_app.config['CACHE_PATH'], run_id)
shutil.rmtree(cache_path, ignore_errors=True)
os.makedirs(cache_path)
temp_animation_path = os.path.join(current_app.config['OUTPUT_PATH'], "temp_images_" + run_id)
shutil.rmtree(temp_animation_path, ignore_errors=True)
os.makedirs(temp_animation_path)
# Set up Logger
queue = multiprocessing.Queue(-1)
f, log_file = tempfile.mkstemp(dir=cache_path, prefix=run_id, suffix=".log")
os.close(f)
# Close any existing handlers
(hand.close() for hand in logger.handlers)
# Remove any existing handlers
logger.handlers = []
logger.setLevel(logging.PROGRESS)
handler = MultiProcessingLogHandler(log_file, queue)
handler.setLevel(logging.PROGRESS)
formatter = logging.Formatter('[%(asctime)s] - %(levelname)s - %(name)s - %(processName)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
# Progress stuff. Hokey!
progress_deque = collections.deque(maxlen=1)
progress_handler = ProgressHandler(progress_deque)
progress_handler.setLevel(logging.PROGRESS)
logger.addHandler(progress_handler)
e = threading.Event()
def save_progress():
while e.wait(5) is not True:
try:
record = progress_deque.pop()
if record == StopIteration:
break
job.meta["updated"] = record[0]
if record is not None and record[1] >= 0:
job.meta["progress"] = record[1]
if isinstance(record[2], unicode) or isinstance(record[2], str):
job.meta["message"] = record[2]
job.save()
except IndexError:
pass
except Exception:
raise
return
t = threading.Thread(name="ProgressUpdater", target=save_progress)
t.daemon = True
t.start()
model = None
try:
logger.progress((0, "Configuring model"))
run = db.Run.find_one( { '_id' : ObjectId(run_id) } )
if run is None:
return "Failed to locate run %s. May have been deleted while task was in the queue?" % run_id
geometry = loads(run['geometry'])
start_depth = run['release_depth']
num_particles = run['particles']
time_step = run['timestep']
num_steps = int(math.ceil((run['duration'] * 24 * 60 * 60) / time_step))
start_time = run['start'].replace(tzinfo = pytz.utc)
shoreline_path = run['shoreline_path'] or app.config.get("SHORE_PATH")
shoreline_feat = run['shoreline_feature']
# Set up output directory/bucket for run
output_formats = ['Shapefile', 'NetCDF', 'Trackline']
# Setup Models
models = []
if run['cached_behavior'] is not None and run['cached_behavior'].get('results', None) is not None:
behavior_data = run['cached_behavior']['results'][0]
l = LarvaBehavior(data=behavior_data)
models.append(l)
models.append(Transport(horizDisp=run['horiz_dispersion'], vertDisp=run['vert_dispersion']))
# Setup ModelController
model = ModelController(geometry=geometry, depth=start_depth, start=start_time, step=time_step, nstep=num_steps, npart=num_particles, models=models, use_bathymetry=True, use_shoreline=True,
time_chunk=run['time_chunk'], horiz_chunk=run['horiz_chunk'], time_method=run['time_method'], shoreline_path=shoreline_path, shoreline_feature=shoreline_feat, reverse_distance=1500)
# Run the model
cache_file = os.path.join(cache_path, run_id + ".nc.cache")
bathy_file = current_app.config['BATHY_PATH']
model.run(run['hydro_path'], output_path=output_path, bathy=bathy_file, output_formats=output_formats, cache=cache_file, remove_cache=False, caching=run['caching'])
# Skip creating movie output_path
"""
from paegan.viz.trajectory import CFTrajectory
logger.info("Creating animation...")
for filename in os.listdir(output_path):
if os.path.splitext(filename)[1][1:] == "nc":
# Found netCDF file
netcdf_file = os.path.join(output_path,filename)
traj = CFTrajectory(netcdf_file)
success = traj.plot_animate(os.path.join(output_path,'animation.avi'), temp_folder=temp_animation_path, bathy=app.config['BATHY_PATH'])
if not success:
logger.info("Could not create animation")
else:
logger.info("Animation saved")
"""
job.meta["outcome"] = "success"
job.save()
return "Successfully ran %s" % run_id
except Exception as exception:
logger.warn("Run FAILED, cleaning up and uploading log.")
logger.warn(exception.message)
job.meta["outcome"] = "failed"
job.save()
raise
finally:
logger.progress((99, "Processing output files"))
# Close the handler so we can upload the log file without a file lock
(hand.close() for hand in logger.handlers)
queue.put(StopIteration)
# Break out of the progress loop
e.set()
t.join()
# Move logfile to output directory
shutil.move(log_file, os.path.join(output_path, 'model.log'))
# Move cachefile to output directory if we made one
if run['caching']:
shutil.move(cache_file, output_path)
output_files = []
for filename in os.listdir(output_path):
outfile = os.path.join(output_path, filename)
output_files.append(outfile)
result_files = []
base_access_url = current_app.config.get('NON_S3_OUTPUT_URL', None)
# Handle results and cleanup
if current_app.config['USE_S3'] is True:
base_access_url = urljoin("http://%s.s3.amazonaws.com/output/" % current_app.config['S3_BUCKET'], run_id)
# Upload results to S3 and remove the local copies
conn = S3Connection()
bucket = conn.get_bucket(current_app.config['S3_BUCKET'])
for outfile in output_files:
# Don't upload the cache file
if os.path.basename(outfile) == os.path.basename(cache_file):
continue
# Upload the outfile with the same as the run name
_, ext = os.path.splitext(outfile)
new_filename = slugify(unicode(run['name'])) + ext
k = Key(bucket)
k.key = "output/%s/%s" % (run_id, new_filename)
k.set_contents_from_filename(outfile)
k.set_acl('public-read')
result_files.append(base_access_url + "/" + new_filename)
os.remove(outfile)
shutil.rmtree(output_path, ignore_errors=True)
else:
for outfile in output_files:
result_files.append(urljoin(base_access_url, run_id) + "/" + os.path.basename(outfile))
shutil.rmtree(temp_animation_path, ignore_errors=True)
# Set output fields
run.output = result_files
run.ended = datetime.utcnow()
run.compute()
run.save()
# Cleanup
logger.removeHandler(handler)
del formatter
del handler
del logger
del model
queue.close()
job.meta["message"] = "Complete"
job.save()
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))
