def test_aggregate_vector_impact(self):
"""Test aggregation results on a vector layer.
created from loadStandardLayers.qgs with:
- a flood in Jakarta like in 2007
- Essential buildings
- be flooded
- kabupaten_jakarta_singlepart.shp
"""
impact_layer = Vector(data=os.path.join(
TESTDATA, 'aggregation_test_impact_vector.shp'),
name='test vector impact')
expected_results = [['JAKARTA BARAT', '87'], ['JAKARTA PUSAT', '117'],
['JAKARTA SELATAN', '22'],
['JAKARTA UTARA', '286'], ['JAKARTA TIMUR', '198']]
self._aggregate(impact_layer, expected_results)
impact_layer = Vector(data=TESTDATA +
'/aggregation_test_impact_vector_small.shp',
name='test vector impact')
expected_results = [['JAKARTA BARAT', '2'], ['JAKARTA PUSAT', '0'],
['JAKARTA SELATAN', '0'], ['JAKARTA UTARA', '1'],
['JAKARTA TIMUR', '0']]
self._aggregate(impact_layer, expected_results)
def test_aggregate_vector_impact(self):
"""Test aggregation results on a vector layer.
created from loadStandardLayers.qgs with:
- a flood in Jakarta like in 2007
- Essential buildings
- be flodded
- kabupaten_jakarta_singlepart.shp
"""
myImpactLayer = Vector(
data=os.path.join(TESTDATA, 'aggregation_test_impact_vector.shp'),
name='test vector impact')
myExpectedResults = [
{0: 'JAKARTA BARAT', 1: '87'},
{0: 'JAKARTA PUSAT', 1: '117'},
{0: 'JAKARTA SELATAN', 1: '22'},
{0: 'JAKARTA UTARA', 1: '286'},
{0: 'JAKARTA TIMUR', 1: '198'}
]
# self._aggregate(myImpactLayer, myExpectedResults)
myImpactLayer = Vector(
data=TESTDATA + '/aggregation_test_impact_vector_small.shp',
name='test vector impact')
myExpectedResults = [
{0: 'JAKARTA BARAT', 1: '87'},
{0: 'JAKARTA PUSAT', 1: '117'},
{0: 'JAKARTA SELATAN', 1: '22'},
{0: 'JAKARTA UTARA', 1: '286'},
{0: 'JAKARTA TIMUR', 1: '198'}
]
# TODO (MB) enable this
self._aggregate(myImpactLayer, myExpectedResults)
def minimum_needs(self, input_layer, population_name):
"""Compute minimum needs given a layer and a column containing pop.
:param input_layer: InaSAFE layer object assumed to contain
population counts
:type input_layer: read_layer
:param population_name: Attribute name that holds population count
:type population_name: str
:returns: Layer with attributes for minimum needs as per Perka 7
:rtype: read_layer
"""
all_attributes = []
for attributes in input_layer.get_data():
# Get population count
population = attributes[population_name]
# Clean up and turn into integer
if population in ['-', None]:
displaced = 0
else:
if type(population) is basestring:
population = str(population).replace(',', '')
try:
displaced = int(population)
except ValueError:
# noinspection PyTypeChecker,PyArgumentList
QtGui.QMessageBox.information(
None, self.tr('Format error'),
self.tr('Please change the value of %1 in attribute '
'%1 to integer format').arg(population).arg(
population_name))
raise ValueError
# Calculate estimated needs based on BNPB Perka 7/2008
# minimum needs
# weekly_needs = {
# 'rice': int(ceil(population * min_rice)),
# 'drinking_water': int(ceil(population * min_drinking_water)),
# 'water': int(ceil(population * min_water)),
# 'family_kits': int(ceil(population * min_family_kits)),
# 'toilets': int(ceil(population * min_toilets))}
# Add to attributes
weekly_needs = evacuated_population_weekly_needs(displaced)
# Record attributes for this feature
all_attributes.append(weekly_needs)
output_layer = Vector(geometry=input_layer.get_geometry(),
data=all_attributes,
projection=input_layer.get_projection())
return output_layer
def test_line_aggregation(self):
"""Test if line aggregation works
"""
data_path = os.path.join(UNITDATA, 'impact',
'aggregation_test_roads.shp')
impact_layer = Vector(data=data_path, name='test vector impact')
expected_results = [[u'JAKARTA BARAT', 0], [u'JAKARTA PUSAT', 4356],
[u'JAKARTA SELATAN', 0], [u'JAKARTA UTARA', 4986],
[u'JAKARTA TIMUR', 5809]]
impact_layer_attributes = [[{
'KAB_NAME': u'JAKARTA BARAT',
'flooded': 0.0,
'length': 7230.864654,
'id': 2,
'aggr_sum': 7230.864654
}],
[{
'KAB_NAME': u'JAKARTA PUSAT',
'flooded': 4356.161093,
'length': 4356.161093,
'id': 3,
'aggr_sum': 4356.161093
}],
[{
'KAB_NAME': u'JAKARTA SELATAN',
'flooded': 0.0,
'length': 3633.317287,
'id': 4,
'aggr_sum': 3633.317287
}],
[{
'KAB_NAME': u'JAKARTA UTARA',
'flooded': 4985.831677,
'length': 4985.831677,
'id': 1,
'aggr_sum': 4985.831677
}],
[{
'KAB_NAME': u'JAKARTA TIMUR',
'flooded': 0.0,
'length': 4503.033629,
'id': 4,
'aggr_sum': 4503.033629
}, {
'KAB_NAME': u'JAKARTA TIMUR',
'flooded': 5809.142247,
'length': 5809.142247,
'id': 1,
'aggr_sum': 5809.142247
}]]
self._aggregate(impact_layer,
expected_results,
impact_layer_attributes=impact_layer_attributes)
def test_aggregate_vector_impact(self):
"""Test aggregation results on a vector layer.
created from loadStandardLayers.qgs with:
- a flood in Jakarta like in 2007
- Essential buildings
- be flooded
- kabupaten_jakarta_singlepart.shp
"""
# Aggregation in sum mode
impact_layer = Vector(data=os.path.join(
TESTDATA, 'aggregation_test_impact_vector.shp'),
name='test vector impact')
expected_results = [['JAKARTA BARAT', '87'], ['JAKARTA PUSAT', '117'],
['JAKARTA SELATAN', '22'],
['JAKARTA UTARA', '286'], ['JAKARTA TIMUR', '198']]
self._aggregate(impact_layer, expected_results)
impact_layer = Vector(data=TESTDATA +
'/aggregation_test_impact_vector_small.shp',
name='test vector impact')
expected_results = [ # Count of inundated polygons:
['JAKARTA BARAT', '2'], ['JAKARTA PUSAT', '0'],
['JAKARTA SELATAN', '0'], ['JAKARTA UTARA', '1'],
['JAKARTA TIMUR', '0']
]
self._aggregate(impact_layer, expected_results)
expected_results = [['Entire area', '3']]
self._aggregate(impact_layer, expected_results, use_aoi_mode=True)
# Aggregation in class_count mode
data_path = os.path.join(
UNITDATA, 'impact',
'aggregation_test_impact_vector_class_count.shp')
impact_layer = Vector(data=data_path, name='test vector impact')
expected_results = [['Entire area', '2', '3', '0']]
self._aggregate(impact_layer, expected_results, use_aoi_mode=True)
expected_results = [['JAKARTA BARAT', '1', '2', '0'],
['JAKARTA PUSAT', '1', '0', '0'],
['JAKARTA SELATAN', '0', '0', '0'],
['JAKARTA UTARA', '0', '1', '0'],
['JAKARTA TIMUR', '0', '0', '0']]
impact_layer_attributes = [
[ # JAKARTA BARAT
{
'INUNDATED': 1,
'depth': 2.0,
'type': None,
'name': None,
'osm_id': None
}, {
'INUNDATED': 1,
'depth': 2.0,
'type': None,
'name': None,
'osm_id': None
}, {
'INUNDATED': 0,
'depth': None,
'type': None,
'name': None,
'osm_id': None
}
],
[ # JAKARTA PUSAT
{
'INUNDATED': 0,
'depth': None,
'type': None,
'name': None,
'osm_id': None
}
],
[
# JAKARTA SELATAN
],
[ # JAKARTA UTARA
{
'INUNDATED': 1,
'depth': None,
'type': None,
'name': None,
'osm_id': None
}
],
[
# JAKARTA TIMUR
]
]
self._aggregate(impact_layer,
expected_results,
impact_layer_attributes=impact_layer_attributes)
def minimum_needs(self, input_layer, population_name):
"""
Args
input_layer: InaSAFE layer object assumed to contain
population counts
population_name: Attribute name that holds population count
Returns
InaSAFE layer with attributes for minimum needs as per Perka 7
"""
needs_attributes = []
for attributes in input_layer.get_data():
attribute_dict = attributes
# Get population count
population = attributes[population_name]
# Clean up and turn into integer
if population in ['-', None]:
displaced = 0
else:
population = str(population).replace(',', '')
try:
displaced = int(population)
except ValueError:
QtGui.QMessageBox.information(
None, self.tr('Format error'),
self.tr('Please change the value of %s in '
'attribute %s to integer format') %
(population, population_name))
raise ValueError
# Calculate estimated needs based on BNPB Perka 7/2008
# minimum bantuan
# 400g per person per day
rice = int(displaced * 2.8)
# 2.5L per person per day
drinking_water = int(displaced * 17.5)
# 15L per person per day
water = int(displaced * 105)
# assume 5 people per family (not in perka)
family_kits = int(displaced / 5)
# 20 people per toilet
toilets = int(displaced / 20)
# Add to attributes
attribute_dict['Beras'] = rice
attribute_dict['Air minum'] = drinking_water
attribute_dict['Air bersih'] = water
attribute_dict['Kit keluarga'] = family_kits
attribute_dict['Jamban'] = toilets
# Record attributes for this feature
needs_attributes.append(attribute_dict)
output_layer = Vector(geometry=input_layer.get_geometry(),
data=needs_attributes,
projection=input_layer.get_projection())
return output_layer