def _select_exact_area(countries=None, reg=None):
"""Extract coordinates of selected countries or region
from NatID grid. If countries are given countries are cut,
if only reg is given, the whole region is cut.
Parameters:
countries: List of countries
reg: List of regions
Raises:
KeyError
Returns:
centroids
"""
lat, lon = get_region_gridpoints(countries=countries, regions=reg,
basemap="isimip", resolution=150)
if reg:
country_isos = region2isos(reg)
else:
country_isos = countries if countries else []
natIDs = country_iso2natid(country_isos)
centroids = Centroids()
centroids.set_lat_lon(lat, lon)
centroids.id = np.arange(centroids.lon.shape[0])
# centroids.set_region_id()
return centroids, country_isos, natIDs
def test_track_to_surge_raster_pass(self):
""" Test set_from_winds with default raster (same as as_pixel=True) """
tc_track = TCTracks()
tc_track.read_processed_ibtracs_csv(TEST_TRACK)
tc_track.calc_random_walk()
tc_track.equal_timestep()
centr_ras = copy.copy(CENTR_TEST_BRB)
centr_ras.set_lat_lon_to_meta(min_resol=1.0e-2)
centr_clean = Centroids()
centr_clean.meta = centr_ras.meta
centr_clean.check()
tc_haz = TropCyclone()
tc_haz.set_from_tracks(tc_track, centroids=centr_clean)
tc_haz.check()
tc_surge = TCSurge()
tc_surge.set_from_winds(tc_haz)
tc_surge.check()
self.assertTrue(tc_surge.centroids is tc_haz.centroids)
self.assertEqual(tc_surge.size, tc_haz.size)
self.assertEqual(tc_surge.fraction.min(), 0)
self.assertEqual(tc_surge.fraction.max(), 1)
self.assertTrue(np.unique(tc_surge.fraction.data).size > 2)
def test_meta_centroids_flood(self):
min_lat, max_lat, min_lon, max_lon = 45.7, 47.8, 7.5, 10.5
cent = Centroids()
cent.set_raster_from_pnt_bounds((min_lon, min_lat, max_lon, max_lat),
res=0.05)
rf_rast = RiverFlood()
rf_rast.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC,
centroids=cent)
self.assertEqual(rf_rast.centroids.shape, (43, 61))
self.assertAlmostEqual(np.min(rf_rast.centroids.lat),
45.70000000000012, 4)
self.assertAlmostEqual(np.max(rf_rast.centroids.lat), 47.8, 4)
self.assertAlmostEqual(np.min(rf_rast.centroids.lon), 7.5, 4)
self.assertAlmostEqual(np.max(rf_rast.centroids.lon),
10.49999999999999, 4)
self.assertAlmostEqual(rf_rast.centroids.lon[90],
8.949999999999996, 4)
self.assertAlmostEqual(rf_rast.centroids.lat[90], 47.75, 4)
self.assertEqual(rf_rast.intensity.shape, (1, 2623))
self.assertAlmostEqual(np.min(rf_rast.intensity), 0.0, 4)
self.assertAlmostEqual(np.max(rf_rast.intensity), 5.8037286, 4)
self.assertEqual(np.argmin(rf_rast.intensity), 0, 4)
self.assertEqual(np.argmax(rf_rast.intensity), 55, 4)
self.assertEqual(rf_rast.fraction.shape, (1, 2623))
self.assertAlmostEqual(np.min(rf_rast.fraction), 0.0, 4)
self.assertAlmostEqual(np.max(rf_rast.fraction), 0.4896, 4)
self.assertEqual(np.argmin(rf_rast.fraction), 0, 4)
self.assertEqual(np.argmax(rf_rast.fraction), 360, 4)
def test_write_fraction_pass(self):
"""Test write_raster with fraction"""
haz_fl = Hazard('FL')
haz_fl.event_id = np.array([1])
haz_fl.date = np.array([1])
haz_fl.frequency = np.array([1])
haz_fl.orig = np.array([1])
haz_fl.event_name = ['1']
haz_fl.intensity = sparse.csr_matrix(np.array([0.5, 0.2, 0.1]))
haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1]) / 2)
haz_fl.centroids = Centroids.from_lat_lon(np.array([1, 2, 3]),
np.array([1, 2, 3]))
haz_fl.check()
haz_fl.write_raster(DATA_DIR.joinpath('test_write_hazard.tif'),
intensity=False)
haz_read = Hazard.from_raster(
[DATA_DIR.joinpath('test_write_hazard.tif')],
files_fraction=[DATA_DIR.joinpath('test_write_hazard.tif')],
haz_type='FL')
self.assertEqual(haz_read.intensity.shape, (1, 9))
self.assertEqual(haz_read.fraction.shape, (1, 9))
self.assertTrue(
np.allclose(np.unique(np.array(haz_read.fraction.toarray())),
np.array([0.0, 0.05, 0.1, 0.25])))
self.assertTrue(
np.allclose(np.unique(np.array(haz_read.intensity.toarray())),
np.array([0.0, 0.05, 0.1, 0.25])))
def select_exact_area_polygon(countries=[], reg=[]):
""" Extract coordinates of selected countries or region
from NatID grid. If countries are given countries are cut,
if only reg is given, the whole region is cut.
Parameters:
countries: List of countries
reg: List of regions
Raises:
AttributeError
Returns:
np.array
"""
centroids = Centroids()
natID_info = pd.read_csv(NAT_REG_ID)
isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC)
isimip_lon = isimip_grid.lon.data
isimip_lat = isimip_grid.lat.data
gridX, gridY = np.meshgrid(isimip_lon, isimip_lat)
if countries:
natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)]
elif reg:
natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)]
else:
centroids.coord = np.zeros((gridX.size, 2))
centroids.coord[:, 1] = gridX.flatten()
centroids.coord[:, 0] = gridY.flatten()
centroids.id = np.arange(centroids.coord.shape[0])
return centroids
isimip_NatIdGrid = isimip_grid.NatIdGrid.data
natID_pos = np.isin(isimip_NatIdGrid, natID)
lon_coordinates = gridX[natID_pos]
lat_coordinates = gridY[natID_pos]
centroids.coord = np.zeros((len(lon_coordinates), 2))
centroids.coord[:, 1] = lon_coordinates
centroids.coord[:, 0] = lat_coordinates
centroids.id = np.arange(centroids.coord.shape[0])
orig_proj = 'epsg:4326'
country = gpd.GeoDataFrame()
country['geometry'] = list(
zip(centroids.coord[:, 1], centroids.coord[:, 0]))
country['geometry'] = country['geometry'].apply(Point)
country.crs = {'init': orig_proj}
points = country.geometry.values
concave_hull, _ = alpha_shape(points, alpha=1)
return concave_hull
def test_centroids_flood(self):
# this is going to go through the meta part
rand_centroids = Centroids()
lat = np.arange(47, 56, 0.2)
lon = np.arange(5, 15, 0.2)
lon, lat = np.meshgrid(lon, lat)
rand_centroids.set_lat_lon(lat.flatten(), lon.flatten())
rf = RiverFlood()
rf.set_from_nc(dph_path=HAZ_DEMO_FLDDPH,
frc_path=HAZ_DEMO_FLDFRC,
centroids=rand_centroids,
ISINatIDGrid=False)
self.assertEqual(rf.date[0], 730303)
self.assertEqual(rf.event_id[0], 0)
self.assertEqual(rf.event_name[0], '2000')
self.assertEqual(rf.orig[0], False)
self.assertAlmostEqual(rf.frequency[0], 1.)
self.assertEqual(rf.centroids.shape, (45, 50))
self.assertAlmostEqual(np.min(rf.centroids.lat), 47.0, 4)
self.assertAlmostEqual(np.max(rf.centroids.lat), 55.8, 4)
self.assertAlmostEqual(np.min(rf.centroids.lon), 5.0, 4)
self.assertAlmostEqual(np.max(rf.centroids.lon), 14.8, 4)
self.assertAlmostEqual(rf.centroids.lon[90], 13.0, 4)
self.assertAlmostEqual(rf.centroids.lat[90], 47.2, 4)
self.assertEqual(rf.intensity.shape, (1, 2250))
self.assertAlmostEqual(np.min(rf.intensity), 0.0, 4)
self.assertAlmostEqual(np.max(rf.intensity), 8.921593, 4)
self.assertEqual(np.argmin(rf.intensity), 0, 4)
self.assertEqual(np.argmax(rf.intensity), 191, 4)
self.assertEqual(rf.fraction.shape, (1, 2250))
self.assertAlmostEqual(np.min(rf.fraction), 0.0, 4)
self.assertAlmostEqual(np.max(rf.fraction), 0.92, 4)
self.assertEqual(np.argmin(rf.fraction), 0, 4)
self.assertEqual(np.argmax(rf.fraction), 1438, 4)
def init_test_centroids(data):
"""define centroids for test data:"""
centroids = Centroids()
grid = np.meshgrid(np.arange(data.lat.min(), data.lat.max()+.5, .5),
np.arange(data.lon.min(), data.lon.max()+.5, .5))
lat = list()
lon = list()
for arrlat, arrlon in zip(list(grid[0]), list(grid[1])):
lat += list(arrlat)
lon += list(arrlon)
centroids.set_lat_lon(np.array(lat), np.array(lon))
centroids.set_lat_lon_to_meta()
return centroids
Test tc_surge module.
"""
import os
import copy
import unittest
import numpy as np
from climada.hazard import TCSurge, TropCyclone, TCTracks
from climada.hazard.centroids import Centroids
DATA_DIR = os.path.join(os.path.dirname(__file__), os.pardir,
'hazard/test/data')
TEST_TRACK = os.path.join(DATA_DIR, "trac_brb_test.csv")
CENTR_TEST_BRB = Centroids()
CENTR_TEST_BRB.read_mat(os.path.join(DATA_DIR, 'centr_brb_test.mat'))
class TestEnd2End(unittest.TestCase):
"""Test reading TC from IBTrACS files"""
def test_track_to_surge_point_pass(self):
""" Test set_from_winds with default points (same as as_pixel=False) """
tc_track = TCTracks()
tc_track.read_processed_ibtracs_csv(TEST_TRACK)
tc_track.calc_random_walk()
tc_track.equal_timestep()
tc_haz = TropCyclone()
tc_haz.set_from_tracks(tc_track, CENTR_TEST_BRB)
class RiverFlood(Hazard):
"""Contains flood events
Flood intensities are calculated by means of the
CaMa-Flood global hydrodynamic model
Attributes:
fla_ev_centr (2d array(n_events x n_centroids)) flooded area in
every centroid for every event
fla_event (1d array(n_events)) total flooded area for every event
fla_ann_centr (2d array(n_years x n_centroids)) flooded area in
every centroid for every event
fla_annual (1d array (n_years)) total flooded area for every year
fla_ann_av (float) average flooded area per year
fla_ev_av (float) average flooded area per event
"""
def __init__(self):
"""Empty constructor"""
Hazard.__init__(self, HAZ_TYPE)
def set_from_nc(self,
flood_dir=None,
dph_path=None,
frc_path=None,
centroids=None,
countries=[],
reg=None,
years=[2000],
rf_model=RF_MODEL[0],
cl_model=CL_MODEL[5],
scenario=SCENARIO[1],
prot_std=PROT_STD):
"""Wrapper to fill hazard from nc_flood file
Parameters:
flood_dir (string): location of flood data
(can be used when different model-runs are considered,
dph_path and frc_path must be None)
dph_path (string): Flood file to read (depth)
frc_path (string): Flood file to read (fraction)
centroids (Centroids): centroids
(area that is considered, reg and country must be None)
countries (list of countries ISO3) selection of countries
(reg must be None!)
reg (list of regions): can be set with region code if whole areas
are considered (if not None, countries and centroids
are ignored)
years (int list): years that are considered
rf_model: run-off model (only when flood_dir is selected)
cl_model: climate model (only when flood_dir is selected)
scenario: climate change scenario (only when flood_dir is selected)
prot_std: protection standard (only when flood_dir is selected)
raises:
NameError
"""
if dph_path is None or frc_path is None:
if flood_dir is not None:
if os.path.exists(flood_dir):
dph_path, frc_path = self._select_model_run(
flood_dir, rf_model, cl_model, scenario, prot_std)
else:
dph_path = HAZ_DEMO_FLDDPH
frc_path = HAZ_DEMO_FLDFRC
LOGGER.warning('Flood directory ' + flood_dir +
' does not exist, setting Demo files ' +
str(dph_path) + ' and ' + str(frc_path))
else:
dph_path = HAZ_DEMO_FLDDPH
frc_path = HAZ_DEMO_FLDFRC
LOGGER.warning('Flood directory not set ' +
', setting Demo files ' + str(dph_path) +
' and ' + str(frc_path))
else:
if not os.path.exists(dph_path):
LOGGER.error('Invalid flood-file path ' + dph_path)
raise NameError
if not os.path.exists(frc_path):
LOGGER.error('Invalid flood-file path ' + frc_path)
raise NameError
if centroids is not None:
self.centroids = centroids
centr_handling = 'align'
elif countries or reg:
self.centroids = RiverFlood.select_exact_area(countries, reg)
centr_handling = 'align'
else:
centr_handling = 'full_hazard'
intensity, fraction = self._read_nc(years, centr_handling, dph_path,
frc_path)
if scenario == 'historical':
self.orig = np.full((self._n_events), True, dtype=bool)
else:
self.orig = np.full((self._n_events), False, dtype=bool)
self.intensity = sparse.csr_matrix(intensity)
self.fraction = sparse.csr_matrix(fraction)
self.event_id = np.arange(1, self._n_events + 1)
self.units = 'm'
self.frequency = np.ones(self._n_events) / self._n_events
return self
def _read_nc(self, years, centr_handling, dph_path, frc_path):
""" extract and flood intesity and fraction from flood
data
Returns:
np.arrays
"""
try:
flood_dph = xr.open_dataset(dph_path)
flood_frc = xr.open_dataset(frc_path)
lon = flood_dph.lon.data
lat = flood_dph.lat.data
time = flood_dph.time.data
event_index = self._select_event(time, years)
self._n_events = len(event_index)
self.date = np.array([
dt.datetime(flood_dph.time[i].dt.year,
flood_dph.time[i].dt.month,
flood_dph.time[i].dt.day).toordinal()
for i in event_index
])
except KeyError:
LOGGER.error('Invalid dimensions or variables in file ' +
dph_path + ' or ' + frc_path)
raise KeyError
except OSError:
LOGGER.error('Problems while reading file ' + dph_path + ' or ' +
frc_path + ' check flood_file specifications')
raise NameError
if centr_handling == 'full_hazard':
if len(event_index) > 1:
LOGGER.warning('Calculates global hazard' +
' advanced memory requirements')
LOGGER.warning('Calculates global hazard, select area with ' +
'countries, reg or centroids in set_from_nc ' +
'to reduce runtime')
self._set_centroids_from_file(lon, lat)
try:
intensity = np.nan_to_num(
np.array([
flood_dph.flddph[i].data.flatten() for i in event_index
]))
fraction = np.nan_to_num(
np.array([
flood_frc.fldfrc[i].data.flatten() for i in event_index
]))
except MemoryError:
LOGGER.error('Too many events for grid size')
raise MemoryError
else:
n_centroids = self.centroids.size
win = self._cut_window(lon, lat)
lon_coord = lon[win[0, 0]:win[1, 0] + 1]
lat_coord = lat[win[0, 1]:win[1, 1] + 1]
dph_window = flood_dph.flddph[event_index, win[0, 1]:win[1, 1] + 1,
win[0, 0]:win[1, 0] + 1].data
frc_window = flood_frc.fldfrc[event_index, win[0, 1]:win[1, 1] + 1,
win[0, 0]:win[1, 0] + 1].data
self.window = win
try:
intensity, fraction = _interpolate(lat_coord, lon_coord,
dph_window, frc_window,
self.centroids.lon,
self.centroids.lat,
n_centroids, self._n_events)
except MemoryError:
LOGGER.error('Too many events for grid size')
raise MemoryError
return intensity, fraction
def _select_model_run(self,
flood_dir,
rf_model,
cl_model,
scenario,
prot_std,
proj=False):
"""Provides paths for selected models to incorporate flood depth
and fraction
Parameters:
flood_dir(string): string folder location of flood data
rf_model (string): run-off model
cl_model (string): climate model
scenario (string): climate change scenario
prot_std (string): protection standard
"""
if proj is False:
final = 'gev_0.1.nc'
dph_file = 'flddph_{}_{}_{}_{}'\
.format(rf_model, cl_model, prot_std, final)
frc_file = 'fldfrc_{}_{}_{}_{}'\
.format(rf_model, cl_model, prot_std, final)
else:
final = 'gev_picontrol_2000_0.1.nc'
dph_file = 'flddph_{}_{}_{}_{}_{}'\
.format(rf_model, cl_model, scenario, prot_std, final)
frc_file = 'fldfrc_{}_{}_{}_{}_{}'\
.format(rf_model, cl_model, scenario, prot_std, final)
dph_path = os.path.join(flood_dir, dph_file)
frc_path = os.path.join(flood_dir, frc_file)
return dph_path, frc_path
def _set_centroids_from_file(self, lon, lat):
self.centroids = Centroids()
gridX, gridY = np.meshgrid(lon, lat)
self.centroids.set_lat_lon(gridY.flatten(), gridX.flatten())
def _select_event(self, time, years):
event_names = pd.to_datetime(time).year
event_index = np.where(np.isin(event_names, years))[0]
if len(event_index) == 0:
LOGGER.error('No events found for selected ' + str(years))
raise AttributeError
self.event_name = list(map(str, pd.to_datetime(time[event_index])))
return event_index
def _cut_window(self, lon, lat):
""" Determine size of window to extract flood data.
Parameters:
lon: flood-file longitude coordinates
lat: flood-file latitude coordinates
Returns:
np.array
"""
lon_min = math.floor(min(self.centroids.coord[:, 1]))
lon_max = math.ceil(max(self.centroids.coord[:, 1]))
lat_min = math.floor(min(self.centroids.coord[:, 0]))
lat_max = math.ceil(max(self.centroids.coord[:, 0]))
diff_lon = np.diff(lon)[0]
diff_lat = np.diff(lat)[0]
win = np.zeros((2, 2), dtype=int)
win[0, 0] = min(
np.where((lon >= lon_min - diff_lon)
& (lon <= lon_max + diff_lon))[0])
win[1, 0] = max(
np.where((lon >= lon_min - diff_lon)
& (lon <= lon_max + diff_lon))[0])
win[0, 1] = min(
np.where((lat >= lat_min - diff_lat)
& (lat <= lat_max + diff_lat))[0])
win[1, 1] = max(
np.where((lat >= lat_min - diff_lat)
& (lat <= lat_max + diff_lat))[0])
return win
def set_flooded_area(self):
""" Calculates flooded area for hazard. sets yearly flooded area and
flooded area per event
Raises:
MemoryError
"""
self.centroids.set_area_pixel()
area_centr = self.centroids.area_pixel
event_years = np.array([
date.fromordinal(self.date[i]).year for i in range(len(self.date))
])
years = np.unique(event_years)
year_ev_mk = self._annual_event_mask(event_years, years)
try:
self.fla_ev_centr = np.zeros(
(self._n_events, len(self.centroids.lon)))
self.fla_ann_centr = np.zeros(
(len(years), len(self.centroids.lon)))
self.fla_ev_centr = np.array(
np.multiply(self.fraction.todense(), area_centr))
self.fla_event = np.sum(self.fla_ev_centr, axis=1)
for year_ind in range(len(years)):
self.fla_ann_centr[year_ind, :] =\
np.sum(self.fla_ev_centr[year_ev_mk[year_ind, :], :],
axis=0)
self.fla_annual = np.sum(self.fla_ann_centr, axis=1)
self.fla_ann_av = np.mean(self.fla_annual)
self.fla_ev_av = np.mean(self.fla_event)
except MemoryError:
self.fla_ev_centr = None
self.tot_fld_area = None
self.fla_ann_centr = None
self.fla_annual = None
self.fla_ann_av = None
self.fla_ev_av = None
LOGGER.warning('Number of events and slected area exceed ' +
'memory capacities, area has not been calculated,' +
' attributes set to None')
def set_flooded_area_cut(self, coordinates, centr_indices=None):
""" Calculates flooded area for any window given with coordinates or
from indices of hazard centroids. sets yearly flooded area and
per event
Parameters:
coordinates(2d array): coordinates of window
centr_indices(1d array): indices of hazard centroid
Raises:
MemoryError
"""
if centr_indices is None:
centr_indices = interpol_index(self.centroids.coord, coordinates)
self.centroids.set_area_pixel()
area_centr = self.centroids.area_pixel[centr_indices]
event_years = np.array([
date.fromordinal(self.date[i]).year for i in range(len(self.date))
])
years = np.unique(event_years)
year_ev_mk = self._annual_event_mask(event_years, years)
try:
self.fla_ev_centr = np.zeros((self._n_events, len(centr_indices)))
self.fla_ann_centr = np.zeros((len(years), len(centr_indices)))
self.fla_ev_centr = np.array(
np.multiply(self.fraction[:, centr_indices].todense(),
area_centr))
self.fla_event = np.sum(self.fla_ev_centr, axis=1)
for year_ind in range(len(years)):
self.fla_ann_centr[year_ind, :] = \
np.sum(self.fla_ev_centr[year_ev_mk[year_ind, :], :],
axis=0)
self.fla_annual = np.sum(self.fla_ann_centr, axis=1)
self.fla_ann_av = np.mean(self.fla_annual)
self.fla_ev_av = np.mean(self.fla_event)
except MemoryError:
self.fla_ev_centr = None
self.fla_event = None
self.fla_ann_centr = None
self.fla_annual = None
self.fla_ann_av = None
self.fla_ev_av = None
LOGGER.warning('Number of events and slected area exceed ' +
'memory capacities, area has not been calculated,' +
' attributes set to None')
def _annual_event_mask(self, event_years, years):
event_mask = np.full((len(years), len(event_years)), False, dtype=bool)
for year_ind in range(len(years)):
events = np.where(event_years == years[year_ind])[0]
event_mask[year_ind, events] = True
return event_mask
def select_window_area(countries=[], reg=[]):
""" Extract coordinates of selected countries or region
from NatID in a rectangular box. If countries are given countries
are cut, if only reg is given, the whole region is cut.
Parameters:
countries: List of countries
reg: List of regions
Raises:
AttributeError
Returns:
np.array
"""
centroids = Centroids()
natID_info = pd.read_csv(NAT_REG_ID)
isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC)
isimip_lon = isimip_grid.lon.data
isimip_lat = isimip_grid.lat.data
gridX, gridY = np.meshgrid(isimip_lon, isimip_lat)
if countries:
if not any(np.isin(natID_info['ISO'], countries)):
LOGGER.error('Country ISO3s ' + str(countries) + ' unknown')
raise KeyError
natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)]
elif reg:
natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)]
if not any(np.isin(natID_info["Reg_name"], reg)):
LOGGER.error('Shortcuts ' + str(reg) + ' unknown')
raise KeyError
else:
centroids.lat = np.zeros((gridX.size))
centroids.lon = np.zeros((gridX.size))
centroids.lon = gridX.flatten()
centroids.lat = gridY.flatten()
centroids.id = np.arange(centroids.lon.shape[0])
centroids.id = np.arange(centroids.lon.shape[0])
return centroids
isimip_NatIdGrid = isimip_grid.NatIdGrid.data
natID_pos = np.isin(isimip_NatIdGrid, natID)
lon_coordinates = gridX[natID_pos]
lat_coordinates = gridY[natID_pos]
lon_min = math.floor(min(lon_coordinates))
if lon_min <= -179:
lon_inmin = 0
else:
lon_inmin = min(np.where((isimip_lon >= lon_min))[0]) - 1
lon_max = math.ceil(max(lon_coordinates))
if lon_max >= 179:
lon_inmax = len(isimip_lon) - 1
else:
lon_inmax = max(np.where((isimip_lon <= lon_max))[0]) + 1
lat_min = math.floor(min(lat_coordinates))
if lat_min <= -89:
lat_inmin = 0
else:
lat_inmin = min(np.where((isimip_lat >= lat_min))[0]) - 1
lat_max = math.ceil(max(lat_coordinates))
if lat_max >= 89:
lat_max = len(isimip_lat) - 1
else:
lat_inmax = max(np.where((isimip_lat <= lat_max))[0]) + 1
lon = isimip_lon[lon_inmin:lon_inmax]
lat = isimip_lat[lat_inmin:lat_inmax]
gridX, gridY = np.meshgrid(lon, lat)
lat = np.zeros((gridX.size))
lon = np.zeros((gridX.size))
lon = gridX.flatten()
lat = gridY.flatten()
centroids.set_lat_lon(lat, lon)
centroids.id = np.arange(centroids.coord.shape[0])
centroids.set_region_id()
return centroids
def select_exact_area(countries=[], reg=[]):
""" Extract coordinates of selected countries or region
from NatID grid. If countries are given countries are cut,
if only reg is given, the whole region is cut.
Parameters:
countries: List of countries
reg: List of regions
Raises:
KeyError
Returns:
centroids
"""
centroids = Centroids()
natID_info = pd.read_csv(NAT_REG_ID)
isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC)
isimip_lon = isimip_grid.lon.data
isimip_lat = isimip_grid.lat.data
gridX, gridY = np.meshgrid(isimip_lon, isimip_lat)
try:
if countries:
if not any(np.isin(natID_info['ISO'], countries)):
LOGGER.error('Country ISO3s ' + str(countries) +
' unknown')
raise KeyError
natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)]
elif reg:
if not any(np.isin(natID_info["Reg_name"], reg)):
LOGGER.error('Shortcuts ' + str(reg) + ' unknown')
raise KeyError
natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)]
else:
centroids.lon = np.zeros((gridX.size))
centroids.lat = np.zeros((gridX.size))
centroids.lon = gridX.flatten()
centroids.lat = gridY.flatten()
centroids.id = np.arange(centroids.lon.shape[0])
return centroids
except KeyError:
LOGGER.error('Selected country or region do ' +
'not match reference file')
raise KeyError
isimip_NatIdGrid = isimip_grid.NatIdGrid.data
natID_pos = np.isin(isimip_NatIdGrid, natID)
lon_coordinates = gridX[natID_pos]
lat_coordinates = gridY[natID_pos]
centroids.set_lat_lon(lat_coordinates, lon_coordinates)
centroids.id = np.arange(centroids.lon.shape[0])
centroids.set_region_id()
return centroids
def select_exact_area_polygon(countries=[], reg=[]):
""" Extract coordinates of selected countries or region
from NatID grid. If countries are given countries are cut,
if only reg is given, the whole region is cut.
Parameters:
countries: List of countries
reg: List of regions
Raises:
AttributeError
Returns:
np.array
"""
centroids = Centroids()
natID_info = pd.read_csv(NAT_REG_ID)
isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC)
isimip_lon = isimip_grid.lon.data
isimip_lat = isimip_grid.lat.data
gridX, gridY = np.meshgrid(isimip_lon, isimip_lat)
if countries:
natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)]
elif reg:
natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)]
else:
centroids.coord = np.zeros((gridX.size, 2))
centroids.coord[:, 1] = gridX.flatten()
centroids.coord[:, 0] = gridY.flatten()
centroids.id = np.arange(centroids.coord.shape[0])
return centroids
isimip_NatIdGrid = isimip_grid.NatIdGrid.data
natID_pos = np.isin(isimip_NatIdGrid, natID)
lon_coordinates = gridX[natID_pos]
lat_coordinates = gridY[natID_pos]
centroids.coord = np.zeros((len(lon_coordinates), 2))
centroids.coord[:, 1] = lon_coordinates
centroids.coord[:, 0] = lat_coordinates
centroids.id = np.arange(centroids.coord.shape[0])
orig_proj = 'epsg:4326'
country = gpd.GeoDataFrame()
country['geometry'] = list(
zip(centroids.coord[:, 1], centroids.coord[:, 0]))
country['geometry'] = country['geometry'].apply(Point)
country.crs = {'init': orig_proj}
points = country.geometry.values
concave_hull, _ = alpha_shape(points, alpha=1)
return concave_hull
def select_exact_area(countries=[], reg=[]):
""" Extract coordinates of selected countries or region
from NatID grid. If countries are given countries are cut,
if only reg is given, the whole region is cut.
Parameters:
countries: List of countries
reg: List of regions
Raises:
KeyError
Returns:
centroids
"""
centroids = Centroids()
natID_info = pd.read_csv(NAT_REG_ID)
isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC)
isimip_lon = isimip_grid.lon.data
isimip_lat = isimip_grid.lat.data
gridX, gridY = np.meshgrid(isimip_lon, isimip_lat)
try:
if countries:
if not any(np.isin(natID_info['ISO'], countries)):
LOGGER.error('Country ISO3s ' + str(countries) +
' unknown')
raise KeyError
natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)]
elif reg:
if not any(np.isin(natID_info["Reg_name"], reg)):
LOGGER.error('Shortcuts ' + str(reg) + ' unknown')
raise KeyError
natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)]
else:
centroids.lon = np.zeros((gridX.size))
centroids.lat = np.zeros((gridX.size))
centroids.lon = gridX.flatten()
centroids.lat = gridY.flatten()
centroids.id = np.arange(centroids.lon.shape[0])
return centroids
except KeyError:
LOGGER.error('Selected country or region do ' +
'not match reference file')
raise KeyError
isimip_NatIdGrid = isimip_grid.NatIdGrid.data
natID_pos = np.isin(isimip_NatIdGrid, natID)
lon_coordinates = gridX[natID_pos]
lat_coordinates = gridY[natID_pos]
centroids.set_lat_lon(lat_coordinates, lon_coordinates)
centroids.id = np.arange(centroids.lon.shape[0])
centroids.set_region_id()
return centroids
def select_window_area(countries=[], reg=[]):
""" Extract coordinates of selected countries or region
from NatID in a rectangular box. If countries are given countries
are cut, if only reg is given, the whole region is cut.
Parameters:
countries: List of countries
reg: List of regions
Raises:
AttributeError
Returns:
np.array
"""
centroids = Centroids()
natID_info = pd.read_csv(NAT_REG_ID)
isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC)
isimip_lon = isimip_grid.lon.data
isimip_lat = isimip_grid.lat.data
gridX, gridY = np.meshgrid(isimip_lon, isimip_lat)
if countries:
if not any(np.isin(natID_info['ISO'], countries)):
LOGGER.error('Country ISO3s ' + str(countries) + ' unknown')
raise KeyError
natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)]
elif reg:
natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)]
if not any(np.isin(natID_info["Reg_name"], reg)):
LOGGER.error('Shortcuts ' + str(reg) + ' unknown')
raise KeyError
else:
centroids.lat = np.zeros((gridX.size))
centroids.lon = np.zeros((gridX.size))
centroids.lon = gridX.flatten()
centroids.lat = gridY.flatten()
centroids.id = np.arange(centroids.lon.shape[0])
centroids.id = np.arange(centroids.lon.shape[0])
return centroids
isimip_NatIdGrid = isimip_grid.NatIdGrid.data
natID_pos = np.isin(isimip_NatIdGrid, natID)
lon_coordinates = gridX[natID_pos]
lat_coordinates = gridY[natID_pos]
lon_min = math.floor(min(lon_coordinates))
if lon_min <= -179:
lon_inmin = 0
else:
lon_inmin = min(np.where((isimip_lon >= lon_min))[0]) - 1
lon_max = math.ceil(max(lon_coordinates))
if lon_max >= 179:
lon_inmax = len(isimip_lon) - 1
else:
lon_inmax = max(np.where((isimip_lon <= lon_max))[0]) + 1
lat_min = math.floor(min(lat_coordinates))
if lat_min <= -89:
lat_inmin = 0
else:
lat_inmin = min(np.where((isimip_lat >= lat_min))[0]) - 1
lat_max = math.ceil(max(lat_coordinates))
if lat_max >= 89:
lat_max = len(isimip_lat) - 1
else:
lat_inmax = max(np.where((isimip_lat <= lat_max))[0]) + 1
lon = isimip_lon[lon_inmin:lon_inmax]
lat = isimip_lat[lat_inmin:lat_inmax]
gridX, gridY = np.meshgrid(lon, lat)
lat = np.zeros((gridX.size))
lon = np.zeros((gridX.size))
lon = gridX.flatten()
lat = gridY.flatten()
centroids.set_lat_lon(lat, lon)
centroids.id = np.arange(centroids.coord.shape[0])
centroids.set_region_id()
return centroids
def _set_centroids_from_file(self, lon, lat):
self.centroids = Centroids()
gridX, gridY = np.meshgrid(lon, lat)
self.centroids.set_lat_lon(gridY.flatten(), gridX.flatten())
def _init_centroids(dis_xarray, centr_res_factor=1):
"""Get centroids from the firms dataset and refactor them.
Parameters:
dis_xarray (xarray): dataset obtained from ISIMIP netcdf
Optional Parameters:
centr_res_factor (float): the factor applied to voluntarly decrease/increase
the centroids resolution
Returns:
centroids (Centroids)
"""
res_data = np.min(
np.abs([
np.diff(dis_xarray.lon.values).min(),
np.diff(dis_xarray.lat.values).min()
]))
centroids = Centroids()
centroids.set_raster_from_pnt_bounds(
(dis_xarray.lon.values.min(), dis_xarray.lat.values.min(),
dis_xarray.lon.values.max(), dis_xarray.lat.values.max()),
res=res_data / centr_res_factor)
centroids.set_meta_to_lat_lon()
centroids.set_area_approx()
centroids.set_on_land()
centroids.empty_geometry_points()
return centroids
