def processing_task(self,
task_id=None,
geo_chunk_id=None,
time_chunk_id=None,
geographic_chunk=None,
time_chunk=None,
**parameters):
"""Process a parameter set and save the results to disk.
Uses the geographic and time chunk id to identify output products.
**params is updated with time and geographic ranges then used to load data.
the task model holds the iterative property that signifies whether the algorithm
is iterative or if all data needs to be loaded at once.
Args:
task_id, geo_chunk_id, time_chunk_id: identification for the main task and what chunk this is processing
geographic_chunk: range of latitude and longitude to load - dict with keys latitude, longitude
time_chunk: list of acquisition dates
parameters: all required kwargs to load data.
Returns:
path to the output product, metadata dict, and a dict containing the geo/time ids
"""
chunk_id = "_".join([str(geo_chunk_id), str(time_chunk_id)])
task = SpectralIndicesTask.objects.get(pk=task_id)
if check_cancel_task(self, task): return
logger.info("Starting chunk: " + chunk_id)
if not os.path.exists(task.get_temp_path()):
return None
metadata = {}
times = list(
map(_get_datetime_range_containing, time_chunk) if task.get_iterative(
) else [_get_datetime_range_containing(time_chunk[0], time_chunk[-1])])
dc = DataAccessApi(config=task.config_path)
updated_params = parameters
updated_params.update(geographic_chunk)
iteration_data = None
for time_index, time in enumerate(times):
updated_params.update({'time': time})
data = dc.get_dataset_by_extent(**updated_params)
if check_cancel_task(self, task): return
if data is None:
logger.info("Empty chunk.")
continue
if 'time' not in data:
logger.info("Invalid chunk.")
continue
clear_mask = task.satellite.get_clean_mask_func()(data)
add_timestamp_data_to_xr(data)
metadata = task.metadata_from_dataset(metadata, data, clear_mask,
updated_params)
iteration_data = task.get_processing_method()(
data,
clean_mask=clear_mask,
intermediate_product=iteration_data,
no_data=task.satellite.no_data_value,
reverse_time=task.get_reverse_time())
if check_cancel_task(self, task): return
task.scenes_processed = F('scenes_processed') + 1
task.save(update_fields=['scenes_processed'])
if iteration_data is None:
return None
path = os.path.join(task.get_temp_path(), chunk_id + ".nc")
export_xarray_to_netcdf(iteration_data, path)
dc.close()
logger.info("Done with chunk: " + chunk_id)
return path, metadata, {
'geo_chunk_id': geo_chunk_id,
'time_chunk_id': time_chunk_id
}
def processing_task(task_id=None,
geo_chunk_id=None,
time_chunk_id=None,
geographic_chunk=None,
time_chunk=None,
**parameters):
"""Process a parameter set and save the results to disk.
Uses the geographic and time chunk id to identify output products.
**params is updated with time and geographic ranges then used to load data.
the task model holds the iterative property that signifies whether the algorithm
is iterative or if all data needs to be loaded at once.
Args:
task_id, geo_chunk_id, time_chunk_id: identification for the main task and what chunk this is processing
geographic_chunk: range of latitude and longitude to load - dict with keys latitude, longitude
time_chunk: list of acquisition dates
parameters: all required kwargs to load data.
Returns:
path to the output product, metadata dict, and a dict containing the geo/time ids
"""
chunk_id = "_".join([str(geo_chunk_id), str(time_chunk_id)])
task = AppNameTask.objects.get(pk=task_id)
logger.info("Starting chunk: " + chunk_id)
if not os.path.exists(task.get_temp_path()):
return None
iteration_data = None
metadata = {}
times = list(
map(_get_datetime_range_containing, time_chunk) if task.get_iterative(
) else [_get_datetime_range_containing(time_chunk[0], time_chunk[-1])])
dc = DataAccessApi(config=task.config_path)
updated_params = parameters
updated_params.update(geographic_chunk)
#updated_params.update({'products': parameters['']})
iteration_data = None
base_index = (task.get_chunk_size()['time'] if task.get_chunk_size()
['time'] is not None else 1) * time_chunk_id
for time_index, time in enumerate(times):
updated_params.update({'time': time})
# TODO: If this is not a multisensory app replace get_stacked_datasets_by_extent with get_dataset_by_extent
data = dc.get_stacked_datasets_by_extent(**updated_params)
if data is None or 'time' not in data:
logger.info("Invalid chunk.")
continue
# TODO: Replace anything here with your processing - do you need to create additional masks? Apply bandmaths? etc.
clear_mask = task.satellite.get_clean_mask_func()(data)
add_timestamp_data_to_xr(data)
metadata = task.metadata_from_dataset(metadata, data, clear_mask,
updated_params)
# TODO: Make sure you're producing everything required for your algorithm.
iteration_data = task.get_processing_method()(
data,
clean_mask=clear_mask,
intermediate_product=iteration_data,
no_data=task.satellite.no_data_value,
reverse_time=task.get_reverse_time())
# TODO: If there is no animation you can remove this block. Otherwise, save off the data that you need.
if task.animated_product.animation_id != "none":
path = os.path.join(
task.get_temp_path(),
"animation_{}_{}.nc".format(str(geo_chunk_id),
str(base_index + time_index)))
if task.animated_product.animation_id == "scene":
#need to clear out all the metadata..
clear_attrs(data)
#can't reindex on time - weird?
export_xarray_to_netcdf(data.isel(time=0).drop('time'), path)
elif task.animated_product.animation_id == "cumulative":
export_xarray_to_netcdf(iteration_data, path)
task.scenes_processed = F('scenes_processed') + 1
task.save()
if iteration_data is None:
return None
path = os.path.join(task.get_temp_path(), chunk_id + ".nc")
export_xarray_to_netcdf(iteration_data, path)
dc.close()
logger.info("Done with chunk: " + chunk_id)
return path, metadata, {
'geo_chunk_id': geo_chunk_id,
'time_chunk_id': time_chunk_id
}
def processing_task(self,
task_id=None,
geo_chunk_id=None,
time_chunk_id=None,
geographic_chunk=None,
time_chunk=None,
**parameters):
"""Process a parameter set and save the results to disk.
Uses the geographic and time chunk id to identify output products.
**params is updated with time and geographic ranges then used to load data.
the task model holds the iterative property that signifies whether the algorithm
is iterative or if all data needs to be loaded at once.
Args:
task_id, geo_chunk_id, time_chunk_id: identification for the main task and what chunk this is processing
geographic_chunk: range of latitude and longitude to load - dict with keys latitude, longitude
time_chunk: list of acquisition dates
parameters: all required kwargs to load data.
Returns:
path to the output product, metadata dict, and a dict containing the geo/time ids
"""
chunk_id = "_".join([str(geo_chunk_id), str(time_chunk_id)])
task = TsmTask.objects.get(pk=task_id)
if check_cancel_task(self, task): return
logger.info("Starting chunk: " + chunk_id)
if not os.path.exists(task.get_temp_path()):
return None
metadata = {}
times = list(
map(_get_datetime_range_containing, time_chunk)
if task.get_iterative() else [_get_datetime_range_containing(time_chunk[0], time_chunk[-1])])
dc = DataAccessApi(config=task.config_path)
updated_params = parameters
updated_params.update(geographic_chunk)
water_analysis = None
tsm_analysis = None
combined_data = None
base_index = (task.get_chunk_size()['time'] if task.get_chunk_size()['time'] is not None else 1) * time_chunk_id
for time_index, time in enumerate(times):
updated_params.update({'time': time})
data = dc.get_stacked_datasets_by_extent(**updated_params)
if check_cancel_task(self, task): return
if data is None:
logger.info("Empty chunk.")
continue
if 'time' not in data:
logger.info("Invalid chunk.")
continue
clear_mask = task.satellite.get_clean_mask_func()(data)
# Ensure data variables have the range of Landsat 7 Collection 1 Level 2
# since the color scales are tailored for that dataset.
platform = task.satellite.platform
collection = task.satellite.collection
level = task.satellite.level
if (platform, collection) != ('LANDSAT_7', 'c1'):
data = \
convert_range(data, from_platform=platform,
from_collection=collection, from_level=level,
to_platform='LANDSAT_7', to_collection='c1', to_level='l2')
wofs_data = task.get_processing_method()(data,
clean_mask=clear_mask,
no_data=task.satellite.no_data_value)
water_analysis = perform_timeseries_analysis(
wofs_data, 'wofs', intermediate_product=water_analysis, no_data=task.satellite.no_data_value)
# clear_mask.data[(data.swir2.values > 100) | (wofs_data.wofs.values == 0)] = False
tsm_data = tsm(data, clean_mask=clear_mask, no_data=task.satellite.no_data_value)
tsm_analysis = perform_timeseries_analysis(
tsm_data, 'tsm', intermediate_product=tsm_analysis, no_data=task.satellite.no_data_value)
if check_cancel_task(self, task): return
combined_data = tsm_analysis
combined_data['wofs'] = water_analysis.total_data
combined_data['wofs_total_clean'] = water_analysis.total_clean
metadata = task.metadata_from_dataset(metadata, tsm_data, clear_mask, updated_params)
if task.animated_product.animation_id != "none":
path = os.path.join(task.get_temp_path(),
"animation_{}_{}.nc".format(str(geo_chunk_id), str(base_index + time_index)))
animated_data = tsm_data.isel(
time=0, drop=True) if task.animated_product.animation_id == "scene" else combined_data
export_xarray_to_netcdf(animated_data, path)
task.scenes_processed = F('scenes_processed') + 1
task.save(update_fields=['scenes_processed'])
if combined_data is None:
return None
path = os.path.join(task.get_temp_path(), chunk_id + ".nc")
export_xarray_to_netcdf(combined_data, path)
dc.close()
logger.info("Done with chunk: " + chunk_id)
return path, metadata, {'geo_chunk_id': geo_chunk_id, 'time_chunk_id': time_chunk_id}
def processing_task(self,
task_id=None,
geo_chunk_id=None,
time_chunk_id=None,
geographic_chunk=None,
time_chunk=None,
**parameters):
"""Process a parameter set and save the results to disk.
Uses the geographic and time chunk id to identify output products.
**params is updated with time and geographic ranges then used to load data.
the task model holds the iterative property that signifies whether the algorithm
is iterative or if all data needs to be loaded at once.
Computes a single SLIP baseline comparison - returns a slip mask and mosaic.
Args:
task_id, geo_chunk_id, time_chunk_id: identification for the main task and what chunk this is processing
geographic_chunk: range of latitude and longitude to load - dict with keys latitude, longitude
time_chunk: list of acquisition dates
parameters: all required kwargs to load data.
Returns:
path to the output product, metadata dict, and a dict containing the geo/time ids
"""
chunk_id = "_".join([str(geo_chunk_id), str(time_chunk_id)])
task = SlipTask.objects.get(pk=task_id)
if check_cancel_task(self, task): return
logger.info("Starting chunk: " + chunk_id)
if not os.path.exists(task.get_temp_path()):
return None
metadata = {}
time_range = _get_datetime_range_containing(time_chunk[0], time_chunk[-1])
dc = DataAccessApi(config=task.config_path)
updated_params = {**parameters}
updated_params.update(geographic_chunk)
updated_params.update({'time': time_range})
data = dc.get_dataset_by_extent(**updated_params)
#grab dem data as well
dem_parameters = {**updated_params}
dem_parameters.update({'product': 'terra_aster_gdm_' + task.area_id, 'platform': 'TERRA'})
dem_parameters.pop('time')
dem_parameters.pop('measurements')
dem_data = dc.get_dataset_by_extent(**dem_parameters)
if 'time' not in data or 'time' not in dem_data:
return None
#target data is most recent, with the baseline being everything else.
target_data = xr.concat([data.isel(time=-1)], 'time')
baseline_data = data.isel(time=slice(None, -1))
target_clear_mask = task.satellite.get_clean_mask_func()(target_data)
baseline_clear_mask = task.satellite.get_clean_mask_func()(baseline_data)
combined_baseline = task.get_processing_method()(baseline_data,
clean_mask=baseline_clear_mask,
no_data=task.satellite.no_data_value,
reverse_time=task.get_reverse_time())
if check_cancel_task(self, task): return
target_data = create_mosaic(
target_data,
clean_mask=target_clear_mask,
no_data=task.satellite.no_data_value,
reverse_time=task.get_reverse_time())
if check_cancel_task(self, task): return
slip_data = compute_slip(combined_baseline, target_data, dem_data, no_data=task.satellite.no_data_value)
target_data['slip'] = slip_data
metadata = task.metadata_from_dataset(
metadata, target_data, target_clear_mask, updated_params, time=data.time.values.astype('M8[ms]').tolist()[-1])
if check_cancel_task(self, task): return
task.scenes_processed = F('scenes_processed') + 1
task.save(update_fields=['scenes_processed'])
path = os.path.join(task.get_temp_path(), chunk_id + ".nc")
clear_attrs(target_data)
export_xarray_to_netcdf(target_data, path)
dc.close()
logger.info("Done with chunk: " + chunk_id)
return path, metadata, {'geo_chunk_id': geo_chunk_id, 'time_chunk_id': time_chunk_id}
def processing_task(self,
task_id=None,
geo_chunk_id=None,
time_chunk_id=None,
geographic_chunk=None,
time_chunk=None,
**parameters):
"""Process a parameter set and save the results to disk.
Uses the geographic and time chunk id to identify output products.
**params is updated with time and geographic ranges then used to load data.
the task model holds the iterative property that signifies whether the algorithm
is iterative or if all data needs to be loaded at once.
Args:
task_id, geo_chunk_id, time_chunk_id: identification for the main task and what chunk this is processing
geographic_chunk: range of latitude and longitude to load - dict with keys latitude, longitude
time_chunk: list of acquisition dates
parameters: all required kwargs to load data.
Returns:
path to the output product, metadata dict, and a dict containing the geo/time ids
"""
chunk_id = "_".join([str(geo_chunk_id), str(time_chunk_id)])
task = NdviAnomalyTask.objects.get(pk=task_id)
if check_cancel_task(self, task): return
logger.info("Starting chunk: " + chunk_id)
if not os.path.exists(task.get_temp_path()):
return None
metadata = {}
base_scene_time_range = parameters['time']
dc = DataAccessApi(config=task.config_path)
updated_params = parameters
updated_params.update(geographic_chunk)
# Generate the baseline data - one time slice at a time
full_dataset = []
for time_index, time in enumerate(time_chunk):
updated_params.update({'time': _get_datetime_range_containing(time)})
data = dc.get_dataset_by_extent(**updated_params)
if check_cancel_task(self, task): return
if data is None or 'time' not in data:
logger.info("Invalid chunk.")
continue
full_dataset.append(data.copy(deep=True))
# load selected scene and mosaic just in case we got two scenes (handles scene boundaries/overlapping data)
updated_params.update({'time': base_scene_time_range})
selected_scene = dc.get_dataset_by_extent(**updated_params)
if check_cancel_task(self, task): return
if len(full_dataset) == 0 or 'time' not in selected_scene:
return None
#concat individual slices over time, compute metadata + mosaic
baseline_data = xr.concat(full_dataset, 'time')
baseline_clear_mask = task.satellite.get_clean_mask_func()(baseline_data)
metadata = task.metadata_from_dataset(metadata, baseline_data, baseline_clear_mask, parameters)
selected_scene_clear_mask = task.satellite.get_clean_mask_func()(selected_scene)
metadata = task.metadata_from_dataset(metadata, selected_scene, selected_scene_clear_mask, parameters)
selected_scene = task.get_processing_method()(selected_scene,
clean_mask=selected_scene_clear_mask,
intermediate_product=None,
no_data=task.satellite.no_data_value)
# we need to re generate the clear mask using the mosaic now.
selected_scene_clear_mask = task.satellite.get_clean_mask_func()(selected_scene)
if check_cancel_task(self, task): return
ndvi_products = compute_ndvi_anomaly(
baseline_data,
selected_scene,
baseline_clear_mask=baseline_clear_mask,
selected_scene_clear_mask=selected_scene_clear_mask,
no_data=task.satellite.no_data_value)
full_product = xr.merge([ndvi_products, selected_scene])
task.scenes_processed = F('scenes_processed') + 1
task.save(update_fields=['scenes_processed'])
path = os.path.join(task.get_temp_path(), chunk_id + ".nc")
export_xarray_to_netcdf(full_product, path)
dc.close()
logger.info("Done with chunk: " + chunk_id)
return path, metadata, {'geo_chunk_id': geo_chunk_id, 'time_chunk_id': time_chunk_id}