def __init__(self, cfg, time_period=0, models=NAMES_UA):
# logging
log = gen.getLogger(gen.getClassName(self))
log.info("start creating ua ensemble monthly average")
# aos
obj = aos.DataObject(('ua_ens_month_avg', time_period, models))
if obj.load():
obj.copy(self)
return (None)
# init
self._time_period = time_period
self._models = models
self.cfg = dict(cfg)
# create list of time_avg_lat_lon 2D-fields
ensemble_time_avg_lat_lon = []
if time_period == '1979-2005':
directory_prefix = pl.Path(
self.cfg['data_folders']['historical']).joinpath('ua/mon')
elif time_period == '2070-2099':
directory_prefix = pl.Path(
self.cfg['data_folders']['rcp85']).joinpath('ua/mon')
else:
raise Exception("ua_ens_avg: time period '%s' not known" %
time_period)
print("Load data from time period %s" % time_period)
log.info("ua_ens_month_avg: Load data from time period %s" %
time_period)
for name in self._models:
print("Loading %s from file" % name)
log.info("Loading %s from file" % name)
directory = directory_prefix.joinpath(name).joinpath('r1i1p1')
tall = time_avg_month_lat_lon_dir(directory,
time_period=time_period)
ensemble_time_avg_lat_lon += [tall]
# check consistancy of grids in the ensemble of time_avg_lat_lon 2D-fields and store grid
tall_0 = ensemble_time_avg_lat_lon[0]
for tall in ensemble_time_avg_lat_lon:
if not tall_0.grid_equal(tall):
raise Exception(
"ua_ens_month_avg: error, grids are not consistent")
self.points = tall_0.points
self.area = tall_0.area
# put ensemble into a 3D array with shape (n_points, 24, n_models)
tall_0 = ensemble_time_avg_lat_lon[0]
n_points = tall_0.data.shape[0]
self.n_models = len(ensemble_time_avg_lat_lon)
self.data = np.zeros((n_points, 24, self.n_models))
for tall, model in zip(ensemble_time_avg_lat_lon,
range(self.n_models)):
self.data[:, :, model] = tall.data
# aos
obj.save(self)
def __init__(self, time_period=0):
# aos
obj = aos.DataObject(('uas_ens_avg', time_period))
if obj.load():
obj.copy(self)
return (None)
# init
self._time_period = time_period
self._models = NAMES_UAS
# create list of time_avg_lat_lon 2D-fields
ensemble_time_avg_lat_lon = []
if (time_period == '1971-2000') or (time_period == '1979-2005'):
directory_prefix = 'data/ModData2/CMIP5/atmos/historical/uas/mon/'
elif (time_period == '2071-2100') or (time_period == '2070-2099'):
directory_prefix = 'data/ModData2/CMIP5/atmos/rcp85/uas/mon/'
else:
raise Exception("uas_ens_avg: time period '%s' not known" %
time_period)
print("Load data from time period %s" % time_period)
for name in self._models:
print("Loading %s from file" % name)
directory = directory_prefix + name + '/r1i1p1/'
uas_list = [
uas(['file', os.path.join(directory, f)])
for f in os.listdir(directory) if f.endswith(".nc")
]
tall = time_avg_lat_lon(['uas list', uas_list],
time_period=time_period)
ensemble_time_avg_lat_lon += [tall]
# check consistancy of grids in the ensemble of time_avg_lat_lon 2D-fields and store grid
tall_0 = ensemble_time_avg_lat_lon[0]
for tall in ensemble_time_avg_lat_lon:
if not tall_0.grid_equal(tall):
raise Exception("uas_ens_avg: error, grids are not consistent")
self.points = tall_0.points
self.area = tall_0.area
# put ensemble into a 2D array: [data,talls]
data = []
for tall in ensemble_time_avg_lat_lon:
data += [tall.data]
self.data = np.hstack(data)
# area
# aos
obj.save(self)
def __init__(self, data):
# aos
obj = aos.DataObject(('ua', data))
if obj.load():
obj.copy(self)
return (None)
# logging
log = gen.getLogger(gen.getClassName(self))
log.info("creating class")
if not data:
raise Exception("ua: Initialization error, provide data")
if data[0] == 'file':
self._init_ua_from_file(data[1])
else:
raise Exception("ua: Initialization error, unknow mode")
# aos
obj.save(self)
def __init__(self, directory, time_period=0):
# logging
log = gen.getLogger(gen.getClassName(self))
log.info("starting")
# aos
obj = aos.DataObject(('time_avg_lat_lon_dir', directory, time_period))
if obj.load():
obj.copy(self)
log.info("loaded")
return (None)
ua_list = []
for f in directory.iterdir():
if '_reg.nc' in str(f):
gen.print_truncate(" Loading file %s" % f)
ua_list = ua_list + [ua(['file', directory.joinpath(f)])]
super(time_avg_lat_lon_dir, self).__init__(['ua list', ua_list],
time_period=time_period)
# aos
obj.save(self)
log.info("created")
def __init__(self, e, n=360, max_length=360, min_length=30):
"""Class initialization
Parameters
----------
e : class
Evaluation class for a single evaluation to run
n : int, optional
Range of starting degrees [0, 360].
max_length : int, optional
Maximum size of averaging window [1, 360).
min_length : int, optional
Minumum size of averaging window [1, 359).
Returns
-------
TYPE
Description
"""
# logging
log = gen.getLogger(gen.getClassName(self))
log.info("start evaluating all [JJA]")
# Error files
self.minima_error_file = 'min_max_error.csv'
self.nan_error_file = 'nan_error.csv'
# aos
obj = aos.DataObject(('EvaluateAll [JJA]', n))
if obj.load():
obj.copy(self)
return (None)
# prepare evaluate
self.n = n
self.min_length = min_length
self.max_length = max_length
x = np.arange(self.n)
y = np.arange(self.min_length, self.max_length + 1)
xx, yy = np.meshgrid(x, y)
individuals = np.transpose(
np.hstack((xx.reshape(-1, 1), yy.reshape(-1, 1))))
# evaluate
self.ret = e(individuals)
# Process / convert results
self.start = individuals[0, :].reshape(-1, 1)
self.length = individuals[1, :].reshape(-1, 1)
self.fitness = self.ret[0, :].reshape(-1, 1)
self.result = pd.DataFrame(
{
'start': self.start.ravel(),
'length': self.length.ravel(),
'fitness': self.fitness.ravel()
},
index=np.arange(len(self.fitness.ravel())))
# Longitudes in input goes from -179.5 to 179.5.
# Here we convert from integer degree (offset) to the proper longitude value.
self.result['start'] -= 179.5
self.save_errors(e.f.n_models)
# aos
obj.save(self)