Datasets:

CM

/

codexglue_code2text_python

like 3

gem/oq-engine

openquake/hazardlib/scalerel/wc1994.py

WC1994.get_std_dev_area

def get_std_dev_area(self, mag, rake): """ Standard deviation for WC1994. Magnitude is ignored. """ assert rake is None or -180 <= rake <= 180 if rake is None: # their "All" case return 0.24 elif (-45 <= rake <= 45) or (rake >= 135) or (rake <= -135): # strike slip return 0.22 elif rake > 0: # thrust/reverse return 0.26 else: # normal return 0.22

python

def get_std_dev_area(self, mag, rake): """ Standard deviation for WC1994. Magnitude is ignored. """ assert rake is None or -180 <= rake <= 180 if rake is None: # their "All" case return 0.24 elif (-45 <= rake <= 45) or (rake >= 135) or (rake <= -135): # strike slip return 0.22 elif rake > 0: # thrust/reverse return 0.26 else: # normal return 0.22

Standard deviation for WC1994. Magnitude is ignored.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/scalerel/wc1994.py#L54-L70

gem/oq-engine

openquake/hazardlib/scalerel/wc1994.py

WC1994.get_std_dev_mag

def get_std_dev_mag(self, rake): """ Standard deviation on the magnitude for the WC1994 area relation. """ assert rake is None or -180 <= rake <= 180 if rake is None: # their "All" case return 0.24 elif (-45 <= rake <= 45) or (rake >= 135) or (rake <= -135): # strike slip return 0.23 elif rake > 0: # thrust/reverse return 0.25 else: # normal return 0.25

python

def get_std_dev_mag(self, rake): """ Standard deviation on the magnitude for the WC1994 area relation. """ assert rake is None or -180 <= rake <= 180 if rake is None: # their "All" case return 0.24 elif (-45 <= rake <= 45) or (rake >= 135) or (rake <= -135): # strike slip return 0.23 elif rake > 0: # thrust/reverse return 0.25 else: # normal return 0.25

Standard deviation on the magnitude for the WC1994 area relation.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/scalerel/wc1994.py#L72-L88

gem/oq-engine

openquake/hazardlib/gsim/mgmpe/generic_gmpe_avgsa.py

GenericGmpeAvgSA.set_parameters

def set_parameters(self): """ Combines the parameters of the GMPE provided at the construction level with the ones assigned to the average GMPE. """ for key in dir(self): if key.startswith('REQUIRES_'): setattr(self, key, getattr(self.gmpe, key)) if key.startswith('DEFINED_'): if not key.endswith('FOR_INTENSITY_MEASURE_TYPES'): setattr(self, key, getattr(self.gmpe, key))

python

def set_parameters(self): """ Combines the parameters of the GMPE provided at the construction level with the ones assigned to the average GMPE. """ for key in dir(self): if key.startswith('REQUIRES_'): setattr(self, key, getattr(self.gmpe, key)) if key.startswith('DEFINED_'): if not key.endswith('FOR_INTENSITY_MEASURE_TYPES'): setattr(self, key, getattr(self.gmpe, key))

Combines the parameters of the GMPE provided at the construction level with the ones assigned to the average GMPE.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/mgmpe/generic_gmpe_avgsa.py#L87-L97

gem/oq-engine

openquake/hazardlib/geo/mesh.py

Mesh.from_points_list

def from_points_list(cls, points): """ Create a mesh object from a collection of points. :param point: List of :class:`~openquake.hazardlib.geo.point.Point` objects. :returns: An instance of :class:`Mesh` with one-dimensional arrays of coordinates from ``points``. """ lons = numpy.zeros(len(points), dtype=float) lats = lons.copy() depths = lons.copy() for i in range(len(points)): lons[i] = points[i].longitude lats[i] = points[i].latitude depths[i] = points[i].depth if not depths.any(): # all points have zero depth, no need to waste memory depths = None return cls(lons, lats, depths)

python

def from_points_list(cls, points): """ Create a mesh object from a collection of points. :param point: List of :class:`~openquake.hazardlib.geo.point.Point` objects. :returns: An instance of :class:`Mesh` with one-dimensional arrays of coordinates from ``points``. """ lons = numpy.zeros(len(points), dtype=float) lats = lons.copy() depths = lons.copy() for i in range(len(points)): lons[i] = points[i].longitude lats[i] = points[i].latitude depths[i] = points[i].depth if not depths.any(): # all points have zero depth, no need to waste memory depths = None return cls(lons, lats, depths)

Create a mesh object from a collection of points. :param point: List of :class:`~openquake.hazardlib.geo.point.Point` objects. :returns: An instance of :class:`Mesh` with one-dimensional arrays of coordinates from ``points``.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L134-L154

gem/oq-engine

openquake/hazardlib/geo/mesh.py

Mesh.get_min_distance

def get_min_distance(self, mesh): """ Compute and return the minimum distance from the mesh to each point in another mesh. :returns: numpy array of distances in km of shape (self.size, mesh.size) Method doesn't make any assumptions on arrangement of the points in either mesh and instead calculates the distance from each point of this mesh to each point of the target mesh and returns the lowest found for each. """ return cdist(self.xyz, mesh.xyz).min(axis=0)

python

def get_min_distance(self, mesh): """ Compute and return the minimum distance from the mesh to each point in another mesh. :returns: numpy array of distances in km of shape (self.size, mesh.size) Method doesn't make any assumptions on arrangement of the points in either mesh and instead calculates the distance from each point of this mesh to each point of the target mesh and returns the lowest found for each. """ return cdist(self.xyz, mesh.xyz).min(axis=0)

Compute and return the minimum distance from the mesh to each point in another mesh. :returns: numpy array of distances in km of shape (self.size, mesh.size) Method doesn't make any assumptions on arrangement of the points in either mesh and instead calculates the distance from each point of this mesh to each point of the target mesh and returns the lowest found for each.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L236-L249

gem/oq-engine

openquake/hazardlib/geo/mesh.py

Mesh.get_closest_points

def get_closest_points(self, mesh): """ Find closest point of this mesh for each point in the other mesh :returns: :class:`Mesh` object of the same shape as `mesh` with closest points from this one at respective indices. """ min_idx = cdist(self.xyz, mesh.xyz).argmin(axis=0) # lose shape if hasattr(mesh, 'shape'): min_idx = min_idx.reshape(mesh.shape) lons = self.lons.take(min_idx) lats = self.lats.take(min_idx) deps = self.depths.take(min_idx) return Mesh(lons, lats, deps)

python

def get_closest_points(self, mesh): """ Find closest point of this mesh for each point in the other mesh :returns: :class:`Mesh` object of the same shape as `mesh` with closest points from this one at respective indices. """ min_idx = cdist(self.xyz, mesh.xyz).argmin(axis=0) # lose shape if hasattr(mesh, 'shape'): min_idx = min_idx.reshape(mesh.shape) lons = self.lons.take(min_idx) lats = self.lats.take(min_idx) deps = self.depths.take(min_idx) return Mesh(lons, lats, deps)

Find closest point of this mesh for each point in the other mesh :returns: :class:`Mesh` object of the same shape as `mesh` with closest points from this one at respective indices.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L251-L265

gem/oq-engine

openquake/hazardlib/geo/mesh.py

Mesh.get_distance_matrix

def get_distance_matrix(self): """ Compute and return distances between each pairs of points in the mesh. This method requires that the coordinate arrays are one-dimensional. NB: the depth of the points is ignored .. warning:: Because of its quadratic space and time complexity this method is safe to use for meshes of up to several thousand points. For mesh of 10k points it needs ~800 Mb for just the resulting matrix and four times that much for intermediate storage. :returns: Two-dimensional numpy array, square matrix of distances. The matrix has zeros on main diagonal and positive distances in kilometers on all other cells. That is, value in cell (3, 5) is the distance between mesh's points 3 and 5 in km, and it is equal to value in cell (5, 3). Uses :func:`openquake.hazardlib.geo.geodetic.geodetic_distance`. """ assert self.lons.ndim == 1 distances = geodetic.geodetic_distance( self.lons.reshape(self.lons.shape + (1, )), self.lats.reshape(self.lats.shape + (1, )), self.lons, self.lats) return numpy.matrix(distances, copy=False)

python

def get_distance_matrix(self): """ Compute and return distances between each pairs of points in the mesh. This method requires that the coordinate arrays are one-dimensional. NB: the depth of the points is ignored .. warning:: Because of its quadratic space and time complexity this method is safe to use for meshes of up to several thousand points. For mesh of 10k points it needs ~800 Mb for just the resulting matrix and four times that much for intermediate storage. :returns: Two-dimensional numpy array, square matrix of distances. The matrix has zeros on main diagonal and positive distances in kilometers on all other cells. That is, value in cell (3, 5) is the distance between mesh's points 3 and 5 in km, and it is equal to value in cell (5, 3). Uses :func:`openquake.hazardlib.geo.geodetic.geodetic_distance`. """ assert self.lons.ndim == 1 distances = geodetic.geodetic_distance( self.lons.reshape(self.lons.shape + (1, )), self.lats.reshape(self.lats.shape + (1, )), self.lons, self.lats) return numpy.matrix(distances, copy=False)

Compute and return distances between each pairs of points in the mesh. This method requires that the coordinate arrays are one-dimensional. NB: the depth of the points is ignored .. warning:: Because of its quadratic space and time complexity this method is safe to use for meshes of up to several thousand points. For mesh of 10k points it needs ~800 Mb for just the resulting matrix and four times that much for intermediate storage. :returns: Two-dimensional numpy array, square matrix of distances. The matrix has zeros on main diagonal and positive distances in kilometers on all other cells. That is, value in cell (3, 5) is the distance between mesh's points 3 and 5 in km, and it is equal to value in cell (5, 3). Uses :func:`openquake.hazardlib.geo.geodetic.geodetic_distance`.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L267-L295

gem/oq-engine

openquake/hazardlib/geo/mesh.py

Mesh._get_proj_convex_hull

def _get_proj_convex_hull(self): """ Create a projection centered in the center of this mesh and define a convex polygon in that projection, enveloping all the points of the mesh. :returns: Tuple of two items: projection function and shapely 2d polygon. Note that the result geometry can be line or point depending on number of points in the mesh and their arrangement. """ # create a projection centered in the center of points collection proj = geo_utils.OrthographicProjection( *geo_utils.get_spherical_bounding_box(self.lons, self.lats)) # project all the points and create a shapely multipoint object. # need to copy an array because otherwise shapely misinterprets it coords = numpy.transpose(proj(self.lons.flat, self.lats.flat)).copy() multipoint = shapely.geometry.MultiPoint(coords) # create a 2d polygon from a convex hull around that multipoint return proj, multipoint.convex_hull

python

def _get_proj_convex_hull(self): """ Create a projection centered in the center of this mesh and define a convex polygon in that projection, enveloping all the points of the mesh. :returns: Tuple of two items: projection function and shapely 2d polygon. Note that the result geometry can be line or point depending on number of points in the mesh and their arrangement. """ # create a projection centered in the center of points collection proj = geo_utils.OrthographicProjection( *geo_utils.get_spherical_bounding_box(self.lons, self.lats)) # project all the points and create a shapely multipoint object. # need to copy an array because otherwise shapely misinterprets it coords = numpy.transpose(proj(self.lons.flat, self.lats.flat)).copy() multipoint = shapely.geometry.MultiPoint(coords) # create a 2d polygon from a convex hull around that multipoint return proj, multipoint.convex_hull

Create a projection centered in the center of this mesh and define a convex polygon in that projection, enveloping all the points of the mesh. :returns: Tuple of two items: projection function and shapely 2d polygon. Note that the result geometry can be line or point depending on number of points in the mesh and their arrangement.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L297-L317

gem/oq-engine

openquake/hazardlib/geo/mesh.py

Mesh.get_joyner_boore_distance

def get_joyner_boore_distance(self, mesh): """ Compute and return Joyner-Boore distance to each point of ``mesh``. Point's depth is ignored. See :meth:`openquake.hazardlib.geo.surface.base.BaseSurface.get_joyner_boore_distance` for definition of this distance. :returns: numpy array of distances in km of the same shape as ``mesh``. Distance value is considered to be zero if a point lies inside the polygon enveloping the projection of the mesh or on one of its edges. """ # we perform a hybrid calculation (geodetic mesh-to-mesh distance # and distance on the projection plane for close points). first, # we find the closest geodetic distance for each point of target # mesh to this one. in general that distance is greater than # the exact distance to enclosing polygon of this mesh and it # depends on mesh spacing. but the difference can be neglected # if calculated geodetic distance is over some threshold. # get the highest slice from the 3D mesh distances = geodetic.min_geodetic_distance( (self.lons, self.lats), (mesh.lons, mesh.lats)) # here we find the points for which calculated mesh-to-mesh # distance is below a threshold. this threshold is arbitrary: # lower values increase the maximum possible error, higher # values reduce the efficiency of that filtering. the maximum # error is equal to the maximum difference between a distance # from site to two adjacent points of the mesh and distance # from site to the line connecting them. thus the error is # a function of distance threshold and mesh spacing. the error # is maximum when the site lies on a perpendicular to the line # connecting points of the mesh and that passes the middle # point between them. the error then can be calculated as # ``err = trsh - d = trsh - \sqrt(trsh^2 - (ms/2)^2)``, where # ``trsh`` and ``d`` are distance to mesh points (the one # we found on the previous step) and distance to the line # connecting them (the actual distance) and ``ms`` is mesh # spacing. the threshold of 40 km gives maximum error of 314 # meters for meshes with spacing of 10 km and 5.36 km for # meshes with spacing of 40 km. if mesh spacing is over # ``(trsh / \sqrt(2)) * 2`` then points lying in the middle # of mesh cells (that is inside the polygon) will be filtered # out by the threshold and have positive distance instead of 0. # so for threshold of 40 km mesh spacing should not be more # than 56 km (typical values are 5 to 10 km). idxs = (distances < 40).nonzero()[0] # indices on the first dimension if not len(idxs): # no point is close enough, return distances as they are return distances # for all the points that are closer than the threshold we need # to recalculate the distance and set it to zero, if point falls # inside the enclosing polygon of the mesh. for doing that we # project both this mesh and the points of the second mesh--selected # by distance threshold--to the same Cartesian space, define # minimum shapely polygon enclosing the mesh and calculate point # to polygon distance, which gives the most accurate value # of distance in km (and that value is zero for points inside # the polygon). proj, polygon = self._get_proj_enclosing_polygon() if not isinstance(polygon, shapely.geometry.Polygon): # either line or point is our enclosing polygon. draw # a square with side of 10 m around in order to have # a proper polygon instead. polygon = polygon.buffer(self.DIST_TOLERANCE, 1) mesh_xx, mesh_yy = proj(mesh.lons[idxs], mesh.lats[idxs]) # replace geodetic distance values for points-closer-than-the-threshold # by more accurate point-to-polygon distance values. distances[idxs] = geo_utils.point_to_polygon_distance( polygon, mesh_xx, mesh_yy) return distances

python

def get_joyner_boore_distance(self, mesh): """ Compute and return Joyner-Boore distance to each point of ``mesh``. Point's depth is ignored. See :meth:`openquake.hazardlib.geo.surface.base.BaseSurface.get_joyner_boore_distance` for definition of this distance. :returns: numpy array of distances in km of the same shape as ``mesh``. Distance value is considered to be zero if a point lies inside the polygon enveloping the projection of the mesh or on one of its edges. """ # we perform a hybrid calculation (geodetic mesh-to-mesh distance # and distance on the projection plane for close points). first, # we find the closest geodetic distance for each point of target # mesh to this one. in general that distance is greater than # the exact distance to enclosing polygon of this mesh and it # depends on mesh spacing. but the difference can be neglected # if calculated geodetic distance is over some threshold. # get the highest slice from the 3D mesh distances = geodetic.min_geodetic_distance( (self.lons, self.lats), (mesh.lons, mesh.lats)) # here we find the points for which calculated mesh-to-mesh # distance is below a threshold. this threshold is arbitrary: # lower values increase the maximum possible error, higher # values reduce the efficiency of that filtering. the maximum # error is equal to the maximum difference between a distance # from site to two adjacent points of the mesh and distance # from site to the line connecting them. thus the error is # a function of distance threshold and mesh spacing. the error # is maximum when the site lies on a perpendicular to the line # connecting points of the mesh and that passes the middle # point between them. the error then can be calculated as # ``err = trsh - d = trsh - \sqrt(trsh^2 - (ms/2)^2)``, where # ``trsh`` and ``d`` are distance to mesh points (the one # we found on the previous step) and distance to the line # connecting them (the actual distance) and ``ms`` is mesh # spacing. the threshold of 40 km gives maximum error of 314 # meters for meshes with spacing of 10 km and 5.36 km for # meshes with spacing of 40 km. if mesh spacing is over # ``(trsh / \sqrt(2)) * 2`` then points lying in the middle # of mesh cells (that is inside the polygon) will be filtered # out by the threshold and have positive distance instead of 0. # so for threshold of 40 km mesh spacing should not be more # than 56 km (typical values are 5 to 10 km). idxs = (distances < 40).nonzero()[0] # indices on the first dimension if not len(idxs): # no point is close enough, return distances as they are return distances # for all the points that are closer than the threshold we need # to recalculate the distance and set it to zero, if point falls # inside the enclosing polygon of the mesh. for doing that we # project both this mesh and the points of the second mesh--selected # by distance threshold--to the same Cartesian space, define # minimum shapely polygon enclosing the mesh and calculate point # to polygon distance, which gives the most accurate value # of distance in km (and that value is zero for points inside # the polygon). proj, polygon = self._get_proj_enclosing_polygon() if not isinstance(polygon, shapely.geometry.Polygon): # either line or point is our enclosing polygon. draw # a square with side of 10 m around in order to have # a proper polygon instead. polygon = polygon.buffer(self.DIST_TOLERANCE, 1) mesh_xx, mesh_yy = proj(mesh.lons[idxs], mesh.lats[idxs]) # replace geodetic distance values for points-closer-than-the-threshold # by more accurate point-to-polygon distance values. distances[idxs] = geo_utils.point_to_polygon_distance( polygon, mesh_xx, mesh_yy) return distances

Compute and return Joyner-Boore distance to each point of ``mesh``. Point's depth is ignored. See :meth:`openquake.hazardlib.geo.surface.base.BaseSurface.get_joyner_boore_distance` for definition of this distance. :returns: numpy array of distances in km of the same shape as ``mesh``. Distance value is considered to be zero if a point lies inside the polygon enveloping the projection of the mesh or on one of its edges.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L319-L393

gem/oq-engine

openquake/hazardlib/geo/mesh.py

Mesh.get_convex_hull

def get_convex_hull(self): """ Get a convex polygon object that contains projections of all the points of the mesh. :returns: Instance of :class:`openquake.hazardlib.geo.polygon.Polygon` that is a convex hull around all the points in this mesh. If the original mesh had only one point, the resulting polygon has a square shape with a side length of 10 meters. If there were only two points, resulting polygon is a stripe 10 meters wide. """ proj, polygon2d = self._get_proj_convex_hull() # if mesh had only one point, the convex hull is a point. if there # were two, it is a line string. we need to return a convex polygon # object, so extend that area-less geometries by some arbitrarily # small distance. if isinstance(polygon2d, (shapely.geometry.LineString, shapely.geometry.Point)): polygon2d = polygon2d.buffer(self.DIST_TOLERANCE, 1) # avoid circular imports from openquake.hazardlib.geo.polygon import Polygon return Polygon._from_2d(polygon2d, proj)

python

def get_convex_hull(self): """ Get a convex polygon object that contains projections of all the points of the mesh. :returns: Instance of :class:`openquake.hazardlib.geo.polygon.Polygon` that is a convex hull around all the points in this mesh. If the original mesh had only one point, the resulting polygon has a square shape with a side length of 10 meters. If there were only two points, resulting polygon is a stripe 10 meters wide. """ proj, polygon2d = self._get_proj_convex_hull() # if mesh had only one point, the convex hull is a point. if there # were two, it is a line string. we need to return a convex polygon # object, so extend that area-less geometries by some arbitrarily # small distance. if isinstance(polygon2d, (shapely.geometry.LineString, shapely.geometry.Point)): polygon2d = polygon2d.buffer(self.DIST_TOLERANCE, 1) # avoid circular imports from openquake.hazardlib.geo.polygon import Polygon return Polygon._from_2d(polygon2d, proj)

Get a convex polygon object that contains projections of all the points of the mesh. :returns: Instance of :class:`openquake.hazardlib.geo.polygon.Polygon` that is a convex hull around all the points in this mesh. If the original mesh had only one point, the resulting polygon has a square shape with a side length of 10 meters. If there were only two points, resulting polygon is a stripe 10 meters wide.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L457-L480

gem/oq-engine

openquake/hazardlib/geo/mesh.py

RectangularMesh.from_points_list

def from_points_list(cls, points): """ Create a rectangular mesh object from a list of lists of points. Lists in a list are supposed to have the same length. :param point: List of lists of :class:`~openquake.hazardlib.geo.point.Point` objects. """ assert points is not None and len(points) > 0 and len(points[0]) > 0, \ 'list of at least one non-empty list of points is required' lons = numpy.zeros((len(points), len(points[0])), dtype=float) lats = lons.copy() depths = lons.copy() num_cols = len(points[0]) for i, row in enumerate(points): assert len(row) == num_cols, \ 'lists of points are not of uniform length' for j, point in enumerate(row): lons[i, j] = point.longitude lats[i, j] = point.latitude depths[i, j] = point.depth if not depths.any(): depths = None return cls(lons, lats, depths)

python

def from_points_list(cls, points): """ Create a rectangular mesh object from a list of lists of points. Lists in a list are supposed to have the same length. :param point: List of lists of :class:`~openquake.hazardlib.geo.point.Point` objects. """ assert points is not None and len(points) > 0 and len(points[0]) > 0, \ 'list of at least one non-empty list of points is required' lons = numpy.zeros((len(points), len(points[0])), dtype=float) lats = lons.copy() depths = lons.copy() num_cols = len(points[0]) for i, row in enumerate(points): assert len(row) == num_cols, \ 'lists of points are not of uniform length' for j, point in enumerate(row): lons[i, j] = point.longitude lats[i, j] = point.latitude depths[i, j] = point.depth if not depths.any(): depths = None return cls(lons, lats, depths)

Create a rectangular mesh object from a list of lists of points. Lists in a list are supposed to have the same length. :param point: List of lists of :class:`~openquake.hazardlib.geo.point.Point` objects.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L497-L521

gem/oq-engine

openquake/hazardlib/geo/mesh.py

RectangularMesh.get_middle_point

def get_middle_point(self): """ Return the middle point of the mesh. :returns: An instance of :class:`~openquake.hazardlib.geo.point.Point`. The middle point is taken from the middle row and a middle column of the mesh if there are odd number of both. Otherwise the geometric mean point of two or four middle points. """ num_rows, num_cols = self.lons.shape mid_row = num_rows // 2 depth = 0 if num_rows & 1 == 1: # there are odd number of rows mid_col = num_cols // 2 if num_cols & 1 == 1: # odd number of columns, we can easily take # the middle point depth = self.depths[mid_row, mid_col] return Point(self.lons[mid_row, mid_col], self.lats[mid_row, mid_col], depth) else: # even number of columns, need to take two middle # points on the middle row lon1, lon2 = self.lons[mid_row, mid_col - 1: mid_col + 1] lat1, lat2 = self.lats[mid_row, mid_col - 1: mid_col + 1] depth1 = self.depths[mid_row, mid_col - 1] depth2 = self.depths[mid_row, mid_col] else: # there are even number of rows. take the row just above # and the one just below the middle and find middle point # of each submesh1 = self[mid_row - 1: mid_row] submesh2 = self[mid_row: mid_row + 1] p1, p2 = submesh1.get_middle_point(), submesh2.get_middle_point() lon1, lat1, depth1 = p1.longitude, p1.latitude, p1.depth lon2, lat2, depth2 = p2.longitude, p2.latitude, p2.depth # we need to find the middle between two points depth = (depth1 + depth2) / 2.0 lon, lat = geo_utils.get_middle_point(lon1, lat1, lon2, lat2) return Point(lon, lat, depth)

python

def get_middle_point(self): """ Return the middle point of the mesh. :returns: An instance of :class:`~openquake.hazardlib.geo.point.Point`. The middle point is taken from the middle row and a middle column of the mesh if there are odd number of both. Otherwise the geometric mean point of two or four middle points. """ num_rows, num_cols = self.lons.shape mid_row = num_rows // 2 depth = 0 if num_rows & 1 == 1: # there are odd number of rows mid_col = num_cols // 2 if num_cols & 1 == 1: # odd number of columns, we can easily take # the middle point depth = self.depths[mid_row, mid_col] return Point(self.lons[mid_row, mid_col], self.lats[mid_row, mid_col], depth) else: # even number of columns, need to take two middle # points on the middle row lon1, lon2 = self.lons[mid_row, mid_col - 1: mid_col + 1] lat1, lat2 = self.lats[mid_row, mid_col - 1: mid_col + 1] depth1 = self.depths[mid_row, mid_col - 1] depth2 = self.depths[mid_row, mid_col] else: # there are even number of rows. take the row just above # and the one just below the middle and find middle point # of each submesh1 = self[mid_row - 1: mid_row] submesh2 = self[mid_row: mid_row + 1] p1, p2 = submesh1.get_middle_point(), submesh2.get_middle_point() lon1, lat1, depth1 = p1.longitude, p1.latitude, p1.depth lon2, lat2, depth2 = p2.longitude, p2.latitude, p2.depth # we need to find the middle between two points depth = (depth1 + depth2) / 2.0 lon, lat = geo_utils.get_middle_point(lon1, lat1, lon2, lat2) return Point(lon, lat, depth)

Return the middle point of the mesh. :returns: An instance of :class:`~openquake.hazardlib.geo.point.Point`. The middle point is taken from the middle row and a middle column of the mesh if there are odd number of both. Otherwise the geometric mean point of two or four middle points.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L523-L566

gem/oq-engine

openquake/hazardlib/geo/mesh.py

RectangularMesh.get_cell_dimensions

def get_cell_dimensions(self): """ Calculate centroid, width, length and area of each mesh cell. :returns: Tuple of four elements, each being 2d numpy array. Each array has both dimensions less by one the dimensions of the mesh, since they represent cells, not vertices. Arrays contain the following cell information: #. centroids, 3d vectors in a Cartesian space, #. length (size along row of points) in km, #. width (size along column of points) in km, #. area in square km. """ points, along_azimuth, updip, diag = self.triangulate() top = along_azimuth[:-1] left = updip[:, :-1] tl_area = geo_utils.triangle_area(top, left, diag) top_length = numpy.sqrt(numpy.sum(top * top, axis=-1)) left_length = numpy.sqrt(numpy.sum(left * left, axis=-1)) bottom = along_azimuth[1:] right = updip[:, 1:] br_area = geo_utils.triangle_area(bottom, right, diag) bottom_length = numpy.sqrt(numpy.sum(bottom * bottom, axis=-1)) right_length = numpy.sqrt(numpy.sum(right * right, axis=-1)) cell_area = tl_area + br_area tl_center = (points[:-1, :-1] + points[:-1, 1:] + points[1:, :-1]) / 3 br_center = (points[:-1, 1:] + points[1:, :-1] + points[1:, 1:]) / 3 cell_center = ((tl_center * tl_area.reshape(tl_area.shape + (1, )) + br_center * br_area.reshape(br_area.shape + (1, ))) / cell_area.reshape(cell_area.shape + (1, ))) cell_length = ((top_length * tl_area + bottom_length * br_area) / cell_area) cell_width = ((left_length * tl_area + right_length * br_area) / cell_area) return cell_center, cell_length, cell_width, cell_area

python

def get_cell_dimensions(self): """ Calculate centroid, width, length and area of each mesh cell. :returns: Tuple of four elements, each being 2d numpy array. Each array has both dimensions less by one the dimensions of the mesh, since they represent cells, not vertices. Arrays contain the following cell information: #. centroids, 3d vectors in a Cartesian space, #. length (size along row of points) in km, #. width (size along column of points) in km, #. area in square km. """ points, along_azimuth, updip, diag = self.triangulate() top = along_azimuth[:-1] left = updip[:, :-1] tl_area = geo_utils.triangle_area(top, left, diag) top_length = numpy.sqrt(numpy.sum(top * top, axis=-1)) left_length = numpy.sqrt(numpy.sum(left * left, axis=-1)) bottom = along_azimuth[1:] right = updip[:, 1:] br_area = geo_utils.triangle_area(bottom, right, diag) bottom_length = numpy.sqrt(numpy.sum(bottom * bottom, axis=-1)) right_length = numpy.sqrt(numpy.sum(right * right, axis=-1)) cell_area = tl_area + br_area tl_center = (points[:-1, :-1] + points[:-1, 1:] + points[1:, :-1]) / 3 br_center = (points[:-1, 1:] + points[1:, :-1] + points[1:, 1:]) / 3 cell_center = ((tl_center * tl_area.reshape(tl_area.shape + (1, )) + br_center * br_area.reshape(br_area.shape + (1, ))) / cell_area.reshape(cell_area.shape + (1, ))) cell_length = ((top_length * tl_area + bottom_length * br_area) / cell_area) cell_width = ((left_length * tl_area + right_length * br_area) / cell_area) return cell_center, cell_length, cell_width, cell_area

Calculate centroid, width, length and area of each mesh cell. :returns: Tuple of four elements, each being 2d numpy array. Each array has both dimensions less by one the dimensions of the mesh, since they represent cells, not vertices. Arrays contain the following cell information: #. centroids, 3d vectors in a Cartesian space, #. length (size along row of points) in km, #. width (size along column of points) in km, #. area in square km.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L704-L746

gem/oq-engine

openquake/hazardlib/geo/mesh.py

RectangularMesh.triangulate

def triangulate(self): """ Convert mesh points to vectors in Cartesian space. :returns: Tuple of four elements, each being 2d numpy array of 3d vectors (the same structure and shape as the mesh itself). Those arrays are: #. points vectors, #. vectors directed from each point (excluding the last column) to the next one in a same row →, #. vectors directed from each point (excluding the first row) to the previous one in a same column ↑, #. vectors pointing from a bottom left point of each mesh cell to top right one ↗. So the last three arrays of vectors allow to construct triangles covering the whole mesh. """ points = geo_utils.spherical_to_cartesian(self.lons, self.lats, self.depths) # triangulate the mesh by defining vectors of triangles edges: # → along_azimuth = points[:, 1:] - points[:, :-1] # ↑ updip = points[:-1] - points[1:] # ↗ diag = points[:-1, 1:] - points[1:, :-1] return points, along_azimuth, updip, diag

python

def triangulate(self): """ Convert mesh points to vectors in Cartesian space. :returns: Tuple of four elements, each being 2d numpy array of 3d vectors (the same structure and shape as the mesh itself). Those arrays are: #. points vectors, #. vectors directed from each point (excluding the last column) to the next one in a same row →, #. vectors directed from each point (excluding the first row) to the previous one in a same column ↑, #. vectors pointing from a bottom left point of each mesh cell to top right one ↗. So the last three arrays of vectors allow to construct triangles covering the whole mesh. """ points = geo_utils.spherical_to_cartesian(self.lons, self.lats, self.depths) # triangulate the mesh by defining vectors of triangles edges: # → along_azimuth = points[:, 1:] - points[:, :-1] # ↑ updip = points[:-1] - points[1:] # ↗ diag = points[:-1, 1:] - points[1:, :-1] return points, along_azimuth, updip, diag

Convert mesh points to vectors in Cartesian space. :returns: Tuple of four elements, each being 2d numpy array of 3d vectors (the same structure and shape as the mesh itself). Those arrays are: #. points vectors, #. vectors directed from each point (excluding the last column) to the next one in a same row →, #. vectors directed from each point (excluding the first row) to the previous one in a same column ↑, #. vectors pointing from a bottom left point of each mesh cell to top right one ↗. So the last three arrays of vectors allow to construct triangles covering the whole mesh.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/mesh.py#L748-L778

gem/oq-engine

openquake/hmtk/seismicity/smoothing/kernels/isotropic_gaussian.py

IsotropicGaussian.smooth_data

def smooth_data(self, data, config, is_3d=False): ''' Applies the smoothing kernel to the data :param np.ndarray data: Raw earthquake count in the form [Longitude, Latitude, Depth, Count] :param dict config: Configuration parameters must contain: * BandWidth: The bandwidth of the kernel (in km) (float) * Length_Limit: Maximum number of standard deviations :returns: * smoothed_value: np.ndarray vector of smoothed values * Total (summed) rate of the original values * Total (summed) rate of the smoothed values ''' max_dist = config['Length_Limit'] * config['BandWidth'] smoothed_value = np.zeros(len(data), dtype=float) for iloc in range(0, len(data)): dist_val = haversine(data[:, 0], data[:, 1], data[iloc, 0], data[iloc, 1]) if is_3d: dist_val = np.sqrt(dist_val.flatten() ** 2.0 + (data[:, 2] - data[iloc, 2]) ** 2.0) id0 = np.where(dist_val <= max_dist)[0] w_val = (np.exp(-(dist_val[id0] ** 2.0) / (config['BandWidth'] ** 2.))).flatten() smoothed_value[iloc] = np.sum(w_val * data[id0, 3]) / np.sum(w_val) return smoothed_value, np.sum(data[:, -1]), np.sum(smoothed_value)

python

def smooth_data(self, data, config, is_3d=False): ''' Applies the smoothing kernel to the data :param np.ndarray data: Raw earthquake count in the form [Longitude, Latitude, Depth, Count] :param dict config: Configuration parameters must contain: * BandWidth: The bandwidth of the kernel (in km) (float) * Length_Limit: Maximum number of standard deviations :returns: * smoothed_value: np.ndarray vector of smoothed values * Total (summed) rate of the original values * Total (summed) rate of the smoothed values ''' max_dist = config['Length_Limit'] * config['BandWidth'] smoothed_value = np.zeros(len(data), dtype=float) for iloc in range(0, len(data)): dist_val = haversine(data[:, 0], data[:, 1], data[iloc, 0], data[iloc, 1]) if is_3d: dist_val = np.sqrt(dist_val.flatten() ** 2.0 + (data[:, 2] - data[iloc, 2]) ** 2.0) id0 = np.where(dist_val <= max_dist)[0] w_val = (np.exp(-(dist_val[id0] ** 2.0) / (config['BandWidth'] ** 2.))).flatten() smoothed_value[iloc] = np.sum(w_val * data[id0, 3]) / np.sum(w_val) return smoothed_value, np.sum(data[:, -1]), np.sum(smoothed_value)

Applies the smoothing kernel to the data :param np.ndarray data: Raw earthquake count in the form [Longitude, Latitude, Depth, Count] :param dict config: Configuration parameters must contain: * BandWidth: The bandwidth of the kernel (in km) (float) * Length_Limit: Maximum number of standard deviations :returns: * smoothed_value: np.ndarray vector of smoothed values * Total (summed) rate of the original values * Total (summed) rate of the smoothed values

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/smoothing/kernels/isotropic_gaussian.py#L69-L99

gem/oq-engine

openquake/commands/purge.py

purge_one

def purge_one(calc_id, user): """ Remove one calculation ID from the database and remove its datastore """ filename = os.path.join(datadir, 'calc_%s.hdf5' % calc_id) err = dbcmd('del_calc', calc_id, user) if err: print(err) elif os.path.exists(filename): # not removed yet os.remove(filename) print('Removed %s' % filename)

python

def purge_one(calc_id, user): """ Remove one calculation ID from the database and remove its datastore """ filename = os.path.join(datadir, 'calc_%s.hdf5' % calc_id) err = dbcmd('del_calc', calc_id, user) if err: print(err) elif os.path.exists(filename): # not removed yet os.remove(filename) print('Removed %s' % filename)

Remove one calculation ID from the database and remove its datastore

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commands/purge.py#L28-L38

gem/oq-engine

openquake/commands/purge.py

purge_all

def purge_all(user=None, fast=False): """ Remove all calculations of the given user """ user = user or getpass.getuser() if os.path.exists(datadir): if fast: shutil.rmtree(datadir) print('Removed %s' % datadir) else: for fname in os.listdir(datadir): mo = re.match('calc_(\d+)\.hdf5', fname) if mo is not None: calc_id = int(mo.group(1)) purge_one(calc_id, user)

python

def purge_all(user=None, fast=False): """ Remove all calculations of the given user """ user = user or getpass.getuser() if os.path.exists(datadir): if fast: shutil.rmtree(datadir) print('Removed %s' % datadir) else: for fname in os.listdir(datadir): mo = re.match('calc_(\d+)\.hdf5', fname) if mo is not None: calc_id = int(mo.group(1)) purge_one(calc_id, user)

Remove all calculations of the given user

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commands/purge.py#L42-L56

gem/oq-engine

openquake/commands/purge.py

purge

def purge(calc_id): """ Remove the given calculation. If you want to remove all calculations, use oq reset. """ if calc_id < 0: try: calc_id = datastore.get_calc_ids(datadir)[calc_id] except IndexError: print('Calculation %d not found' % calc_id) return purge_one(calc_id, getpass.getuser())

python

def purge(calc_id): """ Remove the given calculation. If you want to remove all calculations, use oq reset. """ if calc_id < 0: try: calc_id = datastore.get_calc_ids(datadir)[calc_id] except IndexError: print('Calculation %d not found' % calc_id) return purge_one(calc_id, getpass.getuser())

Remove the given calculation. If you want to remove all calculations, use oq reset.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commands/purge.py#L60-L71

gem/oq-engine

openquake/hmtk/plotting/patch.py

PolygonPatch

def PolygonPatch(polygon, **kwargs): """Constructs a matplotlib patch from a geometric object The `polygon` may be a Shapely or GeoJSON-like object possibly with holes. The `kwargs` are those supported by the matplotlib.patches.Polygon class constructor. Returns an instance of matplotlib.patches.PathPatch. Example (using Shapely Point and a matplotlib axes): >> b = Point(0, 0).buffer(1.0) >> patch = PolygonPatch(b, fc='blue', ec='blue', alpha=0.5) >> axis.add_patch(patch) """ def coding(ob): # The codes will be all "LINETO" commands, except for "MOVETO"s at the # beginning of each subpath n = len(getattr(ob, 'coords', None) or ob) vals = ones(n, dtype=Path.code_type) * Path.LINETO vals[0] = Path.MOVETO return vals if hasattr(polygon, 'geom_type'): # Shapely ptype = polygon.geom_type if ptype == 'Polygon': polygon = [Polygon(polygon)] elif ptype == 'MultiPolygon': polygon = [Polygon(p) for p in polygon] else: raise ValueError( "A polygon or multi-polygon representation is required") else: # GeoJSON polygon = getattr(polygon, '__geo_interface__', polygon) ptype = polygon["type"] if ptype == 'Polygon': polygon = [Polygon(polygon)] elif ptype == 'MultiPolygon': polygon = [Polygon(p) for p in polygon['coordinates']] else: raise ValueError( "A polygon or multi-polygon representation is required") vertices = concatenate([ concatenate([asarray(t.exterior)[:, :2]] + [asarray(r)[:, :2] for r in t.interiors]) for t in polygon]) codes = concatenate([ concatenate([coding(t.exterior)] + [coding(r) for r in t.interiors]) for t in polygon]) return PathPatch(Path(vertices, codes), **kwargs)

python

def PolygonPatch(polygon, **kwargs): """Constructs a matplotlib patch from a geometric object The `polygon` may be a Shapely or GeoJSON-like object possibly with holes. The `kwargs` are those supported by the matplotlib.patches.Polygon class constructor. Returns an instance of matplotlib.patches.PathPatch. Example (using Shapely Point and a matplotlib axes): >> b = Point(0, 0).buffer(1.0) >> patch = PolygonPatch(b, fc='blue', ec='blue', alpha=0.5) >> axis.add_patch(patch) """ def coding(ob): # The codes will be all "LINETO" commands, except for "MOVETO"s at the # beginning of each subpath n = len(getattr(ob, 'coords', None) or ob) vals = ones(n, dtype=Path.code_type) * Path.LINETO vals[0] = Path.MOVETO return vals if hasattr(polygon, 'geom_type'): # Shapely ptype = polygon.geom_type if ptype == 'Polygon': polygon = [Polygon(polygon)] elif ptype == 'MultiPolygon': polygon = [Polygon(p) for p in polygon] else: raise ValueError( "A polygon or multi-polygon representation is required") else: # GeoJSON polygon = getattr(polygon, '__geo_interface__', polygon) ptype = polygon["type"] if ptype == 'Polygon': polygon = [Polygon(polygon)] elif ptype == 'MultiPolygon': polygon = [Polygon(p) for p in polygon['coordinates']] else: raise ValueError( "A polygon or multi-polygon representation is required") vertices = concatenate([ concatenate([asarray(t.exterior)[:, :2]] + [asarray(r)[:, :2] for r in t.interiors]) for t in polygon]) codes = concatenate([ concatenate([coding(t.exterior)] + [coding(r) for r in t.interiors]) for t in polygon]) return PathPatch(Path(vertices, codes), **kwargs)

Constructs a matplotlib patch from a geometric object The `polygon` may be a Shapely or GeoJSON-like object possibly with holes. The `kwargs` are those supported by the matplotlib.patches.Polygon class constructor. Returns an instance of matplotlib.patches.PathPatch. Example (using Shapely Point and a matplotlib axes): >> b = Point(0, 0).buffer(1.0) >> patch = PolygonPatch(b, fc='blue', ec='blue', alpha=0.5) >> axis.add_patch(patch)

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/patch.py#L43-L93

gem/oq-engine

openquake/hazardlib/gsim/kotha_2019.py

KothaEtAl2019.retreive_sigma_mu_data

def retreive_sigma_mu_data(self): """ For the general form of the GMPE this retrieves the sigma mu values from the hdf5 file using the "general" model, i.e. sigma mu factors that are independent of the choice of region or depth """ fle = h5py.File(os.path.join(BASE_PATH, "KothaEtAl2019_SigmaMu_Fixed.hdf5"), "r") self.mags = fle["M"][:] self.dists = fle["R"][:] self.periods = fle["T"][:] self.pga = fle["PGA"][:] self.pgv = fle["PGV"][:] self.s_a = fle["SA"][:] fle.close()

python

def retreive_sigma_mu_data(self): """ For the general form of the GMPE this retrieves the sigma mu values from the hdf5 file using the "general" model, i.e. sigma mu factors that are independent of the choice of region or depth """ fle = h5py.File(os.path.join(BASE_PATH, "KothaEtAl2019_SigmaMu_Fixed.hdf5"), "r") self.mags = fle["M"][:] self.dists = fle["R"][:] self.periods = fle["T"][:] self.pga = fle["PGA"][:] self.pgv = fle["PGV"][:] self.s_a = fle["SA"][:] fle.close()

For the general form of the GMPE this retrieves the sigma mu values from the hdf5 file using the "general" model, i.e. sigma mu factors that are independent of the choice of region or depth

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/kotha_2019.py#L129-L143

gem/oq-engine

openquake/hazardlib/gsim/kotha_2019.py

KothaEtAl2019.get_magnitude_scaling

def get_magnitude_scaling(self, C, mag): """ Returns the magnitude scaling term """ d_m = mag - self.CONSTANTS["Mh"] if mag < self.CONSTANTS["Mh"]: return C["e1"] + C["b1"] * d_m + C["b2"] * (d_m ** 2.0) else: return C["e1"] + C["b3"] * d_m

python

def get_magnitude_scaling(self, C, mag): """ Returns the magnitude scaling term """ d_m = mag - self.CONSTANTS["Mh"] if mag < self.CONSTANTS["Mh"]: return C["e1"] + C["b1"] * d_m + C["b2"] * (d_m ** 2.0) else: return C["e1"] + C["b3"] * d_m

Returns the magnitude scaling term

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/kotha_2019.py#L174-L182

gem/oq-engine

openquake/hazardlib/gsim/kotha_2019.py

KothaEtAl2019.get_distance_term

def get_distance_term(self, C, rup, rjb, imt): """ Returns the distance attenuation factor """ h = self._get_h(C, rup.hypo_depth) rval = np.sqrt(rjb ** 2. + h ** 2.) c3 = self.get_distance_coefficients(C, imt) f_r = (C["c1"] + C["c2"] * (rup.mag - self.CONSTANTS["Mref"])) *\ np.log(rval / self.CONSTANTS["Rref"]) +\ c3 * (rval - self.CONSTANTS["Rref"]) return f_r

python

def get_distance_term(self, C, rup, rjb, imt): """ Returns the distance attenuation factor """ h = self._get_h(C, rup.hypo_depth) rval = np.sqrt(rjb ** 2. + h ** 2.) c3 = self.get_distance_coefficients(C, imt) f_r = (C["c1"] + C["c2"] * (rup.mag - self.CONSTANTS["Mref"])) *\ np.log(rval / self.CONSTANTS["Rref"]) +\ c3 * (rval - self.CONSTANTS["Rref"]) return f_r

Returns the distance attenuation factor

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/kotha_2019.py#L184-L195

gem/oq-engine

openquake/hazardlib/gsim/kotha_2019.py

KothaEtAl2019.get_distance_coefficients

def get_distance_coefficients(self, C, imt): """ Returns the c3 term """ c3 = self.c3[imt]["c3"] if self.c3 else C["c3"] return c3

python

def get_distance_coefficients(self, C, imt): """ Returns the c3 term """ c3 = self.c3[imt]["c3"] if self.c3 else C["c3"] return c3

Returns the c3 term

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/kotha_2019.py#L208-L213

gem/oq-engine

openquake/hazardlib/gsim/kotha_2019.py

KothaEtAl2019.get_sigma_mu_adjustment

def get_sigma_mu_adjustment(self, C, imt, rup, dists): """ Returns the sigma mu adjustment factor """ if imt.name in "PGA PGV": # PGA and PGV are 2D arrays of dimension [nmags, ndists] sigma_mu = getattr(self, imt.name.lower()) if rup.mag <= self.mags[0]: sigma_mu_m = sigma_mu[0, :] elif rup.mag >= self.mags[-1]: sigma_mu_m = sigma_mu[-1, :] else: intpl1 = interp1d(self.mags, sigma_mu, axis=0) sigma_mu_m = intpl1(rup.mag) # Linear interpolation with distance intpl2 = interp1d(self.dists, sigma_mu_m, bounds_error=False, fill_value=(sigma_mu_m[0], sigma_mu_m[-1])) return intpl2(dists.rjb) # In the case of SA the array is of dimension [nmags, ndists, nperiods] # Get values for given magnitude if rup.mag <= self.mags[0]: sigma_mu_m = self.s_a[0, :, :] elif rup.mag >= self.mags[-1]: sigma_mu_m = self.s_a[-1, :, :] else: intpl1 = interp1d(self.mags, self.s_a, axis=0) sigma_mu_m = intpl1(rup.mag) # Get values for period - N.B. ln T, linear sigma mu interpolation if imt.period <= self.periods[0]: sigma_mu_t = sigma_mu_m[:, 0] elif imt.period >= self.periods[-1]: sigma_mu_t = sigma_mu_m[:, -1] else: intpl2 = interp1d(np.log(self.periods), sigma_mu_m, axis=1) sigma_mu_t = intpl2(np.log(imt.period)) intpl3 = interp1d(self.dists, sigma_mu_t, bounds_error=False, fill_value=(sigma_mu_t[0], sigma_mu_t[-1])) return intpl3(dists.rjb)

python

def get_sigma_mu_adjustment(self, C, imt, rup, dists): """ Returns the sigma mu adjustment factor """ if imt.name in "PGA PGV": # PGA and PGV are 2D arrays of dimension [nmags, ndists] sigma_mu = getattr(self, imt.name.lower()) if rup.mag <= self.mags[0]: sigma_mu_m = sigma_mu[0, :] elif rup.mag >= self.mags[-1]: sigma_mu_m = sigma_mu[-1, :] else: intpl1 = interp1d(self.mags, sigma_mu, axis=0) sigma_mu_m = intpl1(rup.mag) # Linear interpolation with distance intpl2 = interp1d(self.dists, sigma_mu_m, bounds_error=False, fill_value=(sigma_mu_m[0], sigma_mu_m[-1])) return intpl2(dists.rjb) # In the case of SA the array is of dimension [nmags, ndists, nperiods] # Get values for given magnitude if rup.mag <= self.mags[0]: sigma_mu_m = self.s_a[0, :, :] elif rup.mag >= self.mags[-1]: sigma_mu_m = self.s_a[-1, :, :] else: intpl1 = interp1d(self.mags, self.s_a, axis=0) sigma_mu_m = intpl1(rup.mag) # Get values for period - N.B. ln T, linear sigma mu interpolation if imt.period <= self.periods[0]: sigma_mu_t = sigma_mu_m[:, 0] elif imt.period >= self.periods[-1]: sigma_mu_t = sigma_mu_m[:, -1] else: intpl2 = interp1d(np.log(self.periods), sigma_mu_m, axis=1) sigma_mu_t = intpl2(np.log(imt.period)) intpl3 = interp1d(self.dists, sigma_mu_t, bounds_error=False, fill_value=(sigma_mu_t[0], sigma_mu_t[-1])) return intpl3(dists.rjb)

Returns the sigma mu adjustment factor

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/kotha_2019.py#L221-L258

gem/oq-engine

openquake/hazardlib/gsim/kotha_2019.py

KothaEtAl2019SERA.get_site_amplification

def get_site_amplification(self, C, sites): """ Returns the linear site amplification term depending on whether the Vs30 is observed of inferred """ ampl = np.zeros(sites.vs30.shape) # For observed vs30 sites ampl[sites.vs30measured] = (C["d0_obs"] + C["d1_obs"] * np.log(sites.vs30[sites.vs30measured])) # For inferred Vs30 sites idx = np.logical_not(sites.vs30measured) ampl[idx] = (C["d0_inf"] + C["d1_inf"] * np.log(sites.vs30[idx])) return ampl

python

def get_site_amplification(self, C, sites): """ Returns the linear site amplification term depending on whether the Vs30 is observed of inferred """ ampl = np.zeros(sites.vs30.shape) # For observed vs30 sites ampl[sites.vs30measured] = (C["d0_obs"] + C["d1_obs"] * np.log(sites.vs30[sites.vs30measured])) # For inferred Vs30 sites idx = np.logical_not(sites.vs30measured) ampl[idx] = (C["d0_inf"] + C["d1_inf"] * np.log(sites.vs30[idx])) return ampl

Returns the linear site amplification term depending on whether the Vs30 is observed of inferred

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/kotha_2019.py#L332-L344

gem/oq-engine

openquake/hazardlib/gsim/kotha_2019.py

KothaEtAl2019SERA.get_stddevs

def get_stddevs(self, C, stddev_shape, stddev_types, sites): """ Returns the standard deviations, with different site standard deviation for inferred vs. observed vs30 sites. """ stddevs = [] tau = C["tau_event"] sigma_s = np.zeros(sites.vs30measured.shape, dtype=float) sigma_s[sites.vs30measured] += C["sigma_s_obs"] sigma_s[np.logical_not(sites.vs30measured)] += C["sigma_s_inf"] phi = np.sqrt(C["phi0"] ** 2.0 + sigma_s ** 2.) for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES if stddev_type == const.StdDev.TOTAL: stddevs.append(np.sqrt(tau ** 2. + phi ** 2.) + np.zeros(stddev_shape)) elif stddev_type == const.StdDev.INTRA_EVENT: stddevs.append(phi + np.zeros(stddev_shape)) elif stddev_type == const.StdDev.INTER_EVENT: stddevs.append(tau + np.zeros(stddev_shape)) return stddevs

python

def get_stddevs(self, C, stddev_shape, stddev_types, sites): """ Returns the standard deviations, with different site standard deviation for inferred vs. observed vs30 sites. """ stddevs = [] tau = C["tau_event"] sigma_s = np.zeros(sites.vs30measured.shape, dtype=float) sigma_s[sites.vs30measured] += C["sigma_s_obs"] sigma_s[np.logical_not(sites.vs30measured)] += C["sigma_s_inf"] phi = np.sqrt(C["phi0"] ** 2.0 + sigma_s ** 2.) for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES if stddev_type == const.StdDev.TOTAL: stddevs.append(np.sqrt(tau ** 2. + phi ** 2.) + np.zeros(stddev_shape)) elif stddev_type == const.StdDev.INTRA_EVENT: stddevs.append(phi + np.zeros(stddev_shape)) elif stddev_type == const.StdDev.INTER_EVENT: stddevs.append(tau + np.zeros(stddev_shape)) return stddevs

Returns the standard deviations, with different site standard deviation for inferred vs. observed vs30 sites.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/kotha_2019.py#L346-L366

gem/oq-engine

openquake/hazardlib/geo/geodetic.py

geodetic_distance

def geodetic_distance(lons1, lats1, lons2, lats2, diameter=2*EARTH_RADIUS): """ Calculate the geodetic distance between two points or two collections of points. Parameters are coordinates in decimal degrees. They could be scalar float numbers or numpy arrays, in which case they should "broadcast together". Implements http://williams.best.vwh.net/avform.htm#Dist :returns: Distance in km, floating point scalar or numpy array of such. """ lons1, lats1, lons2, lats2 = _prepare_coords(lons1, lats1, lons2, lats2) distance = numpy.arcsin(numpy.sqrt( numpy.sin((lats1 - lats2) / 2.0) ** 2.0 + numpy.cos(lats1) * numpy.cos(lats2) * numpy.sin((lons1 - lons2) / 2.0) ** 2.0 )) return diameter * distance

python

def geodetic_distance(lons1, lats1, lons2, lats2, diameter=2*EARTH_RADIUS): """ Calculate the geodetic distance between two points or two collections of points. Parameters are coordinates in decimal degrees. They could be scalar float numbers or numpy arrays, in which case they should "broadcast together". Implements http://williams.best.vwh.net/avform.htm#Dist :returns: Distance in km, floating point scalar or numpy array of such. """ lons1, lats1, lons2, lats2 = _prepare_coords(lons1, lats1, lons2, lats2) distance = numpy.arcsin(numpy.sqrt( numpy.sin((lats1 - lats2) / 2.0) ** 2.0 + numpy.cos(lats1) * numpy.cos(lats2) * numpy.sin((lons1 - lons2) / 2.0) ** 2.0 )) return diameter * distance

Calculate the geodetic distance between two points or two collections of points. Parameters are coordinates in decimal degrees. They could be scalar float numbers or numpy arrays, in which case they should "broadcast together". Implements http://williams.best.vwh.net/avform.htm#Dist :returns: Distance in km, floating point scalar or numpy array of such.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/geodetic.py#L34-L54

gem/oq-engine

openquake/hazardlib/geo/geodetic.py

azimuth

def azimuth(lons1, lats1, lons2, lats2): """ Calculate the azimuth between two points or two collections of points. Parameters are the same as for :func:`geodetic_distance`. Implements an "alternative formula" from http://williams.best.vwh.net/avform.htm#Crs :returns: Azimuth as an angle between direction to north from first point and direction to the second point measured clockwise in decimal degrees. """ lons1, lats1, lons2, lats2 = _prepare_coords(lons1, lats1, lons2, lats2) cos_lat2 = numpy.cos(lats2) true_course = numpy.degrees(numpy.arctan2( numpy.sin(lons1 - lons2) * cos_lat2, numpy.cos(lats1) * numpy.sin(lats2) - numpy.sin(lats1) * cos_lat2 * numpy.cos(lons1 - lons2) )) return (360 - true_course) % 360

python

def azimuth(lons1, lats1, lons2, lats2): """ Calculate the azimuth between two points or two collections of points. Parameters are the same as for :func:`geodetic_distance`. Implements an "alternative formula" from http://williams.best.vwh.net/avform.htm#Crs :returns: Azimuth as an angle between direction to north from first point and direction to the second point measured clockwise in decimal degrees. """ lons1, lats1, lons2, lats2 = _prepare_coords(lons1, lats1, lons2, lats2) cos_lat2 = numpy.cos(lats2) true_course = numpy.degrees(numpy.arctan2( numpy.sin(lons1 - lons2) * cos_lat2, numpy.cos(lats1) * numpy.sin(lats2) - numpy.sin(lats1) * cos_lat2 * numpy.cos(lons1 - lons2) )) return (360 - true_course) % 360

Calculate the azimuth between two points or two collections of points. Parameters are the same as for :func:`geodetic_distance`. Implements an "alternative formula" from http://williams.best.vwh.net/avform.htm#Crs :returns: Azimuth as an angle between direction to north from first point and direction to the second point measured clockwise in decimal degrees.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/geodetic.py#L57-L77

gem/oq-engine

openquake/hazardlib/geo/geodetic.py

min_distance_to_segment

def min_distance_to_segment(seglons, seglats, lons, lats): """ This function computes the shortest distance to a segment in a 2D reference system. :parameter seglons: A list or an array of floats specifying the longitude values of the two vertexes delimiting the segment. :parameter seglats: A list or an array of floats specifying the latitude values of the two vertexes delimiting the segment. :parameter lons: A list or a 1D array of floats specifying the longitude values of the points for which the calculation of the shortest distance is requested. :parameter lats: A list or a 1D array of floats specifying the latitude values of the points for which the calculation of the shortest distance is requested. :returns: An array of the same shape as lons which contains for each point defined by (lons, lats) the shortest distance to the segment. Distances are negative for those points that stay on the 'left side' of the segment direction and whose projection lies within the segment edges. For all other points, distance is positive. """ # Check the size of the seglons, seglats arrays assert len(seglons) == len(seglats) == 2 # Compute the azimuth of the segment seg_azim = azimuth(seglons[0], seglats[0], seglons[1], seglats[1]) # Compute the azimuth of the direction obtained # connecting the first point defining the segment and each site azimuth1 = azimuth(seglons[0], seglats[0], lons, lats) # Compute the azimuth of the direction obtained # connecting the second point defining the segment and each site azimuth2 = azimuth(seglons[1], seglats[1], lons, lats) # Find the points inside the band defined by the two lines perpendicular # to the segment direction passing through the two vertexes of the segment. # For these points the closest distance is the distance from the great arc. idx_in = numpy.nonzero( (numpy.cos(numpy.radians(seg_azim-azimuth1)) >= 0.0) & (numpy.cos(numpy.radians(seg_azim-azimuth2)) <= 0.0)) # Find the points outside the band defined by the two line perpendicular # to the segment direction passing through the two vertexes of the segment. # For these points the closest distance is the minimum of the distance from # the two point vertexes. idx_out = numpy.nonzero( (numpy.cos(numpy.radians(seg_azim-azimuth1)) < 0.0) | (numpy.cos(numpy.radians(seg_azim-azimuth2)) > 0.0)) # Find the indexes of points 'on the left of the segment' idx_neg = numpy.nonzero(numpy.sin(numpy.radians( (azimuth1-seg_azim))) < 0.0) # Now let's compute the distances for the two cases. dists = numpy.zeros_like(lons) if len(idx_in[0]): dists[idx_in] = distance_to_arc( seglons[0], seglats[0], seg_azim, lons[idx_in], lats[idx_in]) if len(idx_out[0]): dists[idx_out] = min_geodetic_distance( (seglons, seglats), (lons[idx_out], lats[idx_out])) # Finally we correct the sign of the distances in order to make sure that # the points on the right semispace defined using as a reference the # direction defined by the segment (i.e. the direction defined by going # from the first point to the second one) have a positive distance and # the others a negative one. dists = abs(dists) dists[idx_neg] = - dists[idx_neg] return dists

python

def min_distance_to_segment(seglons, seglats, lons, lats): """ This function computes the shortest distance to a segment in a 2D reference system. :parameter seglons: A list or an array of floats specifying the longitude values of the two vertexes delimiting the segment. :parameter seglats: A list or an array of floats specifying the latitude values of the two vertexes delimiting the segment. :parameter lons: A list or a 1D array of floats specifying the longitude values of the points for which the calculation of the shortest distance is requested. :parameter lats: A list or a 1D array of floats specifying the latitude values of the points for which the calculation of the shortest distance is requested. :returns: An array of the same shape as lons which contains for each point defined by (lons, lats) the shortest distance to the segment. Distances are negative for those points that stay on the 'left side' of the segment direction and whose projection lies within the segment edges. For all other points, distance is positive. """ # Check the size of the seglons, seglats arrays assert len(seglons) == len(seglats) == 2 # Compute the azimuth of the segment seg_azim = azimuth(seglons[0], seglats[0], seglons[1], seglats[1]) # Compute the azimuth of the direction obtained # connecting the first point defining the segment and each site azimuth1 = azimuth(seglons[0], seglats[0], lons, lats) # Compute the azimuth of the direction obtained # connecting the second point defining the segment and each site azimuth2 = azimuth(seglons[1], seglats[1], lons, lats) # Find the points inside the band defined by the two lines perpendicular # to the segment direction passing through the two vertexes of the segment. # For these points the closest distance is the distance from the great arc. idx_in = numpy.nonzero( (numpy.cos(numpy.radians(seg_azim-azimuth1)) >= 0.0) & (numpy.cos(numpy.radians(seg_azim-azimuth2)) <= 0.0)) # Find the points outside the band defined by the two line perpendicular # to the segment direction passing through the two vertexes of the segment. # For these points the closest distance is the minimum of the distance from # the two point vertexes. idx_out = numpy.nonzero( (numpy.cos(numpy.radians(seg_azim-azimuth1)) < 0.0) | (numpy.cos(numpy.radians(seg_azim-azimuth2)) > 0.0)) # Find the indexes of points 'on the left of the segment' idx_neg = numpy.nonzero(numpy.sin(numpy.radians( (azimuth1-seg_azim))) < 0.0) # Now let's compute the distances for the two cases. dists = numpy.zeros_like(lons) if len(idx_in[0]): dists[idx_in] = distance_to_arc( seglons[0], seglats[0], seg_azim, lons[idx_in], lats[idx_in]) if len(idx_out[0]): dists[idx_out] = min_geodetic_distance( (seglons, seglats), (lons[idx_out], lats[idx_out])) # Finally we correct the sign of the distances in order to make sure that # the points on the right semispace defined using as a reference the # direction defined by the segment (i.e. the direction defined by going # from the first point to the second one) have a positive distance and # the others a negative one. dists = abs(dists) dists[idx_neg] = - dists[idx_neg] return dists

This function computes the shortest distance to a segment in a 2D reference system. :parameter seglons: A list or an array of floats specifying the longitude values of the two vertexes delimiting the segment. :parameter seglats: A list or an array of floats specifying the latitude values of the two vertexes delimiting the segment. :parameter lons: A list or a 1D array of floats specifying the longitude values of the points for which the calculation of the shortest distance is requested. :parameter lats: A list or a 1D array of floats specifying the latitude values of the points for which the calculation of the shortest distance is requested. :returns: An array of the same shape as lons which contains for each point defined by (lons, lats) the shortest distance to the segment. Distances are negative for those points that stay on the 'left side' of the segment direction and whose projection lies within the segment edges. For all other points, distance is positive.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/geodetic.py#L99-L174

gem/oq-engine

openquake/hazardlib/geo/geodetic.py

min_geodetic_distance

def min_geodetic_distance(a, b): """ Compute the minimum distance between first mesh and each point of the second mesh when both are defined on the earth surface. :param a: a pair of (lons, lats) or an array of cartesian coordinates :param b: a pair of (lons, lats) or an array of cartesian coordinates """ if isinstance(a, tuple): a = spherical_to_cartesian(a[0].flatten(), a[1].flatten()) if isinstance(b, tuple): b = spherical_to_cartesian(b[0].flatten(), b[1].flatten()) return cdist(a, b).min(axis=0)

python

def min_geodetic_distance(a, b): """ Compute the minimum distance between first mesh and each point of the second mesh when both are defined on the earth surface. :param a: a pair of (lons, lats) or an array of cartesian coordinates :param b: a pair of (lons, lats) or an array of cartesian coordinates """ if isinstance(a, tuple): a = spherical_to_cartesian(a[0].flatten(), a[1].flatten()) if isinstance(b, tuple): b = spherical_to_cartesian(b[0].flatten(), b[1].flatten()) return cdist(a, b).min(axis=0)

Compute the minimum distance between first mesh and each point of the second mesh when both are defined on the earth surface. :param a: a pair of (lons, lats) or an array of cartesian coordinates :param b: a pair of (lons, lats) or an array of cartesian coordinates

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/geodetic.py#L224-L236

gem/oq-engine

openquake/hazardlib/geo/geodetic.py

intervals_between

def intervals_between(lon1, lat1, depth1, lon2, lat2, depth2, length): """ Find a list of points between two given ones that lie on the same great circle arc and are equally spaced by ``length`` km. :param float lon1, lat1, depth1: Coordinates of a point to start placing intervals from. The first point in the resulting list has these coordinates. :param float lon2, lat2, depth2: Coordinates of the other end of the great circle arc segment to put intervals on. The last resulting point might be closer to the first reference point than the second one or further, since the number of segments is taken as rounded division of length between two reference points and ``length``. :param length: Required distance between two subsequent resulting points, in km. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Rounds the distance between two reference points with respect to ``length`` and calls :func:`npoints_towards`. """ assert length > 0 hdist = geodetic_distance(lon1, lat1, lon2, lat2) vdist = depth2 - depth1 # if this method is called multiple times with coordinates that are # separated by the same distance, because of floating point imprecisions # the total distance may have slightly different values (for instance if # the distance between two set of points is 65 km, total distance can be # 64.9999999999989910 and 65.0000000000020322). These two values bring to # two different values of num_intervals (32 in the first case and 33 in # the second), and this is a problem because for the same distance we # should have the same number of intervals. To reduce potential differences # due to floating point errors, we therefore round total_distance to a # fixed precision (7) total_distance = round(numpy.sqrt(hdist ** 2 + vdist ** 2), 7) num_intervals = int(round(total_distance / length)) if num_intervals == 0: return numpy.array([lon1]), numpy.array([lat1]), numpy.array([depth1]) dist_factor = (length * num_intervals) / total_distance return npoints_towards( lon1, lat1, depth1, azimuth(lon1, lat1, lon2, lat2), hdist * dist_factor, vdist * dist_factor, num_intervals + 1)

python

def intervals_between(lon1, lat1, depth1, lon2, lat2, depth2, length): """ Find a list of points between two given ones that lie on the same great circle arc and are equally spaced by ``length`` km. :param float lon1, lat1, depth1: Coordinates of a point to start placing intervals from. The first point in the resulting list has these coordinates. :param float lon2, lat2, depth2: Coordinates of the other end of the great circle arc segment to put intervals on. The last resulting point might be closer to the first reference point than the second one or further, since the number of segments is taken as rounded division of length between two reference points and ``length``. :param length: Required distance between two subsequent resulting points, in km. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Rounds the distance between two reference points with respect to ``length`` and calls :func:`npoints_towards`. """ assert length > 0 hdist = geodetic_distance(lon1, lat1, lon2, lat2) vdist = depth2 - depth1 # if this method is called multiple times with coordinates that are # separated by the same distance, because of floating point imprecisions # the total distance may have slightly different values (for instance if # the distance between two set of points is 65 km, total distance can be # 64.9999999999989910 and 65.0000000000020322). These two values bring to # two different values of num_intervals (32 in the first case and 33 in # the second), and this is a problem because for the same distance we # should have the same number of intervals. To reduce potential differences # due to floating point errors, we therefore round total_distance to a # fixed precision (7) total_distance = round(numpy.sqrt(hdist ** 2 + vdist ** 2), 7) num_intervals = int(round(total_distance / length)) if num_intervals == 0: return numpy.array([lon1]), numpy.array([lat1]), numpy.array([depth1]) dist_factor = (length * num_intervals) / total_distance return npoints_towards( lon1, lat1, depth1, azimuth(lon1, lat1, lon2, lat2), hdist * dist_factor, vdist * dist_factor, num_intervals + 1)

Find a list of points between two given ones that lie on the same great circle arc and are equally spaced by ``length`` km. :param float lon1, lat1, depth1: Coordinates of a point to start placing intervals from. The first point in the resulting list has these coordinates. :param float lon2, lat2, depth2: Coordinates of the other end of the great circle arc segment to put intervals on. The last resulting point might be closer to the first reference point than the second one or further, since the number of segments is taken as rounded division of length between two reference points and ``length``. :param length: Required distance between two subsequent resulting points, in km. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Rounds the distance between two reference points with respect to ``length`` and calls :func:`npoints_towards`.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/geodetic.py#L259-L302

gem/oq-engine

openquake/hazardlib/geo/geodetic.py

npoints_between

def npoints_between(lon1, lat1, depth1, lon2, lat2, depth2, npoints): """ Find a list of specified number of points between two given ones that are equally spaced along the great circle arc connecting given points. :param float lon1, lat1, depth1: Coordinates of a point to start from. The first point in a resulting list has these coordinates. :param float lon2, lat2, depth2: Coordinates of a point to finish at. The last point in a resulting list has these coordinates. :param npoints: Integer number of points to return. First and last points count, so if there have to be two intervals, ``npoints`` should be 3. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Finds distance between two reference points and calls :func:`npoints_towards`. """ hdist = geodetic_distance(lon1, lat1, lon2, lat2) vdist = depth2 - depth1 rlons, rlats, rdepths = npoints_towards( lon1, lat1, depth1, azimuth(lon1, lat1, lon2, lat2), hdist, vdist, npoints ) # the last point should be left intact rlons[-1] = lon2 rlats[-1] = lat2 rdepths[-1] = depth2 return rlons, rlats, rdepths

python

def npoints_between(lon1, lat1, depth1, lon2, lat2, depth2, npoints): """ Find a list of specified number of points between two given ones that are equally spaced along the great circle arc connecting given points. :param float lon1, lat1, depth1: Coordinates of a point to start from. The first point in a resulting list has these coordinates. :param float lon2, lat2, depth2: Coordinates of a point to finish at. The last point in a resulting list has these coordinates. :param npoints: Integer number of points to return. First and last points count, so if there have to be two intervals, ``npoints`` should be 3. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Finds distance between two reference points and calls :func:`npoints_towards`. """ hdist = geodetic_distance(lon1, lat1, lon2, lat2) vdist = depth2 - depth1 rlons, rlats, rdepths = npoints_towards( lon1, lat1, depth1, azimuth(lon1, lat1, lon2, lat2), hdist, vdist, npoints ) # the last point should be left intact rlons[-1] = lon2 rlats[-1] = lat2 rdepths[-1] = depth2 return rlons, rlats, rdepths

Find a list of specified number of points between two given ones that are equally spaced along the great circle arc connecting given points. :param float lon1, lat1, depth1: Coordinates of a point to start from. The first point in a resulting list has these coordinates. :param float lon2, lat2, depth2: Coordinates of a point to finish at. The last point in a resulting list has these coordinates. :param npoints: Integer number of points to return. First and last points count, so if there have to be two intervals, ``npoints`` should be 3. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Finds distance between two reference points and calls :func:`npoints_towards`.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/geodetic.py#L305-L336

gem/oq-engine

openquake/hazardlib/geo/geodetic.py

npoints_towards

def npoints_towards(lon, lat, depth, azimuth, hdist, vdist, npoints): """ Find a list of specified number of points starting from a given one along a great circle arc with a given azimuth measured in a given point. :param float lon, lat, depth: Coordinates of a point to start from. The first point in a resulting list has these coordinates. :param azimuth: A direction representing a great circle arc together with a reference point. :param hdist: Horizontal (geodetic) distance from reference point to the last point of the resulting list, in km. :param vdist: Vertical (depth) distance between reference and the last point, in km. :param npoints: Integer number of points to return. First and last points count, so if there have to be two intervals, ``npoints`` should be 3. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Implements "completely general but more complicated algorithm" from http://williams.best.vwh.net/avform.htm#LL """ assert npoints > 1 rlon, rlat = numpy.radians(lon), numpy.radians(lat) tc = numpy.radians(360 - azimuth) hdists = numpy.arange(npoints, dtype=float) hdists *= (hdist / EARTH_RADIUS) / (npoints - 1) vdists = numpy.arange(npoints, dtype=float) vdists *= vdist / (npoints - 1) sin_dists = numpy.sin(hdists) cos_dists = numpy.cos(hdists) sin_lat = numpy.sin(rlat) cos_lat = numpy.cos(rlat) sin_lats = sin_lat * cos_dists + cos_lat * sin_dists * numpy.cos(tc) lats = numpy.degrees(numpy.arcsin(sin_lats)) dlon = numpy.arctan2(numpy.sin(tc) * sin_dists * cos_lat, cos_dists - sin_lat * sin_lats) lons = numpy.mod(rlon - dlon + numpy.pi, 2 * numpy.pi) - numpy.pi lons = numpy.degrees(lons) depths = vdists + depth # the first point should be left intact lons[0] = lon lats[0] = lat depths[0] = depth return lons, lats, depths

python

def npoints_towards(lon, lat, depth, azimuth, hdist, vdist, npoints): """ Find a list of specified number of points starting from a given one along a great circle arc with a given azimuth measured in a given point. :param float lon, lat, depth: Coordinates of a point to start from. The first point in a resulting list has these coordinates. :param azimuth: A direction representing a great circle arc together with a reference point. :param hdist: Horizontal (geodetic) distance from reference point to the last point of the resulting list, in km. :param vdist: Vertical (depth) distance between reference and the last point, in km. :param npoints: Integer number of points to return. First and last points count, so if there have to be two intervals, ``npoints`` should be 3. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Implements "completely general but more complicated algorithm" from http://williams.best.vwh.net/avform.htm#LL """ assert npoints > 1 rlon, rlat = numpy.radians(lon), numpy.radians(lat) tc = numpy.radians(360 - azimuth) hdists = numpy.arange(npoints, dtype=float) hdists *= (hdist / EARTH_RADIUS) / (npoints - 1) vdists = numpy.arange(npoints, dtype=float) vdists *= vdist / (npoints - 1) sin_dists = numpy.sin(hdists) cos_dists = numpy.cos(hdists) sin_lat = numpy.sin(rlat) cos_lat = numpy.cos(rlat) sin_lats = sin_lat * cos_dists + cos_lat * sin_dists * numpy.cos(tc) lats = numpy.degrees(numpy.arcsin(sin_lats)) dlon = numpy.arctan2(numpy.sin(tc) * sin_dists * cos_lat, cos_dists - sin_lat * sin_lats) lons = numpy.mod(rlon - dlon + numpy.pi, 2 * numpy.pi) - numpy.pi lons = numpy.degrees(lons) depths = vdists + depth # the first point should be left intact lons[0] = lon lats[0] = lat depths[0] = depth return lons, lats, depths

Find a list of specified number of points starting from a given one along a great circle arc with a given azimuth measured in a given point. :param float lon, lat, depth: Coordinates of a point to start from. The first point in a resulting list has these coordinates. :param azimuth: A direction representing a great circle arc together with a reference point. :param hdist: Horizontal (geodetic) distance from reference point to the last point of the resulting list, in km. :param vdist: Vertical (depth) distance between reference and the last point, in km. :param npoints: Integer number of points to return. First and last points count, so if there have to be two intervals, ``npoints`` should be 3. :returns: Tuple of three 1d numpy arrays: longitudes, latitudes and depths of resulting points respectively. Implements "completely general but more complicated algorithm" from http://williams.best.vwh.net/avform.htm#LL

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/geodetic.py#L339-L393

gem/oq-engine

openquake/hazardlib/geo/geodetic.py

_prepare_coords

def _prepare_coords(lons1, lats1, lons2, lats2): """ Convert two pairs of spherical coordinates in decimal degrees to numpy arrays of radians. Makes sure that respective coordinates in pairs have the same shape. """ lons1 = numpy.radians(lons1) lats1 = numpy.radians(lats1) assert lons1.shape == lats1.shape lons2 = numpy.radians(lons2) lats2 = numpy.radians(lats2) assert lons2.shape == lats2.shape return lons1, lats1, lons2, lats2

python

def _prepare_coords(lons1, lats1, lons2, lats2): """ Convert two pairs of spherical coordinates in decimal degrees to numpy arrays of radians. Makes sure that respective coordinates in pairs have the same shape. """ lons1 = numpy.radians(lons1) lats1 = numpy.radians(lats1) assert lons1.shape == lats1.shape lons2 = numpy.radians(lons2) lats2 = numpy.radians(lats2) assert lons2.shape == lats2.shape return lons1, lats1, lons2, lats2

Convert two pairs of spherical coordinates in decimal degrees to numpy arrays of radians. Makes sure that respective coordinates in pairs have the same shape.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/geodetic.py#L528-L540

gem/oq-engine

openquake/hmtk/sources/simple_fault_source.py

mtkSimpleFaultSource.select_catalogue

def select_catalogue(self, selector, distance, distance_metric='joyner-boore', upper_eq_depth=None, lower_eq_depth=None): ''' Selects earthquakes within a distance of the fault :param selector: Populated instance of :class: `openquake.hmtk.seismicity.selector.CatalogueSelector` :param distance: Distance from point (km) for selection :param str distance_metric Choice of fault source distance metric 'joyner-boore' or 'rupture' :param float upper_eq_depth: Upper hypocentral depth of hypocentres to be selected :param float lower_eq_depth: Lower hypocentral depth of hypocentres to be selected ''' if selector.catalogue.get_number_events() < 1: raise ValueError('No events found in catalogue!') # rupture metric is selected and dip != 90 or 'rupture' if ('rupture' in distance_metric) and (fabs(self.dip - 90) > 1E-5): # Use rupture distance self.catalogue = selector.within_rupture_distance( self.geometry, distance, upper_depth=upper_eq_depth, lower_depth=lower_eq_depth) else: # Use Joyner-Boore distance self.catalogue = selector.within_joyner_boore_distance( self.geometry, distance, upper_depth=upper_eq_depth, lower_depth=lower_eq_depth) if self.catalogue.get_number_events() < 5: # Throw a warning regarding the small number of earthquakes in # the source! warnings.warn('Source %s (%s) has fewer than 5 events' % (self.id, self.name))

python

def select_catalogue(self, selector, distance, distance_metric='joyner-boore', upper_eq_depth=None, lower_eq_depth=None): ''' Selects earthquakes within a distance of the fault :param selector: Populated instance of :class: `openquake.hmtk.seismicity.selector.CatalogueSelector` :param distance: Distance from point (km) for selection :param str distance_metric Choice of fault source distance metric 'joyner-boore' or 'rupture' :param float upper_eq_depth: Upper hypocentral depth of hypocentres to be selected :param float lower_eq_depth: Lower hypocentral depth of hypocentres to be selected ''' if selector.catalogue.get_number_events() < 1: raise ValueError('No events found in catalogue!') # rupture metric is selected and dip != 90 or 'rupture' if ('rupture' in distance_metric) and (fabs(self.dip - 90) > 1E-5): # Use rupture distance self.catalogue = selector.within_rupture_distance( self.geometry, distance, upper_depth=upper_eq_depth, lower_depth=lower_eq_depth) else: # Use Joyner-Boore distance self.catalogue = selector.within_joyner_boore_distance( self.geometry, distance, upper_depth=upper_eq_depth, lower_depth=lower_eq_depth) if self.catalogue.get_number_events() < 5: # Throw a warning regarding the small number of earthquakes in # the source! warnings.warn('Source %s (%s) has fewer than 5 events' % (self.id, self.name))

Selects earthquakes within a distance of the fault :param selector: Populated instance of :class: `openquake.hmtk.seismicity.selector.CatalogueSelector` :param distance: Distance from point (km) for selection :param str distance_metric Choice of fault source distance metric 'joyner-boore' or 'rupture' :param float upper_eq_depth: Upper hypocentral depth of hypocentres to be selected :param float lower_eq_depth: Lower hypocentral depth of hypocentres to be selected

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/sources/simple_fault_source.py#L191-L237

gem/oq-engine

openquake/hmtk/plotting/faults/geology_mfd_plot.py

plot_recurrence_models

def plot_recurrence_models( configs, area, slip, msr, rake, shear_modulus=30.0, disp_length_ratio=1.25E-5, msr_sigma=0., figure_size=(8, 6), filename=None, filetype='png', dpi=300, ax=None): """ Plots a set of recurrence models :param list configs: List of configuration dictionaries """ if ax is None: fig, ax = plt.subplots(figsize=figure_size) else: fig = ax.get_figure() for config in configs: model = RecurrenceBranch(area, slip, msr, rake, shear_modulus, disp_length_ratio, msr_sigma, weight=1.0) model.get_recurrence(config) occurrence = model.recurrence.occur_rates cumulative = np.array([np.sum(occurrence[iloc:]) for iloc in range(0, len(occurrence))]) if 'AndersonLuco' in config['Model_Name']: flt_label = config['Model_Name'] + ' - ' + config['Model_Type'] +\ ' Type' else: flt_label = config['Model_Name'] flt_color = np.random.uniform(0.1, 1.0, 3) ax.semilogy(model.magnitudes, cumulative, '-', label=flt_label, color=flt_color, linewidth=2.) ax.semilogy(model.magnitudes, model.recurrence.occur_rates, '--', color=flt_color, linewidth=2.) ax.set_xlabel('Magnitude') ax.set_ylabel('Annual Rate') ax.legend(bbox_to_anchor=(1.1, 1.0)) _save_image(fig, filename, filetype, dpi)

python

def plot_recurrence_models( configs, area, slip, msr, rake, shear_modulus=30.0, disp_length_ratio=1.25E-5, msr_sigma=0., figure_size=(8, 6), filename=None, filetype='png', dpi=300, ax=None): """ Plots a set of recurrence models :param list configs: List of configuration dictionaries """ if ax is None: fig, ax = plt.subplots(figsize=figure_size) else: fig = ax.get_figure() for config in configs: model = RecurrenceBranch(area, slip, msr, rake, shear_modulus, disp_length_ratio, msr_sigma, weight=1.0) model.get_recurrence(config) occurrence = model.recurrence.occur_rates cumulative = np.array([np.sum(occurrence[iloc:]) for iloc in range(0, len(occurrence))]) if 'AndersonLuco' in config['Model_Name']: flt_label = config['Model_Name'] + ' - ' + config['Model_Type'] +\ ' Type' else: flt_label = config['Model_Name'] flt_color = np.random.uniform(0.1, 1.0, 3) ax.semilogy(model.magnitudes, cumulative, '-', label=flt_label, color=flt_color, linewidth=2.) ax.semilogy(model.magnitudes, model.recurrence.occur_rates, '--', color=flt_color, linewidth=2.) ax.set_xlabel('Magnitude') ax.set_ylabel('Annual Rate') ax.legend(bbox_to_anchor=(1.1, 1.0)) _save_image(fig, filename, filetype, dpi)

Plots a set of recurrence models :param list configs: List of configuration dictionaries

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/faults/geology_mfd_plot.py#L69-L105

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_area_source_geometry

def build_area_source_geometry(area_source): """ Returns the area source geometry as a Node :param area_source: Area source model as an instance of the :class: `openquake.hazardlib.source.area.AreaSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ geom = [] for lon_lat in zip(area_source.polygon.lons, area_source.polygon.lats): geom.extend(lon_lat) poslist_node = Node("gml:posList", text=geom) linear_ring_node = Node("gml:LinearRing", nodes=[poslist_node]) exterior_node = Node("gml:exterior", nodes=[linear_ring_node]) polygon_node = Node("gml:Polygon", nodes=[exterior_node]) upper_depth_node = Node( "upperSeismoDepth", text=area_source.upper_seismogenic_depth) lower_depth_node = Node( "lowerSeismoDepth", text=area_source.lower_seismogenic_depth) return Node( "areaGeometry", {'discretization': area_source.area_discretization}, nodes=[polygon_node, upper_depth_node, lower_depth_node])

python

def build_area_source_geometry(area_source): """ Returns the area source geometry as a Node :param area_source: Area source model as an instance of the :class: `openquake.hazardlib.source.area.AreaSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ geom = [] for lon_lat in zip(area_source.polygon.lons, area_source.polygon.lats): geom.extend(lon_lat) poslist_node = Node("gml:posList", text=geom) linear_ring_node = Node("gml:LinearRing", nodes=[poslist_node]) exterior_node = Node("gml:exterior", nodes=[linear_ring_node]) polygon_node = Node("gml:Polygon", nodes=[exterior_node]) upper_depth_node = Node( "upperSeismoDepth", text=area_source.upper_seismogenic_depth) lower_depth_node = Node( "lowerSeismoDepth", text=area_source.lower_seismogenic_depth) return Node( "areaGeometry", {'discretization': area_source.area_discretization}, nodes=[polygon_node, upper_depth_node, lower_depth_node])

Returns the area source geometry as a Node :param area_source: Area source model as an instance of the :class: `openquake.hazardlib.source.area.AreaSource` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L35-L58

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_point_source_geometry

def build_point_source_geometry(point_source): """ Returns the poing source geometry as a Node :param point_source: Point source model as an instance of the :class: `openquake.hazardlib.source.point.PointSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ xy = point_source.location.x, point_source.location.y pos_node = Node("gml:pos", text=xy) point_node = Node("gml:Point", nodes=[pos_node]) upper_depth_node = Node( "upperSeismoDepth", text=point_source.upper_seismogenic_depth) lower_depth_node = Node( "lowerSeismoDepth", text=point_source.lower_seismogenic_depth) return Node( "pointGeometry", nodes=[point_node, upper_depth_node, lower_depth_node])

python

def build_point_source_geometry(point_source): """ Returns the poing source geometry as a Node :param point_source: Point source model as an instance of the :class: `openquake.hazardlib.source.point.PointSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ xy = point_source.location.x, point_source.location.y pos_node = Node("gml:pos", text=xy) point_node = Node("gml:Point", nodes=[pos_node]) upper_depth_node = Node( "upperSeismoDepth", text=point_source.upper_seismogenic_depth) lower_depth_node = Node( "lowerSeismoDepth", text=point_source.lower_seismogenic_depth) return Node( "pointGeometry", nodes=[point_node, upper_depth_node, lower_depth_node])

Returns the poing source geometry as a Node :param point_source: Point source model as an instance of the :class: `openquake.hazardlib.source.point.PointSource` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L61-L80

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_linestring_node

def build_linestring_node(line, with_depth=False): """ Parses a line to a Node class :param line: Line as instance of :class:`openquake.hazardlib.geo.line.Line` :param bool with_depth: Include the depth values (True) or not (False): :returns: Instance of :class:`openquake.baselib.node.Node` """ geom = [] for p in line.points: if with_depth: geom.extend((p.x, p.y, p.z)) else: geom.extend((p.x, p.y)) poslist_node = Node("gml:posList", text=geom) return Node("gml:LineString", nodes=[poslist_node])

python

def build_linestring_node(line, with_depth=False): """ Parses a line to a Node class :param line: Line as instance of :class:`openquake.hazardlib.geo.line.Line` :param bool with_depth: Include the depth values (True) or not (False): :returns: Instance of :class:`openquake.baselib.node.Node` """ geom = [] for p in line.points: if with_depth: geom.extend((p.x, p.y, p.z)) else: geom.extend((p.x, p.y)) poslist_node = Node("gml:posList", text=geom) return Node("gml:LineString", nodes=[poslist_node])

Parses a line to a Node class :param line: Line as instance of :class:`openquake.hazardlib.geo.line.Line` :param bool with_depth: Include the depth values (True) or not (False): :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L83-L101

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_simple_fault_geometry

def build_simple_fault_geometry(fault_source): """ Returns the simple fault source geometry as a Node :param fault_source: Simple fault source model as an instance of the :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ linestring_node = build_linestring_node(fault_source.fault_trace, with_depth=False) dip_node = Node("dip", text=fault_source.dip) upper_depth_node = Node( "upperSeismoDepth", text=fault_source.upper_seismogenic_depth) lower_depth_node = Node( "lowerSeismoDepth", text=fault_source.lower_seismogenic_depth) return Node("simpleFaultGeometry", nodes=[linestring_node, dip_node, upper_depth_node, lower_depth_node])

python

def build_simple_fault_geometry(fault_source): """ Returns the simple fault source geometry as a Node :param fault_source: Simple fault source model as an instance of the :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ linestring_node = build_linestring_node(fault_source.fault_trace, with_depth=False) dip_node = Node("dip", text=fault_source.dip) upper_depth_node = Node( "upperSeismoDepth", text=fault_source.upper_seismogenic_depth) lower_depth_node = Node( "lowerSeismoDepth", text=fault_source.lower_seismogenic_depth) return Node("simpleFaultGeometry", nodes=[linestring_node, dip_node, upper_depth_node, lower_depth_node])

Returns the simple fault source geometry as a Node :param fault_source: Simple fault source model as an instance of the :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L104-L123

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_complex_fault_geometry

def build_complex_fault_geometry(fault_source): """ Returns the complex fault source geometry as a Node :param fault_source: Complex fault source model as an instance of the :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ num_edges = len(fault_source.edges) edge_nodes = [] for iloc, edge in enumerate(fault_source.edges): if iloc == 0: # Top Edge node_name = "faultTopEdge" elif iloc == (num_edges - 1): # Bottom edge node_name = "faultBottomEdge" else: # Intermediate edge node_name = "intermediateEdge" edge_nodes.append( Node(node_name, nodes=[build_linestring_node(edge, with_depth=True)])) return Node("complexFaultGeometry", nodes=edge_nodes)

python

def build_complex_fault_geometry(fault_source): """ Returns the complex fault source geometry as a Node :param fault_source: Complex fault source model as an instance of the :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ num_edges = len(fault_source.edges) edge_nodes = [] for iloc, edge in enumerate(fault_source.edges): if iloc == 0: # Top Edge node_name = "faultTopEdge" elif iloc == (num_edges - 1): # Bottom edge node_name = "faultBottomEdge" else: # Intermediate edge node_name = "intermediateEdge" edge_nodes.append( Node(node_name, nodes=[build_linestring_node(edge, with_depth=True)])) return Node("complexFaultGeometry", nodes=edge_nodes)

Returns the complex fault source geometry as a Node :param fault_source: Complex fault source model as an instance of the :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L126-L152

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_evenly_discretised_mfd

def build_evenly_discretised_mfd(mfd): """ Returns the evenly discretized MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.evenly_discretized.EvenlyDiscretizedMFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ occur_rates = Node("occurRates", text=mfd.occurrence_rates) return Node("incrementalMFD", {"binWidth": mfd.bin_width, "minMag": mfd.min_mag}, nodes=[occur_rates])

python

def build_evenly_discretised_mfd(mfd): """ Returns the evenly discretized MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.evenly_discretized.EvenlyDiscretizedMFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ occur_rates = Node("occurRates", text=mfd.occurrence_rates) return Node("incrementalMFD", {"binWidth": mfd.bin_width, "minMag": mfd.min_mag}, nodes=[occur_rates])

Returns the evenly discretized MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.evenly_discretized.EvenlyDiscretizedMFD` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L156-L169

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_truncated_gr_mfd

def build_truncated_gr_mfd(mfd): """ Parses the truncated Gutenberg Richter MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.truncated_gr.TruncatedGRMFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ return Node("truncGutenbergRichterMFD", {"aValue": mfd.a_val, "bValue": mfd.b_val, "minMag": mfd.min_mag, "maxMag": mfd.max_mag})

python

def build_truncated_gr_mfd(mfd): """ Parses the truncated Gutenberg Richter MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.truncated_gr.TruncatedGRMFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ return Node("truncGutenbergRichterMFD", {"aValue": mfd.a_val, "bValue": mfd.b_val, "minMag": mfd.min_mag, "maxMag": mfd.max_mag})

Parses the truncated Gutenberg Richter MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.truncated_gr.TruncatedGRMFD` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L173-L185

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_arbitrary_mfd

def build_arbitrary_mfd(mfd): """ Parses the arbitrary MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.arbitrary.ArbitraryMFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ magnitudes = Node("magnitudes", text=mfd.magnitudes) occur_rates = Node("occurRates", text=mfd.occurrence_rates) return Node("arbitraryMFD", nodes=[magnitudes, occur_rates])

python

def build_arbitrary_mfd(mfd): """ Parses the arbitrary MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.arbitrary.ArbitraryMFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ magnitudes = Node("magnitudes", text=mfd.magnitudes) occur_rates = Node("occurRates", text=mfd.occurrence_rates) return Node("arbitraryMFD", nodes=[magnitudes, occur_rates])

Parses the arbitrary MFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.arbitrary.ArbitraryMFD` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L189-L201

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_youngs_coppersmith_mfd

def build_youngs_coppersmith_mfd(mfd): """ Parses the Youngs & Coppersmith MFD as a node. Note that the MFD does not hold the total moment rate, but only the characteristic rate. Therefore the node is written to the characteristic rate version regardless of whether or not it was originally created from total moment rate :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.youngs_coppersmith_1985. YoungsCoppersmith1985MFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ return Node("YoungsCoppersmithMFD", {"minMag": mfd.min_mag, "bValue": mfd.b_val, "characteristicMag": mfd.char_mag, "characteristicRate": mfd.char_rate, "binWidth": mfd.bin_width})

python

def build_youngs_coppersmith_mfd(mfd): """ Parses the Youngs & Coppersmith MFD as a node. Note that the MFD does not hold the total moment rate, but only the characteristic rate. Therefore the node is written to the characteristic rate version regardless of whether or not it was originally created from total moment rate :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.youngs_coppersmith_1985. YoungsCoppersmith1985MFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ return Node("YoungsCoppersmithMFD", {"minMag": mfd.min_mag, "bValue": mfd.b_val, "characteristicMag": mfd.char_mag, "characteristicRate": mfd.char_rate, "binWidth": mfd.bin_width})

Parses the Youngs & Coppersmith MFD as a node. Note that the MFD does not hold the total moment rate, but only the characteristic rate. Therefore the node is written to the characteristic rate version regardless of whether or not it was originally created from total moment rate :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.youngs_coppersmith_1985. YoungsCoppersmith1985MFD` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L205-L223

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_multi_mfd

def build_multi_mfd(mfd): """ Parses the MultiMFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.multi_mfd.MultiMFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ node = Node("multiMFD", dict(kind=mfd.kind, size=mfd.size)) for name in sorted(mfd.kwargs): values = mfd.kwargs[name] if name in ('magnitudes', 'occurRates'): if len(values[0]) > 1: # tested in multipoint_test.py values = list(numpy.concatenate(values)) else: values = sum(values, []) node.append(Node(name, text=values)) if 'occurRates' in mfd.kwargs: lengths = [len(rates) for rates in mfd.kwargs['occurRates']] node.append(Node('lengths', text=lengths)) return node

python

def build_multi_mfd(mfd): """ Parses the MultiMFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.multi_mfd.MultiMFD` :returns: Instance of :class:`openquake.baselib.node.Node` """ node = Node("multiMFD", dict(kind=mfd.kind, size=mfd.size)) for name in sorted(mfd.kwargs): values = mfd.kwargs[name] if name in ('magnitudes', 'occurRates'): if len(values[0]) > 1: # tested in multipoint_test.py values = list(numpy.concatenate(values)) else: values = sum(values, []) node.append(Node(name, text=values)) if 'occurRates' in mfd.kwargs: lengths = [len(rates) for rates in mfd.kwargs['occurRates']] node.append(Node('lengths', text=lengths)) return node

Parses the MultiMFD as a Node :param mfd: MFD as instance of :class: `openquake.hazardlib.mfd.multi_mfd.MultiMFD` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L227-L249

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_nodal_plane_dist

def build_nodal_plane_dist(npd): """ Returns the nodal plane distribution as a Node instance :param npd: Nodal plane distribution as instance of :class: `openquake.hazardlib.pmf.PMF` :returns: Instance of :class:`openquake.baselib.node.Node` """ npds = [] for prob, npd in npd.data: nodal_plane = Node( "nodalPlane", {"dip": npd.dip, "probability": prob, "strike": npd.strike, "rake": npd.rake}) npds.append(nodal_plane) return Node("nodalPlaneDist", nodes=npds)

python

def build_nodal_plane_dist(npd): """ Returns the nodal plane distribution as a Node instance :param npd: Nodal plane distribution as instance of :class: `openquake.hazardlib.pmf.PMF` :returns: Instance of :class:`openquake.baselib.node.Node` """ npds = [] for prob, npd in npd.data: nodal_plane = Node( "nodalPlane", {"dip": npd.dip, "probability": prob, "strike": npd.strike, "rake": npd.rake}) npds.append(nodal_plane) return Node("nodalPlaneDist", nodes=npds)

Returns the nodal plane distribution as a Node instance :param npd: Nodal plane distribution as instance of :class: `openquake.hazardlib.pmf.PMF` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L252-L268

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_hypo_depth_dist

def build_hypo_depth_dist(hdd): """ Returns the hypocentral depth distribution as a Node instance :param hdd: Hypocentral depth distribution as an instance of :class: `openquake.hzardlib.pmf.PMF` :returns: Instance of :class:`openquake.baselib.node.Node` """ hdds = [] for (prob, depth) in hdd.data: hdds.append( Node("hypoDepth", {"depth": depth, "probability": prob})) return Node("hypoDepthDist", nodes=hdds)

python

def build_hypo_depth_dist(hdd): """ Returns the hypocentral depth distribution as a Node instance :param hdd: Hypocentral depth distribution as an instance of :class: `openquake.hzardlib.pmf.PMF` :returns: Instance of :class:`openquake.baselib.node.Node` """ hdds = [] for (prob, depth) in hdd.data: hdds.append( Node("hypoDepth", {"depth": depth, "probability": prob})) return Node("hypoDepthDist", nodes=hdds)

Returns the hypocentral depth distribution as a Node instance :param hdd: Hypocentral depth distribution as an instance of :class: `openquake.hzardlib.pmf.PMF` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L271-L285

gem/oq-engine

openquake/hazardlib/sourcewriter.py

get_distributed_seismicity_source_nodes

def get_distributed_seismicity_source_nodes(source): """ Returns list of nodes of attributes common to all distributed seismicity source classes :param source: Seismic source as instance of :class: `openquake.hazardlib.source.area.AreaSource` or :class: `openquake.hazardlib.source.point.PointSource` :returns: List of instances of :class:`openquake.baselib.node.Node` """ source_nodes = [] # parse msr source_nodes.append( Node("magScaleRel", text=source.magnitude_scaling_relationship.__class__.__name__)) # Parse aspect ratio source_nodes.append( Node("ruptAspectRatio", text=source.rupture_aspect_ratio)) # Parse MFD source_nodes.append(obj_to_node(source.mfd)) # Parse nodal plane distribution source_nodes.append( build_nodal_plane_dist(source.nodal_plane_distribution)) # Parse hypocentral depth distribution source_nodes.append( build_hypo_depth_dist(source.hypocenter_distribution)) return source_nodes

python

def get_distributed_seismicity_source_nodes(source): """ Returns list of nodes of attributes common to all distributed seismicity source classes :param source: Seismic source as instance of :class: `openquake.hazardlib.source.area.AreaSource` or :class: `openquake.hazardlib.source.point.PointSource` :returns: List of instances of :class:`openquake.baselib.node.Node` """ source_nodes = [] # parse msr source_nodes.append( Node("magScaleRel", text=source.magnitude_scaling_relationship.__class__.__name__)) # Parse aspect ratio source_nodes.append( Node("ruptAspectRatio", text=source.rupture_aspect_ratio)) # Parse MFD source_nodes.append(obj_to_node(source.mfd)) # Parse nodal plane distribution source_nodes.append( build_nodal_plane_dist(source.nodal_plane_distribution)) # Parse hypocentral depth distribution source_nodes.append( build_hypo_depth_dist(source.hypocenter_distribution)) return source_nodes

Returns list of nodes of attributes common to all distributed seismicity source classes :param source: Seismic source as instance of :class: `openquake.hazardlib.source.area.AreaSource` or :class: `openquake.hazardlib.source.point.PointSource` :returns: List of instances of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L288-L316

gem/oq-engine

openquake/hazardlib/sourcewriter.py

get_fault_source_nodes

def get_fault_source_nodes(source): """ Returns list of nodes of attributes common to all fault source classes :param source: Fault source as instance of :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` or :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: List of instances of :class:`openquake.baselib.node.Node` """ source_nodes = [] # parse msr source_nodes.append( Node( "magScaleRel", text=source.magnitude_scaling_relationship.__class__.__name__)) # Parse aspect ratio source_nodes.append( Node("ruptAspectRatio", text=source.rupture_aspect_ratio)) # Parse MFD source_nodes.append(obj_to_node(source.mfd)) # Parse Rake source_nodes.append(Node("rake", text=source.rake)) if len(getattr(source, 'hypo_list', [])): source_nodes.append(build_hypo_list_node(source.hypo_list)) if len(getattr(source, 'slip_list', [])): source_nodes.append(build_slip_list_node(source.slip_list)) return source_nodes

python

def get_fault_source_nodes(source): """ Returns list of nodes of attributes common to all fault source classes :param source: Fault source as instance of :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` or :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: List of instances of :class:`openquake.baselib.node.Node` """ source_nodes = [] # parse msr source_nodes.append( Node( "magScaleRel", text=source.magnitude_scaling_relationship.__class__.__name__)) # Parse aspect ratio source_nodes.append( Node("ruptAspectRatio", text=source.rupture_aspect_ratio)) # Parse MFD source_nodes.append(obj_to_node(source.mfd)) # Parse Rake source_nodes.append(Node("rake", text=source.rake)) if len(getattr(source, 'hypo_list', [])): source_nodes.append(build_hypo_list_node(source.hypo_list)) if len(getattr(source, 'slip_list', [])): source_nodes.append(build_slip_list_node(source.slip_list)) return source_nodes

Returns list of nodes of attributes common to all fault source classes :param source: Fault source as instance of :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` or :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: List of instances of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L348-L376

gem/oq-engine

openquake/hazardlib/sourcewriter.py

get_source_attributes

def get_source_attributes(source): """ Retreives a dictionary of source attributes from the source class :param source: Seismic source as instance of :class: `openquake.hazardlib.source.base.BaseSeismicSource` :returns: Dictionary of source attributes """ attrs = {"id": source.source_id, "name": source.name, "tectonicRegion": source.tectonic_region_type} if isinstance(source, NonParametricSeismicSource): if source.data[0][0].weight is not None: weights = [] for data in source.data: weights.append(data[0].weight) attrs['rup_weights'] = numpy.array(weights) print(attrs) return attrs

python

def get_source_attributes(source): """ Retreives a dictionary of source attributes from the source class :param source: Seismic source as instance of :class: `openquake.hazardlib.source.base.BaseSeismicSource` :returns: Dictionary of source attributes """ attrs = {"id": source.source_id, "name": source.name, "tectonicRegion": source.tectonic_region_type} if isinstance(source, NonParametricSeismicSource): if source.data[0][0].weight is not None: weights = [] for data in source.data: weights.append(data[0].weight) attrs['rup_weights'] = numpy.array(weights) print(attrs) return attrs

Retreives a dictionary of source attributes from the source class :param source: Seismic source as instance of :class: `openquake.hazardlib.source.base.BaseSeismicSource` :returns: Dictionary of source attributes

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L379-L399

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_area_source_node

def build_area_source_node(area_source): """ Parses an area source to a Node class :param area_source: Area source as instance of :class: `openquake.hazardlib.source.area.AreaSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ # parse geometry source_nodes = [build_area_source_geometry(area_source)] # parse common distributed attributes source_nodes.extend(get_distributed_seismicity_source_nodes(area_source)) return Node( "areaSource", get_source_attributes(area_source), nodes=source_nodes)

python

def build_area_source_node(area_source): """ Parses an area source to a Node class :param area_source: Area source as instance of :class: `openquake.hazardlib.source.area.AreaSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ # parse geometry source_nodes = [build_area_source_geometry(area_source)] # parse common distributed attributes source_nodes.extend(get_distributed_seismicity_source_nodes(area_source)) return Node( "areaSource", get_source_attributes(area_source), nodes=source_nodes)

Parses an area source to a Node class :param area_source: Area source as instance of :class: `openquake.hazardlib.source.area.AreaSource` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L403-L418

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_simple_fault_source_node

def build_simple_fault_source_node(fault_source): """ Parses a simple fault source to a Node class :param fault_source: Simple fault source as instance of :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ # Parse geometry source_nodes = [build_simple_fault_geometry(fault_source)] # Parse common fault source attributes source_nodes.extend(get_fault_source_nodes(fault_source)) return Node("simpleFaultSource", get_source_attributes(fault_source), nodes=source_nodes)

python

def build_simple_fault_source_node(fault_source): """ Parses a simple fault source to a Node class :param fault_source: Simple fault source as instance of :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ # Parse geometry source_nodes = [build_simple_fault_geometry(fault_source)] # Parse common fault source attributes source_nodes.extend(get_fault_source_nodes(fault_source)) return Node("simpleFaultSource", get_source_attributes(fault_source), nodes=source_nodes)

Parses a simple fault source to a Node class :param fault_source: Simple fault source as instance of :class: `openquake.hazardlib.source.simple_fault.SimpleFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L524-L540

gem/oq-engine

openquake/hazardlib/sourcewriter.py

build_complex_fault_source_node

def build_complex_fault_source_node(fault_source): """ Parses a complex fault source to a Node class :param fault_source: Simple fault source as instance of :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ # Parse geometry source_nodes = [build_complex_fault_geometry(fault_source)] # Parse common fault source attributes source_nodes.extend(get_fault_source_nodes(fault_source)) return Node("complexFaultSource", get_source_attributes(fault_source), nodes=source_nodes)

python

def build_complex_fault_source_node(fault_source): """ Parses a complex fault source to a Node class :param fault_source: Simple fault source as instance of :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node` """ # Parse geometry source_nodes = [build_complex_fault_geometry(fault_source)] # Parse common fault source attributes source_nodes.extend(get_fault_source_nodes(fault_source)) return Node("complexFaultSource", get_source_attributes(fault_source), nodes=source_nodes)

Parses a complex fault source to a Node class :param fault_source: Simple fault source as instance of :class: `openquake.hazardlib.source.complex_fault.ComplexFaultSource` :returns: Instance of :class:`openquake.baselib.node.Node`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L544-L560

gem/oq-engine

openquake/hazardlib/sourcewriter.py

write_source_model

def write_source_model(dest, sources_or_groups, name=None, investigation_time=None): """ Writes a source model to XML. :param dest: Destination path :param sources_or_groups: Source model in different formats :param name: Name of the source model (if missing, extracted from the filename) """ if isinstance(sources_or_groups, nrml.SourceModel): with open(dest, 'wb') as f: nrml.write([obj_to_node(sources_or_groups)], f, '%s') return if isinstance(sources_or_groups[0], sourceconverter.SourceGroup): groups = sources_or_groups else: # passed a list of sources srcs_by_trt = groupby( sources_or_groups, operator.attrgetter('tectonic_region_type')) groups = [sourceconverter.SourceGroup(trt, srcs_by_trt[trt]) for trt in srcs_by_trt] name = name or os.path.splitext(os.path.basename(dest))[0] nodes = list(map(obj_to_node, sorted(groups))) attrs = {"name": name} if investigation_time is not None: attrs['investigation_time'] = investigation_time source_model = Node("sourceModel", attrs, nodes=nodes) with open(dest, 'wb') as f: nrml.write([source_model], f, '%s') return dest

python

def write_source_model(dest, sources_or_groups, name=None, investigation_time=None): """ Writes a source model to XML. :param dest: Destination path :param sources_or_groups: Source model in different formats :param name: Name of the source model (if missing, extracted from the filename) """ if isinstance(sources_or_groups, nrml.SourceModel): with open(dest, 'wb') as f: nrml.write([obj_to_node(sources_or_groups)], f, '%s') return if isinstance(sources_or_groups[0], sourceconverter.SourceGroup): groups = sources_or_groups else: # passed a list of sources srcs_by_trt = groupby( sources_or_groups, operator.attrgetter('tectonic_region_type')) groups = [sourceconverter.SourceGroup(trt, srcs_by_trt[trt]) for trt in srcs_by_trt] name = name or os.path.splitext(os.path.basename(dest))[0] nodes = list(map(obj_to_node, sorted(groups))) attrs = {"name": name} if investigation_time is not None: attrs['investigation_time'] = investigation_time source_model = Node("sourceModel", attrs, nodes=nodes) with open(dest, 'wb') as f: nrml.write([source_model], f, '%s') return dest

Writes a source model to XML. :param dest: Destination path :param sources_or_groups: Source model in different formats :param name: Name of the source model (if missing, extracted from the filename)

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/sourcewriter.py#L610-L641

gem/oq-engine

openquake/hazardlib/gsim/sharma_2009.py

SharmaEtAl2009._get_stddevs

def _get_stddevs(self, coeffs, stddev_types, num_sites): """ Return total sigma as reported in Table 2, p. 1202. """ stddevs = [] for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES stddevs.append(coeffs['sigma'] + np.zeros(num_sites)) return np.array(stddevs)

python

def _get_stddevs(self, coeffs, stddev_types, num_sites): """ Return total sigma as reported in Table 2, p. 1202. """ stddevs = [] for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES stddevs.append(coeffs['sigma'] + np.zeros(num_sites)) return np.array(stddevs)

Return total sigma as reported in Table 2, p. 1202.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/sharma_2009.py#L121-L129

gem/oq-engine

openquake/hazardlib/gsim/sharma_2009.py

SharmaEtAl2009.get_fault_type_dummy_variables

def get_fault_type_dummy_variables(self, rup): """ Fault-type classification dummy variable based on rup.rake. "``H`` is 1 for a strike-slip mechanism and 0 for a reverse mechanism" (p. 1201). Note: UserWarning is raised if mechanism is determined to be normal faulting, since as summarized in Table 2 on p. 1197 the data used for regression included only reverse and stike-slip events. """ # normal faulting is_normal = np.array( self.RAKE_THRESH < -rup.rake < (180. - self.RAKE_THRESH)) # reverse raulting is_reverse = np.array( self.RAKE_THRESH < rup.rake < (180. - self.RAKE_THRESH)) if not self.ALREADY_WARNED and is_normal.any(): # make sure that the warning is printed only once to avoid # flooding the terminal msg = ('Normal faulting not supported by %s; ' 'treating as strike-slip' % type(self).__name__) warnings.warn(msg, UserWarning) self.ALREADY_WARNED = True is_strike_slip = ~is_reverse | is_normal is_strike_slip = is_strike_slip.astype(float) return is_strike_slip

python

def get_fault_type_dummy_variables(self, rup): """ Fault-type classification dummy variable based on rup.rake. "``H`` is 1 for a strike-slip mechanism and 0 for a reverse mechanism" (p. 1201). Note: UserWarning is raised if mechanism is determined to be normal faulting, since as summarized in Table 2 on p. 1197 the data used for regression included only reverse and stike-slip events. """ # normal faulting is_normal = np.array( self.RAKE_THRESH < -rup.rake < (180. - self.RAKE_THRESH)) # reverse raulting is_reverse = np.array( self.RAKE_THRESH < rup.rake < (180. - self.RAKE_THRESH)) if not self.ALREADY_WARNED and is_normal.any(): # make sure that the warning is printed only once to avoid # flooding the terminal msg = ('Normal faulting not supported by %s; ' 'treating as strike-slip' % type(self).__name__) warnings.warn(msg, UserWarning) self.ALREADY_WARNED = True is_strike_slip = ~is_reverse | is_normal is_strike_slip = is_strike_slip.astype(float) return is_strike_slip

Fault-type classification dummy variable based on rup.rake. "``H`` is 1 for a strike-slip mechanism and 0 for a reverse mechanism" (p. 1201). Note: UserWarning is raised if mechanism is determined to be normal faulting, since as summarized in Table 2 on p. 1197 the data used for regression included only reverse and stike-slip events.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/sharma_2009.py#L176-L208

gem/oq-engine

openquake/hmtk/parsers/strain/strain_csv_parser.py

ReadStrainCsv.read_data

def read_data(self, scaling_factor=1E-9, strain_headers=None): ''' Reads the data from the csv file :param float scaling_factor: Scaling factor used for all strain values (default 1E-9 for nanostrain) :param list strain_headers: List of the variables in the file that correspond to strain parameters :returns: strain - Strain model as an instance of the :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain ''' if strain_headers: self.strain.data_variables = strain_headers else: self.strain.data_variables = STRAIN_VARIABLES datafile = open(self.filename, 'r') reader = csv.DictReader(datafile) self.strain.data = dict([(name, []) for name in reader.fieldnames]) for row in reader: for name in row.keys(): if 'region' in name.lower(): self.strain.data[name].append(row[name]) elif name in self.strain.data_variables: self.strain.data[name].append( scaling_factor * float(row[name])) else: self.strain.data[name].append(float(row[name])) for key in self.strain.data.keys(): if 'region' in key: self.strain.data[key] = np.array(self.strain.data[key], dtype='S13') else: self.strain.data[key] = np.array(self.strain.data[key]) self._check_invalid_longitudes() if 'region' not in self.strain.data: print('No tectonic regionalisation found in input file!') self.strain.data_variables = self.strain.data.keys() # Update data with secondary data (i.e. 2nd invariant, e1h, e2h etc. self.strain.get_secondary_strain_data() return self.strain

python

def read_data(self, scaling_factor=1E-9, strain_headers=None): ''' Reads the data from the csv file :param float scaling_factor: Scaling factor used for all strain values (default 1E-9 for nanostrain) :param list strain_headers: List of the variables in the file that correspond to strain parameters :returns: strain - Strain model as an instance of the :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain ''' if strain_headers: self.strain.data_variables = strain_headers else: self.strain.data_variables = STRAIN_VARIABLES datafile = open(self.filename, 'r') reader = csv.DictReader(datafile) self.strain.data = dict([(name, []) for name in reader.fieldnames]) for row in reader: for name in row.keys(): if 'region' in name.lower(): self.strain.data[name].append(row[name]) elif name in self.strain.data_variables: self.strain.data[name].append( scaling_factor * float(row[name])) else: self.strain.data[name].append(float(row[name])) for key in self.strain.data.keys(): if 'region' in key: self.strain.data[key] = np.array(self.strain.data[key], dtype='S13') else: self.strain.data[key] = np.array(self.strain.data[key]) self._check_invalid_longitudes() if 'region' not in self.strain.data: print('No tectonic regionalisation found in input file!') self.strain.data_variables = self.strain.data.keys() # Update data with secondary data (i.e. 2nd invariant, e1h, e2h etc. self.strain.get_secondary_strain_data() return self.strain

Reads the data from the csv file :param float scaling_factor: Scaling factor used for all strain values (default 1E-9 for nanostrain) :param list strain_headers: List of the variables in the file that correspond to strain parameters :returns: strain - Strain model as an instance of the :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/parsers/strain/strain_csv_parser.py#L82-L132

gem/oq-engine

openquake/hmtk/parsers/strain/strain_csv_parser.py

ReadStrainCsv._check_invalid_longitudes

def _check_invalid_longitudes(self): ''' Checks to ensure that all longitudes are in the range -180. to 180 ''' idlon = self.strain.data['longitude'] > 180. if np.any(idlon): self.strain.data['longitude'][idlon] = \ self.strain.data['longitude'][idlon] - 360.

python

def _check_invalid_longitudes(self): ''' Checks to ensure that all longitudes are in the range -180. to 180 ''' idlon = self.strain.data['longitude'] > 180. if np.any(idlon): self.strain.data['longitude'][idlon] = \ self.strain.data['longitude'][idlon] - 360.

Checks to ensure that all longitudes are in the range -180. to 180

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/parsers/strain/strain_csv_parser.py#L134-L141

gem/oq-engine

openquake/hmtk/parsers/strain/strain_csv_parser.py

WriteStrainCsv.write_file

def write_file(self, strain, scaling_factor=1E-9): ''' Main writer function for the csv file :param strain: Instance of :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain :param float scaling_factor: Scaling factor used for all strain values (default 1E-9 for nanostrain) ''' if not isinstance(strain, GeodeticStrain): raise ValueError('Strain data must be instance of GeodeticStrain') for key in strain.data.keys(): if key in strain.data_variables: # Return strain value back to original scaling if key in ['longitude', 'latitude']: continue strain.data[key] = strain.data[key] / scaling_factor # Slice seismicity rates into separate dictionary vectors strain, output_variables = self.slice_rates_to_data(strain) outfile = open(self.filename, 'wt') print('Writing strain data to file %s' % self.filename) writer = csv.DictWriter(outfile, fieldnames=output_variables) writer.writeheader() for iloc in range(0, strain.get_number_observations()): row_dict = {} for key in output_variables: if len(strain.data[key]) > 0: # Ignores empty dictionary attributes row_dict[key] = strain.data[key][iloc] writer.writerow(row_dict) outfile.close() print('done!')

python

def write_file(self, strain, scaling_factor=1E-9): ''' Main writer function for the csv file :param strain: Instance of :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain :param float scaling_factor: Scaling factor used for all strain values (default 1E-9 for nanostrain) ''' if not isinstance(strain, GeodeticStrain): raise ValueError('Strain data must be instance of GeodeticStrain') for key in strain.data.keys(): if key in strain.data_variables: # Return strain value back to original scaling if key in ['longitude', 'latitude']: continue strain.data[key] = strain.data[key] / scaling_factor # Slice seismicity rates into separate dictionary vectors strain, output_variables = self.slice_rates_to_data(strain) outfile = open(self.filename, 'wt') print('Writing strain data to file %s' % self.filename) writer = csv.DictWriter(outfile, fieldnames=output_variables) writer.writeheader() for iloc in range(0, strain.get_number_observations()): row_dict = {} for key in output_variables: if len(strain.data[key]) > 0: # Ignores empty dictionary attributes row_dict[key] = strain.data[key][iloc] writer.writerow(row_dict) outfile.close() print('done!')

Main writer function for the csv file :param strain: Instance of :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain :param float scaling_factor: Scaling factor used for all strain values (default 1E-9 for nanostrain)

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/parsers/strain/strain_csv_parser.py#L160-L196

gem/oq-engine

openquake/hmtk/parsers/strain/strain_csv_parser.py

WriteStrainCsv.slice_rates_to_data

def slice_rates_to_data(self, strain): ''' For the strain data, checks to see if seismicity rates have been calculated. If so, each column in the array is sliced and stored as a single vector in the strain.data dictionary with the corresponding magnitude as a key. :param strain: Instance of :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain :returns: strain - Instance of strain class with updated data dictionary output_variables - Updated list of headers ''' output_variables = list(strain.data) cond = (isinstance(strain.target_magnitudes, np.ndarray) or isinstance(strain.target_magnitudes, list)) if cond: magnitude_list = ['%.3f' % mag for mag in strain.target_magnitudes] else: return strain, output_variables # Ensure that the number of rows in the rate array corresponds to the # number of observations assert np.shape(strain.seismicity_rate)[0] == \ strain.get_number_observations() for iloc, magnitude in enumerate(magnitude_list): strain.data[magnitude] = strain.seismicity_rate[:, iloc] output_variables.extend(magnitude_list) return strain, output_variables

python

def slice_rates_to_data(self, strain): ''' For the strain data, checks to see if seismicity rates have been calculated. If so, each column in the array is sliced and stored as a single vector in the strain.data dictionary with the corresponding magnitude as a key. :param strain: Instance of :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain :returns: strain - Instance of strain class with updated data dictionary output_variables - Updated list of headers ''' output_variables = list(strain.data) cond = (isinstance(strain.target_magnitudes, np.ndarray) or isinstance(strain.target_magnitudes, list)) if cond: magnitude_list = ['%.3f' % mag for mag in strain.target_magnitudes] else: return strain, output_variables # Ensure that the number of rows in the rate array corresponds to the # number of observations assert np.shape(strain.seismicity_rate)[0] == \ strain.get_number_observations() for iloc, magnitude in enumerate(magnitude_list): strain.data[magnitude] = strain.seismicity_rate[:, iloc] output_variables.extend(magnitude_list) return strain, output_variables

For the strain data, checks to see if seismicity rates have been calculated. If so, each column in the array is sliced and stored as a single vector in the strain.data dictionary with the corresponding magnitude as a key. :param strain: Instance of :class: openquake.hmtk.strain.geodetic_strain.GeodeticStrain :returns: strain - Instance of strain class with updated data dictionary output_variables - Updated list of headers

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/parsers/strain/strain_csv_parser.py#L198-L228

gem/oq-engine

openquake/baselib/__init__.py

read

def read(*paths, **validators): """ Load the configuration, make each section available in a separate dict. The configuration location can specified via an environment variable: - OQ_CONFIG_FILE In the absence of this environment variable the following paths will be used: - sys.prefix + /openquake.cfg when in a virtualenv - /etc/openquake/openquake.cfg outside of a virtualenv If those files are missing, the fallback is the source code: - openquake/engine/openquake.cfg Please note: settings in the site configuration file are overridden by settings with the same key names in the OQ_CONFIG_FILE openquake.cfg. """ paths = config.paths + list(paths) parser = configparser.ConfigParser() found = parser.read(os.path.normpath(os.path.expanduser(p)) for p in paths) if not found: raise IOError('No configuration file found in %s' % str(paths)) config.found = found config.clear() for section in parser.sections(): config[section] = sec = DotDict(parser.items(section)) for k, v in sec.items(): sec[k] = validators.get(k, lambda x: x)(v)

python

def read(*paths, **validators): """ Load the configuration, make each section available in a separate dict. The configuration location can specified via an environment variable: - OQ_CONFIG_FILE In the absence of this environment variable the following paths will be used: - sys.prefix + /openquake.cfg when in a virtualenv - /etc/openquake/openquake.cfg outside of a virtualenv If those files are missing, the fallback is the source code: - openquake/engine/openquake.cfg Please note: settings in the site configuration file are overridden by settings with the same key names in the OQ_CONFIG_FILE openquake.cfg. """ paths = config.paths + list(paths) parser = configparser.ConfigParser() found = parser.read(os.path.normpath(os.path.expanduser(p)) for p in paths) if not found: raise IOError('No configuration file found in %s' % str(paths)) config.found = found config.clear() for section in parser.sections(): config[section] = sec = DotDict(parser.items(section)) for k, v in sec.items(): sec[k] = validators.get(k, lambda x: x)(v)

Load the configuration, make each section available in a separate dict. The configuration location can specified via an environment variable: - OQ_CONFIG_FILE In the absence of this environment variable the following paths will be used: - sys.prefix + /openquake.cfg when in a virtualenv - /etc/openquake/openquake.cfg outside of a virtualenv If those files are missing, the fallback is the source code: - openquake/engine/openquake.cfg Please note: settings in the site configuration file are overridden by settings with the same key names in the OQ_CONFIG_FILE openquake.cfg.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/baselib/__init__.py#L56-L84

gem/oq-engine

openquake/baselib/__init__.py

boolean

def boolean(flag): """ Convert string in boolean """ s = flag.lower() if s in ('1', 'yes', 'true'): return True elif s in ('0', 'no', 'false'): return False raise ValueError('Unknown flag %r' % s)

python

def boolean(flag): """ Convert string in boolean """ s = flag.lower() if s in ('1', 'yes', 'true'): return True elif s in ('0', 'no', 'false'): return False raise ValueError('Unknown flag %r' % s)

Convert string in boolean

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/baselib/__init__.py#L90-L99

gem/oq-engine

openquake/hazardlib/gsim/atkinson_boore_2006.py

AtkinsonBoore2006._get_mean

def _get_mean(self, vs30, mag, rrup, imt, scale_fac): """ Compute and return mean """ C_HR, C_BC, C_SR, SC = self._extract_coeffs(imt) rrup = self._clip_distances(rrup) f0 = self._compute_f0_factor(rrup) f1 = self._compute_f1_factor(rrup) f2 = self._compute_f2_factor(rrup) pga_bc = self._get_pga_bc( f0, f1, f2, SC, mag, rrup, vs30, scale_fac ) # compute mean values for hard-rock sites (vs30 >= 2000), # and non-hard-rock sites (vs30 < 2000) and add soil amplification # term mean = np.zeros_like(vs30) self._compute_mean(C_HR, f0, f1, f2, SC, mag, rrup, vs30 >= 2000.0, mean, scale_fac) self._compute_mean(C_BC, f0, f1, f2, SC, mag, rrup, vs30 < 2000.0, mean, scale_fac) self._compute_soil_amplification(C_SR, vs30, pga_bc, mean) # convert from base 10 to base e if imt == PGV(): mean = np.log(10 ** mean) else: # convert from cm/s**2 to g mean = np.log((10 ** mean) * 1e-2 / g) return mean

python

def _get_mean(self, vs30, mag, rrup, imt, scale_fac): """ Compute and return mean """ C_HR, C_BC, C_SR, SC = self._extract_coeffs(imt) rrup = self._clip_distances(rrup) f0 = self._compute_f0_factor(rrup) f1 = self._compute_f1_factor(rrup) f2 = self._compute_f2_factor(rrup) pga_bc = self._get_pga_bc( f0, f1, f2, SC, mag, rrup, vs30, scale_fac ) # compute mean values for hard-rock sites (vs30 >= 2000), # and non-hard-rock sites (vs30 < 2000) and add soil amplification # term mean = np.zeros_like(vs30) self._compute_mean(C_HR, f0, f1, f2, SC, mag, rrup, vs30 >= 2000.0, mean, scale_fac) self._compute_mean(C_BC, f0, f1, f2, SC, mag, rrup, vs30 < 2000.0, mean, scale_fac) self._compute_soil_amplification(C_SR, vs30, pga_bc, mean) # convert from base 10 to base e if imt == PGV(): mean = np.log(10 ** mean) else: # convert from cm/s**2 to g mean = np.log((10 ** mean) * 1e-2 / g) return mean

Compute and return mean

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/atkinson_boore_2006.py#L109-L142

gem/oq-engine

openquake/hazardlib/gsim/atkinson_boore_2006.py

AtkinsonBoore2006._get_pga_bc

def _get_pga_bc(self, f0, f1, f2, SC, mag, rrup, vs30, scale_fac): """ Compute and return PGA on BC boundary """ pga_bc = np.zeros_like(vs30) self._compute_mean(self.COEFFS_BC[PGA()], f0, f1, f2, SC, mag, rrup, vs30 < 2000.0, pga_bc, scale_fac) return (10 ** pga_bc) * 1e-2 / g

python

def _get_pga_bc(self, f0, f1, f2, SC, mag, rrup, vs30, scale_fac): """ Compute and return PGA on BC boundary """ pga_bc = np.zeros_like(vs30) self._compute_mean(self.COEFFS_BC[PGA()], f0, f1, f2, SC, mag, rrup, vs30 < 2000.0, pga_bc, scale_fac) return (10 ** pga_bc) * 1e-2 / g

Compute and return PGA on BC boundary

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/atkinson_boore_2006.py#L144-L152

gem/oq-engine

openquake/hazardlib/gsim/atkinson_boore_2006.py

AtkinsonBoore2006._extract_coeffs

def _extract_coeffs(self, imt): """ Extract dictionaries of coefficients specific to required intensity measure type. """ C_HR = self.COEFFS_HARD_ROCK[imt] C_BC = self.COEFFS_BC[imt] C_SR = self.COEFFS_SOIL_RESPONSE[imt] SC = self.COEFFS_STRESS[imt] return C_HR, C_BC, C_SR, SC

python

def _extract_coeffs(self, imt): """ Extract dictionaries of coefficients specific to required intensity measure type. """ C_HR = self.COEFFS_HARD_ROCK[imt] C_BC = self.COEFFS_BC[imt] C_SR = self.COEFFS_SOIL_RESPONSE[imt] SC = self.COEFFS_STRESS[imt] return C_HR, C_BC, C_SR, SC

Extract dictionaries of coefficients specific to required intensity measure type.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/atkinson_boore_2006.py#L154-L164

gem/oq-engine

openquake/calculators/getters.py

PmapGetter.init

def init(self): """ Read the poes and set the .data attribute with the hazard curves """ if hasattr(self, 'data'): # already initialized return if isinstance(self.dstore, str): self.dstore = hdf5.File(self.dstore, 'r') else: self.dstore.open('r') # if not if self.sids is None: self.sids = self.dstore['sitecol'].sids oq = self.dstore['oqparam'] self.imtls = oq.imtls self.poes = self.poes or oq.poes self.data = {} try: hcurves = self.get_hcurves(self.imtls) # shape (R, N) except IndexError: # no data return for sid, hcurve_by_rlz in zip(self.sids, hcurves.T): self.data[sid] = datadict = {} for rlzi, hcurve in enumerate(hcurve_by_rlz): datadict[rlzi] = lst = [None for imt in self.imtls] for imti, imt in enumerate(self.imtls): lst[imti] = hcurve[imt]

python

def init(self): """ Read the poes and set the .data attribute with the hazard curves """ if hasattr(self, 'data'): # already initialized return if isinstance(self.dstore, str): self.dstore = hdf5.File(self.dstore, 'r') else: self.dstore.open('r') # if not if self.sids is None: self.sids = self.dstore['sitecol'].sids oq = self.dstore['oqparam'] self.imtls = oq.imtls self.poes = self.poes or oq.poes self.data = {} try: hcurves = self.get_hcurves(self.imtls) # shape (R, N) except IndexError: # no data return for sid, hcurve_by_rlz in zip(self.sids, hcurves.T): self.data[sid] = datadict = {} for rlzi, hcurve in enumerate(hcurve_by_rlz): datadict[rlzi] = lst = [None for imt in self.imtls] for imti, imt in enumerate(self.imtls): lst[imti] = hcurve[imt]

Read the poes and set the .data attribute with the hazard curves

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/getters.py#L66-L91

gem/oq-engine

openquake/calculators/getters.py

PmapGetter.get_mean

def get_mean(self, grp=None): """ Compute the mean curve as a ProbabilityMap :param grp: if not None must be a string of the form "grp-XX"; in that case returns the mean considering only the contribution for group XX """ self.init() if len(self.weights) == 1: # one realization # the standard deviation is zero pmap = self.get(0, grp) for sid, pcurve in pmap.items(): array = numpy.zeros(pcurve.array.shape[:-1] + (2,)) array[:, 0] = pcurve.array[:, 0] pcurve.array = array return pmap else: # multiple realizations dic = ({g: self.dstore['poes/' + g] for g in self.dstore['poes']} if grp is None else {grp: self.dstore['poes/' + grp]}) pmaps = self.rlzs_assoc.combine_pmaps(dic) return stats.compute_pmap_stats( pmaps, [stats.mean_curve, stats.std_curve], self.weights, self.imtls)

python

def get_mean(self, grp=None): """ Compute the mean curve as a ProbabilityMap :param grp: if not None must be a string of the form "grp-XX"; in that case returns the mean considering only the contribution for group XX """ self.init() if len(self.weights) == 1: # one realization # the standard deviation is zero pmap = self.get(0, grp) for sid, pcurve in pmap.items(): array = numpy.zeros(pcurve.array.shape[:-1] + (2,)) array[:, 0] = pcurve.array[:, 0] pcurve.array = array return pmap else: # multiple realizations dic = ({g: self.dstore['poes/' + g] for g in self.dstore['poes']} if grp is None else {grp: self.dstore['poes/' + grp]}) pmaps = self.rlzs_assoc.combine_pmaps(dic) return stats.compute_pmap_stats( pmaps, [stats.mean_curve, stats.std_curve], self.weights, self.imtls)

Compute the mean curve as a ProbabilityMap :param grp: if not None must be a string of the form "grp-XX"; in that case returns the mean considering only the contribution for group XX

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/getters.py#L189-L212

gem/oq-engine

openquake/calculators/getters.py

GmfGetter.init

def init(self): """ Initialize the computers. Should be called on the workers """ if hasattr(self, 'computers'): # init already called return with hdf5.File(self.rupgetter.filename, 'r') as parent: self.weights = parent['weights'].value self.computers = [] for ebr in self.rupgetter.get_ruptures(self.srcfilter): sitecol = self.sitecol.filtered(ebr.sids) try: computer = calc.gmf.GmfComputer( ebr, sitecol, self.oqparam.imtls, self.cmaker, self.oqparam.truncation_level, self.correl_model) except FarAwayRupture: # due to numeric errors, ruptures within the maximum_distance # when written, can be outside when read; I found a case with # a distance of 99.9996936 km over a maximum distance of 100 km continue self.computers.append(computer)

python

def init(self): """ Initialize the computers. Should be called on the workers """ if hasattr(self, 'computers'): # init already called return with hdf5.File(self.rupgetter.filename, 'r') as parent: self.weights = parent['weights'].value self.computers = [] for ebr in self.rupgetter.get_ruptures(self.srcfilter): sitecol = self.sitecol.filtered(ebr.sids) try: computer = calc.gmf.GmfComputer( ebr, sitecol, self.oqparam.imtls, self.cmaker, self.oqparam.truncation_level, self.correl_model) except FarAwayRupture: # due to numeric errors, ruptures within the maximum_distance # when written, can be outside when read; I found a case with # a distance of 99.9996936 km over a maximum distance of 100 km continue self.computers.append(computer)

Initialize the computers. Should be called on the workers

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/getters.py#L306-L326

gem/oq-engine

openquake/hazardlib/gsim/skarlatoudis_2013.py

SkarlatoudisEtAlSSlab2013._compute_forearc_backarc_term

def _compute_forearc_backarc_term(self, C, sites, dists, rup): """ Compute back-arc term of Equation 3 """ # flag 1 (R < 335 & R >= 205) flag1 = np.zeros(len(dists.rhypo)) ind1 = np.logical_and((dists.rhypo < 335), (dists.rhypo >= 205)) flag1[ind1] = 1.0 # flag 2 (R >= 335) flag2 = np.zeros(len(dists.rhypo)) ind2 = (dists.rhypo >= 335) flag2[ind2] = 1.0 # flag 3 (R < 240 & R >= 140) flag3 = np.zeros(len(dists.rhypo)) ind3 = np.logical_and((dists.rhypo < 240), (dists.rhypo >= 140)) flag3[ind3] = 1.0 # flag 4 (R >= 240) flag4 = np.zeros(len(dists.rhypo)) ind4 = (dists.rhypo >= 240) flag4[ind4] = 1.0 A = flag1 * ((205 - dists.rhypo)/150) + flag2 B = flag3 * ((140 - dists.rhypo)/100) + flag4 if (rup.hypo_depth < 80): FHR = A else: FHR = B H0 = 100 # Heaviside function if (rup.hypo_depth >= H0): H = 1 else: H = 0 # ARC = 0 for back-arc - ARC = 1 for forearc ARC = np.zeros(len(sites.backarc)) idxarc = (sites.backarc == 1) ARC[idxarc] = 1.0 return ((C['c41'] * (1 - ARC) * H) + (C['c42'] * (1 - ARC) * H * FHR) + (C['c51'] * ARC * H) + (C['c52'] * ARC * H * FHR))

python

def _compute_forearc_backarc_term(self, C, sites, dists, rup): """ Compute back-arc term of Equation 3 """ # flag 1 (R < 335 & R >= 205) flag1 = np.zeros(len(dists.rhypo)) ind1 = np.logical_and((dists.rhypo < 335), (dists.rhypo >= 205)) flag1[ind1] = 1.0 # flag 2 (R >= 335) flag2 = np.zeros(len(dists.rhypo)) ind2 = (dists.rhypo >= 335) flag2[ind2] = 1.0 # flag 3 (R < 240 & R >= 140) flag3 = np.zeros(len(dists.rhypo)) ind3 = np.logical_and((dists.rhypo < 240), (dists.rhypo >= 140)) flag3[ind3] = 1.0 # flag 4 (R >= 240) flag4 = np.zeros(len(dists.rhypo)) ind4 = (dists.rhypo >= 240) flag4[ind4] = 1.0 A = flag1 * ((205 - dists.rhypo)/150) + flag2 B = flag3 * ((140 - dists.rhypo)/100) + flag4 if (rup.hypo_depth < 80): FHR = A else: FHR = B H0 = 100 # Heaviside function if (rup.hypo_depth >= H0): H = 1 else: H = 0 # ARC = 0 for back-arc - ARC = 1 for forearc ARC = np.zeros(len(sites.backarc)) idxarc = (sites.backarc == 1) ARC[idxarc] = 1.0 return ((C['c41'] * (1 - ARC) * H) + (C['c42'] * (1 - ARC) * H * FHR) + (C['c51'] * ARC * H) + (C['c52'] * ARC * H * FHR))

Compute back-arc term of Equation 3

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/skarlatoudis_2013.py#L177-L219

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

AmplificationTable._build_data

def _build_data(self, amplification_group): """ Creates the numpy array tables from the hdf5 tables """ # Determine shape of the tables n_levels = len(amplification_group) # Checks the first group in the amplification group and returns the # shape of the SA array - implicitly assumes the SA array in all # amplification groups is the same shape level = next(iter(amplification_group)) n_d, n_p, n_m = amplification_group[level]["IMLs/SA"].shape assert n_d == len(self.distances), (n_d, len(self.distances)) assert n_m == len(self.magnitudes), (n_m, len(self.magnitudes)) # Instantiate the arrays with ones self.mean = {"SA": numpy.ones([n_d, n_p, n_m, n_levels]), "PGA": numpy.ones([n_d, 1, n_m, n_levels]), "PGV": numpy.ones([n_d, 1, n_m, n_levels])} self.sigma = {} for stddev_type in [const.StdDev.TOTAL, const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT]: level = next(iter(amplification_group)) if stddev_type in amplification_group[level]: self.sigma[stddev_type] = deepcopy(self.mean) for iloc, (level, amp_model) in enumerate(amplification_group.items()): if "SA" in amp_model["IMLs"]: if iloc == 0: self.periods = amp_model["IMLs/T"][:] else: assert numpy.allclose(self.periods, amp_model["IMLs/T"][:]) for imt in ["SA", "PGA", "PGV"]: if imt in amp_model["IMLs"]: self.mean[imt][:, :, :, self.argidx[iloc]] = \ amp_model["IMLs/" + imt][:] for stddev_type in self.sigma: self.sigma[stddev_type][imt][ :, :, :, self.argidx[iloc]] = \ amp_model["/".join([stddev_type, imt])][:] self.shape = (n_d, n_p, n_m, n_levels)

python

def _build_data(self, amplification_group): """ Creates the numpy array tables from the hdf5 tables """ # Determine shape of the tables n_levels = len(amplification_group) # Checks the first group in the amplification group and returns the # shape of the SA array - implicitly assumes the SA array in all # amplification groups is the same shape level = next(iter(amplification_group)) n_d, n_p, n_m = amplification_group[level]["IMLs/SA"].shape assert n_d == len(self.distances), (n_d, len(self.distances)) assert n_m == len(self.magnitudes), (n_m, len(self.magnitudes)) # Instantiate the arrays with ones self.mean = {"SA": numpy.ones([n_d, n_p, n_m, n_levels]), "PGA": numpy.ones([n_d, 1, n_m, n_levels]), "PGV": numpy.ones([n_d, 1, n_m, n_levels])} self.sigma = {} for stddev_type in [const.StdDev.TOTAL, const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT]: level = next(iter(amplification_group)) if stddev_type in amplification_group[level]: self.sigma[stddev_type] = deepcopy(self.mean) for iloc, (level, amp_model) in enumerate(amplification_group.items()): if "SA" in amp_model["IMLs"]: if iloc == 0: self.periods = amp_model["IMLs/T"][:] else: assert numpy.allclose(self.periods, amp_model["IMLs/T"][:]) for imt in ["SA", "PGA", "PGV"]: if imt in amp_model["IMLs"]: self.mean[imt][:, :, :, self.argidx[iloc]] = \ amp_model["IMLs/" + imt][:] for stddev_type in self.sigma: self.sigma[stddev_type][imt][ :, :, :, self.argidx[iloc]] = \ amp_model["/".join([stddev_type, imt])][:] self.shape = (n_d, n_p, n_m, n_levels)

Creates the numpy array tables from the hdf5 tables

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L112-L150

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

AmplificationTable.get_amplification_factors

def get_amplification_factors(self, imt, sctx, rctx, dists, stddev_types): """ Returns the amplification factors for the given rupture and site conditions. :param imt: Intensity measure type as an instance of the :class: `openquake.hazardlib.imt` :param sctx: SiteCollection instance :param rctx: Rupture instance :param dists: Source to site distances (km) :param stddev_types: List of required standard deviation types :returns: * mean_amp - Amplification factors applied to the median ground motion * sigma_amps - List of modification factors applied to the standard deviations of ground motion """ dist_level_table = self.get_mean_table(imt, rctx) sigma_tables = self.get_sigma_tables(imt, rctx, stddev_types) mean_interpolator = interp1d(self.values, numpy.log10(dist_level_table), axis=1) sigma_interpolators = [interp1d(self.values, sigma_table, axis=1) for sigma_table in sigma_tables] if self.element == "Rupture": mean_amp = 10.0 ** mean_interpolator( getattr(rctx, self.parameter))[0] * numpy.ones_like(dists) sigma_amps = [] for sig_interpolator in sigma_interpolators: sigma_amps.append(sig_interpolator( getattr(rctx, self.parameter))[0] * numpy.ones_like(dists)) else: mean_amp = 10.0 ** mean_interpolator( getattr(sctx, self.parameter))[0, :] sigma_amps = [] for sig_interpolator in sigma_interpolators: sigma_amps.append(sig_interpolator( getattr(sctx, self.parameter))[0, :] * numpy.ones_like(dists)) return mean_amp, sigma_amps

python

def get_amplification_factors(self, imt, sctx, rctx, dists, stddev_types): """ Returns the amplification factors for the given rupture and site conditions. :param imt: Intensity measure type as an instance of the :class: `openquake.hazardlib.imt` :param sctx: SiteCollection instance :param rctx: Rupture instance :param dists: Source to site distances (km) :param stddev_types: List of required standard deviation types :returns: * mean_amp - Amplification factors applied to the median ground motion * sigma_amps - List of modification factors applied to the standard deviations of ground motion """ dist_level_table = self.get_mean_table(imt, rctx) sigma_tables = self.get_sigma_tables(imt, rctx, stddev_types) mean_interpolator = interp1d(self.values, numpy.log10(dist_level_table), axis=1) sigma_interpolators = [interp1d(self.values, sigma_table, axis=1) for sigma_table in sigma_tables] if self.element == "Rupture": mean_amp = 10.0 ** mean_interpolator( getattr(rctx, self.parameter))[0] * numpy.ones_like(dists) sigma_amps = [] for sig_interpolator in sigma_interpolators: sigma_amps.append(sig_interpolator( getattr(rctx, self.parameter))[0] * numpy.ones_like(dists)) else: mean_amp = 10.0 ** mean_interpolator( getattr(sctx, self.parameter))[0, :] sigma_amps = [] for sig_interpolator in sigma_interpolators: sigma_amps.append(sig_interpolator( getattr(sctx, self.parameter))[0, :] * numpy.ones_like(dists)) return mean_amp, sigma_amps

Returns the amplification factors for the given rupture and site conditions. :param imt: Intensity measure type as an instance of the :class: `openquake.hazardlib.imt` :param sctx: SiteCollection instance :param rctx: Rupture instance :param dists: Source to site distances (km) :param stddev_types: List of required standard deviation types :returns: * mean_amp - Amplification factors applied to the median ground motion * sigma_amps - List of modification factors applied to the standard deviations of ground motion

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L158-L202

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

AmplificationTable.get_mean_table

def get_mean_table(self, imt, rctx): """ Returns amplification factors for the mean, given the rupture and intensity measure type. :returns: amplification table as an array of [Number Distances, Number Levels] """ # Levels by Distances if imt.name in 'PGA PGV': interpolator = interp1d(self.magnitudes, numpy.log10(self.mean[imt.name]), axis=2) output_table = 10.0 ** ( interpolator(rctx.mag).reshape(self.shape[0], self.shape[3])) else: # For spectral accelerations - need two step process # Interpolate period - log-log space interpolator = interp1d(numpy.log10(self.periods), numpy.log10(self.mean["SA"]), axis=1) period_table = interpolator(numpy.log10(imt.period)) # Interpolate magnitude - linear-log space mag_interpolator = interp1d(self.magnitudes, period_table, axis=1) output_table = 10.0 ** mag_interpolator(rctx.mag) return output_table

python

def get_mean_table(self, imt, rctx): """ Returns amplification factors for the mean, given the rupture and intensity measure type. :returns: amplification table as an array of [Number Distances, Number Levels] """ # Levels by Distances if imt.name in 'PGA PGV': interpolator = interp1d(self.magnitudes, numpy.log10(self.mean[imt.name]), axis=2) output_table = 10.0 ** ( interpolator(rctx.mag).reshape(self.shape[0], self.shape[3])) else: # For spectral accelerations - need two step process # Interpolate period - log-log space interpolator = interp1d(numpy.log10(self.periods), numpy.log10(self.mean["SA"]), axis=1) period_table = interpolator(numpy.log10(imt.period)) # Interpolate magnitude - linear-log space mag_interpolator = interp1d(self.magnitudes, period_table, axis=1) output_table = 10.0 ** mag_interpolator(rctx.mag) return output_table

Returns amplification factors for the mean, given the rupture and intensity measure type. :returns: amplification table as an array of [Number Distances, Number Levels]

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L204-L229

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

AmplificationTable.get_sigma_tables

def get_sigma_tables(self, imt, rctx, stddev_types): """ Returns modification factors for the standard deviations, given the rupture and intensity measure type. :returns: List of standard deviation modification tables, each as an array of [Number Distances, Number Levels] """ output_tables = [] for stddev_type in stddev_types: # For PGA and PGV only needs to apply magnitude interpolation if imt.name in 'PGA PGV': interpolator = interp1d(self.magnitudes, self.sigma[stddev_type][imt.name], axis=2) output_tables.append( interpolator(rctx.mag).reshape(self.shape[0], self.shape[3])) else: # For spectral accelerations - need two step process # Interpolate period interpolator = interp1d(numpy.log10(self.periods), self.sigma[stddev_type]["SA"], axis=1) period_table = interpolator(numpy.log10(imt.period)) mag_interpolator = interp1d(self.magnitudes, period_table, axis=1) output_tables.append(mag_interpolator(rctx.mag)) return output_tables

python

def get_sigma_tables(self, imt, rctx, stddev_types): """ Returns modification factors for the standard deviations, given the rupture and intensity measure type. :returns: List of standard deviation modification tables, each as an array of [Number Distances, Number Levels] """ output_tables = [] for stddev_type in stddev_types: # For PGA and PGV only needs to apply magnitude interpolation if imt.name in 'PGA PGV': interpolator = interp1d(self.magnitudes, self.sigma[stddev_type][imt.name], axis=2) output_tables.append( interpolator(rctx.mag).reshape(self.shape[0], self.shape[3])) else: # For spectral accelerations - need two step process # Interpolate period interpolator = interp1d(numpy.log10(self.periods), self.sigma[stddev_type]["SA"], axis=1) period_table = interpolator(numpy.log10(imt.period)) mag_interpolator = interp1d(self.magnitudes, period_table, axis=1) output_tables.append(mag_interpolator(rctx.mag)) return output_tables

Returns modification factors for the standard deviations, given the rupture and intensity measure type. :returns: List of standard deviation modification tables, each as an array of [Number Distances, Number Levels]

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L231-L263

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

GMPETable.init

def init(self, fle=None): """ Executes the preprocessing steps at the instantiation stage to read in the tables from hdf5 and hold them in memory. """ if fle is None: fname = self.kwargs.get('gmpe_table', self.GMPE_TABLE) if fname is None: raise ValueError('You forgot to set GMPETable.GMPE_TABLE!') elif os.path.isabs(fname): self.GMPE_TABLE = fname else: # NB: (hackish) GMPE_DIR must be set externally self.GMPE_TABLE = os.path.abspath( os.path.join(self.GMPE_DIR, fname)) fle = h5py.File(self.GMPE_TABLE, "r") try: # this is the format inside the datastore self.distance_type = fle["distance_type"].value except KeyError: # this is the original format outside the datastore self.distance_type = decode(fle["Distances"].attrs["metric"]) self.REQUIRES_DISTANCES = set([self.distance_type]) # Load in magnitude self.m_w = fle["Mw"][:] # Load in distances self.distances = fle["Distances"][:] # Load intensity measure types and levels self.imls = hdf_arrays_to_dict(fle["IMLs"]) self.DEFINED_FOR_INTENSITY_MEASURE_TYPES = set(self._supported_imts()) if "SA" in self.imls and "T" not in self.imls: raise ValueError("Spectral Acceleration must be accompanied by " "periods") # Get the standard deviations self._setup_standard_deviations(fle) if "Amplification" in fle: self._setup_amplification(fle)

python

def init(self, fle=None): """ Executes the preprocessing steps at the instantiation stage to read in the tables from hdf5 and hold them in memory. """ if fle is None: fname = self.kwargs.get('gmpe_table', self.GMPE_TABLE) if fname is None: raise ValueError('You forgot to set GMPETable.GMPE_TABLE!') elif os.path.isabs(fname): self.GMPE_TABLE = fname else: # NB: (hackish) GMPE_DIR must be set externally self.GMPE_TABLE = os.path.abspath( os.path.join(self.GMPE_DIR, fname)) fle = h5py.File(self.GMPE_TABLE, "r") try: # this is the format inside the datastore self.distance_type = fle["distance_type"].value except KeyError: # this is the original format outside the datastore self.distance_type = decode(fle["Distances"].attrs["metric"]) self.REQUIRES_DISTANCES = set([self.distance_type]) # Load in magnitude self.m_w = fle["Mw"][:] # Load in distances self.distances = fle["Distances"][:] # Load intensity measure types and levels self.imls = hdf_arrays_to_dict(fle["IMLs"]) self.DEFINED_FOR_INTENSITY_MEASURE_TYPES = set(self._supported_imts()) if "SA" in self.imls and "T" not in self.imls: raise ValueError("Spectral Acceleration must be accompanied by " "periods") # Get the standard deviations self._setup_standard_deviations(fle) if "Amplification" in fle: self._setup_amplification(fle)

Executes the preprocessing steps at the instantiation stage to read in the tables from hdf5 and hold them in memory.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L306-L342

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

GMPETable._setup_amplification

def _setup_amplification(self, fle): """ If amplification data is specified then reads into memory and updates the required rupture and site parameters """ self.amplification = AmplificationTable(fle["Amplification"], self.m_w, self.distances) if self.amplification.element == "Sites": self.REQUIRES_SITES_PARAMETERS = set( [self.amplification.parameter]) elif self.amplification.element == "Rupture": # set the site and rupture parameters on the instance self.REQUIRES_SITES_PARAMETERS = set() self.REQUIRES_RUPTURE_PARAMETERS = ( self.REQUIRES_RUPTURE_PARAMETERS | {self.amplification.parameter})

python

def _setup_amplification(self, fle): """ If amplification data is specified then reads into memory and updates the required rupture and site parameters """ self.amplification = AmplificationTable(fle["Amplification"], self.m_w, self.distances) if self.amplification.element == "Sites": self.REQUIRES_SITES_PARAMETERS = set( [self.amplification.parameter]) elif self.amplification.element == "Rupture": # set the site and rupture parameters on the instance self.REQUIRES_SITES_PARAMETERS = set() self.REQUIRES_RUPTURE_PARAMETERS = ( self.REQUIRES_RUPTURE_PARAMETERS | {self.amplification.parameter})

If amplification data is specified then reads into memory and updates the required rupture and site parameters

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L364-L380

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

GMPETable._supported_imts

def _supported_imts(self): """ Updates the list of supported IMTs from the tables """ imt_list = [] for key in self.imls: if "SA" in key: imt_list.append(imt_module.SA) elif key == "T": continue else: try: factory = getattr(imt_module, key) except Exception: continue imt_list.append(factory) return imt_list

python

def _supported_imts(self): """ Updates the list of supported IMTs from the tables """ imt_list = [] for key in self.imls: if "SA" in key: imt_list.append(imt_module.SA) elif key == "T": continue else: try: factory = getattr(imt_module, key) except Exception: continue imt_list.append(factory) return imt_list

Updates the list of supported IMTs from the tables

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L382-L398

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

GMPETable.get_mean_and_stddevs

def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types): """ Returns the mean and standard deviations """ # Return Distance Tables imls = self._return_tables(rctx.mag, imt, "IMLs") # Get distance vector for the given magnitude idx = numpy.searchsorted(self.m_w, rctx.mag) dists = self.distances[:, 0, idx - 1] # Get mean and standard deviations mean = self._get_mean(imls, dctx, dists) stddevs = self._get_stddevs(dists, rctx.mag, dctx, imt, stddev_types) if self.amplification: # Apply amplification mean_amp, sigma_amp = self.amplification.get_amplification_factors( imt, sctx, rctx, getattr(dctx, self.distance_type), stddev_types) mean = numpy.log(mean) + numpy.log(mean_amp) for iloc in range(len(stddev_types)): stddevs[iloc] *= sigma_amp[iloc] return mean, stddevs else: return numpy.log(mean), stddevs

python

def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types): """ Returns the mean and standard deviations """ # Return Distance Tables imls = self._return_tables(rctx.mag, imt, "IMLs") # Get distance vector for the given magnitude idx = numpy.searchsorted(self.m_w, rctx.mag) dists = self.distances[:, 0, idx - 1] # Get mean and standard deviations mean = self._get_mean(imls, dctx, dists) stddevs = self._get_stddevs(dists, rctx.mag, dctx, imt, stddev_types) if self.amplification: # Apply amplification mean_amp, sigma_amp = self.amplification.get_amplification_factors( imt, sctx, rctx, getattr(dctx, self.distance_type), stddev_types) mean = numpy.log(mean) + numpy.log(mean_amp) for iloc in range(len(stddev_types)): stddevs[iloc] *= sigma_amp[iloc] return mean, stddevs else: return numpy.log(mean), stddevs

Returns the mean and standard deviations

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L400-L425

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

GMPETable._get_stddevs

def _get_stddevs(self, dists, mag, dctx, imt, stddev_types): """ Returns the total standard deviation of the intensity measure level from the tables. :param fle: HDF5 data stream as instance of :class:`h5py.File` :param distances: The distance vector for the given magnitude and IMT :param key: The distance type :param mag: The rupture magnitude """ stddevs = [] for stddev_type in stddev_types: if stddev_type not in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES: raise ValueError("Standard Deviation type %s not supported" % stddev_type) sigma = self._return_tables(mag, imt, stddev_type) interpolator_std = interp1d(dists, sigma, bounds_error=False) stddev = interpolator_std(getattr(dctx, self.distance_type)) stddev[getattr(dctx, self.distance_type) < dists[0]] = sigma[0] stddev[getattr(dctx, self.distance_type) > dists[-1]] = sigma[-1] stddevs.append(stddev) return stddevs

python

def _get_stddevs(self, dists, mag, dctx, imt, stddev_types): """ Returns the total standard deviation of the intensity measure level from the tables. :param fle: HDF5 data stream as instance of :class:`h5py.File` :param distances: The distance vector for the given magnitude and IMT :param key: The distance type :param mag: The rupture magnitude """ stddevs = [] for stddev_type in stddev_types: if stddev_type not in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES: raise ValueError("Standard Deviation type %s not supported" % stddev_type) sigma = self._return_tables(mag, imt, stddev_type) interpolator_std = interp1d(dists, sigma, bounds_error=False) stddev = interpolator_std(getattr(dctx, self.distance_type)) stddev[getattr(dctx, self.distance_type) < dists[0]] = sigma[0] stddev[getattr(dctx, self.distance_type) > dists[-1]] = sigma[-1] stddevs.append(stddev) return stddevs

Returns the total standard deviation of the intensity measure level from the tables. :param fle: HDF5 data stream as instance of :class:`h5py.File` :param distances: The distance vector for the given magnitude and IMT :param key: The distance type :param mag: The rupture magnitude

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L454-L480

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

GMPETable._return_tables

def _return_tables(self, mag, imt, val_type): """ Returns the vector of ground motions or standard deviations corresponding to the specific magnitude and intensity measure type. :param val_type: String indicating the type of data {"IMLs", "Total", "Inter" etc} """ if imt.name in 'PGA PGV': # Get scalar imt if val_type == "IMLs": iml_table = self.imls[imt.name][:] else: iml_table = self.stddevs[val_type][imt.name][:] n_d, n_s, n_m = iml_table.shape iml_table = iml_table.reshape([n_d, n_m]) else: if val_type == "IMLs": periods = self.imls["T"][:] iml_table = self.imls["SA"][:] else: periods = self.stddevs[val_type]["T"][:] iml_table = self.stddevs[val_type]["SA"][:] low_period = round(periods[0], 7) high_period = round(periods[-1], 7) if (round(imt.period, 7) < low_period) or ( round(imt.period, 7) > high_period): raise ValueError("Spectral period %.3f outside of valid range " "(%.3f to %.3f)" % (imt.period, periods[0], periods[-1])) # Apply log-log interpolation for spectral period interpolator = interp1d(numpy.log10(periods), numpy.log10(iml_table), axis=1) iml_table = 10. ** interpolator(numpy.log10(imt.period)) return self.apply_magnitude_interpolation(mag, iml_table)

python

def _return_tables(self, mag, imt, val_type): """ Returns the vector of ground motions or standard deviations corresponding to the specific magnitude and intensity measure type. :param val_type: String indicating the type of data {"IMLs", "Total", "Inter" etc} """ if imt.name in 'PGA PGV': # Get scalar imt if val_type == "IMLs": iml_table = self.imls[imt.name][:] else: iml_table = self.stddevs[val_type][imt.name][:] n_d, n_s, n_m = iml_table.shape iml_table = iml_table.reshape([n_d, n_m]) else: if val_type == "IMLs": periods = self.imls["T"][:] iml_table = self.imls["SA"][:] else: periods = self.stddevs[val_type]["T"][:] iml_table = self.stddevs[val_type]["SA"][:] low_period = round(periods[0], 7) high_period = round(periods[-1], 7) if (round(imt.period, 7) < low_period) or ( round(imt.period, 7) > high_period): raise ValueError("Spectral period %.3f outside of valid range " "(%.3f to %.3f)" % (imt.period, periods[0], periods[-1])) # Apply log-log interpolation for spectral period interpolator = interp1d(numpy.log10(periods), numpy.log10(iml_table), axis=1) iml_table = 10. ** interpolator(numpy.log10(imt.period)) return self.apply_magnitude_interpolation(mag, iml_table)

Returns the vector of ground motions or standard deviations corresponding to the specific magnitude and intensity measure type. :param val_type: String indicating the type of data {"IMLs", "Total", "Inter" etc}

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L482-L518

gem/oq-engine

openquake/hazardlib/gsim/gmpe_table.py

GMPETable.apply_magnitude_interpolation

def apply_magnitude_interpolation(self, mag, iml_table): """ Interpolates the tables to the required magnitude level :param float mag: Magnitude :param iml_table: Intensity measure level table """ # do not allow "mag" to exceed maximum table magnitude if mag > self.m_w[-1]: mag = self.m_w[-1] # Get magnitude values if mag < self.m_w[0] or mag > self.m_w[-1]: raise ValueError("Magnitude %.2f outside of supported range " "(%.2f to %.2f)" % (mag, self.m_w[0], self.m_w[-1])) # It is assumed that log10 of the spectral acceleration scales # linearly (or approximately linearly) with magnitude m_interpolator = interp1d(self.m_w, numpy.log10(iml_table), axis=1) return 10.0 ** m_interpolator(mag)

python

def apply_magnitude_interpolation(self, mag, iml_table): """ Interpolates the tables to the required magnitude level :param float mag: Magnitude :param iml_table: Intensity measure level table """ # do not allow "mag" to exceed maximum table magnitude if mag > self.m_w[-1]: mag = self.m_w[-1] # Get magnitude values if mag < self.m_w[0] or mag > self.m_w[-1]: raise ValueError("Magnitude %.2f outside of supported range " "(%.2f to %.2f)" % (mag, self.m_w[0], self.m_w[-1])) # It is assumed that log10 of the spectral acceleration scales # linearly (or approximately linearly) with magnitude m_interpolator = interp1d(self.m_w, numpy.log10(iml_table), axis=1) return 10.0 ** m_interpolator(mag)

Interpolates the tables to the required magnitude level :param float mag: Magnitude :param iml_table: Intensity measure level table

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/gmpe_table.py#L520-L542

gem/oq-engine

openquake/hazardlib/gsim/sadigh_1997.py

SadighEtAl1997._get_mean_deep_soil

def _get_mean_deep_soil(self, mag, rake, rrup, is_reverse, imt): """ Calculate and return the mean intensity for deep soil sites. Implements an equation from table 4. """ if mag <= self.NEAR_FIELD_SATURATION_MAG: c4 = self.COEFFS_SOIL_IMT_INDEPENDENT['c4lowmag'] c5 = self.COEFFS_SOIL_IMT_INDEPENDENT['c5lowmag'] else: c4 = self.COEFFS_SOIL_IMT_INDEPENDENT['c4himag'] c5 = self.COEFFS_SOIL_IMT_INDEPENDENT['c5himag'] c2 = self.COEFFS_SOIL_IMT_INDEPENDENT['c2'] c3 = self.COEFFS_SOIL_IMT_INDEPENDENT['c3'] C = self.COEFFS_SOIL[imt] if is_reverse: c1 = self.COEFFS_SOIL_IMT_INDEPENDENT['c1r'] c6 = C['c6r'] else: c1 = self.COEFFS_SOIL_IMT_INDEPENDENT['c1ss'] c6 = C['c6ss'] # clip mag if greater than 8.5. This is to avoid # ValueError: negative number cannot be raised to a fractional power mag = 8.5 if mag > 8.5 else mag return (c1 + c2 * mag + c6 + C['c7'] * ((8.5 - mag) ** 2.5) - c3 * numpy.log(rrup + c4 * numpy.exp(c5 * mag)))

python

def _get_mean_deep_soil(self, mag, rake, rrup, is_reverse, imt): """ Calculate and return the mean intensity for deep soil sites. Implements an equation from table 4. """ if mag <= self.NEAR_FIELD_SATURATION_MAG: c4 = self.COEFFS_SOIL_IMT_INDEPENDENT['c4lowmag'] c5 = self.COEFFS_SOIL_IMT_INDEPENDENT['c5lowmag'] else: c4 = self.COEFFS_SOIL_IMT_INDEPENDENT['c4himag'] c5 = self.COEFFS_SOIL_IMT_INDEPENDENT['c5himag'] c2 = self.COEFFS_SOIL_IMT_INDEPENDENT['c2'] c3 = self.COEFFS_SOIL_IMT_INDEPENDENT['c3'] C = self.COEFFS_SOIL[imt] if is_reverse: c1 = self.COEFFS_SOIL_IMT_INDEPENDENT['c1r'] c6 = C['c6r'] else: c1 = self.COEFFS_SOIL_IMT_INDEPENDENT['c1ss'] c6 = C['c6ss'] # clip mag if greater than 8.5. This is to avoid # ValueError: negative number cannot be raised to a fractional power mag = 8.5 if mag > 8.5 else mag return (c1 + c2 * mag + c6 + C['c7'] * ((8.5 - mag) ** 2.5) - c3 * numpy.log(rrup + c4 * numpy.exp(c5 * mag)))

Calculate and return the mean intensity for deep soil sites. Implements an equation from table 4.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/sadigh_1997.py#L114-L139

gem/oq-engine

openquake/hazardlib/gsim/sadigh_1997.py

SadighEtAl1997._get_mean_rock

def _get_mean_rock(self, mag, _rake, rrup, is_reverse, imt): """ Calculate and return the mean intensity for rock sites. Implements an equation from table 2. """ if mag <= self.NEAR_FIELD_SATURATION_MAG: C = self.COEFFS_ROCK_LOWMAG[imt] else: C = self.COEFFS_ROCK_HIMAG[imt] # clip mag if greater than 8.5. This is to avoid # ValueError: negative number cannot be raised to a fractional power mag = 8.5 if mag > 8.5 else mag mean = ( C['c1'] + C['c2'] * mag + C['c3'] * ((8.5 - mag) ** 2.5) + C['c4'] * numpy.log(rrup + numpy.exp(C['c5'] + C['c6'] * mag)) + C['c7'] * numpy.log(rrup + 2) ) if is_reverse: # footnote in table 2 says that for reverse ruptures # the mean amplitude value should be multiplied by 1.2 mean += 0.1823215567939546 # == log(1.2) return mean

python

def _get_mean_rock(self, mag, _rake, rrup, is_reverse, imt): """ Calculate and return the mean intensity for rock sites. Implements an equation from table 2. """ if mag <= self.NEAR_FIELD_SATURATION_MAG: C = self.COEFFS_ROCK_LOWMAG[imt] else: C = self.COEFFS_ROCK_HIMAG[imt] # clip mag if greater than 8.5. This is to avoid # ValueError: negative number cannot be raised to a fractional power mag = 8.5 if mag > 8.5 else mag mean = ( C['c1'] + C['c2'] * mag + C['c3'] * ((8.5 - mag) ** 2.5) + C['c4'] * numpy.log(rrup + numpy.exp(C['c5'] + C['c6'] * mag)) + C['c7'] * numpy.log(rrup + 2) ) if is_reverse: # footnote in table 2 says that for reverse ruptures # the mean amplitude value should be multiplied by 1.2 mean += 0.1823215567939546 # == log(1.2) return mean

Calculate and return the mean intensity for rock sites. Implements an equation from table 2.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/sadigh_1997.py#L141-L163

gem/oq-engine

openquake/hazardlib/gsim/sadigh_1997.py

SadighEtAl1997._get_stddev_rock

def _get_stddev_rock(self, mag, imt): """ Calculate and return total standard deviation for rock sites. Implements formulae from table 3. """ C = self.COEFFS_ROCK_STDDERR[imt] if mag > C['maxmag']: return C['maxsigma'] else: return C['sigma0'] + C['magfactor'] * mag

python

def _get_stddev_rock(self, mag, imt): """ Calculate and return total standard deviation for rock sites. Implements formulae from table 3. """ C = self.COEFFS_ROCK_STDDERR[imt] if mag > C['maxmag']: return C['maxsigma'] else: return C['sigma0'] + C['magfactor'] * mag

Calculate and return total standard deviation for rock sites. Implements formulae from table 3.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/sadigh_1997.py#L165-L175

gem/oq-engine

openquake/hazardlib/gsim/sadigh_1997.py

SadighEtAl1997._get_stddev_deep_soil

def _get_stddev_deep_soil(self, mag, imt): """ Calculate and return total standard deviation for deep soil sites. Implements formulae from the last column of table 4. """ # footnote from table 4 says that stderr for magnitudes over 7 # is equal to one of magnitude 7. if mag > 7: mag = 7 C = self.COEFFS_SOIL[imt] return C['sigma0'] + C['magfactor'] * mag

python

def _get_stddev_deep_soil(self, mag, imt): """ Calculate and return total standard deviation for deep soil sites. Implements formulae from the last column of table 4. """ # footnote from table 4 says that stderr for magnitudes over 7 # is equal to one of magnitude 7. if mag > 7: mag = 7 C = self.COEFFS_SOIL[imt] return C['sigma0'] + C['magfactor'] * mag

Calculate and return total standard deviation for deep soil sites. Implements formulae from the last column of table 4.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/sadigh_1997.py#L177-L188

gem/oq-engine

openquake/commands/zip.py

zip

def zip(what, archive_zip='', risk_file=''): """ Zip into an archive one or two job.ini files with all related files """ if os.path.isdir(what): oqzip.zip_all(what) elif what.endswith('.xml') and '<logicTree' in open(what).read(512): # hack to see if the NRML file is of kind logicTree oqzip.zip_source_model(what, archive_zip) elif what.endswith('.xml') and '<exposureModel' in open(what).read(512): # hack to see if the NRML file is of kind exposureModel oqzip.zip_exposure(what, archive_zip) elif what.endswith('.ini'): # a job.ini oqzip.zip_job(what, archive_zip, risk_file) else: sys.exit('Cannot zip %s' % what)

python

def zip(what, archive_zip='', risk_file=''): """ Zip into an archive one or two job.ini files with all related files """ if os.path.isdir(what): oqzip.zip_all(what) elif what.endswith('.xml') and '<logicTree' in open(what).read(512): # hack to see if the NRML file is of kind logicTree oqzip.zip_source_model(what, archive_zip) elif what.endswith('.xml') and '<exposureModel' in open(what).read(512): # hack to see if the NRML file is of kind exposureModel oqzip.zip_exposure(what, archive_zip) elif what.endswith('.ini'): # a job.ini oqzip.zip_job(what, archive_zip, risk_file) else: sys.exit('Cannot zip %s' % what)

Zip into an archive one or two job.ini files with all related files

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commands/zip.py#L25-L40

gem/oq-engine

openquake/commands/reduce.py

reduce

def reduce(fname, reduction_factor): """ Produce a submodel from `fname` by sampling the nodes randomly. Supports source models, site models and exposure models. As a special case, it is also able to reduce .csv files by sampling the lines. This is a debugging utility to reduce large computations to small ones. """ if fname.endswith('.csv'): with open(fname) as f: line = f.readline() # read the first line if csv.Sniffer().has_header(line): header = line all_lines = f.readlines() else: header = None f.seek(0) all_lines = f.readlines() lines = general.random_filter(all_lines, reduction_factor) shutil.copy(fname, fname + '.bak') print('Copied the original file in %s.bak' % fname) _save_csv(fname, lines, header) print('Extracted %d lines out of %d' % (len(lines), len(all_lines))) return elif fname.endswith('.npy'): array = numpy.load(fname) shutil.copy(fname, fname + '.bak') print('Copied the original file in %s.bak' % fname) arr = numpy.array(general.random_filter(array, reduction_factor)) numpy.save(fname, arr) print('Extracted %d rows out of %d' % (len(arr), len(array))) return node = nrml.read(fname) model = node[0] if model.tag.endswith('exposureModel'): total = len(model.assets) model.assets.nodes = general.random_filter( model.assets, reduction_factor) num_nodes = len(model.assets) elif model.tag.endswith('siteModel'): total = len(model) model.nodes = general.random_filter(model, reduction_factor) num_nodes = len(model) elif model.tag.endswith('sourceModel'): reduce_source_model(fname, reduction_factor) return elif model.tag.endswith('logicTree'): for smpath in logictree.collect_info(fname).smpaths: reduce_source_model(smpath, reduction_factor) return else: raise RuntimeError('Unknown model tag: %s' % model.tag) save_bak(fname, node, num_nodes, total)

python

def reduce(fname, reduction_factor): """ Produce a submodel from `fname` by sampling the nodes randomly. Supports source models, site models and exposure models. As a special case, it is also able to reduce .csv files by sampling the lines. This is a debugging utility to reduce large computations to small ones. """ if fname.endswith('.csv'): with open(fname) as f: line = f.readline() # read the first line if csv.Sniffer().has_header(line): header = line all_lines = f.readlines() else: header = None f.seek(0) all_lines = f.readlines() lines = general.random_filter(all_lines, reduction_factor) shutil.copy(fname, fname + '.bak') print('Copied the original file in %s.bak' % fname) _save_csv(fname, lines, header) print('Extracted %d lines out of %d' % (len(lines), len(all_lines))) return elif fname.endswith('.npy'): array = numpy.load(fname) shutil.copy(fname, fname + '.bak') print('Copied the original file in %s.bak' % fname) arr = numpy.array(general.random_filter(array, reduction_factor)) numpy.save(fname, arr) print('Extracted %d rows out of %d' % (len(arr), len(array))) return node = nrml.read(fname) model = node[0] if model.tag.endswith('exposureModel'): total = len(model.assets) model.assets.nodes = general.random_filter( model.assets, reduction_factor) num_nodes = len(model.assets) elif model.tag.endswith('siteModel'): total = len(model) model.nodes = general.random_filter(model, reduction_factor) num_nodes = len(model) elif model.tag.endswith('sourceModel'): reduce_source_model(fname, reduction_factor) return elif model.tag.endswith('logicTree'): for smpath in logictree.collect_info(fname).smpaths: reduce_source_model(smpath, reduction_factor) return else: raise RuntimeError('Unknown model tag: %s' % model.tag) save_bak(fname, node, num_nodes, total)

Produce a submodel from `fname` by sampling the nodes randomly. Supports source models, site models and exposure models. As a special case, it is also able to reduce .csv files by sampling the lines. This is a debugging utility to reduce large computations to small ones.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commands/reduce.py#L60-L111

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

downsample_mesh

def downsample_mesh(mesh, tol=1.0): """ Returns a mesh sampled at a lower resolution - if the difference in azimuth is larger than the specified tolerance a turn is assumed :returns: Downsampled mesh as instance of :class: openquake.hazardlib.geo.mesh.RectangularMesh """ idx = _find_turning_points(mesh, tol) if mesh.depths is not None: return RectangularMesh(lons=mesh.lons[:, idx], lats=mesh.lats[:, idx], depths=mesh.depths[:, idx]) else: return RectangularMesh(lons=mesh.lons[:, idx], lats=mesh.lats[:, idx])

python

def downsample_mesh(mesh, tol=1.0): """ Returns a mesh sampled at a lower resolution - if the difference in azimuth is larger than the specified tolerance a turn is assumed :returns: Downsampled mesh as instance of :class: openquake.hazardlib.geo.mesh.RectangularMesh """ idx = _find_turning_points(mesh, tol) if mesh.depths is not None: return RectangularMesh(lons=mesh.lons[:, idx], lats=mesh.lats[:, idx], depths=mesh.depths[:, idx]) else: return RectangularMesh(lons=mesh.lons[:, idx], lats=mesh.lats[:, idx])

Returns a mesh sampled at a lower resolution - if the difference in azimuth is larger than the specified tolerance a turn is assumed :returns: Downsampled mesh as instance of :class: openquake.hazardlib.geo.mesh.RectangularMesh

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L64-L80

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

downsample_trace

def downsample_trace(mesh, tol=1.0): """ Downsamples the upper edge of a fault within a rectangular mesh, retaining node points only if changes in direction on the order of tol are found :returns: Downsampled edge as a numpy array of [long, lat, depth] """ idx = _find_turning_points(mesh, tol) if mesh.depths is not None: return numpy.column_stack([mesh.lons[0, idx], mesh.lats[0, idx], mesh.depths[0, idx]]) else: return numpy.column_stack([mesh.lons[0, idx], mesh.lats[0, idx]])

python

def downsample_trace(mesh, tol=1.0): """ Downsamples the upper edge of a fault within a rectangular mesh, retaining node points only if changes in direction on the order of tol are found :returns: Downsampled edge as a numpy array of [long, lat, depth] """ idx = _find_turning_points(mesh, tol) if mesh.depths is not None: return numpy.column_stack([mesh.lons[0, idx], mesh.lats[0, idx], mesh.depths[0, idx]]) else: return numpy.column_stack([mesh.lons[0, idx], mesh.lats[0, idx]])

Downsamples the upper edge of a fault within a rectangular mesh, retaining node points only if changes in direction on the order of tol are found :returns: Downsampled edge as a numpy array of [long, lat, depth]

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L83-L97

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

BaseSurface.get_ry0_distance

def get_ry0_distance(self, mesh): """ Compute the minimum distance between each point of a mesh and the great circle arcs perpendicular to the average strike direction of the fault trace and passing through the end-points of the trace. :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points to calculate Ry0-distance to. :returns: Numpy array of distances in km. """ # This computes ry0 by using an average strike direction top_edge = self.mesh[0:1] mean_strike = self.get_strike() dst1 = geodetic.distance_to_arc(top_edge.lons[0, 0], top_edge.lats[0, 0], (mean_strike + 90.) % 360, mesh.lons, mesh.lats) dst2 = geodetic.distance_to_arc(top_edge.lons[0, -1], top_edge.lats[0, -1], (mean_strike + 90.) % 360, mesh.lons, mesh.lats) # Find the points on the rupture # Get the shortest distance from the two lines idx = numpy.sign(dst1) == numpy.sign(dst2) dst = numpy.zeros_like(dst1) dst[idx] = numpy.fmin(numpy.abs(dst1[idx]), numpy.abs(dst2[idx])) return dst

python

def get_ry0_distance(self, mesh): """ Compute the minimum distance between each point of a mesh and the great circle arcs perpendicular to the average strike direction of the fault trace and passing through the end-points of the trace. :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points to calculate Ry0-distance to. :returns: Numpy array of distances in km. """ # This computes ry0 by using an average strike direction top_edge = self.mesh[0:1] mean_strike = self.get_strike() dst1 = geodetic.distance_to_arc(top_edge.lons[0, 0], top_edge.lats[0, 0], (mean_strike + 90.) % 360, mesh.lons, mesh.lats) dst2 = geodetic.distance_to_arc(top_edge.lons[0, -1], top_edge.lats[0, -1], (mean_strike + 90.) % 360, mesh.lons, mesh.lats) # Find the points on the rupture # Get the shortest distance from the two lines idx = numpy.sign(dst1) == numpy.sign(dst2) dst = numpy.zeros_like(dst1) dst[idx] = numpy.fmin(numpy.abs(dst1[idx]), numpy.abs(dst2[idx])) return dst

Compute the minimum distance between each point of a mesh and the great circle arcs perpendicular to the average strike direction of the fault trace and passing through the end-points of the trace. :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points to calculate Ry0-distance to. :returns: Numpy array of distances in km.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L148-L180

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

BaseSurface.get_rx_distance

def get_rx_distance(self, mesh): """ Compute distance between each point of mesh and surface's great circle arc. Distance is measured perpendicular to the rupture strike, from the surface projection of the updip edge of the rupture, with the down dip direction being positive (this distance is usually called ``Rx``). In other words, is the horizontal distance to top edge of rupture measured perpendicular to the strike. Values on the hanging wall are positive, values on the footwall are negative. :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points to calculate Rx-distance to. :returns: Numpy array of distances in km. """ top_edge = self.mesh[0:1] dists = [] if top_edge.lons.shape[1] < 3: i = 0 p1 = Point( top_edge.lons[0, i], top_edge.lats[0, i], top_edge.depths[0, i] ) p2 = Point( top_edge.lons[0, i + 1], top_edge.lats[0, i + 1], top_edge.depths[0, i + 1] ) azimuth = p1.azimuth(p2) dists.append( geodetic.distance_to_arc( p1.longitude, p1.latitude, azimuth, mesh.lons, mesh.lats ) ) else: for i in range(top_edge.lons.shape[1] - 1): p1 = Point( top_edge.lons[0, i], top_edge.lats[0, i], top_edge.depths[0, i] ) p2 = Point( top_edge.lons[0, i + 1], top_edge.lats[0, i + 1], top_edge.depths[0, i + 1] ) # Swapping if i == 0: pt = p1 p1 = p2 p2 = pt # Computing azimuth and distance if i == 0 or i == top_edge.lons.shape[1] - 2: azimuth = p1.azimuth(p2) tmp = geodetic.distance_to_semi_arc(p1.longitude, p1.latitude, azimuth, mesh.lons, mesh.lats) else: tmp = geodetic.min_distance_to_segment( numpy.array([p1.longitude, p2.longitude]), numpy.array([p1.latitude, p2.latitude]), mesh.lons, mesh.lats) # Correcting the sign of the distance if i == 0: tmp *= -1 dists.append(tmp) # Computing distances dists = numpy.array(dists) iii = abs(dists).argmin(axis=0) dst = dists[iii, list(range(dists.shape[1]))] return dst

python

def get_rx_distance(self, mesh): """ Compute distance between each point of mesh and surface's great circle arc. Distance is measured perpendicular to the rupture strike, from the surface projection of the updip edge of the rupture, with the down dip direction being positive (this distance is usually called ``Rx``). In other words, is the horizontal distance to top edge of rupture measured perpendicular to the strike. Values on the hanging wall are positive, values on the footwall are negative. :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points to calculate Rx-distance to. :returns: Numpy array of distances in km. """ top_edge = self.mesh[0:1] dists = [] if top_edge.lons.shape[1] < 3: i = 0 p1 = Point( top_edge.lons[0, i], top_edge.lats[0, i], top_edge.depths[0, i] ) p2 = Point( top_edge.lons[0, i + 1], top_edge.lats[0, i + 1], top_edge.depths[0, i + 1] ) azimuth = p1.azimuth(p2) dists.append( geodetic.distance_to_arc( p1.longitude, p1.latitude, azimuth, mesh.lons, mesh.lats ) ) else: for i in range(top_edge.lons.shape[1] - 1): p1 = Point( top_edge.lons[0, i], top_edge.lats[0, i], top_edge.depths[0, i] ) p2 = Point( top_edge.lons[0, i + 1], top_edge.lats[0, i + 1], top_edge.depths[0, i + 1] ) # Swapping if i == 0: pt = p1 p1 = p2 p2 = pt # Computing azimuth and distance if i == 0 or i == top_edge.lons.shape[1] - 2: azimuth = p1.azimuth(p2) tmp = geodetic.distance_to_semi_arc(p1.longitude, p1.latitude, azimuth, mesh.lons, mesh.lats) else: tmp = geodetic.min_distance_to_segment( numpy.array([p1.longitude, p2.longitude]), numpy.array([p1.latitude, p2.latitude]), mesh.lons, mesh.lats) # Correcting the sign of the distance if i == 0: tmp *= -1 dists.append(tmp) # Computing distances dists = numpy.array(dists) iii = abs(dists).argmin(axis=0) dst = dists[iii, list(range(dists.shape[1]))] return dst

Compute distance between each point of mesh and surface's great circle arc. Distance is measured perpendicular to the rupture strike, from the surface projection of the updip edge of the rupture, with the down dip direction being positive (this distance is usually called ``Rx``). In other words, is the horizontal distance to top edge of rupture measured perpendicular to the strike. Values on the hanging wall are positive, values on the footwall are negative. :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points to calculate Rx-distance to. :returns: Numpy array of distances in km.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L182-L266

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

BaseSurface.get_top_edge_depth

def get_top_edge_depth(self): """ Return minimum depth of surface's top edge. :returns: Float value, the vertical distance between the earth surface and the shallowest point in surface's top edge in km. """ top_edge = self.mesh[0:1] if top_edge.depths is None: return 0 else: return numpy.min(top_edge.depths)

python

def get_top_edge_depth(self): """ Return minimum depth of surface's top edge. :returns: Float value, the vertical distance between the earth surface and the shallowest point in surface's top edge in km. """ top_edge = self.mesh[0:1] if top_edge.depths is None: return 0 else: return numpy.min(top_edge.depths)

Return minimum depth of surface's top edge. :returns: Float value, the vertical distance between the earth surface and the shallowest point in surface's top edge in km.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L268-L280

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

BaseSurface.get_area

def get_area(self): """ Compute area as the sum of the mesh cells area values. """ mesh = self.mesh _, _, _, area = mesh.get_cell_dimensions() return numpy.sum(area)

python

def get_area(self): """ Compute area as the sum of the mesh cells area values. """ mesh = self.mesh _, _, _, area = mesh.get_cell_dimensions() return numpy.sum(area)

Compute area as the sum of the mesh cells area values.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L290-L297

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

BaseSurface.get_surface_boundaries

def get_surface_boundaries(self): """ Returns the boundaries in the same format as a multiplanar surface, with two one-element lists of lons and lats """ mesh = self.mesh lons = numpy.concatenate((mesh.lons[0, :], mesh.lons[1:, -1], mesh.lons[-1, :-1][::-1], mesh.lons[:-1, 0][::-1])) lats = numpy.concatenate((mesh.lats[0, :], mesh.lats[1:, -1], mesh.lats[-1, :-1][::-1], mesh.lats[:-1, 0][::-1])) return [lons], [lats]

python

def get_surface_boundaries(self): """ Returns the boundaries in the same format as a multiplanar surface, with two one-element lists of lons and lats """ mesh = self.mesh lons = numpy.concatenate((mesh.lons[0, :], mesh.lons[1:, -1], mesh.lons[-1, :-1][::-1], mesh.lons[:-1, 0][::-1])) lats = numpy.concatenate((mesh.lats[0, :], mesh.lats[1:, -1], mesh.lats[-1, :-1][::-1], mesh.lats[:-1, 0][::-1])) return [lons], [lats]

Returns the boundaries in the same format as a multiplanar surface, with two one-element lists of lons and lats

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L326-L340

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

BaseSurface.get_resampled_top_edge

def get_resampled_top_edge(self, angle_var=0.1): """ This methods computes a simplified representation of a fault top edge by removing the points that are not describing a change of direction, provided a certain tolerance angle. :param float angle_var: Number representing the maximum deviation (in degrees) admitted without the creation of a new segment :returns: A :class:`~openquake.hazardlib.geo.line.Line` representing the rupture surface's top edge. """ mesh = self.mesh top_edge = [Point(mesh.lons[0][0], mesh.lats[0][0], mesh.depths[0][0])] for i in range(len(mesh.triangulate()[1][0]) - 1): v1 = numpy.asarray(mesh.triangulate()[1][0][i]) v2 = numpy.asarray(mesh.triangulate()[1][0][i + 1]) cosang = numpy.dot(v1, v2) sinang = numpy.linalg.norm(numpy.cross(v1, v2)) angle = math.degrees(numpy.arctan2(sinang, cosang)) if abs(angle) > angle_var: top_edge.append(Point(mesh.lons[0][i + 1], mesh.lats[0][i + 1], mesh.depths[0][i + 1])) top_edge.append(Point(mesh.lons[0][-1], mesh.lats[0][-1], mesh.depths[0][-1])) line_top_edge = Line(top_edge) return line_top_edge

python

def get_resampled_top_edge(self, angle_var=0.1): """ This methods computes a simplified representation of a fault top edge by removing the points that are not describing a change of direction, provided a certain tolerance angle. :param float angle_var: Number representing the maximum deviation (in degrees) admitted without the creation of a new segment :returns: A :class:`~openquake.hazardlib.geo.line.Line` representing the rupture surface's top edge. """ mesh = self.mesh top_edge = [Point(mesh.lons[0][0], mesh.lats[0][0], mesh.depths[0][0])] for i in range(len(mesh.triangulate()[1][0]) - 1): v1 = numpy.asarray(mesh.triangulate()[1][0][i]) v2 = numpy.asarray(mesh.triangulate()[1][0][i + 1]) cosang = numpy.dot(v1, v2) sinang = numpy.linalg.norm(numpy.cross(v1, v2)) angle = math.degrees(numpy.arctan2(sinang, cosang)) if abs(angle) > angle_var: top_edge.append(Point(mesh.lons[0][i + 1], mesh.lats[0][i + 1], mesh.depths[0][i + 1])) top_edge.append(Point(mesh.lons[0][-1], mesh.lats[0][-1], mesh.depths[0][-1])) line_top_edge = Line(top_edge) return line_top_edge

This methods computes a simplified representation of a fault top edge by removing the points that are not describing a change of direction, provided a certain tolerance angle. :param float angle_var: Number representing the maximum deviation (in degrees) admitted without the creation of a new segment :returns: A :class:`~openquake.hazardlib.geo.line.Line` representing the rupture surface's top edge.

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L342-L375

gem/oq-engine

openquake/hazardlib/geo/surface/base.py

BaseSurface.get_hypo_location

def get_hypo_location(self, mesh_spacing, hypo_loc=None): """ The method determines the location of the hypocentre within the rupture :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points :param mesh_spacing: The desired distance between two adjacent points in source's ruptures' mesh, in km. Mainly this parameter allows to balance the trade-off between time needed to compute the distance between the rupture surface and a site and the precision of that computation. :param hypo_loc: Hypocentre location as fraction of rupture plane, as a tuple of (Along Strike, Down Dip), e.g. a hypocentre located in the centroid of the rupture would be input as (0.5, 0.5), whereas a hypocentre located in a position 3/4 along the length, and 1/4 of the way down dip of the rupture plane would be entered as (0.75, 0.25). :returns: Hypocentre location as instance of :class:`~openquake.hazardlib.geo.point.Point` """ mesh = self.mesh centroid = mesh.get_middle_point() if hypo_loc is None: return centroid total_len_y = (len(mesh.depths) - 1) * mesh_spacing y_distance = hypo_loc[1] * total_len_y y_node = int(numpy.round(y_distance / mesh_spacing)) total_len_x = (len(mesh.lons[y_node]) - 1) * mesh_spacing x_distance = hypo_loc[0] * total_len_x x_node = int(numpy.round(x_distance / mesh_spacing)) hypocentre = Point(mesh.lons[y_node][x_node], mesh.lats[y_node][x_node], mesh.depths[y_node][x_node]) return hypocentre

python

def get_hypo_location(self, mesh_spacing, hypo_loc=None): """ The method determines the location of the hypocentre within the rupture :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points :param mesh_spacing: The desired distance between two adjacent points in source's ruptures' mesh, in km. Mainly this parameter allows to balance the trade-off between time needed to compute the distance between the rupture surface and a site and the precision of that computation. :param hypo_loc: Hypocentre location as fraction of rupture plane, as a tuple of (Along Strike, Down Dip), e.g. a hypocentre located in the centroid of the rupture would be input as (0.5, 0.5), whereas a hypocentre located in a position 3/4 along the length, and 1/4 of the way down dip of the rupture plane would be entered as (0.75, 0.25). :returns: Hypocentre location as instance of :class:`~openquake.hazardlib.geo.point.Point` """ mesh = self.mesh centroid = mesh.get_middle_point() if hypo_loc is None: return centroid total_len_y = (len(mesh.depths) - 1) * mesh_spacing y_distance = hypo_loc[1] * total_len_y y_node = int(numpy.round(y_distance / mesh_spacing)) total_len_x = (len(mesh.lons[y_node]) - 1) * mesh_spacing x_distance = hypo_loc[0] * total_len_x x_node = int(numpy.round(x_distance / mesh_spacing)) hypocentre = Point(mesh.lons[y_node][x_node], mesh.lats[y_node][x_node], mesh.depths[y_node][x_node]) return hypocentre

The method determines the location of the hypocentre within the rupture :param mesh: :class:`~openquake.hazardlib.geo.mesh.Mesh` of points :param mesh_spacing: The desired distance between two adjacent points in source's ruptures' mesh, in km. Mainly this parameter allows to balance the trade-off between time needed to compute the distance between the rupture surface and a site and the precision of that computation. :param hypo_loc: Hypocentre location as fraction of rupture plane, as a tuple of (Along Strike, Down Dip), e.g. a hypocentre located in the centroid of the rupture would be input as (0.5, 0.5), whereas a hypocentre located in a position 3/4 along the length, and 1/4 of the way down dip of the rupture plane would be entered as (0.75, 0.25). :returns: Hypocentre location as instance of :class:`~openquake.hazardlib.geo.point.Point`

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/geo/surface/base.py#L377-L414

gem/oq-engine

openquake/engine/tools/viewlog.py

viewlog

def viewlog(calc_id, host='localhost', port=8000): """ Extract the log of the given calculation ID from the WebUI """ base_url = 'http://%s:%s/v1/calc/' % (host, port) start = 0 psize = 10 # page size try: while True: url = base_url + '%d/log/%d:%d' % (calc_id, start, start + psize) rows = json.load(urlopen(url)) for row in rows: print(' '.join(row)) start += len(rows) time.sleep(1) except: pass

python

def viewlog(calc_id, host='localhost', port=8000): """ Extract the log of the given calculation ID from the WebUI """ base_url = 'http://%s:%s/v1/calc/' % (host, port) start = 0 psize = 10 # page size try: while True: url = base_url + '%d/log/%d:%d' % (calc_id, start, start + psize) rows = json.load(urlopen(url)) for row in rows: print(' '.join(row)) start += len(rows) time.sleep(1) except: pass

Extract the log of the given calculation ID from the WebUI

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/engine/tools/viewlog.py#L33-L49

gem/oq-engine

openquake/baselib/parallel.py

pickle_sequence

def pickle_sequence(objects): """ Convert an iterable of objects into a list of pickled objects. If the iterable contains copies, the pickling will be done only once. If the iterable contains objects already pickled, they will not be pickled again. :param objects: a sequence of objects to pickle """ cache = {} out = [] for obj in objects: obj_id = id(obj) if obj_id not in cache: if isinstance(obj, Pickled): # already pickled cache[obj_id] = obj else: # pickle the object cache[obj_id] = Pickled(obj) out.append(cache[obj_id]) return out

python

def pickle_sequence(objects): """ Convert an iterable of objects into a list of pickled objects. If the iterable contains copies, the pickling will be done only once. If the iterable contains objects already pickled, they will not be pickled again. :param objects: a sequence of objects to pickle """ cache = {} out = [] for obj in objects: obj_id = id(obj) if obj_id not in cache: if isinstance(obj, Pickled): # already pickled cache[obj_id] = obj else: # pickle the object cache[obj_id] = Pickled(obj) out.append(cache[obj_id]) return out

Convert an iterable of objects into a list of pickled objects. If the iterable contains copies, the pickling will be done only once. If the iterable contains objects already pickled, they will not be pickled again. :param objects: a sequence of objects to pickle

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/baselib/parallel.py#L294-L313

gem/oq-engine

openquake/baselib/parallel.py

check_mem_usage

def check_mem_usage(soft_percent=None, hard_percent=None): """ Display a warning if we are running out of memory """ soft_percent = soft_percent or config.memory.soft_mem_limit hard_percent = hard_percent or config.memory.hard_mem_limit used_mem_percent = psutil.virtual_memory().percent if used_mem_percent > hard_percent: raise MemoryError('Using more memory than allowed by configuration ' '(Used: %d%% / Allowed: %d%%)! Shutting down.' % (used_mem_percent, hard_percent)) elif used_mem_percent > soft_percent: msg = 'Using over %d%% of the memory in %s!' return msg % (used_mem_percent, socket.gethostname())

python

def check_mem_usage(soft_percent=None, hard_percent=None): """ Display a warning if we are running out of memory """ soft_percent = soft_percent or config.memory.soft_mem_limit hard_percent = hard_percent or config.memory.hard_mem_limit used_mem_percent = psutil.virtual_memory().percent if used_mem_percent > hard_percent: raise MemoryError('Using more memory than allowed by configuration ' '(Used: %d%% / Allowed: %d%%)! Shutting down.' % (used_mem_percent, hard_percent)) elif used_mem_percent > soft_percent: msg = 'Using over %d%% of the memory in %s!' return msg % (used_mem_percent, socket.gethostname())

Display a warning if we are running out of memory

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/baselib/parallel.py#L370-L383

gem/oq-engine

openquake/baselib/parallel.py

init_workers

def init_workers(): """Waiting function, used to wake up the process pool""" setproctitle('oq-worker') # unregister raiseMasterKilled in oq-workers to avoid deadlock # since processes are terminated via pool.terminate() signal.signal(signal.SIGTERM, signal.SIG_DFL) # prctl is still useful (on Linux) to terminate all spawned processes # when master is killed via SIGKILL try: import prctl except ImportError: pass else: # if the parent dies, the children die prctl.set_pdeathsig(signal.SIGKILL)

python

def init_workers(): """Waiting function, used to wake up the process pool""" setproctitle('oq-worker') # unregister raiseMasterKilled in oq-workers to avoid deadlock # since processes are terminated via pool.terminate() signal.signal(signal.SIGTERM, signal.SIG_DFL) # prctl is still useful (on Linux) to terminate all spawned processes # when master is killed via SIGKILL try: import prctl except ImportError: pass else: # if the parent dies, the children die prctl.set_pdeathsig(signal.SIGKILL)

Waiting function, used to wake up the process pool

8294553a0b8aba33fd96437a35065d03547d0040

https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/baselib/parallel.py#L567-L581