def get_mesh_hcurves(oqparam):
"""
Read CSV data in the format `lon lat, v1-vN, w1-wN, ...`.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
the mesh of points and the data as a dictionary
imt -> array of curves for each site
"""
imtls = oqparam.imtls
lon_lats = set()
data = AccumDict() # imt -> list of arrays
ncols = len(imtls) + 1 # lon_lat + curve_per_imt ...
csvfile = oqparam.inputs["hazard_curves"]
for line, row in enumerate(csv.reader(csvfile), 1):
try:
if len(row) != ncols:
raise ValueError("Expected %d columns, found %d" % ncols, len(row))
x, y = row[0].split()
lon_lat = valid.longitude(x), valid.latitude(y)
if lon_lat in lon_lats:
raise DuplicatedPoint(lon_lat)
lon_lats.add(lon_lat)
for i, imt_ in enumerate(imtls, 1):
values = valid.decreasing_probabilities(row[i])
if len(values) != len(imtls[imt_]):
raise ValueError("Found %d values, expected %d" % (len(values), len(imtls([imt_]))))
data += {imt_: [numpy.array(values)]}
except (ValueError, DuplicatedPoint) as err:
raise err.__class__("%s: file %s, line %d" % (err, csvfile, line))
lons, lats = zip(*sorted(lon_lats))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
return mesh, {imt: numpy.array(lst) for imt, lst in data.items()}
def test_latitude(self):
self.assertEqual(valid.latitude('1'), 1.0)
self.assertEqual(valid.latitude('90'), 90.0)
with self.assertRaises(ValueError):
valid.latitude('91')
with self.assertRaises(ValueError):
valid.latitude('-91')
def split_coords_2d(seq):
"""
:param seq: a flat list with lons and lats
:returns: a validated list of pairs (lon, lat)
>>> split_coords_2d([1.1, 2.1, 2.2, 2.3])
[(1.1, 2.1), (2.2, 2.3)]
"""
lons, lats = [], []
for i, el in enumerate(seq):
if i % 2 == 0:
lons.append(valid.longitude(el))
elif i % 2 == 1:
lats.append(valid.latitude(el))
return list(zip(lons, lats))
def split_coords_2d(seq):
"""
:param seq: a flat list with lons and lats
:returns: a validated list of pairs (lon, lat)
>>> split_coords_2d([1.1, 2.1, 2.2, 2.3])
[(1.1, 2.1), (2.2, 2.3)]
"""
lons, lats = [], []
for i, el in enumerate(seq):
if i % 2 == 0:
lons.append(valid.longitude(el))
elif i % 2 == 1:
lats.append(valid.latitude(el))
return list(zip(lons, lats))
def split_coords_3d(seq):
"""
:param seq: a flat list with lons, lats and depths
:returns: a validated list of (lon, lat, depths) triplets
>>> split_coords_3d([1.1, 2.1, 0.1, 2.3, 2.4, 0.1])
[(1.1, 2.1, 0.1), (2.3, 2.4, 0.1)]
"""
lons, lats, depths = [], [], []
for i, el in enumerate(seq):
if i % 3 == 0:
lons.append(valid.longitude(el))
elif i % 3 == 1:
lats.append(valid.latitude(el))
elif i % 3 == 2:
depths.append(valid.depth(el))
return list(zip(lons, lats, depths))
def split_coords_3d(seq):
"""
:param seq: a flat list with lons, lats and depths
:returns: a validated list of (lon, lat, depths) triplets
>>> split_coords_3d([1.1, 2.1, 0.1, 2.3, 2.4, 0.1])
[(1.1, 2.1, 0.1), (2.3, 2.4, 0.1)]
"""
lons, lats, depths = [], [], []
for i, el in enumerate(seq):
if i % 3 == 0:
lons.append(valid.longitude(el))
elif i % 3 == 1:
lats.append(valid.latitude(el))
elif i % 3 == 2:
depths.append(valid.depth(el))
return list(zip(lons, lats, depths))
def get_mesh_csvdata(csvfile, imts, num_values, validvalues):
"""
Read CSV data in the format `IMT lon lat value1 ... valueN`.
:param csvfile:
a file or file-like object with the CSV data
:param imts:
a list of intensity measure types
:param num_values:
dictionary with the number of expected values per IMT
:param validvalues:
validation function for the values
:returns:
the mesh of points and the data as a dictionary
imt -> array of curves for each site
"""
number_of_values = dict(zip(imts, num_values))
lon_lats = {imt: set() for imt in imts}
data = AccumDict() # imt -> list of arrays
check_imt = valid.Choice(*imts)
for line, row in enumerate(csv.reader(csvfile, delimiter=' '), 1):
try:
imt = check_imt(row[0])
lon_lat = valid.longitude(row[1]), valid.latitude(row[2])
if lon_lat in lon_lats[imt]:
raise DuplicatedPoint(lon_lat)
lon_lats[imt].add(lon_lat)
values = validvalues(' '.join(row[3:]))
if len(values) != number_of_values[imt]:
raise ValueError('Found %d values, expected %d' %
(len(values), number_of_values[imt]))
except (ValueError, DuplicatedPoint) as err:
raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
data += {imt: [numpy.array(values)]}
points = lon_lats.pop(imts[0])
for other_imt, other_points in lon_lats.iteritems():
if points != other_points:
raise ValueError('Inconsistent locations between %s and %s' %
(imts[0], other_imt))
lons, lats = zip(*sorted(points))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
return mesh, {imt: numpy.array(lst) for imt, lst in data.iteritems()}
def get_mesh_csvdata(csvfile, imts, num_values, validvalues):
"""
Read CSV data in the format `IMT lon lat value1 ... valueN`.
:param csvfile:
a file or file-like object with the CSV data
:param imts:
a list of intensity measure types
:param num_values:
dictionary with the number of expected values per IMT
:param validvalues:
validation function for the values
:returns:
the mesh of points and the data as a dictionary
imt -> array of curves for each site
"""
number_of_values = dict(zip(imts, num_values))
lon_lats = {imt: set() for imt in imts}
data = AccumDict() # imt -> list of arrays
check_imt = valid.Choice(*imts)
for line, row in enumerate(csv.reader(csvfile, delimiter=' '), 1):
try:
imt = check_imt(row[0])
lon_lat = valid.longitude(row[1]), valid.latitude(row[2])
if lon_lat in lon_lats[imt]:
raise DuplicatedPoint(lon_lat)
lon_lats[imt].add(lon_lat)
values = validvalues(' '.join(row[3:]))
if len(values) != number_of_values[imt]:
raise ValueError('Found %d values, expected %d' %
(len(values), number_of_values[imt]))
except (ValueError, DuplicatedPoint) as err:
raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
data += {imt: [numpy.array(values)]}
points = lon_lats.pop(imts[0])
for other_imt, other_points in lon_lats.items():
if points != other_points:
raise ValueError('Inconsistent locations between %s and %s' %
(imts[0], other_imt))
lons, lats = zip(*sorted(points))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
return mesh, {imt: numpy.array(lst) for imt, lst in data.items()}
def get_mesh_hcurves(oqparam):
"""
Read CSV data in the format `lon lat, v1-vN, w1-wN, ...`.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
the mesh of points and the data as a dictionary
imt -> array of curves for each site
"""
imtls = oqparam.imtls
lon_lats = set()
data = AccumDict() # imt -> list of arrays
ncols = len(imtls) + 1 # lon_lat + curve_per_imt ...
csvfile = oqparam.inputs['hazard_curves']
for line, row in enumerate(csv.reader(csvfile), 1):
try:
if len(row) != ncols:
raise ValueError('Expected %d columns, found %d' % ncols,
len(row))
x, y = row[0].split()
lon_lat = valid.longitude(x), valid.latitude(y)
if lon_lat in lon_lats:
raise DuplicatedPoint(lon_lat)
lon_lats.add(lon_lat)
for i, imt_ in enumerate(imtls, 1):
values = valid.decreasing_probabilities(row[i])
if len(values) != len(imtls[imt_]):
raise ValueError('Found %d values, expected %d' %
(len(values), len(imtls([imt_]))))
data += {imt_: [numpy.array(values)]}
except (ValueError, DuplicatedPoint) as err:
raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
lons, lats = zip(*sorted(lon_lats))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
return mesh, {imt: numpy.array(lst) for imt, lst in data.items()}
def insert_datum(self, asset_data):
"""
Insert a single asset entry.
:param asset_data:
an instance of :class:`openquake.commonlib.risk_parsers.AssetData`
"""
for cost_type in self.cost_types:
if not any(cost_type == cost.cost_type
for cost in asset_data.costs):
if cost_type in self.ignore_missing_costs:
LOG.warn('asset %s, %s cost is missing',
asset_data.asset_ref, cost_type)
else:
raise ValueError("Invalid Exposure. "
"Missing cost %s for asset %s" % (
cost_type, asset_data.asset_ref))
asset = models.ExposureData.objects.create(
exposure_model=self.model,
asset_ref=asset_data.asset_ref,
taxonomy=asset_data.taxonomy,
area=asset_data.area,
number_of_units=asset_data.number,
site="POINT(%s %s)" % (valid.longitude(asset_data.site.longitude),
valid.latitude(asset_data.site.latitude)))
model = asset_data.exposure_metadata
deductible_is_absolute = model.conversions.deductible_is_absolute
insurance_limit_is_absolute = (
model.conversions.insurance_limit_is_absolute)
for cost in asset_data.costs:
cost_type = self.cost_types.get(cost.cost_type, None)
if cost_type is None:
raise ValueError("Invalid Exposure. Missing conversion "
"for cost type %s" % cost.cost_type)
if cost.retrofitted is not None:
retrofitted = models.ExposureData.per_asset_value(
cost.retrofitted, cost_type.retrofitted_conversion,
asset_data.area,
model.conversions.area_type,
asset_data.number,
model.asset_category)
else:
retrofitted = None
converted_cost = models.ExposureData.per_asset_value(
cost.value, cost_type.conversion,
asset_data.area,
model.conversions.area_type,
asset_data.number,
model.asset_category)
models.Cost.objects.create(
exposure_data=asset,
cost_type=cost_type,
converted_cost=converted_cost,
converted_retrofitted_cost=retrofitted,
deductible_absolute=models.make_absolute(
cost.deductible,
converted_cost,
deductible_is_absolute),
insurance_limit_absolute=models.make_absolute(
cost.limit,
converted_cost,
insurance_limit_is_absolute))
for odata in asset_data.occupancy:
models.Occupancy.objects.create(exposure_data=asset,
occupants=odata.occupants,
period=odata.period)
