def perform_task_chunking(parameters, task_id=None):
"""Chunk parameter sets into more manageable sizes
Uses functions provided by the task model to create a group of
parameter sets that make up the arg.
Args:
parameters: parameter stream containing all kwargs to load data
Returns:
parameters with a list of geographic and time ranges
"""
if parameters is None:
return None
task = SlipTask.objects.get(pk=task_id)
dc = DataAccessApi(config=task.config_path)
dates = dc.list_acquisition_dates(**parameters)
task_chunk_sizing = task.get_chunk_size()
geographic_chunks = create_geographic_chunks(
longitude=parameters['longitude'],
latitude=parameters['latitude'],
geographic_chunk_size=task_chunk_sizing['geographic'])
time_chunks = generate_baseline(dates, task.baseline_length)
logger.info("Time chunks: {}, Geo chunks: {}".format(len(time_chunks), len(geographic_chunks)))
dc.close()
task.update_status("WAIT", "Chunked parameter set.")
return {'parameters': parameters, 'geographic_chunks': geographic_chunks, 'time_chunks': time_chunks}
def perform_task_chunking(parameters, task_id=None):
"""Chunk parameter sets into more manageable sizes
Uses functions provided by the task model to create a group of
parameter sets that make up the arg.
Args:
parameters: parameter stream containing all kwargs to load data
Returns:
parameters with a list of geographic and time ranges
"""
if parameters is None:
return None
task = NdviAnomalyTask.objects.get(pk=task_id)
dc = DataAccessApi(config=task.config_path)
dates = dc.list_acquisition_dates(**parameters)
task_chunk_sizing = task.get_chunk_size()
geographic_chunks = create_geographic_chunks(
longitude=parameters['longitude'],
latitude=parameters['latitude'],
geographic_chunk_size=task_chunk_sizing['geographic'])
grouped_dates_params = {**parameters}
grouped_dates_params.update({
'time': (datetime(1000, 1,
1), task.time_start - timedelta(microseconds=1))
})
acquisitions = dc.list_acquisition_dates(**grouped_dates_params)
grouped_dates = group_datetimes_by_month(
acquisitions,
months=list(map(int, task.baseline_selection.split(","))))
# create a single monolithic list of all acq. dates - there should be only one.
time_chunks = []
for date_group in grouped_dates:
time_chunks.extend(grouped_dates[date_group])
# time chunks casted to a list, essnetially.
time_chunks = [time_chunks]
logger.info("Time chunks: {}, Geo chunks: {}".format(
len(time_chunks), len(geographic_chunks)))
dc.close()
task.update_status("WAIT", "Chunked parameter set.")
return {
'parameters': parameters,
'geographic_chunks': geographic_chunks,
'time_chunks': time_chunks
}
def perform_task_chunking(parameters, task_id=None):
"""Chunk parameter sets into more manageable sizes
Uses functions provided by the task model to create a group of
parameter sets that make up the arg.
Args:
parameters: parameter stream containing all kwargs to load data
Returns:
parameters with a list of geographic and time ranges
"""
if parameters is None:
return None
task = CoastalChangeTask.objects.get(pk=task_id)
dc = DataAccessApi(config=task.config_path)
dates = dc.list_acquisition_dates(**parameters)
task_chunk_sizing = task.get_chunk_size()
geographic_chunks = create_geographic_chunks(
longitude=parameters['longitude'],
latitude=parameters['latitude'],
geographic_chunk_size=task_chunk_sizing['geographic'])
grouped_dates = group_datetimes_by_year(dates)
# we need to pair these with the first year - subsequent years.
time_chunks = None
if task.animated_product.animation_id == 'none':
# first and last only
time_chunks = [[
grouped_dates[task.time_start], grouped_dates[task.time_end]
]]
else:
initial_year = grouped_dates.pop(task.time_start)
time_chunks = [[initial_year, grouped_dates[year]]
for year in grouped_dates]
logger.info("Time chunks: {}, Geo chunks: {}".format(
len(time_chunks), len(geographic_chunks)))
dc.close()
task.update_status("WAIT", "Chunked parameter set.")
return {
'parameters': parameters,
'geographic_chunks': geographic_chunks,
'time_chunks': time_chunks
}