t12_K,
t37_K,
t_clim_K,
sun_zenith_angle,
sat_zenith_angle)
if np.isnan(st_truth_K):
# No need to do more for this pixel, if the output is not a number.
continue
swath_input_id = db.insert_swath_values(
str(avhrr_model.satellite_id),
surface_temp=st_truth_K, # float(true_st_K),
t_11=float(t11_K),
t_12=float(t12_K),
sat_zenith_angle=sat_zenith_angle,
sun_zenith_angle=sun_zenith_angle,
cloudmask=int(avhrr_model.cloudmask[row_index, col_index]),
swath_datetime=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
lat=float(lat),
lon=float(lon)
)
# Do the perturbations...
for i in range(int(args["--number-of-perturbations"])):
perturbed_t11_K = random.gauss(t11_K, sigma_11)
perturbed_t12_K = random.gauss(t12_K, sigma_12)
perturbed_t37_K = random.gauss(t37_K, sigma_37) \
if t37_K is None or np.isnan(t37_K) else np.NaN
# Missing climatology
def populate_from_files(database_filename,
avhrr_filename,
sun_sat_angle_filename,
cloudmask_filename,
sea_ice_fraction_data_directory,
number_of_perturbations,
run_in_parallel=False):
"""
Populate the database with perturbed values.
"""
LOG.info("db_filename: %s" % (database_filename))
LOG.info("avhrr_filename: %s" % (avhrr_filename))
LOG.info("sunsatangle_filename: %s" % (sunsatangle_filename))
LOG.info("cloudmask_filename: %s" % (cloudmask_filename))
LOG.info("sea_ice_fraction_data_directory: %s" %
(sea_ice_fraction_data_directory))
# Reading in the input file.
# The file is cached, so that when the values are read, they are read
# from memory, and not from the file system. This speeds up the
# calculations.
with models.avhrr_hdf5.Hdf5(avhrr_filename, sun_sat_angle_filename,
cloudmask_filename) as avhrr_model:
LOG.info(avhrr_model)
assert (avhrr_model.lat.shape == avhrr_model.lon.shape)
# Get the sigma values based on the satellite id.
sigmas = eustace.sigmas.get_sigmas(avhrr_model.satellite_id)
LOG.info(sigmas)
sea_ice_fractions = get_sea_ice_fractions(
sea_ice_fraction_data_directory, avhrr_filename)
if sea_ice_fractions is not None:
assert (avhrr_model.lat.shape == sea_ice_fractions.shape)
# Some book keeping...
total_perturbed_st_count = 0
counter = 0
# Set the random seed, so that the results are the same
# the next time the exact same system is is run.
random.seed(1)
output_queue = mp.Queue()
number_of_cpus = mp.cpu_count()
number_of_processes_started = 0
number_of_processes_finished = 0
# Book keeping.
start_time = datetime.datetime.now()
# Using the coefficients based on the satellite id.
with eustace.coefficients.Coefficients(
avhrr_model.satellite_id) as coeff:
## Using a ram disk speeds up the calculations, quite a lot.
## Creating ramdisk:
# mkdir /tmp/ramdisk
#
## A 3Gb ram disk.
# mount -t tmpfs -o size=3072m tmpfs /tmp/ramdisk
#
## or
#
## For a 12Gb ram disk.
# mount -t tmpfs -o size=$((12 * 1024))m tmpfs /tmp/ramdisk
#
#
## Defining the database.
with eustace.db.Db(database_filename) as db:
# Rows.
for row_index in np.arange(avhrr_model.lon.shape[0]):
# Some diagnostics while running.
LOG.info(
"ROW: %i. total st_count: %i. total_time: %s. sts./sec: %f"
% (row_index, total_perturbed_st_count,
str(datetime.datetime.now() - start_time),
(total_perturbed_st_count /
(datetime.datetime.now() -
start_time).total_seconds())))
# Cols.
for col_index in np.arange(avhrr_model.lon.shape[1]):
counter += 1
# Reading in the values.
cloudmask = avhrr_model.cloudmask[row_index, col_index]
if cloudmask != 1 and cloudmask != 4:
LOG.debug("Bad cloudmask: %i" % (cloudmask))
continue
# T11 is channel 4.
t11_K = avhrr_model.ch4[row_index, col_index]
# T12 is channel 5.
t12_K = avhrr_model.ch5[row_index, col_index]
# T37 is channel 3b.
t37_K = avhrr_model.ch3b[row_index, col_index]
if np.isnan(t11_K) or np.isnan(t12_K):
# t11 and t12 are both needed for all calculations.
# Is something wrong if they are both missing?
# Consider what to do.
raise RuntimeException("Missing T11 or T12")
# Angles.
sun_zenith_angle = float(
avhrr_model.sun_zenith_angle[row_index, col_index])
sat_zenith_angle = float(
avhrr_model.sat_zenith_angle[row_index, col_index])
# Missing climatology. Using t11_K in stead.
t_clim_K = t11_K
# Lat / lon.
lat = avhrr_model.lat[row_index, col_index]
lon = avhrr_model.lon[row_index, col_index]
if lat is None or np.isnan(
lat) or lon is None or np.isnan(lon):
continue
# Pick algorithm.
algorithm = eustace.surface_temperature.select_surface_temperature_algorithm(
sun_zenith_angle, t11_K, t37_K)
# Calculate the temperature.
st_truth_K = eustace.surface_temperature.get_surface_temperature(
algorithm, coeff, t11_K, t12_K, t37_K, t_clim_K,
sun_zenith_angle, sat_zenith_angle)
if np.isnan(st_truth_K):
# No need to do more for this pixel, if the output is not a number.
continue
if sea_ice_fractions is not None:
if sea_ice_fractions[row_index][
col_index] is None or np.isnan(
sea_ice_fractions[row_index]
[col_index]):
sea_ice_fraction = None
else:
sea_ice_fraction = float(
sea_ice_fractions[row_index][col_index])
else:
sea_ice_fraction = None
swath_input_id = db.insert_swath_values(
str(avhrr_model.satellite_id),
surface_temp=st_truth_K, # float(true_st_K),
t_11=float(t11_K),
t_12=float(t12_K),
sat_zenith_angle=sat_zenith_angle,
sun_zenith_angle=sun_zenith_angle,
cloudmask=int(avhrr_model.cloudmask[row_index,
col_index]),
swath_datetime=avhrr_model.swath_datetime,
lat=float(lat),
lon=float(lon),
sea_ice_fraction=sea_ice_fraction)
if not run_in_parallel:
# WARNING!
# If the number of perturbations is a small number, it is much faster
# to run sequencially!!
perturbations = eustace.surface_temperature.get_n_perturbed_temeratures(
coeff,
number_of_perturbations,
t11_K,
t12_K,
t37_K,
t_clim_K,
sigmas["sigma_11"],
sigmas["sigma_12"],
sigmas["sigma_37"],
sun_zenith_angle,
sat_zenith_angle,
random_seed=counter)
num_inserted = db.insert_many_perturbations(
swath_input_id, perturbations)
total_perturbed_st_count += num_inserted
else:
# This starts a process running a number of perturbations
# and inserts the result in the in the output queue.
perturbate_in_parallel(output_queue,
swath_input_id,
coeff,
number_of_perturbations,
t11_K,
t12_K,
t37_K,
t_clim_K,
sigmas["sigma_11"],
sigmas["sigma_12"],
sigmas["sigma_37"],
sun_zenith_angle,
sat_zenith_angle,
random_seed=counter)
number_of_processes_started += 1
if number_of_processes_started > number_of_cpus:
# Get will wait forever, for the process to finish.
swath_input_id, perturbations = output_queue.get(
)
number_of_processes_finished += 1
num_inserted = db.insert_many_perturbations(
swath_input_id, perturbations)
total_perturbed_st_count += num_inserted
if run_in_parallel:
while number_of_processes_started > number_of_processes_finished:
swath_input_id, perturbations = output_queue.get()
number_of_processes_finished += 1
db.insert_many_perturbations(swath_input_id,
perturbations)
# FIN.
LOG.info("Finished perturbing '%s'." %
(avhrr_model.avhrr_filename))
def populate_from_files(database_filename, avhrr_filename, sun_sat_angle_filename,
cloudmask_filename, sea_ice_fraction_data_directory,
number_of_perturbations, run_in_parallel = False
):
"""
Populate the database with perturbed values.
"""
LOG.info("db_filename: %s" % (database_filename))
LOG.info("avhrr_filename: %s" % (avhrr_filename))
LOG.info("sunsatangle_filename: %s" % (sunsatangle_filename))
LOG.info("cloudmask_filename: %s" % (cloudmask_filename))
LOG.info("sea_ice_fraction_data_directory: %s" % (sea_ice_fraction_data_directory))
# Reading in the input file.
# The file is cached, so that when the values are read, they are read
# from memory, and not from the file system. This speeds up the
# calculations.
with models.avhrr_hdf5.Hdf5(avhrr_filename,
sun_sat_angle_filename,
cloudmask_filename) as avhrr_model:
LOG.info(avhrr_model)
assert(avhrr_model.lat.shape == avhrr_model.lon.shape)
# Get the sigma values based on the satellite id.
sigmas = eustace.sigmas.get_sigmas(avhrr_model.satellite_id)
LOG.info(sigmas)
sea_ice_fractions = get_sea_ice_fractions(sea_ice_fraction_data_directory, avhrr_filename)
if sea_ice_fractions is not None:
assert(avhrr_model.lat.shape == sea_ice_fractions.shape)
# Some book keeping...
total_perturbed_st_count = 0
counter = 0
# Set the random seed, so that the results are the same
# the next time the exact same system is is run.
random.seed(1)
output_queue = mp.Queue()
number_of_cpus = mp.cpu_count()
number_of_processes_started = 0
number_of_processes_finished = 0
# Book keeping.
start_time = datetime.datetime.now()
# Using the coefficients based on the satellite id.
with eustace.coefficients.Coefficients(avhrr_model.satellite_id) as coeff:
## Using a ram disk speeds up the calculations, quite a lot.
## Creating ramdisk:
# mkdir /tmp/ramdisk
#
## A 3Gb ram disk.
# mount -t tmpfs -o size=3072m tmpfs /tmp/ramdisk
#
## or
#
## For a 12Gb ram disk.
# mount -t tmpfs -o size=$((12 * 1024))m tmpfs /tmp/ramdisk
#
#
## Defining the database.
with eustace.db.Db(database_filename) as db:
# Rows.
for row_index in np.arange(avhrr_model.lon.shape[0]):
# Some diagnostics while running.
LOG.info("ROW: %i. total st_count: %i. total_time: %s. sts./sec: %f" %
(row_index, total_perturbed_st_count,
str(datetime.datetime.now() - start_time),
(total_perturbed_st_count / (datetime.datetime.now() -
start_time).total_seconds())))
# Cols.
for col_index in np.arange(avhrr_model.lon.shape[1]):
counter += 1
# Reading in the values.
cloudmask = avhrr_model.cloudmask[row_index, col_index]
if cloudmask != 1 and cloudmask != 4:
LOG.debug("Bad cloudmask: %i" % (cloudmask))
continue
# T11 is channel 4.
t11_K = avhrr_model.ch4[row_index, col_index]
# T12 is channel 5.
t12_K = avhrr_model.ch5[row_index, col_index]
# T37 is channel 3b.
t37_K = avhrr_model.ch3b[row_index, col_index]
if np.isnan(t11_K) or np.isnan(t12_K):
# t11 and t12 are both needed for all calculations.
# Is something wrong if they are both missing?
# Consider what to do.
raise RuntimeException("Missing T11 or T12")
# Angles.
sun_zenith_angle = float(avhrr_model.sun_zenith_angle[row_index, col_index])
sat_zenith_angle = float(avhrr_model.sat_zenith_angle[row_index, col_index])
# Missing climatology. Using t11_K in stead.
t_clim_K = t11_K
# Lat / lon.
lat = avhrr_model.lat[row_index, col_index]
lon = avhrr_model.lon[row_index, col_index]
if lat is None or np.isnan(lat) or lon is None or np.isnan(lon):
continue
# Pick algorithm.
algorithm = eustace.surface_temperature.select_surface_temperature_algorithm(
sun_zenith_angle,
t11_K,
t37_K)
# Calculate the temperature.
st_truth_K = eustace.surface_temperature.get_surface_temperature(algorithm,
coeff,
t11_K,
t12_K,
t37_K,
t_clim_K,
sun_zenith_angle,
sat_zenith_angle)
if np.isnan(st_truth_K):
# No need to do more for this pixel, if the output is not a number.
continue
if sea_ice_fractions is not None:
if sea_ice_fractions[row_index][col_index] is None or np.isnan(sea_ice_fractions[row_index][col_index]):
sea_ice_fraction = None
else:
sea_ice_fraction = float(sea_ice_fractions[row_index][col_index])
else:
sea_ice_fraction = None
swath_input_id = db.insert_swath_values(
str(avhrr_model.satellite_id),
surface_temp=st_truth_K, # float(true_st_K),
t_11=float(t11_K),
t_12=float(t12_K),
sat_zenith_angle=sat_zenith_angle,
sun_zenith_angle=sun_zenith_angle,
cloudmask=int(avhrr_model.cloudmask[row_index, col_index]),
swath_datetime=avhrr_model.swath_datetime,
lat=float(lat),
lon=float(lon),
sea_ice_fraction=sea_ice_fraction
)
if not run_in_parallel:
# WARNING!
# If the number of perturbations is a small number, it is much faster
# to run sequencially!!
perturbations = eustace.surface_temperature.get_n_perturbed_temeratures(coeff,
number_of_perturbations,
t11_K,
t12_K,
t37_K,
t_clim_K,
sigmas["sigma_11"],
sigmas["sigma_12"],
sigmas["sigma_37"],
sun_zenith_angle,
sat_zenith_angle,
random_seed=counter)
num_inserted = db.insert_many_perturbations(swath_input_id, perturbations)
total_perturbed_st_count += num_inserted
else:
# This starts a process running a number of perturbations
# and inserts the result in the in the output queue.
perturbate_in_parallel(output_queue,
swath_input_id,
coeff,
number_of_perturbations,
t11_K,
t12_K,
t37_K,
t_clim_K,
sigmas["sigma_11"],
sigmas["sigma_12"],
sigmas["sigma_37"],
sun_zenith_angle,
sat_zenith_angle,
random_seed=counter)
number_of_processes_started += 1
if number_of_processes_started > number_of_cpus:
# Get will wait forever, for the process to finish.
swath_input_id, perturbations = output_queue.get()
number_of_processes_finished += 1
num_inserted = db.insert_many_perturbations(swath_input_id, perturbations)
total_perturbed_st_count += num_inserted
if run_in_parallel:
while number_of_processes_started > number_of_processes_finished:
swath_input_id, perturbations = output_queue.get()
number_of_processes_finished += 1
db.insert_many_perturbations(swath_input_id, perturbations)
# FIN.
LOG.info("Finished perturbing '%s'." % (avhrr_model.avhrr_filename))
