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gem/oq-engine | openquake/hazardlib/gsim/tavakoli_pezeshk_2005.py | TavakoliPezeshk2005._compute_anelastic_attenuation_term | def _compute_anelastic_attenuation_term(self, C, rrup, mag):
"""
Compute magnitude-distance scaling term as defined in equation 21,
page 2291 (Tavakoli and Pezeshk, 2005)
"""
r = (rrup**2. + (C['c5'] * np.exp(C['c6'] * mag +
C['c7'] * (8.5 - mag)**2.5))**2.)**.5
f3 = ((C['c4'] + C['c13'] * mag) * np.log(r) +
(C['c8'] + C['c12'] * mag) * r)
return f3 | python | def _compute_anelastic_attenuation_term(self, C, rrup, mag):
r = (rrup**2. + (C['c5'] * np.exp(C['c6'] * mag +
C['c7'] * (8.5 - mag)**2.5))**2.)**.5
f3 = ((C['c4'] + C['c13'] * mag) * np.log(r) +
(C['c8'] + C['c12'] * mag) * r)
return f3 | [
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gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | edge_node | def edge_node(name, points):
"""
:param name: 'faultTopEdge', 'intermediateEdge' or 'faultBottomEdge'
:param points: a list of Point objects
:returns: a Node of kind faultTopEdge, intermediateEdge or faultBottomEdge
"""
line = []
for point in points:
line.append(point.longitude)
line.append(point.latitude)
line.append(point.depth)
pos = Node('gml:posList', {}, line)
node = Node(name, nodes=[Node('gml:LineString', nodes=[pos])])
return node | python | def edge_node(name, points):
line = []
for point in points:
line.append(point.longitude)
line.append(point.latitude)
line.append(point.depth)
pos = Node('gml:posList', {}, line)
node = Node(name, nodes=[Node('gml:LineString', nodes=[pos])])
return node | [
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:param points: a list of Point objects
:returns: a Node of kind faultTopEdge, intermediateEdge or faultBottomEdge | [
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gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | complex_fault_node | def complex_fault_node(edges):
"""
:param edges: a list of lists of points
:returns: a Node of kind complexFaultGeometry
"""
node = Node('complexFaultGeometry')
node.append(edge_node('faultTopEdge', edges[0]))
for edge in edges[1:-1]:
node.append(edge_node('intermediateEdge', edge))
node.append(edge_node('faultBottomEdge', edges[-1]))
return node | python | def complex_fault_node(edges):
node = Node('complexFaultGeometry')
node.append(edge_node('faultTopEdge', edges[0]))
for edge in edges[1:-1]:
node.append(edge_node('intermediateEdge', edge))
node.append(edge_node('faultBottomEdge', edges[-1]))
return node | [
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:returns: a Node of kind complexFaultGeometry | [
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gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.get_dip | def get_dip(self):
"""
Return the fault dip as the average dip over the mesh.
The average dip is defined as the weighted mean inclination
of all the mesh cells. See
:meth:`openquake.hazardlib.geo.mesh.RectangularMesh.get_mean_inclination_and_azimuth`
:returns:
The average dip, in decimal degrees.
"""
# uses the same approach as in simple fault surface
if self.dip is None:
mesh = self.mesh
self.dip, self.strike = mesh.get_mean_inclination_and_azimuth()
return self.dip | python | def get_dip(self):
if self.dip is None:
mesh = self.mesh
self.dip, self.strike = mesh.get_mean_inclination_and_azimuth()
return self.dip | [
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gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.check_aki_richards_convention | def check_aki_richards_convention(cls, edges):
"""
Verify that surface (as defined by corner points) conforms with Aki and
Richard convention (i.e. surface dips right of surface strike)
This method doesn't have to be called by hands before creating the
surface object, because it is called from :meth:`from_fault_data`.
"""
# 1) extract 4 corner points of surface mesh
# 2) compute cross products between left and right edges and top edge
# (these define vectors normal to the surface)
# 3) compute dot products between cross product results and
# position vectors associated with upper left and right corners (if
# both angles are less then 90 degrees then the surface is correctly
# defined)
ul = edges[0].points[0]
ur = edges[0].points[-1]
bl = edges[-1].points[0]
br = edges[-1].points[-1]
ul, ur, bl, br = spherical_to_cartesian(
[ul.longitude, ur.longitude, bl.longitude, br.longitude],
[ul.latitude, ur.latitude, bl.latitude, br.latitude],
[ul.depth, ur.depth, bl.depth, br.depth],
)
top_edge = ur - ul
left_edge = bl - ul
right_edge = br - ur
left_cross_top = numpy.cross(left_edge, top_edge)
right_cross_top = numpy.cross(right_edge, top_edge)
left_cross_top /= numpy.sqrt(numpy.dot(left_cross_top, left_cross_top))
right_cross_top /= numpy.sqrt(
numpy.dot(right_cross_top, right_cross_top)
)
ul /= numpy.sqrt(numpy.dot(ul, ul))
ur /= numpy.sqrt(numpy.dot(ur, ur))
# rounding to 1st digit, to avoid ValueError raised for floating point
# imprecision
angle_ul = round(
numpy.degrees(numpy.arccos(numpy.dot(ul, left_cross_top))), 1
)
angle_ur = round(
numpy.degrees(numpy.arccos(numpy.dot(ur, right_cross_top))), 1
)
if (angle_ul > 90) or (angle_ur > 90):
raise ValueError(
"Surface does not conform with Aki & Richards convention"
) | python | def check_aki_richards_convention(cls, edges):
ul = edges[0].points[0]
ur = edges[0].points[-1]
bl = edges[-1].points[0]
br = edges[-1].points[-1]
ul, ur, bl, br = spherical_to_cartesian(
[ul.longitude, ur.longitude, bl.longitude, br.longitude],
[ul.latitude, ur.latitude, bl.latitude, br.latitude],
[ul.depth, ur.depth, bl.depth, br.depth],
)
top_edge = ur - ul
left_edge = bl - ul
right_edge = br - ur
left_cross_top = numpy.cross(left_edge, top_edge)
right_cross_top = numpy.cross(right_edge, top_edge)
left_cross_top /= numpy.sqrt(numpy.dot(left_cross_top, left_cross_top))
right_cross_top /= numpy.sqrt(
numpy.dot(right_cross_top, right_cross_top)
)
ul /= numpy.sqrt(numpy.dot(ul, ul))
ur /= numpy.sqrt(numpy.dot(ur, ur))
angle_ul = round(
numpy.degrees(numpy.arccos(numpy.dot(ul, left_cross_top))), 1
)
angle_ur = round(
numpy.degrees(numpy.arccos(numpy.dot(ur, right_cross_top))), 1
)
if (angle_ul > 90) or (angle_ur > 90):
raise ValueError(
"Surface does not conform with Aki & Richards convention"
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gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.check_surface_validity | def check_surface_validity(cls, edges):
"""
Check validity of the surface.
Project edge points to vertical plane anchored to surface upper left
edge and with strike equal to top edge strike. Check that resulting
polygon is valid.
This method doesn't have to be called by hands before creating the
surface object, because it is called from :meth:`from_fault_data`.
"""
# extract coordinates of surface boundary (as defined from edges)
full_boundary = []
left_boundary = []
right_boundary = []
for i in range(1, len(edges) - 1):
left_boundary.append(edges[i].points[0])
right_boundary.append(edges[i].points[-1])
full_boundary.extend(edges[0].points)
full_boundary.extend(right_boundary)
full_boundary.extend(edges[-1].points[::-1])
full_boundary.extend(left_boundary[::-1])
lons = [p.longitude for p in full_boundary]
lats = [p.latitude for p in full_boundary]
depths = [p.depth for p in full_boundary]
# define reference plane. Corner points are separated by an arbitrary
# distance of 10 km. The mesh spacing is set to 2 km. Both corner
# distance and mesh spacing values do not affect the algorithm results.
ul = edges[0].points[0]
strike = ul.azimuth(edges[0].points[-1])
dist = 10.
ur = ul.point_at(dist, 0, strike)
bl = Point(ul.longitude, ul.latitude, ul.depth + dist)
br = bl.point_at(dist, 0, strike)
# project surface boundary to reference plane and check for
# validity.
ref_plane = PlanarSurface.from_corner_points(ul, ur, br, bl)
_, xx, yy = ref_plane._project(
spherical_to_cartesian(lons, lats, depths))
coords = [(x, y) for x, y in zip(xx, yy)]
p = shapely.geometry.Polygon(coords)
if not p.is_valid:
raise ValueError('Edges points are not in the right order') | python | def check_surface_validity(cls, edges):
full_boundary = []
left_boundary = []
right_boundary = []
for i in range(1, len(edges) - 1):
left_boundary.append(edges[i].points[0])
right_boundary.append(edges[i].points[-1])
full_boundary.extend(edges[0].points)
full_boundary.extend(right_boundary)
full_boundary.extend(edges[-1].points[::-1])
full_boundary.extend(left_boundary[::-1])
lons = [p.longitude for p in full_boundary]
lats = [p.latitude for p in full_boundary]
depths = [p.depth for p in full_boundary]
ul = edges[0].points[0]
strike = ul.azimuth(edges[0].points[-1])
dist = 10.
ur = ul.point_at(dist, 0, strike)
bl = Point(ul.longitude, ul.latitude, ul.depth + dist)
br = bl.point_at(dist, 0, strike)
ref_plane = PlanarSurface.from_corner_points(ul, ur, br, bl)
_, xx, yy = ref_plane._project(
spherical_to_cartesian(lons, lats, depths))
coords = [(x, y) for x, y in zip(xx, yy)]
p = shapely.geometry.Polygon(coords)
if not p.is_valid:
raise ValueError('Edges points are not in the right order') | [
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gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.check_fault_data | def check_fault_data(cls, edges, mesh_spacing):
"""
Verify the fault data and raise ``ValueError`` if anything is wrong.
This method doesn't have to be called by hands before creating the
surface object, because it is called from :meth:`from_fault_data`.
"""
if not len(edges) >= 2:
raise ValueError("at least two edges are required")
if not all(len(edge) >= 2 for edge in edges):
raise ValueError("at least two points must be defined "
"in each edge")
if not mesh_spacing > 0.0:
raise ValueError("mesh spacing must be positive")
cls.check_surface_validity(edges)
cls.check_aki_richards_convention(edges) | python | def check_fault_data(cls, edges, mesh_spacing):
if not len(edges) >= 2:
raise ValueError("at least two edges are required")
if not all(len(edge) >= 2 for edge in edges):
raise ValueError("at least two points must be defined "
"in each edge")
if not mesh_spacing > 0.0:
raise ValueError("mesh spacing must be positive")
cls.check_surface_validity(edges)
cls.check_aki_richards_convention(edges) | [
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gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.from_fault_data | def from_fault_data(cls, edges, mesh_spacing):
"""
Create and return a fault surface using fault source data.
:param edges:
A list of at least two horizontal edges of the surface
as instances of :class:`openquake.hazardlib.geo.line.Line`. The
list should be in top-to-bottom order (the shallowest edge first).
:param mesh_spacing:
Distance between two subsequent points in a mesh, in km.
:returns:
An instance of :class:`ComplexFaultSurface` created using
that data.
:raises ValueError:
If requested mesh spacing is too big for the surface geometry
(doesn't allow to put a single mesh cell along length and/or
width).
Uses :meth:`check_fault_data` for checking parameters.
"""
cls.check_fault_data(edges, mesh_spacing)
surface_nodes = [complex_fault_node(edges)]
mean_length = numpy.mean([edge.get_length() for edge in edges])
num_hor_points = int(round(mean_length / mesh_spacing)) + 1
if num_hor_points <= 1:
raise ValueError(
'mesh spacing %.1f km is too big for mean length %.1f km' %
(mesh_spacing, mean_length)
)
edges = [edge.resample_to_num_points(num_hor_points).points
for i, edge in enumerate(edges)]
vert_edges = [Line(v_edge) for v_edge in zip(*edges)]
mean_width = numpy.mean([v_edge.get_length() for v_edge in vert_edges])
num_vert_points = int(round(mean_width / mesh_spacing)) + 1
if num_vert_points <= 1:
raise ValueError(
'mesh spacing %.1f km is too big for mean width %.1f km' %
(mesh_spacing, mean_width)
)
points = zip(*[v_edge.resample_to_num_points(num_vert_points).points
for v_edge in vert_edges])
mesh = RectangularMesh.from_points_list(list(points))
assert 1 not in mesh.shape
self = cls(mesh)
self.surface_nodes = surface_nodes
return self | python | def from_fault_data(cls, edges, mesh_spacing):
cls.check_fault_data(edges, mesh_spacing)
surface_nodes = [complex_fault_node(edges)]
mean_length = numpy.mean([edge.get_length() for edge in edges])
num_hor_points = int(round(mean_length / mesh_spacing)) + 1
if num_hor_points <= 1:
raise ValueError(
'mesh spacing %.1f km is too big for mean length %.1f km' %
(mesh_spacing, mean_length)
)
edges = [edge.resample_to_num_points(num_hor_points).points
for i, edge in enumerate(edges)]
vert_edges = [Line(v_edge) for v_edge in zip(*edges)]
mean_width = numpy.mean([v_edge.get_length() for v_edge in vert_edges])
num_vert_points = int(round(mean_width / mesh_spacing)) + 1
if num_vert_points <= 1:
raise ValueError(
'mesh spacing %.1f km is too big for mean width %.1f km' %
(mesh_spacing, mean_width)
)
points = zip(*[v_edge.resample_to_num_points(num_vert_points).points
for v_edge in vert_edges])
mesh = RectangularMesh.from_points_list(list(points))
assert 1 not in mesh.shape
self = cls(mesh)
self.surface_nodes = surface_nodes
return self | [
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:param mesh_spacing:
Distance between two subsequent points in a mesh, in km.
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gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.surface_projection_from_fault_data | def surface_projection_from_fault_data(cls, edges):
"""
Get a surface projection of the complex fault surface.
:param edges:
A list of horizontal edges of the surface as instances
of :class:`openquake.hazardlib.geo.line.Line`.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon`
describing the surface projection of the complex fault.
"""
# collect lons and lats of all the vertices of all the edges
lons = []
lats = []
for edge in edges:
for point in edge:
lons.append(point.longitude)
lats.append(point.latitude)
lons = numpy.array(lons, dtype=float)
lats = numpy.array(lats, dtype=float)
return Mesh(lons, lats, depths=None).get_convex_hull() | python | def surface_projection_from_fault_data(cls, edges):
lons = []
lats = []
for edge in edges:
for point in edge:
lons.append(point.longitude)
lats.append(point.latitude)
lons = numpy.array(lons, dtype=float)
lats = numpy.array(lats, dtype=float)
return Mesh(lons, lats, depths=None).get_convex_hull() | [
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gem/oq-engine | openquake/calculators/base.py | fix_ones | def fix_ones(pmap):
"""
Physically, an extremely small intensity measure level can have an
extremely large probability of exceedence, however that probability
cannot be exactly 1 unless the level is exactly 0. Numerically, the
PoE can be 1 and this give issues when calculating the damage (there
is a log(0) in
:class:`openquake.risklib.scientific.annual_frequency_of_exceedence`).
Here we solve the issue by replacing the unphysical probabilities 1
with .9999999999999999 (the float64 closest to 1).
"""
for sid in pmap:
array = pmap[sid].array
array[array == 1.] = .9999999999999999
return pmap | python | def fix_ones(pmap):
for sid in pmap:
array = pmap[sid].array
array[array == 1.] = .9999999999999999
return pmap | [
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gem/oq-engine | openquake/calculators/base.py | build_weights | def build_weights(realizations, imt_dt):
"""
:returns: an array with the realization weights of shape (R, M)
"""
arr = numpy.zeros((len(realizations), len(imt_dt.names)))
for m, imt in enumerate(imt_dt.names):
arr[:, m] = [rlz.weight[imt] for rlz in realizations]
return arr | python | def build_weights(realizations, imt_dt):
arr = numpy.zeros((len(realizations), len(imt_dt.names)))
for m, imt in enumerate(imt_dt.names):
arr[:, m] = [rlz.weight[imt] for rlz in realizations]
return arr | [
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gem/oq-engine | openquake/calculators/base.py | set_array | def set_array(longarray, shortarray):
"""
:param longarray: a numpy array of floats of length L >= l
:param shortarray: a numpy array of floats of length l
Fill `longarray` with the values of `shortarray`, starting from the left.
If `shortarry` is shorter than `longarray`, then the remaining elements on
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"""
longarray[:len(shortarray)] = shortarray
longarray[len(shortarray):] = numpy.nan | python | def set_array(longarray, shortarray):
longarray[:len(shortarray)] = shortarray
longarray[len(shortarray):] = numpy.nan | [
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gem/oq-engine | openquake/calculators/base.py | check_time_event | def check_time_event(oqparam, occupancy_periods):
"""
Check the `time_event` parameter in the datastore, by comparing
with the periods found in the exposure.
"""
time_event = oqparam.time_event
if time_event and time_event not in occupancy_periods:
raise ValueError(
'time_event is %s in %s, but the exposure contains %s' %
(time_event, oqparam.inputs['job_ini'],
', '.join(occupancy_periods))) | python | def check_time_event(oqparam, occupancy_periods):
time_event = oqparam.time_event
if time_event and time_event not in occupancy_periods:
raise ValueError(
'time_event is %s in %s, but the exposure contains %s' %
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', '.join(occupancy_periods))) | [
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gem/oq-engine | openquake/calculators/base.py | build_hmaps | def build_hmaps(hcurves_by_kind, slice_, imtls, poes, monitor):
"""
Build hazard maps from a slice of hazard curves.
:returns: a pair ({kind: hmaps}, slice)
"""
dic = {}
for kind, hcurves in hcurves_by_kind.items():
dic[kind] = calc.make_hmap_array(hcurves, imtls, poes, len(hcurves))
return dic, slice_ | python | def build_hmaps(hcurves_by_kind, slice_, imtls, poes, monitor):
dic = {}
for kind, hcurves in hcurves_by_kind.items():
dic[kind] = calc.make_hmap_array(hcurves, imtls, poes, len(hcurves))
return dic, slice_ | [
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gem/oq-engine | openquake/calculators/base.py | get_gmv_data | def get_gmv_data(sids, gmfs, events):
"""
Convert an array of shape (N, E, M) into an array of type gmv_data_dt
"""
N, E, M = gmfs.shape
gmv_data_dt = numpy.dtype(
[('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (M,)))])
# NB: ordering of the loops: first site, then event
lst = [(event['rlz'], sids[s], ei, gmfs[s, ei])
for s in numpy.arange(N, dtype=U32)
for ei, event in enumerate(events)]
return numpy.array(lst, gmv_data_dt) | python | def get_gmv_data(sids, gmfs, events):
N, E, M = gmfs.shape
gmv_data_dt = numpy.dtype(
[('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (M,)))])
lst = [(event['rlz'], sids[s], ei, gmfs[s, ei])
for s in numpy.arange(N, dtype=U32)
for ei, event in enumerate(events)]
return numpy.array(lst, gmv_data_dt) | [
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gem/oq-engine | openquake/calculators/base.py | save_gmfs | def save_gmfs(calculator):
"""
:param calculator: a scenario_risk/damage or event_based_risk calculator
:returns: a pair (eids, R) where R is the number of realizations
"""
dstore = calculator.datastore
oq = calculator.oqparam
logging.info('Reading gmfs from file')
if oq.inputs['gmfs'].endswith('.csv'):
# TODO: check if import_gmfs can be removed
eids = import_gmfs(
dstore, oq.inputs['gmfs'], calculator.sitecol.complete.sids)
else: # XML
eids, gmfs = readinput.eids, readinput.gmfs
E = len(eids)
events = numpy.zeros(E, rupture.events_dt)
events['eid'] = eids
calculator.eids = eids
if hasattr(oq, 'number_of_ground_motion_fields'):
if oq.number_of_ground_motion_fields != E:
raise RuntimeError(
'Expected %d ground motion fields, found %d' %
(oq.number_of_ground_motion_fields, E))
else: # set the number of GMFs from the file
oq.number_of_ground_motion_fields = E
# NB: save_gmfs redefine oq.sites in case of GMFs from XML or CSV
if oq.inputs['gmfs'].endswith('.xml'):
haz_sitecol = readinput.get_site_collection(oq)
N, E, M = gmfs.shape
save_gmf_data(dstore, haz_sitecol, gmfs[haz_sitecol.sids],
oq.imtls, events) | python | def save_gmfs(calculator):
dstore = calculator.datastore
oq = calculator.oqparam
logging.info('Reading gmfs from file')
if oq.inputs['gmfs'].endswith('.csv'):
eids = import_gmfs(
dstore, oq.inputs['gmfs'], calculator.sitecol.complete.sids)
else:
eids, gmfs = readinput.eids, readinput.gmfs
E = len(eids)
events = numpy.zeros(E, rupture.events_dt)
events['eid'] = eids
calculator.eids = eids
if hasattr(oq, 'number_of_ground_motion_fields'):
if oq.number_of_ground_motion_fields != E:
raise RuntimeError(
'Expected %d ground motion fields, found %d' %
(oq.number_of_ground_motion_fields, E))
else:
oq.number_of_ground_motion_fields = E
if oq.inputs['gmfs'].endswith('.xml'):
haz_sitecol = readinput.get_site_collection(oq)
N, E, M = gmfs.shape
save_gmf_data(dstore, haz_sitecol, gmfs[haz_sitecol.sids],
oq.imtls, events) | [
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gem/oq-engine | openquake/calculators/base.py | save_gmf_data | def save_gmf_data(dstore, sitecol, gmfs, imts, events=()):
"""
:param dstore: a :class:`openquake.baselib.datastore.DataStore` instance
:param sitecol: a :class:`openquake.hazardlib.site.SiteCollection` instance
:param gmfs: an array of shape (N, E, M)
:param imts: a list of IMT strings
:param events: E event IDs or the empty tuple
"""
if len(events) == 0:
E = gmfs.shape[1]
events = numpy.zeros(E, rupture.events_dt)
events['eid'] = numpy.arange(E, dtype=U64)
dstore['events'] = events
offset = 0
gmfa = get_gmv_data(sitecol.sids, gmfs, events)
dstore['gmf_data/data'] = gmfa
dic = general.group_array(gmfa, 'sid')
lst = []
all_sids = sitecol.complete.sids
for sid in all_sids:
rows = dic.get(sid, ())
n = len(rows)
lst.append((offset, offset + n))
offset += n
dstore['gmf_data/imts'] = ' '.join(imts)
dstore['gmf_data/indices'] = numpy.array(lst, U32) | python | def save_gmf_data(dstore, sitecol, gmfs, imts, events=()):
if len(events) == 0:
E = gmfs.shape[1]
events = numpy.zeros(E, rupture.events_dt)
events['eid'] = numpy.arange(E, dtype=U64)
dstore['events'] = events
offset = 0
gmfa = get_gmv_data(sitecol.sids, gmfs, events)
dstore['gmf_data/data'] = gmfa
dic = general.group_array(gmfa, 'sid')
lst = []
all_sids = sitecol.complete.sids
for sid in all_sids:
rows = dic.get(sid, ())
n = len(rows)
lst.append((offset, offset + n))
offset += n
dstore['gmf_data/imts'] = ' '.join(imts)
dstore['gmf_data/indices'] = numpy.array(lst, U32) | [
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gem/oq-engine | openquake/calculators/base.py | get_idxs | def get_idxs(data, eid2idx):
"""
Convert from event IDs to event indices.
:param data: an array with a field eid
:param eid2idx: a dictionary eid -> idx
:returns: the array of event indices
"""
uniq, inv = numpy.unique(data['eid'], return_inverse=True)
idxs = numpy.array([eid2idx[eid] for eid in uniq])[inv]
return idxs | python | def get_idxs(data, eid2idx):
uniq, inv = numpy.unique(data['eid'], return_inverse=True)
idxs = numpy.array([eid2idx[eid] for eid in uniq])[inv]
return idxs | [
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gem/oq-engine | openquake/calculators/base.py | import_gmfs | def import_gmfs(dstore, fname, sids):
"""
Import in the datastore a ground motion field CSV file.
:param dstore: the datastore
:param fname: the CSV file
:param sids: the site IDs (complete)
:returns: event_ids, num_rlzs
"""
array = writers.read_composite_array(fname).array
# has header rlzi, sid, eid, gmv_PGA, ...
imts = [name[4:] for name in array.dtype.names[3:]]
n_imts = len(imts)
gmf_data_dt = numpy.dtype(
[('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (n_imts,)))])
# store the events
eids = numpy.unique(array['eid'])
eids.sort()
E = len(eids)
eid2idx = dict(zip(eids, range(E)))
events = numpy.zeros(E, rupture.events_dt)
events['eid'] = eids
dstore['events'] = events
# store the GMFs
dic = general.group_array(array.view(gmf_data_dt), 'sid')
lst = []
offset = 0
for sid in sids:
n = len(dic.get(sid, []))
lst.append((offset, offset + n))
if n:
offset += n
gmvs = dic[sid]
gmvs['eid'] = get_idxs(gmvs, eid2idx)
gmvs['rlzi'] = 0 # effectively there is only 1 realization
dstore.extend('gmf_data/data', gmvs)
dstore['gmf_data/indices'] = numpy.array(lst, U32)
dstore['gmf_data/imts'] = ' '.join(imts)
sig_eps_dt = [('eid', U64), ('sig', (F32, n_imts)), ('eps', (F32, n_imts))]
dstore['gmf_data/sigma_epsilon'] = numpy.zeros(0, sig_eps_dt)
dstore['weights'] = numpy.ones((1, n_imts))
return eids | python | def import_gmfs(dstore, fname, sids):
array = writers.read_composite_array(fname).array
imts = [name[4:] for name in array.dtype.names[3:]]
n_imts = len(imts)
gmf_data_dt = numpy.dtype(
[('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (n_imts,)))])
eids = numpy.unique(array['eid'])
eids.sort()
E = len(eids)
eid2idx = dict(zip(eids, range(E)))
events = numpy.zeros(E, rupture.events_dt)
events['eid'] = eids
dstore['events'] = events
dic = general.group_array(array.view(gmf_data_dt), 'sid')
lst = []
offset = 0
for sid in sids:
n = len(dic.get(sid, []))
lst.append((offset, offset + n))
if n:
offset += n
gmvs = dic[sid]
gmvs['eid'] = get_idxs(gmvs, eid2idx)
gmvs['rlzi'] = 0
dstore.extend('gmf_data/data', gmvs)
dstore['gmf_data/indices'] = numpy.array(lst, U32)
dstore['gmf_data/imts'] = ' '.join(imts)
sig_eps_dt = [('eid', U64), ('sig', (F32, n_imts)), ('eps', (F32, n_imts))]
dstore['gmf_data/sigma_epsilon'] = numpy.zeros(0, sig_eps_dt)
dstore['weights'] = numpy.ones((1, n_imts))
return eids | [
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:param dstore: the datastore
:param fname: the CSV file
:param sids: the site IDs (complete)
:returns: event_ids, num_rlzs | [
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gem/oq-engine | openquake/calculators/base.py | BaseCalculator.monitor | def monitor(self, operation='', **kw):
"""
:returns: a new Monitor instance
"""
mon = self._monitor(operation, hdf5=self.datastore.hdf5)
self._monitor.calc_id = mon.calc_id = self.datastore.calc_id
vars(mon).update(kw)
return mon | python | def monitor(self, operation='', **kw):
mon = self._monitor(operation, hdf5=self.datastore.hdf5)
self._monitor.calc_id = mon.calc_id = self.datastore.calc_id
vars(mon).update(kw)
return mon | [
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gem/oq-engine | openquake/calculators/base.py | BaseCalculator.save_params | def save_params(self, **kw):
"""
Update the current calculation parameters and save engine_version
"""
if ('hazard_calculation_id' in kw and
kw['hazard_calculation_id'] is None):
del kw['hazard_calculation_id']
vars(self.oqparam).update(**kw)
self.datastore['oqparam'] = self.oqparam # save the updated oqparam
attrs = self.datastore['/'].attrs
attrs['engine_version'] = engine_version
attrs['date'] = datetime.now().isoformat()[:19]
if 'checksum32' not in attrs:
attrs['checksum32'] = readinput.get_checksum32(self.oqparam)
self.datastore.flush() | python | def save_params(self, **kw):
if ('hazard_calculation_id' in kw and
kw['hazard_calculation_id'] is None):
del kw['hazard_calculation_id']
vars(self.oqparam).update(**kw)
self.datastore['oqparam'] = self.oqparam
attrs = self.datastore['/'].attrs
attrs['engine_version'] = engine_version
attrs['date'] = datetime.now().isoformat()[:19]
if 'checksum32' not in attrs:
attrs['checksum32'] = readinput.get_checksum32(self.oqparam)
self.datastore.flush() | [
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gem/oq-engine | openquake/calculators/base.py | BaseCalculator.check_precalc | def check_precalc(self, precalc_mode):
"""
Defensive programming against users providing an incorrect
pre-calculation ID (with ``--hazard-calculation-id``).
:param precalc_mode:
calculation_mode of the previous calculation
"""
calc_mode = self.oqparam.calculation_mode
ok_mode = self.accept_precalc
if calc_mode != precalc_mode and precalc_mode not in ok_mode:
raise InvalidCalculationID(
'In order to run a calculation of kind %r, '
'you need to provide a calculation of kind %r, '
'but you provided a %r instead' %
(calc_mode, ok_mode, precalc_mode)) | python | def check_precalc(self, precalc_mode):
calc_mode = self.oqparam.calculation_mode
ok_mode = self.accept_precalc
if calc_mode != precalc_mode and precalc_mode not in ok_mode:
raise InvalidCalculationID(
'In order to run a calculation of kind %r, '
'you need to provide a calculation of kind %r, '
'but you provided a %r instead' %
(calc_mode, ok_mode, precalc_mode)) | [
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gem/oq-engine | openquake/calculators/base.py | BaseCalculator.run | def run(self, pre_execute=True, concurrent_tasks=None, close=True, **kw):
"""
Run the calculation and return the exported outputs.
"""
with self._monitor:
self._monitor.username = kw.get('username', '')
self._monitor.hdf5 = self.datastore.hdf5
if concurrent_tasks is None: # use the job.ini parameter
ct = self.oqparam.concurrent_tasks
else: # used the parameter passed in the command-line
ct = concurrent_tasks
if ct == 0: # disable distribution temporarily
oq_distribute = os.environ.get('OQ_DISTRIBUTE')
os.environ['OQ_DISTRIBUTE'] = 'no'
if ct != self.oqparam.concurrent_tasks:
# save the used concurrent_tasks
self.oqparam.concurrent_tasks = ct
self.save_params(**kw)
try:
if pre_execute:
self.pre_execute()
self.result = self.execute()
if self.result is not None:
self.post_execute(self.result)
self.before_export()
self.export(kw.get('exports', ''))
except Exception:
if kw.get('pdb'): # post-mortem debug
tb = sys.exc_info()[2]
traceback.print_tb(tb)
pdb.post_mortem(tb)
else:
logging.critical('', exc_info=True)
raise
finally:
# cleanup globals
if ct == 0: # restore OQ_DISTRIBUTE
if oq_distribute is None: # was not set
del os.environ['OQ_DISTRIBUTE']
else:
os.environ['OQ_DISTRIBUTE'] = oq_distribute
readinput.pmap = None
readinput.exposure = None
readinput.gmfs = None
readinput.eids = None
self._monitor.flush()
if close: # in the engine we close later
self.result = None
try:
self.datastore.close()
except (RuntimeError, ValueError):
# sometimes produces errors but they are difficult to
# reproduce
logging.warning('', exc_info=True)
return getattr(self, 'exported', {}) | python | def run(self, pre_execute=True, concurrent_tasks=None, close=True, **kw):
with self._monitor:
self._monitor.username = kw.get('username', '')
self._monitor.hdf5 = self.datastore.hdf5
if concurrent_tasks is None:
ct = self.oqparam.concurrent_tasks
else:
ct = concurrent_tasks
if ct == 0:
oq_distribute = os.environ.get('OQ_DISTRIBUTE')
os.environ['OQ_DISTRIBUTE'] = 'no'
if ct != self.oqparam.concurrent_tasks:
self.oqparam.concurrent_tasks = ct
self.save_params(**kw)
try:
if pre_execute:
self.pre_execute()
self.result = self.execute()
if self.result is not None:
self.post_execute(self.result)
self.before_export()
self.export(kw.get('exports', ''))
except Exception:
if kw.get('pdb'):
tb = sys.exc_info()[2]
traceback.print_tb(tb)
pdb.post_mortem(tb)
else:
logging.critical('', exc_info=True)
raise
finally:
if ct == 0:
if oq_distribute is None:
del os.environ['OQ_DISTRIBUTE']
else:
os.environ['OQ_DISTRIBUTE'] = oq_distribute
readinput.pmap = None
readinput.exposure = None
readinput.gmfs = None
readinput.eids = None
self._monitor.flush()
if close:
self.result = None
try:
self.datastore.close()
except (RuntimeError, ValueError):
logging.warning('', exc_info=True)
return getattr(self, 'exported', {}) | [
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gem/oq-engine | openquake/calculators/base.py | BaseCalculator.export | def export(self, exports=None):
"""
Export all the outputs in the datastore in the given export formats.
Individual outputs are not exported if there are multiple realizations.
"""
self.exported = getattr(self.precalc, 'exported', {})
if isinstance(exports, tuple):
fmts = exports
elif exports: # is a string
fmts = exports.split(',')
elif isinstance(self.oqparam.exports, tuple):
fmts = self.oqparam.exports
else: # is a string
fmts = self.oqparam.exports.split(',')
keys = set(self.datastore)
has_hcurves = ('hcurves-stats' in self.datastore or
'hcurves-rlzs' in self.datastore)
if has_hcurves:
keys.add('hcurves')
for fmt in fmts:
if not fmt:
continue
for key in sorted(keys): # top level keys
if 'rlzs' in key and self.R > 1:
continue # skip individual curves
self._export((key, fmt))
if has_hcurves and self.oqparam.hazard_maps:
self._export(('hmaps', fmt))
if has_hcurves and self.oqparam.uniform_hazard_spectra:
self._export(('uhs', fmt)) | python | def export(self, exports=None):
self.exported = getattr(self.precalc, 'exported', {})
if isinstance(exports, tuple):
fmts = exports
elif exports:
fmts = exports.split(',')
elif isinstance(self.oqparam.exports, tuple):
fmts = self.oqparam.exports
else:
fmts = self.oqparam.exports.split(',')
keys = set(self.datastore)
has_hcurves = ('hcurves-stats' in self.datastore or
'hcurves-rlzs' in self.datastore)
if has_hcurves:
keys.add('hcurves')
for fmt in fmts:
if not fmt:
continue
for key in sorted(keys):
if 'rlzs' in key and self.R > 1:
continue
self._export((key, fmt))
if has_hcurves and self.oqparam.hazard_maps:
self._export(('hmaps', fmt))
if has_hcurves and self.oqparam.uniform_hazard_spectra:
self._export(('uhs', fmt)) | [
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gem/oq-engine | openquake/calculators/base.py | BaseCalculator.before_export | def before_export(self):
"""
Set the attributes nbytes
"""
# sanity check that eff_ruptures have been set, i.e. are not -1
try:
csm_info = self.datastore['csm_info']
except KeyError:
csm_info = self.datastore['csm_info'] = self.csm.info
for sm in csm_info.source_models:
for sg in sm.src_groups:
assert sg.eff_ruptures != -1, sg
for key in self.datastore:
self.datastore.set_nbytes(key)
self.datastore.flush() | python | def before_export(self):
try:
csm_info = self.datastore['csm_info']
except KeyError:
csm_info = self.datastore['csm_info'] = self.csm.info
for sm in csm_info.source_models:
for sg in sm.src_groups:
assert sg.eff_ruptures != -1, sg
for key in self.datastore:
self.datastore.set_nbytes(key)
self.datastore.flush() | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.block_splitter | def block_splitter(self, sources, weight=get_weight, key=lambda src: 1):
"""
:param sources: a list of sources
:param weight: a weight function (default .weight)
:param key: None or 'src_group_id'
:returns: an iterator over blocks of sources
"""
ct = self.oqparam.concurrent_tasks or 1
maxweight = self.csm.get_maxweight(weight, ct, source.MINWEIGHT)
if not hasattr(self, 'logged'):
if maxweight == source.MINWEIGHT:
logging.info('Using minweight=%d', source.MINWEIGHT)
else:
logging.info('Using maxweight=%d', maxweight)
self.logged = True
return general.block_splitter(sources, maxweight, weight, key) | python | def block_splitter(self, sources, weight=get_weight, key=lambda src: 1):
ct = self.oqparam.concurrent_tasks or 1
maxweight = self.csm.get_maxweight(weight, ct, source.MINWEIGHT)
if not hasattr(self, 'logged'):
if maxweight == source.MINWEIGHT:
logging.info('Using minweight=%d', source.MINWEIGHT)
else:
logging.info('Using maxweight=%d', maxweight)
self.logged = True
return general.block_splitter(sources, maxweight, weight, key) | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.src_filter | def src_filter(self):
"""
:returns: a SourceFilter/UcerfFilter
"""
oq = self.oqparam
self.hdf5cache = self.datastore.hdf5cache()
sitecol = self.sitecol.complete if self.sitecol else None
if 'ucerf' in oq.calculation_mode:
return UcerfFilter(sitecol, oq.maximum_distance, self.hdf5cache)
return SourceFilter(sitecol, oq.maximum_distance, self.hdf5cache) | python | def src_filter(self):
oq = self.oqparam
self.hdf5cache = self.datastore.hdf5cache()
sitecol = self.sitecol.complete if self.sitecol else None
if 'ucerf' in oq.calculation_mode:
return UcerfFilter(sitecol, oq.maximum_distance, self.hdf5cache)
return SourceFilter(sitecol, oq.maximum_distance, self.hdf5cache) | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.rtree_filter | def rtree_filter(self):
"""
:returns: an RtreeFilter
"""
return RtreeFilter(self.src_filter.sitecol,
self.oqparam.maximum_distance,
self.src_filter.filename) | python | def rtree_filter(self):
return RtreeFilter(self.src_filter.sitecol,
self.oqparam.maximum_distance,
self.src_filter.filename) | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.N | def N(self):
"""
:returns: the total number of sites
"""
if hasattr(self, 'sitecol'):
return len(self.sitecol.complete) if self.sitecol else None
return len(self.datastore['sitecol/array']) | python | def N(self):
if hasattr(self, 'sitecol'):
return len(self.sitecol.complete) if self.sitecol else None
return len(self.datastore['sitecol/array']) | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.read_inputs | def read_inputs(self):
"""
Read risk data and sources if any
"""
oq = self.oqparam
self._read_risk_data()
self.check_overflow() # check if self.sitecol is too large
if ('source_model_logic_tree' in oq.inputs and
oq.hazard_calculation_id is None):
self.csm = readinput.get_composite_source_model(
oq, self.monitor(), srcfilter=self.src_filter)
self.init() | python | def read_inputs(self):
oq = self.oqparam
self._read_risk_data()
self.check_overflow()
if ('source_model_logic_tree' in oq.inputs and
oq.hazard_calculation_id is None):
self.csm = readinput.get_composite_source_model(
oq, self.monitor(), srcfilter=self.src_filter)
self.init() | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.pre_execute | def pre_execute(self):
"""
Check if there is a previous calculation ID.
If yes, read the inputs by retrieving the previous calculation;
if not, read the inputs directly.
"""
oq = self.oqparam
if 'gmfs' in oq.inputs or 'multi_peril' in oq.inputs:
# read hazard from files
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with gmfs_file')
self.read_inputs()
if 'gmfs' in oq.inputs:
save_gmfs(self)
else:
self.save_multi_peril()
elif 'hazard_curves' in oq.inputs: # read hazard from file
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with hazard_curves')
haz_sitecol = readinput.get_site_collection(oq)
# NB: horrible: get_site_collection calls get_pmap_from_nrml
# that sets oq.investigation_time, so it must be called first
self.load_riskmodel() # must be after get_site_collection
self.read_exposure(haz_sitecol) # define .assets_by_site
self.datastore['poes/grp-00'] = fix_ones(readinput.pmap)
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc()
elif oq.hazard_calculation_id:
parent = util.read(oq.hazard_calculation_id)
self.check_precalc(parent['oqparam'].calculation_mode)
self.datastore.parent = parent
# copy missing parameters from the parent
params = {name: value for name, value in
vars(parent['oqparam']).items()
if name not in vars(self.oqparam)}
self.save_params(**params)
self.read_inputs()
oqp = parent['oqparam']
if oqp.investigation_time != oq.investigation_time:
raise ValueError(
'The parent calculation was using investigation_time=%s'
' != %s' % (oqp.investigation_time, oq.investigation_time))
if oqp.minimum_intensity != oq.minimum_intensity:
raise ValueError(
'The parent calculation was using minimum_intensity=%s'
' != %s' % (oqp.minimum_intensity, oq.minimum_intensity))
missing_imts = set(oq.risk_imtls) - set(oqp.imtls)
if missing_imts:
raise ValueError(
'The parent calculation is missing the IMT(s) %s' %
', '.join(missing_imts))
elif self.__class__.precalc:
calc = calculators[self.__class__.precalc](
self.oqparam, self.datastore.calc_id)
calc.run()
self.param = calc.param
self.sitecol = calc.sitecol
self.assetcol = calc.assetcol
self.riskmodel = calc.riskmodel
if hasattr(calc, 'rlzs_assoc'):
self.rlzs_assoc = calc.rlzs_assoc
else:
# this happens for instance for a scenario_damage without
# rupture, gmfs, multi_peril
raise InvalidFile(
'%(job_ini)s: missing gmfs_csv, multi_peril_csv' %
oq.inputs)
if hasattr(calc, 'csm'): # no scenario
self.csm = calc.csm
else:
self.read_inputs()
if self.riskmodel:
self.save_riskmodel() | python | def pre_execute(self):
oq = self.oqparam
if 'gmfs' in oq.inputs or 'multi_peril' in oq.inputs:
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with gmfs_file')
self.read_inputs()
if 'gmfs' in oq.inputs:
save_gmfs(self)
else:
self.save_multi_peril()
elif 'hazard_curves' in oq.inputs:
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with hazard_curves')
haz_sitecol = readinput.get_site_collection(oq)
self.load_riskmodel()
self.read_exposure(haz_sitecol)
self.datastore['poes/grp-00'] = fix_ones(readinput.pmap)
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc()
elif oq.hazard_calculation_id:
parent = util.read(oq.hazard_calculation_id)
self.check_precalc(parent['oqparam'].calculation_mode)
self.datastore.parent = parent
params = {name: value for name, value in
vars(parent['oqparam']).items()
if name not in vars(self.oqparam)}
self.save_params(**params)
self.read_inputs()
oqp = parent['oqparam']
if oqp.investigation_time != oq.investigation_time:
raise ValueError(
'The parent calculation was using investigation_time=%s'
' != %s' % (oqp.investigation_time, oq.investigation_time))
if oqp.minimum_intensity != oq.minimum_intensity:
raise ValueError(
'The parent calculation was using minimum_intensity=%s'
' != %s' % (oqp.minimum_intensity, oq.minimum_intensity))
missing_imts = set(oq.risk_imtls) - set(oqp.imtls)
if missing_imts:
raise ValueError(
'The parent calculation is missing the IMT(s) %s' %
', '.join(missing_imts))
elif self.__class__.precalc:
calc = calculators[self.__class__.precalc](
self.oqparam, self.datastore.calc_id)
calc.run()
self.param = calc.param
self.sitecol = calc.sitecol
self.assetcol = calc.assetcol
self.riskmodel = calc.riskmodel
if hasattr(calc, 'rlzs_assoc'):
self.rlzs_assoc = calc.rlzs_assoc
else:
raise InvalidFile(
'%(job_ini)s: missing gmfs_csv, multi_peril_csv' %
oq.inputs)
if hasattr(calc, 'csm'):
self.csm = calc.csm
else:
self.read_inputs()
if self.riskmodel:
self.save_riskmodel() | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.init | def init(self):
"""
To be overridden to initialize the datasets needed by the calculation
"""
oq = self.oqparam
if not oq.risk_imtls:
if self.datastore.parent:
oq.risk_imtls = (
self.datastore.parent['oqparam'].risk_imtls)
if 'precalc' in vars(self):
self.rlzs_assoc = self.precalc.rlzs_assoc
elif 'csm_info' in self.datastore:
csm_info = self.datastore['csm_info']
if oq.hazard_calculation_id and 'gsim_logic_tree' in oq.inputs:
# redefine the realizations by reading the weights from the
# gsim_logic_tree_file that could be different from the parent
csm_info.gsim_lt = logictree.GsimLogicTree(
oq.inputs['gsim_logic_tree'], set(csm_info.trts))
self.rlzs_assoc = csm_info.get_rlzs_assoc()
elif hasattr(self, 'csm'):
self.check_floating_spinning()
self.rlzs_assoc = self.csm.info.get_rlzs_assoc()
else: # build a fake; used by risk-from-file calculators
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc() | python | def init(self):
oq = self.oqparam
if not oq.risk_imtls:
if self.datastore.parent:
oq.risk_imtls = (
self.datastore.parent['oqparam'].risk_imtls)
if 'precalc' in vars(self):
self.rlzs_assoc = self.precalc.rlzs_assoc
elif 'csm_info' in self.datastore:
csm_info = self.datastore['csm_info']
if oq.hazard_calculation_id and 'gsim_logic_tree' in oq.inputs:
csm_info.gsim_lt = logictree.GsimLogicTree(
oq.inputs['gsim_logic_tree'], set(csm_info.trts))
self.rlzs_assoc = csm_info.get_rlzs_assoc()
elif hasattr(self, 'csm'):
self.check_floating_spinning()
self.rlzs_assoc = self.csm.info.get_rlzs_assoc()
else:
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc() | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.R | def R(self):
"""
:returns: the number of realizations
"""
try:
return self.csm.info.get_num_rlzs()
except AttributeError: # no self.csm
return self.datastore['csm_info'].get_num_rlzs() | python | def R(self):
try:
return self.csm.info.get_num_rlzs()
except AttributeError:
return self.datastore['csm_info'].get_num_rlzs() | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.read_exposure | def read_exposure(self, haz_sitecol=None): # after load_risk_model
"""
Read the exposure, the riskmodel and update the attributes
.sitecol, .assetcol
"""
with self.monitor('reading exposure', autoflush=True):
self.sitecol, self.assetcol, discarded = (
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if len(discarded):
self.datastore['discarded'] = discarded
if hasattr(self, 'rup'):
# this is normal for the case of scenario from rupture
logging.info('%d assets were discarded because too far '
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'to show them and `oq plot_assets` to plot '
'them' % len(discarded))
elif not self.oqparam.discard_assets: # raise an error
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(
'%d assets were discarded; use `oq show discarded` to'
' show them and `oq plot_assets` to plot them' %
len(discarded))
# reduce the riskmodel to the relevant taxonomies
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
if len(self.riskmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.riskmodel.taxonomies), len(taxonomies))
self.riskmodel = self.riskmodel.reduce(taxonomies)
return readinput.exposure | python | def read_exposure(self, haz_sitecol=None):
with self.monitor('reading exposure', autoflush=True):
self.sitecol, self.assetcol, discarded = (
readinput.get_sitecol_assetcol(
self.oqparam, haz_sitecol, self.riskmodel.loss_types))
if len(discarded):
self.datastore['discarded'] = discarded
if hasattr(self, 'rup'):
logging.info('%d assets were discarded because too far '
'from the rupture; use `oq show discarded` '
'to show them and `oq plot_assets` to plot '
'them' % len(discarded))
elif not self.oqparam.discard_assets:
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(
'%d assets were discarded; use `oq show discarded` to'
' show them and `oq plot_assets` to plot them' %
len(discarded))
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
if len(self.riskmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.riskmodel.taxonomies), len(taxonomies))
self.riskmodel = self.riskmodel.reduce(taxonomies)
return readinput.exposure | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.load_riskmodel | def load_riskmodel(self):
# to be called before read_exposure
# NB: this is called even if there is no risk model
"""
Read the risk model and set the attribute .riskmodel.
The riskmodel can be empty for hazard calculations.
Save the loss ratios (if any) in the datastore.
"""
logging.info('Reading the risk model if present')
self.riskmodel = readinput.get_risk_model(self.oqparam)
if not self.riskmodel:
parent = self.datastore.parent
if 'risk_model' in parent:
self.riskmodel = riskinput.CompositeRiskModel.read(parent)
return
if self.oqparam.ground_motion_fields and not self.oqparam.imtls:
raise InvalidFile('No intensity_measure_types specified in %s' %
self.oqparam.inputs['job_ini'])
self.save_params() | python | def load_riskmodel(self):
logging.info('Reading the risk model if present')
self.riskmodel = readinput.get_risk_model(self.oqparam)
if not self.riskmodel:
parent = self.datastore.parent
if 'risk_model' in parent:
self.riskmodel = riskinput.CompositeRiskModel.read(parent)
return
if self.oqparam.ground_motion_fields and not self.oqparam.imtls:
raise InvalidFile('No intensity_measure_types specified in %s' %
self.oqparam.inputs['job_ini'])
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.save_riskmodel | def save_riskmodel(self):
"""
Save the risk models in the datastore
"""
self.datastore['risk_model'] = rm = self.riskmodel
self.datastore['taxonomy_mapping'] = self.riskmodel.tmap
attrs = self.datastore.getitem('risk_model').attrs
attrs['min_iml'] = hdf5.array_of_vstr(sorted(rm.min_iml.items()))
self.datastore.set_nbytes('risk_model') | python | def save_riskmodel(self):
self.datastore['risk_model'] = rm = self.riskmodel
self.datastore['taxonomy_mapping'] = self.riskmodel.tmap
attrs = self.datastore.getitem('risk_model').attrs
attrs['min_iml'] = hdf5.array_of_vstr(sorted(rm.min_iml.items()))
self.datastore.set_nbytes('risk_model') | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.store_rlz_info | def store_rlz_info(self, eff_ruptures=None):
"""
Save info about the composite source model inside the csm_info dataset
"""
if hasattr(self, 'csm'): # no scenario
self.csm.info.update_eff_ruptures(eff_ruptures)
self.rlzs_assoc = self.csm.info.get_rlzs_assoc(
self.oqparam.sm_lt_path)
if not self.rlzs_assoc:
raise RuntimeError('Empty logic tree: too much filtering?')
self.datastore['csm_info'] = self.csm.info
R = len(self.rlzs_assoc.realizations)
logging.info('There are %d realization(s)', R)
if self.oqparam.imtls:
self.datastore['weights'] = arr = build_weights(
self.rlzs_assoc.realizations, self.oqparam.imt_dt())
self.datastore.set_attrs('weights', nbytes=arr.nbytes)
if hasattr(self, 'hdf5cache'): # no scenario
with hdf5.File(self.hdf5cache, 'r+') as cache:
cache['weights'] = arr
if 'event_based' in self.oqparam.calculation_mode and R >= TWO16:
# rlzi is 16 bit integer in the GMFs, so there is hard limit or R
raise ValueError(
'The logic tree has %d realizations, the maximum '
'is %d' % (R, TWO16))
elif R > 10000:
logging.warning(
'The logic tree has %d realizations(!), please consider '
'sampling it', R)
self.datastore.flush() | python | def store_rlz_info(self, eff_ruptures=None):
if hasattr(self, 'csm'):
self.csm.info.update_eff_ruptures(eff_ruptures)
self.rlzs_assoc = self.csm.info.get_rlzs_assoc(
self.oqparam.sm_lt_path)
if not self.rlzs_assoc:
raise RuntimeError('Empty logic tree: too much filtering?')
self.datastore['csm_info'] = self.csm.info
R = len(self.rlzs_assoc.realizations)
logging.info('There are %d realization(s)', R)
if self.oqparam.imtls:
self.datastore['weights'] = arr = build_weights(
self.rlzs_assoc.realizations, self.oqparam.imt_dt())
self.datastore.set_attrs('weights', nbytes=arr.nbytes)
if hasattr(self, 'hdf5cache'):
with hdf5.File(self.hdf5cache, 'r+') as cache:
cache['weights'] = arr
if 'event_based' in self.oqparam.calculation_mode and R >= TWO16:
raise ValueError(
'The logic tree has %d realizations, the maximum '
'is %d' % (R, TWO16))
elif R > 10000:
logging.warning(
'The logic tree has %d realizations(!), please consider '
'sampling it', R)
self.datastore.flush() | [
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gem/oq-engine | openquake/calculators/base.py | HazardCalculator.store_source_info | def store_source_info(self, calc_times):
"""
Save (weight, num_sites, calc_time) inside the source_info dataset
"""
if calc_times:
source_info = self.datastore['source_info']
arr = numpy.zeros((len(source_info), 3), F32)
ids, vals = zip(*sorted(calc_times.items()))
arr[numpy.array(ids)] = vals
source_info['weight'] += arr[:, 0]
source_info['num_sites'] += arr[:, 1]
source_info['calc_time'] += arr[:, 2] | python | def store_source_info(self, calc_times):
if calc_times:
source_info = self.datastore['source_info']
arr = numpy.zeros((len(source_info), 3), F32)
ids, vals = zip(*sorted(calc_times.items()))
arr[numpy.array(ids)] = vals
source_info['weight'] += arr[:, 0]
source_info['num_sites'] += arr[:, 1]
source_info['calc_time'] += arr[:, 2] | [
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gem/oq-engine | openquake/calculators/base.py | RiskCalculator.read_shakemap | def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
extra = self.riskmodel.get_extra_imts(oq.risk_imtls)
if extra:
logging.warning('There are risk functions for not available IMTs '
'which will be ignored: %s' % extra)
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'],
oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol = assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(
shakemap, oq.spatial_correlation, oq.cross_correlation,
oq.site_effects, oq.truncation_level, E, oq.random_seed,
oq.imtls)
save_gmf_data(self.datastore, sitecol, gmfs, imts)
return sitecol, assetcol | python | def read_shakemap(self, haz_sitecol, assetcol):
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
extra = self.riskmodel.get_extra_imts(oq.risk_imtls)
if extra:
logging.warning('There are risk functions for not available IMTs '
'which will be ignored: %s' % extra)
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'],
oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol = assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(
shakemap, oq.spatial_correlation, oq.cross_correlation,
oq.site_effects, oq.truncation_level, E, oq.random_seed,
oq.imtls)
save_gmf_data(self.datastore, sitecol, gmfs, imts)
return sitecol, assetcol | [
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gem/oq-engine | openquake/calculators/base.py | RiskCalculator.build_riskinputs | def build_riskinputs(self, kind):
"""
:param kind:
kind of hazard getter, can be 'poe' or 'gmf'
:returns:
a list of RiskInputs objects, sorted by IMT.
"""
logging.info('Building risk inputs from %d realization(s)', self.R)
imtls = self.oqparam.imtls
if not set(self.oqparam.risk_imtls) & set(imtls):
rsk = ', '.join(self.oqparam.risk_imtls)
haz = ', '.join(imtls)
raise ValueError('The IMTs in the risk models (%s) are disjoint '
"from the IMTs in the hazard (%s)" % (rsk, haz))
self.riskmodel.taxonomy = self.assetcol.tagcol.taxonomy
with self.monitor('building riskinputs', autoflush=True):
riskinputs = list(self._gen_riskinputs(kind))
assert riskinputs
logging.info('Built %d risk inputs', len(riskinputs))
return riskinputs | python | def build_riskinputs(self, kind):
logging.info('Building risk inputs from %d realization(s)', self.R)
imtls = self.oqparam.imtls
if not set(self.oqparam.risk_imtls) & set(imtls):
rsk = ', '.join(self.oqparam.risk_imtls)
haz = ', '.join(imtls)
raise ValueError('The IMTs in the risk models (%s) are disjoint '
"from the IMTs in the hazard (%s)" % (rsk, haz))
self.riskmodel.taxonomy = self.assetcol.tagcol.taxonomy
with self.monitor('building riskinputs', autoflush=True):
riskinputs = list(self._gen_riskinputs(kind))
assert riskinputs
logging.info('Built %d risk inputs', len(riskinputs))
return riskinputs | [
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gem/oq-engine | openquake/calculators/base.py | RiskCalculator.get_getter | def get_getter(self, kind, sid):
"""
:param kind: 'poe' or 'gmf'
:param sid: a site ID
:returns: a PmapGetter or GmfDataGetter
"""
hdf5cache = getattr(self, 'hdf5cache', None)
if hdf5cache:
dstore = hdf5cache
elif (self.oqparam.hazard_calculation_id and
'gmf_data' not in self.datastore):
# 'gmf_data' in self.datastore happens for ShakeMap calculations
self.datastore.parent.close() # make sure it is closed
dstore = self.datastore.parent
else:
dstore = self.datastore
if kind == 'poe': # hcurves, shape (R, N)
getter = getters.PmapGetter(dstore, self.rlzs_assoc, [sid])
else: # gmf
getter = getters.GmfDataGetter(dstore, [sid], self.R)
if dstore is self.datastore:
getter.init()
return getter | python | def get_getter(self, kind, sid):
hdf5cache = getattr(self, 'hdf5cache', None)
if hdf5cache:
dstore = hdf5cache
elif (self.oqparam.hazard_calculation_id and
'gmf_data' not in self.datastore):
self.datastore.parent.close()
dstore = self.datastore.parent
else:
dstore = self.datastore
if kind == 'poe':
getter = getters.PmapGetter(dstore, self.rlzs_assoc, [sid])
else:
getter = getters.GmfDataGetter(dstore, [sid], self.R)
if dstore is self.datastore:
getter.init()
return getter | [
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gem/oq-engine | openquake/calculators/base.py | RiskCalculator.execute | def execute(self):
"""
Parallelize on the riskinputs and returns a dictionary of results.
Require a `.core_task` to be defined with signature
(riskinputs, riskmodel, rlzs_assoc, monitor).
"""
if not hasattr(self, 'riskinputs'): # in the reportwriter
return
res = Starmap.apply(
self.core_task.__func__,
(self.riskinputs, self.riskmodel, self.param, self.monitor()),
concurrent_tasks=self.oqparam.concurrent_tasks or 1,
weight=get_weight
).reduce(self.combine)
return res | python | def execute(self):
if not hasattr(self, 'riskinputs'):
return
res = Starmap.apply(
self.core_task.__func__,
(self.riskinputs, self.riskmodel, self.param, self.monitor()),
concurrent_tasks=self.oqparam.concurrent_tasks or 1,
weight=get_weight
).reduce(self.combine)
return res | [
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gem/oq-engine | openquake/baselib/zeromq.py | bind | def bind(end_point, socket_type):
"""
Bind to a zmq URL; raise a proper error if the URL is invalid; return
a zmq socket.
"""
sock = context.socket(socket_type)
try:
sock.bind(end_point)
except zmq.error.ZMQError as exc:
sock.close()
raise exc.__class__('%s: %s' % (exc, end_point))
return sock | python | def bind(end_point, socket_type):
sock = context.socket(socket_type)
try:
sock.bind(end_point)
except zmq.error.ZMQError as exc:
sock.close()
raise exc.__class__('%s: %s' % (exc, end_point))
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gem/oq-engine | openquake/baselib/zeromq.py | Socket.send | def send(self, obj):
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if the socket type is REQ.
:param obj:
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self.zsocket.send_pyobj(obj)
self.num_sent += 1
if self.socket_type == zmq.REQ:
return self.zsocket.recv_pyobj() | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | angular_distance | def angular_distance(km, lat, lat2=None):
"""
Return the angular distance of two points at the given latitude.
>>> '%.3f' % angular_distance(100, lat=40)
'1.174'
>>> '%.3f' % angular_distance(100, lat=80)
'5.179'
"""
if lat2 is not None:
# use the largest latitude to compute the angular distance
lat = max(abs(lat), abs(lat2))
return km * KM_TO_DEGREES / math.cos(lat * DEGREES_TO_RAD) | python | def angular_distance(km, lat, lat2=None):
if lat2 is not None:
lat = max(abs(lat), abs(lat2))
return km * KM_TO_DEGREES / math.cos(lat * DEGREES_TO_RAD) | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | assoc | def assoc(objects, sitecol, assoc_dist, mode, asset_refs=()):
"""
Associate geographic objects to a site collection.
:param objects:
something with .lons, .lats or ['lon'] ['lat'], or a list of lists
of objects with a .location attribute (i.e. assets_by_site)
:param assoc_dist:
the maximum distance for association
:param mode:
if 'strict' fail if at least one site is not associated
if 'error' fail if all sites are not associated
:returns: (filtered site collection, filtered objects)
"""
if isinstance(objects, numpy.ndarray) or hasattr(objects, 'lons'):
# objects is a geo array with lon, lat fields or a mesh-like instance
return _GeographicObjects(objects).assoc(sitecol, assoc_dist, mode)
else: # objects is the list assets_by_site
return _GeographicObjects(sitecol).assoc2(
objects, assoc_dist, mode, asset_refs) | python | def assoc(objects, sitecol, assoc_dist, mode, asset_refs=()):
if isinstance(objects, numpy.ndarray) or hasattr(objects, 'lons'):
return _GeographicObjects(objects).assoc(sitecol, assoc_dist, mode)
else:
return _GeographicObjects(sitecol).assoc2(
objects, assoc_dist, mode, asset_refs) | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | clean_points | def clean_points(points):
"""
Given a list of :class:`~openquake.hazardlib.geo.point.Point` objects,
return a new list with adjacent duplicate points removed.
"""
if not points:
return points
result = [points[0]]
for point in points:
if point != result[-1]:
result.append(point)
return result | python | def clean_points(points):
if not points:
return points
result = [points[0]]
for point in points:
if point != result[-1]:
result.append(point)
return result | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | line_intersects_itself | def line_intersects_itself(lons, lats, closed_shape=False):
"""
Return ``True`` if line of points intersects itself.
Line with the last point repeating the first one considered
intersecting itself.
The line is defined by lists (or numpy arrays) of points'
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:param closed_shape:
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"""
assert len(lons) == len(lats)
if len(lons) <= 3:
# line can not intersect itself unless there are
# at least four points
return False
west, east, north, south = get_spherical_bounding_box(lons, lats)
proj = OrthographicProjection(west, east, north, south)
xx, yy = proj(lons, lats)
if not shapely.geometry.LineString(list(zip(xx, yy))).is_simple:
return True
if closed_shape:
xx, yy = proj(numpy.roll(lons, 1), numpy.roll(lats, 1))
if not shapely.geometry.LineString(list(zip(xx, yy))).is_simple:
return True
return False | python | def line_intersects_itself(lons, lats, closed_shape=False):
assert len(lons) == len(lats)
if len(lons) <= 3:
return False
west, east, north, south = get_spherical_bounding_box(lons, lats)
proj = OrthographicProjection(west, east, north, south)
xx, yy = proj(lons, lats)
if not shapely.geometry.LineString(list(zip(xx, yy))).is_simple:
return True
if closed_shape:
xx, yy = proj(numpy.roll(lons, 1), numpy.roll(lats, 1))
if not shapely.geometry.LineString(list(zip(xx, yy))).is_simple:
return True
return False | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | get_bounding_box | def get_bounding_box(obj, maxdist):
"""
Return the dilated bounding box of a geometric object.
:param obj:
an object with method .get_bounding_box, or with an attribute .polygon
or a list of locations
:param maxdist: maximum distance in km
"""
if hasattr(obj, 'get_bounding_box'):
return obj.get_bounding_box(maxdist)
elif hasattr(obj, 'polygon'):
bbox = obj.polygon.get_bbox()
else:
if isinstance(obj, list): # a list of locations
lons = numpy.array([loc.longitude for loc in obj])
lats = numpy.array([loc.latitude for loc in obj])
else: # assume an array with fields lon, lat
lons, lats = obj['lon'], obj['lat']
min_lon, max_lon = lons.min(), lons.max()
if cross_idl(min_lon, max_lon):
lons %= 360
bbox = lons.min(), lats.min(), lons.max(), lats.max()
a1 = min(maxdist * KM_TO_DEGREES, 90)
a2 = min(angular_distance(maxdist, bbox[1], bbox[3]), 180)
return bbox[0] - a2, bbox[1] - a1, bbox[2] + a2, bbox[3] + a1 | python | def get_bounding_box(obj, maxdist):
if hasattr(obj, 'get_bounding_box'):
return obj.get_bounding_box(maxdist)
elif hasattr(obj, 'polygon'):
bbox = obj.polygon.get_bbox()
else:
if isinstance(obj, list):
lons = numpy.array([loc.longitude for loc in obj])
lats = numpy.array([loc.latitude for loc in obj])
else:
lons, lats = obj['lon'], obj['lat']
min_lon, max_lon = lons.min(), lons.max()
if cross_idl(min_lon, max_lon):
lons %= 360
bbox = lons.min(), lats.min(), lons.max(), lats.max()
a1 = min(maxdist * KM_TO_DEGREES, 90)
a2 = min(angular_distance(maxdist, bbox[1], bbox[3]), 180)
return bbox[0] - a2, bbox[1] - a1, bbox[2] + a2, bbox[3] + a1 | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | get_spherical_bounding_box | def get_spherical_bounding_box(lons, lats):
"""
Given a collection of points find and return the bounding box,
as a pair of longitudes and a pair of latitudes.
Parameters define longitudes and latitudes of a point collection
respectively in a form of lists or numpy arrays.
:return:
A tuple of four items. These items represent western, eastern,
northern and southern borders of the bounding box respectively.
Values are floats in decimal degrees.
:raises ValueError:
If points collection has the longitudinal extent of more than
180 degrees (it is impossible to define a single hemisphere
bound to poles that would contain the whole collection).
"""
north, south = numpy.max(lats), numpy.min(lats)
west, east = numpy.min(lons), numpy.max(lons)
assert (-180 <= west <= 180) and (-180 <= east <= 180), (west, east)
if get_longitudinal_extent(west, east) < 0:
# points are lying on both sides of the international date line
# (meridian 180). the actual west longitude is the lowest positive
# longitude and east one is the highest negative.
if hasattr(lons, 'flatten'):
# fixes test_surface_crossing_international_date_line
lons = lons.flatten()
west = min(lon for lon in lons if lon > 0)
east = max(lon for lon in lons if lon < 0)
if not all((get_longitudinal_extent(west, lon) >= 0
and get_longitudinal_extent(lon, east) >= 0)
for lon in lons):
raise ValueError('points collection has longitudinal extent '
'wider than 180 deg')
return SphericalBB(west, east, north, south) | python | def get_spherical_bounding_box(lons, lats):
north, south = numpy.max(lats), numpy.min(lats)
west, east = numpy.min(lons), numpy.max(lons)
assert (-180 <= west <= 180) and (-180 <= east <= 180), (west, east)
if get_longitudinal_extent(west, east) < 0:
if hasattr(lons, 'flatten'):
lons = lons.flatten()
west = min(lon for lon in lons if lon > 0)
east = max(lon for lon in lons if lon < 0)
if not all((get_longitudinal_extent(west, lon) >= 0
and get_longitudinal_extent(lon, east) >= 0)
for lon in lons):
raise ValueError('points collection has longitudinal extent '
'wider than 180 deg')
return SphericalBB(west, east, north, south) | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | get_middle_point | def get_middle_point(lon1, lat1, lon2, lat2):
"""
Given two points return the point exactly in the middle lying on the same
great circle arc.
Parameters are point coordinates in degrees.
:returns:
Tuple of longitude and latitude of the point in the middle.
"""
if lon1 == lon2 and lat1 == lat2:
return lon1, lat1
dist = geodetic.geodetic_distance(lon1, lat1, lon2, lat2)
azimuth = geodetic.azimuth(lon1, lat1, lon2, lat2)
return geodetic.point_at(lon1, lat1, azimuth, dist / 2.0) | python | def get_middle_point(lon1, lat1, lon2, lat2):
if lon1 == lon2 and lat1 == lat2:
return lon1, lat1
dist = geodetic.geodetic_distance(lon1, lat1, lon2, lat2)
azimuth = geodetic.azimuth(lon1, lat1, lon2, lat2)
return geodetic.point_at(lon1, lat1, azimuth, dist / 2.0) | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | cartesian_to_spherical | def cartesian_to_spherical(vectors):
"""
Return the spherical coordinates for coordinates in Cartesian space.
This function does an opposite to :func:`spherical_to_cartesian`.
:param vectors:
Array of 3d vectors in Cartesian space of shape (..., 3)
:returns:
Tuple of three arrays of the same shape as ``vectors`` representing
longitude (decimal degrees), latitude (decimal degrees) and depth (km)
in specified order.
"""
rr = numpy.sqrt(numpy.sum(vectors * vectors, axis=-1))
xx, yy, zz = vectors.T
lats = numpy.degrees(numpy.arcsin((zz / rr).clip(-1., 1.)))
lons = numpy.degrees(numpy.arctan2(yy, xx))
depths = EARTH_RADIUS - rr
return lons.T, lats.T, depths | python | def cartesian_to_spherical(vectors):
rr = numpy.sqrt(numpy.sum(vectors * vectors, axis=-1))
xx, yy, zz = vectors.T
lats = numpy.degrees(numpy.arcsin((zz / rr).clip(-1., 1.)))
lons = numpy.degrees(numpy.arctan2(yy, xx))
depths = EARTH_RADIUS - rr
return lons.T, lats.T, depths | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | triangle_area | def triangle_area(e1, e2, e3):
"""
Get the area of triangle formed by three vectors.
Parameters are three three-dimensional numpy arrays representing
vectors of triangle's edges in Cartesian space.
:returns:
Float number, the area of the triangle in squared units of coordinates,
or numpy array of shape of edges with one dimension less.
Uses Heron formula, see http://mathworld.wolfram.com/HeronsFormula.html.
"""
# calculating edges length
e1_length = numpy.sqrt(numpy.sum(e1 * e1, axis=-1))
e2_length = numpy.sqrt(numpy.sum(e2 * e2, axis=-1))
e3_length = numpy.sqrt(numpy.sum(e3 * e3, axis=-1))
# calculating half perimeter
s = (e1_length + e2_length + e3_length) / 2.0
# applying Heron's formula
return numpy.sqrt(s * (s - e1_length) * (s - e2_length) * (s - e3_length)) | python | def triangle_area(e1, e2, e3):
e1_length = numpy.sqrt(numpy.sum(e1 * e1, axis=-1))
e2_length = numpy.sqrt(numpy.sum(e2 * e2, axis=-1))
e3_length = numpy.sqrt(numpy.sum(e3 * e3, axis=-1))
s = (e1_length + e2_length + e3_length) / 2.0
return numpy.sqrt(s * (s - e1_length) * (s - e2_length) * (s - e3_length)) | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | normalized | def normalized(vector):
"""
Get unit vector for a given one.
:param vector:
Numpy vector as coordinates in Cartesian space, or an array of such.
:returns:
Numpy array of the same shape and structure where all vectors are
normalized. That is, each coordinate component is divided by its
vector's length.
"""
length = numpy.sum(vector * vector, axis=-1)
length = numpy.sqrt(length.reshape(length.shape + (1, )))
return vector / length | python | def normalized(vector):
length = numpy.sum(vector * vector, axis=-1)
length = numpy.sqrt(length.reshape(length.shape + (1, )))
return vector / length | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | point_to_polygon_distance | def point_to_polygon_distance(polygon, pxx, pyy):
"""
Calculate the distance to polygon for each point of the collection
on the 2d Cartesian plane.
:param polygon:
Shapely "Polygon" geometry object.
:param pxx:
List or numpy array of abscissae values of points to calculate
the distance from.
:param pyy:
Same structure as ``pxx``, but with ordinate values.
:returns:
Numpy array of distances in units of coordinate system. Points
that lie inside the polygon have zero distance.
"""
pxx = numpy.array(pxx)
pyy = numpy.array(pyy)
assert pxx.shape == pyy.shape
if pxx.ndim == 0:
pxx = pxx.reshape((1, ))
pyy = pyy.reshape((1, ))
result = numpy.array([
polygon.distance(shapely.geometry.Point(pxx.item(i), pyy.item(i)))
for i in range(pxx.size)
])
return result.reshape(pxx.shape) | python | def point_to_polygon_distance(polygon, pxx, pyy):
pxx = numpy.array(pxx)
pyy = numpy.array(pyy)
assert pxx.shape == pyy.shape
if pxx.ndim == 0:
pxx = pxx.reshape((1, ))
pyy = pyy.reshape((1, ))
result = numpy.array([
polygon.distance(shapely.geometry.Point(pxx.item(i), pyy.item(i)))
for i in range(pxx.size)
])
return result.reshape(pxx.shape) | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | cross_idl | def cross_idl(lon1, lon2, *lons):
"""
Return True if two longitude values define line crossing international date
line.
>>> cross_idl(-45, 45)
False
>>> cross_idl(-180, -179)
False
>>> cross_idl(180, 179)
False
>>> cross_idl(45, -45)
False
>>> cross_idl(0, 0)
False
>>> cross_idl(-170, 170)
True
>>> cross_idl(170, -170)
True
>>> cross_idl(-180, 180)
True
"""
lons = (lon1, lon2) + lons
l1, l2 = min(lons), max(lons)
# a line crosses the international date line if the end positions
# have different sign and they are more than 180 degrees longitude apart
return l1 * l2 < 0 and abs(l1 - l2) > 180 | python | def cross_idl(lon1, lon2, *lons):
lons = (lon1, lon2) + lons
l1, l2 = min(lons), max(lons)
return l1 * l2 < 0 and abs(l1 - l2) > 180 | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | normalize_lons | def normalize_lons(l1, l2):
"""
An international date line safe way of returning a range of longitudes.
>>> normalize_lons(20, 30) # no IDL within the range
[(20, 30)]
>>> normalize_lons(-17, +17) # no IDL within the range
[(-17, 17)]
>>> normalize_lons(-178, +179)
[(-180, -178), (179, 180)]
>>> normalize_lons(178, -179)
[(-180, -179), (178, 180)]
>>> normalize_lons(179, -179)
[(-180, -179), (179, 180)]
>>> normalize_lons(177, -176)
[(-180, -176), (177, 180)]
"""
if l1 > l2: # exchange lons
l1, l2 = l2, l1
delta = l2 - l1
if l1 < 0 and l2 > 0 and delta > 180:
return [(-180, l1), (l2, 180)]
elif l1 > 0 and l2 > 180 and delta < 180:
return [(l1, 180), (-180, l2 - 360)]
elif l1 < -180 and l2 < 0 and delta < 180:
return [(l1 + 360, 180), (l2, -180)]
return [(l1, l2)] | python | def normalize_lons(l1, l2):
if l1 > l2:
l1, l2 = l2, l1
delta = l2 - l1
if l1 < 0 and l2 > 0 and delta > 180:
return [(-180, l1), (l2, 180)]
elif l1 > 0 and l2 > 180 and delta < 180:
return [(l1, 180), (-180, l2 - 360)]
elif l1 < -180 and l2 < 0 and delta < 180:
return [(l1 + 360, 180), (l2, -180)]
return [(l1, l2)] | [
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>>> normalize_lons(20, 30) # no IDL within the range
[(20, 30)]
>>> normalize_lons(-17, +17) # no IDL within the range
[(-17, 17)]
>>> normalize_lons(-178, +179)
[(-180, -178), (179, 180)]
>>> normalize_lons(178, -179)
[(-180, -179), (178, 180)]
>>> normalize_lons(179, -179)
[(-180, -179), (179, 180)]
>>> normalize_lons(177, -176)
[(-180, -176), (177, 180)] | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | within | def within(bbox, lonlat_index):
"""
:param bbox: a bounding box in lon, lat
:param lonlat_index: an rtree index in lon, lat
:returns: array of indices within the bounding box
"""
lon1, lat1, lon2, lat2 = bbox
set_ = set()
for l1, l2 in normalize_lons(lon1, lon2):
box = (l1, lat1, l2, lat2)
set_ |= set(lonlat_index.intersection(box))
return numpy.array(sorted(set_), numpy.uint32) | python | def within(bbox, lonlat_index):
lon1, lat1, lon2, lat2 = bbox
set_ = set()
for l1, l2 in normalize_lons(lon1, lon2):
box = (l1, lat1, l2, lat2)
set_ |= set(lonlat_index.intersection(box))
return numpy.array(sorted(set_), numpy.uint32) | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | plane_fit | def plane_fit(points):
"""
This fits an n-dimensional plane to a set of points. See
http://stackoverflow.com/questions/12299540/plane-fitting-to-4-or-more-xyz-points
:parameter points:
An instance of :class:~numpy.ndarray. The number of columns must be
equal to three.
:return:
A point on the plane and the normal to the plane.
"""
points = numpy.transpose(points)
points = numpy.reshape(points, (numpy.shape(points)[0], -1))
assert points.shape[0] < points.shape[1], points.shape
ctr = points.mean(axis=1)
x = points - ctr[:, None]
M = numpy.dot(x, x.T)
return ctr, numpy.linalg.svd(M)[0][:, -1] | python | def plane_fit(points):
points = numpy.transpose(points)
points = numpy.reshape(points, (numpy.shape(points)[0], -1))
assert points.shape[0] < points.shape[1], points.shape
ctr = points.mean(axis=1)
x = points - ctr[:, None]
M = numpy.dot(x, x.T)
return ctr, numpy.linalg.svd(M)[0][:, -1] | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | _GeographicObjects.get_closest | def get_closest(self, lon, lat, depth=0):
"""
Get the closest object to the given longitude and latitude
and its distance.
:param lon: longitude in degrees
:param lat: latitude in degrees
:param depth: depth in km (default 0)
:returns: (object, distance)
"""
xyz = spherical_to_cartesian(lon, lat, depth)
min_dist, idx = self.kdtree.query(xyz)
return self.objects[idx], min_dist | python | def get_closest(self, lon, lat, depth=0):
xyz = spherical_to_cartesian(lon, lat, depth)
min_dist, idx = self.kdtree.query(xyz)
return self.objects[idx], min_dist | [
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gem/oq-engine | openquake/hazardlib/geo/utils.py | _GeographicObjects.assoc | def assoc(self, sitecol, assoc_dist, mode):
"""
:param sitecol: a (filtered) site collection
:param assoc_dist: the maximum distance for association
:param mode: 'strict', 'warn' or 'filter'
:returns: filtered site collection, filtered objects, discarded
"""
assert mode in 'strict warn filter', mode
dic = {}
discarded = []
for sid, lon, lat in zip(sitecol.sids, sitecol.lons, sitecol.lats):
obj, distance = self.get_closest(lon, lat)
if assoc_dist is None:
dic[sid] = obj # associate all
elif distance <= assoc_dist:
dic[sid] = obj # associate within
elif mode == 'warn':
dic[sid] = obj # associate outside
logging.warning(
'The closest vs30 site (%.1f %.1f) is distant more than %d'
' km from site #%d (%.1f %.1f)', obj['lon'], obj['lat'],
int(distance), sid, lon, lat)
elif mode == 'filter':
discarded.append(obj)
elif mode == 'strict':
raise SiteAssociationError(
'There is nothing closer than %s km '
'to site (%s %s)' % (assoc_dist, lon, lat))
if not dic:
raise SiteAssociationError(
'No sites could be associated within %s km' % assoc_dist)
return (sitecol.filtered(dic),
numpy.array([dic[sid] for sid in sorted(dic)]),
discarded) | python | def assoc(self, sitecol, assoc_dist, mode):
assert mode in 'strict warn filter', mode
dic = {}
discarded = []
for sid, lon, lat in zip(sitecol.sids, sitecol.lons, sitecol.lats):
obj, distance = self.get_closest(lon, lat)
if assoc_dist is None:
dic[sid] = obj
elif distance <= assoc_dist:
dic[sid] = obj
elif mode == 'warn':
dic[sid] = obj
logging.warning(
'The closest vs30 site (%.1f %.1f) is distant more than %d'
' km from site
int(distance), sid, lon, lat)
elif mode == 'filter':
discarded.append(obj)
elif mode == 'strict':
raise SiteAssociationError(
'There is nothing closer than %s km '
'to site (%s %s)' % (assoc_dist, lon, lat))
if not dic:
raise SiteAssociationError(
'No sites could be associated within %s km' % assoc_dist)
return (sitecol.filtered(dic),
numpy.array([dic[sid] for sid in sorted(dic)]),
discarded) | [
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:param mode: 'strict', 'warn' or 'filter'
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gem/oq-engine | openquake/hazardlib/geo/utils.py | _GeographicObjects.assoc2 | def assoc2(self, assets_by_site, assoc_dist, mode, asset_refs):
"""
Associated a list of assets by site to the site collection used
to instantiate GeographicObjects.
:param assets_by_sites: a list of lists of assets
:param assoc_dist: the maximum distance for association
:param mode: 'strict', 'warn' or 'filter'
:param asset_ref: ID of the assets are a list of strings
:returns: filtered site collection, filtered assets by site, discarded
"""
assert mode in 'strict filter', mode
self.objects.filtered # self.objects must be a SiteCollection
asset_dt = numpy.dtype(
[('asset_ref', vstr), ('lon', F32), ('lat', F32)])
assets_by_sid = collections.defaultdict(list)
discarded = []
for assets in assets_by_site:
lon, lat = assets[0].location
obj, distance = self.get_closest(lon, lat)
if distance <= assoc_dist:
# keep the assets, otherwise discard them
assets_by_sid[obj['sids']].extend(assets)
elif mode == 'strict':
raise SiteAssociationError(
'There is nothing closer than %s km '
'to site (%s %s)' % (assoc_dist, lon, lat))
else:
discarded.extend(assets)
sids = sorted(assets_by_sid)
if not sids:
raise SiteAssociationError(
'Could not associate any site to any assets within the '
'asset_hazard_distance of %s km' % assoc_dist)
assets_by_site = [
sorted(assets_by_sid[sid], key=operator.attrgetter('ordinal'))
for sid in sids]
data = [(asset_refs[asset.ordinal],) + asset.location
for asset in discarded]
discarded = numpy.array(data, asset_dt)
return self.objects.filtered(sids), assets_by_site, discarded | python | def assoc2(self, assets_by_site, assoc_dist, mode, asset_refs):
assert mode in 'strict filter', mode
self.objects.filtered
asset_dt = numpy.dtype(
[('asset_ref', vstr), ('lon', F32), ('lat', F32)])
assets_by_sid = collections.defaultdict(list)
discarded = []
for assets in assets_by_site:
lon, lat = assets[0].location
obj, distance = self.get_closest(lon, lat)
if distance <= assoc_dist:
assets_by_sid[obj['sids']].extend(assets)
elif mode == 'strict':
raise SiteAssociationError(
'There is nothing closer than %s km '
'to site (%s %s)' % (assoc_dist, lon, lat))
else:
discarded.extend(assets)
sids = sorted(assets_by_sid)
if not sids:
raise SiteAssociationError(
'Could not associate any site to any assets within the '
'asset_hazard_distance of %s km' % assoc_dist)
assets_by_site = [
sorted(assets_by_sid[sid], key=operator.attrgetter('ordinal'))
for sid in sids]
data = [(asset_refs[asset.ordinal],) + asset.location
for asset in discarded]
discarded = numpy.array(data, asset_dt)
return self.objects.filtered(sids), assets_by_site, discarded | [
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] | train | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/utils.py#L134-L174 |
gem/oq-engine | openquake/risklib/read_nrml.py | get_vulnerability_functions_04 | def get_vulnerability_functions_04(node, fname):
"""
:param node:
a vulnerabilityModel node
:param fname:
path to the vulnerability file
:returns:
a dictionary imt, vf_id -> vulnerability function
"""
logging.warning('Please upgrade %s to NRML 0.5', fname)
# NB: the IMTs can be duplicated and with different levels, each
# vulnerability function in a set will get its own levels
imts = set()
vf_ids = set()
# imt, vf_id -> vulnerability function
vmodel = scientific.VulnerabilityModel(**node.attrib)
for vset in node:
imt_str = vset.IML['IMT']
imls = ~vset.IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
vf_id = vfun['vulnerabilityFunctionID']
if vf_id in vf_ids:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(vf_id, fname, vfun.lineno))
vf_ids.add(vf_id)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vmodel[imt_str, vf_id] = scientific.VulnerabilityFunction(
vf_id, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
return vmodel | python | def get_vulnerability_functions_04(node, fname):
logging.warning('Please upgrade %s to NRML 0.5', fname)
imts = set()
vf_ids = set()
vmodel = scientific.VulnerabilityModel(**node.attrib)
for vset in node:
imt_str = vset.IML['IMT']
imls = ~vset.IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
vf_id = vfun['vulnerabilityFunctionID']
if vf_id in vf_ids:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(vf_id, fname, vfun.lineno))
vf_ids.add(vf_id)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vmodel[imt_str, vf_id] = scientific.VulnerabilityFunction(
vf_id, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
return vmodel | [
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gem/oq-engine | openquake/risklib/read_nrml.py | get_vulnerability_functions_05 | def get_vulnerability_functions_05(node, fname):
"""
:param node:
a vulnerabilityModel node
:param fname:
path of the vulnerability filter
:returns:
a dictionary imt, vf_id -> vulnerability function
"""
# NB: the IMTs can be duplicated and with different levels, each
# vulnerability function in a set will get its own levels
vf_ids = set()
vmodel = scientific.VulnerabilityModel(**node.attrib)
# imt, vf_id -> vulnerability function
for vfun in node.getnodes('vulnerabilityFunction'):
with context(fname, vfun):
imt = vfun.imls['imt']
imls = numpy.array(~vfun.imls)
vf_id = vfun['id']
if vf_id in vf_ids:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(vf_id, fname, vfun.lineno))
vf_ids.add(vf_id)
num_probs = None
if vfun['dist'] == 'PM':
loss_ratios, probs = [], []
for probabilities in vfun[1:]:
loss_ratios.append(probabilities['lr'])
probs.append(valid.probabilities(~probabilities))
if num_probs is None:
num_probs = len(probs[-1])
elif len(probs[-1]) != num_probs:
raise ValueError(
'Wrong number of probabilities (expected %d, '
'got %d) in %s, line %d' %
(num_probs, len(probs[-1]), fname,
probabilities.lineno))
all_probs = numpy.array(probs)
assert all_probs.shape == (len(loss_ratios), len(imls)), (
len(loss_ratios), len(imls))
vmodel[imt, vf_id] = (
scientific.VulnerabilityFunctionWithPMF(
vf_id, imt, imls, numpy.array(loss_ratios),
all_probs))
# the seed will be set by readinput.get_risk_model
else:
with context(fname, vfun):
loss_ratios = ~vfun.meanLRs
coefficients = ~vfun.covLRs
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.meanLRs.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, '
'line %d' % (len(coefficients), len(imls), fname,
vfun.covLRs.lineno))
with context(fname, vfun):
vmodel[imt, vf_id] = scientific.VulnerabilityFunction(
vf_id, imt, imls, loss_ratios, coefficients,
vfun['dist'])
return vmodel | python | def get_vulnerability_functions_05(node, fname):
vf_ids = set()
vmodel = scientific.VulnerabilityModel(**node.attrib)
for vfun in node.getnodes('vulnerabilityFunction'):
with context(fname, vfun):
imt = vfun.imls['imt']
imls = numpy.array(~vfun.imls)
vf_id = vfun['id']
if vf_id in vf_ids:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(vf_id, fname, vfun.lineno))
vf_ids.add(vf_id)
num_probs = None
if vfun['dist'] == 'PM':
loss_ratios, probs = [], []
for probabilities in vfun[1:]:
loss_ratios.append(probabilities['lr'])
probs.append(valid.probabilities(~probabilities))
if num_probs is None:
num_probs = len(probs[-1])
elif len(probs[-1]) != num_probs:
raise ValueError(
'Wrong number of probabilities (expected %d, '
'got %d) in %s, line %d' %
(num_probs, len(probs[-1]), fname,
probabilities.lineno))
all_probs = numpy.array(probs)
assert all_probs.shape == (len(loss_ratios), len(imls)), (
len(loss_ratios), len(imls))
vmodel[imt, vf_id] = (
scientific.VulnerabilityFunctionWithPMF(
vf_id, imt, imls, numpy.array(loss_ratios),
all_probs))
else:
with context(fname, vfun):
loss_ratios = ~vfun.meanLRs
coefficients = ~vfun.covLRs
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.meanLRs.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, '
'line %d' % (len(coefficients), len(imls), fname,
vfun.covLRs.lineno))
with context(fname, vfun):
vmodel[imt, vf_id] = scientific.VulnerabilityFunction(
vf_id, imt, imls, loss_ratios, coefficients,
vfun['dist'])
return vmodel | [
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:returns:
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gem/oq-engine | openquake/risklib/read_nrml.py | ffconvert | def ffconvert(fname, limit_states, ff, min_iml=1E-10):
"""
Convert a fragility function into a numpy array plus a bunch
of attributes.
:param fname: path to the fragility model file
:param limit_states: expected limit states
:param ff: fragility function node
:returns: a pair (array, dictionary)
"""
with context(fname, ff):
ffs = ff[1:]
imls = ff.imls
nodamage = imls.attrib.get('noDamageLimit')
if nodamage == 0:
# use a cutoff to avoid log(0) in GMPE.to_distribution_values
logging.warning('Found a noDamageLimit=0 in %s, line %s, '
'using %g instead', fname, ff.lineno, min_iml)
nodamage = min_iml
with context(fname, imls):
attrs = dict(format=ff['format'],
imt=imls['imt'],
id=ff['id'],
nodamage=nodamage)
LS = len(limit_states)
if LS != len(ffs):
with context(fname, ff):
raise InvalidFile('expected %d limit states, found %d' %
(LS, len(ffs)))
if ff['format'] == 'continuous':
minIML = float(imls['minIML'])
if minIML == 0:
# use a cutoff to avoid log(0) in GMPE.to_distribution_values
logging.warning('Found minIML=0 in %s, line %s, using %g instead',
fname, imls.lineno, min_iml)
minIML = min_iml
attrs['minIML'] = minIML
attrs['maxIML'] = float(imls['maxIML'])
array = numpy.zeros(LS, [('mean', F64), ('stddev', F64)])
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
if ls != node['ls']:
with context(fname, node):
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
array['mean'][i] = node['mean']
array['stddev'][i] = node['stddev']
elif ff['format'] == 'discrete':
attrs['imls'] = ~imls
valid.check_levels(attrs['imls'], attrs['imt'], min_iml)
num_poes = len(attrs['imls'])
array = numpy.zeros((LS, num_poes))
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
with context(fname, node):
if ls != node['ls']:
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
poes = (~node if isinstance(~node, list)
else valid.probabilities(~node))
if len(poes) != num_poes:
raise InvalidFile('expected %s, found' %
(num_poes, len(poes)))
array[i, :] = poes
# NB: the format is constrained in nrml.FragilityNode to be either
# discrete or continuous, there is no third option
return array, attrs | python | def ffconvert(fname, limit_states, ff, min_iml=1E-10):
with context(fname, ff):
ffs = ff[1:]
imls = ff.imls
nodamage = imls.attrib.get('noDamageLimit')
if nodamage == 0:
logging.warning('Found a noDamageLimit=0 in %s, line %s, '
'using %g instead', fname, ff.lineno, min_iml)
nodamage = min_iml
with context(fname, imls):
attrs = dict(format=ff['format'],
imt=imls['imt'],
id=ff['id'],
nodamage=nodamage)
LS = len(limit_states)
if LS != len(ffs):
with context(fname, ff):
raise InvalidFile('expected %d limit states, found %d' %
(LS, len(ffs)))
if ff['format'] == 'continuous':
minIML = float(imls['minIML'])
if minIML == 0:
logging.warning('Found minIML=0 in %s, line %s, using %g instead',
fname, imls.lineno, min_iml)
minIML = min_iml
attrs['minIML'] = minIML
attrs['maxIML'] = float(imls['maxIML'])
array = numpy.zeros(LS, [('mean', F64), ('stddev', F64)])
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
if ls != node['ls']:
with context(fname, node):
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
array['mean'][i] = node['mean']
array['stddev'][i] = node['stddev']
elif ff['format'] == 'discrete':
attrs['imls'] = ~imls
valid.check_levels(attrs['imls'], attrs['imt'], min_iml)
num_poes = len(attrs['imls'])
array = numpy.zeros((LS, num_poes))
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
with context(fname, node):
if ls != node['ls']:
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
poes = (~node if isinstance(~node, list)
else valid.probabilities(~node))
if len(poes) != num_poes:
raise InvalidFile('expected %s, found' %
(num_poes, len(poes)))
array[i, :] = poes
return array, attrs | [
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:param fname: path to the fragility model file
:param limit_states: expected limit states
:param ff: fragility function node
:returns: a pair (array, dictionary) | [
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gem/oq-engine | openquake/risklib/read_nrml.py | get_fragility_model | def get_fragility_model(node, fname):
"""
:param node:
a vulnerabilityModel node
:param fname:
path to the vulnerability file
:returns:
a dictionary imt, ff_id -> fragility function list
"""
with context(fname, node):
fid = node['id']
asset_category = node['assetCategory']
loss_type = node['lossCategory']
description = ~node.description
limit_states = ~node.limitStates
ffs = node[2:]
fmodel = scientific.FragilityModel(
fid, asset_category, loss_type, description, limit_states)
for ff in ffs:
array, attrs = ffconvert(fname, limit_states, ff)
attrs['id'] = ff['id']
ffl = scientific.FragilityFunctionList(array, **attrs)
fmodel[ff.imls['imt'], ff['id']] = ffl
return fmodel | python | def get_fragility_model(node, fname):
with context(fname, node):
fid = node['id']
asset_category = node['assetCategory']
loss_type = node['lossCategory']
description = ~node.description
limit_states = ~node.limitStates
ffs = node[2:]
fmodel = scientific.FragilityModel(
fid, asset_category, loss_type, description, limit_states)
for ff in ffs:
array, attrs = ffconvert(fname, limit_states, ff)
attrs['id'] = ff['id']
ffl = scientific.FragilityFunctionList(array, **attrs)
fmodel[ff.imls['imt'], ff['id']] = ffl
return fmodel | [
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:param fname:
path to the vulnerability file
:returns:
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gem/oq-engine | openquake/risklib/read_nrml.py | convert_fragility_model_04 | def convert_fragility_model_04(node, fname, fmcounter=itertools.count(1)):
"""
:param node:
an :class:`openquake.commonib.node.Node` in NRML 0.4
:param fname:
path of the fragility file
:returns:
an :class:`openquake.commonib.node.Node` in NRML 0.5
"""
convert_type = {"lognormal": "logncdf"}
new = Node('fragilityModel',
dict(assetCategory='building',
lossCategory='structural',
id='fm_%d_converted_from_NRML_04' %
next(fmcounter)))
with context(fname, node):
fmt = node['format']
descr = ~node.description
limit_states = ~node.limitStates
new.append(Node('description', {}, descr))
new.append((Node('limitStates', {}, ' '.join(limit_states))))
for ffs in node[2:]:
IML = ffs.IML
# NB: noDamageLimit = None is different than zero
nodamage = ffs.attrib.get('noDamageLimit')
ff = Node('fragilityFunction', {'format': fmt})
ff['id'] = ~ffs.taxonomy
ff['shape'] = convert_type[ffs.attrib.get('type', 'lognormal')]
if fmt == 'continuous':
with context(fname, IML):
attr = dict(imt=IML['IMT'],
minIML=IML['minIML'],
maxIML=IML['maxIML'])
if nodamage is not None:
attr['noDamageLimit'] = nodamage
ff.append(Node('imls', attr))
for ffc in ffs[2:]:
with context(fname, ffc):
ls = ffc['ls']
param = ffc.params
with context(fname, param):
m, s = param['mean'], param['stddev']
ff.append(Node('params', dict(ls=ls, mean=m, stddev=s)))
else: # discrete
with context(fname, IML):
imls = ' '.join(map(str, (~IML)[1]))
attr = dict(imt=IML['IMT'])
if nodamage is not None:
attr['noDamageLimit'] = nodamage
ff.append(Node('imls', attr, imls))
for ffd in ffs[2:]:
ls = ffd['ls']
with context(fname, ffd):
poes = ' '.join(map(str, ~ffd.poEs))
ff.append(Node('poes', dict(ls=ls), poes))
new.append(ff)
return new | python | def convert_fragility_model_04(node, fname, fmcounter=itertools.count(1)):
convert_type = {"lognormal": "logncdf"}
new = Node('fragilityModel',
dict(assetCategory='building',
lossCategory='structural',
id='fm_%d_converted_from_NRML_04' %
next(fmcounter)))
with context(fname, node):
fmt = node['format']
descr = ~node.description
limit_states = ~node.limitStates
new.append(Node('description', {}, descr))
new.append((Node('limitStates', {}, ' '.join(limit_states))))
for ffs in node[2:]:
IML = ffs.IML
nodamage = ffs.attrib.get('noDamageLimit')
ff = Node('fragilityFunction', {'format': fmt})
ff['id'] = ~ffs.taxonomy
ff['shape'] = convert_type[ffs.attrib.get('type', 'lognormal')]
if fmt == 'continuous':
with context(fname, IML):
attr = dict(imt=IML['IMT'],
minIML=IML['minIML'],
maxIML=IML['maxIML'])
if nodamage is not None:
attr['noDamageLimit'] = nodamage
ff.append(Node('imls', attr))
for ffc in ffs[2:]:
with context(fname, ffc):
ls = ffc['ls']
param = ffc.params
with context(fname, param):
m, s = param['mean'], param['stddev']
ff.append(Node('params', dict(ls=ls, mean=m, stddev=s)))
else:
with context(fname, IML):
imls = ' '.join(map(str, (~IML)[1]))
attr = dict(imt=IML['IMT'])
if nodamage is not None:
attr['noDamageLimit'] = nodamage
ff.append(Node('imls', attr, imls))
for ffd in ffs[2:]:
ls = ffd['ls']
with context(fname, ffd):
poes = ' '.join(map(str, ~ffd.poEs))
ff.append(Node('poes', dict(ls=ls), poes))
new.append(ff)
return new | [
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an :class:`openquake.commonib.node.Node` in NRML 0.4
:param fname:
path of the fragility file
:returns:
an :class:`openquake.commonib.node.Node` in NRML 0.5 | [
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gem/oq-engine | openquake/risklib/read_nrml.py | get_fragility_model_04 | def get_fragility_model_04(fmodel, fname):
"""
:param fmodel:
a fragilityModel node
:param fname:
path of the fragility file
:returns:
an :class:`openquake.risklib.scientific.FragilityModel` instance
"""
logging.warning('Please upgrade %s to NRML 0.5', fname)
node05 = convert_fragility_model_04(fmodel, fname)
node05.limitStates.text = node05.limitStates.text.split()
return get_fragility_model(node05, fname) | python | def get_fragility_model_04(fmodel, fname):
logging.warning('Please upgrade %s to NRML 0.5', fname)
node05 = convert_fragility_model_04(fmodel, fname)
node05.limitStates.text = node05.limitStates.text.split()
return get_fragility_model(node05, fname) | [
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gem/oq-engine | openquake/risklib/read_nrml.py | taxonomy | def taxonomy(value):
"""
Any ASCII character goes into a taxonomy, except spaces.
"""
try:
value.encode('ascii')
except UnicodeEncodeError:
raise ValueError('tag %r is not ASCII' % value)
if re.search(r'\s', value):
raise ValueError('The taxonomy %r contains whitespace chars' % value)
return value | python | def taxonomy(value):
try:
value.encode('ascii')
except UnicodeEncodeError:
raise ValueError('tag %r is not ASCII' % value)
if re.search(r'\s', value):
raise ValueError('The taxonomy %r contains whitespace chars' % value)
return value | [
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gem/oq-engine | openquake/risklib/read_nrml.py | update_validators | def update_validators():
"""
Call this to updade the global nrml.validators
"""
validators.update({
'fragilityFunction.id': valid.utf8, # taxonomy
'vulnerabilityFunction.id': valid.utf8, # taxonomy
'consequenceFunction.id': valid.utf8, # taxonomy
'asset.id': valid.asset_id,
'costType.name': valid.cost_type,
'costType.type': valid.cost_type_type,
'cost.type': valid.cost_type,
'area.type': valid.name,
'isAbsolute': valid.boolean,
'insuranceLimit': valid.positivefloat,
'deductible': valid.positivefloat,
'occupants': valid.positivefloat,
'value': valid.positivefloat,
'retrofitted': valid.positivefloat,
'number': valid.compose(valid.positivefloat, valid.nonzero),
'vulnerabilitySetID': str, # any ASCII string is fine
'vulnerabilityFunctionID': str, # any ASCII string is fine
'lossCategory': valid.utf8, # a description field
'lr': valid.probability,
'lossRatio': valid.positivefloats,
'coefficientsVariation': valid.positivefloats,
'probabilisticDistribution': valid.Choice('LN', 'BT'),
'dist': valid.Choice('LN', 'BT', 'PM'),
'meanLRs': valid.positivefloats,
'covLRs': valid.positivefloats,
'format': valid.ChoiceCI('discrete', 'continuous'),
'mean': valid.positivefloat,
'stddev': valid.positivefloat,
'minIML': valid.positivefloat,
'maxIML': valid.positivefloat,
'limitStates': valid.namelist,
'noDamageLimit': valid.NoneOr(valid.positivefloat),
'loss_type': valid_loss_types,
'losses': valid.positivefloats,
'averageLoss': valid.positivefloat,
'stdDevLoss': valid.positivefloat,
'ffs.type': valid.ChoiceCI('lognormal'),
'assetLifeExpectancy': valid.positivefloat,
'interestRate': valid.positivefloat,
'lossType': valid_loss_types,
'aalOrig': valid.positivefloat,
'aalRetr': valid.positivefloat,
'ratio': valid.positivefloat,
'cf': asset_mean_stddev,
'damage': damage_triple,
'damageStates': valid.namelist,
'taxonomy': taxonomy,
'tagNames': valid.namelist,
}) | python | def update_validators():
validators.update({
'fragilityFunction.id': valid.utf8,
'vulnerabilityFunction.id': valid.utf8,
'consequenceFunction.id': valid.utf8,
'asset.id': valid.asset_id,
'costType.name': valid.cost_type,
'costType.type': valid.cost_type_type,
'cost.type': valid.cost_type,
'area.type': valid.name,
'isAbsolute': valid.boolean,
'insuranceLimit': valid.positivefloat,
'deductible': valid.positivefloat,
'occupants': valid.positivefloat,
'value': valid.positivefloat,
'retrofitted': valid.positivefloat,
'number': valid.compose(valid.positivefloat, valid.nonzero),
'vulnerabilitySetID': str,
'vulnerabilityFunctionID': str,
'lossCategory': valid.utf8,
'lr': valid.probability,
'lossRatio': valid.positivefloats,
'coefficientsVariation': valid.positivefloats,
'probabilisticDistribution': valid.Choice('LN', 'BT'),
'dist': valid.Choice('LN', 'BT', 'PM'),
'meanLRs': valid.positivefloats,
'covLRs': valid.positivefloats,
'format': valid.ChoiceCI('discrete', 'continuous'),
'mean': valid.positivefloat,
'stddev': valid.positivefloat,
'minIML': valid.positivefloat,
'maxIML': valid.positivefloat,
'limitStates': valid.namelist,
'noDamageLimit': valid.NoneOr(valid.positivefloat),
'loss_type': valid_loss_types,
'losses': valid.positivefloats,
'averageLoss': valid.positivefloat,
'stdDevLoss': valid.positivefloat,
'ffs.type': valid.ChoiceCI('lognormal'),
'assetLifeExpectancy': valid.positivefloat,
'interestRate': valid.positivefloat,
'lossType': valid_loss_types,
'aalOrig': valid.positivefloat,
'aalRetr': valid.positivefloat,
'ratio': valid.positivefloat,
'cf': asset_mean_stddev,
'damage': damage_triple,
'damageStates': valid.namelist,
'taxonomy': taxonomy,
'tagNames': valid.namelist,
}) | [
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gem/oq-engine | openquake/calculators/extract.py | get_info | def get_info(dstore):
"""
:returns: {'stats': dic, 'loss_types': dic, 'num_rlzs': R}
"""
oq = dstore['oqparam']
stats = {stat: s for s, stat in enumerate(oq.hazard_stats())}
loss_types = {lt: l for l, lt in enumerate(oq.loss_dt().names)}
imt = {imt: i for i, imt in enumerate(oq.imtls)}
num_rlzs = dstore['csm_info'].get_num_rlzs()
return dict(stats=stats, num_rlzs=num_rlzs, loss_types=loss_types,
imtls=oq.imtls, investigation_time=oq.investigation_time,
poes=oq.poes, imt=imt, uhs_dt=oq.uhs_dt()) | python | def get_info(dstore):
oq = dstore['oqparam']
stats = {stat: s for s, stat in enumerate(oq.hazard_stats())}
loss_types = {lt: l for l, lt in enumerate(oq.loss_dt().names)}
imt = {imt: i for i, imt in enumerate(oq.imtls)}
num_rlzs = dstore['csm_info'].get_num_rlzs()
return dict(stats=stats, num_rlzs=num_rlzs, loss_types=loss_types,
imtls=oq.imtls, investigation_time=oq.investigation_time,
poes=oq.poes, imt=imt, uhs_dt=oq.uhs_dt()) | [
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gem/oq-engine | openquake/calculators/extract.py | parse | def parse(query_string, info={}):
"""
:returns: a normalized query_dict as in the following examples:
>>> parse('kind=stats', {'stats': {'mean': 0, 'max': 1}})
{'kind': ['mean', 'max'], 'k': [0, 1], 'rlzs': False}
>>> parse('kind=rlzs', {'stats': {}, 'num_rlzs': 3})
{'kind': ['rlz-000', 'rlz-001', 'rlz-002'], 'k': [0, 1, 2], 'rlzs': True}
>>> parse('kind=mean', {'stats': {'mean': 0, 'max': 1}})
{'kind': ['mean'], 'k': [0], 'rlzs': False}
>>> parse('kind=rlz-3&imt=PGA&site_id=0', {'stats': {}})
{'kind': ['rlz-3'], 'imt': ['PGA'], 'site_id': [0], 'k': [3], 'rlzs': True}
"""
qdic = parse_qs(query_string)
loss_types = info.get('loss_types', [])
for key, val in qdic.items(): # for instance, convert site_id to an int
if key == 'loss_type':
qdic[key] = [loss_types[k] for k in val]
else:
qdic[key] = [lit_eval(v) for v in val]
if info:
qdic['k'], qdic['kind'], qdic['rlzs'] = _normalize(qdic['kind'], info)
return qdic | python | def parse(query_string, info={}):
qdic = parse_qs(query_string)
loss_types = info.get('loss_types', [])
for key, val in qdic.items():
if key == 'loss_type':
qdic[key] = [loss_types[k] for k in val]
else:
qdic[key] = [lit_eval(v) for v in val]
if info:
qdic['k'], qdic['kind'], qdic['rlzs'] = _normalize(qdic['kind'], info)
return qdic | [
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>>> parse('kind=stats', {'stats': {'mean': 0, 'max': 1}})
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>>> parse('kind=rlzs', {'stats': {}, 'num_rlzs': 3})
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>>> parse('kind=mean', {'stats': {'mean': 0, 'max': 1}})
{'kind': ['mean'], 'k': [0], 'rlzs': False}
>>> parse('kind=rlz-3&imt=PGA&site_id=0', {'stats': {}})
{'kind': ['rlz-3'], 'imt': ['PGA'], 'site_id': [0], 'k': [3], 'rlzs': True} | [
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gem/oq-engine | openquake/calculators/extract.py | barray | def barray(iterlines):
"""
Array of bytes
"""
lst = [line.encode('utf-8') for line in iterlines]
arr = numpy.array(lst)
return arr | python | def barray(iterlines):
lst = [line.encode('utf-8') for line in iterlines]
arr = numpy.array(lst)
return arr | [
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gem/oq-engine | openquake/calculators/extract.py | extract_ | def extract_(dstore, dspath):
"""
Extracts an HDF5 path object from the datastore, for instance
extract(dstore, 'sitecol').
"""
obj = dstore[dspath]
if isinstance(obj, Dataset):
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return ArrayWrapper(numpy.array(list(obj)), obj.attrs)
else:
return obj | python | def extract_(dstore, dspath):
obj = dstore[dspath]
if isinstance(obj, Dataset):
return ArrayWrapper(obj.value, obj.attrs)
elif isinstance(obj, Group):
return ArrayWrapper(numpy.array(list(obj)), obj.attrs)
else:
return obj | [
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gem/oq-engine | openquake/calculators/extract.py | extract_realizations | def extract_realizations(dstore, dummy):
"""
Extract an array of realizations. Use it as /extract/realizations
"""
rlzs = dstore['csm_info'].rlzs
dt = [('ordinal', U32), ('weight', F32), ('gsims', '<S64')]
arr = numpy.zeros(len(rlzs), dt)
arr['ordinal'] = rlzs['ordinal']
arr['weight'] = rlzs['weight']
arr['gsims'] = rlzs['branch_path'] # this is used in scenario by QGIS
return arr | python | def extract_realizations(dstore, dummy):
rlzs = dstore['csm_info'].rlzs
dt = [('ordinal', U32), ('weight', F32), ('gsims', '<S64')]
arr = numpy.zeros(len(rlzs), dt)
arr['ordinal'] = rlzs['ordinal']
arr['weight'] = rlzs['weight']
arr['gsims'] = rlzs['branch_path']
return arr | [
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gem/oq-engine | openquake/calculators/extract.py | extract_exposure_metadata | def extract_exposure_metadata(dstore, what):
"""
Extract the loss categories and the tags of the exposure.
Use it as /extract/exposure_metadata
"""
dic = {}
dic1, dic2 = dstore['assetcol/tagcol'].__toh5__()
dic.update(dic1)
dic.update(dic2)
if 'asset_risk' in dstore:
dic['multi_risk'] = sorted(
set(dstore['asset_risk'].dtype.names) -
set(dstore['assetcol/array'].dtype.names))
names = [name for name in dstore['assetcol/array'].dtype.names
if name.startswith(('value-', 'number', 'occupants_'))
and not name.endswith('_None')]
return ArrayWrapper(numpy.array(names), dic) | python | def extract_exposure_metadata(dstore, what):
dic = {}
dic1, dic2 = dstore['assetcol/tagcol'].__toh5__()
dic.update(dic1)
dic.update(dic2)
if 'asset_risk' in dstore:
dic['multi_risk'] = sorted(
set(dstore['asset_risk'].dtype.names) -
set(dstore['assetcol/array'].dtype.names))
names = [name for name in dstore['assetcol/array'].dtype.names
if name.startswith(('value-', 'number', 'occupants_'))
and not name.endswith('_None')]
return ArrayWrapper(numpy.array(names), dic) | [
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gem/oq-engine | openquake/calculators/extract.py | extract_assets | def extract_assets(dstore, what):
"""
Extract an array of assets, optionally filtered by tag.
Use it as /extract/assets?taxonomy=RC&taxonomy=MSBC&occupancy=RES
"""
qdict = parse(what)
dic = {}
dic1, dic2 = dstore['assetcol/tagcol'].__toh5__()
dic.update(dic1)
dic.update(dic2)
arr = dstore['assetcol/array'].value
for tag, vals in qdict.items():
cond = numpy.zeros(len(arr), bool)
for val in vals:
tagidx, = numpy.where(dic[tag] == val)
cond |= arr[tag] == tagidx
arr = arr[cond]
return ArrayWrapper(arr, dic) | python | def extract_assets(dstore, what):
qdict = parse(what)
dic = {}
dic1, dic2 = dstore['assetcol/tagcol'].__toh5__()
dic.update(dic1)
dic.update(dic2)
arr = dstore['assetcol/array'].value
for tag, vals in qdict.items():
cond = numpy.zeros(len(arr), bool)
for val in vals:
tagidx, = numpy.where(dic[tag] == val)
cond |= arr[tag] == tagidx
arr = arr[cond]
return ArrayWrapper(arr, dic) | [
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gem/oq-engine | openquake/calculators/extract.py | extract_asset_values | def extract_asset_values(dstore, sid):
"""
Extract an array of asset values for the given sid. Use it as
/extract/asset_values/0
:returns:
(aid, loss_type1, ..., loss_typeN) composite array
"""
if sid:
return extract(dstore, 'asset_values')[int(sid)]
assetcol = extract(dstore, 'assetcol')
asset_refs = assetcol.asset_refs
assets_by_site = assetcol.assets_by_site()
lts = assetcol.loss_types
dt = numpy.dtype([('aref', asset_refs.dtype), ('aid', numpy.uint32)] +
[(str(lt), numpy.float32) for lt in lts])
data = []
for assets in assets_by_site:
vals = numpy.zeros(len(assets), dt)
for a, asset in enumerate(assets):
vals[a]['aref'] = asset_refs[a]
vals[a]['aid'] = asset['ordinal']
for lt in lts:
vals[a][lt] = asset['value-' + lt]
data.append(vals)
return data | python | def extract_asset_values(dstore, sid):
if sid:
return extract(dstore, 'asset_values')[int(sid)]
assetcol = extract(dstore, 'assetcol')
asset_refs = assetcol.asset_refs
assets_by_site = assetcol.assets_by_site()
lts = assetcol.loss_types
dt = numpy.dtype([('aref', asset_refs.dtype), ('aid', numpy.uint32)] +
[(str(lt), numpy.float32) for lt in lts])
data = []
for assets in assets_by_site:
vals = numpy.zeros(len(assets), dt)
for a, asset in enumerate(assets):
vals[a]['aref'] = asset_refs[a]
vals[a]['aid'] = asset['ordinal']
for lt in lts:
vals[a][lt] = asset['value-' + lt]
data.append(vals)
return data | [
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gem/oq-engine | openquake/calculators/extract.py | extract_asset_tags | def extract_asset_tags(dstore, tagname):
"""
Extract an array of asset tags for the given tagname. Use it as
/extract/asset_tags or /extract/asset_tags/taxonomy
"""
tagcol = dstore['assetcol/tagcol']
if tagname:
yield tagname, barray(tagcol.gen_tags(tagname))
for tagname in tagcol.tagnames:
yield tagname, barray(tagcol.gen_tags(tagname)) | python | def extract_asset_tags(dstore, tagname):
tagcol = dstore['assetcol/tagcol']
if tagname:
yield tagname, barray(tagcol.gen_tags(tagname))
for tagname in tagcol.tagnames:
yield tagname, barray(tagcol.gen_tags(tagname)) | [
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gem/oq-engine | openquake/calculators/extract.py | get_mesh | def get_mesh(sitecol, complete=True):
"""
:returns:
a lon-lat or lon-lat-depth array depending if the site collection
is at sea level or not
"""
sc = sitecol.complete if complete else sitecol
if sc.at_sea_level():
mesh = numpy.zeros(len(sc), [('lon', F64), ('lat', F64)])
mesh['lon'] = sc.lons
mesh['lat'] = sc.lats
else:
mesh = numpy.zeros(len(sc), [('lon', F64), ('lat', F64),
('depth', F64)])
mesh['lon'] = sc.lons
mesh['lat'] = sc.lats
mesh['depth'] = sc.depths
return mesh | python | def get_mesh(sitecol, complete=True):
sc = sitecol.complete if complete else sitecol
if sc.at_sea_level():
mesh = numpy.zeros(len(sc), [('lon', F64), ('lat', F64)])
mesh['lon'] = sc.lons
mesh['lat'] = sc.lats
else:
mesh = numpy.zeros(len(sc), [('lon', F64), ('lat', F64),
('depth', F64)])
mesh['lon'] = sc.lons
mesh['lat'] = sc.lats
mesh['depth'] = sc.depths
return mesh | [
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gem/oq-engine | openquake/calculators/extract.py | hazard_items | def hazard_items(dic, mesh, *extras, **kw):
"""
:param dic: dictionary of arrays of the same shape
:param mesh: a mesh array with lon, lat fields of the same length
:param extras: optional triples (field, dtype, values)
:param kw: dictionary of parameters (like investigation_time)
:returns: a list of pairs (key, value) suitable for storage in .npz format
"""
for item in kw.items():
yield item
arr = dic[next(iter(dic))]
dtlist = [(str(field), arr.dtype) for field in sorted(dic)]
for field, dtype, values in extras:
dtlist.append((str(field), dtype))
array = numpy.zeros(arr.shape, dtlist)
for field in dic:
array[field] = dic[field]
for field, dtype, values in extras:
array[field] = values
yield 'all', util.compose_arrays(mesh, array) | python | def hazard_items(dic, mesh, *extras, **kw):
for item in kw.items():
yield item
arr = dic[next(iter(dic))]
dtlist = [(str(field), arr.dtype) for field in sorted(dic)]
for field, dtype, values in extras:
dtlist.append((str(field), dtype))
array = numpy.zeros(arr.shape, dtlist)
for field in dic:
array[field] = dic[field]
for field, dtype, values in extras:
array[field] = values
yield 'all', util.compose_arrays(mesh, array) | [
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gem/oq-engine | openquake/calculators/extract.py | extract_hcurves | def extract_hcurves(dstore, what):
"""
Extracts hazard curves. Use it as /extract/hcurves?kind=mean or
/extract/hcurves?kind=rlz-0, /extract/hcurves?kind=stats,
/extract/hcurves?kind=rlzs etc
"""
info = get_info(dstore)
if what == '': # npz exports for QGIS
sitecol = dstore['sitecol']
mesh = get_mesh(sitecol, complete=False)
dic = _get_dict(dstore, 'hcurves-stats', info['imtls'], info['stats'])
yield from hazard_items(
dic, mesh, investigation_time=info['investigation_time'])
return
params = parse(what, info)
if 'imt' in params:
[imt] = params['imt']
slc = info['imtls'](imt)
else:
slc = ALL
sids = params.get('site_id', ALL)
if params['rlzs']:
dset = dstore['hcurves-rlzs']
for k in params['k']:
yield 'rlz-%03d' % k, hdf5.extract(dset, sids, k, slc)[:, 0]
else:
dset = dstore['hcurves-stats']
stats = list(info['stats'])
for k in params['k']:
yield stats[k], hdf5.extract(dset, sids, k, slc)[:, 0]
yield from params.items() | python | def extract_hcurves(dstore, what):
info = get_info(dstore)
if what == '':
sitecol = dstore['sitecol']
mesh = get_mesh(sitecol, complete=False)
dic = _get_dict(dstore, 'hcurves-stats', info['imtls'], info['stats'])
yield from hazard_items(
dic, mesh, investigation_time=info['investigation_time'])
return
params = parse(what, info)
if 'imt' in params:
[imt] = params['imt']
slc = info['imtls'](imt)
else:
slc = ALL
sids = params.get('site_id', ALL)
if params['rlzs']:
dset = dstore['hcurves-rlzs']
for k in params['k']:
yield 'rlz-%03d' % k, hdf5.extract(dset, sids, k, slc)[:, 0]
else:
dset = dstore['hcurves-stats']
stats = list(info['stats'])
for k in params['k']:
yield stats[k], hdf5.extract(dset, sids, k, slc)[:, 0]
yield from params.items() | [
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gem/oq-engine | openquake/calculators/extract.py | extract_hmaps | def extract_hmaps(dstore, what):
"""
Extracts hazard maps. Use it as /extract/hmaps?imt=PGA
"""
info = get_info(dstore)
if what == '': # npz exports for QGIS
sitecol = dstore['sitecol']
mesh = get_mesh(sitecol, complete=False)
dic = _get_dict(dstore, 'hmaps-stats',
{imt: info['poes'] for imt in info['imtls']},
info['stats'])
yield from hazard_items(
dic, mesh, investigation_time=info['investigation_time'])
return
params = parse(what, info)
if 'imt' in params:
[imt] = params['imt']
m = info['imt'][imt]
s = slice(m, m + 1)
else:
s = ALL
if params['rlzs']:
dset = dstore['hmaps-rlzs']
for k in params['k']:
yield 'rlz-%03d' % k, hdf5.extract(dset, ALL, k, s, ALL)[:, 0]
else:
dset = dstore['hmaps-stats']
stats = list(info['stats'])
for k in params['k']:
yield stats[k], hdf5.extract(dset, ALL, k, s, ALL)[:, 0]
yield from params.items() | python | def extract_hmaps(dstore, what):
info = get_info(dstore)
if what == '':
sitecol = dstore['sitecol']
mesh = get_mesh(sitecol, complete=False)
dic = _get_dict(dstore, 'hmaps-stats',
{imt: info['poes'] for imt in info['imtls']},
info['stats'])
yield from hazard_items(
dic, mesh, investigation_time=info['investigation_time'])
return
params = parse(what, info)
if 'imt' in params:
[imt] = params['imt']
m = info['imt'][imt]
s = slice(m, m + 1)
else:
s = ALL
if params['rlzs']:
dset = dstore['hmaps-rlzs']
for k in params['k']:
yield 'rlz-%03d' % k, hdf5.extract(dset, ALL, k, s, ALL)[:, 0]
else:
dset = dstore['hmaps-stats']
stats = list(info['stats'])
for k in params['k']:
yield stats[k], hdf5.extract(dset, ALL, k, s, ALL)[:, 0]
yield from params.items() | [
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gem/oq-engine | openquake/calculators/extract.py | extract_uhs | def extract_uhs(dstore, what):
"""
Extracts uniform hazard spectra. Use it as /extract/uhs?kind=mean or
/extract/uhs?kind=rlz-0, etc
"""
info = get_info(dstore)
if what == '': # npz exports for QGIS
sitecol = dstore['sitecol']
mesh = get_mesh(sitecol, complete=False)
dic = {}
for stat, s in info['stats'].items():
hmap = dstore['hmaps-stats'][:, s]
dic[stat] = calc.make_uhs(hmap, info)
yield from hazard_items(
dic, mesh, investigation_time=info['investigation_time'])
return
params = parse(what, info)
periods = []
for m, imt in enumerate(info['imtls']):
if imt == 'PGA' or imt.startswith('SA'):
periods.append(m)
if 'site_id' in params:
sids = params['site_id']
else:
sids = ALL
if params['rlzs']:
dset = dstore['hmaps-rlzs']
for k in params['k']:
yield ('rlz-%03d' % k,
hdf5.extract(dset, sids, k, periods, ALL)[:, 0])
else:
dset = dstore['hmaps-stats']
stats = list(info['stats'])
for k in params['k']:
yield stats[k], hdf5.extract(dset, sids, k, periods, ALL)[:, 0]
yield from params.items() | python | def extract_uhs(dstore, what):
info = get_info(dstore)
if what == '':
sitecol = dstore['sitecol']
mesh = get_mesh(sitecol, complete=False)
dic = {}
for stat, s in info['stats'].items():
hmap = dstore['hmaps-stats'][:, s]
dic[stat] = calc.make_uhs(hmap, info)
yield from hazard_items(
dic, mesh, investigation_time=info['investigation_time'])
return
params = parse(what, info)
periods = []
for m, imt in enumerate(info['imtls']):
if imt == 'PGA' or imt.startswith('SA'):
periods.append(m)
if 'site_id' in params:
sids = params['site_id']
else:
sids = ALL
if params['rlzs']:
dset = dstore['hmaps-rlzs']
for k in params['k']:
yield ('rlz-%03d' % k,
hdf5.extract(dset, sids, k, periods, ALL)[:, 0])
else:
dset = dstore['hmaps-stats']
stats = list(info['stats'])
for k in params['k']:
yield stats[k], hdf5.extract(dset, sids, k, periods, ALL)[:, 0]
yield from params.items() | [
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gem/oq-engine | openquake/calculators/extract.py | extract_agg_curves | def extract_agg_curves(dstore, what):
"""
Aggregate loss curves of the given loss type and tags for
event based risk calculations. Use it as
/extract/agg_curves/structural?taxonomy=RC&zipcode=20126
:returns:
array of shape (S, P), being P the number of return periods
and S the number of statistics
"""
from openquake.calculators.export.loss_curves import get_loss_builder
oq = dstore['oqparam']
loss_type, tags = get_loss_type_tags(what)
if 'curves-stats' in dstore: # event_based_risk
losses = _get_curves(dstore['curves-stats'], oq.lti[loss_type])
stats = dstore['curves-stats'].attrs['stats']
elif 'curves-rlzs' in dstore: # event_based_risk, 1 rlz
losses = _get_curves(dstore['curves-rlzs'], oq.lti[loss_type])
assert losses.shape[1] == 1, 'There must be a single realization'
stats = [b'mean'] # suitable to be stored as hdf5 attribute
else:
raise KeyError('No curves found in %s' % dstore)
res = _filter_agg(dstore['assetcol'], losses, tags, stats)
cc = dstore['assetcol/cost_calculator']
res.units = cc.get_units(loss_types=[loss_type])
res.return_periods = get_loss_builder(dstore).return_periods
return res | python | def extract_agg_curves(dstore, what):
from openquake.calculators.export.loss_curves import get_loss_builder
oq = dstore['oqparam']
loss_type, tags = get_loss_type_tags(what)
if 'curves-stats' in dstore:
losses = _get_curves(dstore['curves-stats'], oq.lti[loss_type])
stats = dstore['curves-stats'].attrs['stats']
elif 'curves-rlzs' in dstore:
losses = _get_curves(dstore['curves-rlzs'], oq.lti[loss_type])
assert losses.shape[1] == 1, 'There must be a single realization'
stats = [b'mean']
else:
raise KeyError('No curves found in %s' % dstore)
res = _filter_agg(dstore['assetcol'], losses, tags, stats)
cc = dstore['assetcol/cost_calculator']
res.units = cc.get_units(loss_types=[loss_type])
res.return_periods = get_loss_builder(dstore).return_periods
return res | [
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/extract/agg_curves/structural?taxonomy=RC&zipcode=20126
:returns:
array of shape (S, P), being P the number of return periods
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gem/oq-engine | openquake/calculators/extract.py | extract_agg_losses | def extract_agg_losses(dstore, what):
"""
Aggregate losses of the given loss type and tags. Use it as
/extract/agg_losses/structural?taxonomy=RC&zipcode=20126
/extract/agg_losses/structural?taxonomy=RC&zipcode=*
:returns:
an array of shape (T, R) if one of the tag names has a `*` value
an array of shape (R,), being R the number of realizations
an array of length 0 if there is no data for the given tags
"""
loss_type, tags = get_loss_type_tags(what)
if not loss_type:
raise ValueError('loss_type not passed in agg_losses/<loss_type>')
l = dstore['oqparam'].lti[loss_type]
if 'losses_by_asset' in dstore: # scenario_risk
stats = None
losses = dstore['losses_by_asset'][:, :, l]['mean']
elif 'avg_losses-stats' in dstore: # event_based_risk, classical_risk
stats = dstore['avg_losses-stats'].attrs['stats']
losses = dstore['avg_losses-stats'][:, :, l]
elif 'avg_losses-rlzs' in dstore: # event_based_risk, classical_risk
stats = [b'mean']
losses = dstore['avg_losses-rlzs'][:, :, l]
else:
raise KeyError('No losses found in %s' % dstore)
return _filter_agg(dstore['assetcol'], losses, tags, stats) | python | def extract_agg_losses(dstore, what):
loss_type, tags = get_loss_type_tags(what)
if not loss_type:
raise ValueError('loss_type not passed in agg_losses/<loss_type>')
l = dstore['oqparam'].lti[loss_type]
if 'losses_by_asset' in dstore:
stats = None
losses = dstore['losses_by_asset'][:, :, l]['mean']
elif 'avg_losses-stats' in dstore:
stats = dstore['avg_losses-stats'].attrs['stats']
losses = dstore['avg_losses-stats'][:, :, l]
elif 'avg_losses-rlzs' in dstore:
stats = [b'mean']
losses = dstore['avg_losses-rlzs'][:, :, l]
else:
raise KeyError('No losses found in %s' % dstore)
return _filter_agg(dstore['assetcol'], losses, tags, stats) | [
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gem/oq-engine | openquake/calculators/extract.py | extract_agg_damages | def extract_agg_damages(dstore, what):
"""
Aggregate damages of the given loss type and tags. Use it as
/extract/agg_damages/structural?taxonomy=RC&zipcode=20126
:returns:
array of shape (R, D), being R the number of realizations and D the
number of damage states, or an array of length 0 if there is no data
for the given tags
"""
loss_type, tags = get_loss_type_tags(what)
if 'dmg_by_asset' in dstore: # scenario_damage
lti = dstore['oqparam'].lti[loss_type]
losses = dstore['dmg_by_asset'][:, :, lti, 0]
else:
raise KeyError('No damages found in %s' % dstore)
return _filter_agg(dstore['assetcol'], losses, tags) | python | def extract_agg_damages(dstore, what):
loss_type, tags = get_loss_type_tags(what)
if 'dmg_by_asset' in dstore:
lti = dstore['oqparam'].lti[loss_type]
losses = dstore['dmg_by_asset'][:, :, lti, 0]
else:
raise KeyError('No damages found in %s' % dstore)
return _filter_agg(dstore['assetcol'], losses, tags) | [
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gem/oq-engine | openquake/calculators/extract.py | extract_aggregate | def extract_aggregate(dstore, what):
"""
/extract/aggregate/avg_losses?
kind=mean&loss_type=structural&tag=taxonomy&tag=occupancy
"""
name, qstring = what.split('?', 1)
info = get_info(dstore)
qdic = parse(qstring, info)
suffix = '-rlzs' if qdic['rlzs'] else '-stats'
tagnames = qdic.get('tag', [])
assetcol = dstore['assetcol']
ltypes = qdic.get('loss_type', [])
if ltypes:
array = dstore[name + suffix][:, qdic['k'][0], ltypes[0]]
else:
array = dstore[name + suffix][:, qdic['k'][0]]
aw = ArrayWrapper(assetcol.aggregate_by(tagnames, array), {})
for tagname in tagnames:
setattr(aw, tagname, getattr(assetcol.tagcol, tagname))
aw.tagnames = encode(tagnames)
if not ltypes:
aw.extra = ('loss_type',) + tuple(info['loss_types'])
return aw | python | def extract_aggregate(dstore, what):
name, qstring = what.split('?', 1)
info = get_info(dstore)
qdic = parse(qstring, info)
suffix = '-rlzs' if qdic['rlzs'] else '-stats'
tagnames = qdic.get('tag', [])
assetcol = dstore['assetcol']
ltypes = qdic.get('loss_type', [])
if ltypes:
array = dstore[name + suffix][:, qdic['k'][0], ltypes[0]]
else:
array = dstore[name + suffix][:, qdic['k'][0]]
aw = ArrayWrapper(assetcol.aggregate_by(tagnames, array), {})
for tagname in tagnames:
setattr(aw, tagname, getattr(assetcol.tagcol, tagname))
aw.tagnames = encode(tagnames)
if not ltypes:
aw.extra = ('loss_type',) + tuple(info['loss_types'])
return aw | [
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gem/oq-engine | openquake/calculators/extract.py | build_damage_dt | def build_damage_dt(dstore, mean_std=True):
"""
:param dstore: a datastore instance
:param mean_std: a flag (default True)
:returns:
a composite dtype loss_type -> (mean_ds1, stdv_ds1, ...) or
loss_type -> (ds1, ds2, ...) depending on the flag mean_std
"""
oq = dstore['oqparam']
damage_states = ['no_damage'] + list(
dstore.get_attr('risk_model', 'limit_states'))
dt_list = []
for ds in damage_states:
ds = str(ds)
if mean_std:
dt_list.append(('%s_mean' % ds, F32))
dt_list.append(('%s_stdv' % ds, F32))
else:
dt_list.append((ds, F32))
damage_dt = numpy.dtype(dt_list)
loss_types = oq.loss_dt().names
return numpy.dtype([(lt, damage_dt) for lt in loss_types]) | python | def build_damage_dt(dstore, mean_std=True):
oq = dstore['oqparam']
damage_states = ['no_damage'] + list(
dstore.get_attr('risk_model', 'limit_states'))
dt_list = []
for ds in damage_states:
ds = str(ds)
if mean_std:
dt_list.append(('%s_mean' % ds, F32))
dt_list.append(('%s_stdv' % ds, F32))
else:
dt_list.append((ds, F32))
damage_dt = numpy.dtype(dt_list)
loss_types = oq.loss_dt().names
return numpy.dtype([(lt, damage_dt) for lt in loss_types]) | [
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gem/oq-engine | openquake/calculators/extract.py | build_damage_array | def build_damage_array(data, damage_dt):
"""
:param data: an array of shape (A, L, 1, D) or (A, L, 2, D)
:param damage_dt: a damage composite data type loss_type -> states
:returns: a composite array of length N and dtype damage_dt
"""
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dmg = numpy.zeros(A, damage_dt)
for a in range(A):
for l, lt in enumerate(damage_dt.names):
std = any(f for f in damage_dt[lt].names if f.endswith('_stdv'))
if MS == 1 or not std: # there is only the mean value
dmg[lt][a] = tuple(data[a, l, 0])
else: # there are both mean and stddev
# data[a, l].T has shape (D, 2)
dmg[lt][a] = tuple(numpy.concatenate(data[a, l].T))
return dmg | python | def build_damage_array(data, damage_dt):
A, L, MS, D = data.shape
dmg = numpy.zeros(A, damage_dt)
for a in range(A):
for l, lt in enumerate(damage_dt.names):
std = any(f for f in damage_dt[lt].names if f.endswith('_stdv'))
if MS == 1 or not std:
dmg[lt][a] = tuple(data[a, l, 0])
else:
dmg[lt][a] = tuple(numpy.concatenate(data[a, l].T))
return dmg | [
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gem/oq-engine | openquake/calculators/extract.py | extract_mfd | def extract_mfd(dstore, what):
"""
Display num_ruptures by magnitude for event based calculations.
Example: http://127.0.0.1:8800/v1/calc/30/extract/event_based_mfd
"""
dd = collections.defaultdict(int)
for rup in dstore['ruptures'].value:
dd[rup['mag']] += 1
dt = numpy.dtype([('mag', float), ('freq', int)])
magfreq = numpy.array(sorted(dd.items(), key=operator.itemgetter(0)), dt)
return magfreq | python | def extract_mfd(dstore, what):
dd = collections.defaultdict(int)
for rup in dstore['ruptures'].value:
dd[rup['mag']] += 1
dt = numpy.dtype([('mag', float), ('freq', int)])
magfreq = numpy.array(sorted(dd.items(), key=operator.itemgetter(0)), dt)
return magfreq | [
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gem/oq-engine | openquake/calculators/extract.py | extract_src_loss_table | def extract_src_loss_table(dstore, loss_type):
"""
Extract the source loss table for a give loss type, ordered in decreasing
order. Example:
http://127.0.0.1:8800/v1/calc/30/extract/src_loss_table/structural
"""
oq = dstore['oqparam']
li = oq.lti[loss_type]
source_ids = dstore['source_info']['source_id']
idxs = dstore['ruptures'].value[['srcidx', 'grp_id']]
losses = dstore['rup_loss_table'][:, li]
slt = numpy.zeros(len(source_ids), [('grp_id', U32), (loss_type, F32)])
for loss, (srcidx, grp_id) in zip(losses, idxs):
slt[srcidx][loss_type] += loss
slt[srcidx]['grp_id'] = grp_id
slt = util.compose_arrays(source_ids, slt, 'source_id')
slt.sort(order=loss_type)
return slt[::-1] | python | def extract_src_loss_table(dstore, loss_type):
oq = dstore['oqparam']
li = oq.lti[loss_type]
source_ids = dstore['source_info']['source_id']
idxs = dstore['ruptures'].value[['srcidx', 'grp_id']]
losses = dstore['rup_loss_table'][:, li]
slt = numpy.zeros(len(source_ids), [('grp_id', U32), (loss_type, F32)])
for loss, (srcidx, grp_id) in zip(losses, idxs):
slt[srcidx][loss_type] += loss
slt[srcidx]['grp_id'] = grp_id
slt = util.compose_arrays(source_ids, slt, 'source_id')
slt.sort(order=loss_type)
return slt[::-1] | [
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gem/oq-engine | openquake/calculators/extract.py | extract_mean_std_curves | def extract_mean_std_curves(dstore, what):
"""
Yield imls/IMT and poes/IMT containg mean and stddev for all sites
"""
getter = getters.PmapGetter(dstore)
arr = getter.get_mean().array
for imt in getter.imtls:
yield 'imls/' + imt, getter.imtls[imt]
yield 'poes/' + imt, arr[:, getter.imtls(imt)] | python | def extract_mean_std_curves(dstore, what):
getter = getters.PmapGetter(dstore)
arr = getter.get_mean().array
for imt in getter.imtls:
yield 'imls/' + imt, getter.imtls[imt]
yield 'poes/' + imt, arr[:, getter.imtls(imt)] | [
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gem/oq-engine | openquake/calculators/extract.py | losses_by_tag | def losses_by_tag(dstore, tag):
"""
Statistical average losses by tag. For instance call
$ oq extract losses_by_tag/occupancy
"""
dt = [(tag, vstr)] + dstore['oqparam'].loss_dt_list()
aids = dstore['assetcol/array'][tag]
dset, stats = _get(dstore, 'avg_losses')
arr = dset.value
tagvalues = dstore['assetcol/tagcol/' + tag][1:] # except tagvalue="?"
for s, stat in enumerate(stats):
out = numpy.zeros(len(tagvalues), dt)
for li, (lt, lt_dt) in enumerate(dt[1:]):
for i, tagvalue in enumerate(tagvalues):
out[i][tag] = tagvalue
counts = arr[aids == i + 1, s, li].sum()
if counts:
out[i][lt] = counts
yield stat, out | python | def losses_by_tag(dstore, tag):
dt = [(tag, vstr)] + dstore['oqparam'].loss_dt_list()
aids = dstore['assetcol/array'][tag]
dset, stats = _get(dstore, 'avg_losses')
arr = dset.value
tagvalues = dstore['assetcol/tagcol/' + tag][1:]
for s, stat in enumerate(stats):
out = numpy.zeros(len(tagvalues), dt)
for li, (lt, lt_dt) in enumerate(dt[1:]):
for i, tagvalue in enumerate(tagvalues):
out[i][tag] = tagvalue
counts = arr[aids == i + 1, s, li].sum()
if counts:
out[i][lt] = counts
yield stat, out | [
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gem/oq-engine | openquake/calculators/extract.py | extract_rupture | def extract_rupture(dstore, serial):
"""
Extract information about the given event index.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/rupture/1066
"""
ridx = list(dstore['ruptures']['serial']).index(int(serial))
[getter] = getters.gen_rupture_getters(dstore, slice(ridx, ridx + 1))
yield from getter.get_rupdict().items() | python | def extract_rupture(dstore, serial):
ridx = list(dstore['ruptures']['serial']).index(int(serial))
[getter] = getters.gen_rupture_getters(dstore, slice(ridx, ridx + 1))
yield from getter.get_rupdict().items() | [
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gem/oq-engine | openquake/calculators/extract.py | extract_event_info | def extract_event_info(dstore, eidx):
"""
Extract information about the given event index.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/event_info/0
"""
event = dstore['events'][int(eidx)]
serial = int(event['eid'] // TWO32)
ridx = list(dstore['ruptures']['serial']).index(serial)
[getter] = getters.gen_rupture_getters(dstore, slice(ridx, ridx + 1))
rupdict = getter.get_rupdict()
rlzi = event['rlz']
rlzs_assoc = dstore['csm_info'].get_rlzs_assoc()
gsim = rlzs_assoc.gsim_by_trt[rlzi][rupdict['trt']]
for key, val in rupdict.items():
yield key, val
yield 'rlzi', rlzi
yield 'gsim', repr(gsim) | python | def extract_event_info(dstore, eidx):
event = dstore['events'][int(eidx)]
serial = int(event['eid'] // TWO32)
ridx = list(dstore['ruptures']['serial']).index(serial)
[getter] = getters.gen_rupture_getters(dstore, slice(ridx, ridx + 1))
rupdict = getter.get_rupdict()
rlzi = event['rlz']
rlzs_assoc = dstore['csm_info'].get_rlzs_assoc()
gsim = rlzs_assoc.gsim_by_trt[rlzi][rupdict['trt']]
for key, val in rupdict.items():
yield key, val
yield 'rlzi', rlzi
yield 'gsim', repr(gsim) | [
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gem/oq-engine | openquake/calculators/extract.py | get_ruptures_within | def get_ruptures_within(dstore, bbox):
"""
Extract the ruptures within the given bounding box, a string
minlon,minlat,maxlon,maxlat.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/ruptures_with/8,44,10,46
"""
minlon, minlat, maxlon, maxlat = map(float, bbox.split(','))
hypo = dstore['ruptures']['hypo'].T # shape (3, N)
mask = ((minlon <= hypo[0]) * (minlat <= hypo[1]) *
(maxlon >= hypo[0]) * (maxlat >= hypo[1]))
return dstore['ruptures'][mask] | python | def get_ruptures_within(dstore, bbox):
minlon, minlat, maxlon, maxlat = map(float, bbox.split(','))
hypo = dstore['ruptures']['hypo'].T
mask = ((minlon <= hypo[0]) * (minlat <= hypo[1]) *
(maxlon >= hypo[0]) * (maxlat >= hypo[1]))
return dstore['ruptures'][mask] | [
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gem/oq-engine | openquake/calculators/extract.py | extract_source_geom | def extract_source_geom(dstore, srcidxs):
"""
Extract the geometry of a given sources
Example:
http://127.0.0.1:8800/v1/calc/30/extract/source_geom/1,2,3
"""
for i in srcidxs.split(','):
rec = dstore['source_info'][int(i)]
geom = dstore['source_geom'][rec['gidx1']:rec['gidx2']]
yield rec['source_id'], geom | python | def extract_source_geom(dstore, srcidxs):
for i in srcidxs.split(','):
rec = dstore['source_info'][int(i)]
geom = dstore['source_geom'][rec['gidx1']:rec['gidx2']]
yield rec['source_id'], geom | [
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gem/oq-engine | openquake/calculators/extract.py | WebExtractor.get | def get(self, what):
"""
:param what: what to extract
:returns: an ArrayWrapper instance
"""
url = '%s/v1/calc/%d/extract/%s' % (self.server, self.calc_id, what)
logging.info('GET %s', url)
resp = self.sess.get(url)
if resp.status_code != 200:
raise WebAPIError(resp.text)
npz = numpy.load(io.BytesIO(resp.content))
attrs = {k: npz[k] for k in npz if k != 'array'}
try:
arr = npz['array']
except KeyError:
arr = ()
return ArrayWrapper(arr, attrs) | python | def get(self, what):
url = '%s/v1/calc/%d/extract/%s' % (self.server, self.calc_id, what)
logging.info('GET %s', url)
resp = self.sess.get(url)
if resp.status_code != 200:
raise WebAPIError(resp.text)
npz = numpy.load(io.BytesIO(resp.content))
attrs = {k: npz[k] for k in npz if k != 'array'}
try:
arr = npz['array']
except KeyError:
arr = ()
return ArrayWrapper(arr, attrs) | [
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gem/oq-engine | openquake/calculators/extract.py | WebExtractor.dump | def dump(self, fname):
"""
Dump the remote datastore on a local path.
"""
url = '%s/v1/calc/%d/datastore' % (self.server, self.calc_id)
resp = self.sess.get(url, stream=True)
down = 0
with open(fname, 'wb') as f:
logging.info('Saving %s', fname)
for chunk in resp.iter_content(CHUNKSIZE):
f.write(chunk)
down += len(chunk)
println('Downloaded {:,} bytes'.format(down))
print() | python | def dump(self, fname):
url = '%s/v1/calc/%d/datastore' % (self.server, self.calc_id)
resp = self.sess.get(url, stream=True)
down = 0
with open(fname, 'wb') as f:
logging.info('Saving %s', fname)
for chunk in resp.iter_content(CHUNKSIZE):
f.write(chunk)
down += len(chunk)
println('Downloaded {:,} bytes'.format(down))
print() | [
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