st_greater_than = 260
st_less_than = None
algo = eustace.surface_temperature.ST_ALGORITHM.IST
elif algorithm == eustace.surface_temperature.ST_ALGORITHM.IST + "_GT_240_LT_260":
st_less_than = 260
st_greater_than = 240
algo = eustace.surface_temperature.ST_ALGORITHM.IST
else:
st_less_than = None
st_greater_than = None
algo = algorithm
y_array = np.array([row[0] for row in db.get_perturbed_values(swath_variables=None,
lat_less_than=args["--lat-lt"],
lat_greater_than=args["--lat-gt"],
tb_11_minus_tb_12_limit=args["--t11-t12-limit"],
st_less_than=st_less_than,
st_greater_than=st_greater_than,
algorithm=algo,
limit=limit)])
LOG.debug("Took: %s" % (str(datetime.datetime.now() - t)))
# Number of samples - total.
LOG.info("Number of samples: %i." %(len(y_array)))
# Make sure that there are no nan in the array.
y_array_is_not_nan = y_array[~np.isnan(y_array)]
# Number of samples.
LOG.info("Number of samples without NaN: %i." %(len(y_array_is_not_nan)))
LOG.debug("Calculating the average.")
x_arrays[variable]=[]
random.seed(1)
LOG.debug("Get the values from the database.")
t = datetime.datetime.now()
with eustace.db.Db(args["<database-filename>"]) as db:
# Where sql used for the title in the plots.
where_sql = db.build_where_sql(lat_less_than=args["--lat-lt"],
lat_greater_than=args["--lat-gt"],
tb_11_minus_tb_12_limit=args["--t11-t12-limit"],
algorithm=args["--algorithm"])
# Get the values.
for row in db.get_perturbed_values(variable_names,
lat_less_than=args["--lat-lt"],
lat_greater_than=args["--lat-gt"],
tb_11_minus_tb_12_limit=args["--t11-t12-limit"],
algorithm=args["--algorithm"],
limit=limit):
y_array.append(row[0])
for i in range(len(variable_names)):
if row[i + 1] == None:
x_arrays[variable_names[i]].append(np.NaN)
else:
x_arrays[variable_names[i]].append(row[i + 1])
LOG.debug("Took: %s" % (str(datetime.datetime.now() - t)))
LOG.info("%i samples" %(len(y_array)))
y_array = np.array(y_array)
y_array_is_not_nan = y_array[~np.isnan(y_array)]
average_all = np.average(y_array_is_not_nan)
