def _centroids_from_nc(file_name):
""" Construct Centroids from the grid described by 'latitude' and
'longitude' variables in a netCDF file.
"""
LOGGER.info('Constructing centroids from %s', file_name)
cent = Centroids()
ncdf = xr.open_dataset(file_name)
if hasattr(ncdf, 'latitude'):
lats = ncdf.latitude.data
lons = ncdf.longitude.data
elif hasattr(ncdf, 'lat'):
lats = ncdf.lat.data
lons = ncdf.lon.data
elif hasattr(ncdf, 'lat_1'):
lats = ncdf.lat_1.data
lons = ncdf.lon_1.data
else:
raise AttributeError('netcdf file has no field named latitude or '
'other know abrivation for coordinates.')
ncdf.close()
lats, lons = np.array(
[np.repeat(lats, len(lons)),
np.tile(lons, len(lats))])
cent = Centroids()
cent.set_lat_lon(lats, lons)
cent.set_area_pixel()
cent.set_on_land()
return cent
def test_on_land(self):
""" Test set_on_land """
centr_ras = Centroids()
centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60))
centr_ras.set_on_land()
centr_ras.check()
self.assertTrue(np.array_equal(centr_ras.on_land, np.ones(60 * 50, bool)))
def test_on_land(self):
"""Test set_on_land"""
centr = Centroids()
centr.lat, centr.lon, centr.geometry = self.data_vector()
centr.geometry.crs = {'init': 'epsg:4326'}
centr.set_on_land()
centr.set_region_id()
centr.region_id[centr.region_id > 0] = 1
self.assertTrue(
np.array_equal(centr.on_land.astype(int), centr.region_id))
def _centroids_creation(firms, centr_res_factor):
""" Compute centroids for the firms dataset.
The number of centroids is defined according to the data resolution.
Parameters:
firms (dataframe): dataset obtained from FIRMS data
centr_res_factor (float): the factor applied to voluntarly decrease/increase
the centroids resolution
Returns:
centroids (Centroids)
res_data (float): data resolution (km)
"""
LOGGER.info('Defining the resolution of the centroids.')
# Resolution of the centroids depends on the data origin.
# Resolution in km.
if 'instrument' in firms.columns:
if firms['instrument'].any() == 'MODIS':
res_data = 1.0
else:
res_data = 0.375 # For VIIRS data
else:
res_data = RES_DATA # For undefined data origin, defined by user
LOGGER.info('Computing centroids.')
centroids = Centroids()
dlat_km = abs(firms['latitude'].min() - firms['latitude'].max()) * ONE_LAT_KM
dlon_km = abs(firms['longitude'].min() - firms['longitude'].max()) * ONE_LAT_KM* \
np.cos(np.radians((abs(firms['latitude'].min() - firms['latitude'].max()))/2))
nb_centr_lat = int(dlat_km/res_data * centr_res_factor)
nb_centr_lon = int(dlon_km/res_data * centr_res_factor)
coord = (np.mgrid[firms['latitude'].min() : firms['latitude'].max() : \
complex(0, nb_centr_lat), firms['longitude'].min() : firms['longitude'].max() : \
complex(0, nb_centr_lon)]).reshape(2, nb_centr_lat*nb_centr_lon).transpose()
centroids.set_lat_lon(coord[:, 0], coord[:, 1])
# centroids.set_raster_from_pnt_bounds((firms['longitude'].min(),
# firms['latitude'].min(), firms['longitude'].max(), firms['latitude'].max()),
# res=res_data/centr_res_factor) ---> propagation works?
# Calculate the area attributed to each centroid
centroids.set_area_approx()
# Calculate if the centroids is on land or not
centroids.set_on_land()
# Calculate to distance to coast
centroids.set_dist_coast()
# Create on land grid
centroids.land = centroids.on_land.reshape((nb_centr_lat, nb_centr_lon)).astype(int)
centroids.nb_centr_lat = nb_centr_lat
centroids.nb_centr_lon = nb_centr_lon
centroids.empty_geometry_points()
return centroids, res_data
def _centroids_from_nc(file_name):
"""Construct Centroids from the grid described by 'latitude' and
'longitude' variables in a netCDF file.
"""
LOGGER.info('Constructing centroids from %s', file_name)
cent = Centroids()
ncdf = xr.open_dataset(file_name)
create_meshgrid = True
if hasattr(ncdf, 'latitude'):
lats = ncdf.latitude.data
lons = ncdf.longitude.data
elif hasattr(ncdf, 'lat'):
lats = ncdf.lat.data
lons = ncdf.lon.data
elif hasattr(ncdf, 'lat_1'):
if len(ncdf.lon_1.shape)>1 & \
(ncdf.lon_1.shape == ncdf.lat_1.shape) \
:
lats = ncdf.lat_1.data.flatten()
lons = ncdf.lon_1.data.flatten()
create_meshgrid = False
else:
lats = ncdf.lat_1.data
lons = ncdf.lon_1.data
elif hasattr(ncdf, 'clat'):
lats = ncdf.clat.data
lons = ncdf.clon.data
if ncdf.clat.attrs['units'] == 'radian':
lats = np.rad2deg(lats)
lons = np.rad2deg(lons)
create_meshgrid = False
else:
raise AttributeError('netcdf file has no field named latitude or '
'other know abrivation for coordinates.')
ncdf.close()
if create_meshgrid:
lats, lons = np.array(
[np.repeat(lats, len(lons)),
np.tile(lons, len(lats))])
cent = Centroids()
cent.set_lat_lon(lats, lons)
cent.set_area_pixel()
cent.set_on_land()
return cent
def test_elevation_3_pass(self):
""" test set_elevation """
centr = Centroids()
centr.lat, centr.lon, centr.geometry = self.data_vector()
centr.geometry.crs = DEF_CRS
centr.set_elevation(product='SRTM3', resampling=Resampling.nearest)
centr.set_on_land()
self.assertTrue(np.all(centr.elevation[centr.on_land] > 0))
self.assertTrue(
np.all(
centr.elevation[np.logical_not(centr.on_land)] == DEM_NODATA))
self.assertEqual(centr.elevation[0], 23)
self.assertEqual(centr.elevation[1], 92)
self.assertEqual(centr.elevation[2], 69)
self.assertEqual(centr.elevation[3], 77)
self.assertEqual(centr.elevation[4], 133)
self.assertEqual(centr.elevation[5], 41)
self.assertEqual(centr.elevation.min(), DEM_NODATA)
def def_ori_centroids(min_lon, min_lat, max_lon, max_lat, centr_res_factor):
res_data = 0.375 # For VIIRS data
centroids = Centroids()
dlat_km = abs(min_lat - max_lat) * ONE_LAT_KM
dlon_km = abs(min_lon - max_lon) * ONE_LAT_KM * np.cos(
np.radians((abs(min_lat - max_lat)) / 2))
nb_centr_lat = int(dlat_km / res_data * centr_res_factor)
nb_centr_lon = int(dlon_km / res_data * centr_res_factor)
coord = (np.mgrid[min_lat : max_lat : complex(0, nb_centr_lat), \
min_lon : max_lon : complex(0, nb_centr_lon)]). \
reshape(2, nb_centr_lat*nb_centr_lon).transpose()
centroids.set_lat_lon(coord[:, 0], coord[:, 1])
# Calculate the area attributed to each centroid
centroids.set_area_approx()
# Calculate if the centroids is on land or not
centroids.set_on_land()
# Create on land grid
centroids.empty_geometry_points()
return centroids
def _centroids_creation(firms, res_data, centr_res_factor):
""" Get centroids from the firms dataset and refactor them.
Parameters:
firms (DataFrame): dataset obtained from FIRMS data
res_data (float): FIRMS instrument resolution in degrees
centr_res_factor (float): the factor applied to voluntarly decrease/increase
the centroids resolution
Returns:
centroids (Centroids)
"""
centroids = Centroids()
centroids.set_raster_from_pnt_bounds((firms['longitude'].min(), \
firms['latitude'].min(), firms['longitude'].max(), \
firms['latitude'].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
def def_ori_centroids(firms, centr_res_factor):
# res_data in km
if firms['instrument'].any() == 'MODIS':
res_data = 1.0
else:
res_data = 0.375 # For VIIRS data
centroids = Centroids()
dlat_km = abs(firms['latitude'].min() -
firms['latitude'].max()) * ONE_LAT_KM
dlon_km = abs(firms['longitude'].min() - firms['longitude'].max()) * ONE_LAT_KM* \
np.cos(np.radians((abs(firms['latitude'].min() - firms['latitude'].max()))/2))
nb_centr_lat = int(dlat_km / res_data * centr_res_factor)
nb_centr_lon = int(dlon_km / res_data * centr_res_factor)
coord = (np.mgrid[firms['latitude'].min() : firms['latitude'].max() : complex(0, nb_centr_lat), \
firms['longitude'].min() : firms['longitude'].max() : complex(0, nb_centr_lon)]). \
reshape(2, nb_centr_lat*nb_centr_lon).transpose()
centroids.set_lat_lon(coord[:, 0], coord[:, 1])
centroids.set_area_approx()
centroids.set_on_land()
centroids.empty_geometry_points()
return centroids, res_data
