def main ():
no_upload = False
# Create the output log
uu.initiate_log()
os.chdir(cn.docker_base_dir)
# The list of tiles to iterate through
tile_id_list = uu.tile_list_s3(cn.WHRC_biomass_2000_unmasked_dir)
# tile_id_list = ["00N_000E", "00N_050W", "00N_060W", "00N_010E", "00N_020E", "00N_030E", "00N_040E", "10N_000E", "10N_010E", "10N_010W", "10N_020E", "10N_020W"] # test tiles
# tile_id_list = ['00N_110E'] # test tile
uu.print_log(tile_id_list)
uu.print_log("There are {} tiles to process".format(str(len(tile_id_list))) + "\n")
# By definition, this script is for the biomass swap analysis (replacing WHRC AGB with Saatchi/JPL AGB)
sensit_type = 'biomass_swap'
# Downloads a pan-tropical raster that has the erroneous integer values in the oceans removed
uu.s3_file_download(cn.JPL_raw_dir, cn.JPL_raw_name, sensit_type)
# Converts the Saatchi AGB vrt to Hansen tiles
source_raster = cn.JPL_raw_name
out_pattern = cn.pattern_JPL_unmasked_processed
dt = 'Float32'
pool = multiprocessing.Pool(cn.count-5) # count-5 peaks at 320GB of memory
pool.map(partial(uu.mp_warp_to_Hansen, source_raster=source_raster, out_pattern=out_pattern, dt=dt, no_upload=no_upload), tile_id_list)
# Checks if each tile has data in it. Only tiles with data are uploaded.
upload_dir = cn.JPL_processed_dir
pattern = cn.pattern_JPL_unmasked_processed
pool = multiprocessing.Pool(cn.count - 5) # count-5 peaks at 410GB of memory
pool.map(partial(uu.check_and_upload, upload_dir=upload_dir, pattern=pattern), tile_id_list)
'-l',
required=True,
help=
'List of tile ids to use in the model. Should be of form 00N_110E or 00N_110E,00N_120E or all.'
)
parser.add_argument('--run-date',
'-d',
required=False,
help='Date of run. Must be format YYYYMMDD.')
args = parser.parse_args()
sensit_type = args.model_type
tile_id_list = args.tile_id_list
run_date = args.run_date
# Disables upload to s3 if no AWS credentials are found in environment
if not uu.check_aws_creds():
no_upload = True
uu.print_log("s3 credentials not found. Uploading to s3 disabled.")
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list,
sensit_type=sensit_type,
run_date=run_date)
# Checks whether the sensitivity analysis and tile_id_list arguments are valid
uu.check_sensit_type(sensit_type)
tile_id_list = uu.tile_id_list_check(tile_id_list)
mp_annual_gain_rate_mangrove(sensit_type=sensit_type,
tile_id_list=tile_id_list,
run_date=run_date)
'-l',
required=True,
help=
'List of tile ids to use in the model. Should be of form 00N_110E or 00N_110E,00N_120E or all.'
)
parser.add_argument('--run-date',
'-d',
required=False,
help='Date of run. Must be format YYYYMMDD.')
parser.add_argument('--no-upload',
'-nu',
action='store_true',
help='Disables uploading of outputs to s3')
args = parser.parse_args()
tile_id_list = args.tile_id_list
run_date = args.run_date
no_upload = args.no_upload
# Disables upload to s3 if no AWS credentials are found in environment
if not uu.check_aws_creds():
no_upload = True
uu.print_log("s3 credentials not found. Uploading to s3 disabled.")
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list,
run_date=run_date,
no_upload=no_upload)
mp_mangrove_processing(tile_id_list=tile_id_list,
run_date=run_date,
no_upload=no_upload)
pool = multiprocessing.Pool(processes)
pool.map(partial(uu.check_and_delete_if_empty, output_pattern=output_pattern), tile_id_list)
pool.close()
pool.join()
uu.print_log('\n')
# Uploads output tiles to s3
for i in range(0, len(output_dir_list)):
uu.upload_final_set(output_dir_list[i], output_pattern_list[i])
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Create tiles of the annual AGB and BGB gain rates for mangrove forests')
parser.add_argument('--tile_id_list', '-l', required=True,
help='List of tile ids to use in the model. Should be of form 00N_110E or 00N_110E,00N_120E or all.')
parser.add_argument('--run-date', '-d', required=False,
help='Date of run. Must be format YYYYMMDD.')
args = parser.parse_args()
tile_id_list = args.tile_id_list
run_date = args.run_date
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list, run_date=run_date)
# Checks whether the tile_id_list argument is valid
tile_id_list = uu.tile_id_list_check(tile_id_list)
mp_prep_other_inputs(tile_id_list=tile_id_list, run_date=run_date)
def main():
# Create the output log
uu.initiate_log()
os.chdir(cn.docker_base_dir)
# List of tiles that could be run. This list is only used to create the FIA region tiles if they don't already exist.
tile_id_list = uu.tile_list_s3(cn.WHRC_biomass_2000_unmasked_dir)
# tile_id_list = ['50N_130W'] # test tiles
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) + "\n")
# Downloads the Mekong loss folder. Each year of loss has its own raster
uu.s3_folder_download(cn.Mekong_loss_raw_dir, cn.docker_base_dir,
sensit_type)
# The list of all annual loss rasters
annual_loss_list = glob.glob('Loss_20*tif')
uu.print_log(annual_loss_list)
uu.print_log(
"Creating first year of loss Hansen tiles for Mekong region...")
# Recodes raw loss rasters with their loss year (for model years only)
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(Mekong_loss.recode_tiles, annual_loss_list)
# Makes a single raster of all first loss year pixels in the Mekong (i.e. where loss occurred in multiple years,
# the earlier loss gets)
uu.print_log("Merging all loss years within model range...")
loss_composite = "Mekong_loss_2001_2015.tif"
cmd = [
'gdal_merge.py', '-o', loss_composite, '-co', 'COMPRESS=LZW',
'-a_nodata', '0', '-ot', 'Byte', "Mekong_loss_recoded_2015.tif",
"Mekong_loss_recoded_2014.tif", "Mekong_loss_recoded_2013.tif",
"Mekong_loss_recoded_2012.tif", "Mekong_loss_recoded_2011.tif",
"Mekong_loss_recoded_2010.tif", "Mekong_loss_recoded_2009.tif",
"Mekong_loss_recoded_2008.tif", "Mekong_loss_recoded_2007.tif",
"Mekong_loss_recoded_2006.tif", "Mekong_loss_recoded_2005.tif",
"Mekong_loss_recoded_2004.tif", "Mekong_loss_recoded_2003.tif",
"Mekong_loss_recoded_2002.tif", "Mekong_loss_recoded_2001.tif"
]
# Solution for adding subprocess output to log is from https://stackoverflow.com/questions/21953835/run-subprocess-and-print-output-to-logging
process = Popen(cmd, stdout=PIPE, stderr=STDOUT)
with process.stdout:
uu.log_subprocess_output(process.stdout)
# Creates Hansen tiles out of the composite Mekong loss
source_raster = loss_composite
out_pattern = cn.pattern_Mekong_loss_processed
dt = 'Byte'
pool.map(
partial(uu.mp_warp_to_Hansen,
source_raster=source_raster,
out_pattern=out_pattern,
dt=dt), tile_id_list)
# This is necessary for changing NoData values to 0s (so they are recognized as 0s)
pool.map(Mekong_loss.recode_tiles, tile_id_list)
# Only uploads tiles that actually have Mekong loss in them
upload_dir = cn.Mekong_loss_processed_dir
pattern = cn.pattern_Mekong_loss_processed
pool.map(
partial(uu.check_and_upload, upload_dir=upload_dir, pattern=pattern),
tile_id_list)
pool.join()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Create planted forest carbon gain rate tiles')
parser.add_argument('--gadm-tile-index', '-gi', required=True,
help='Shapefile of 1x1 degree tiles of countries that contain planted forests (i.e. countries with planted forests rasterized to 1x1 deg). If no shapefile, write None.')
parser.add_argument('--planted-tile-index', '-pi', required=True,
help='Shapefile of 1x1 degree tiles of that contain planted forests (i.e. planted forest extent rasterized to 1x1 deg). If no shapefile, write None.')
# # This is the beginning of adding a way to have the model run on a selected area, rather than globally. I didn't finish implementing it, though.
# parser.add_argument('--bounding-box', '-bb', required=False, type=int, nargs='+',
# help='The bounding box of the tiles to be update, supplied in the order min-x, max-x, min-y, max-y. They must be at 10 degree increments.')
args = parser.parse_args()
# Creates the directory and shapefile names for the two possible arguments (index shapefiles)
gadm_index = os.path.split(args.gadm_tile_index)
gadm_index_path = gadm_index[0]
gadm_index_shp = gadm_index[1]
gadm_index_shp = gadm_index_shp[:-4]
planted_index = os.path.split(args.planted_tile_index)
planted_index_path = planted_index[0]
planted_index_shp = planted_index[1]
planted_index_shp = planted_index_shp[:-4]
# Create the output log
uu.initiate_log()
mp_plantation_preparation(gadm_index_shp=gadm_index_shp, planted_index_shp=planted_index_shp)
required=False,
help='Date of run. Must be format YYYYMMDD.')
parser.add_argument('--no-upload',
'-nu',
action='store_true',
help='Disables uploading of outputs to s3')
args = parser.parse_args()
sensit_type = args.model_type
tile_id_list = args.tile_id_list
run_date = args.run_date
no_upload = args.no_upload
# Disables upload to s3 if no AWS credentials are found in environment
if not uu.check_aws_creds():
no_upload = True
uu.print_log("s3 credentials not found. Uploading to s3 disabled.")
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list,
sensit_type=sensit_type,
run_date=run_date,
no_upload=no_upload)
# Checks whether the sensitivity analysis and tile_id_list arguments are valid
uu.check_sensit_type(sensit_type)
tile_id_list = uu.tile_id_list_check(tile_id_list)
mp_annual_gain_rate_IPCC_defaults(sensit_type=sensit_type,
tile_id_list=tile_id_list,
run_date=run_date,
no_upload=no_upload)
'-t',
required=True,
help='{}'.format(cn.model_type_arg_help))
parser.add_argument('--run-date',
'-d',
required=False,
help='Date of run. Must be format YYYYMMDD.')
args = parser.parse_args()
sensit_type = args.model_type
tile_id_list = args.tile_id_list
emitted_pools = args.emitted_pools_to_use
run_date = args.run_date
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list,
sensit_type=sensit_type,
run_date=run_date,
emitted_pools=emitted_pools)
# Checks whether the sensitivity analysis argument is valid
uu.check_sensit_type(sensit_type)
# Checks whether the sensitivity analysis and tile_id_list arguments are valid
uu.check_sensit_type(sensit_type)
if 's3://' in tile_id_list:
tile_id_list = uu.tile_list_s3(tile_id_list, 'std')
else:
tile_id_list = uu.tile_id_list_check(tile_id_list)
mp_calculate_gross_emissions(sensit_type=sensit_type,
def main():
no_upload = False
sensit_type = "legal_Amazon_loss"
# Create the output log
uu.initiate_log()
os.chdir(cn.docker_base_dir)
Brazil_stages = ['all', 'create_forest_extent', 'create_loss']
# The argument for what kind of model run is being done: standard conditions or a sensitivity analysis run
parser = argparse.ArgumentParser(
description=
'Create tiles of forest extent in legal Amazon in 2000 and annual loss according to PRODES'
)
parser.add_argument(
'--stages',
'-s',
required=True,
help=
'Stages of creating Brazil legal Amazon-specific gross cumulative removals. Options are {}'
.format(Brazil_stages))
parser.add_argument(
'--run_through',
'-r',
required=True,
help=
'Options: true or false. true: run named stage and following stages. false: run only named stage.'
)
args = parser.parse_args()
stage_input = args.stages
run_through = args.run_through
# Checks the validity of the two arguments. If either one is invalid, the script ends.
if (stage_input not in Brazil_stages):
uu.exception_log(
no_upload, 'Invalid stage selection. Please provide a stage from',
Brazil_stages)
else:
pass
if (run_through not in ['true', 'false']):
uu.exception_log(
no_upload,
'Invalid run through option. Please enter true or false.')
else:
pass
actual_stages = uu.analysis_stages(Brazil_stages, stage_input, run_through,
sensit_type)
uu.print_log(actual_stages)
# By definition, this script is for US-specific removals
sensit_type = 'legal_Amazon_loss'
# List of output directories and output file name patterns
master_output_dir_list = [
cn.Brazil_forest_extent_2000_processed_dir,
cn.Brazil_annual_loss_processed_dir
]
master_output_pattern_list = [
cn.pattern_Brazil_forest_extent_2000_processed,
cn.pattern_Brazil_annual_loss_processed
]
# Creates forest extent 2000 raster from multiple PRODES forest extent rasters
###NOTE: Didn't redo this for model v1.2.0, so I don't know if it still works.
if 'create_forest_extent' in actual_stages:
uu.print_log('Creating forest extent tiles')
# List of tiles that could be run. This list is only used to create the FIA region tiles if they don't already exist.
tile_id_list = uu.tile_list_s3(cn.WHRC_biomass_2000_unmasked_dir)
# tile_id_list = ["00N_000E", "00N_050W", "00N_060W", "00N_010E", "00N_020E", "00N_030E", "00N_040E", "10N_000E", "10N_010E", "10N_010W", "10N_020E", "10N_020W"] # test tiles
# tile_id_list = ['50N_130W'] # test tiles
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) +
"\n")
# Downloads input rasters and lists them
uu.s3_folder_download(cn.Brazil_forest_extent_2000_raw_dir,
cn.docker_base_dir, sensit_type)
raw_forest_extent_inputs = glob.glob(
'*_AMZ_warped_*tif') # The list of tiles to merge
# Gets the resolution of a more recent PRODES raster, which has a higher resolution. The merged output matches that.
raw_forest_extent_input_2019 = glob.glob('*2019_AMZ_warped_*tif')
prodes_2019 = gdal.Open(raw_forest_extent_input_2019[0])
transform_2019 = prodes_2019.GetGeoTransform()
pixelSizeX = transform_2019[1]
pixelSizeY = -transform_2019[5]
uu.print_log(pixelSizeX)
uu.print_log(pixelSizeY)
# This merges all six rasters together, so it takes a lot of memory and time. It seems to repeatedly max out
# at about 300 GB as it progresses abot 15% each time; then the memory drops back to 0 and slowly increases.
cmd = [
'gdal_merge.py', '-o',
'{}.tif'.format(cn.pattern_Brazil_forest_extent_2000_merged),
'-co', 'COMPRESS=LZW', '-a_nodata', '0', '-n', '0', '-ot', 'Byte',
'-ps', '{}'.format(pixelSizeX), '{}'.format(pixelSizeY),
raw_forest_extent_inputs[0], raw_forest_extent_inputs[1],
raw_forest_extent_inputs[2], raw_forest_extent_inputs[3],
raw_forest_extent_inputs[4], raw_forest_extent_inputs[5]
]
uu.log_subprocess_output_full(cmd)
# Uploads the merged forest extent raster to s3 for future reference
uu.upload_final_set(cn.Brazil_forest_extent_2000_merged_dir,
cn.pattern_Brazil_forest_extent_2000_merged)
# Creates legal Amazon extent 2000 tiles
source_raster = '{}.tif'.format(
cn.pattern_Brazil_forest_extent_2000_merged)
out_pattern = cn.pattern_Brazil_forest_extent_2000_processed
dt = 'Byte'
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(
partial(uu.mp_warp_to_Hansen,
source_raster=source_raster,
out_pattern=out_pattern,
dt=dt,
no_upload=no_upload), tile_id_list)
# Checks if each tile has data in it. Only tiles with data are uploaded.
upload_dir = master_output_dir_list[0]
pattern = master_output_pattern_list[0]
pool = multiprocessing.Pool(cn.count - 5)
pool.map(
partial(uu.check_and_upload,
upload_dir=upload_dir,
pattern=pattern), tile_id_list)
# Creates annual loss raster for 2001-2019 from multiples PRODES rasters
if 'create_loss' in actual_stages:
uu.print_log('Creating annual PRODES loss tiles')
tile_id_list = uu.tile_list_s3(
cn.Brazil_forest_extent_2000_processed_dir)
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) +
"\n")
# Downloads input rasters and lists them
cmd = [
'aws', 's3', 'cp', cn.Brazil_annual_loss_raw_dir, '.',
'--recursive'
]
uu.log_subprocess_output_full(cmd)
uu.print_log(
"Input loss rasters downloaded. Getting resolution of recent raster..."
)
# Gets the resolution of the more recent PRODES raster, which has a higher resolution. The merged output matches that.
raw_forest_extent_input_2019 = glob.glob('Prodes2019_*tif')
prodes_2019 = gdal.Open(raw_forest_extent_input_2019[0])
transform_2019 = prodes_2019.GetGeoTransform()
pixelSizeX = transform_2019[1]
pixelSizeY = -transform_2019[5]
uu.print_log(" Recent raster resolution: {0} by {1}".format(
pixelSizeX, pixelSizeY))
# This merges both loss rasters together, so it takes a lot of memory and time. It seems to max out
# at about 180 GB, then go back to 0.
# This took about 8 minutes.
uu.print_log(
"Merging input loss rasters into a composite for all years...")
cmd = [
'gdal_merge.py', '-o',
'{}.tif'.format(cn.pattern_Brazil_annual_loss_merged), '-co',
'COMPRESS=LZW', '-a_nodata', '0', '-n', '0', '-ot', 'Byte', '-ps',
'{}'.format(pixelSizeX), '{}'.format(pixelSizeY),
'Prodes2019_annual_loss_2008_2019.tif',
'Prodes2014_annual_loss_2001_2007.tif'
]
uu.log_subprocess_output_full(cmd)
uu.print_log(" Loss rasters combined into composite")
# Uploads the merged loss raster to s3 for future reference
uu.upload_final_set(cn.Brazil_annual_loss_merged_dir,
cn.pattern_Brazil_annual_loss_merged)
# Creates annual loss 2001-2015 tiles
uu.print_log("Warping composite PRODES loss to Hansen tiles...")
source_raster = '{}.tif'.format(cn.pattern_Brazil_annual_loss_merged)
out_pattern = cn.pattern_Brazil_annual_loss_processed
dt = 'Byte'
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(
partial(uu.mp_warp_to_Hansen,
source_raster=source_raster,
out_pattern=out_pattern,
dt=dt,
no_upload=no_upload), tile_id_list)
uu.print_log(" PRODES composite loss raster warped to Hansen tiles")
# Checks if each tile has data in it. Only tiles with data are uploaded.
# In practice, every Amazon tile has loss in it but I figured I'd do this just to be thorough.
upload_dir = master_output_dir_list[1]
pattern = master_output_pattern_list[1]
pool = multiprocessing.Pool(cn.count - 5)
pool.map(
partial(uu.check_and_upload,
upload_dir=upload_dir,
pattern=pattern), tile_id_list)
# Creates forest age category tiles
if 'forest_age_category' in actual_stages:
uu.print_log('Creating forest age category tiles')
# Files to download for this script.
download_dict = {
cn.Brazil_annual_loss_processed_dir:
[cn.pattern_Brazil_annual_loss_processed],
cn.gain_dir: [cn.pattern_gain],
cn.WHRC_biomass_2000_non_mang_non_planted_dir:
[cn.pattern_WHRC_biomass_2000_non_mang_non_planted],
cn.planted_forest_type_unmasked_dir:
[cn.pattern_planted_forest_type_unmasked],
cn.mangrove_biomass_2000_dir: [cn.pattern_mangrove_biomass_2000],
cn.Brazil_forest_extent_2000_processed_dir:
[cn.pattern_Brazil_forest_extent_2000_processed]
}
tile_id_list = uu.tile_list_s3(
cn.Brazil_forest_extent_2000_processed_dir)
# tile_id_list = ['00N_050W']
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) +
"\n")
# Downloads input files or entire directories, depending on how many tiles are in the tile_id_list
for key, values in download_dict.items():
dir = key
pattern = values[0]
uu.s3_flexible_download(dir, pattern, cn.docker_base_dir,
sensit_type, tile_id_list)
# If the model run isn't the standard one, the output directory and file names are changed
if sensit_type != 'std':
uu.print_log(
"Changing output directory and file name pattern based on sensitivity analysis"
)
stage_output_dir_list = uu.alter_dirs(sensit_type,
master_output_dir_list)
stage_output_pattern_list = uu.alter_patterns(
sensit_type, master_output_pattern_list)
output_pattern = stage_output_pattern_list[2]
# This configuration of the multiprocessing call is necessary for passing multiple arguments to the main function
# It is based on the example here: http://spencerimp.blogspot.com/2015/12/python-multiprocess-with-multiple.html
# With processes=30, peak usage was about 350 GB using WHRC AGB.
# processes=26 maxes out above 480 GB for biomass_swap, so better to use fewer than that.
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(
partial(legal_AMZ_loss.legal_Amazon_forest_age_category,
sensit_type=sensit_type,
output_pattern=output_pattern), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
#
# legal_AMZ_loss.legal_Amazon_forest_age_category(tile_id, sensit_type, output_pattern)
# Uploads output from this stage
uu.upload_final_set(stage_output_dir_list[2],
stage_output_pattern_list[2])
# Creates tiles of the number of years of removals
if 'gain_year_count' in actual_stages:
uu.print_log('Creating gain year count tiles for natural forest')
# Files to download for this script.
download_dict = {
cn.Brazil_annual_loss_processed_dir:
[cn.pattern_Brazil_annual_loss_processed],
cn.gain_dir: [cn.pattern_gain],
cn.WHRC_biomass_2000_non_mang_non_planted_dir:
[cn.pattern_WHRC_biomass_2000_non_mang_non_planted],
cn.planted_forest_type_unmasked_dir:
[cn.pattern_planted_forest_type_unmasked],
cn.mangrove_biomass_2000_dir: [cn.pattern_mangrove_biomass_2000],
cn.Brazil_forest_extent_2000_processed_dir:
[cn.pattern_Brazil_forest_extent_2000_processed]
}
tile_id_list = uu.tile_list_s3(
cn.Brazil_forest_extent_2000_processed_dir)
# tile_id_list = ['00N_050W']
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) +
"\n")
# Downloads input files or entire directories, depending on how many tiles are in the tile_id_list
for key, values in download_dict.items():
dir = key
pattern = values[0]
uu.s3_flexible_download(dir, pattern, cn.docker_base_dir,
sensit_type, tile_id_list)
# If the model run isn't the standard one, the output directory and file names are changed
if sensit_type != 'std':
uu.print_log(
"Changing output directory and file name pattern based on sensitivity analysis"
)
stage_output_dir_list = uu.alter_dirs(sensit_type,
master_output_dir_list)
stage_output_pattern_list = uu.alter_patterns(
sensit_type, master_output_pattern_list)
output_pattern = stage_output_pattern_list[3]
pool = multiprocessing.Pool(int(cn.count / 3))
pool.map(
partial(
legal_AMZ_loss.legal_Amazon_create_gain_year_count_loss_only,
sensit_type=sensit_type), tile_id_list)
pool.map(
partial(
legal_AMZ_loss.legal_Amazon_create_gain_year_count_no_change,
sensit_type=sensit_type), tile_id_list)
pool.map(
partial(legal_AMZ_loss.
legal_Amazon_create_gain_year_count_loss_and_gain_standard,
sensit_type=sensit_type), tile_id_list)
pool = multiprocessing.Pool(
int(cn.count / 8)
) # count/5 uses more than 160GB of memory. count/8 uses about 120GB of memory.
pool.map(
partial(legal_AMZ_loss.legal_Amazon_create_gain_year_count_merge,
output_pattern=output_pattern), tile_id_list)
# # For single processor use
# for tile_id in tile_id_list:
# legal_AMZ_loss.legal_Amazon_create_gain_year_count_loss_only(tile_id, sensit_type)
#
# for tile_id in tile_id_list:
# legal_AMZ_loss.legal_Amazon_create_gain_year_count_no_change(tile_id, sensit_type)
#
# for tile_id in tile_id_list:
# legal_AMZ_loss.legal_Amazon_create_gain_year_count_loss_and_gain_standard(tile_id, sensit_type)
#
# for tile_id in tile_id_list:
# legal_AMZ_loss.legal_Amazon_create_gain_year_count_merge(tile_id, output_pattern)
# Intermediate output tiles for checking outputs
uu.upload_final_set(stage_output_dir_list[3], "growth_years_loss_only")
uu.upload_final_set(stage_output_dir_list[3], "growth_years_gain_only")
uu.upload_final_set(stage_output_dir_list[3], "growth_years_no_change")
uu.upload_final_set(stage_output_dir_list[3],
"growth_years_loss_and_gain")
# Uploads output from this stage
uu.upload_final_set(stage_output_dir_list[3],
stage_output_pattern_list[3])
# Creates tiles of annual AGB and BGB gain rate for non-mangrove, non-planted forest using the standard model
# removal function
if 'annual_removals' in actual_stages:
uu.print_log('Creating annual removals for natural forest')
# Files to download for this script.
download_dict = {
cn.age_cat_IPCC_dir: [cn.pattern_age_cat_IPCC],
cn.cont_eco_dir: [cn.pattern_cont_eco_processed],
cn.plant_pre_2000_processed_dir: [cn.pattern_plant_pre_2000]
}
tile_id_list = uu.tile_list_s3(
cn.Brazil_forest_extent_2000_processed_dir)
# tile_id_list = ['00N_050W']
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) +
"\n")
# If the model run isn't the standard one, the output directory and file names are changed.
# This adapts just the relevant items in the output directory and pattern lists (annual removals).
if sensit_type != 'std':
uu.print_log(
"Changing output directory and file name pattern based on sensitivity analysis"
)
stage_output_dir_list = uu.alter_dirs(sensit_type,
master_output_dir_list[4:6])
stage_output_pattern_list = uu.alter_patterns(
sensit_type, master_output_pattern_list[4:6])
# Downloads input files or entire directories, depending on how many tiles are in the tile_id_list
for key, values in download_dict.items():
dir = key
pattern = values[0]
uu.s3_flexible_download(dir, pattern, cn.docker_base_dir,
sensit_type, tile_id_list)
# Table with IPCC Table 4.9 default gain rates
cmd = [
'aws', 's3', 'cp',
os.path.join(cn.gain_spreadsheet_dir, cn.gain_spreadsheet),
cn.docker_base_dir
]
# Solution for adding subprocess output to log is from https://stackoverflow.com/questions/21953835/run-subprocess-and-print-output-to-logging
process = Popen(cmd, stdout=PIPE, stderr=STDOUT)
with process.stdout:
uu.log_subprocess_output(process.stdout)
pd.options.mode.chained_assignment = None
# Imports the table with the ecozone-continent codes and the carbon gain rates
gain_table = pd.read_excel(
"{}".format(cn.gain_spreadsheet),
sheet_name="natrl fores gain, for std model")
# Removes rows with duplicate codes (N. and S. America for the same ecozone)
gain_table_simplified = gain_table.drop_duplicates(subset='gainEcoCon',
keep='first')
# Converts gain table from wide to long, so each continent-ecozone-age category has its own row
gain_table_cont_eco_age = pd.melt(gain_table_simplified,
id_vars=['gainEcoCon'],
value_vars=[
'growth_primary',
'growth_secondary_greater_20',
'growth_secondary_less_20'
])
gain_table_cont_eco_age = gain_table_cont_eco_age.dropna()
# Creates a table that has just the continent-ecozone combinations for adding to the dictionary.
# These will be used whenever there is just a continent-ecozone pixel without a forest age pixel.
# Assigns removal rate of 0 when there's no age category.
gain_table_con_eco_only = gain_table_cont_eco_age
gain_table_con_eco_only = gain_table_con_eco_only.drop_duplicates(
subset='gainEcoCon', keep='first')
gain_table_con_eco_only['value'] = 0
gain_table_con_eco_only['cont_eco_age'] = gain_table_con_eco_only[
'gainEcoCon']
# Creates a code for each age category so that each continent-ecozone-age combo can have its own unique value
age_dict = {
'growth_primary': 10000,
'growth_secondary_greater_20': 20000,
'growth_secondary_less_20': 30000
}
# Creates a unique value for each continent-ecozone-age category
gain_table_cont_eco_age = gain_table_cont_eco_age.replace(
{"variable": age_dict})
gain_table_cont_eco_age['cont_eco_age'] = gain_table_cont_eco_age[
'gainEcoCon'] + gain_table_cont_eco_age['variable']
# Merges the table of just continent-ecozone codes and the table of continent-ecozone-age codes
gain_table_all_combos = pd.concat(
[gain_table_con_eco_only, gain_table_cont_eco_age])
# Converts the continent-ecozone-age codes and corresponding gain rates to a dictionary
gain_table_dict = pd.Series(
gain_table_all_combos.value.values,
index=gain_table_all_combos.cont_eco_age).to_dict()
# Adds a dictionary entry for where the ecozone-continent-age code is 0 (not in a continent)
gain_table_dict[0] = 0
# Adds a dictionary entry for each forest age code for pixels that have forest age but no continent-ecozone
for key, value in age_dict.items():
gain_table_dict[value] = 0
# Converts all the keys (continent-ecozone-age codes) to float type
gain_table_dict = {
float(key): value
for key, value in gain_table_dict.items()
}
uu.print_log(gain_table_dict)
# This configuration of the multiprocessing call is necessary for passing multiple arguments to the main function
# It is based on the example here: http://spencerimp.blogspot.com/2015/12/python-multiprocess-with-multiple.html
# processes=24 peaks at about 440 GB of memory on an r4.16xlarge machine
output_pattern_list = stage_output_pattern_list
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(
partial(annual_gain_rate_natrl_forest.annual_gain_rate,
sensit_type=sensit_type,
gain_table_dict=gain_table_dict,
output_pattern_list=output_pattern_list), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile in tile_id_list:
#
# annual_gain_rate_natrl_forest.annual_gain_rate(tile, sensit_type, gain_table_dict, stage_output_pattern_list)
# Uploads outputs from this stage
for i in range(0, len(stage_output_dir_list)):
uu.upload_final_set(stage_output_dir_list[i],
stage_output_pattern_list[i])
# Creates tiles of cumulative AGCO2 and BGCO2 gain rate for non-mangrove, non-planted forest using the standard model
# removal function
if 'cumulative_removals' in actual_stages:
uu.print_log('Creating cumulative removals for natural forest')
# Files to download for this script.
download_dict = {
cn.annual_gain_AGB_IPCC_defaults_dir:
[cn.pattern_annual_gain_AGB_IPCC_defaults],
cn.annual_gain_BGB_natrl_forest_dir:
[cn.pattern_annual_gain_BGB_natrl_forest],
cn.gain_year_count_natrl_forest_dir:
[cn.pattern_gain_year_count_natrl_forest]
}
tile_id_list = uu.tile_list_s3(
cn.Brazil_forest_extent_2000_processed_dir)
# tile_id_list = ['00N_050W']
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) +
"\n")
# If the model run isn't the standard one, the output directory and file names are changed.
# This adapts just the relevant items in the output directory and pattern lists (cumulative removals).
if sensit_type != 'std':
uu.print_log(
"Changing output directory and file name pattern based on sensitivity analysis"
)
stage_output_dir_list = uu.alter_dirs(sensit_type,
master_output_dir_list[6:8])
stage_output_pattern_list = uu.alter_patterns(
sensit_type, master_output_pattern_list[6:8])
# Downloads input files or entire directories, depending on how many tiles are in the tile_id_list
for key, values in download_dict.items():
dir = key
pattern = values[0]
uu.s3_flexible_download(dir, pattern, cn.docker_base_dir,
sensit_type, tile_id_list)
# Calculates cumulative aboveground carbon gain in non-mangrove planted forests
output_pattern_list = stage_output_pattern_list
pool = multiprocessing.Pool(int(cn.count / 3))
pool.map(
partial(cumulative_gain_natrl_forest.cumulative_gain_AGCO2,
output_pattern_list=output_pattern_list,
sensit_type=sensit_type), tile_id_list)
# Calculates cumulative belowground carbon gain in non-mangrove planted forests
pool = multiprocessing.Pool(int(cn.count / 3))
pool.map(
partial(cumulative_gain_natrl_forest.cumulative_gain_BGCO2,
output_pattern_list=output_pattern_list,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# cumulative_gain_natrl_forest.cumulative_gain_AGCO2(tile_id, stage_output_pattern_list[0], sensit_type)
#
# for tile_id in tile_id_list:
# cumulative_gain_natrl_forest.cumulative_gain_BGCO2(tile_id, stage_output_pattern_list[1], sensit_type)
# Uploads outputs from this stage
for i in range(0, len(stage_output_dir_list)):
uu.upload_final_set(stage_output_dir_list[i],
stage_output_pattern_list[i])
# Creates tiles of annual gain rate and cumulative removals for all forest types (above + belowground)
if 'removals_merged' in actual_stages:
uu.print_log(
'Creating annual and cumulative removals for all forest types combined (above + belowground)'
)
# Files to download for this script
download_dict = {
cn.annual_gain_AGB_mangrove_dir:
[cn.pattern_annual_gain_AGB_mangrove],
cn.annual_gain_AGB_planted_forest_non_mangrove_dir:
[cn.pattern_annual_gain_AGB_planted_forest_non_mangrove],
cn.annual_gain_AGB_IPCC_defaults_dir:
[cn.pattern_annual_gain_AGB_IPCC_defaults],
cn.annual_gain_BGB_mangrove_dir:
[cn.pattern_annual_gain_BGB_mangrove],
cn.annual_gain_BGB_planted_forest_non_mangrove_dir:
[cn.pattern_annual_gain_BGB_planted_forest_non_mangrove],
cn.annual_gain_BGB_natrl_forest_dir:
[cn.pattern_annual_gain_BGB_natrl_forest],
cn.cumul_gain_AGCO2_mangrove_dir:
[cn.pattern_cumul_gain_AGCO2_mangrove],
cn.cumul_gain_AGCO2_planted_forest_non_mangrove_dir:
[cn.pattern_cumul_gain_AGCO2_planted_forest_non_mangrove],
cn.cumul_gain_AGCO2_natrl_forest_dir:
[cn.pattern_cumul_gain_AGCO2_natrl_forest],
cn.cumul_gain_BGCO2_mangrove_dir:
[cn.pattern_cumul_gain_BGCO2_mangrove],
cn.cumul_gain_BGCO2_planted_forest_non_mangrove_dir:
[cn.pattern_cumul_gain_BGCO2_planted_forest_non_mangrove],
cn.cumul_gain_BGCO2_natrl_forest_dir:
[cn.pattern_cumul_gain_BGCO2_natrl_forest]
}
tile_id_list = uu.tile_list_s3(
cn.Brazil_forest_extent_2000_processed_dir)
# tile_id_list = ['00N_050W']
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) +
"\n")
# If the model run isn't the standard one, the output directory and file names are changed.
# This adapts just the relevant items in the output directory and pattern lists (cumulative removals).
if sensit_type != 'std':
uu.print_log(
"Changing output directory and file name pattern based on sensitivity analysis"
)
stage_output_dir_list = uu.alter_dirs(sensit_type,
master_output_dir_list[8:10])
stage_output_pattern_list = uu.alter_patterns(
sensit_type, master_output_pattern_list[8:10])
# Downloads input files or entire directories, depending on how many tiles are in the tile_id_list
for key, values in download_dict.items():
dir = key
pattern = values[0]
uu.s3_flexible_download(dir, pattern, cn.docker_base_dir,
sensit_type, tile_id_list)
# For multiprocessing
output_pattern_list = stage_output_pattern_list
pool = multiprocessing.Pool(int(cn.count / 3))
pool.map(
partial(merge_cumulative_annual_gain_all_forest_types.gain_merge,
output_pattern_list=output_pattern_list,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# merge_cumulative_annual_gain_all_forest_types.gain_merge(tile_id, output_pattern_list, sensit_type)
# Uploads output tiles to s3
for i in range(0, len(stage_output_dir_list)):
uu.upload_final_set(stage_output_dir_list[i],
stage_output_pattern_list[i])
# Creates carbon emitted_pools in loss year
if 'carbon_pools' in actual_stages:
uu.print_log('Creating emissions year carbon emitted_pools')
# Specifies that carbon emitted_pools are created for loss year rather than in 2000
extent = 'loss'
# Files to download for this script
download_dict = {
cn.mangrove_biomass_2000_dir: [cn.pattern_mangrove_biomass_2000],
cn.cont_eco_dir: [cn.pattern_cont_eco_processed],
cn.bor_tem_trop_processed_dir: [cn.pattern_bor_tem_trop_processed],
cn.precip_processed_dir: [cn.pattern_precip],
cn.elevation_processed_dir: [cn.pattern_elevation],
cn.soil_C_full_extent_2000_dir:
[cn.pattern_soil_C_full_extent_2000],
cn.gain_dir: [cn.pattern_gain],
cn.cumul_gain_AGCO2_mangrove_dir:
[cn.pattern_cumul_gain_AGCO2_mangrove],
cn.cumul_gain_AGCO2_planted_forest_non_mangrove_dir:
[cn.pattern_cumul_gain_AGCO2_planted_forest_non_mangrove],
cn.cumul_gain_AGCO2_natrl_forest_dir:
[cn.pattern_cumul_gain_AGCO2_natrl_forest],
cn.annual_gain_AGB_mangrove_dir:
[cn.pattern_annual_gain_AGB_mangrove],
cn.annual_gain_AGB_planted_forest_non_mangrove_dir:
[cn.pattern_annual_gain_AGB_planted_forest_non_mangrove],
cn.annual_gain_AGB_IPCC_defaults_dir:
[cn.pattern_annual_gain_AGB_IPCC_defaults]
}
# Adds the correct AGB tiles to the download dictionary depending on the model run
if sensit_type == 'biomass_swap':
download_dict[cn.JPL_processed_dir] = [
cn.pattern_JPL_unmasked_processed
]
else:
download_dict[cn.WHRC_biomass_2000_unmasked_dir] = [
cn.pattern_WHRC_biomass_2000_unmasked
]
# Adds the correct loss tile to the download dictionary depending on the model run
if sensit_type == 'legal_Amazon_loss':
download_dict[cn.Brazil_annual_loss_processed_dir] = [
cn.pattern_Brazil_annual_loss_processed
]
else:
download_dict[cn.loss_dir] = ['']
tile_id_list = uu.tile_list_s3(
cn.Brazil_forest_extent_2000_processed_dir)
# tile_id_list = ['00N_050W']
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))) +
"\n")
for key, values in download_dict.items():
dir = key
pattern = values[0]
uu.s3_flexible_download(dir, pattern, cn.docker_base_dir,
sensit_type, tile_id_list)
# If the model run isn't the standard one, the output directory and file names are changed
if sensit_type != 'std':
uu.print_log(
"Changing output directory and file name pattern based on sensitivity analysis"
)
stage_output_dir_list = uu.alter_dirs(
sensit_type, master_output_dir_list[10:16])
stage_output_pattern_list = uu.alter_patterns(
sensit_type, master_output_pattern_list[10:16])
# Table with IPCC Wetland Supplement Table 4.4 default mangrove gain rates
cmd = [
'aws', 's3', 'cp',
os.path.join(cn.gain_spreadsheet_dir, cn.gain_spreadsheet),
cn.docker_base_dir
]
# Solution for adding subprocess output to log is from https://stackoverflow.com/questions/21953835/run-subprocess-and-print-output-to-logging
process = Popen(cmd, stdout=PIPE, stderr=STDOUT)
with process.stdout:
uu.log_subprocess_output(process.stdout)
pd.options.mode.chained_assignment = None
# Imports the table with the ecozone-continent codes and the carbon gain rates
gain_table = pd.read_excel("{}".format(cn.gain_spreadsheet),
sheet_name="mangrove gain, for model")
# Removes rows with duplicate codes (N. and S. America for the same ecozone)
gain_table_simplified = gain_table.drop_duplicates(subset='gainEcoCon',
keep='first')
mang_BGB_AGB_ratio = create_carbon_pools.mangrove_pool_ratio_dict(
gain_table_simplified, cn.below_to_above_trop_dry_mang,
cn.below_to_above_trop_wet_mang, cn.below_to_above_subtrop_mang)
mang_deadwood_AGB_ratio = create_carbon_pools.mangrove_pool_ratio_dict(
gain_table_simplified, cn.deadwood_to_above_trop_dry_mang,
cn.deadwood_to_above_trop_wet_mang,
cn.deadwood_to_above_subtrop_mang)
mang_litter_AGB_ratio = create_carbon_pools.mangrove_pool_ratio_dict(
gain_table_simplified, cn.litter_to_above_trop_dry_mang,
cn.litter_to_above_trop_wet_mang, cn.litter_to_above_subtrop_mang)
if extent == 'loss':
uu.print_log(
"Creating tiles of emitted aboveground carbon (carbon 2000 + carbon accumulation until loss year)"
)
# 16 processors seems to use more than 460 GB-- I don't know exactly how much it uses because I stopped it at 460
# 14 processors maxes out at 410-415 GB
# Creates a single filename pattern to pass to the multiprocessor call
pattern = stage_output_pattern_list[0]
pool = multiprocessing.Pool(int(cn.count / 4))
pool.map(
partial(create_carbon_pools.create_emitted_AGC,
pattern=pattern,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# create_carbon_pools.create_emitted_AGC(tile_id, stage_output_pattern_list[0], sensit_type)
uu.upload_final_set(stage_output_dir_list[0],
stage_output_pattern_list[0])
elif extent == '2000':
uu.print_log("Creating tiles of aboveground carbon in 2000")
# 16 processors seems to use more than 460 GB-- I don't know exactly how much it uses because I stopped it at 460
# 14 processors maxes out at 415 GB
# Creates a single filename pattern to pass to the multiprocessor call
pattern = stage_output_pattern_list[0]
pool = multiprocessing.Pool(processes=14)
pool.map(
partial(create_carbon_pools.create_2000_AGC,
pattern=pattern,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# create_carbon_pools.create_2000_AGC(tile_id, output_pattern_list[0], sensit_type)
uu.upload_final_set(stage_output_dir_list[0],
stage_output_pattern_list[0])
else:
uu.exception_log(no_upload, "Extent argument not valid")
uu.print_log("Creating tiles of belowground carbon")
# 18 processors used between 300 and 400 GB memory, so it was okay on a r4.16xlarge spot machine
# Creates a single filename pattern to pass to the multiprocessor call
pattern = stage_output_pattern_list[1]
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(
partial(create_carbon_pools.create_BGC,
mang_BGB_AGB_ratio=mang_BGB_AGB_ratio,
extent=extent,
pattern=pattern,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# create_carbon_pools.create_BGC(tile_id, mang_BGB_AGB_ratio, extent, stage_output_pattern_list[1], sensit_type)
uu.upload_final_set(stage_output_dir_list[1],
stage_output_pattern_list[1])
uu.print_log("Creating tiles of deadwood carbon")
# processes=16 maxes out at about 430 GB
# Creates a single filename pattern to pass to the multiprocessor call
pattern = stage_output_pattern_list[2]
pool = multiprocessing.Pool(int(cn.count / 4))
pool.map(
partial(create_carbon_pools.create_deadwood,
mang_deadwood_AGB_ratio=mang_deadwood_AGB_ratio,
extent=extent,
pattern=pattern,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# create_carbon_pools.create_deadwood(tile_id, mang_deadwood_AGB_ratio, extent, stage_output_pattern_list[2], sensit_type)
uu.upload_final_set(stage_output_dir_list[2],
stage_output_pattern_list[2])
uu.print_log("Creating tiles of litter carbon")
# Creates a single filename pattern to pass to the multiprocessor call
pattern = stage_output_pattern_list[3]
pool = multiprocessing.Pool(int(cn.count / 4))
pool.map(
partial(create_carbon_pools.create_litter,
mang_litter_AGB_ratio=mang_litter_AGB_ratio,
extent=extent,
pattern=pattern,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# create_carbon_pools.create_litter(tile_id, mang_litter_AGB_ratio, extent, stage_output_pattern_list[3], sensit_type)
uu.upload_final_set(stage_output_dir_list[3],
stage_output_pattern_list[3])
if extent == 'loss':
uu.print_log("Creating tiles of soil carbon")
# Creates a single filename pattern to pass to the multiprocessor call
pattern = stage_output_pattern_list[4]
pool = multiprocessing.Pool(int(cn.count / 3))
pool.map(
partial(create_carbon_pools.create_soil,
pattern=pattern,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# create_carbon_pools.create_soil(tile_id, stage_output_pattern_list[4], sensit_type)
uu.upload_final_set(stage_output_dir_list[4],
stage_output_pattern_list[4])
elif extent == '2000':
uu.print_log("Skipping soil for 2000 carbon pool calculation")
else:
uu.exception_log(no_upload, "Extent argument not valid")
uu.print_log("Creating tiles of total carbon")
# I tried several different processor numbers for this. Ended up using 14 processors, which used about 380 GB memory
# at peak. Probably could've handled 16 processors on an r4.16xlarge machine but I didn't feel like taking the time to check.
# Creates a single filename pattern to pass to the multiprocessor call
pattern = stage_output_pattern_list[5]
pool = multiprocessing.Pool(int(cn.count / 4))
pool.map(
partial(create_carbon_pools.create_total_C,
extent=extent,
pattern=pattern,
sensit_type=sensit_type), tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in tile_id_list:
# create_carbon_pools.create_total_C(tile_id, extent, stage_output_pattern_list[5], sensit_type)
uu.upload_final_set(stage_output_dir_list[5],
stage_output_pattern_list[5])
def main():
os.chdir(cn.docker_base_dir)
# List of possible model stages to run (not including mangrove and planted forest stages)
model_stages = [
'all', 'model_extent', 'forest_age_category_IPCC',
'annual_removals_IPCC', 'annual_removals_all_forest_types',
'gain_year_count', 'gross_removals_all_forest_types', 'carbon_pools',
'gross_emissions', 'net_flux', 'aggregate'
]
# The argument for what kind of model run is being done: standard conditions or a sensitivity analysis run
parser = argparse.ArgumentParser(
description='Run the full carbon flux model')
parser.add_argument('--model-type',
'-t',
required=True,
help='{}'.format(cn.model_type_arg_help))
parser.add_argument(
'--stages',
'-s',
required=True,
help='Stages for running the flux model. Options are {}'.format(
model_stages))
parser.add_argument(
'--run-through',
'-r',
required=True,
help=
'Options: true or false. true: run named stage and following stages. false: run only named stage.'
)
parser.add_argument('--run-date',
'-d',
required=False,
help='Date of run. Must be format YYYYMMDD.')
parser.add_argument(
'--tile-id-list',
'-l',
required=True,
help=
'List of tile ids to use in the model. Should be of form 00N_110E or 00N_110E,00N_120E or all.'
)
parser.add_argument(
'--carbon-pool-extent',
'-ce',
required=False,
help=
'Time period for which carbon emitted_pools should be calculated: loss, 2000, loss,2000, or 2000,loss'
)
parser.add_argument(
'--emitted-pools-to-use',
'-p',
required=False,
help=
'Options are soil_only or biomass_soil. Former only considers emissions from soil. Latter considers emissions from biomass and soil.'
)
parser.add_argument(
'--tcd-threshold',
'-tcd',
required=False,
help=
'Tree cover density threshold above which pixels will be included in the aggregation.'
)
parser.add_argument(
'--std-net-flux-aggreg',
'-sagg',
required=False,
help=
'The s3 standard model net flux aggregated tif, for comparison with the sensitivity analysis map'
)
parser.add_argument(
'--mangroves',
'-ma',
required=False,
help=
'Include mangrove removal rate and standard deviation tile creation step (before model extent). true or false.'
)
parser.add_argument(
'--us-rates',
'-us',
required=False,
help=
'Include US removal rate and standard deviation tile creation step (before model extent). true or false.'
)
parser.add_argument(
'--per-pixel-results',
'-ppr',
required=False,
help=
'Include per pixel result calculations for gross emissions (all gases, all pools), gross removals, and net flux. true or false.'
)
parser.add_argument('--log-note',
'-ln',
required=False,
help='Note to include in log header about model run.')
args = parser.parse_args()
sensit_type = args.model_type
stage_input = args.stages
run_through = args.run_through
run_date = args.run_date
tile_id_list = args.tile_id_list
carbon_pool_extent = args.carbon_pool_extent
emitted_pools = args.emitted_pools_to_use
thresh = args.tcd_threshold
if thresh is not None:
thresh = int(thresh)
std_net_flux = args.std_net_flux_aggreg
include_mangroves = args.mangroves
include_us = args.us_rates
include_per_pixel = args.per_pixel_results
log_note = args.log_note
# Start time for script
script_start = datetime.datetime.now()
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list,
sensit_type=sensit_type,
run_date=run_date,
stage_input=stage_input,
run_through=run_through,
carbon_pool_extent=carbon_pool_extent,
emitted_pools=emitted_pools,
thresh=thresh,
std_net_flux=std_net_flux,
include_mangroves=include_mangroves,
include_us=include_us,
include_per_pixel=include_per_pixel,
log_note=log_note)
# Checks the validity of the model stage arguments. If either one is invalid, the script ends.
if (stage_input not in model_stages):
uu.exception_log(
'Invalid stage selection. Please provide a stage from',
model_stages)
else:
pass
if (run_through not in ['true', 'false']):
uu.exception_log(
'Invalid run through option. Please enter true or false.')
else:
pass
# Generates the list of stages to run
actual_stages = uu.analysis_stages(model_stages,
stage_input,
run_through,
include_mangroves=include_mangroves,
include_us=include_us,
include_per_pixel=include_per_pixel)
uu.print_log("Analysis stages to run:", actual_stages)
# Reports how much storage is being used with files
uu.check_storage()
# Checks whether the sensitivity analysis argument is valid
uu.check_sensit_type(sensit_type)
# Checks if the carbon pool type is specified if the stages to run includes carbon pool generation.
# Does this up front so the user knows before the run begins that information is missing.
if ('carbon_pools' in actual_stages) & (carbon_pool_extent not in [
'loss', '2000', 'loss,2000', '2000,loss'
]):
uu.exception_log(
"Invalid carbon_pool_extent input. Please choose loss, 2000, loss,2000 or 2000,loss."
)
# Checks if the correct c++ script has been compiled for the pool option selected.
# Does this up front so that the user is prompted to compile the C++ before the script starts running, if necessary.
if 'gross_emissions' in actual_stages:
if emitted_pools == 'biomass_soil':
# Some sensitivity analyses have specific gross emissions scripts.
# The rest of the sensitivity analyses and the standard model can all use the same, generic gross emissions script.
if sensit_type in ['no_shifting_ag', 'convert_to_grassland']:
if os.path.exists('{0}/calc_gross_emissions_{1}.exe'.format(
cn.c_emis_compile_dst, sensit_type)):
uu.print_log(
"C++ for {} already compiled.".format(sensit_type))
else:
uu.exception_log(
'Must compile standard {} model C++...'.format(
sensit_type))
else:
if os.path.exists(
'{0}/calc_gross_emissions_generic.exe'.format(
cn.c_emis_compile_dst)):
uu.print_log("C++ for generic emissions already compiled.")
else:
uu.exception_log('Must compile generic emissions C++...')
elif (emitted_pools == 'soil_only') & (sensit_type == 'std'):
if os.path.exists('{0}/calc_gross_emissions_soil_only.exe'.format(
cn.c_emis_compile_dst)):
uu.print_log("C++ for generic emissions already compiled.")
else:
uu.exception_log('Must compile soil_only C++...')
else:
uu.exception_log(
'Pool and/or sensitivity analysis option not valid for gross emissions'
)
# Checks whether the canopy cover argument is valid up front.
if 'aggregate' in actual_stages:
if thresh < 0 or thresh > 99:
uu.exception_log(
'Invalid tcd. Please provide an integer between 0 and 99.')
else:
pass
# If the tile_list argument is an s3 folder, the list of tiles in it is created
if 's3://' in tile_id_list:
tile_id_list = uu.tile_list_s3(tile_id_list, 'std')
uu.print_log(tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(tile_id_list))),
"\n")
# Otherwise, check that the tile list argument is valid. "all" is the way to specify that all tiles should be processed
else:
tile_id_list = uu.tile_id_list_check(tile_id_list)
# List of output directories and output file name patterns.
# The directory list is only used for counting tiles in output folders at the end of the model
output_dir_list = [
cn.model_extent_dir, cn.age_cat_IPCC_dir,
cn.annual_gain_AGB_IPCC_defaults_dir,
cn.annual_gain_BGB_IPCC_defaults_dir,
cn.stdev_annual_gain_AGB_IPCC_defaults_dir, cn.removal_forest_type_dir,
cn.annual_gain_AGC_all_types_dir, cn.annual_gain_BGC_all_types_dir,
cn.annual_gain_AGC_BGC_all_types_dir,
cn.stdev_annual_gain_AGC_all_types_dir, cn.gain_year_count_dir,
cn.cumul_gain_AGCO2_all_types_dir, cn.cumul_gain_BGCO2_all_types_dir,
cn.cumul_gain_AGCO2_BGCO2_all_types_dir
]
# Prepends the mangrove and US output directories if mangroves are included
if 'annual_removals_mangrove' in actual_stages:
output_dir_list = [
cn.annual_gain_AGB_mangrove_dir, cn.annual_gain_BGB_mangrove_dir,
cn.stdev_annual_gain_AGB_mangrove_dir
] + output_dir_list
if 'annual_removals_us' in actual_stages:
output_dir_list = [
cn.annual_gain_AGC_BGC_natrl_forest_US_dir,
cn.stdev_annual_gain_AGC_BGC_natrl_forest_US_dir
] + output_dir_list
# Adds the carbon directories depending on which carbon emitted_pools are being generated: 2000 and/or emissions year
if 'carbon_pools' in actual_stages:
if 'loss' in carbon_pool_extent:
output_dir_list = output_dir_list + [
cn.AGC_emis_year_dir, cn.BGC_emis_year_dir,
cn.deadwood_emis_year_2000_dir, cn.litter_emis_year_2000_dir,
cn.soil_C_emis_year_2000_dir, cn.total_C_emis_year_dir
]
if '2000' in carbon_pool_extent:
output_dir_list = output_dir_list + [
cn.AGC_2000_dir, cn.BGC_2000_dir, cn.deadwood_2000_dir,
cn.litter_2000_dir, cn.soil_C_full_extent_2000_dir,
cn.total_C_2000_dir
]
# Adds the biomass_soil output directories or the soil_only output directories depending on the model run
if 'gross_emissions' in actual_stages:
if emitted_pools == 'biomass_soil':
output_dir_list = output_dir_list + [
cn.gross_emis_commod_biomass_soil_dir,
cn.gross_emis_shifting_ag_biomass_soil_dir,
cn.gross_emis_forestry_biomass_soil_dir,
cn.gross_emis_wildfire_biomass_soil_dir,
cn.gross_emis_urban_biomass_soil_dir,
cn.gross_emis_no_driver_biomass_soil_dir,
cn.gross_emis_all_gases_all_drivers_biomass_soil_dir,
cn.gross_emis_co2_only_all_drivers_biomass_soil_dir,
cn.gross_emis_non_co2_all_drivers_biomass_soil_dir,
cn.gross_emis_nodes_biomass_soil_dir
]
else:
output_dir_list = output_dir_list + [
cn.gross_emis_commod_soil_only_dir,
cn.gross_emis_shifting_ag_soil_only_dir,
cn.gross_emis_forestry_soil_only_dir,
cn.gross_emis_wildfire_soil_only_dir,
cn.gross_emis_urban_soil_only_dir,
cn.gross_emis_no_driver_soil_only_dir,
cn.gross_emis_all_gases_all_drivers_soil_only_dir,
cn.gross_emis_co2_only_all_drivers_soil_only_dir,
cn.gross_emis_non_co2_all_drivers_soil_only_dir,
cn.gross_emis_nodes_soil_only_dir
]
output_dir_list = output_dir_list + [
cn.net_flux_dir, cn.cumul_gain_AGCO2_BGCO2_all_types_per_pixel_dir,
cn.gross_emis_all_gases_all_drivers_biomass_soil_per_pixel_dir,
cn.net_flux_per_pixel_dir
]
# Output patterns aren't actually used in the script-- here just for reference.
output_pattern_list = [
cn.pattern_model_extent, cn.pattern_age_cat_IPCC,
cn.pattern_annual_gain_AGB_IPCC_defaults,
cn.pattern_annual_gain_BGB_IPCC_defaults,
cn.pattern_stdev_annual_gain_AGB_IPCC_defaults,
cn.pattern_removal_forest_type, cn.pattern_annual_gain_AGC_all_types,
cn.pattern_annual_gain_BGC_all_types,
cn.pattern_annual_gain_AGC_BGC_all_types,
cn.pattern_stdev_annual_gain_AGC_all_types, cn.pattern_gain_year_count,
cn.pattern_cumul_gain_AGCO2_all_types,
cn.pattern_cumul_gain_BGCO2_all_types,
cn.pattern_cumul_gain_AGCO2_BGCO2_all_types
]
# Prepends the mangrove and US output pattern if mangroves are included
if 'annual_removals_mangrove' in actual_stages:
output_pattern_list = [
cn.pattern_annual_gain_AGB_mangrove,
cn.pattern_annual_gain_BGB_mangrove,
cn.pattern_stdev_annual_gain_AGB_mangrove
] + output_pattern_list
if 'annual_removals_us' in actual_stages:
output_pattern_list = [
cn.pattern_annual_gain_AGC_BGC_natrl_forest_US,
cn.pattern_stdev_annual_gain_AGC_BGC_natrl_forest_US
] + output_pattern_list
# Adds the soil carbon patterns depending on which carbon emitted_pools are being generated: 2000 and/or emissions year
if 'carbon_pools' in actual_stages:
if 'loss' in carbon_pool_extent:
output_pattern_list = output_pattern_list + [
cn.pattern_AGC_emis_year, cn.pattern_BGC_emis_year,
cn.pattern_deadwood_emis_year_2000,
cn.pattern_litter_emis_year_2000,
cn.pattern_soil_C_emis_year_2000, cn.pattern_total_C_emis_year
]
if '2000' in carbon_pool_extent:
output_pattern_list = output_pattern_list + [
cn.pattern_AGC_2000, cn.pattern_BGC_2000,
cn.pattern_deadwood_2000, cn.pattern_litter_2000,
cn.pattern_soil_C_full_extent_2000, cn.pattern_total_C_2000
]
# Adds the biomass_soil output patterns or the soil_only output directories depending on the model run
if 'gross_emissions' in actual_stages:
if emitted_pools == 'biomass_soil':
output_pattern_list = output_pattern_list + [
cn.pattern_gross_emis_commod_biomass_soil,
cn.pattern_gross_emis_shifting_ag_biomass_soil,
cn.pattern_gross_emis_forestry_biomass_soil,
cn.pattern_gross_emis_wildfire_biomass_soil,
cn.pattern_gross_emis_urban_biomass_soil,
cn.pattern_gross_emis_no_driver_biomass_soil,
cn.pattern_gross_emis_co2_only_all_drivers_biomass_soil,
cn.pattern_gross_emis_non_co2_all_drivers_biomass_soil,
cn.pattern_gross_emis_all_gases_all_drivers_biomass_soil
]
else:
output_pattern_list = output_pattern_list + [
cn.pattern_gross_emis_commod_soil_only,
cn.pattern_gross_emis_shifting_ag_soil_only,
cn.pattern_gross_emis_forestry_soil_only,
cn.pattern_gross_emis_wildfire_soil_only,
cn.pattern_gross_emis_urban_soil_only,
cn.pattern_gross_emis_no_driver_soil_only,
cn.pattern_gross_emis_all_gases_all_drivers_soil_only,
cn.pattern_gross_emis_co2_only_all_drivers_soil_only,
cn.pattern_gross_emis_non_co2_all_drivers_soil_only,
cn.pattern_gross_emis_nodes_soil_only
]
output_pattern_list = output_pattern_list + [
cn.pattern_net_flux,
cn.pattern_cumul_gain_AGCO2_BGCO2_all_types_per_pixel,
cn.pattern_gross_emis_all_gases_all_drivers_biomass_soil_per_pixel,
cn.pattern_net_flux_per_pixel
]
# Creates tiles of annual AGB and BGB gain rate and AGB stdev for mangroves using the standard model
# removal function
if 'annual_removals_mangrove' in actual_stages:
uu.print_log(":::::Creating tiles of annual removals for mangrove")
start = datetime.datetime.now()
mp_annual_gain_rate_mangrove(sensit_type,
tile_id_list,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for annual_gain_rate_mangrove:",
elapsed_time, "\n")
# Creates tiles of annual AGC+BGC gain rate and AGC stdev for US-specific removals using the standard model
# removal function
if 'annual_removals_us' in actual_stages:
uu.print_log(":::::Creating tiles of annual removals for US")
start = datetime.datetime.now()
mp_US_removal_rates(sensit_type, tile_id_list, run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for annual_gain_rate_us:",
elapsed_time, "\n")
# Creates model extent tiles
if 'model_extent' in actual_stages:
uu.print_log(":::::Creating tiles of model extent")
start = datetime.datetime.now()
mp_model_extent(sensit_type, tile_id_list, run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for model_extent:", elapsed_time,
"\n", "\n")
# Creates age category tiles for natural forests
if 'forest_age_category_IPCC' in actual_stages:
uu.print_log(
":::::Creating tiles of forest age categories for IPCC removal rates"
)
start = datetime.datetime.now()
mp_forest_age_category_IPCC(sensit_type,
tile_id_list,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for forest_age_category_IPCC:",
elapsed_time, "\n", "\n")
# Creates tiles of annual AGB and BGB gain rates using IPCC Table 4.9 defaults
if 'annual_removals_IPCC' in actual_stages:
uu.print_log(
":::::Creating tiles of annual aboveground and belowground removal rates using IPCC defaults"
)
start = datetime.datetime.now()
mp_annual_gain_rate_IPCC_defaults(sensit_type,
tile_id_list,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for annual_gain_rate_IPCC:",
elapsed_time, "\n", "\n")
# Creates tiles of annual AGC and BGC removal factors for the entire model, combining removal factors from all forest types
if 'annual_removals_all_forest_types' in actual_stages:
uu.print_log(
":::::Creating tiles of annual aboveground and belowground removal rates for all forest types"
)
start = datetime.datetime.now()
mp_annual_gain_rate_AGC_BGC_all_forest_types(sensit_type,
tile_id_list,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(
":::::Processing time for annual_gain_rate_AGC_BGC_all_forest_types:",
elapsed_time, "\n", "\n")
# Creates tiles of the number of years of removals for all model pixels (across all forest types)
if 'gain_year_count' in actual_stages:
uu.print_log(
":::::Freeing up memory for gain year count creation by deleting unneeded tiles"
)
tiles_to_delete = []
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_mangrove_biomass_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_WHRC_biomass_2000_unmasked)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_annual_gain_AGB_mangrove)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_annual_gain_BGB_mangrove)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_BGC_natrl_forest_Europe)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_BGC_planted_forest_unmasked)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_BGC_natrl_forest_US)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_natrl_forest_young)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_age_cat_IPCC)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGB_IPCC_defaults)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_BGB_IPCC_defaults)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_BGC_all_types)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_ifl_primary)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_planted_forest_type_unmasked)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_plant_pre_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGB_mangrove)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_BGC_natrl_forest_Europe)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_BGC_planted_forest_unmasked)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_BGC_natrl_forest_US)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_natrl_forest_young)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGB_IPCC_defaults)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_all_types)))
uu.print_log(" Deleting", len(tiles_to_delete), "tiles...")
for tile_to_delete in tiles_to_delete:
os.remove(tile_to_delete)
uu.print_log(":::::Deleted unneeded tiles")
uu.check_storage()
uu.print_log(
":::::Creating tiles of gain year count for all removal pixels")
start = datetime.datetime.now()
mp_gain_year_count_all_forest_types(sensit_type,
tile_id_list,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for gain_year_count:", elapsed_time,
"\n", "\n")
# Creates tiles of gross removals for all forest types (aboveground, belowground, and above+belowground)
if 'gross_removals_all_forest_types' in actual_stages:
uu.print_log(
":::::Creating gross removals for all forest types combined (above + belowground) tiles'"
)
start = datetime.datetime.now()
mp_gross_removals_all_forest_types(sensit_type,
tile_id_list,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(
":::::Processing time for gross_removals_all_forest_types:",
elapsed_time, "\n", "\n")
# Creates carbon emitted_pools in loss year
if 'carbon_pools' in actual_stages:
uu.print_log(
":::::Freeing up memory for carbon pool creation by deleting unneeded tiles"
)
tiles_to_delete = []
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_model_extent)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_annual_gain_AGB_mangrove)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_annual_gain_BGB_mangrove)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_BGC_natrl_forest_Europe)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_BGC_planted_forest_unmasked)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_BGC_natrl_forest_US)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_natrl_forest_young)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_age_cat_IPCC)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGB_IPCC_defaults)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_BGB_IPCC_defaults)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_annual_gain_BGC_all_types)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_annual_gain_AGC_BGC_all_types)))
tiles_to_delete.extend(glob.glob('*growth_years*tif'))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_gain_year_count)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_cumul_gain_BGCO2_all_types)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_cumul_gain_AGCO2_BGCO2_all_types)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_ifl_primary)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_planted_forest_type_unmasked)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_plant_pre_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGB_mangrove)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_BGC_natrl_forest_Europe)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_BGC_planted_forest_unmasked)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_BGC_natrl_forest_US)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_natrl_forest_young)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGB_IPCC_defaults)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_stdev_annual_gain_AGC_all_types)))
uu.print_log(" Deleting", len(tiles_to_delete), "tiles...")
for tile_to_delete in tiles_to_delete:
os.remove(tile_to_delete)
uu.print_log(":::::Deleted unneeded tiles")
uu.check_storage()
uu.print_log(":::::Creating carbon pool tiles")
start = datetime.datetime.now()
mp_create_carbon_pools(sensit_type,
tile_id_list,
carbon_pool_extent,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for create_carbon_pools:",
elapsed_time, "\n", "\n")
# Creates gross emissions tiles by driver, gas, and all emissions combined
if 'gross_emissions' in actual_stages:
uu.print_log(
":::::Freeing up memory for gross emissions creation by deleting unneeded tiles"
)
tiles_to_delete = []
# tiles_to_delete.extend(glob.glob('*{}*tif'.format(cn.pattern_removal_forest_type)))
tiles_to_delete.extend(glob.glob('*{}*tif'.format(
cn.pattern_AGC_2000)))
tiles_to_delete.extend(glob.glob('*{}*tif'.format(
cn.pattern_BGC_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_deadwood_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_litter_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_total_C_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_elevation)))
tiles_to_delete.extend(glob.glob('*{}*tif'.format(cn.pattern_precip)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_annual_gain_AGC_all_types)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_cumul_gain_AGCO2_all_types)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_cont_eco_processed)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_WHRC_biomass_2000_unmasked)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_mangrove_biomass_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_removal_forest_type)))
uu.print_log(" Deleting", len(tiles_to_delete), "tiles...")
uu.print_log(tiles_to_delete)
for tile_to_delete in tiles_to_delete:
os.remove(tile_to_delete)
uu.print_log(":::::Deleted unneeded tiles")
uu.check_storage()
uu.print_log(":::::Creating gross emissions tiles")
start = datetime.datetime.now()
mp_calculate_gross_emissions(sensit_type,
tile_id_list,
emitted_pools,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for gross_emissions:", elapsed_time,
"\n", "\n")
# Creates net flux tiles (gross emissions - gross removals)
if 'net_flux' in actual_stages:
uu.print_log(
":::::Freeing up memory for net flux creation by deleting unneeded tiles"
)
tiles_to_delete = []
tiles_to_delete.extend(glob.glob('*{}*tif'.format(cn.pattern_loss)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_non_co2_all_drivers_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_co2_only_all_drivers_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_commod_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_shifting_ag_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_forestry_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_wildfire_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_urban_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_no_driver_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_gross_emis_nodes_biomass_soil)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_AGC_emis_year)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_BGC_emis_year)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_deadwood_emis_year_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_litter_emis_year_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_soil_C_emis_year_2000)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_total_C_emis_year)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_peat_mask)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_ifl_primary)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(
cn.pattern_planted_forest_type_unmasked)))
tiles_to_delete.extend(glob.glob('*{}*tif'.format(cn.pattern_drivers)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_climate_zone)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_bor_tem_trop_processed)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_burn_year)))
tiles_to_delete.extend(
glob.glob('*{}*tif'.format(cn.pattern_plant_pre_2000)))
uu.print_log(" Deleting", len(tiles_to_delete), "tiles...")
for tile_to_delete in tiles_to_delete:
os.remove(tile_to_delete)
uu.print_log(":::::Deleted unneeded tiles")
uu.check_storage()
uu.print_log(":::::Creating net flux tiles")
start = datetime.datetime.now()
mp_net_flux(sensit_type, tile_id_list, run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for net_flux:", elapsed_time, "\n",
"\n")
# Aggregates gross emissions, gross removals, and net flux to coarser resolution.
# For sensitivity analyses, creates percent difference and sign change maps compared to standard model net flux.
if 'aggregate' in actual_stages:
uu.print_log(":::::Creating 4x4 km aggregate maps")
start = datetime.datetime.now()
mp_aggregate_results_to_4_km(sensit_type,
thresh,
tile_id_list,
std_net_flux=std_net_flux,
run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for aggregate:", elapsed_time, "\n",
"\n")
# Converts gross emissions, gross removals and net flux from per hectare rasters to per pixel rasters
if 'per_pixel_results' in actual_stages:
uu.print_log(":::::Creating per pixel versions of main model outputs")
start = datetime.datetime.now()
mp_output_per_pixel(sensit_type, tile_id_list, run_date=run_date)
end = datetime.datetime.now()
elapsed_time = end - start
uu.check_storage()
uu.print_log(":::::Processing time for per pixel raster creation:",
elapsed_time, "\n", "\n")
uu.print_log(":::::Counting tiles output to each folder")
# Modifies output directory names to make them match those used during the model run.
# The tiles in each of these directories and counted and logged.
# If the model run isn't the standard one, the output directory and file names are changed
if sensit_type != 'std':
uu.print_log(
"Modifying output directory and file name pattern based on sensitivity analysis"
)
output_dir_list = uu.alter_dirs(sensit_type, output_dir_list)
# Changes the date in the output directories. This date was used during the model run.
# This replaces the date in constants_and_names.
if run_date:
output_dir_list = uu.replace_output_dir_date(output_dir_list, run_date)
for output in output_dir_list:
tile_count = uu.count_tiles_s3(output)
uu.print_log("Total tiles in", output, ": ", tile_count)
script_end = datetime.datetime.now()
script_elapsed_time = script_end - script_start
uu.print_log(":::::Processing time for entire run:", script_elapsed_time,
"\n")
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=
'Create tiles of the annual AGB and BGB gain rates for mangrove forests'
)
parser.add_argument('--model-type',
'-t',
required=True,
help='{}'.format(cn.model_type_arg_help))
parser.add_argument(
'--tile_id_list',
'-l',
required=True,
help=
'List of tile ids to use in the model. Should be of form 00N_110E or 00N_110E,00N_120E or all.'
)
args = parser.parse_args()
sensit_type = args.model_type
tile_id_list = args.tile_id_list
# Create the output log
uu.initiate_log(sensit_type=sensit_type, tile_id_list=tile_id_list)
# Checks whether the sensitivity analysis and tile_id_list arguments are valid
uu.check_sensit_type(sensit_type)
tile_id_list = uu.tile_id_list_check(tile_id_list)
mp_tile_statistics(sensit_type=sensit_type, tile_id_list=tile_id_list)
def main():
# Create the output log
uu.initiate_log()
os.chdir(cn.docker_base_dir)
# Files to download for this script.
download_dict = {
cn.gain_dir: [cn.pattern_gain],
cn.annual_gain_AGB_IPCC_defaults_dir:
[cn.pattern_annual_gain_AGB_IPCC_defaults]
}
# List of tiles that could be run. This list is only used to create the FIA region tiles if they don't already exist.
tile_id_list = uu.tile_list_s3(cn.annual_gain_AGB_IPCC_defaults_dir)
# tile_id_list = ["00N_000E", "00N_050W", "00N_060W", "00N_010E", "00N_020E", "00N_030E", "00N_040E", "10N_000E", "10N_010E", "10N_010W", "10N_020E", "10N_020W"] # test tiles
# tile_id_list = ['50N_130W'] # test tiles
# List of output directories and output file name patterns
output_dir_list = [
cn.US_annual_gain_AGB_natrl_forest_dir,
cn.US_annual_gain_BGB_natrl_forest_dir
]
output_pattern_list = [
cn.pattern_US_annual_gain_AGB_natrl_forest,
cn.pattern_US_annual_gain_BGB_natrl_forest
]
# By definition, this script is for US-specific removals
sensit_type = 'US_removals'
# Counts how many processed FIA region tiles there are on s3 already. 16 tiles cover the continental US.
FIA_regions_tile_count = uu.count_tiles_s3(cn.FIA_regions_processed_dir)
# Only creates FIA region tiles if they don't already exist on s3.
if FIA_regions_tile_count == 16:
uu.print_log("FIA region tiles already created. Copying to s3 now...")
uu.s3_flexible_download(cn.FIA_regions_processed_dir,
cn.pattern_FIA_regions_processed,
cn.docker_base_dir, 'std', 'all')
else:
uu.print_log(
"FIA region tiles do not exist. Creating tiles, then copying to s3 for future use..."
)
uu.s3_file_download(
os.path.join(cn.FIA_regions_raw_dir, cn.name_FIA_regions_raw),
cn.docker_base_dir, 'std')
cmd = ['unzip', '-o', '-j', cn.name_FIA_regions_raw]
# Solution for adding subprocess output to log is from https://stackoverflow.com/questions/21953835/run-subprocess-and-print-output-to-logging
process = Popen(cmd, stdout=PIPE, stderr=STDOUT)
with process.stdout:
uu.log_subprocess_output(process.stdout)
# Converts the region shapefile to Hansen tiles
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(US_removal_rates.prep_FIA_regions, tile_id_list)
# List of FIA region tiles on the spot machine. Only this list is used for the rest of the script.
US_tile_list = uu.tile_list_spot_machine(
cn.docker_base_dir, '{}.tif'.format(cn.pattern_FIA_regions_processed))
US_tile_id_list = [i[0:8] for i in US_tile_list]
# US_tile_id_list = ['50N_130W'] # For testing
uu.print_log(US_tile_id_list)
uu.print_log(
"There are {} tiles to process".format(str(len(US_tile_id_list))) +
"\n")
# Counts how many processed forest age category tiles there are on s3 already. 16 tiles cover the continental US.
US_age_tile_count = uu.count_tiles_s3(cn.US_forest_age_cat_processed_dir)
# Only creates FIA forest age category tiles if they don't already exist on s3.
if US_age_tile_count == 16:
uu.print_log(
"Forest age category tiles already created. Copying to spot machine now..."
)
uu.s3_flexible_download(cn.US_forest_age_cat_processed_dir,
cn.pattern_US_forest_age_cat_processed, '',
'std', US_tile_id_list)
else:
uu.print_log(
"Southern forest age category tiles do not exist. Creating tiles, then copying to s3 for future use..."
)
uu.s3_file_download(
os.path.join(cn.US_forest_age_cat_raw_dir,
cn.name_US_forest_age_cat_raw), cn.docker_base_dir,
'std')
# Converts the national forest age category raster to Hansen tiles
source_raster = cn.name_US_forest_age_cat_raw
out_pattern = cn.pattern_US_forest_age_cat_processed
dt = 'Int16'
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(
partial(uu.mp_warp_to_Hansen,
source_raster=source_raster,
out_pattern=out_pattern,
dt=dt), US_tile_id_list)
uu.upload_final_set(cn.US_forest_age_cat_processed_dir,
cn.pattern_US_forest_age_cat_processed)
# Counts how many processed FIA forest group tiles there are on s3 already. 16 tiles cover the continental US.
FIA_forest_group_tile_count = uu.count_tiles_s3(
cn.FIA_forest_group_processed_dir)
# Only creates FIA forest group tiles if they don't already exist on s3.
if FIA_forest_group_tile_count == 16:
uu.print_log(
"FIA forest group tiles already created. Copying to spot machine now..."
)
uu.s3_flexible_download(cn.FIA_forest_group_processed_dir,
cn.pattern_FIA_forest_group_processed, '',
'std', US_tile_id_list)
else:
uu.print_log(
"FIA forest group tiles do not exist. Creating tiles, then copying to s3 for future use..."
)
uu.s3_file_download(
os.path.join(cn.FIA_forest_group_raw_dir,
cn.name_FIA_forest_group_raw), cn.docker_base_dir,
'std')
# Converts the national forest group raster to Hansen forest group tiles
source_raster = cn.name_FIA_forest_group_raw
out_pattern = cn.pattern_FIA_forest_group_processed
dt = 'Byte'
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(
partial(uu.mp_warp_to_Hansen,
source_raster=source_raster,
out_pattern=out_pattern,
dt=dt), US_tile_id_list)
uu.upload_final_set(cn.FIA_forest_group_processed_dir,
cn.pattern_FIA_forest_group_processed)
# Downloads input files or entire directories, depending on how many tiles are in the tile_id_list
for key, values in download_dict.items():
dir = key
pattern = values[0]
uu.s3_flexible_download(dir, pattern, cn.docker_base_dir, sensit_type,
US_tile_id_list)
# Table with US-specific removal rates
cmd = [
'aws', 's3', 'cp',
os.path.join(cn.gain_spreadsheet_dir, cn.table_US_removal_rate),
cn.docker_base_dir
]
# Solution for adding subprocess output to log is from https://stackoverflow.com/questions/21953835/run-subprocess-and-print-output-to-logging
process = Popen(cmd, stdout=PIPE, stderr=STDOUT)
with process.stdout:
uu.log_subprocess_output(process.stdout)
# Imports the table with the region-group-age AGB removal rates
gain_table = pd.read_excel("{}".format(cn.table_US_removal_rate),
sheet_name="US_rates_for_model")
# Converts gain table from wide to long, so each region-group-age category has its own row
gain_table_group_region_by_age = pd.melt(
gain_table,
id_vars=['FIA_region_code', 'forest_group_code'],
value_vars=['growth_young', 'growth_middle', 'growth_old'])
gain_table_group_region_by_age = gain_table_group_region_by_age.dropna()
# In the forest age category raster, each category has this value
age_dict = {
'growth_young': 1000,
'growth_middle': 2000,
'growth_old': 3000
}
# Creates a unique value for each forest group-region-age category in the table.
# Although these rates are applied to all standard gain model pixels at first, they are not ultimately used for
# pixels that have Hansen gain (see below).
gain_table_group_region_age = gain_table_group_region_by_age.replace(
{"variable": age_dict})
gain_table_group_region_age[
'age_cat'] = gain_table_group_region_age['variable'] * 10
gain_table_group_region_age['group_region_age_combined'] = gain_table_group_region_age['age_cat'] + \
gain_table_group_region_age['forest_group_code']*100 + \
gain_table_group_region_age['FIA_region_code']
# Converts the forest group-region-age codes and corresponding gain rates to a dictionary,
# where the key is the unique group-region-age code and the value is the AGB removal rate.
gain_table_group_region_age_dict = pd.Series(
gain_table_group_region_age.value.values,
index=gain_table_group_region_age.group_region_age_combined).to_dict()
uu.print_log(gain_table_group_region_age_dict)
# Creates a unique value for each forest group-region category using just young forest rates.
# These are assigned to Hansen gain pixels, which automatically get the young forest rate, regardless of the
# forest age category raster.
gain_table_group_region = gain_table_group_region_age.drop(
gain_table_group_region_age[
gain_table_group_region_age.age_cat != 10000].index)
gain_table_group_region['group_region_combined'] = gain_table_group_region['forest_group_code']*100 + \
gain_table_group_region['FIA_region_code']
# Converts the forest group-region codes and corresponding gain rates to a dictionary,
# where the key is the unique group-region code (youngest age category) and the value is the AGB removal rate.
gain_table_group_region_dict = pd.Series(
gain_table_group_region.value.values,
index=gain_table_group_region.group_region_combined).to_dict()
uu.print_log(gain_table_group_region_dict)
# count/2 on a m4.16xlarge maxes out at about 230 GB of memory (processing 16 tiles at once), so it's okay on an m4.16xlarge
pool = multiprocessing.Pool(int(cn.count / 2))
pool.map(
partial(
US_removal_rates.US_removal_rate_calc,
gain_table_group_region_age_dict=gain_table_group_region_age_dict,
gain_table_group_region_dict=gain_table_group_region_dict,
output_pattern_list=output_pattern_list,
sensit_type=sensit_type), US_tile_id_list)
pool.close()
pool.join()
# # For single processor use
# for tile_id in US_tile_id_list:
#
# US_removal_rates.US_removal_rate_calc(tile_id, gain_table_group_region_age_dict, gain_table_group_region_dict,
# output_pattern_list, sensit_type)
# Uploads output tiles to s3
for i in range(0, len(output_dir_list)):
uu.upload_final_set(output_dir_list[i], output_pattern_list[i])
action='store_true',
help='Disables uploading of outputs to s3')
args = parser.parse_args()
sensit_type = args.model_type
tile_id_list = args.tile_id_list
std_net_flux = args.std_net_flux_aggreg
thresh = args.tcd_threshold
thresh = int(thresh)
no_upload = args.no_upload
# Disables upload to s3 if no AWS credentials are found in environment
if not uu.check_aws_creds():
no_upload = True
uu.print_log("s3 credentials not found. Uploading to s3 disabled.")
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list,
sensit_type=sensit_type,
thresh=thresh,
std_net_flux=std_net_flux,
no_upload=no_upload)
# Checks whether the sensitivity analysis and tile_id_list arguments are valid
uu.check_sensit_type(sensit_type)
tile_id_list = uu.tile_id_list_check(tile_id_list)
mp_aggregate_results_to_4_km(sensit_type=sensit_type,
tile_id_list=tile_id_list,
thresh=thresh,
std_net_flux=std_net_flux,
no_upload=no_upload)
uu.check_storage()
if __name__ == '__main__':
# The argument for what kind of model run is being done: standard conditions or a sensitivity analysis run
parser = argparse.ArgumentParser(
description='Create tiles of the number of years of carbon gain for mangrove forests')
parser.add_argument('--model-type', '-t', required=True,
help='{}'.format(cn.model_type_arg_help))
parser.add_argument('--tile_id_list', '-l', required=True,
help='List of tile ids to use in the model. Should be of form 00N_110E or 00N_110E,00N_120E or all.')
parser.add_argument('--carbon_pool_extent', '-ce', required=True,
help='Extent over which carbon emitted_pools should be calculated: loss, 2000, loss,2000, or 2000,loss')
parser.add_argument('--run-date', '-d', required=False,
help='Date of run. Must be format YYYYMMDD.')
args = parser.parse_args()
sensit_type = args.model_type
tile_id_list = args.tile_id_list
carbon_pool_extent = args.carbon_pool_extent # Tells the pool creation functions to calculate carbon emitted_pools as they were at the year of loss in loss pixels only
run_date = args.run_date
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list, sensit_type=sensit_type, run_date=run_date, carbon_pool_extent=carbon_pool_extent)
# Checks whether the sensitivity analysis and tile_id_list arguments are valid
uu.check_sensit_type(sensit_type)
tile_id_list = uu.tile_id_list_check(tile_id_list)
mp_create_carbon_pools(sensit_type=sensit_type, tile_id_list=tile_id_list,
carbon_pool_extent=carbon_pool_extent, run_date=run_date)
help='Extent over which carbon emitted_pools should be calculated: loss, 2000, loss,2000, or 2000,loss')
parser.add_argument('--run-date', '-d', required=False,
help='Date of run. Must be format YYYYMMDD.')
parser.add_argument('--no-upload', '-nu', action='store_true',
help='Disables uploading of outputs to s3')
parser.add_argument('--save-intermediates', '-si', action='store_true',
help='Saves intermediate model outputs rather than deleting them to save storage')
args = parser.parse_args()
sensit_type = args.model_type
tile_id_list = args.tile_id_list
carbon_pool_extent = args.carbon_pool_extent # Tells the pool creation functions to calculate carbon emitted_pools as they were at the year of loss in loss pixels only
run_date = args.run_date
no_upload = args.no_upload
save_intermediates = args.save_intermediates
# Disables upload to s3 if no AWS credentials are found in environment
if not uu.check_aws_creds():
no_upload = True
uu.print_log("s3 credentials not found. Uploading to s3 disabled.")
# Create the output log
uu.initiate_log(tile_id_list=tile_id_list, sensit_type=sensit_type, run_date=run_date,
carbon_pool_extent=carbon_pool_extent, no_upload=no_upload, save_intermediates=save_intermediates)
# Checks whether the sensitivity analysis and tile_id_list arguments are valid
uu.check_sensit_type(sensit_type)
tile_id_list = uu.tile_id_list_check(tile_id_list)
mp_create_carbon_pools(sensit_type=sensit_type, tile_id_list=tile_id_list,
carbon_pool_extent=carbon_pool_extent, run_date=run_date, no_upload=no_upload,
save_intermediates=save_intermediates)
