def generate_animation(index, combined_data):
base_index = (task.get_chunk_size()['time'] if
task.get_chunk_size()['time'] is not None else 1) * index
for index in range((task.get_chunk_size()['time'] if
task.get_chunk_size()['time'] is not None else 1)):
path = os.path.join(task.get_temp_path(),
"animation_{}.nc".format(base_index + index))
if os.path.exists(path):
animated_data = xr.open_dataset(path, autoclose=True)
if task.animated_product.animation_id == "cumulative":
animated_data = xr.concat([animated_data], 'time')
animated_data['time'] = [0]
clear_mask = create_cfmask_clean_mask(
animated_data.cf_mask
) if 'cf_mask' in animated_data else create_bit_mask(
animated_data.pixel_qa, [1, 2])
animated_data = task.get_processing_method()(
animated_data,
clean_mask=clear_mask,
intermediate_product=combined_data)
path = os.path.join(
task.get_temp_path(),
"animation_{}.png".format(base_index + index))
write_png_from_xr(path,
animated_data,
bands=[
task.query_type.red,
task.query_type.green,
task.query_type.blue
],
scale=(0, 4096))
def _apply_band_math(dataset):
clear_mask = create_cfmask_clean_mask(
dataset.cf_mask) if 'cf_mask' in dataset else create_bit_mask(
dataset.pixel_qa, [1, 2])
# mask out water manually. Necessary for frac. cover.
wofs = wofs_classify(dataset, clean_mask=clear_mask, mosaic=True)
clear_mask[wofs.wofs.values == 1] = False
return frac_coverage_classify(dataset, clean_mask=clear_mask)
def recombine_time_chunks(chunks, task_id=None):
"""Recombine processed chunks over the time index.
Open time chunked processed datasets and recombine them using the same function
that was used to process them. This assumes an iterative algorithm - if it is not, then it will
simply return the data again.
Args:
chunks: list of the return from the processing_task function - path, metadata, and {chunk ids}
Returns:
path to the output product, metadata dict, and a dict containing the geo/time ids
"""
logger.info("RECOMBINE_TIME")
#sorting based on time id - earlier processed first as they're incremented e.g. 0, 1, 2..
chunks = chunks if isinstance(chunks, list) else [chunks]
chunks = [chunk for chunk in chunks if chunk is not None]
if len(chunks) == 0:
return None
total_chunks = sorted(chunks, key=lambda x: x[0])
task = FractionalCoverTask.objects.get(pk=task_id)
geo_chunk_id = total_chunks[0][2]['geo_chunk_id']
time_chunk_id = total_chunks[0][2]['time_chunk_id']
metadata = {}
combined_data = None
for index, chunk in enumerate(total_chunks):
metadata.update(chunk[1])
data = xr.open_dataset(chunk[0], autoclose=True)
if combined_data is None:
combined_data = data
continue
#give time an indice to keep mosaicking from breaking.
data = xr.concat([data], 'time')
data['time'] = [0]
clear_mask = create_cfmask_clean_mask(
data.cf_mask) if 'cf_mask' in data else create_bit_mask(
data.pixel_qa, [1, 2])
combined_data = task.get_processing_method()(
data, clean_mask=clear_mask, intermediate_product=combined_data)
if combined_data is None:
return None
path = os.path.join(task.get_temp_path(),
"recombined_time_{}.nc".format(geo_chunk_id))
combined_data.to_netcdf(path)
logger.info("Done combining time chunks for geo: " + str(geo_chunk_id))
return path, metadata, {
'geo_chunk_id': geo_chunk_id,
'time_chunk_id': time_chunk_id
}
def _compute_mosaic(time):
updated_params.update({'time': time})
data = dc.get_dataset_by_extent(**updated_params)
if data is None or 'time' not in data:
logger.info("Invalid chunk.")
return None, None
clear_mask = create_cfmask_clean_mask(data.cf_mask) if 'cf_mask' in data else create_bit_mask(data.pixel_qa,
[1, 2])
metadata = task.metadata_from_dataset({}, data, clear_mask, updated_params)
return task.get_processing_method()(data, clean_mask=clear_mask), metadata
def get_clean_mask_func(self):
"""Get the func required to generate a clear mask for a dataset. Defaults to returning all True
The returned func will be specific to the measurements or product/platform and will take a single xr dataset
argument, returning a boolean np array of the same shape as ds.
"""
def return_all_true(ds):
return np.full(ds[self.get_measurements()[0]].shape(), True)
options = {
'bit_mask': lambda ds: create_bit_mask(ds.pixel_qa, [1, 2]),
'cf_mask': lambda ds: create_cfmask_clean_mask(ds.cf_mask),
'default': lambda ds: return_all_true
}
key = 'bit_mask' if 'pixel_qa' in self.get_measurements(
) else 'cf_mask' if 'cf_mask' in self.get_measurements() else 'default'
return options.get(key, return_all_true)
def get_clean_mask_func(self):
"""Get the func required to generate a clear mask for a dataset. Defaults to returning all True
The returned func will be specific to the measurements or product/platform and will take a single xr dataset
argument, returning a boolean np array of the same shape as ds.
"""
def return_all_true(ds):
return np.full(ds[self.get_measurements()[0]].shape(), True)
options = {
'bit_mask': lambda ds: \
landsat_qa_clean_mask(ds, platform=self.platform,
collection=self.collection, level=self.level),
'cf_mask': lambda ds: create_cfmask_clean_mask(ds.cf_mask),
'default': lambda ds: return_all_true
}
key = 'bit_mask' if 'pixel_qa' in self.get_measurements() or \
self.name == 'Landsat 8 Collection 2 Level 2' else \
'cf_mask' if 'cf_mask' in self.get_measurements() else \
'default'
return options.get(key, return_all_true)
def recombine_time_chunks(chunks, task_id=None):
"""Recombine processed chunks over the time index.
Open time chunked processed datasets and recombine them using the same function
that was used to process them. This assumes an iterative algorithm - if it is not, then it will
simply return the data again.
Args:
chunks: list of the return from the processing_task function - path, metadata, and {chunk ids}
Returns:
path to the output product, metadata dict, and a dict containing the geo/time ids
"""
logger.info("RECOMBINE_TIME")
#sorting based on time id - earlier processed first as they're incremented e.g. 0, 1, 2..
chunks = chunks if isinstance(chunks, list) else [chunks]
chunks = [chunk for chunk in chunks if chunk is not None]
total_chunks = sorted(chunks, key=lambda x: x[0]) if isinstance(
chunks, list) else [chunks]
task = AppNameTask.objects.get(pk=task_id)
geo_chunk_id = total_chunks[0][2]['geo_chunk_id']
time_chunk_id = total_chunks[0][2]['time_chunk_id']
metadata = {}
#TODO: If there is no animation, remove this block. Otherwise, compute the data needed to create each frame.
def generate_animation(index, combined_data):
base_index = (task.get_chunk_size()['time'] if
task.get_chunk_size()['time'] is not None else 1) * index
for index in range((task.get_chunk_size()['time'] if
task.get_chunk_size()['time'] is not None else 1)):
path = os.path.join(task.get_temp_path(),
"animation_{}.nc".format(base_index + index))
if os.path.exists(path):
animated_data = xr.open_dataset(path, autoclose=True)
if task.animated_product.animation_id == "cumulative":
animated_data = xr.concat([animated_data], 'time')
animated_data['time'] = [0]
clear_mask = create_cfmask_clean_mask(
animated_data.cf_mask
) if 'cf_mask' in animated_data else create_bit_mask(
animated_data.pixel_qa, [1, 2])
animated_data = task.get_processing_method()(
animated_data,
clean_mask=clear_mask,
intermediate_product=combined_data)
path = os.path.join(
task.get_temp_path(),
"animation_{}.png".format(base_index + index))
write_png_from_xr(path,
animated_data,
bands=[
task.query_type.red,
task.query_type.green,
task.query_type.blue
],
scale=(0, 4096))
combined_data = None
for index, chunk in enumerate(total_chunks):
metadata.update(chunk[1])
data = xr.open_dataset(chunk[0], autoclose=True)
if combined_data is None:
# TODO: If there is no animation, remove this.
if task.animated_product.animation_id != "none":
generate_animation(index, combined_data)
combined_data = data
continue
#give time an indice to keep mosaicking from breaking.
data = xr.concat([data], 'time')
data['time'] = [0]
clear_mask = create_cfmask_clean_mask(
data.cf_mask) if 'cf_mask' in data else create_bit_mask(
data.pixel_qa, [1, 2])
combined_data = task.get_processing_method()(
data, clean_mask=clear_mask, intermediate_product=combined_data)
# if we're animating, combine it all and save to disk.
# TODO: If there is no animation, remove this.
if task.animated_product.animation_id != "none":
generate_animation(index, combined_data)
path = os.path.join(task.get_temp_path(),
"recombined_time_{}.nc".format(geo_chunk_id))
combined_data.to_netcdf(path)
logger.info("Done combining time chunks for geo: " + str(geo_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 = {}
def _get_datetime_range_containing(*time_ranges):
return (min(time_ranges) - timedelta(microseconds=1),
max(time_ranges) + timedelta(microseconds=1))
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 = create_cfmask_clean_mask(
data.cf_mask) if 'cf_mask' in data else create_bit_mask(
data.pixel_qa, [1, 2])
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)
# 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?
data.isel(time=0).drop('time').to_netcdf(path)
elif task.animated_product.animation_id == "cumulative":
iteration_data.to_netcdf(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")
iteration_data.to_netcdf(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 = FractionalCoverTask.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 = {}
def _get_datetime_range_containing(*time_ranges):
return (min(time_ranges) - timedelta(microseconds=1),
max(time_ranges) + timedelta(microseconds=1))
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})
data = dc.get_stacked_datasets_by_extent(**updated_params)
if data is None or 'time' not in data:
logger.info("Invalid chunk.")
continue
clear_mask = create_cfmask_clean_mask(
data.cf_mask) if 'cf_mask' in data else create_bit_mask(
data.pixel_qa, [1, 2])
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)
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")
iteration_data.to_netcdf(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 recombine_time_chunks(chunks, task_id=None):
"""Recombine processed chunks over the time index.
Open time chunked processed datasets and recombine them using the same function
that was used to process them. This assumes an iterative algorithm - if it is not, then it will
simply return the data again.
Args:
chunks: list of the return from the processing_task function - path, metadata, and {chunk ids}
Returns:
path to the output product, metadata dict, and a dict containing the geo/time ids
"""
logger.info("RECOMBINE_TIME")
#sorting based on time id - earlier processed first as they're incremented e.g. 0, 1, 2..
chunks = chunks if isinstance(chunks, list) else [chunks]
chunks = [chunk for chunk in chunks if chunk is not None]
if len(chunks) == 0:
return None
total_chunks = sorted(chunks, key=lambda x: x[0])
task = SlipTask.objects.get(pk=task_id)
geo_chunk_id = total_chunks[0][2]['geo_chunk_id']
time_chunk_id = total_chunks[0][2]['time_chunk_id']
metadata = {}
combined_data = None
combined_slip = None
for index, chunk in enumerate(reversed(total_chunks)):
metadata.update(chunk[1])
data = xr.open_dataset(chunk[0], autoclose=True)
if combined_data is None:
combined_data = data.drop('slip')
# since this is going to interact with data/mosaicking, it needs a time dim
combined_slip = xr.concat([data.slip.copy(deep=True)], 'time')
continue
#give time an indice to keep mosaicking from breaking.
data = xr.concat([data], 'time')
data['time'] = [0]
clear_mask = create_cfmask_clean_mask(
data.cf_mask) if 'cf_mask' in data else create_bit_mask(
data.pixel_qa, [1, 2])
# modify clean mask so that only slip pixels that are still zero will be used. This will show all the pixels that caused the flag.
clear_mask[xr.concat([combined_slip], 'time').values == 1] = False
combined_data = create_mosaic(data.drop('slip'),
clean_mask=clear_mask,
intermediate_product=combined_data)
combined_slip.values[combined_slip.values == 0] = data.slip.values[
combined_slip.values == 0]
# Since we added a time dim to combined_slip, we need to remove it here.
combined_data['slip'] = combined_slip.isel(time=0, drop=True)
path = os.path.join(task.get_temp_path(),
"recombined_time_{}.nc".format(geo_chunk_id))
combined_data.to_netcdf(path)
logger.info("Done combining time chunks for geo: " + str(geo_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.
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)
logger.info("Starting chunk: " + chunk_id)
if not os.path.exists(task.get_temp_path()):
return None
metadata = {}
def _get_datetime_range_containing(*time_ranges):
return (min(time_ranges) - timedelta(microseconds=1),
max(time_ranges) + timedelta(microseconds=1))
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 = create_cfmask_clean_mask(
target_data.cf_mask) if 'cf_mask' in target_data else create_bit_mask(
target_data.pixel_qa, [1, 2])
baseline_clear_mask = create_cfmask_clean_mask(
baseline_data.cf_mask
) if 'cf_mask' in baseline_data else create_bit_mask(
baseline_data.pixel_qa, [1, 2])
combined_baseline = task.get_processing_method()(
baseline_data, clean_mask=baseline_clear_mask)
target_data = create_mosaic(target_data, clean_mask=target_clear_mask)
slip_data = compute_slip(combined_baseline, target_data, dem_data)
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])
task.scenes_processed = F('scenes_processed') + 1
task.save()
path = os.path.join(task.get_temp_path(), chunk_id + ".nc")
clear_attrs(target_data)
target_data.to_netcdf(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 = NdviAnomalyTask.objects.get(pk=task_id)
logger.info("Starting chunk: " + chunk_id)
if not os.path.exists(task.get_temp_path()):
return None
metadata = {}
def _get_datetime_range_containing(*time_ranges):
return (min(time_ranges) - timedelta(microseconds=1),
max(time_ranges) + timedelta(microseconds=1))
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 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 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 = create_cfmask_clean_mask(
baseline_data.cf_mask
) if 'cf_mask' in baseline_data else create_bit_mask(
baseline_data.pixel_qa, [1, 2])
metadata = task.metadata_from_dataset(metadata, baseline_data,
baseline_clear_mask, parameters)
selected_scene_clear_mask = create_cfmask_clean_mask(
selected_scene.cf_mask
) if 'cf_mask' in selected_scene else create_bit_mask(
selected_scene.pixel_qa, [1, 2])
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)
# we need to re generate the clear mask using the mosaic now.
selected_scene_clear_mask = create_cfmask_clean_mask(
selected_scene.cf_mask
) if 'cf_mask' in selected_scene else create_bit_mask(
selected_scene.pixel_qa, [1, 2])
ndvi_products = compute_ndvi_anomaly(
baseline_data,
selected_scene,
baseline_clear_mask=baseline_clear_mask,
selected_scene_clear_mask=selected_scene_clear_mask)
full_product = xr.merge([ndvi_products, selected_scene])
task.scenes_processed = F('scenes_processed') + 1
task.save()
path = os.path.join(task.get_temp_path(), chunk_id + ".nc")
full_product.to_netcdf(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 = TsmTask.objects.get(pk=task_id)
logger.info("Starting chunk: " + chunk_id)
if not os.path.exists(task.get_temp_path()):
return None
metadata = {}
def _get_datetime_range_containing(*time_ranges):
return (min(time_ranges) - timedelta(microseconds=1),
max(time_ranges) + timedelta(microseconds=1))
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['']})
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 data is None or 'time' not in data:
logger.info("Invalid chunk.")
continue
clear_mask = create_cfmask_clean_mask(
data.cf_mask) if 'cf_mask' in data else create_bit_mask(
data.pixel_qa, [1, 2])
wofs_data = task.get_processing_method()(data,
clean_mask=clear_mask,
enforce_float64=True,
no_data=-9999)
water_analysis = perform_timeseries_analysis(
wofs_data,
'wofs',
intermediate_product=water_analysis,
no_data=-9999)
clear_mask[(data.swir2.values > 100) |
(wofs_data.wofs.values == 0)] = False
tsm_data = tsm(data, clean_mask=clear_mask, no_data=-9999)
tsm_analysis = perform_timeseries_analysis(
tsm_data, 'tsm', intermediate_product=tsm_analysis, no_data=-9999)
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
animated_data.to_netcdf(path)
task.scenes_processed = F('scenes_processed') + 1
task.save()
if combined_data is None:
return None
path = os.path.join(task.get_temp_path(), chunk_id + ".nc")
combined_data.to_netcdf(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
}
