def populate_datamart(dbsession):
# AdminLevelType
for i in [
("COU", "Country", "Administrative division of level 0"),
("PRO", "Province", "Administrative division of level 1"),
("REG", "Region", "Administrative division of level 2"),
]:
if AdminLevelType.get(dbsession, i[0]):
continue
r = AdminLevelType()
r.mnemonic, r.title, r.description = i
dbsession.add(r)
# HazardLevel
for i in [
("HIG", "High", 1),
("MED", "Medium", 2),
("LOW", "Low", 3),
("VLO", "Very low", 4),
]:
if HazardLevel.get(dbsession, i[0]):
continue
r = HazardLevel()
r.mnemonic, r.title, r.order = i
dbsession.add(r)
# HazardType
for i in [
("FL", "River flood", 1),
("UF", "Urban flood", 2),
("CF", "Coastal flood", 3),
("EQ", "Earthquake", 4),
("LS", "Landslide", 5),
("TS", "Tsunami", 6),
("VA", "Volcano", 7),
("CY", "Cyclone", 8),
("DG", "Water scarcity", 9),
("EH", "Extreme heat", 10),
("WF", "Wildfire", 11),
("AP", "Air pollution", 12),
]:
if HazardType.get(dbsession, i[0]):
continue
r = HazardType()
r.mnemonic, r.title, r.order = i
dbsession.add(r)
# HazardCategory
hazardlevels = dbsession.query(HazardLevel)
for hazardtype in dbsession.query(HazardType):
for hazardlevel in hazardlevels:
if HazardCategory.get(dbsession, hazardtype, hazardlevel):
continue
r = HazardCategory()
r.hazardtype = hazardtype
r.hazardlevel = hazardlevel
r.general_recommendation = "General recommendation for {} {}".format(
hazardtype.mnemonic, hazardlevel.mnemonic)
dbsession.add(r)
def hazardcategories(request):
hazard_types = request.dbsession.query(HazardType).order_by(
HazardType.order)
hazard_levels = []
for level in ["HIG", "MED", "LOW", "VLO"]:
hazard_levels.append(HazardLevel.get(request.dbsession, level))
return {"hazard_types": hazard_types, "hazard_levels": hazard_levels}
def test_not_corresponding_rasters(self, open_mock):
"""Difference in origin, resolution or size must not complete"""
hazardset_id = "notpreprocessed"
hazardtype = HazardType.get(DBSession, "FL")
regions = DBSession.query(Region).all()
hazardset = HazardSet(
id=hazardset_id,
hazardtype=hazardtype,
local=False,
data_lastupdated_date=datetime.now(),
metadata_lastupdated_date=datetime.now(),
regions=regions,
)
DBSession.add(hazardset)
for level in ["HIG", "MED", "LOW"]:
layer = Layer(
hazardlevel=HazardLevel.get(DBSession, level),
mask=False,
return_period=None,
data_lastupdated_date=datetime.now(),
metadata_lastupdated_date=datetime.now(),
geonode_id=new_geonode_id(DBSession),
download_url="test",
calculation_method_quality=5,
scientific_quality=1,
local=False,
downloaded=True,
)
hazardset.layers.append(layer)
mask_layer = Layer(
hazardlevel=None,
mask=True,
return_period=None,
data_lastupdated_date=datetime.now(),
metadata_lastupdated_date=datetime.now(),
geonode_id=new_geonode_id(DBSession),
download_url="test",
calculation_method_quality=5,
scientific_quality=1,
local=False,
downloaded=True,
)
hazardset.layers.append(mask_layer)
DBSession.flush()
self.completer().execute()
hazardset = DBSession.query(HazardSet).one()
self.assertEqual(
hazardset.complete_error,
"All layers should have the same origin, resolution and size",
)
self.assertEqual(hazardset.complete, False)
def technical_rec_process(request, obj):
if request.method == "GET":
hazard_types = request.dbsession.query(HazardType).order_by(
HazardType.order)
hazard_levels = []
for level in ["HIG", "MED", "LOW", "VLO"]:
hazard_levels.append(HazardLevel.get(request.dbsession, level))
if obj.id is None:
action = request.route_url("admin_technical_rec_new")
else:
action = request.route_url("admin_technical_rec_edit", id=obj.id)
return {
"obj": obj,
"action": action,
"hazard_types": hazard_types,
"hazard_levels": hazard_levels,
}
if request.method == "POST":
obj.text = request.POST.get("text")
obj.detail = request.POST.get("detail")
if inspect(obj).transient:
request.dbsession.add(obj)
associations = request.POST.getall("associations")
records = obj.hazardcategory_associations
# Remove unchecked ones
for record in records:
if record.hazardcategory.name() not in associations:
request.dbsession.delete(record)
# Add new ones
for association in associations:
hazardtype, hazardlevel = association.split(" - ")
if not obj.has_association(hazardtype, hazardlevel):
hazardcategory = HazardCategory.get(request.dbsession,
hazardtype, hazardlevel)
order = (request.dbsession.query(
func.coalesce(func.cast(func.max(HcTr.order), Integer), 0)
).select_from(HcTr).filter(
HcTr.hazardcategory_id == hazardcategory.id).first()[0] +
1)
record = HcTr(hazardcategory=hazardcategory, order=order)
obj.hazardcategory_associations.append(record)
request.dbsession.flush()
return HTTPFound(request.route_url("admin_technical_rec"))
def make_layers():
layers = []
for level in ("HIG", "MED", "LOW"):
layer = Layer()
layer.hazardlevel = HazardLevel.get(level)
layer.return_period = 1
layer.hazardunit = "m"
layer.data_lastupdated_date = datetime.now()
layer.metadata_lastupdated_date = datetime.now()
layer.geonode_id = new_geonode_id()
layer.download_url = "http://something"
layer.calculation_method_quality = 5
layer.scientific_quality = 1
layer.local = False
layer.downloaded = True
layers.append(layer)
return layers
def preprocessed_hazardlevel(self, geometry):
hazardlevel = None
reader = self.readers[0]
for polygon in geometry.geoms:
if not polygon.intersects(self.bbox):
continue
window = reader.window(*polygon.bounds)
data = reader.read(1, window=window, masked=True)
if data.shape[0] * data.shape[1] == 0:
continue
if data.mask.all():
continue
geometry_mask = features.geometry_mask(
[polygon],
out_shape=data.shape,
transform=reader.window_transform(window),
all_touched=True,
)
data.mask = data.mask | geometry_mask
del geometry_mask
if data.mask.all():
continue
for level in ("HIG", "MED", "LOW", "VLO"):
level_obj = HazardLevel.get(self.dbsession, level)
if level_obj <= hazardlevel:
break
if level in self.type_settings["values"]:
values = self.type_settings["values"][level]
for value in values:
if value in data:
hazardlevel = level_obj
break
return hazardlevel
def test_open_exception(self, open_mock):
"""Test handling of open exception"""
hazardset_id = "notpreprocessed"
hazardtype = HazardType.get(DBSession, "EQ")
regions = DBSession.query(Region).all()
hazardset = HazardSet(
id=hazardset_id,
hazardtype=hazardtype,
local=False,
data_lastupdated_date=datetime.now(),
metadata_lastupdated_date=datetime.now(),
regions=regions,
)
DBSession.add(hazardset)
for level in ["HIG", "MED", "LOW"]:
layer = Layer(
hazardlevel=HazardLevel.get(DBSession, level),
mask=False,
return_period=None,
data_lastupdated_date=datetime.now(),
metadata_lastupdated_date=datetime.now(),
geonode_id=new_geonode_id(DBSession),
download_url="test",
calculation_method_quality=5,
scientific_quality=1,
local=False,
downloaded=True,
)
hazardset.layers.append(layer)
DBSession.flush()
self.completer().execute()
hazardset = DBSession.query(HazardSet).one()
self.assertEqual(hazardset.complete_error,
"Error opening layer notpreprocessed")
self.assertEqual(hazardset.complete, False)
def select_levels(self):
for hazardset in self.dbsession.query(HazardSet):
type_settings = self.settings["hazard_types"][hazardset.hazardtype.mnemonic]
preprocessed = "values" in type_settings
if preprocessed:
continue
for level_mne in ('HIG', 'MED', 'LOW'):
level = HazardLevel.get(self.dbsession, level_mne)
self.select_layer_for_level(hazardset, level)
if type_settings.get("mask_return_period"):
self.select_mask_layer(hazardset)
# Purge superseeded layers
self.dbsession.query(Layer) \
.filter(Layer.hazardset_id == hazardset.id) \
.filter(Layer.hazardlevel_id.is_(None)) \
.filter(Layer.mask.is_(False)) \
.delete()
def harvest_layer(self, object):
logger.info("Harvesting layer {id} - {title}".format(**object))
title = object["title"]
# we need to retrieve more information on this layer
# since the regions array is not advertised by the main
# regions listing from GeoNode
o = self.fetch("api/layers/{id}/".format(**object))
if "regions" not in list(o.keys()):
warning(object, 'Attribute "regions" is missing')
region_ids = []
for r in o.get("regions", []):
# r is like "/api/regions/1/"
region_ids.append(r.split("/")[3])
if len(region_ids) == 0:
regions = []
else:
regions = self.dbsession.query(Region).filter(Region.id.in_(region_ids)).all()
hazardset_id = o['hazard_set']
if not hazardset_id:
logger.info(" hazard_set is empty")
return False
# FIXME: temporary override
if hazardset_id in excluded_hazardsets:
logger.info(" hazard_set {} is excluded, skipping")
return False
hazardtype = self.check_hazard_type(o)
if not hazardtype:
return False
type_settings = self.settings["hazard_types"][hazardtype.mnemonic]
preprocessed = "values" in type_settings
local = "GLOBAL" not in hazardset_id
mask = False
if preprocessed is True:
hazardlevel = None
# harvest hazard_unit for preprocessed layers
hazard_unit = o['hazard_unit']
if o['hazard_period']:
logger.info(' return period found in preprocessed hazardset')
return False
hazard_period = None
else:
try:
hazard_period = int(o['hazard_period'])
except:
hazard_period = None
if hazard_period is None:
logger.info(' no return period found')
return False
hazardlevel = None
for level in ("LOW", "MED", "HIG"):
if between(hazard_period, type_settings["return_periods"][level]):
hazardlevel = HazardLevel.get(self.dbsession, level)
break
if "mask_return_period" in type_settings and between(
hazard_period, type_settings["mask_return_period"]
):
mask = True
if hazardlevel is None and not mask:
logger.info(" No corresponding hazard_level")
return False
hazard_unit = o['hazard_unit']
if hazard_unit == '':
logger.info(' hazard_unit is empty')
return False
if o['srid'] != 'EPSG:4326':
logger.info(' srid is different from "EPSG:4326"')
return False
data_update_date = parse_date(o['data_update_date'])
if not data_update_date:
warning(o, 'data_update_date is empty: set to {}'.format(datetime.fromtimestamp(0)))
# We use a very old date for good comparison in decision tree
data_update_date = datetime.fromtimestamp(0)
metadata_update_date = parse_date(o['metadata_update_date'])
if not metadata_update_date:
warning(o, 'metadata_update_date is empty: set to {}'.format(datetime.fromtimestamp(0)))
# We use a very old date for good comparison in decision tree
metadata_update_date = datetime.fromtimestamp(0)
calculation_method_quality = o['calculation_method_quality']
if not calculation_method_quality:
warning(o, 'calculation_method_quality is empty: skip layer')
return False
calculation_method_quality = int(float(calculation_method_quality))
scientific_quality = o['scientific_quality']
if not scientific_quality:
warning(o, 'scientific_quality is empty')
return False
scientific_quality = int(float(scientific_quality))
download_url = o['download_url']
if not download_url:
warning(o, 'download_url is empty')
return False
hazardset = self.dbsession.query(HazardSet).get(hazardset_id)
# Create hazardset before layer
if hazardset is None:
logger.info(" Create new hazardset {}".format(hazardset_id))
hazardset = HazardSet()
hazardset.id = hazardset_id
hazardset.hazardtype = hazardtype
self.dbsession.add(hazardset)
# get detail_url and owner_organization from last updated layer
geonode = self.settings["geonode"]
geonode_base_url = geonode["url"]
if o['detail_url'] and not mask:
hazardset.detail_url = geonode_base_url + o['detail_url']
if o['owner']['organization'] and not mask:
hazardset.owner_organization = o['owner']['organization']
if not mask:
hazardset.regions = regions
layer = self.dbsession.query(Layer).get(o['id'])
if layer is None:
logger.info(" Create new Layer {}".format(title))
layer = Layer()
layer.geonode_id = o['id']
layer.hazardset = hazardset
layer.mask = False
else:
# If data has changed
if (
layer.data_lastupdated_date != data_update_date
or layer.download_url != download_url
):
logger.info(" Invalidate downloaded")
layer.downloaded = False
hazardset.complete = False
hazardset.processed = None
# Remove file from cache
layer.download_url = download_url
path = self.layer_path(layer)
if os.path.isfile(path):
os.unlink(path)
# Some hazardset fields are calculated during completing
if (
layer.calculation_method_quality != calculation_method_quality
or layer.scientific_quality != scientific_quality
or layer.metadata_lastupdated_date != metadata_update_date
):
logger.info(" Invalidate complete")
hazardset.complete = False
# Some fields invalidate outputs
if layer.hazardunit != hazard_unit:
logger.info(" Invalidate processed")
hazardset.processed = None
typename = o.get("typename", None)
if typename is None:
warning(o, 'Attribute "typename" is missing')
layer.typename = typename
layer.return_period = hazard_period
layer.hazardunit = hazard_unit
layer.data_lastupdated_date = data_update_date
layer.metadata_lastupdated_date = metadata_update_date
layer.download_url = download_url
# TODO: retrieve quality attributes
layer.calculation_method_quality = calculation_method_quality
layer.scientific_quality = scientific_quality
layer.local = local
layer.set_harvested(True)
self.dbsession.flush()
return True
def populate_notpreprocessed(type, unit):
hazardset_id = "notpreprocessed"
hazardtype = HazardType.get(DBSession, type)
hazardtype_settings = settings["hazard_types"][hazardtype.mnemonic]
regions = DBSession.query(Region).all()
print("Populating hazardset {}".format(hazardset_id))
hazardset = HazardSet(
id=hazardset_id,
hazardtype=hazardtype,
local=False,
data_lastupdated_date=datetime.now(),
metadata_lastupdated_date=datetime.now(),
regions=regions,
)
DBSession.add(hazardset)
return_periods = hazardtype_settings["return_periods"]
for level in ("HIG", "MED", "LOW"):
hazardlevel = HazardLevel.get(DBSession, level)
level_return_periods = return_periods[level]
if isinstance(level_return_periods, list):
return_period = level_return_periods[0]
else:
return_period = level_return_periods
layer = Layer(
hazardlevel=hazardlevel,
mask=False,
return_period=return_period,
hazardunit=unit,
data_lastupdated_date=datetime.now(),
metadata_lastupdated_date=datetime.now(),
geonode_id=new_geonode_id(DBSession),
download_url="test",
calculation_method_quality=5,
scientific_quality=1,
local=False,
downloaded=True,
)
hazardset.layers.append(layer)
mask_return_periods = hazardtype_settings["mask_return_period"]
if isinstance(mask_return_periods, list):
mask_return_period = mask_return_periods[0]
else:
mask_return_period = mask_return_periods
layer = Layer(
hazardlevel=None,
mask=True,
return_period=mask_return_period,
hazardunit=unit,
data_lastupdated_date=datetime.now(),
metadata_lastupdated_date=datetime.now(),
geonode_id=new_geonode_id(DBSession),
download_url="test",
calculation_method_quality=5,
scientific_quality=1,
local=False,
downloaded=True,
)
hazardset.layers.append(layer)
hazardset.complete = True
DBSession.flush()
def process_hazardset(self, hazardset_id):
hazardset = self.dbsession.query(HazardSet).get(hazardset_id)
if hazardset is None:
raise ProcessException("Hazardset {} does not exist.".format(hazardset_id))
chrono = datetime.datetime.now()
if hazardset.processed is not None:
if self.force:
hazardset.processed = None
else:
raise ProcessException(
"Hazardset {} has already been processed.".format(hazardset.id)
)
logger.info(" Cleaning previous outputs")
self.dbsession.query(Output).filter(Output.hazardset_id == hazardset.id).delete()
self.dbsession.flush()
self.type_settings = self.settings["hazard_types"][
hazardset.hazardtype.mnemonic
]
hazardset.processing_error = None
with rasterio.Env():
try:
logger.info(" Opening raster files")
# Open rasters
self.layers = {}
self.readers = {}
if "values" in list(self.type_settings.keys()):
# preprocessed layer
layer = (
self.dbsession.query(Layer)
.filter(Layer.hazardset_id == hazardset.id)
.one()
)
reader = rasterio.open(self.layer_path(layer))
self.layers[0] = layer
self.readers[0] = reader
else:
for level in ("HIG", "MED", "LOW"):
hazardlevel = HazardLevel.get(self.dbsession, level)
layer = (
self.dbsession.query(Layer)
.filter(Layer.hazardset_id == hazardset.id)
.filter(Layer.hazardlevel_id == hazardlevel.id)
.one()
)
reader = rasterio.open(self.layer_path(layer))
self.layers[level] = layer
self.readers[level] = reader
if "mask_return_period" in self.type_settings:
layer = (
self.dbsession.query(Layer)
.filter(Layer.hazardset_id == hazardset.id)
.filter(Layer.mask.is_(True))
.one()
)
reader = rasterio.open(self.layer_path(layer))
self.layers["mask"] = layer
self.readers["mask"] = reader
outputs, error = self.create_outputs(hazardset)
if error:
hazardset.processing_error = error
if outputs:
self.dbsession.add_all(outputs)
else:
hazardset.processing_error = 'No output generated'
finally:
logger.info(" Closing raster files")
for key, reader in self.readers.items():
if reader and not reader.closed:
reader.close()
if not hazardset.processing_error:
hazardset.processed = datetime.datetime.now()
logger.info(
" Successfully processed {},"
" {} outputs generated in {}".format(
hazardset.id, len(outputs), datetime.datetime.now() - chrono
)
)
else:
logger.info(' Process of {} failed in {}, {}'
.format(hazardset.id,
datetime.datetime.now() - chrono,
hazardset.processing_error))
self.dbsession.flush()
def notpreprocessed_hazardlevel(self, hazardtype, geometry):
level_vlo = HazardLevel.get(self.dbsession, "VLO")
hazardlevel = None
# Create some optimization caches
polygons = {}
bboxes = {}
geometry_masks = {} # Storage for the geometry geometry_masks
inverted_comparison = (
"inverted_comparison" in self.type_settings
and self.type_settings["inverted_comparison"]
)
for level in ("HIG", "MED", "LOW"):
layer = self.layers[level]
reader = self.readers[level]
threshold = self.get_threshold(
hazardtype, layer.local, layer.hazardlevel.mnemonic, layer.hazardunit
)
for i in range(0, len(geometry.geoms)):
if i not in polygons:
polygon = geometry.geoms[i]
bbox = polygon.bounds
polygons[i] = polygon
bboxes[i] = bbox
else:
polygon = polygons[i]
bbox = bboxes[i]
if not polygon.intersects(self.bbox):
continue
window = reader.window(*bbox)
# data: MaskedArray
data = reader.read(1, window=window, masked=True)
# check if data is empty (cols x rows)
if data.shape[0] * data.shape[1] == 0:
continue
# all data is masked which means that all is NODATA
if data.mask.all():
continue
if inverted_comparison:
data = data < threshold
else:
data = data > threshold
# some hazard types have a specific mask layer with very low
# return period which should be used as mask for other layers
# for example River Flood
if "mask_return_period" in self.type_settings:
mask_layer = self.layers["mask"]
mask_reader = self.readers["mask"]
mask_threshold = self.get_threshold(
hazardtype, mask_layer.local, "MASK", mask_layer.hazardunit
)
mask_window = mask_reader.window(*bbox)
mask = self.readers["mask"].read(1, window=mask_window, masked=True)
if inverted_comparison:
mask = mask < mask_threshold
else:
mask = mask > mask_threshold
# apply the specific layer mask
data.mask = ma.getmaskarray(data) | mask.filled(False)
del mask
if data.mask.all():
continue
if i in geometry_masks:
geometry_mask = geometry_masks[i]
else:
geometry_mask = features.geometry_mask(
[polygon],
out_shape=data.shape,
transform=reader.window_transform(window),
all_touched=True,
)
geometry_masks[i] = geometry_mask
data.mask = ma.getmaskarray(data) | geometry_mask
del geometry_mask
# If at least one value is True this means that there's
# at least one raw value > threshold
if data.any():
hazardlevel = layer.hazardlevel
break
# check one last time is array is filled with NODATA
if data.mask.all():
continue
# Here we have at least one value lower than the current level
# threshold
if hazardlevel is None:
hazardlevel = level_vlo
# we got a value for the level, no need to go further, this will be
# the highest one
if hazardlevel == layer.hazardlevel:
break
return hazardlevel
def populate_processing():
hazardtype_eq = HazardType.get("EQ")
hazardtype_fl = HazardType.get("FL")
hazardset_id = "hazardset1"
print("Populating hazardset {}".format(hazardset_id))
hazardset1 = HazardSet()
hazardset1.id = hazardset_id
hazardset1.hazardtype = hazardtype_eq
hazardset1.local = False
hazardset1.calculation_method_quality = 0
hazardset1.data_lastupdated_date = datetime.now()
hazardset1.metadata_lastupdated_date = datetime.now()
hazardset1.complete = True
hazardset1.layers.extend(make_layers())
DBSession.add(hazardset1)
hazardset_id = "hazardset1b"
print("Populating hazardset {}".format(hazardset_id))
hazardset1b = HazardSet()
hazardset1b.id = hazardset_id
hazardset1b.hazardtype = hazardtype_fl
hazardset1b.local = True
hazardset1b.calculation_method_quality = 3
hazardset1b.data_lastupdated_date = datetime.now()
hazardset1b.metadata_lastupdated_date = datetime.now()
hazardset1b.complete = True
hazardset1b.layers.extend(make_layers())
DBSession.add(hazardset1b)
# create hazardsets 2, 3 ... 5
hazardset_id = "hazardset2"
# calculation_method_quality = 1 / scientific_quality = 0
print("Populating hazardset {}".format(hazardset_id))
hazardset2 = HazardSet()
hazardset2.id = hazardset_id
hazardset2.hazardtype = hazardtype_eq
hazardset2.local = True
hazardset2.calculation_method_quality = 1
hazardset2.scientific_quality = 0
hazardset2.data_lastupdated_date = datetime.now()
hazardset2.metadata_lastupdated_date = datetime.now()
hazardset2.complete = True
hazardset2.layers.extend(make_layers())
DBSession.add(hazardset2)
hazardset_id = "hazardset3"
# date = 2015-01-01 / global /
# calculation_method_quality = 1 / scientific_quality = 2
print("Populating hazardset {}".format(hazardset_id))
hazardset3 = HazardSet()
hazardset3.id = hazardset_id
hazardset3.hazardtype = hazardtype_eq
hazardset3.local = False
hazardset3.calculation_method_quality = 1
hazardset3.scientific_quality = 2
hazardset3.data_lastupdated_date = datetime(2015, 1, 1, 0, 0)
hazardset3.metadata_lastupdated_date = datetime.now()
hazardset3.complete = True
hazardset3.layers.extend(make_layers())
DBSession.add(hazardset3)
hazardset_id = "hazardset4"
# date = 2015-01-01 / local /
# calculation_method_quality = 1 / scientific_quality = 2
print("Populating hazardset {}".format(hazardset_id))
hazardset4 = HazardSet()
hazardset4.id = hazardset_id
hazardset4.hazardtype = hazardtype_eq
hazardset4.local = True
hazardset4.calculation_method_quality = 1
hazardset4.scientific_quality = 2
hazardset4.data_lastupdated_date = datetime(2015, 1, 1, 0, 0)
hazardset4.metadata_lastupdated_date = datetime.now()
hazardset4.complete = True
hazardset4.layers.extend(make_layers())
DBSession.add(hazardset4)
hazardset_id = "hazardset5"
# date = now() / local /
# calculation_method_quality = 1 / scientific_quality = 2
print("Populating hazardset {}".format(hazardset_id))
hazardset5 = HazardSet()
hazardset5.id = hazardset_id
hazardset5.hazardtype = hazardtype_eq
hazardset5.local = True
hazardset5.calculation_method_quality = 1
hazardset5.scientific_quality = 2
hazardset5.data_lastupdated_date = datetime.now()
hazardset5.metadata_lastupdated_date = datetime.now()
hazardset5.complete = True
hazardset5.layers.extend(make_layers())
DBSession.add(hazardset5)
hazardset_id = "hazardset6"
# date = now() / global /
# calculation_method_quality = 1 / scientific_quality = 2
print("Populating hazardset {}".format(hazardset_id))
hazardset6 = HazardSet()
hazardset6.id = hazardset_id
hazardset6.hazardtype = hazardtype_eq
hazardset6.local = False
hazardset6.calculation_method_quality = 1
hazardset6.scientific_quality = 2
hazardset6.data_lastupdated_date = datetime.now()
hazardset6.metadata_lastupdated_date = datetime.now()
hazardset6.complete = True
hazardset6.layers.extend(make_layers())
DBSession.add(hazardset6)
# populate output table
# admin div (code 30) has only one hazardset for EQ
admin30 = (DBSession.query(AdministrativeDivision).filter(
AdministrativeDivision.code == 30).one())
# => test outcome = hazardset1
output1 = Output()
output1.hazardset = hazardset1
output1.administrativedivision = admin30
output1.hazardlevel = HazardLevel.get("HIG")
DBSession.add(output1)
# admin div (code 30) also has another hazardset
# but this one is for FL
# => test outcome = hazardset1b
output1b = Output()
output1b.hazardset = hazardset1b
output1b.administrativedivision = admin30
output1b.hazardlevel = HazardLevel.get("NPR")
DBSession.add(output1b)
# admin div (code 31) has 2 hazardsets,
# one with a higher calculation_method_quality
# => test outcome = hazardset2
admin31 = (DBSession.query(AdministrativeDivision).filter(
AdministrativeDivision.code == 31).one())
output2 = Output()
output2.hazardset = hazardset1 # calculation_method_quality = 0
output2.administrativedivision = admin31
output2.hazardlevel = HazardLevel.get("MED")
DBSession.add(output2)
output3 = Output()
output3.hazardset = hazardset2 # calculation_method_quality = 1
output3.administrativedivision = admin31
output3.hazardlevel = HazardLevel.get("LOW")
DBSession.add(output3)
# admin div (code 32) has 2 hazardsets,
# both share the same calculation_method_quality,
# one with a higher scientific_quality
# => test outcome = hazardset3
admin32 = (DBSession.query(AdministrativeDivision).filter(
AdministrativeDivision.code == 32).one())
output4 = Output()
output4.hazardset = hazardset2
# calculation_method_quality = 1 / scientific_quality = 0
output4.administrativedivision = admin32
output4.hazardlevel = HazardLevel.get("MED")
DBSession.add(output4)
output5 = Output()
output5.hazardset = hazardset3
# calculation_method_quality = 1 / scientific_quality = 2
output5.administrativedivision = admin32
output5.hazardlevel = HazardLevel.get("LOW")
DBSession.add(output5)
# admin div (code 33) has 2 hazardsets,
# both share the same ratings, one is global, one local
# => test outcome = hazardset4
admin33 = (DBSession.query(AdministrativeDivision).filter(
AdministrativeDivision.code == 33).one())
output6 = Output()
output6.hazardset = hazardset3
# global / calculation_method_quality = 1 / scientific_quality = 2
output6.administrativedivision = admin33
output6.hazardlevel = HazardLevel.get("MED")
DBSession.add(output6)
output7 = Output()
output7.hazardset = hazardset4
# local / calculation_method_quality = 1 / scientific_quality = 2
output7.administrativedivision = admin33
output7.hazardlevel = HazardLevel.get("LOW")
DBSession.add(output7)
# admin div (code 34) has 2 hazardsets,
# both share the same ratings, are local, one is more recent
# => test outcome = hazardset5
admin34 = (DBSession.query(AdministrativeDivision).filter(
AdministrativeDivision.code == 34).one())
output8 = Output()
output8.hazardset = hazardset4
# date = 2015-01-01 / local /
# calculation_method_quality = 1 / scientific_quality = 2
output8.administrativedivision = admin34
output8.hazardlevel = HazardLevel.get("MED")
DBSession.add(output8)
output9 = Output()
output9.hazardset = hazardset5
# date = now() / local /
# calculation_method_quality = 1 / scientific_quality = 2
output9.administrativedivision = admin34
output9.hazardlevel = HazardLevel.get("LOW")
DBSession.add(output9)
# admin div (code 35) has 2 hazardsets,
# both share the same ratings, are global, one is more recent
# => test outcome = hazardset6
admin35 = (DBSession.query(AdministrativeDivision).filter(
AdministrativeDivision.code == 35).one())
output10 = Output()
output10.hazardset = hazardset3
# date = 2015-01-01 / global /
# calculation_method_quality = 1 / scientific_quality = 2
output10.administrativedivision = admin35
output10.hazardlevel = HazardLevel.get("MED")
DBSession.add(output10)
output11 = Output()
output11.hazardset = hazardset6
# date = now() / global /
# calculation_method_quality = 1 / scientific_quality = 2
output11.administrativedivision = admin35
output11.hazardlevel = HazardLevel.get("LOW")
DBSession.add(output11)
DBSession.flush()
HazardSet,
HazardType,
Layer,
Region,
FurtherResource,
ClimateChangeRecommendation,
HazardTypeFurtherResourceAssociation,
ClimateChangeRecAdministrativeDivisionAssociation as CcrAd,
HazardCategoryAdministrativeDivisionAssociation,
Contact,
ContactAdministrativeDivisionHazardTypeAssociation as CAdHt,
)
from ..analytics import GoogleAnalytics
# An object for the "no data" category type.
_hazardlevel_nodata = HazardLevel()
_hazardlevel_nodata.mnemonic = "no-data"
_hazardlevel_nodata.title = "No Data"
_hazardlevel_nodata.description = "No data for this hazard type."
_hazardlevel_nodata.order = float("inf")
@view_config(route_name="report_overview", renderer="templates/report.jinja2")
@view_config(route_name="report_overview_slash",
renderer="templates/report.jinja2")
@view_config(route_name="report", renderer="templates/report.jinja2")
@view_config(route_name="report_print", renderer="templates/pdf_report.jinja2")
def report(request):
try:
division_code = request.matchdict.get("divisioncode")
except: