def test_area_approx(self):
"""Test set_area_approx"""
centr = Centroids()
centr.lat, centr.lon, centr.geometry = self.data_vector()
centr.geometry.crs = {'init': 'epsg:4326'}
with self.assertRaises(ValueError):
centr.set_area_approx()
def test_area_approx(self):
""" Test set_area_approx """
centr_ras = Centroids()
centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60))
centr_ras.set_area_approx()
approx_dim = centr_ras.meta['transform'][0]*111*1000*centr_ras.meta['transform'][0]*111*1000
self.assertEqual(centr_ras.area_pixel.size, centr_ras.size)
self.assertEqual(np.unique(centr_ras.area_pixel).size, 60)
self.assertTrue(np.array_equal((approx_dim/np.unique(centr_ras.area_pixel)).astype(int),
np.ones(60)))
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 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
