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)
args = parser.parse_args()
print args
print
tif_filename = convert_netcdf2tif(args.filename, args.hours,
verbose=True)
# Tag each polygon with Y if it contains at least one pixel
# exceeding a specific threshold (e.g. 0.3m).
if args.regions is not None:
print 'Tagging %s as "affected" or not' % args.regions
polygons = read_layer(args.regions)
grid = read_layer(tif_filename)
res = tag_polygons_by_grid(polygons, grid,
threshold=0.3,
tag='affected')
# Keep only those that are affected (speeds things up a lot,
# but will reduce overall bounding box for buildings under
# consideration)
# geom = res.get_geometry()
# data = res.get_data()
# new_geom = []
# new_data = []
#
# for i, d in enumerate(data):
# if d['affected']:
# g = geom[i]
# new_geom.append(g)
# new_data.append(d)
'flooded or not'))
args = parser.parse_args()
print args
print
tif_filename = convert_netcdf2tif(args.filename, args.hours, verbose=True)
# Tag each polygon with Y if it contains at least one pixel
# exceeding a specific threshold (e.g. 0.3m).
if args.regions is not None:
print 'Tagging %s as "affected" or not' % args.regions
polygons = read_layer(args.regions)
grid = read_layer(tif_filename)
res = tag_polygons_by_grid(polygons,
grid,
threshold=0.3,
tag='affected')
# Keep only those that are affected (speeds things up a lot,
# but will reduce overall bounding box for buildings under
# consideration)
#geom = res.get_geometry()
#data = res.get_data()
#new_geom = []
#new_data = []
#
#for i, d in enumerate(data):
# if d['affected']:
# g = geom[i]
# new_geom.append(g)
# new_data.append(d)
def process_flood_event(netcdf_file=None, hours=24):
"""A function to process this_netcdf_file to a forecast file.
:param netcdf_file: The netcdf file. If it's None the download it.
:param hours: Positive integer determining how many bands to use.
:type hours: int
"""
print 'Start flood forecasting'
if netcdf_file is None:
# retrieve data from the web
netcdf_file = download_file_url(netcdf_url, forecast_directory)
else:
netcdf_file = download_file_url(netcdf_url,
name=netcdf_file,
download_directory=forecast_directory)
print 'Do flood forecasting for %s ...' % netcdf_file
## check if a forecasting file has been created or not
# is_exist, polyforecast_filepath = get_result_file_name(this_netcdf_file,
# hours)
#
#if is_exist:
# print 'Current flood forecasting has been already created.'
# print 'You can look it at %s' % polyforecast_filepath
# return
# convert to tif
# tif_file = polyforecast_filepath.replace('_regions.shp', '.tif')
tif_filename = convert_netcdf2tif(netcdf_file,
hours,
verbose=False,
output_dir=flood_directory)
print 'tif_file', tif_filename
tif_file = read_layer(tif_filename)
# check if there is another file with the same name
# if so, do not do the forecasting
polyforecast_filepath = tif_filename.replace('.tif', '_regions.shp')
zip_filename = polyforecast_filepath.replace('.shp', '.zip')
if os.path.isfile(zip_filename):
print('File %s is exist, so we do not do the forecasting' %
zip_filename)
else:
polygons = read_layer(polygons_path)
result = tag_polygons_by_grid(polygons,
tif_file,
threshold=0.3,
tag='affected')
new_geom = result.get_geometry()
new_data = result.get_data()
date = os.path.split(netcdf_file)[-1].split('_')[0]
v = Vector(geometry=new_geom,
data=new_data,
projection=result.projection,
keywords={
'category':
'hazard',
'subcategory':
'flood',
'title': ('%d hour flood forecast regions '
'in Jakarta at %s' % (hours, date))
})
print 'polyforecast_filepath', polyforecast_filepath
v.write_to_file(polyforecast_filepath)
print 'Wrote tagged polygons to %s' % polyforecast_filepath
# zip all file
if os.path.isfile(zip_filename):
print 'Has been zipped to %s' % zip_filename
else:
zip_shp(polyforecast_filepath,
extra_ext=['.keywords'],
remove_file=True)
print 'Zipped to %s' % zip_filename
def processFloodEvent(netcdf_file=None, hours=24):
"""A function to process netcdf_file to a forecast file.
"""
print 'Start flood forecasting'
if netcdf_file is None:
# retrieve data from the web
netcdf_file = download_file_url(netcdf_url, forecast_directory)
else:
netcdf_file = download_file_url(netcdf_url, name=netcdf_file,
download_directory=forecast_directory)
print 'Do flood forecasting for %s ...' % netcdf_file
# # check if a forecasting file has been created or not
# is_exist, polyforecast_filepath = get_result_file_name(netcdf_file, hours)
#
# if is_exist:
# print 'Current flood forecasting has been already created.'
# print 'You can look it at %s' % polyforecast_filepath
# return
# convert to tif
# tif_file = polyforecast_filepath.replace('_regions.shp', '.tif')
tif_filename = convert_netcdf2tif(netcdf_file, hours,
verbose=False, output_dir=flood_directory)
print 'tif_file', tif_filename
tif_file = read_layer(tif_filename)
# check if there is another file with the same name
# if so, do not do the forecasting
polyforecast_filepath = tif_filename.replace('.tif', '_regions.shp')
zip_filename = polyforecast_filepath.replace('.shp', '.zip')
if os.path.isfile(zip_filename):
print ('File %s is exist, so we do not do the forecasting'
% zip_filename)
else:
my_polygons = read_layer(polygons_path)
my_result = tag_polygons_by_grid(my_polygons, tif_file, threshold=0.3,
tag='affected')
new_geom = my_result.get_geometry()
new_data = my_result.get_data()
date = os.path.split(netcdf_file)[-1].split('_')[0]
v = Vector(geometry=new_geom, data=new_data,
projection=my_result.projection,
keywords={'category': 'hazard',
'subcategory': 'flood',
'title': ('%d hour flood forecast regions '
'in Jakarta at %s' % (hours,
date))})
print 'polyforecast_filepath', polyforecast_filepath
v.write_to_file(polyforecast_filepath)
print 'Wrote tagged polygons to %s' % polyforecast_filepath
# zip all file
if os.path.isfile(zip_filename):
print 'Has been zipped to %s' % zip_filename
else:
zip_shp(polyforecast_filepath, extra_ext=['.keywords'],
remove_file=True)
print 'Zipped to %s' % zip_filename
"--regions", metavar="regions", type=str, help=("Administrative areas to be flagged as " "flooded or not")
)
args = parser.parse_args()
print args
print
tif_filename = convert_netcdf2tif(args.filename, args.hours)
# Tag each polygon with Y if it contains at least one pixel
# exceeding a specific threshold (e.g. 0.3m).
if args.regions is not None:
print 'Tagging %s as "Flooded" or not' % args.regions
polygons = read_layer(args.regions)
grid = read_layer(tif_filename)
res = tag_polygons_by_grid(polygons, grid, threshold=0.3, tag="Flooded")
# Keep only those that are affected (speeds things up a lot,
# but will reduce overall bounding box for buildings under
# consideration)
# geom = res.get_geometry()
# data = res.get_data()
# new_geom = []
# new_data = []
#
# for i, d in enumerate(data):
# if d['Flooded']:
# g = geom[i]
# new_geom.append(g)
# new_data.append(d)
'flooded or not'))
args = parser.parse_args()
print args
print
tif_filename = convert_netcdf2tif(args.filename, args.hours)
# Tag each polygon with Y if it contains at least one pixel
# exceeding a specific threshold (e.g. 0.3m).
if args.regions is not None:
print 'Tagging %s as "Flooded" or not' % args.regions
polygons = read_layer(args.regions)
grid = read_layer(tif_filename)
res = tag_polygons_by_grid(polygons,
grid,
threshold=0.3,
tag='Flooded')
# Keep only those that are affected (speeds things up a lot,
# but will reduce overall bounding box for buildings under
# consideration)
#geom = res.get_geometry()
#data = res.get_data()
#new_geom = []
#new_data = []
#
#for i, d in enumerate(data):
# if d['Flooded']:
# g = geom[i]
# new_geom.append(g)
# new_data.append(d)
