def test_tag_regions_by_flood(self):
"""Regions can be tagged correctly with data from flood forecasts
"""
threshold = 0.3
label = 'affected'
tif_filename = convert_netcdf2tif(self.nc_filename, 24, verbose=False)
region_filename = os.path.join(TESTDATA, 'rw_jakarta_singlepart.shp')
grid = read_layer(tif_filename)
polygons = read_layer(region_filename)
res = tag_polygons_by_grid(polygons, grid,
threshold=threshold,
tag=label)
os.remove(tif_filename)
geom = res.get_geometry()
data = res.get_data()
# Check correctness of affected regions
affected_geom = []
affected_data = []
for i, d in enumerate(data):
if d[label]:
g = geom[i]
affected_geom.append(g)
affected_data.append(d)
assert len(affected_geom) == 37
assert len(affected_data) == 37
# Check that every grid point exceeding threshold lies inside
# one of the polygons marked as affected
P, V = grid.to_vector_points()
flooded_points_geom = []
flooded_points_data = []
for i, point in enumerate(P):
val = V[i]
if val > threshold:
# Point that is flooded must be in one of the tagged polygons
found = False
for polygon in affected_geom:
if is_inside_polygon(point, polygon):
found = True
msg = ('No affected polygon was found for point [%f, %f] '
'with value %f' % (point[0], point[1], val))
verify(found, msg)
# Collected flooded points for visualisation
flooded_points_geom.append(point)
flooded_points_data.append({'depth': val})
# To generate files for visual inspection.
# See
# https://raw.github.com/AIFDR/inasafe/master/files/flood_tagging_test.png
# https://github.com/AIFDR/inasafe/blob/master/files/flood_tagging_test.tgz
tmp_filename = unique_filename(prefix='grid', suffix='.tif')
grid.write_to_file(tmp_filename)
#print 'Grid written to ', tmp_filename
tmp_filename = unique_filename(prefix='regions', suffix='.shp')
res.write_to_file(tmp_filename)
#print 'Regions written to ', tmp_filename
tmp_filename = unique_filename(prefix='flooded_points', suffix='.shp')
v = Vector(geometry=flooded_points_geom, data=flooded_points_data)
v.write_to_file(tmp_filename)
def test_tag_regions_by_flood(self):
"""Regions can be tagged correctly with data from flood forecasts
"""
threshold = 0.3
label = 'affected'
tif_filename = convert_netcdf2tif(self.nc_filename, 24, verbose=False)
region_filename = os.path.join(TESTDATA, 'rw_jakarta_singlepart.shp')
grid = read_layer(tif_filename)
polygons = read_layer(region_filename)
res = tag_polygons_by_grid(polygons,
grid,
threshold=threshold,
tag=label)
os.remove(tif_filename)
geom = res.get_geometry()
data = res.get_data()
# Check correctness of affected regions
affected_geom = []
affected_data = []
for i, d in enumerate(data):
if d[label]:
g = geom[i]
affected_geom.append(g)
affected_data.append(d)
assert len(affected_geom) == 37
assert len(affected_data) == 37
# Check that every grid point exceeding threshold lies inside
# one of the polygons marked as affected
P, V = grid.to_vector_points()
flooded_points_geom = []
flooded_points_data = []
for i, point in enumerate(P):
val = V[i]
if val > threshold:
# Point that is flooded must be in one of the tagged polygons
found = False
for polygon in affected_geom:
if is_inside_polygon(point, polygon):
found = True
msg = ('No affected polygon was found for point [%f, %f] '
'with value %f' % (point[0], point[1], val))
verify(found, msg)
# Collected flooded points for visualisation
flooded_points_geom.append(point)
flooded_points_data.append({'depth': val})
# To generate files for visual inspection.
# See
# https://raw.github.com/AIFDR/inasafe/master/files/flood_tagging_test.png
# https://github.com/AIFDR/inasafe/blob/master/files/flood_tagging_test.tgz
tmp_filename = unique_filename(prefix='grid', suffix='.tif')
grid.write_to_file(tmp_filename)
#print 'Grid written to ', tmp_filename
tmp_filename = unique_filename(prefix='regions', suffix='.shp')
res.write_to_file(tmp_filename)
#print 'Regions written to ', tmp_filename
tmp_filename = unique_filename(prefix='flooded_points', suffix='.shp')
v = Vector(geometry=flooded_points_geom, data=flooded_points_data)
v.write_to_file(tmp_filename)
def test_convert_netcdf2tif(self):
"""NetCDF flood forecasts can be converted to tif
"""
# First check that input file is as expected
from Scientific.IO.NetCDF import NetCDFFile
fid = NetCDFFile(self.nc_filename)
x = fid.variables['x'][:] # Longitudes
y = fid.variables['y'][:] # Latitudes
inundation_depth = fid.variables['Inundation_Depth'][:]
T = inundation_depth.shape[0] # Number of time steps
M = inundation_depth.shape[1] # Steps in the y direction
N = inundation_depth.shape[2] # Steps in the x direction
assert T == 71
assert M == 162 # Latitudes
assert N == 160 # Longitudes
assert len(x) == N
assert len(y) == M
# Pick a max value in an area known to have flooding
# (approximately picked with ncview)
max_136_51 = max(inundation_depth[:, 136, 51])
assert numpy.allclose(max_136_51, 1.58)
#print max_136_51
#print 'y[136]', y[136] # Lat
#print 'x[51]', x[51] # Lon
assert numpy.allclose(x[51], 106.7777)
assert numpy.allclose(y[136], -6.124634)
# Run script over all hours
all_hours_tif = convert_netcdf2tif(self.nc_filename, T, verbose=False)
msg = 'Expected file %s did not exist' % all_hours_tif
assert os.path.isfile(all_hours_tif), msg
# Read resulting layer and check
L = read_layer(all_hours_tif)
D = L.get_data()
os.remove(all_hours_tif)
# Check point taking up-down flip into account
assert numpy.allclose(D[-136 - 1, 51], max_136_51)
# Run script for one hour and check first band
one_hour_tif = convert_netcdf2tif(self.nc_filename, 1, verbose=False)
D = read_layer(one_hour_tif).get_data()
assert numpy.allclose(max(D.flat), 0.74) # Checked band 1 with QGIS
# Characterisation test of location of max inundation
assert D[28, 53] == max(D.flat)
assert numpy.allclose(y[28], -6.3199)
assert numpy.allclose(x[53], 106.781)
os.remove(one_hour_tif)
return
def test_convert_netcdf2tif(self):
"""NetCDF flood forecasts can be converted to tif
"""
# First check that input file is as expected
from Scientific.IO.NetCDF import NetCDFFile
fid = NetCDFFile(self.nc_filename)
x = fid.variables['x'][:] # Longitudes
y = fid.variables['y'][:] # Latitudes
inundation_depth = fid.variables['Inundation_Depth'][:]
T = inundation_depth.shape[0] # Number of time steps
M = inundation_depth.shape[1] # Steps in the y direction
N = inundation_depth.shape[2] # Steps in the x direction
assert T == 71
assert M == 162 # Latitudes
assert N == 160 # Longitudes
assert len(x) == N
assert len(y) == M
# Pick a max value in an area known to have flooding
# (approximately picked with ncview)
max_136_51 = max(inundation_depth[:, 136, 51])
assert numpy.allclose(max_136_51, 1.58)
#print max_136_51
#print 'y[136]', y[136] # Lat
#print 'x[51]', x[51] # Lon
assert numpy.allclose(x[51], 106.7777)
assert numpy.allclose(y[136], -6.124634)
# Run script over all hours
all_hours_tif = convert_netcdf2tif(self.nc_filename, T, verbose=False)
msg = 'Expected file %s did not exist' % all_hours_tif
assert os.path.isfile(all_hours_tif), msg
# Read resulting layer and check
L = read_layer(all_hours_tif)
D = L.get_data()
os.remove(all_hours_tif)
# Check point taking up-down flip into account
assert numpy.allclose(D[-136 - 1, 51], max_136_51)
# Run script for one hour and check first band
one_hour_tif = convert_netcdf2tif(self.nc_filename, 1, verbose=False)
D = read_layer(one_hour_tif).get_data()
assert numpy.allclose(max(D.flat), 0.74) # Checked band 1 with QGIS
# Characterisation test of location of max inundation
assert D[28, 53] == max(D.flat)
assert numpy.allclose(y[28], -6.3199)
assert numpy.allclose(x[53], 106.781)
os.remove(one_hour_tif)
return