def test_native_raster_resolution(self):
"""Raster layer retains native resolution through Geoserver
Raster layer can be uploaded and downloaded again with
native resolution. This is one test for ticket #103
"""
hazard_filename = os.path.join(UNITDATA, 'hazard',
'jakarta_flood_design.tif')
# Get reference values
H = read_layer(hazard_filename)
A_ref = H.get_data(nan=True)
depth_min_ref, depth_max_ref = H.get_extrema()
# Upload to internal geonode
hazard_layer = save_to_geonode(hazard_filename, user=self.user)
hazard_name = '%s:%s' % (hazard_layer.workspace, hazard_layer.name)
# Download data again with native resolution
bbox = get_bounding_box_string(hazard_filename)
H = download(INTERNAL_SERVER_URL, hazard_name, bbox)
A = H.get_data(nan=True)
# Compare shapes
msg = ('Shape of downloaded raster was [%i, %i]. '
'Expected [%i, %i].' %
(A.shape[0], A.shape[1], A_ref.shape[0], A_ref.shape[1]))
assert numpy.allclose(A_ref.shape, A.shape, rtol=0, atol=0), msg
# Compare extrema to values reference values (which have also been
# verified by QGIS for this layer and tested in test_engine.py)
depth_min, depth_max = H.get_extrema()
msg = ('Extrema of downloaded file were [%f, %f] but '
'expected [%f, %f]' %
(depth_min, depth_max, depth_min_ref, depth_max_ref))
assert numpy.allclose([depth_min, depth_max],
[depth_min_ref, depth_max_ref],
rtol=1.0e-6,
atol=1.0e-10), msg
# Compare data number by number
assert nanallclose(A, A_ref, rtol=1.0e-8)
def test_native_raster_resolution(self):
"""Raster layer retains native resolution through Geoserver
Raster layer can be uploaded and downloaded again with
native resolution. This is one test for ticket #103
"""
hazard_filename = os.path.join(UNITDATA, 'hazard', 'jakarta_flood_design.tif')
# Get reference values
H = read_layer(hazard_filename)
A_ref = H.get_data(nan=True)
depth_min_ref, depth_max_ref = H.get_extrema()
# Upload to internal geonode
hazard_layer = save_to_geonode(hazard_filename, user=self.user)
hazard_name = '%s:%s' % (hazard_layer.workspace, hazard_layer.name)
# Download data again with native resolution
bbox = get_bounding_box_string(hazard_filename)
H = download(INTERNAL_SERVER_URL, hazard_name, bbox)
A = H.get_data(nan=True)
# Compare shapes
msg = ('Shape of downloaded raster was [%i, %i]. '
'Expected [%i, %i].' % (A.shape[0], A.shape[1],
A_ref.shape[0], A_ref.shape[1]))
assert numpy.allclose(A_ref.shape, A.shape, rtol=0, atol=0), msg
# Compare extrema to values reference values (which have also been
# verified by QGIS for this layer and tested in test_engine.py)
depth_min, depth_max = H.get_extrema()
msg = ('Extrema of downloaded file were [%f, %f] but '
'expected [%f, %f]' % (depth_min, depth_max,
depth_min_ref, depth_max_ref))
assert numpy.allclose([depth_min, depth_max],
[depth_min_ref, depth_max_ref],
rtol=1.0e-6, atol=1.0e-10), msg
# Compare data number by number
assert nanallclose(A, A_ref, rtol=1.0e-8)
def calculate(request, save_output=save_file_to_geonode):
start = datetime.datetime.now()
if request.method == 'GET':
# FIXME: Add a basic form here to be able to generate the POST request.
return HttpResponse('This should be accessed by robots, not humans.'
'In other words using HTTP POST instead of GET.')
elif request.method == 'POST':
data = request.POST
impact_function_name = data['impact_function']
hazard_server = data['hazard_server']
hazard_layer = data['hazard']
exposure_server = data['exposure_server']
exposure_layer = data['exposure']
requested_bbox = data['bbox']
keywords = data['keywords']
if request.user.is_anonymous():
theuser = get_valid_user()
else:
theuser = request.user
# Create entry in database
calculation = Calculation(user=theuser,
run_date=start,
hazard_server=hazard_server,
hazard_layer=hazard_layer,
exposure_server=exposure_server,
exposure_layer=exposure_layer,
impact_function=impact_function_name,
success=False)
# Wrap main computation loop in try except to catch and present
# messages and stack traces in the application
try:
# Get metadata
haz_metadata = get_metadata(hazard_server, hazard_layer)
exp_metadata = get_metadata(exposure_server, exposure_layer)
# Determine common resolution in case of raster layers
raster_resolution = get_common_resolution(haz_metadata, exp_metadata)
# Get reconciled bounding boxes
haz_bbox, exp_bbox, imp_bbox = get_bounding_boxes(haz_metadata,
exp_metadata,
requested_bbox)
# Record layers to download
download_layers = [(hazard_server, hazard_layer, haz_bbox),
(exposure_server, exposure_layer, exp_bbox)]
# Add linked layers if any FIXME: STILL TODO!
# Get selected impact function
plugins = get_admissible_plugins()
msg = ('Could not find "%s" in "%s"' % (
impact_function_name, plugins.keys()))
assert impact_function_name in plugins, msg
impact_function = plugins.get(impact_function_name)
impact_function_source = inspect.getsource(impact_function)
# Record information calculation object and save it
calculation.impact_function_source = impact_function_source
calculation.bbox = bboxlist2string(imp_bbox)
calculation.save()
# Start computation
msg = 'Performing requested calculation'
#logger.info(msg)
# Download selected layer objects
layers = []
for server, layer_name, bbox in download_layers:
msg = ('- Downloading layer %s from %s'
% (layer_name, server))
#logger.info(msg)
L = download(server, layer_name, bbox, raster_resolution)
layers.append(L)
# Calculate result using specified impact function
msg = ('- Calculating impact using %s' % impact_function_name)
#logger.info(msg)
impact_file = calculate_impact(layers=layers,
impact_fcn=impact_function)
# Upload result to internal GeoServer
msg = ('- Uploading impact layer %s' % impact_file.name)
# Determine layer title for upload
output_kw = impact_file.get_keywords()
title = impact_file.get_name() + " using " + output_kw['hazard_title'] + \
" and " + output_kw['exposure_title']
result = save_output(impact_file.filename,
title=title,
user=theuser, overwrite=False)
except Exception, e:
# FIXME: Reimplement error saving for calculation.
# FIXME (Ole): Why should we reimplement?
# This is dangerous. Try to raise an exception
# e.g. in get_metadata_from_layer. Things will silently fail.
# See issue #170
#logger.error(e)
errors = e.__str__()
trace = exception_format(e)
calculation.errors = errors
calculation.stacktrace = trace
calculation.save()
jsondata = json.dumps({'errors': errors, 'stacktrace': trace})
return HttpResponse(jsondata, mimetype='application/json')
def test_the_earthquake_fatality_estimation_allen(self):
"""Fatality computation computed correctly with GeoServer Data
"""
# Simulate bounding box from application
viewport_bbox_string = '104.3,-8.2,110.04,-5.17'
# Upload exposure data for this test
name = 'Population_2010'
exposure_filename = '%s/%s.asc' % (TESTDATA, name)
exposure_layer = save_to_geonode(exposure_filename,
user=self.user, overwrite=True)
workspace = exposure_layer.workspace
layer_name = exposure_layer.name
msg = 'Expected layer name to be "%s". Got %s' % (name, layer_name)
assert layer_name == name.lower(), msg
exposure_name = '%s:%s' % (workspace, layer_name)
# Check metadata
assert_bounding_box_matches(exposure_layer, exposure_filename)
exp_bbox_string = get_bounding_box_string(exposure_filename)
check_layer(exposure_layer, full=True)
# Now we know that exposure layer is good, lets upload some
# hazard layers and do the calculations
filename = 'lembang_mmi_hazmap.asc'
# Save
hazard_filename = '%s/%s' % (TESTDATA, filename)
hazard_layer = save_to_geonode(hazard_filename,
user=self.user, overwrite=True)
hazard_name = '%s:%s' % (hazard_layer.workspace,
hazard_layer.name)
# Check metadata
assert_bounding_box_matches(hazard_layer, hazard_filename)
haz_bbox_string = get_bounding_box_string(hazard_filename)
check_layer(hazard_layer, full=True)
calculate_url = reverse('safe-calculate')
# Run calculation
c = Client()
rv = c.post(calculate_url, data=dict(
hazard_server=INTERNAL_SERVER_URL,
hazard=hazard_name,
exposure_server=INTERNAL_SERVER_URL,
exposure=exposure_name,
#bbox=viewport_bbox_string,
bbox=exp_bbox_string, # This one reproduced the
# crash for lembang
impact_function='I T B Fatality Function',
keywords='test,shakemap,usgs'))
self.assertEqual(rv.status_code, 200)
self.assertEqual(rv['Content-Type'], 'application/json')
data = json.loads(rv.content)
if 'errors' in data:
errors = data['errors']
if errors is not None:
msg = ('The server returned the error message: %s'
% str(errors))
raise Exception(msg)
assert 'success' in data
assert 'hazard_layer' in data
assert 'exposure_layer' in data
assert 'run_duration' in data
assert 'run_date' in data
assert 'layer' in data
assert data['success']
# Download result and check
layer = data['layer']
layer_id = layer['id']
result_layer = download(INTERNAL_SERVER_URL,
layer_id,
get_bounding_box_string(hazard_filename))
assert os.path.exists(result_layer.filename)
def test_earthquake_exposure_plugin(self):
"""Population exposure to individual MMI levels can be computed
"""
# Upload exposure data for this test
# FIXME (Ole): While this dataset is ok for testing,
# note that is has been resampled without scaling
# so numbers are about 25 times too large.
# Consider replacing test populations dataset for good measures,
# just in case any one accidentally started using this dataset
# for real.
name = 'Population_2010'
exposure_filename = '%s/%s.asc' % (TESTDATA, name)
exposure_layer = save_to_geonode(exposure_filename,
user=self.user, overwrite=True)
exposure_name = '%s:%s' % (exposure_layer.workspace,
exposure_layer.name)
# Check metadata
assert_bounding_box_matches(exposure_layer, exposure_filename)
exp_bbox_string = get_bounding_box_string(exposure_filename)
check_layer(exposure_layer, full=True)
# Upload hazard data
filename = 'lembang_mmi_hazmap.asc'
hazard_filename = '%s/%s' % (TESTDATA, filename)
hazard_layer = save_to_geonode(hazard_filename,
user=self.user, overwrite=True)
hazard_name = '%s:%s' % (hazard_layer.workspace,
hazard_layer.name)
# Check metadata
assert_bounding_box_matches(hazard_layer, hazard_filename)
haz_bbox_string = get_bounding_box_string(hazard_filename)
check_layer(hazard_layer, full=True)
calculate_url = reverse('safe-calculate')
# Run calculation
c = Client()
rv = c.post(calculate_url, data=dict(
hazard_server=INTERNAL_SERVER_URL,
hazard=hazard_name,
exposure_server=INTERNAL_SERVER_URL,
exposure=exposure_name,
bbox=haz_bbox_string,
impact_function='Earthquake Building Damage Function',
keywords='test,population,exposure,usgs'))
self.assertEqual(rv.status_code, 200)
self.assertEqual(rv['Content-Type'], 'application/json')
data = json.loads(rv.content)
if 'errors' in data:
errors = data['errors']
if errors is not None:
msg = ('The server returned the error message: %s'
% str(errors))
raise Exception(msg)
assert 'success' in data
assert 'hazard_layer' in data
assert 'exposure_layer' in data
assert 'run_duration' in data
assert 'run_date' in data
assert 'layer' in data
assert data['success']
# Download result and check
layer_name = data['layer'].split('/')[-1]
result_layer = download(INTERNAL_SERVER_URL,
layer_name,
get_bounding_box_string(hazard_filename))
assert os.path.exists(result_layer.filename)
# Check calculated values
keywords = result_layer.get_keywords()
assert 'mmi-classes' in keywords
assert 'affected-population' in keywords
mmi_classes = [int(x) for x in keywords['mmi-classes'].split('_')]
count = [float(x) for x in keywords['affected-population'].split('_')]
# Brute force count for each population level
population = download(INTERNAL_SERVER_URL,
exposure_name,
get_bounding_box_string(hazard_filename))
intensity = download(INTERNAL_SERVER_URL,
hazard_name,
get_bounding_box_string(hazard_filename))
# Extract data
H = intensity.get_data(nan=0)
P = population.get_data(nan=0)
brutecount = {}
for mmi in mmi_classes:
brutecount[mmi] = 0
for i in range(P.shape[0]):
for j in range(P.shape[1]):
mmi = H[i, j]
if not numpy.isnan(mmi):
mmi_class = int(round(mmi))
pop = P[i, j]
if not numpy.isnan(pop):
brutecount[mmi_class] += pop
for i, mmi in enumerate(mmi_classes):
assert numpy.allclose(count[i], brutecount[mmi], rtol=1.0e-6)
def test_data_resampling_example(self):
"""Raster data is unchanged when going through geonode
"""
# Name file names for hazard level, exposure and expected fatalities
hazard_filename = os.path.join(TESTDATA, '..', 'hazard', 'maumere_aos_depth_20m_land_wgs84.asc')
exposure_filename = os.path.join(TESTDATA, 'maumere_pop_prj.shp')
#------------
# Hazard data
#------------
# Read hazard input data for reference
H_ref = read_layer(hazard_filename)
A_ref = H_ref.get_data()
depth_min_ref, depth_max_ref = H_ref.get_extrema()
# Upload to internal geonode
hazard_layer = save_to_geonode(hazard_filename, user=self.user)
hazard_name = '%s:%s' % (hazard_layer.workspace, hazard_layer.name)
# Download data again
bbox = get_bounding_box_string(hazard_filename) # The biggest
H = download(INTERNAL_SERVER_URL, hazard_name, bbox)
A = H.get_data()
depth_min, depth_max = H.get_extrema()
# FIXME (Ole): The layer read from file is single precision only:
# Issue #17
# Here's the explanation why interpolation below produce slightly
# different results (but why?)
# The layer read from file is single precision which may be due to
# the way it is converted from ASC to TIF. In other words the
# problem may be in raster.write_to_file. Float64 is
# specified there, so this is a mystery.
#print 'A', A.dtype # Double precision
#print 'A_ref', A_ref.dtype # Single precision
# Compare extrema to values from numpy array
assert numpy.allclose(depth_max, numpy.nanmax(A),
rtol=1.0e-12, atol=1.0e-12)
assert numpy.allclose(depth_max_ref, numpy.nanmax(A_ref),
rtol=1.0e-12, atol=1.0e-12)
# Compare to reference
assert numpy.allclose([depth_min, depth_max],
[depth_min_ref, depth_max_ref],
rtol=1.0e-12, atol=1.0e-12)
# Compare extrema to values read off QGIS for this layer
assert numpy.allclose([depth_min, depth_max], [0.0, 16.68],
rtol=1.0e-6, atol=1.0e-10)
# Investigate difference visually
#from matplotlib.pyplot import matshow, show
#matshow(A)
#matshow(A_ref)
#matshow(A - A_ref)
#show()
#print
for i in range(A.shape[0]):
for j in range(A.shape[1]):
if not numpy.isnan(A[i, j]):
err = abs(A[i, j] - A_ref[i, j])
if err > 0:
msg = ('%i, %i: %.15f, %.15f, %.15f'
% (i, j, A[i, j], A_ref[i, j], err))
raise Exception(msg)
#if A[i,j] > 16:
# print i, j, A[i, j], A_ref[i, j]
# Compare elements (nan & numbers)
id_nan = numpy.isnan(A)
id_nan_ref = numpy.isnan(A_ref)
assert numpy.all(id_nan == id_nan_ref)
assert numpy.allclose(A[-id_nan], A_ref[-id_nan],
rtol=1.0e-15, atol=1.0e-15)
#print 'MAX', A[245, 283], A_ref[245, 283]
#print 'MAX: %.15f %.15f %.15f' %(A[245, 283], A_ref[245, 283])
assert numpy.allclose(A[245, 283], A_ref[245, 283],
rtol=1.0e-15, atol=1.0e-15)
#--------------
# Exposure data
#--------------
# Read exposure input data for reference
E_ref = read_layer(exposure_filename)
# Upload to internal geonode
exposure_layer = save_to_geonode(exposure_filename, user=self.user)
exposure_name = '%s:%s' % (exposure_layer.workspace,
exposure_layer.name)
# Download data again
E = download(INTERNAL_SERVER_URL, exposure_name, bbox)
# Check exposure data against reference
coordinates = E.get_geometry()
coordinates_ref = E_ref.get_geometry()
assert numpy.allclose(coordinates, coordinates_ref,
rtol=1.0e-12, atol=1.0e-12)
attributes = E.get_data()
attributes_ref = E_ref.get_data()
for i, att in enumerate(attributes):
att_ref = attributes_ref[i]
for key in att:
assert att[key] == att_ref[key]
# Test safe's interpolation function
I = assign_hazard_values_to_exposure_data(H, E, attribute_name='depth')
icoordinates = I.get_geometry()
I_ref = assign_hazard_values_to_exposure_data(H_ref, E_ref, attribute_name='depth')
icoordinates_ref = I_ref.get_geometry()
assert numpy.allclose(coordinates,
icoordinates,
rtol=1.0e-12, atol=1.0e-12)
assert numpy.allclose(coordinates,
icoordinates_ref,
rtol=1.0e-12, atol=1.0e-12)
iattributes = I.get_data()
assert numpy.allclose(icoordinates, coordinates)
N = len(icoordinates)
assert N == 891
# Set tolerance for single precision until issue #17 has been fixed
# It appears that the single precision leads to larger interpolation
# errors
rtol_issue17 = 2.0e-3
atol_issue17 = 1.0e-4
# Verify interpolated values with test result
for i in range(N):
interpolated_depth_ref = I_ref.get_data()[i]['depth']
interpolated_depth = iattributes[i]['depth']
assert nanallclose(interpolated_depth,
interpolated_depth_ref,
rtol=rtol_issue17, atol=atol_issue17)
pointid = attributes[i]['POINTID']
if pointid == 263:
#print i, pointid, attributes[i],
#print interpolated_depth, coordinates[i]
# Check that location is correct
assert numpy.allclose(coordinates[i],
[122.20367299, -8.61300358],
rtol=1.0e-7, atol=1.0e-12)
# This is known to be outside inundation area so should
# near zero
assert numpy.allclose(interpolated_depth, 0.0,
rtol=1.0e-12, atol=1.0e-12)
if pointid == 148:
# Check that location is correct
#print coordinates[i]
assert numpy.allclose(coordinates[i],
[122.2045912, -8.608483265],
rtol=1.0e-7, atol=1.0e-12)
# This is in an inundated area with a surrounding depths of
# 4.531, 3.911
# 2.675, 2.583
assert interpolated_depth < 4.531
assert interpolated_depth < 3.911
assert interpolated_depth > 2.583
assert interpolated_depth > 2.675
#print interpolated_depth
# This is a characterisation test for bilinear interpolation
assert numpy.allclose(interpolated_depth, 3.62477215491,
rtol=rtol_issue17, atol=1.0e-12)
# Check that interpolated points are within range
msg = ('Interpolated depth %f at point %i was outside extrema: '
'[%f, %f]. ' % (interpolated_depth, i,
depth_min, depth_max))
if not numpy.isnan(interpolated_depth):
assert depth_min <= interpolated_depth <= depth_max, msg
def test_keywords_download(self):
"""Keywords are downloaded from GeoServer along with layer data
"""
# Upload test data
filenames = ['padang_tsunami_mw8.tif',]
filenames = ['jakarta_flood_design.tif',]
layers = []
paths = []
for filename in filenames:
basename, ext = os.path.splitext(filename)
path = os.path.join(UNITDATA, 'hazard', filename)
# Upload to GeoNode
layer = save_to_geonode(path, user=self.user, overwrite=True)
# Record layer and file
layers.append(layer)
paths.append(path)
# Check integrity
for i, layer in enumerate(layers):
# Get reference keyword dictionary from file
L = read_layer(paths[i])
ref_keywords = L.get_keywords()
# Get keywords metadata from GeoServer
layer_name = '%s:%s' % (layer.workspace, layer.name)
metadata = get_metadata(INTERNAL_SERVER_URL,
layer_name)
assert 'keywords' in metadata
geo_keywords = metadata['keywords']
msg = ('Uploaded keywords were not as expected: I got %s '
'but expected %s' % (geo_keywords, ref_keywords))
for kw in ref_keywords:
# Check that all keywords were uploaded
# It is OK for new automatic keywords to have appeared
# (e.g. resolution) - see issue #171
assert kw in geo_keywords, msg
assert ref_keywords[kw] == geo_keywords[kw], msg
# Download data
bbox = get_bounding_box_string(paths[i])
H = download(INTERNAL_SERVER_URL, layer_name, bbox)
dwn_keywords = H.get_keywords()
msg = ('Downloaded keywords were not as expected: I got %s '
'but expected %s' % (dwn_keywords, geo_keywords))
assert geo_keywords == dwn_keywords, msg
# Check that the layer and its .keyword file is there.
msg = 'Downloaded layer %s was not found' % H.filename
assert os.path.isfile(H.filename), msg
kw_filename = os.path.splitext(H.filename)[0] + '.keywords'
msg = 'Downloaded keywords file %s was not found' % kw_filename
assert os.path.isfile(kw_filename), msg
# Check that keywords are OK when reading downloaded file
L = read_layer(H.filename)
read_keywords = L.get_keywords()
msg = ('Keywords in downloaded file %s were not as expected: '
'I got %s but expected %s'
% (kw_filename, read_keywords, geo_keywords))
assert read_keywords == geo_keywords, msg
def test_raster_scaling(self):
"""Raster layers can be scaled when resampled
This is a test for ticket #168
Native test .asc data has
ncols 5525
nrows 2050
cellsize 0.0083333333333333
Scaling is necessary for raster data that represents density
such as population per km^2
"""
for test_filename in ['jakarta_flood_design.tif']:
raster_filename = os.path.join(UNITDATA, 'hazard', test_filename)
# Get reference values
R = read_layer(raster_filename)
R_min_ref, R_max_ref = R.get_extrema()
native_resolution = R.get_resolution()
# Upload to internal geonode
raster_layer = save_to_geonode(raster_filename, user=self.user)
raster_name = '%s:%s' % (raster_layer.workspace,
raster_layer.name)
# Test for a range of resolutions
for res in [0.01, 0.005, 0.002, 0.001, 0.0005, 0.0002]:
bbox = get_bounding_box_string(raster_filename)
R = download(INTERNAL_SERVER_URL, raster_name,
bbox, resolution=res)
A_native = R.get_data(scaling=False)
A_scaled = R.get_data(scaling=True)
sigma = (R.get_resolution()[0] / native_resolution[0]) ** 2
# Compare extrema
expected_scaled_max = sigma * numpy.nanmax(A_native)
msg = ('Resampled raster was not rescaled correctly: '
'max(A_scaled) was %f but expected %f'
% (numpy.nanmax(A_scaled), expected_scaled_max))
assert numpy.allclose(expected_scaled_max,
numpy.nanmax(A_scaled),
rtol=1.0e0, atol=1.0e-1), msg
expected_scaled_min = sigma * numpy.nanmin(A_native)
msg = ('Resampled raster was not rescaled correctly: '
'min(A_scaled) was %f but expected %f'
% (numpy.nanmin(A_scaled), expected_scaled_min))
assert numpy.allclose(expected_scaled_min,
numpy.nanmin(A_scaled),
rtol=1.0e0, atol=1.0e-1), msg
# Compare elementwise
msg = 'Resampled raster was not rescaled correctly'
assert nanallclose(A_native * sigma, A_scaled,
rtol=1.0e0, atol=1.0e-1), msg
# Check that it also works with manual scaling
A_manual = R.get_data(scaling=sigma)
msg = 'Resampled raster was not rescaled correctly'
assert nanallclose(A_manual, A_scaled,
rtol=1.0e0, atol=1.0e-1), msg
# Check that an exception is raised for bad arguments
try:
R.get_data(scaling='bad')
except:
pass
else:
msg = 'String argument should have raised exception'
raise Exception(msg)
try:
R.get_data(scaling='(1, 3)')
except:
pass
else:
msg = 'Tuple argument should have raised exception'
raise Exception(msg)
# Check None option without existence of density keyword
A_none = R.get_data(scaling=None)
msg = 'Data should not have changed'
assert nanallclose(A_native, A_none,
rtol=1.0e-0, atol=1.0e-1), msg
# Try with None and density keyword
R.keywords['density'] = 'true'
A_none = R.get_data(scaling=None)
msg = 'Resampled raster was not rescaled correctly'
assert nanallclose(A_scaled, A_none,
rtol=1.0e2, atol=1.0e2), msg
R.keywords['density'] = 'Yes'
A_none = R.get_data(scaling=None)
msg = 'Resampled raster was not rescaled correctly'
assert nanallclose(A_scaled, A_none,
rtol=1.0e2, atol=1.0e2), msg
R.keywords['density'] = 'False'
A_none = R.get_data(scaling=None)
msg = 'Data should not have changed'
assert nanallclose(A_native, A_none,
rtol=1.0e2, atol=1.0e2), msg
R.keywords['density'] = 'no'
A_none = R.get_data(scaling=None)
msg = 'Data should not have changed'
assert nanallclose(A_native, A_none,
rtol=1.0e2, atol=1.0e2), msg
def test_specified_raster_resolution(self):
"""Raster layers can be downloaded with specific resolution
This is another test for ticket #103
Native test data:
maumere....asc
ncols 931
nrows 463
cellsize 0.00018
Population_Jakarta
ncols 638
nrows 649
cellsize 0.00045228819716044
Population_2010
ncols 5525
nrows 2050
cellsize 0.0083333333333333
Here we download it at a range of fixed resolutions that
are both coarser and finer, and check that the dimensions
of the downloaded matrix are as expected.
We also check that the extrema of the subsampled matrix are sane
"""
hazard_filename = os.path.join(UNITDATA, 'hazard', 'jakarta_flood_design.tif')
# Get reference values
H = read_layer(hazard_filename)
depth_min_ref, depth_max_ref = H.get_extrema()
native_resolution = H.get_resolution()
# Upload to internal geonode
hazard_layer = save_to_geonode(hazard_filename, user=self.user)
hazard_name = '%s:%s' % (hazard_layer.workspace,
hazard_layer.name)
# Test for a range of resolutions
for res in [0.01, 0.005, 0.002, 0.001, 0.0005, # Coarser
0.0002, 0.0001, 0.00006, 0.00003]: # Finer
# Set bounding box
bbox = get_bounding_box_string(hazard_filename)
compare_extrema = True
bb = bboxstring2list(bbox)
# Download data at specified resolution
H = download(INTERNAL_SERVER_URL, hazard_name,
bbox, resolution=res)
A = H.get_data()
# Verify that data has the requested bobx and resolution
actual_bbox = H.get_bounding_box()
msg = ('Bounding box for %s was not as requested. I got %s '
'but '
'expected %s' % (hazard_name, actual_bbox, bb))
assert numpy.allclose(actual_bbox, bb, rtol=1.0e-6)
# FIXME (Ole): How do we sensibly resolve the issue with
# resx, resy vs one resolution (issue #173)
actual_resolution = H.get_resolution()[0]
# FIXME (Ole): Resolution is often far from the requested
# see issue #102
# Here we have to accept up to 5%
tolerance102 = 5.0e-2
msg = ('Resolution of %s was not as requested. I got %s but '
'expected %s' % (hazard_name, actual_resolution, res))
assert numpy.allclose(actual_resolution, res,
rtol=tolerance102), msg
# Determine expected shape from bbox (W, S, E, N)
ref_rows = int(round((bb[3] - bb[1]) / res))
ref_cols = int(round((bb[2] - bb[0]) / res))
# Compare shapes (generally, this may differ by 1)
msg = ('Shape of downloaded raster was [%i, %i]. '
'Expected [%i, %i].' % (A.shape[0], A.shape[1],
ref_rows, ref_cols))
assert (ref_rows == A.shape[0] and
ref_cols == A.shape[1]), msg
# Assess that the range of the interpolated data is sane
if not compare_extrema:
continue
# For these test sets we get exact match of the minimum
msg = ('Minimum of %s resampled at resolution %f '
'was %f. Expected %f.' % (hazard_layer.name,
res,
numpy.nanmin(A),
depth_min_ref))
assert numpy.allclose(depth_min_ref, numpy.nanmin(A),
rtol=0.0, atol=0.0), msg
# At the maximum it depends on the subsampling
msg = ('Maximum of %s resampled at resolution %f '
'was %f. Expected %f.' % (hazard_layer.name,
res,
numpy.nanmax(A),
depth_max_ref))
if res < native_resolution[0]:
# When subsampling to finer resolutions we expect a
# close match
assert numpy.allclose(depth_max_ref, numpy.nanmax(A),
rtol=1.0e-10, atol=1.0e-8), msg
elif res < native_resolution[0] * 10:
# When upsampling to coarser resolutions we expect
# ballpark match (~20%)
assert numpy.allclose(depth_max_ref, numpy.nanmax(A),
rtol=0.17, atol=0.0), msg
else:
# Upsampling to very coarse resolutions, just want sanity
assert 0 < numpy.nanmax(A) <= depth_max_ref
def test_data_resampling_example(self):
"""Raster data is unchanged when going through geonode
"""
# Name file names for hazard level, exposure and expected fatalities
hazard_filename = os.path.join(TESTDATA, '..', 'hazard',
'maumere_aos_depth_20m_land_wgs84.asc')
exposure_filename = os.path.join(TESTDATA, 'maumere_pop_prj.shp')
#------------
# Hazard data
#------------
# Read hazard input data for reference
H_ref = read_layer(hazard_filename)
A_ref = H_ref.get_data()
depth_min_ref, depth_max_ref = H_ref.get_extrema()
# Upload to internal geonode
hazard_layer = save_to_geonode(hazard_filename, user=self.user)
hazard_name = '%s:%s' % (hazard_layer.workspace, hazard_layer.name)
# Download data again
bbox = get_bounding_box_string(hazard_filename) # The biggest
H = download(INTERNAL_SERVER_URL, hazard_name, bbox)
A = H.get_data()
depth_min, depth_max = H.get_extrema()
# FIXME (Ole): The layer read from file is single precision only:
# Issue #17
# Here's the explanation why interpolation below produce slightly
# different results (but why?)
# The layer read from file is single precision which may be due to
# the way it is converted from ASC to TIF. In other words the
# problem may be in raster.write_to_file. Float64 is
# specified there, so this is a mystery.
#print 'A', A.dtype # Double precision
#print 'A_ref', A_ref.dtype # Single precision
# Compare extrema to values from numpy array
assert numpy.allclose(depth_max,
numpy.nanmax(A),
rtol=1.0e-12,
atol=1.0e-12)
assert numpy.allclose(depth_max_ref,
numpy.nanmax(A_ref),
rtol=1.0e-12,
atol=1.0e-12)
# Compare to reference
assert numpy.allclose([depth_min, depth_max],
[depth_min_ref, depth_max_ref],
rtol=1.0e-12,
atol=1.0e-12)
# Compare extrema to values read off QGIS for this layer
assert numpy.allclose([depth_min, depth_max], [0.0, 16.68],
rtol=1.0e-6,
atol=1.0e-10)
# Investigate difference visually
#from matplotlib.pyplot import matshow, show
#matshow(A)
#matshow(A_ref)
#matshow(A - A_ref)
#show()
#print
for i in range(A.shape[0]):
for j in range(A.shape[1]):
if not numpy.isnan(A[i, j]):
err = abs(A[i, j] - A_ref[i, j])
if err > 0:
msg = ('%i, %i: %.15f, %.15f, %.15f' %
(i, j, A[i, j], A_ref[i, j], err))
raise Exception(msg)
#if A[i,j] > 16:
# print i, j, A[i, j], A_ref[i, j]
# Compare elements (nan & numbers)
id_nan = numpy.isnan(A)
id_nan_ref = numpy.isnan(A_ref)
assert numpy.all(id_nan == id_nan_ref)
assert numpy.allclose(A[-id_nan],
A_ref[-id_nan],
rtol=1.0e-15,
atol=1.0e-15)
#print 'MAX', A[245, 283], A_ref[245, 283]
#print 'MAX: %.15f %.15f %.15f' %(A[245, 283], A_ref[245, 283])
assert numpy.allclose(A[245, 283],
A_ref[245, 283],
rtol=1.0e-15,
atol=1.0e-15)
#--------------
# Exposure data
#--------------
# Read exposure input data for reference
E_ref = read_layer(exposure_filename)
# Upload to internal geonode
exposure_layer = save_to_geonode(exposure_filename, user=self.user)
exposure_name = '%s:%s' % (exposure_layer.workspace,
exposure_layer.name)
# Download data again
E = download(INTERNAL_SERVER_URL, exposure_name, bbox)
# Check exposure data against reference
coordinates = E.get_geometry()
coordinates_ref = E_ref.get_geometry()
assert numpy.allclose(coordinates,
coordinates_ref,
rtol=1.0e-12,
atol=1.0e-12)
attributes = E.get_data()
attributes_ref = E_ref.get_data()
for i, att in enumerate(attributes):
att_ref = attributes_ref[i]
for key in att:
assert att[key] == att_ref[key]
# Test safe's interpolation function
I = assign_hazard_values_to_exposure_data(H, E, attribute_name='depth')
icoordinates = I.get_geometry()
I_ref = assign_hazard_values_to_exposure_data(H_ref,
E_ref,
attribute_name='depth')
icoordinates_ref = I_ref.get_geometry()
assert numpy.allclose(coordinates,
icoordinates,
rtol=1.0e-12,
atol=1.0e-12)
assert numpy.allclose(coordinates,
icoordinates_ref,
rtol=1.0e-12,
atol=1.0e-12)
iattributes = I.get_data()
assert numpy.allclose(icoordinates, coordinates)
N = len(icoordinates)
assert N == 891
# Set tolerance for single precision until issue #17 has been fixed
# It appears that the single precision leads to larger interpolation
# errors
rtol_issue17 = 2.0e-3
atol_issue17 = 1.0e-4
# Verify interpolated values with test result
for i in range(N):
interpolated_depth_ref = I_ref.get_data()[i]['depth']
interpolated_depth = iattributes[i]['depth']
assert nanallclose(interpolated_depth,
interpolated_depth_ref,
rtol=rtol_issue17,
atol=atol_issue17)
pointid = attributes[i]['POINTID']
if pointid == 263:
#print i, pointid, attributes[i],
#print interpolated_depth, coordinates[i]
# Check that location is correct
assert numpy.allclose(coordinates[i],
[122.20367299, -8.61300358],
rtol=1.0e-7,
atol=1.0e-12)
# This is known to be outside inundation area so should
# near zero
assert numpy.allclose(interpolated_depth,
0.0,
rtol=1.0e-12,
atol=1.0e-12)
if pointid == 148:
# Check that location is correct
#print coordinates[i]
assert numpy.allclose(coordinates[i],
[122.2045912, -8.608483265],
rtol=1.0e-7,
atol=1.0e-12)
# This is in an inundated area with a surrounding depths of
# 4.531, 3.911
# 2.675, 2.583
assert interpolated_depth < 4.531
assert interpolated_depth < 3.911
assert interpolated_depth > 2.583
assert interpolated_depth > 2.675
#print interpolated_depth
# This is a characterisation test for bilinear interpolation
assert numpy.allclose(interpolated_depth,
3.62477215491,
rtol=rtol_issue17,
atol=1.0e-12)
# Check that interpolated points are within range
msg = ('Interpolated depth %f at point %i was outside extrema: '
'[%f, %f]. ' %
(interpolated_depth, i, depth_min, depth_max))
if not numpy.isnan(interpolated_depth):
assert depth_min <= interpolated_depth <= depth_max, msg
def test_keywords_download(self):
"""Keywords are downloaded from GeoServer along with layer data
"""
# Upload test data
filenames = [
'padang_tsunami_mw8.tif',
]
filenames = [
'jakarta_flood_design.tif',
]
layers = []
paths = []
for filename in filenames:
basename, ext = os.path.splitext(filename)
path = os.path.join(UNITDATA, 'hazard', filename)
# Upload to GeoNode
layer = save_to_geonode(path, user=self.user, overwrite=True)
# Record layer and file
layers.append(layer)
paths.append(path)
# Check integrity
for i, layer in enumerate(layers):
# Get reference keyword dictionary from file
L = read_layer(paths[i])
ref_keywords = L.get_keywords()
# Get keywords metadata from GeoServer
layer_name = '%s:%s' % (layer.workspace, layer.name)
metadata = get_metadata(INTERNAL_SERVER_URL, layer_name)
assert 'keywords' in metadata
geo_keywords = metadata['keywords']
msg = ('Uploaded keywords were not as expected: I got %s '
'but expected %s' % (geo_keywords, ref_keywords))
for kw in ref_keywords:
# Check that all keywords were uploaded
# It is OK for new automatic keywords to have appeared
# (e.g. resolution) - see issue #171
assert kw in geo_keywords, msg
assert ref_keywords[kw] == geo_keywords[kw], msg
# Download data
bbox = get_bounding_box_string(paths[i])
H = download(INTERNAL_SERVER_URL, layer_name, bbox)
dwn_keywords = H.get_keywords()
msg = ('Downloaded keywords were not as expected: I got %s '
'but expected %s' % (dwn_keywords, geo_keywords))
assert geo_keywords == dwn_keywords, msg
# Check that the layer and its .keyword file is there.
msg = 'Downloaded layer %s was not found' % H.filename
assert os.path.isfile(H.filename), msg
kw_filename = os.path.splitext(H.filename)[0] + '.keywords'
msg = 'Downloaded keywords file %s was not found' % kw_filename
assert os.path.isfile(kw_filename), msg
# Check that keywords are OK when reading downloaded file
L = read_layer(H.filename)
read_keywords = L.get_keywords()
msg = ('Keywords in downloaded file %s were not as expected: '
'I got %s but expected %s' %
(kw_filename, read_keywords, geo_keywords))
assert read_keywords == geo_keywords, msg
def test_raster_scaling(self):
"""Raster layers can be scaled when resampled
This is a test for ticket #168
Native test .asc data has
ncols 5525
nrows 2050
cellsize 0.0083333333333333
Scaling is necessary for raster data that represents density
such as population per km^2
"""
for test_filename in ['jakarta_flood_design.tif']:
raster_filename = os.path.join(UNITDATA, 'hazard', test_filename)
# Get reference values
R = read_layer(raster_filename)
R_min_ref, R_max_ref = R.get_extrema()
native_resolution = R.get_resolution()
# Upload to internal geonode
raster_layer = save_to_geonode(raster_filename, user=self.user)
raster_name = '%s:%s' % (raster_layer.workspace, raster_layer.name)
# Test for a range of resolutions
for res in [0.01, 0.005, 0.002, 0.001, 0.0005, 0.0002]:
bbox = get_bounding_box_string(raster_filename)
R = download(INTERNAL_SERVER_URL,
raster_name,
bbox,
resolution=res)
A_native = R.get_data(scaling=False)
A_scaled = R.get_data(scaling=True)
sigma = (R.get_resolution()[0] / native_resolution[0])**2
# Compare extrema
expected_scaled_max = sigma * numpy.nanmax(A_native)
msg = ('Resampled raster was not rescaled correctly: '
'max(A_scaled) was %f but expected %f' %
(numpy.nanmax(A_scaled), expected_scaled_max))
assert numpy.allclose(expected_scaled_max,
numpy.nanmax(A_scaled),
rtol=1.0e0,
atol=1.0e-1), msg
expected_scaled_min = sigma * numpy.nanmin(A_native)
msg = ('Resampled raster was not rescaled correctly: '
'min(A_scaled) was %f but expected %f' %
(numpy.nanmin(A_scaled), expected_scaled_min))
assert numpy.allclose(expected_scaled_min,
numpy.nanmin(A_scaled),
rtol=1.0e0,
atol=1.0e-1), msg
# Compare elementwise
msg = 'Resampled raster was not rescaled correctly'
assert nanallclose(A_native * sigma,
A_scaled,
rtol=1.0e0,
atol=1.0e-1), msg
# Check that it also works with manual scaling
A_manual = R.get_data(scaling=sigma)
msg = 'Resampled raster was not rescaled correctly'
assert nanallclose(A_manual, A_scaled, rtol=1.0e0,
atol=1.0e-1), msg
# Check that an exception is raised for bad arguments
try:
R.get_data(scaling='bad')
except:
pass
else:
msg = 'String argument should have raised exception'
raise Exception(msg)
try:
R.get_data(scaling='(1, 3)')
except:
pass
else:
msg = 'Tuple argument should have raised exception'
raise Exception(msg)
# Check None option without existence of density keyword
A_none = R.get_data(scaling=None)
msg = 'Data should not have changed'
assert nanallclose(A_native, A_none, rtol=1.0e-0,
atol=1.0e-1), msg
# Try with None and density keyword
R.keywords['density'] = 'true'
A_none = R.get_data(scaling=None)
msg = 'Resampled raster was not rescaled correctly'
assert nanallclose(A_scaled, A_none, rtol=1.0e2,
atol=1.0e2), msg
R.keywords['density'] = 'Yes'
A_none = R.get_data(scaling=None)
msg = 'Resampled raster was not rescaled correctly'
assert nanallclose(A_scaled, A_none, rtol=1.0e2,
atol=1.0e2), msg
R.keywords['density'] = 'False'
A_none = R.get_data(scaling=None)
msg = 'Data should not have changed'
assert nanallclose(A_native, A_none, rtol=1.0e2,
atol=1.0e2), msg
R.keywords['density'] = 'no'
A_none = R.get_data(scaling=None)
msg = 'Data should not have changed'
assert nanallclose(A_native, A_none, rtol=1.0e2,
atol=1.0e2), msg
def test_specified_raster_resolution(self):
"""Raster layers can be downloaded with specific resolution
This is another test for ticket #103
Native test data:
maumere....asc
ncols 931
nrows 463
cellsize 0.00018
Population_Jakarta
ncols 638
nrows 649
cellsize 0.00045228819716044
Population_2010
ncols 5525
nrows 2050
cellsize 0.0083333333333333
Here we download it at a range of fixed resolutions that
are both coarser and finer, and check that the dimensions
of the downloaded matrix are as expected.
We also check that the extrema of the subsampled matrix are sane
"""
hazard_filename = os.path.join(UNITDATA, 'hazard',
'jakarta_flood_design.tif')
# Get reference values
H = read_layer(hazard_filename)
depth_min_ref, depth_max_ref = H.get_extrema()
native_resolution = H.get_resolution()
# Upload to internal geonode
hazard_layer = save_to_geonode(hazard_filename, user=self.user)
hazard_name = '%s:%s' % (hazard_layer.workspace, hazard_layer.name)
# Test for a range of resolutions
for res in [
0.01,
0.005,
0.002,
0.001,
0.0005, # Coarser
0.0002,
0.0001,
0.00006,
0.00003
]: # Finer
# Set bounding box
bbox = get_bounding_box_string(hazard_filename)
compare_extrema = True
bb = bboxstring2list(bbox)
# Download data at specified resolution
H = download(INTERNAL_SERVER_URL,
hazard_name,
bbox,
resolution=res)
A = H.get_data()
# Verify that data has the requested bobx and resolution
actual_bbox = H.get_bounding_box()
msg = ('Bounding box for %s was not as requested. I got %s '
'but '
'expected %s' % (hazard_name, actual_bbox, bb))
assert numpy.allclose(actual_bbox, bb, rtol=1.0e-6)
# FIXME (Ole): How do we sensibly resolve the issue with
# resx, resy vs one resolution (issue #173)
actual_resolution = H.get_resolution()[0]
# FIXME (Ole): Resolution is often far from the requested
# see issue #102
# Here we have to accept up to 5%
tolerance102 = 5.0e-2
msg = ('Resolution of %s was not as requested. I got %s but '
'expected %s' % (hazard_name, actual_resolution, res))
assert numpy.allclose(actual_resolution, res,
rtol=tolerance102), msg
# Determine expected shape from bbox (W, S, E, N)
ref_rows = int(round((bb[3] - bb[1]) / res))
ref_cols = int(round((bb[2] - bb[0]) / res))
# Compare shapes (generally, this may differ by 1)
msg = ('Shape of downloaded raster was [%i, %i]. '
'Expected [%i, %i].' %
(A.shape[0], A.shape[1], ref_rows, ref_cols))
assert (ref_rows == A.shape[0] and ref_cols == A.shape[1]), msg
# Assess that the range of the interpolated data is sane
if not compare_extrema:
continue
# For these test sets we get exact match of the minimum
msg = ('Minimum of %s resampled at resolution %f '
'was %f. Expected %f.' %
(hazard_layer.name, res, numpy.nanmin(A), depth_min_ref))
assert numpy.allclose(depth_min_ref,
numpy.nanmin(A),
rtol=0.0,
atol=0.0), msg
# At the maximum it depends on the subsampling
msg = ('Maximum of %s resampled at resolution %f '
'was %f. Expected %f.' %
(hazard_layer.name, res, numpy.nanmax(A), depth_max_ref))
if res < native_resolution[0]:
# When subsampling to finer resolutions we expect a
# close match
assert numpy.allclose(depth_max_ref,
numpy.nanmax(A),
rtol=1.0e-10,
atol=1.0e-8), msg
elif res < native_resolution[0] * 10:
# When upsampling to coarser resolutions we expect
# ballpark match (~20%)
assert numpy.allclose(depth_max_ref,
numpy.nanmax(A),
rtol=0.17,
atol=0.0), msg
else:
# Upsampling to very coarse resolutions, just want sanity
assert 0 < numpy.nanmax(A) <= depth_max_ref
def calculate(request, save_output=save_file_to_geonode):
start = datetime.datetime.now()
if request.method == 'GET':
# FIXME: Add a basic form here to be able to generate the POST request.
return HttpResponse('This should be accessed by robots, not humans.'
'In other words using HTTP POST instead of GET.')
elif request.method == 'POST':
data = request.POST
impact_function_name = data['impact_function']
hazard_server = data['hazard_server']
hazard_layer = data['hazard']
exposure_server = data['exposure_server']
exposure_layer = data['exposure']
requested_bbox = data['bbox']
keywords = data['keywords']
if request.user.is_anonymous():
theuser = get_valid_user()
else:
theuser = request.user
# Create entry in database
calculation = Calculation(user=theuser,
run_date=start,
hazard_server=hazard_server,
hazard_layer=hazard_layer,
exposure_server=exposure_server,
exposure_layer=exposure_layer,
impact_function=impact_function_name,
success=False)
# Wrap main computation loop in try except to catch and present
# messages and stack traces in the application
try:
# Get metadata
haz_metadata = get_metadata(hazard_server, hazard_layer)
exp_metadata = get_metadata(exposure_server, exposure_layer)
# Determine common resolution in case of raster layers
raster_resolution = get_common_resolution(haz_metadata, exp_metadata)
# Get reconciled bounding boxes
haz_bbox, exp_bbox, imp_bbox = get_bounding_boxes(
haz_metadata, exp_metadata, requested_bbox)
# Record layers to download
download_layers = [(hazard_server, hazard_layer, haz_bbox),
(exposure_server, exposure_layer, exp_bbox)]
# Add linked layers if any FIXME: STILL TODO!
# Get selected impact function
plugins = get_admissible_plugins()
msg = ('Could not find "%s" in "%s"' %
(impact_function_name, plugins.keys()))
assert impact_function_name in plugins, msg
impact_function = plugins.get(impact_function_name)
impact_function_source = inspect.getsource(impact_function)
# Record information calculation object and save it
calculation.impact_function_source = impact_function_source
calculation.bbox = bboxlist2string(imp_bbox)
calculation.save()
# Start computation
msg = 'Performing requested calculation'
#logger.info(msg)
# Download selected layer objects
layers = []
for server, layer_name, bbox in download_layers:
msg = ('- Downloading layer %s from %s' % (layer_name, server))
#logger.info(msg)
L = download(server, layer_name, bbox, raster_resolution)
layers.append(L)
# Calculate result using specified impact function
msg = ('- Calculating impact using %s' % impact_function_name)
#logger.info(msg)
impact_file = calculate_impact(layers=layers,
impact_fcn=impact_function)
# Upload result to internal GeoServer
msg = ('- Uploading impact layer %s' % impact_file.name)
# Determine layer title for upload
output_kw = impact_file.get_keywords()
title = impact_file.get_name() + " using " + output_kw['hazard_title'] + \
" and " + output_kw['exposure_title']
result = save_output(impact_file.filename,
title=title,
user=theuser,
overwrite=False)
except Exception, e:
# FIXME: Reimplement error saving for calculation.
# FIXME (Ole): Why should we reimplement?
# This is dangerous. Try to raise an exception
# e.g. in get_metadata_from_layer. Things will silently fail.
# See issue #170
#logger.error(e)
errors = e.__str__()
trace = exception_format(e)
calculation.errors = errors
calculation.stacktrace = trace
calculation.save()
jsondata = json.dumps({'errors': errors, 'stacktrace': trace})
return HttpResponse(jsondata, mimetype='application/json')
def test_the_earthquake_fatality_estimation_allen(self):
"""Fatality computation computed correctly with GeoServer Data
"""
# Simulate bounding box from application
viewport_bbox_string = '104.3,-8.2,110.04,-5.17'
# Upload exposure data for this test
name = 'Population_2010'
exposure_filename = '%s/%s.asc' % (TESTDATA, name)
exposure_layer = save_to_geonode(exposure_filename,
user=self.user,
overwrite=True)
workspace = exposure_layer.workspace
layer_name = exposure_layer.name
msg = 'Expected layer name to be "%s". Got %s' % (name, layer_name)
assert layer_name == name.lower(), msg
exposure_name = '%s:%s' % (workspace, layer_name)
# Check metadata
assert_bounding_box_matches(exposure_layer, exposure_filename)
exp_bbox_string = get_bounding_box_string(exposure_filename)
check_layer(exposure_layer, full=True)
# Now we know that exposure layer is good, lets upload some
# hazard layers and do the calculations
filename = 'lembang_mmi_hazmap.asc'
# Save
hazard_filename = '%s/%s' % (TESTDATA, filename)
hazard_layer = save_to_geonode(hazard_filename,
user=self.user,
overwrite=True)
hazard_name = '%s:%s' % (hazard_layer.workspace, hazard_layer.name)
# Check metadata
assert_bounding_box_matches(hazard_layer, hazard_filename)
haz_bbox_string = get_bounding_box_string(hazard_filename)
check_layer(hazard_layer, full=True)
calculate_url = reverse('safe-calculate')
# Run calculation
c = Client()
rv = c.post(
calculate_url,
data=dict(
hazard_server=INTERNAL_SERVER_URL,
hazard=hazard_name,
exposure_server=INTERNAL_SERVER_URL,
exposure=exposure_name,
#bbox=viewport_bbox_string,
bbox=exp_bbox_string, # This one reproduced the
# crash for lembang
impact_function='I T B Fatality Function',
keywords='test,shakemap,usgs'))
self.assertEqual(rv.status_code, 200)
self.assertEqual(rv['Content-Type'], 'application/json')
data = json.loads(rv.content)
if 'errors' in data:
errors = data['errors']
if errors is not None:
msg = ('The server returned the error message: %s' %
str(errors))
raise Exception(msg)
assert 'success' in data
assert 'hazard_layer' in data
assert 'exposure_layer' in data
assert 'run_duration' in data
assert 'run_date' in data
assert 'layer' in data
assert data['success']
# Download result and check
layer = data['layer']
layer_id = layer['id']
result_layer = download(INTERNAL_SERVER_URL, layer_id,
get_bounding_box_string(hazard_filename))
assert os.path.exists(result_layer.filename)
def test_earthquake_exposure_plugin(self):
"""Population exposure to individual MMI levels can be computed
"""
# Upload exposure data for this test
# FIXME (Ole): While this dataset is ok for testing,
# note that is has been resampled without scaling
# so numbers are about 25 times too large.
# Consider replacing test populations dataset for good measures,
# just in case any one accidentally started using this dataset
# for real.
name = 'Population_2010'
exposure_filename = '%s/%s.asc' % (TESTDATA, name)
exposure_layer = save_to_geonode(exposure_filename,
user=self.user,
overwrite=True)
exposure_name = '%s:%s' % (exposure_layer.workspace,
exposure_layer.name)
# Check metadata
assert_bounding_box_matches(exposure_layer, exposure_filename)
exp_bbox_string = get_bounding_box_string(exposure_filename)
check_layer(exposure_layer, full=True)
# Upload hazard data
filename = 'lembang_mmi_hazmap.asc'
hazard_filename = '%s/%s' % (TESTDATA, filename)
hazard_layer = save_to_geonode(hazard_filename,
user=self.user,
overwrite=True)
hazard_name = '%s:%s' % (hazard_layer.workspace, hazard_layer.name)
# Check metadata
assert_bounding_box_matches(hazard_layer, hazard_filename)
haz_bbox_string = get_bounding_box_string(hazard_filename)
check_layer(hazard_layer, full=True)
calculate_url = reverse('safe-calculate')
# Run calculation
c = Client()
rv = c.post(calculate_url,
data=dict(
hazard_server=INTERNAL_SERVER_URL,
hazard=hazard_name,
exposure_server=INTERNAL_SERVER_URL,
exposure=exposure_name,
bbox=haz_bbox_string,
impact_function='Earthquake Building Damage Function',
keywords='test,population,exposure,usgs'))
self.assertEqual(rv.status_code, 200)
self.assertEqual(rv['Content-Type'], 'application/json')
data = json.loads(rv.content)
if 'errors' in data:
errors = data['errors']
if errors is not None:
msg = ('The server returned the error message: %s' %
str(errors))
raise Exception(msg)
assert 'success' in data
assert 'hazard_layer' in data
assert 'exposure_layer' in data
assert 'run_duration' in data
assert 'run_date' in data
assert 'layer' in data
assert data['success']
# Download result and check
layer_name = data['layer'].split('/')[-1]
result_layer = download(INTERNAL_SERVER_URL, layer_name,
get_bounding_box_string(hazard_filename))
assert os.path.exists(result_layer.filename)
# Check calculated values
keywords = result_layer.get_keywords()
assert 'mmi-classes' in keywords
assert 'affected-population' in keywords
mmi_classes = [int(x) for x in keywords['mmi-classes'].split('_')]
count = [float(x) for x in keywords['affected-population'].split('_')]
# Brute force count for each population level
population = download(INTERNAL_SERVER_URL, exposure_name,
get_bounding_box_string(hazard_filename))
intensity = download(INTERNAL_SERVER_URL, hazard_name,
get_bounding_box_string(hazard_filename))
# Extract data
H = intensity.get_data(nan=0)
P = population.get_data(nan=0)
brutecount = {}
for mmi in mmi_classes:
brutecount[mmi] = 0
for i in range(P.shape[0]):
for j in range(P.shape[1]):
mmi = H[i, j]
if not numpy.isnan(mmi):
mmi_class = int(round(mmi))
pop = P[i, j]
if not numpy.isnan(pop):
brutecount[mmi_class] += pop
for i, mmi in enumerate(mmi_classes):
assert numpy.allclose(count[i], brutecount[mmi], rtol=1.0e-6)