def test_epsg_osr(self, grid):
"""Return the spatial reference object of an EPSG code"""
# Check that a bad EPSG code raises an exception
# with pytest.raises(ValueError):
with pytest.raises(SystemExit):
gdc.epsg_osr(-1)
# Check that an OSR object is returned
assert isinstance(
gdc.epsg_osr(grid.epsg), type(osr.SpatialReference()))
def test_matching_spatref(self, grid):
"""Compare the PROJ4 strings of two spatial reference objects"""
a = grid.osr
b = gdc.proj4_osr(grid.proj4)
c = gdc.wkt_osr(grid.wkt)
d = gdc.epsg_osr(grid.epsg)
assert gdc.matching_spatref(a, b)
assert gdc.matching_spatref(a, c)
assert gdc.matching_spatref(a, d)
def parse_spatial_reference(ini, section='SPATIAL'):
""""""
# MANDATORY PARAMETERS
# section, input_name, output_name, description, get_type
param_list = [
# Output spatial reference
['output_snap', 'snap', str],
['output_cs', 'cellsize', float],
['output_proj', 'crs', str]
]
for input_name, output_name, get_type in param_list:
get_param(ini, section, input_name, output_name, get_type)
# Convert snap points to list
ini[section]['snap'] = [
float(i) for i in ini[section]['snap'].split(',')
if i.strip().isdigit()][:2]
# Compute snap points separately
ini[section]['snap_x'], ini[section]['snap_y'] = ini[section]['snap']
# Compute OSR from EGSG code
try:
ini[section]['osr'] = gdc.epsg_osr(
int(ini[section]['crs'].split(':')[1]))
except Exception as e:
logging.error(
'\nERROR: The output projection could not be converted to a '
'spatial reference object\n {}'.format(
ini[section]['crs']))
logging.exception(' {}'.format(e))
sys.exit()
logging.debug(' Snap: {} {}'.format(
ini[section]['snap_x'], ini[section]['snap_y']))
logging.debug(' Cellsize: {}'.format(ini[section]['cellsize']))
logging.debug(' CRS: {}'.format(ini[section]['crs']))
def ee_image_download(ini_path=None, overwrite_flag=False):
"""Earth Engine Annual Mean Image Download
Parameters
----------
ini_path : str
overwrite_flag : bool, optional
If True, overwrite existing files (the default is False).
"""
logging.info('\nEarth Engine EDDI Image Download')
# 12 month EDDI
aggregation_days = 365
export_name = 'eddi_12month'
output_name = 'eddi.12month'
eddi_date_list = [
'0131', '0228', '0331', '0430', '0531', '0630', '0731', '0831', '0930',
'1031', '1130', '1231'
]
# eddi_date_list = ['0930', '1231']
# eddi_date_list = ['{:02d}01'.format(m) for m in range(1, 13)]
# eddi_date_list = []
eddi_folder = 'eddi'
# Do we need to support separate EDDI years?
# start_year = 1984
# end_year = 2016
#
climo_year_start = 1979
climo_year_end = 2017
# Read config file
# ini = inputs.ini_parse(ini_path, section='IMAGE')
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='SPATIAL')
inputs.parse_section(ini, section='EXPORT')
inputs.parse_section(ini, section='IMAGES')
nodata_value = -9999
# Manually set output spatial reference
logging.info('\nHardcoding GRIDMET snap, cellsize and spatial reference')
ini['output_x'], ini['output_y'] = -124.79299639209513, 49.41685579737572
ini['SPATIAL']['cellsize'] = 0.041666001963701
# ini['SPATIAL']['cellsize'] = [0.041666001963701, 0.041666001489718]
# ini['output_x'] = -124.79166666666666666667
# ini['output_y'] = 25.04166666666666666667
# ini['SPATIAL']['cellsize'] = 1. / 24
ini['SPATIAL']['osr'] = gdc.epsg_osr(4326)
# ini['SPATIAL']['osr'] = gdc.epsg_osr(4269)
ini['SPATIAL']['crs'] = 'EPSG:4326'
logging.debug(' Snap: {} {}'.format(ini['output_x'], ini['output_y']))
logging.debug(' Cellsize: {}'.format(ini['SPATIAL']['cellsize']))
logging.debug(' OSR: {}'.format(ini['SPATIAL']['osr']))
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# Merge geometries
if ini['INPUTS']['merge_geom_flag']:
merge_geom = ogr.Geometry(ogr.wkbMultiPolygon)
for zone in zone_geom_list:
zone_multipolygon = ogr.ForceToMultiPolygon(
ogr.CreateGeometryFromJson(json.dumps(zone[2])))
for zone_polygon in zone_multipolygon:
merge_geom.AddGeometry(zone_polygon)
# merge_json = json.loads(merge_mp.ExportToJson())
zone_geom_list = [[
0, ini['INPUTS']['zone_filename'],
json.loads(merge_geom.ExportToJson())
]]
ini['INPUTS']['zone_field'] = ''
# Need zone_shp_path projection to build EE geometries
zone_osr = gdc.feature_path_osr(ini['INPUTS']['zone_shp_path'])
zone_proj = gdc.osr_wkt(zone_osr)
# zone_proj = ee.Projection(zone_proj).wkt().getInfo()
# zone_proj = zone_proj.replace('\n', '').replace(' ', '')
logging.debug(' Zone Projection: {}'.format(zone_proj))
# Initialize Earth Engine API key
logging.info('\nInitializing Earth Engine')
ee.Initialize()
utils.ee_request(ee.Number(1).getInfo())
# Get current running tasks
tasks = utils.get_ee_tasks()
# Download images for each feature separately
for zone_fid, zone_name, zone_json in zone_geom_list:
zone_name = zone_name.replace(' ', '_')
logging.info('ZONE: {} (FID: {})'.format(zone_name, zone_fid))
# Build EE geometry object for zonal stats
zone_geom = ee.Geometry(zone_json, zone_proj, False)
# Project the zone_geom to the GRIDMET projection
# if zone_proj != output_proj:
zone_geom = zone_geom.transform(ini['SPATIAL']['crs'], 0.001)
# Get the extent from the Earth Engine geometry object?
zone_extent = zone_geom.bounds().getInfo()['coordinates'][0]
zone_extent = gdc.Extent([
min(zip(*zone_extent)[0]),
min(zip(*zone_extent)[1]),
max(zip(*zone_extent)[0]),
max(zip(*zone_extent)[1])
])
# # Use GDAL and geometry json to build extent, transform, and shape
# zone_extent = gdc.Extent(
# ogr.CreateGeometryFromJson(json.dumps(zone_json)).GetEnvelope())
# # zone_extent = gdc.Extent(zone_geom.GetEnvelope())
# zone_extent.ymin, zone_extent.xmax = zone_extent.xmax, zone_extent.ymin
# Adjust extent to match raster
zone_extent = zone_extent.adjust_to_snap('EXPAND', ini['output_x'],
ini['output_y'],
ini['SPATIAL']['cellsize'])
zone_geo = zone_extent.geo(ini['SPATIAL']['cellsize'])
zone_transform = gdc.geo_2_ee_transform(zone_geo)
zone_transform = '[' + ','.join(map(str, zone_transform)) + ']'
zone_shape = zone_extent.shape(ini['SPATIAL']['cellsize'])
logging.debug(' Zone Shape: {}'.format(zone_shape))
logging.debug(' Zone Transform: {}'.format(zone_transform))
logging.debug(' Zone Extent: {}'.format(zone_extent))
# logging.debug(' Geom: {}'.format(zone_geom.getInfo()))
# output_transform = zone_transform[:]
output_transform = '[' + ','.join(map(str, zone_transform)) + ']'
output_shape = '[{1}x{0}]'.format(*zone_shape)
logging.debug(' Output Projection: {}'.format(ini['SPATIAL']['crs']))
logging.debug(' Output Transform: {}'.format(output_transform))
logging.debug(' Output Shape: {}'.format(output_shape))
zone_eddi_ws = os.path.join(ini['IMAGES']['output_ws'], zone_name,
eddi_folder)
if not os.path.isdir(zone_eddi_ws):
os.makedirs(zone_eddi_ws)
# GRIDMET PDSI
# Process each image in the collection by date
export_list = []
export_list = list(
date_range(datetime.datetime(ini['INPUTS']['start_year'], 1, 1),
datetime.datetime(ini['INPUTS']['end_year'], 12, 31),
skip_leap_days=True))
# Filter list to only keep last dekad of October and December
if eddi_date_list:
export_list = [
tgt_dt for tgt_dt in export_list
if tgt_dt.strftime('%m%d') in eddi_date_list
]
for tgt_dt in export_list:
date_str = tgt_dt.strftime('%Y%m%d')
logging.info('{} {}'.format(tgt_dt.strftime('%Y-%m-%d'),
output_name))
if tgt_dt >= datetime.datetime.today():
logging.info(' Date after current date, skipping')
continue
# Rename to match naming style from getDownloadURL
# image_name.band.tif
export_id = '{}_{}_{}'.format(ini['INPUTS']['zone_filename'],
date_str, export_name.lower())
output_id = '{}_{}'.format(date_str, output_name)
export_path = os.path.join(ini['EXPORT']['export_ws'],
export_id + '.tif')
output_path = os.path.join(zone_eddi_ws, output_id + '.tif')
logging.debug(' Export: {}'.format(export_path))
logging.debug(' Output: {}'.format(output_path))
if overwrite_flag:
if export_id in tasks.keys():
logging.debug(' Task already submitted, cancelling')
ee.data.cancelTask(tasks[export_id])
del tasks[export_id]
if os.path.isfile(export_path):
logging.debug(' Export image already exists, removing')
utils.remove_file(export_path)
# os.remove(export_path)
if os.path.isfile(output_path):
logging.debug(' Output image already exists, removing')
utils.remove_file(output_path)
# os.remove(output_path)
else:
if os.path.isfile(export_path):
logging.debug(' Export image already exists, moving')
shutil.move(export_path, output_path)
gdc.raster_path_set_nodata(output_path, nodata_value)
# DEADBEEF - should raster stats be computed?
# gdc.raster_statistics(output_path)
continue
elif os.path.isfile(output_path):
logging.debug(' Output image already exists, skipping')
continue
elif export_id in tasks.keys():
logging.debug(' Task already submitted, skipping')
continue
eddi_image = ee_eddi_image(tgt_dt.strftime('%Y-%m-%d'),
agg_days=aggregation_days,
variable='eddi',
year_start=climo_year_start,
year_end=climo_year_end)
logging.debug(' Building export task')
# if ini['EXPORT']['export_dest'] == 'gdrive':
task = ee.batch.Export.image.toDrive(
image=eddi_image,
description=export_id,
# folder=ini['EXPORT']['export_folder'],
fileNamePrefix=export_id,
dimensions=output_shape,
crs=ini['SPATIAL']['crs'],
crsTransform=output_transform)
# elif ini['EXPORT']['export_dest'] == 'gdrive':
# task = ee.batch.Export.image.toCloudStorage(
# image=eddi_image,
# description=export_id,
# bucket=ini['EXPORT']['export_folder'],
# fileNamePrefix=export_id,
# dimensions=output_shape,
# crs=ini['SPATIAL']['crs'],
# crsTransform=output_transform)
logging.debug(' Starting export task')
utils.ee_request(task.start())
def ee_image_download(ini_path=None, overwrite_flag=False):
"""Earth Engine Annual Mean Image Download
Parameters
----------
ini_path : str
overwrite_flag : bool, optional
If True, overwrite existing files (the default is False).
"""
logging.info('\nEarth Engine GRIDMET Image Download')
# Do we need to support separate GRIDMET years?
# start_year = 1984
# end_year = 2016
gridmet_download_bands = {
'eto': 'ETo',
'pr': 'PPT',
}
# If false, script will export annual and water year total images
gridmet_monthly_flag = False
gridmet_flag = True
pdsi_flag = False
pdsi_date_list = [
'0120',
'0220',
'0320',
'0420',
'0520',
'0620',
'0720',
'0820',
'0920',
'1020',
'1120',
'1220',
]
# pdsi_date_list = ['0920', '1220']
# pdsi_date_list = []
if gridmet_monthly_flag:
gridmet_folder = 'gridmet_monthly'
else:
gridmet_folder = 'gridmet_annual'
if not pdsi_date_list:
pdsi_folder = 'pdsi_full'
else:
pdsi_folder = 'pdsi'
# Read config file
ini = inputs.read(ini_path)
inputs.parse_section(ini, section='INPUTS')
inputs.parse_section(ini, section='SPATIAL')
inputs.parse_section(ini, section='EXPORT')
inputs.parse_section(ini, section='IMAGES')
nodata_value = -9999
# Manually set output spatial reference
logging.info('\nHardcoding GRIDMET snap, cellsize and spatial reference')
ini['output_x'], ini['output_y'] = -124.79299639209513, 49.41685579737572
ini['SPATIAL']['cellsize'] = 0.041666001963701
# ini['SPATIAL']['cellsize'] = [0.041666001963701, 0.041666001489718]
# ini['output_x'], ini['output_y'] = -124.79166666666666666667, 25.04166666666666666667
# ini['SPATIAL']['cellsize'] = 1. / 24
ini['SPATIAL']['osr'] = gdc.epsg_osr(4326)
# ini['SPATIAL']['osr'] = gdc.epsg_osr(4269)
ini['SPATIAL']['crs'] = 'EPSG:4326'
logging.debug(' Snap: {} {}'.format(ini['output_x'], ini['output_y']))
logging.debug(' Cellsize: {}'.format(ini['SPATIAL']['cellsize']))
logging.debug(' OSR: {}'.format(ini['SPATIAL']['osr']))
# Get ee features from shapefile
zone_geom_list = gdc.shapefile_2_geom_list_func(
ini['INPUTS']['zone_shp_path'],
zone_field=ini['INPUTS']['zone_field'],
reverse_flag=False)
# Filter features by FID before merging geometries
if ini['INPUTS']['fid_keep_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] in ini['INPUTS']['fid_keep_list']
]
if ini['INPUTS']['fid_skip_list']:
zone_geom_list = [
zone_obj for zone_obj in zone_geom_list
if zone_obj[0] not in ini['INPUTS']['fid_skip_list']
]
# Merge geometries
if ini['INPUTS']['merge_geom_flag']:
merge_geom = ogr.Geometry(ogr.wkbMultiPolygon)
for zone in zone_geom_list:
zone_multipolygon = ogr.ForceToMultiPolygon(
ogr.CreateGeometryFromJson(json.dumps(zone[2])))
for zone_polygon in zone_multipolygon:
merge_geom.AddGeometry(zone_polygon)
# merge_json = json.loads(merge_mp.ExportToJson())
zone_geom_list = [[
0, ini['INPUTS']['zone_filename'],
json.loads(merge_geom.ExportToJson())
]]
ini['INPUTS']['zone_field'] = ''
# Need zone_shp_path projection to build EE geometries
zone_osr = gdc.feature_path_osr(ini['INPUTS']['zone_shp_path'])
zone_proj = gdc.osr_wkt(zone_osr)
# zone_proj = ee.Projection(zone_proj).wkt().getInfo()
# zone_proj = zone_proj.replace('\n', '').replace(' ', '')
logging.debug(' Zone Projection: {}'.format(zone_proj))
# Initialize Earth Engine API key
logging.info('\nInitializing Earth Engine')
ee.Initialize()
utils.ee_request(ee.Number(1).getInfo())
# Get current running tasks
tasks = utils.get_ee_tasks()
# Download images for each feature separately
for zone_fid, zone_name, zone_json in zone_geom_list:
zone_name = zone_name.replace(' ', '_')
logging.info('ZONE: {} (FID: {})'.format(zone_name, zone_fid))
# Build EE geometry object for zonal stats
zone_geom = ee.Geometry(zone_json, zone_proj, False)
# Project the zone_geom to the GRIDMET projection
# if zone_proj != output_proj:
zone_geom = zone_geom.transform(ini['SPATIAL']['crs'], 0.001)
# Get the extent from the Earth Engine geometry object?
zone_extent = zone_geom.bounds().getInfo()['coordinates'][0]
zone_extent = gdc.Extent([
min(zip(*zone_extent)[0]),
min(zip(*zone_extent)[1]),
max(zip(*zone_extent)[0]),
max(zip(*zone_extent)[1])
])
# # Use GDAL and geometry json to build extent, transform, and shape
# zone_extent = gdc.Extent(
# ogr.CreateGeometryFromJson(json.dumps(zone_json)).GetEnvelope())
# # zone_extent = gdc.Extent(zone_geom.GetEnvelope())
# zone_extent.ymin, zone_extent.xmax = zone_extent.xmax, zone_extent.ymin
# Adjust extent to match raster
zone_extent = zone_extent.adjust_to_snap('EXPAND',
ini['SPATIAL']['snap_x'],
ini['SPATIAL']['snap_y'],
ini['SPATIAL']['cellsize'])
zone_geo = zone_extent.geo(ini['SPATIAL']['cellsize'])
zone_transform = gdc.geo_2_ee_transform(zone_geo)
zone_transform = '[' + ','.join(map(str, zone_transform)) + ']'
zone_shape = zone_extent.shape(ini['SPATIAL']['cellsize'])
logging.debug(' Zone Shape: {}'.format(zone_shape))
logging.debug(' Zone Transform: {}'.format(zone_transform))
logging.debug(' Zone Extent: {}'.format(zone_extent))
# logging.debug(' Geom: {}'.format(zone_geom.getInfo()))
# output_transform = zone_transform[:]
output_transform = '[' + ','.join(map(str, zone_transform)) + ']'
output_shape = '[{1}x{0}]'.format(*zone_shape)
logging.debug(' Output Projection: {}'.format(ini['SPATIAL']['crs']))
logging.debug(' Output Transform: {}'.format(output_transform))
logging.debug(' Output Shape: {}'.format(output_shape))
zone_gridmet_ws = os.path.join(ini['IMAGES']['output_ws'], zone_name,
gridmet_folder)
zone_pdsi_ws = os.path.join(ini['IMAGES']['output_ws'], zone_name,
pdsi_folder)
if not os.path.isdir(zone_gridmet_ws):
os.makedirs(zone_gridmet_ws)
if not os.path.isdir(zone_pdsi_ws):
os.makedirs(zone_pdsi_ws)
# GRIDMET PPT & ETo
if gridmet_flag:
# Process each image in the collection by date
export_list = []
for year in range(ini['INPUTS']['start_year'],
ini['INPUTS']['end_year'] + 1):
for b_key, b_name in sorted(gridmet_download_bands.items()):
if gridmet_monthly_flag:
# Monthly
for start_month in range(1, 13):
start_dt = datetime.datetime(year, start_month, 1)
end_dt = (start_dt + relativedelta(months=1) -
datetime.timedelta(0, 1))
export_list.append([
start_dt, end_dt,
'{:04d}{:02d}'.format(year, start_month),
b_key, b_name
])
else:
# Calendar year
export_list.append([
datetime.datetime(year, 1, 1),
datetime.datetime(year + 1, 1, 1),
'{:04d}'.format(year), b_key, b_name
])
# Water year
export_list.append([
datetime.datetime(year - 1, 10, 1),
datetime.datetime(year, 10, 1) -
datetime.timedelta(0, 1), '{:04d}wy'.format(year),
b_key, b_name
])
for start_dt, end_dt, date_str, b_key, b_name in export_list:
logging.info('{} {}'.format(date_str, b_name))
if end_dt > datetime.datetime.today():
logging.info(' End date after current date, skipping')
continue
# Rename to match naming style from getDownloadURL
# image_name.band.tif
export_id = '{}_{}_gridmet_{}'.format(
ini['INPUTS']['zone_filename'], date_str, b_name.lower())
output_id = '{}_gridmet.{}'.format(date_str, b_name.lower())
export_path = os.path.join(ini['EXPORT']['export_ws'],
export_id + '.tif')
output_path = os.path.join(zone_gridmet_ws, output_id + '.tif')
logging.debug(' Export: {}'.format(export_path))
logging.debug(' Output: {}'.format(output_path))
if overwrite_flag:
if export_id in tasks.keys():
logging.debug(' Task already submitted, cancelling')
ee.data.cancelTask(tasks[export_id])
del tasks[export_id]
if os.path.isfile(export_path):
logging.debug(
' Export image already exists, removing')
utils.remove_file(export_path)
# os.remove(export_path)
if os.path.isfile(output_path):
logging.debug(
' Output image already exists, removing')
utils.remove_file(output_path)
# os.remove(output_path)
else:
if os.path.isfile(export_path):
logging.debug(' Export image already exists, moving')
shutil.move(export_path, output_path)
gdc.raster_path_set_nodata(output_path, nodata_value)
# DEADBEEF - should raster stats be computed?
# gdc.raster_statistics(output_path)
# subprocess.check_output([
# 'gdalwarp',
# '-ot', float_output_type, '-overwrite',
# '-of', 'GTiff', '-co', 'COMPRESS=LZW',
# '-srcnodata', str(nodata_value),
# '-dstnodata', '{:f}'.format(float_nodata_value),
# export_path, output_path])
# with open(os.devnull, 'w') as devnull:
# subprocess.check_call(
# ['gdalinfo', '-stats', output_path],
# stdout=devnull)
# subprocess.check_output(
# ['gdalmanage', 'delete', export_path])
continue
elif os.path.isfile(output_path):
logging.debug(
' Output image already exists, skipping')
continue
elif export_id in tasks.keys():
logging.debug(' Task already submitted, skipping')
continue
# GRIDMET collection is available in EarthEngine
gridmet_coll = ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')\
.filterDate(start_dt, end_dt) \
.select([b_key])
gridmet_image = ee.Image(gridmet_coll.sum())
logging.debug(' Starting download task')
# if ini['EXPORT']['export_dest'] == 'gdrive':
task = ee.batch.Export.image.toDrive(
image=gridmet_image,
description=export_id,
# folder=ini['EXPORT']['export_folder'],
fileNamePrefix=export_id,
dimensions=output_shape,
crs=ini['SPATIAL']['crs'],
crsTransform=output_transform)
# elif ini['EXPORT']['export_dest'] == 'gdrive':
# task = ee.batch.Export.image.toCloudStorage(
# image=gridmet_image,
# description=export_id,
# bucket=ini['EXPORT']['export_folder'],
# fileNamePrefix=export_id,
# dimensions=output_shape,
# crs=ini['SPATIAL']['crs'],
# crsTransform=output_transform)
logging.debug(' Starting export task')
utils.ee_request(task.start())
# GRIDMET PDSI
if pdsi_flag:
# Process each image in the collection by date
export_list = []
b_name = 'pdsi'
for year in range(ini['INPUTS']['start_year'],
ini['INPUTS']['end_year'] + 1):
# Dekad
for start_month in range(1, 13):
for start_day, end_day in zip([1, 10, 20], [10, 20, 30]):
if start_month == 12 and start_day == 20:
# Go to the first day of the next year (and month)
start_dt = datetime.datetime(
year, start_month, start_day)
end_dt = datetime.datetime(year + 1, 1, 1)
elif start_month < 12 and start_day == 20:
# Go to the first day of the next month
start_dt = datetime.datetime(
year, start_month, start_day)
end_dt = datetime.datetime(year, start_month + 1,
1)
else:
start_dt = datetime.datetime(
year, start_month, start_day)
end_dt = datetime.datetime(year, start_month,
end_day)
end_dt = end_dt - datetime.timedelta(0, 1)
export_list.append([
start_dt, end_dt, '{:04d}{:02d}{:02d}'.format(
year, start_month, start_day), b_name
])
# Filter list to only keep last dekad of October and December
if pdsi_date_list:
export_list = [[
start_dt, end_dt, date_str, b_name
] for start_dt, end_dt, date_str, b_name in export_list
if start_dt.strftime('%m%d') in pdsi_date_list]
for start_dt, end_dt, date_str, b_name in export_list:
logging.info('{} {}'.format(date_str, b_name))
# Rename to match naming style from getDownloadURL
# image_name.band.tif
export_id = '{}_{}_{}'.format(
os.path.splitext(
ini['INPUTS']['zone_filename'])[0].lower(), date_str,
b_name.lower())
output_id = '{}_{}'.format(date_str, b_name.lower())
export_path = os.path.join(ini['EXPORT']['export_ws'],
export_id + '.tif')
output_path = os.path.join(zone_pdsi_ws, output_id + '.tif')
logging.debug(' Export: {}'.format(export_path))
logging.debug(' Output: {}'.format(output_path))
if overwrite_flag:
if export_id in tasks.keys():
logging.debug(' Task already submitted, cancelling')
ee.data.cancelTask(tasks[export_id])
del tasks[export_id]
if os.path.isfile(export_path):
logging.debug(
' Export image already exists, removing')
utils.remove_file(export_path)
# os.remove(export_path)
if os.path.isfile(output_path):
logging.debug(
' Output image already exists, removing')
utils.remove_file(output_path)
# os.remove(output_path)
else:
if os.path.isfile(export_path):
logging.debug(' Export image already exists, moving')
shutil.move(export_path, output_path)
gdc.raster_path_set_nodata(output_path, nodata_value)
# DEADBEEF - should raster stats be computed?
# gdc.raster_statistics(output_path)
continue
elif os.path.isfile(output_path):
logging.debug(
' Output image already exists, skipping')
continue
elif export_id in tasks.keys():
logging.debug(' Task already submitted, skipping')
continue
# PDSI collection is available in EarthEngine
# Index the PDSI image directly
pdsi_image = ee.Image('IDAHO_EPSCOR/PDSI/{}'.format(
start_dt.strftime('%Y%m%d')))
# pdsi_coll = ee.ImageCollection('IDAHO_EPSCOR/PDSI')\
# .filterDate(start_dt, end_dt) \
# .select(['pdsi'])
# pdsi_image = ee.Image(pdsi_coll.mean())
logging.debug(' Building export task')
# if ini['EXPORT']['export_dest'] == 'gdrive':
task = ee.batch.Export.image.toDrive(
image=pdsi_image,
description=export_id,
# folder=ini['EXPORT']['export_folder'],
fileNamePrefix=export_id,
dimensions=output_shape,
crs=ini['SPATIAL']['crs'],
crsTransform=output_transform)
# elif ini['EXPORT']['export_dest'] == 'cloud':
# task = ee.batch.Export.image.toCloudStorage(
# image=pdsi_image,
# description=export_id,
# bucket=ini['EXPORT']['export_folder'],
# fileNamePrefix=export_id,
# dimensions=output_shape,
# crs=ini['SPATIAL']['crs'],
# crsTransform=output_transform)
logging.debug(' Starting export task')
utils.ee_request(task.start())