def test_folder_contents(self):
"""If input is one folder name, return a list with containg files.
Folder names are not contained."""
file_name = DEMO_DIR
out = get_file_names(file_name)
for file in out:
self.assertEqual('.', Path(file).suffix[0])
file_name = DEMO_DIR.parent
out = get_file_names(file_name)
for file in out:
self.assertNotEqual('', Path(file).suffix)
def test_folder_contents(self):
"""If input is one folder name, return a list with containg files.
Folder names are not contained."""
file_name = os.path.join(DATA_DIR, 'demo')
out = get_file_names(file_name)
for file in out:
self.assertEqual('.', os.path.splitext(file)[1][0])
file_name = DATA_DIR
out = get_file_names(file_name)
for file in out:
self.assertNotEqual('', os.path.splitext(file)[1])
def test_wrong_argument(self):
"""If the input contains a non-existing file, an empyt directory or a pattern that is not
matched, the method should raise a ValueError."""
empty_dir = DEMO_DIR.parent
with self.assertRaises(ValueError) as ve:
get_file_names(str(empty_dir))
self.assertIn("no files", str(ve.exception))
no_file = 'this is not a file'
with self.assertRaises(ValueError) as ve:
get_file_names(no_file)
self.assertIn("cannot find", str(ve.exception))
def fetch_ecmwf(self, path=None, files=None):
"""
Fetch and read latest ECMWF TC track predictions from the FTP
dissemination server into instance. Use path argument to use local
files instead.
Parameters:
path (str, list(str)): A location in the filesystem. Either a
path to a single BUFR TC track file, or a folder containing
only such files, or a globbing pattern. Passed to
climada.util.files_handler.get_file_names
files (file-like): An explicit list of file objects, bypassing
get_file_names
"""
if path is None and files is None:
files = self.fetch_bufr_ftp()
elif files is None:
files = get_file_names(path)
for i, file in tqdm.tqdm(enumerate(files, 1), desc='Processing',
unit='files', total=len(files)):
try:
file.seek(0) # reset cursor if opened file instance
except AttributeError:
pass
self.read_one_bufr_tc(file, id_no=i)
try:
file.close() # discard if tempfile
except AttributeError:
pass
def test_folder_contents(self):
"""If input is one folder name, return a list with containg files.
Folder names are not contained."""
file_name = DEMO_DIR
out = get_file_names(file_name)
self.assertGreater(len(out), 0)
for file in out:
self.assertTrue(Path(file).is_file())
def test_globbing(self):
"""If input is a glob pattern, return a list of matching visible
files; omit folders.
"""
file_name = DEMO_DIR
out = get_file_names(f'{file_name}/*')
tmp_files = [
str(f) for f in Path(file_name).iterdir()
if f.is_file() and not f.name.startswith('.')
]
self.assertListEqual(sorted(tmp_files), sorted(out))
def test_globbing(self):
""" If input is a glob pattern, return a list of matching visible
files; omit folders.
"""
file_name = os.path.join(DATA_DIR, 'demo')
out = get_file_names(file_name)
tmp_files = os.listdir(file_name)
tmp_files = [os.path.join(file_name, f) for f in tmp_files]
tmp_files = [f for f in tmp_files if not os.path.isdir(f)
and not os.path.basename(os.path.normpath(f)).startswith('.')]
self.assertEqual(len(tmp_files), len(out))
self.assertEqual(sorted(tmp_files), sorted(out))
def read(self, files, descriptions='', var_names=None):
""" Read and check centroids.
Parameters:
files (str or list(str)): absolute file name(s) or folder name
containing the files to read
descriptions (str or list(str), optional): one description of the
data or a description of each data file
var_names (dict or list(dict), default): name of the variables in
the file (default: check def_source_vars() function)
Raises:
TypeError, ValueError
"""
all_files = get_file_names(files)
desc_list = to_list(len(all_files), descriptions, 'descriptions')
var_list = to_list(len(all_files), var_names, 'var_names')
self.clear()
for file, desc, var in zip(all_files, desc_list, var_list):
self.append(Centroids._read_one(file, desc, var))
from rasterio import Affine
from climada.hazard import Hazard
from climada.entity import Exposures, ImpactFuncSet, ImpactFunc
from climada.entity.exposures.base import INDICATOR_CENTR, INDICATOR_IF
from climada.engine import Impact
from climada.util.coordinates import write_raster
from climada.util.files_handler import get_file_names
logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
level=logging.INFO)
HAZ_TYPE = 'FL' # hazard type abbreviation (CLIMADA convention), default: 'FL'
HAZARD_PTH = '/<SET PATH>' # The directory which contains hazard files
HAZ_FILES = get_file_names(HAZARD_PTH)
EXP_POP_PTH = '/<SET PATH>' # The directory which contains exposure files
SAVE_PTH = '/<SET PATH>' # Destinated path for the output raster files
RCP = 'historical' # historical simulation
HYDRO_MODEL = ['H08', 'LPJmL', 'MPI-HM', 'ORCHIDEE', 'PCR-GLOBWB', 'WaterGAP2'] # 6 * hydrological model
GCM_MODEL = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'] # 4 * climate model
YEAR = np.arange(1970,2010,10) # Years for the computation
DST_META = {'width': 8640, 'height': 3347, 'crs': {'init':'epsg:4326'}, \
'transform': Affine(0.04166666666666666, 0.0, -180.0,
def test_several_file_copy(self):
"""If input is a list with several file names, return the same list"""
file_name = [GLB_CENTROIDS_MAT, ENT_TEMPLATE_XLS]
out = get_file_names(file_name)
self.assertEqual(file_name, out)
def test_one_file_copy(self):
"""If input is one file name, return a list with this file name"""
file_name = GLB_CENTROIDS_MAT
out = get_file_names(file_name)
self.assertEqual([file_name], out)
def read_footprints(self,
path,
description=None,
ref_raster=None,
centroids=None,
files_omit='fp_era20c_1990012515_701_0.nc'):
""" Clear instance and read WISC footprints into it. Read Assumes that
all footprints have the same coordinates as the first file listed/first
file in dir.
Parameters:
path (str, list(str)): A location in the filesystem. Either a
path to a single netCDF WISC footprint, or a folder
containing only footprints, or a globbing pattern to one or
more footprints.
description (str, optional): description of the events, defaults
to 'WISC historical hazard set'
ref_raster (str, optional): Reference netCDF file from which to
construct a new barebones Centroids instance. Defaults to
the first file in path.
centroids (Centroids, optional): A Centroids struct, overriding
ref_raster
files_omit (str, list(str), optional): List of files to omit;
defaults to one duplicate storm present in the WISC set as
of 2018-09-10.
"""
self.clear()
file_names = get_file_names(path)
if ref_raster is not None and centroids is not None:
LOGGER.warning('Overriding ref_raster with centroids')
if centroids is not None:
pass
elif ref_raster is not None:
centroids = self._centroids_from_nc(ref_raster)
elif ref_raster is None:
centroids = self._centroids_from_nc(file_names[0])
if isinstance(files_omit, str):
files_omit = [files_omit]
LOGGER.info('Commencing to iterate over netCDF files.')
for file_name in file_names:
if any(fo in file_name for fo in files_omit):
LOGGER.info("Omitting file %s", file_name)
continue
new_haz = self._read_one_nc(file_name, centroids)
if new_haz is not None:
self.append(new_haz)
self.event_id = np.arange(1, len(self.event_id) + 1)
self.frequency = np.divide(np.ones_like(
self.date), (last_year(self.date) - first_year(self.date)))
self.tag = TagHazard(HAZ_TYPE,
'Hazard set not saved by default',
description='WISC historical hazard set.')
if description is not None:
self.tag.description = description
def from_footprints(cls,
path,
description=None,
ref_raster=None,
centroids=None,
files_omit='fp_era20c_1990012515_701_0.nc',
combine_threshold=None,
intensity_thres=None):
"""Create new StormEurope object from WISC footprints.
Assumes that all footprints have the same coordinates as the first file listed/first
file in dir.
Parameters
----------
path : str, list(str)
A location in the filesystem. Either a
path to a single netCDF WISC footprint, or a folder
containing only footprints, or a globbing pattern to one or
more footprints.
description : str, optional
description of the events, defaults
to 'WISC historical hazard set'
ref_raster : str, optional
Reference netCDF file from which to
construct a new barebones Centroids instance. Defaults to
the first file in path.
centroids : Centroids, optional
A Centroids struct, overriding
ref_raster
files_omit : str, list(str), optional
List of files to omit;
defaults to one duplicate storm present in the WISC set as
of 2018-09-10.
combine_threshold : int, optional
threshold for combining events
in number of days. if the difference of the dates (self.date)
of two events is smaller or equal to this threshold, the two
events are combined into one.
Default is None, Advised for WISC is 2
intensity_thres : float, optional
Intensity threshold for storage in m/s. Default: class attribute
StormEurope.intensity_thres (same as used by WISC SSI calculations)
Returns
-------
haz : StormEurope
StormEurope object with data from WISC footprints.
"""
intensity_thres = cls.intensity_thres if intensity_thres is None else intensity_thres
file_names = get_file_names(path)
if ref_raster is not None and centroids is not None:
LOGGER.warning('Overriding ref_raster with centroids')
if centroids is not None:
pass
elif ref_raster is not None:
centroids = cls._centroids_from_nc(ref_raster)
elif ref_raster is None:
centroids = cls._centroids_from_nc(file_names[0])
if isinstance(files_omit, str):
files_omit = [files_omit]
LOGGER.info('Commencing to iterate over netCDF files.')
haz = cls()
for file_name in file_names:
if any(fo in file_name for fo in files_omit):
LOGGER.info("Omitting file %s", file_name)
continue
new_haz = cls._read_one_nc(file_name, centroids, intensity_thres)
if new_haz is not None:
haz.append(new_haz)
haz.event_id = np.arange(1, len(haz.event_id) + 1)
haz.frequency = np.divide(
np.ones_like(haz.date),
np.max([(last_year(haz.date) - first_year(haz.date)), 1]))
haz.tag = TagHazard(HAZ_TYPE,
'Hazard set not saved by default',
description='WISC historical hazard set.')
if description is not None:
haz.tag.description = description
if combine_threshold is not None:
LOGGER.info('Combining events with small difference in date.')
difference_date = np.diff(haz.date)
for event_id_i in haz.event_id[np.append(
difference_date <= combine_threshold, False)]:
event_ids = [event_id_i, event_id_i + 1]
haz._combine_events(event_ids)
return haz
