def flagging_operation(reference_time_string, operation_index, input_directory,
iteration, output_directory, window_range,
count_threshold, target_flag):
# get dates in the month to be processed in this operation
reference_time = parser.parse(reference_time_string)
processing_dates = operation_dates(reference_time, operation_index)
# derive flags for each day
for processdate in processing_dates:
# check that directory exists and if not then make it
if not os.path.exists(
os.path.join(output_directory, str(processdate.year))):
os.makedirs(os.path.join(output_directory, str(processdate.year)))
flag_values = local_window_constraint_checks(
input_directory, output_directory, iteration, processdate,
window_range, count_threshold, target_flag)
# set flag values for output
#flag_values = numpy.zeros( definitions.GLOBAL_FIELD_SHAPE[1:], FLAG_TYPE )
# save to NetCDF
outputfile = os.path.join(
output_directory, '{:04d}'.format(processdate.year),
'eustace_analysis_{:d}_qc_flags_{:04d}{:02d}{:02d}.nc'.format(
iteration, processdate.year, processdate.month,
processdate.day))
save_flag_file(flag_values, processdate, outputfile)
def flagging_operation(
reference_time_string,
operation_index,
analysis_directory,
iteration,
output_directory,
percentile_directory,
):
# get dates in the month to be processed in this operation
reference_time = parser.parse(reference_time_string)
processing_dates = operation_dates(reference_time, operation_index)
# derive flags for each day
for processdate in processing_dates:
# check that directory exists and if not then make it
if not os.path.exists(
os.path.join(output_directory, str(processdate.year))):
os.makedirs(os.path.join(output_directory, str(processdate.year)))
# compute flags
flag_values = location_threshold_checks(analysis_directory,
percentile_directory,
output_directory, iteration,
processdate)
#save
outputfile = os.path.join(
output_directory, '{:04d}'.format(processdate.year),
'eustace_analysis_{:d}_qc_flags_{:04d}{:02d}{:02d}.nc'.format(
iteration, processdate.year, processdate.month,
processdate.day))
save_flag_file(flag_values, processdate, outputfile)
def flagging_operation(reference_time_string, operation_index, input_directory,
iteration, output_directory, start_year, end_year):
# get dates in the month to be processed in this operation
reference_time = parser.parse(reference_time_string)
processing_dates = operation_dates(reference_time, operation_index)
# derive flags for each day
for processdate in processing_dates:
# check that directory exists and if not then make it
if not os.path.exists(
os.path.join(output_directory, str(processdate.year))):
os.makedirs(os.path.join(output_directory, str(processdate.year)))
flag_values = calendar_constraint_checks(input_directory,
output_directory, iteration,
processdate, start_year,
end_year, CALENDAR_DAY_FLAG)
# save to NetCDF
outputfile = os.path.join(
output_directory, '{:04d}'.format(processdate.year),
'eustace_analysis_{:d}_qc_flags_{:04d}{:02d}{:02d}.nc'.format(
iteration, processdate.year, processdate.month,
processdate.day))
save_flag_file(flag_values, processdate, outputfile)
def flagging_operation(
reference_time_string,
operation_index,
analysis_directory,
iteration,
output_directory,
):
# get dates in the month to be processed in this operation
reference_time = parser.parse(reference_time_string)
processing_dates = operation_dates(reference_time, operation_index)
# derive flags for each day
for processdate in processing_dates:
# check that directory exists and if not then make it
if not os.path.exists(
os.path.join(output_directory, str(processdate.year))):
os.makedirs(os.path.join(output_directory, str(processdate.year)))
# compute flags
areal_flag_values = areal_checks(analysis_directory, output_directory,
iteration, processdate,
QUARTILE_LIMIT)
extreme_flag_values = extremes_checks(analysis_directory,
output_directory, iteration,
processdate, EXTREME_LOWER_LIMIT,
EXTREME_UPPER_LIMIT)
# join flags
flag_values = areal_flag_values | extreme_flag_values
#save
outputfile = os.path.join(
output_directory, '{:04d}'.format(processdate.year),
'eustace_analysis_{:d}_qc_flags_{:04d}{:02d}{:02d}.nc'.format(
iteration, processdate.year, processdate.month,
processdate.day))
save_flag_file(flag_values, processdate, outputfile)
def flagging_operation(reference_time_string, operation_index,
analysis_directory, iteration, output_directory,
climatology_limit, largescale_limit,
constraint_threshold):
# get dates in the month to be processed in this operation
reference_time = parser.parse(reference_time_string)
processing_dates = operation_dates(reference_time, operation_index)
# derive flags for each day
for processdate in processing_dates:
# check that directory exists and if not then make it
if not os.path.exists(
os.path.join(output_directory, str(processdate.year))):
os.makedirs(os.path.join(output_directory, str(processdate.year)))
# compute flags
climatology_flag_values = climatology_uncertainty_checks(
analysis_directory, output_directory, iteration, processdate,
climatology_limit)
large_scale_flag_values = large_scale_uncertainty_checks(
analysis_directory, output_directory, iteration, processdate,
largescale_limit)
local_influence_flag_values = local_influence_checks(
analysis_directory, output_directory, iteration, processdate,
constraint_threshold)
marine_flag = get_marine_flag()
# join flags
flag_values = climatology_flag_values | large_scale_flag_values | local_influence_flag_values | marine_flag
#save
outputfile = os.path.join(
output_directory, '{:04d}'.format(processdate.year),
'eustace_analysis_{:d}_qc_flags_{:04d}{:02d}{:02d}.nc'.format(
iteration, processdate.year, processdate.month,
processdate.day))
save_flag_file(flag_values, processdate, outputfile)
def latent_variable_flag(input_directory, output_directory, iteration, processing_dates):
# manually setup the analysis model for the R1413 run - Warning: the eustace svn revision must be correct for the global bias model interpretation to that run analysis
storage_climatology = SpaceTimeComponentSolutionStorageBatched_Files( statefilename_read='/work/scratch/cmorice/advanced_standard/climatology_solution_9/climatology_solution_9.pickle',
sample_filename_read='/work/scratch/cmorice/advanced_standard/climatology_solution_sample_9/climatology_solution_sample_9.pickle',
prior_sample_filename_read='/work/scratch/cmorice/advanced_standard/climatology_solution_prior_sample_9/climatology_solution_prior_sample_9.pickle',
keep_in_memory = True )
storage_large_scale = SpaceTimeComponentSolutionStorageBatched_Files( statefilename_read='/work/scratch/cmorice/advanced_standard/large_scale_solution_9/large_scale_solution_9.pickle',
sample_filename_read='/work/scratch/cmorice/advanced_standard/large_scale_solution_sample_9/large_scale_solution_sample_9.pickle',
prior_sample_filename_read='/work/scratch/cmorice/advanced_standard/large_scale_solution_prior_sample_9/large_scale_solution_prior_sample_9.pickle',
keep_in_memory = True )
storage_local = eustace.analysis.advanced_standard.components.storage_files_batch.SpatialComponentSolutionStorageIndexed_Files()
covariates_descriptor = "/gws/nopw/j04/eustace/data/internal/climatology_covariates/covariates.json"
insitu_biases = True
breakpoints_file = "/gws/nopw/j04/eustace/data/internal/D1.7/daily/eustace_stations_global_R001127_daily_status.nc"
global_biases = True
global_biases_group_list = ["surfaceairmodel_ice_global" , "surfaceairmodel_land_global", "surfaceairmodel_ocean_global"]
compute_uncertainties = False
method = 'EXACT'
compute_sample = False
sample_size = definitions.GLOBAL_SAMPLE_SHAPE[3]
compute_prior_sample = False
print 'VERSION: {0}'.format(get_revision_id_for_module(eustace))
# Build analysis system
analysissystem = AnalysisSystem_EUSTACE(storage_climatology, storage_large_scale, storage_local,
covariates_descriptor, insitu_biases, breakpoints_file, global_biases, global_biases_group_list,
compute_uncertainties, method)
grid_resolution = [180. / definitions.GLOBAL_FIELD_SHAPE[1], 360. / definitions.GLOBAL_FIELD_SHAPE[2]]
latitudes=numpy.linspace(-90.+grid_resolution[0]/2., 90.-grid_resolution[0]/2, num=definitions.GLOBAL_FIELD_SHAPE[1])
longitudes=numpy.linspace(-180.+grid_resolution[1]/2., 180.-grid_resolution[1]/2, num=definitions.GLOBAL_FIELD_SHAPE[2])
#timebase = TimeBaseDays(eustace.timeutils.epoch.EPOCH)
#processdates = [timebase.number_to_datetime(daynumber) for daynumber in time_indices]
# get times as understood by the analysis sustem
time_indices =[eustace.timeutils.epoch.days_since_epoch(t) for t in processing_dates]
cell_sampling = [1, 1]
blocking = 10
# thinned set of sample indices for inclusion in output product
sample_indices = range(definitions.GLOBAL_SAMPLE_SHAPE[3])
climatology_projector = None
large_scale_projector = None
local_projector = None
for ( inner_index, time_index, processdate ) in zip( range(len(time_indices)), time_indices, processing_dates ):
print time_index
# initialise flags
flag_values = numpy.zeros( definitions.GLOBAL_FIELD_SHAPE[1:], FLAG_TYPE )
# Configure output grid
outputstructure = OutputRectilinearGridStructure(time_index, processdate,
latitudes=latitudes,
longitudes=longitudes)
# climatology component
print 'Evaluating: climatology'
if climatology_projector is None:
climatology_projector = Projector(latitudes, longitudes, grid_resolution, time_index, cell_sampling, blocking)
climatology_projector.set_component(analysissystem.components[0])
latent_climatology_constraint = evaluate_latent_variable_constraint(climatology_projector)
climatology_projector.update_time_index(time_index, keep_design = False)
climatology_projector.evaluate_design_matrix()
climatology_statistic = evaluate_constraint_statistic(climatology_projector, latent_climatology_constraint, CONSTRAINT_THRESHOLD).reshape(definitions.GLOBAL_FIELD_SHAPE[1:])
flag_values[climatology_statistic] = flag_values[climatology_statistic] | CLIMATOLOGY_LATENT_FLAG
# large scale component
print 'Evaluating: large-scale'
if large_scale_projector is None:
large_scale_projector = Projector(latitudes, longitudes, grid_resolution, time_index, cell_sampling, blocking)
large_scale_projector.set_component(analysissystem.components[1])
latent_large_scale_constraint = evaluate_latent_variable_constraint(large_scale_projector)
large_scale_projector.update_time_index(time_index, keep_design = False)
large_scale_projector.evaluate_design_matrix()
large_scale_statistic = evaluate_constraint_statistic(large_scale_projector, latent_large_scale_constraint, CONSTRAINT_THRESHOLD).reshape(definitions.GLOBAL_FIELD_SHAPE[1:])
flag_values[large_scale_statistic] = flag_values[large_scale_statistic] | LARGE_SCALE_LATENT_FLAG
outputfile = os.path.join(output_directory, '{:04d}'.format(processdate.year), 'eustace_analysis_{:d}_qc_flags_{:04d}{:02d}{:02d}.nc'.format(iteration, processdate.year, processdate.month, processdate.day))
save_flag_file(flag_values, processdate, outputfile)
