def process_message(self, connector, host, secret_key, resource, parameters):
self.start_message(resource)
# Load metadata from dataset
for fname in resource['local_paths']:
if fname.endswith('_dataset_metadata.json'):
all_dsmd = load_json_file(fname)
terra_md_full = get_terraref_metadata(all_dsmd)
if 'spatial_metadata' in terra_md_full:
spatial_meta = terra_md_full['spatial_metadata']
else:
spatial_meta = None
if not spatial_meta:
ValueError("No spatial metadata found.")
# Determine which files in dataset need clipping
files_to_process = {}
for f in resource['local_paths']:
if f.startswith("ir_geotiff") and f.endswith(".tif"):
sensor_name = "ir_geotiff"
filename = os.path.basename(f)
files_to_process[filename] = {
"path": f,
"bounds": spatial_meta['flirIrCamera']['bounding_box']
}
elif f.startswith("rgb_geotiff") and f.endswith(".tif"):
sensor_name = "rgb_geotiff"
filename = os.path.basename(f)
if f.endswith("_left.tif"): side = "left"
else: side = "right"
files_to_process[filename] = {
"path": f,
"bounds": spatial_meta[side]['bounding_box']
}
elif f.endswith(".las"):
sensor_name = "laser3d_las"
filename = os.path.basename(f)
files_to_process[filename] = {
"path": f,
"bounds": get_las_extents(f)
}
# TODO: Add case for laser3d heightmap
# Fetch experiment name from terra metadata
timestamp = resource['dataset_info']['name'].split(" - ")[1]
season_name, experiment_name, updated_experiment = get_season_and_experiment(timestamp, 'plotclipper', terra_md_full)
if None in [season_name, experiment_name]:
raise ValueError("season and experiment could not be determined")
# Determine script name
target_scan = "unknown_scan"
if 'gantry_variable_metadata' in terra_md_full:
if 'script_name' in terra_md_full['gantry_variable_metadata']:
target_scan = terra_md_full['gantry_variable_metadata']['script_name']
if 'script_hash' in terra_md_full['gantry_variable_metadata']:
target_scan += ' '+terra_md_full['gantry_variable_metadata']['script_hash']
all_plots = get_site_boundaries(timestamp.split("__")[0], city='Maricopa')
uploaded_file_ids = []
for filename in files_to_process:
file_path = files_to_process[filename]["path"]
file_bounds = files_to_process[filename]["bounds"]
overlap_plots = find_plots_intersect_boundingbox(file_bounds, all_plots, fullmac=True)
if len(overlap_plots) > 0:
self.log_info(resource, "Attempting to clip %s into %s plot shards" % (filename, len(overlap_plots)))
for plotname in overlap_plots:
plot_bounds = overlap_plots[plotname]
tuples = geojson_to_tuples_betydb(yaml.safe_load(plot_bounds))
plot_display_name = self.sensors.get_display_name(sensor=sensor_name) + " (By Plot)"
leaf_dataset = plot_display_name + ' - ' + plotname + " - " + timestamp.split("__")[0]
self.log_info(resource, "Hierarchy: %s / %s / %s / %s / %s / %s / %s" % (season_name, experiment_name, plot_display_name,
timestamp[:4], timestamp[5:7], timestamp[8:10], leaf_dataset))
target_dsid = build_dataset_hierarchy_crawl(host, secret_key, self.clowder_user, self.clowder_pass, self.clowderspace,
season_name, experiment_name, plot_display_name,
timestamp[:4], timestamp[5:7], timestamp[8:10], leaf_ds_name=leaf_dataset)
out_file = self.sensors.create_sensor_path(timestamp, plot=plotname, subsensor=sensor_name, filename=filename)
if not os.path.exists(os.path.dirname(out_file)):
os.makedirs(os.path.dirname(out_file))
if filename.endswith(".tif") and (not file_exists(out_file) or self.overwrite):
"""If file is a geoTIFF, simply clip it and upload it to Clowder"""
clip_raster(file_path, tuples, out_path=out_file)
found_in_dest = check_file_in_dataset(connector, host, secret_key, target_dsid, merged_out, remove=self.overwrite)
if not found_in_dest or self.overwrite:
fileid = upload_to_dataset(connector, host, secret_key, target_dsid, merged_out)
uploaded_file_ids.append(host + ("" if host.endswith("/") else "/") + "files/" + fileid)
self.created += 1
self.bytes += os.path.getsize(merged_out)
elif filename.endswith(".las"):
"""If file is LAS, we can merge with any existing scan+plot output safely"""
merged_out = os.path.join(os.path.dirname(out_file), target_scan+"_merged.las")
merged_txt = merged_out.replace(".las", "_contents.txt")
already_merged = False
if os.path.exists(merged_txt):
# Check if contents
with open(merged_txt, 'r') as contents:
for entry in contents.readlines():
if entry.strip() == file_path:
already_merged = True
break
if not already_merged:
clip_las(file_path, tuples, out_path=out_file, merged_path=merged_out)
with open(merged_txt, 'a') as contents:
contents.write(file_path+"\n")
# Upload the individual plot shards for optimizing las2height later
found_in_dest = check_file_in_dataset(connector, host, secret_key, target_dsid, out_file, remove=self.overwrite)
if not found_in_dest or self.overwrite:
fileid = upload_to_dataset(connector, host, secret_key, target_dsid, out_file)
uploaded_file_ids.append(host + ("" if host.endswith("/") else "/") + "files/" + fileid)
self.created += 1
self.bytes += os.path.getsize(out_file)
# Upload the merged result if necessary
found_in_dest = check_file_in_dataset(connector, host, secret_key, target_dsid, merged_out, remove=self.overwrite)
if not found_in_dest or self.overwrite:
fileid = upload_to_dataset(connector, host, secret_key, target_dsid, merged_out)
uploaded_file_ids.append(host + ("" if host.endswith("/") else "/") + "files/" + fileid)
self.created += 1
self.bytes += os.path.getsize(merged_out)
# Trigger las2height extractor
submit_extraction(connector, host, secret_key, target_dsid, "terra.3dscanner.las2height")
# Tell Clowder this is completed so subsequent file updates don't daisy-chain
extractor_md = build_metadata(host, self.extractor_info, resource['id'], {
"files_created": uploaded_file_ids
}, 'dataset')
self.log_info(resource, "uploading extractor metadata to Level_1 dataset")
remove_metadata(connector, host, secret_key, resource['id'], self.extractor_info['name'])
upload_metadata(connector, host, secret_key, resource['id'], extractor_md)
self.end_message(resource)
def process_message(self, connector, host, secret_key, resource,
parameters):
self.start_message(resource)
# Write the CSV to the same directory as the source file
ds_info = get_info(connector, host, secret_key,
resource['parent']['id'])
timestamp = ds_info['name'].split(" - ")[1]
time_fmt = timestamp + "T12:00:00-07:00"
rootdir = self.sensors.create_sensor_path(timestamp,
sensor="rgb_fullfield",
ext=".csv")
out_csv = os.path.join(
os.path.dirname(rootdir),
resource['name'].replace(".tif", "_canopycover_bety.csv"))
out_geo = os.path.join(
os.path.dirname(rootdir),
resource['name'].replace(".tif", "_canopycover_geo.csv"))
# TODO: What should happen if CSV already exists? If we're here, there's no completed metadata...
self.log_info(resource, "Writing BETY CSV to %s" % out_csv)
csv_file = open(out_csv, 'w')
(fields, traits) = get_traits_table()
csv_file.write(','.join(map(str, fields)) + '\n')
self.log_info(resource, "Writing Geostreams CSV to %s" % out_geo)
geo_file = open(out_geo, 'w')
geo_file.write(','.join([
'site', 'trait', 'lat', 'lon', 'dp_time', 'source', 'value',
'timestamp'
]) + '\n')
# Get full list of experiment plots using date as filter
all_plots = get_site_boundaries(timestamp, city='Maricopa')
self.log_info(resource,
"found %s plots on %s" % (len(all_plots), timestamp))
successful_plots = 0
for plotname in all_plots:
if plotname.find("KSU") > -1:
self.log_info(resource, "skipping %s" % plotname)
continue
bounds = all_plots[plotname]
tuples = geojson_to_tuples_betydb(yaml.safe_load(bounds))
centroid_lonlat = json.loads(
centroid_from_geojson(bounds))["coordinates"]
# Use GeoJSON string to clip full field to this plot
try:
pxarray = clip_raster(resource['local_paths'][0], tuples)
if pxarray is not None:
if len(pxarray.shape) < 3:
self.log_error(
resource, "unexpected array shape for %s (%s)" %
(plotname, pxarray.shape))
continue
ccVal = calculate_canopycover_masked(
rollaxis(pxarray, 0, 3))
if (ccVal > -1):
# Prepare and submit datapoint
geo_file.write(','.join([
plotname, 'Canopy Cover',
str(centroid_lonlat[1]),
str(centroid_lonlat[0]), time_fmt, host +
("" if host.endswith("/") else "/") + "files/" +
resource['id'],
str(ccVal), timestamp
]) + '\n')
successful_plots += 1
if successful_plots % 10 == 0:
self.log_info(
resource, "processed %s/%s plots" %
(successful_plots, len(all_plots)))
else:
continue
except:
self.log_error(resource,
"error generating cc for %s" % plotname)
continue
if (ccVal > -1):
traits['canopy_cover'] = str(ccVal)
traits['site'] = plotname
traits['local_datetime'] = timestamp + "T12:00:00"
trait_list = generate_traits_list(traits)
csv_file.write(','.join(map(str, trait_list)) + '\n')
csv_file.close()
geo_file.close()
# Upload this CSV to Clowder
fileid = upload_to_dataset(connector, host, self.clowder_user,
self.clowder_pass, resource['parent']['id'],
out_csv)
geoid = upload_to_dataset(connector, host, self.clowder_user,
self.clowder_pass, resource['parent']['id'],
out_geo)
# Add metadata to original dataset indicating this was run
self.log_info(resource, "updating file metadata")
ext_meta = build_metadata(host, self.extractor_info, resource['id'],
{"files_created": [fileid, geoid]}, 'file')
upload_metadata(connector, host, secret_key, resource['id'], ext_meta)
# Trigger separate extractors
self.log_info(resource, "triggering BETY extractor on %s" % fileid)
submit_extraction(connector, host, secret_key, fileid, "terra.betydb")
self.log_info(resource,
"triggering geostreams extractor on %s" % geoid)
submit_extraction(connector, host, secret_key, geoid,
"terra.geostreams")
self.end_message(resource)
def perform_process(transformer: transformer_class.Transformer, check_md: dict, transformer_md: dict, full_md: dict) -> dict:
"""Performs the processing of the data
Arguments:
transformer: instance of transformer class
Return:
Returns a dictionary with the results of processing
"""
# Setup local variables
timestamp = dateutil.parser.parse(check_md['timestamp'])
datestamp = timestamp.strftime("%Y-%m-%d")
localtime = timestamp.strftime("%Y-%m-%dT%H:%M:%S")
geo_csv_filename = os.path.join(check_md['working_folder'], "canopycover_geostreams.csv")
bety_csv_filename = os.path.join(check_md['working_folder'], "canopycover.csv")
geo_file = open(geo_csv_filename, 'w')
bety_file = open(bety_csv_filename, 'w')
(fields, traits) = get_traits_table()
# Setup default trait values
if not transformer.args.germplasmName is None:
traits['species'] = transformer.args.germplasmName
if not transformer.args.citationAuthor is None:
traits['citation_author'] = transformer.args.citationAuthor
if not transformer.args.citationTitle is None:
traits['citation_title'] = transformer.args.citationTitle
if not transformer.args.citationYear is None:
traits['citation_year'] = transformer.args.citationYear
else:
traits['citation_year'] = (timestamp.year)
geo_csv_header = ','.join(['site', 'trait', 'lat', 'lon', 'dp_time', 'source', 'value', 'timestamp'])
bety_csv_header = ','.join(map(str, fields))
if geo_file:
geo_file.write(geo_csv_header + "\n")
if bety_file:
bety_file.write(bety_csv_header + "\n")
all_plots = get_site_boundaries(datestamp, city='Maricopa')
logging.debug("Found %s plots for date %s", str(len(all_plots)), str(datestamp))
# Loop through finding all image files
image_exts = SUPPORTED_IMAGE_EXTS
num_files = 0
total_plots_calculated = 0
logging.debug("Looking for images with an extension of: %s", ",".join(image_exts))
for one_file in check_md['list_files']():
ext = os.path.splitext(one_file)[1]
if not ext or not ext in image_exts:
logging.debug("Skipping non-supported file '%s'", one_file)
continue
image_bounds = get_image_bounds(one_file)
if not image_bounds:
logging.info("Image file does not appear to be geo-referenced '%s'", one_file)
continue
overlap_plots = find_plots_intersect_boundingbox(image_bounds, all_plots, fullmac=True)
num_plots = len(overlap_plots)
if not num_plots or num_plots < 0:
logging.info("No plots intersect file '%s'", one_file)
continue
num_files += 1
image_spatial_ref = get_spatial_reference_from_json(image_bounds)
for plot_name in overlap_plots:
plot_bounds = convert_json_geometry(overlap_plots[plot_name], image_spatial_ref)
tuples = geojson_to_tuples_betydb(yaml.safe_load(plot_bounds))
centroid = json.loads(centroid_from_geojson(plot_bounds))["coordinates"]
try:
logging.debug("Clipping raster to plot")
pxarray = clip_raster(one_file, tuples, os.path.join(check_md['working_folder'], "temp.tif"))
if pxarray is not None:
if len(pxarray.shape) < 3:
logging.warning("Unexpected image dimensions for file '%s'", one_file)
logging.warning(" expected 3 and received %s", str(pxarray.shape))
break
logging.debug("Calculating canopy cover")
cc_val = calculate_canopycover_masked(np.rollaxis(pxarray, 0, 3))
# Write the datapoint geographically and otherwise
logging.debug("Writing to CSV files")
if geo_file:
csv_data = ','.join([plot_name,
'Canopy Cover',
str(centroid[1]),
str(centroid[0]),
localtime,
one_file,
str(cc_val),
datestamp])
geo_file.write(csv_data + "\n")
if bety_file:
traits['canopy_cover'] = str(cc_val)
traits['site'] = plot_name
traits['local_datetime'] = localtime
trait_list = generate_traits_list(traits)
csv_data = ','.join(map(str, trait_list))
bety_file.write(csv_data + "\n")
total_plots_calculated += 1
else:
continue
except Exception as ex:
logging.warning("Exception caught while processing canopy cover: %s", str(ex))
logging.warning("Error generating canopy cover for '%s'", one_file)
logging.warning(" plot name: '%s'", plot_name)
continue
# Check that we got something
if not num_files:
return {'code': -1000, 'error': "No files were processed"}
if not total_plots_calculated:
return {'code': -1001, 'error': "No plots intersected with the images provided"}
# Setup the metadata for returning files
file_md = []
if geo_file:
file_md.append({'path': geo_csv_filename, 'key': 'csv'})
if bety_file:
file_md.append({'path': bety_csv_filename, 'key': 'csv'})
# Perform cleanup
if geo_file:
geo_file.close()
del geo_file
if bety_file:
bety_file.close()
del bety_file
return {'code': 0, 'files': file_md}
def process_message(self, connector, host, secret_key, resource, parameters):
self.start_message(resource)
# Get full list of experiment plots using date as filter
ds_info = get_info(connector, host, secret_key, resource['parent']['id'])
timestamp = ds_info['name'].split(" - ")[1]
time_fmt = timestamp+"T12:00:00-07:00"
out_csv = self.sensors.create_sensor_path(timestamp, sensor="ir_meantemp", opts=["bety"])
out_geo = out_csv.replace("_bety", "_geo")
# TODO: What should happen if CSV already exists? If we're here, there's no completed metadata...
self.log_info(resource, "Writing BETY CSV to %s" % out_csv)
csv_file = open(out_csv, 'w')
(fields, traits) = get_traits_table()
csv_file.write(','.join(map(str, fields)) + '\n')
self.log_info(resource, "Writing Geostreams CSV to %s" % out_geo)
geo_file = open(out_geo, 'w')
geo_file.write(','.join(['site', 'trait', 'lat', 'lon', 'dp_time', 'source', 'value', 'timestamp']) + '\n')
successful_plots = 0
nan_plots = 0
all_plots = get_site_boundaries(timestamp, city='Maricopa')
for plotname in all_plots:
if plotname.find("KSU") > -1:
self.log_info(resource, "skipping %s" % plotname)
continue
bounds = all_plots[plotname]
tuples = geojson_to_tuples_betydb(yaml.safe_load(bounds))
centroid_lonlat = json.loads(centroid_from_geojson(bounds))["coordinates"]
# Use GeoJSON string to clip full field to this plot
pxarray = clip_raster(resource['local_paths'][0], tuples)
# Filter out any
pxarray[pxarray < 0] = numpy.nan
mean_tc = numpy.nanmean(pxarray) - 273.15
# Create BETY-ready CSV
if not numpy.isnan(mean_tc):
geo_file.write(','.join([plotname,
'IR Surface Temperature',
str(centroid_lonlat[1]),
str(centroid_lonlat[0]),
time_fmt,
host + ("" if host.endswith("/") else "/") + "files/" + resource['id'],
str(mean_tc),
timestamp]) + '\n')
traits['surface_temperature'] = str(mean_tc)
traits['site'] = plotname
traits['local_datetime'] = timestamp+"T12:00:00"
trait_list = generate_traits_list(traits)
csv_file.write(','.join(map(str, trait_list)) + '\n')
else:
nan_plots += 1
successful_plots += 1
self.log_info(resource, "skipped %s of %s plots due to NaN" % (nan_plots, len(all_plots)))
# submit CSV to BETY
csv_file.close()
geo_file.close()
# Upload CSVs to Clowder
fileid = upload_to_dataset(connector, host, self.clowder_user, self.clowder_pass, resource['parent']['id'], out_csv)
geoid = upload_to_dataset(connector, host, self.clowder_user, self.clowder_pass, resource['parent']['id'], out_geo)
# Tell Clowder this is completed so subsequent file updates don't daisy-chain
self.log_info(resource, "updating file metadata")
metadata = build_metadata(host, self.extractor_info, resource['parent']['id'], {
"total_plots": len(all_plots),
"plots_processed": successful_plots,
"blank_plots": nan_plots,
"files_created": [fileid, geoid],
"betydb_link": "https://terraref.ncsa.illinois.edu/bety/api/beta/variables?name=surface_temperature"
}, 'dataset')
upload_metadata(connector, host, secret_key, resource['parent']['id'], metadata)
# Trigger downstream extractors
self.log_info(resource, "triggering BETY extractor on %s" % fileid)
submit_extraction(connector, host, secret_key, fileid, "terra.betydb")
self.log_info(resource, "triggering geostreams extractor on %s" % geoid)
submit_extraction(connector, host, secret_key, geoid, "terra.geostreams")
self.end_message(resource)
def perform_process(transformer: transformer_class.Transformer, check_md: dict,
transformer_md: list, full_md: list) -> dict:
"""Performs the processing of the data
Arguments:
transformer: instance of transformer class
Return:
Returns a dictionary with the results of processing
"""
# pylint: disable=unused-argument
# Disabling pylint checks because resolving them would make code unreadable
# pylint: disable=too-many-branches, too-many-statements, too-many-locals
# Setup local variables
start_timestamp = datetime.datetime.now()
timestamp = dateutil.parser.parse(check_md['timestamp'])
datestamp = timestamp.strftime("%Y-%m-%d")
localtime = timestamp.strftime("%Y-%m-%dT%H:%M:%S")
geo_csv_filename = os.path.join(check_md['working_folder'],
"meantemp_geostreams.csv")
bety_csv_filename = os.path.join(check_md['working_folder'],
"meantemp.csv")
geo_file = open(geo_csv_filename, 'w')
bety_file = open(bety_csv_filename, 'w')
(fields, traits) = get_traits_table()
# Setup default trait values
if transformer.args.citationAuthor is not None:
traits['citation_author'] = transformer.args.citationAuthor
if transformer.args.citationTitle is not None:
traits['citation_title'] = transformer.args.citationTitle
if transformer.args.citationYear is not None:
traits['citation_year'] = transformer.args.citationYear
else:
traits['citation_year'] = timestamp.year
geo_csv_header = ','.join([
'site', 'trait', 'lat', 'lon', 'dp_time', 'source', 'value',
'timestamp'
])
bety_csv_header = ','.join(map(str, fields))
if geo_file:
geo_file.write(geo_csv_header + "\n")
if bety_file:
bety_file.write(bety_csv_header + "\n")
all_plots = get_site_boundaries(datestamp, city='Maricopa')
logging.debug("Found %s plots for date %s", str(len(all_plots)),
str(datestamp))
# Loop through finding all image files
image_exts = SUPPORTED_IMAGE_EXTS
num_files = 0
number_empty_plots = 0
total_plots_calculated = 0
total_files = 0
processed_plots = 0
logging.debug("Looking for images with an extension of: %s",
",".join(image_exts))
for one_file in check_md['list_files']():
total_files += 1
ext = os.path.splitext(one_file)[1]
if not ext or ext not in image_exts:
logging.debug("Skipping non-supported file '%s'", one_file)
continue
image_bounds = get_image_bounds(one_file)
if not image_bounds:
logging.info(
"Image file does not appear to be geo-referenced '%s'",
one_file)
continue
overlap_plots = find_plots_intersect_boundingbox(image_bounds,
all_plots,
fullmac=True)
num_plots = len(overlap_plots)
if not num_plots or num_plots < 0:
logging.info("No plots intersect file '%s'", one_file)
continue
num_files += 1
image_spatial_ref = get_spatial_reference_from_json(image_bounds)
for plot_name in overlap_plots:
processed_plots += 1
plot_bounds = convert_json_geometry(overlap_plots[plot_name],
image_spatial_ref)
tuples = geojson_to_tuples_betydb(yaml.safe_load(plot_bounds))
centroid = json.loads(
centroid_from_geojson(plot_bounds))["coordinates"]
try:
logging.debug("Clipping raster to plot")
clip_path = os.path.join(check_md['working_folder'],
"temp.tif")
pxarray = clip_raster(one_file, tuples, clip_path)
if os.path.exists(clip_path):
os.remove(clip_path)
if pxarray is not None:
logging.debug("Calculating mean temperature")
pxarray[pxarray < 0] = np.nan
mean_tc = np.nanmean(pxarray) - 273.15
# Check for empty plots
if np.isnan(mean_tc):
number_empty_plots += 1
continue
# Write the data point geographically and otherwise
logging.debug("Writing to CSV files")
if geo_file:
csv_data = ','.join([
plot_name, 'IR Surface Temperature',
str(centroid[1]),
str(centroid[0]), localtime, one_file,
str(mean_tc), datestamp
])
geo_file.write(csv_data + "\n")
if bety_file:
traits['surface_temperature'] = str(mean_tc)
traits['site'] = plot_name
traits['local_datetime'] = localtime
trait_list = generate_traits_list(traits)
csv_data = ','.join(map(str, trait_list))
bety_file.write(csv_data + "\n")
total_plots_calculated += 1
else:
continue
except Exception as ex:
logging.warning(
"Exception caught while processing mean temperature: %s",
str(ex))
logging.warning("Error generating mean temperature for '%s'",
one_file)
logging.warning(" plot name: '%s'", plot_name)
continue
# Check that we got something
if not num_files:
return {'code': -1000, 'error': "No files were processed"}
if not total_plots_calculated:
return {
'code': -1001,
'error': "No plots intersected with the images provided"
}
# Setup the metadata for returning files
file_md = []
if geo_file:
file_md.append({'path': geo_csv_filename, 'key': 'csv'})
if bety_file:
file_md.append({'path': bety_csv_filename, 'key': 'csv'})
# Perform cleanup
if geo_file:
geo_file.close()
if bety_file:
bety_file.close()
return {
'code': 0,
'files': file_md,
configuration.TRANSFORMER_NAME: {
'version': configuration.TRANSFORMER_VERSION,
'utc_timestamp': datetime.datetime.utcnow().isoformat(),
'processing_time': str(datetime.datetime.now() - start_timestamp),
'total_file_count': total_files,
'processed_file_count': num_files,
'total_plots_processed': processed_plots,
'empty_plots': number_empty_plots
}
}
geo_csv = args.input.replace(".tif", "_canopycover_geo.csv")
csv_file = open(bety_csv, 'w')
(fields, traits) = get_traits_table()
csv_file.write(','.join(map(str, fields)) + '\n')
geo_file = open(geo_csv, 'w')
geo_file.write(','.join([
'site', 'trait', 'lat', 'lon', 'dp_time', 'source', 'value', 'timestamp'
]) + '\n')
all_plots = get_site_boundaries(date, city='Maricopa')
successful_plots = 0
for plotname in all_plots:
logger.debug("Processing plot %s" % plotname)
bounds = all_plots[plotname]
tuples = geojson_to_tuples_betydb(yaml.safe_load(bounds))
centroid_lonlat = json.loads(centroid_from_geojson(bounds))["coordinates"]
# Use GeoJSON string to clip full field to this plot
try:
pxarray = clip_raster(args.input, tuples)
if pxarray is not None:
pxarray = rollaxis(pxarray, 0, 3)
if len(pxarray.shape) < 3:
logger.error("unexpected array shape for %s (%s)" %
(plotname, pxarray.shape))
continue
ccVal = terraref.stereo_rgb.calculate_canopycover(pxarray)
successful_plots += 1
