# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2023 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
"""
Module :mod:`openquake.hazardlib.site` defines :class:`Site`.
"""
import numpy
from scipy.spatial import distance
from shapely import geometry
from openquake.baselib.general import not_equal, get_duplicates
from openquake.hazardlib.geo.utils import (
fix_lon, cross_idl, _GeographicObjects, geohash, spherical_to_cartesian)
from openquake.hazardlib.geo.mesh import Mesh
U32LIMIT = 2 ** 32
ampcode_dt = (numpy.string_, 4)
param = dict(
vs30measured='reference_vs30_type',
vs30='reference_vs30_value',
z1pt0='reference_depth_to_1pt0km_per_sec',
z2pt5='reference_depth_to_2pt5km_per_sec',
backarc='reference_backarc')
[docs]class Site(object):
"""
Site object represents a geographical location defined by its position
as well as its soil characteristics.
:param location:
Instance of :class:`~openquake.hazardlib.geo.point.Point` representing
where the site is located.
:param vs30:
Average shear wave velocity in the top 30 m, in m/s.
:param z1pt0:
Vertical distance from earth surface to the layer where seismic waves
start to propagate with a speed above 1.0 km/sec, in meters.
:param z2pt5:
Vertical distance from earth surface to the layer where seismic waves
start to propagate with a speed above 2.5 km/sec, in km.
:raises ValueError:
If any of ``vs30``, ``z1pt0`` or ``z2pt5`` is zero or negative.
.. note::
:class:`Sites <Site>` are pickleable
"""
def __init__(self, location, vs30=numpy.nan,
z1pt0=numpy.nan, z2pt5=numpy.nan, **extras):
if not numpy.isnan(vs30) and vs30 <= 0:
raise ValueError('vs30 must be positive')
if not numpy.isnan(z1pt0) and z1pt0 <= 0:
raise ValueError('z1pt0 must be positive')
if not numpy.isnan(z2pt5) and z2pt5 <= 0:
raise ValueError('z2pt5 must be positive')
self.location = location
self.vs30 = vs30
self.z1pt0 = z1pt0
self.z2pt5 = z2pt5
for param, val in extras.items():
assert param in site_param_dt, param
setattr(self, param, val)
def __str__(self):
"""
>>> import openquake.hazardlib
>>> loc = openquake.hazardlib.geo.point.Point(1, 2, 3)
>>> str(Site(loc, 760.0, 100.0, 5.0))
'<Location=<Latitude=2.000000, Longitude=1.000000, Depth=3.0000>, \
Vs30=760.0000, Depth1.0km=100.0000, Depth2.5km=5.0000>'
"""
return (
"<Location=%s, Vs30=%.4f, Depth1.0km=%.4f, "
"Depth2.5km=%.4f>") % (
self.location, self.vs30, self.z1pt0, self.z2pt5)
def __hash__(self):
return hash((self.location.x, self.location.y))
def __eq__(self, other):
return (self.location.x, self.location.y) == (
other.location.x, other.location.y)
def __repr__(self):
"""
>>> import openquake.hazardlib
>>> loc = openquake.hazardlib.geo.point.Point(1, 2, 3)
>>> site = Site(loc, 760.0, 100.0, 5.0)
>>> str(site) == repr(site)
True
"""
return self.__str__()
def _extract(array_or_float, indices):
try: # if array
return array_or_float[indices]
except TypeError: # if float
return array_or_float
# dtype of each valid site parameter
site_param_dt = {
'sids': numpy.uint32,
'lon': numpy.float64,
'lat': numpy.float64,
'depth': numpy.float64,
'vs30': numpy.float64,
'kappa0': numpy.float64,
'vs30measured': bool,
'z1pt0': numpy.float64,
'z2pt5': numpy.float64,
'siteclass': (numpy.string_, 1),
'geohash': (numpy.string_, 6),
'z1pt4': numpy.float64,
'backarc': numpy.uint8, # 0=forearc,1=backarc,2=alongarc
'xvf': numpy.float64,
'soiltype': numpy.uint32,
'bas': bool,
# Parameters for site amplification
'ampcode': ampcode_dt,
'ec8': (numpy.string_, 1),
'ec8_p18': (numpy.string_, 2),
'h800': numpy.float64,
'geology': (numpy.string_, 20),
'amplfactor': numpy.float64,
'fpeak': numpy.float64,
# Fundamental period and and amplitude of HVRSR spectra
'THV': numpy.float64,
'PHV': numpy.float64,
# parameters for secondary perils
'friction_mid': numpy.float64,
'cohesion_mid': numpy.float64,
'saturation': numpy.float64,
'dry_density': numpy.float64,
'Fs': numpy.float64,
'crit_accel': numpy.float64,
'unit': (numpy.string_, 5),
'liq_susc_cat': (numpy.string_, 2),
'dw': numpy.float64,
'yield_acceleration': numpy.float64,
'slope': numpy.float64,
'gwd': numpy.float64,
'cti': numpy.float64,
'dc': numpy.float64,
'dr': numpy.float64,
'dwb': numpy.float64,
'hwater': numpy.float64,
'precip': numpy.float64,
# parameters for YoudEtAl2002
'freeface_ratio': numpy.float64,
'T_15': numpy.float64,
'D50_15': numpy.float64,
'F_15': numpy.float64,
'T_eq': numpy.float64,
# other parameters
'custom_site_id': (numpy.string_, 8),
'region': numpy.uint32,
'in_cshm': bool # used in mcverry
}
[docs]class SiteCollection(object):
"""\
A collection of :class:`sites <Site>`.
Instances of this class are intended to represent a large collection
of sites in a most efficient way in terms of memory usage. The most
common usage is to instantiate it as `SiteCollection.from_points`, by
passing the set of required parameters, which must be a subset of the
following parameters:
%s
.. note::
If a :class:`SiteCollection` is created from sites containing only
lon and lat, iterating over the collection will yield
:class:`Sites <Site>` with a reference depth of 0.0 (the sea level).
Otherwise, it is possible to model the sites on a realistic
topographic surface by specifying the `depth` of each site.
:param sites:
A list of instances of :class:`Site` class.
""" % '\n'.join(' - %s: %s' % item
for item in sorted(site_param_dt.items())
if item[0] not in ('lon', 'lat'))
[docs] @classmethod
def from_usgs_shakemap(cls, shakemap_array):
"""
Build a site collection from a shakemap array
"""
self = object.__new__(cls)
self.complete = self
n = len(shakemap_array)
dtype = numpy.dtype([(p, site_param_dt[p])
for p in 'sids lon lat depth vs30'.split()])
self.array = arr = numpy.zeros(n, dtype)
arr['sids'] = numpy.arange(n, dtype=numpy.uint32)
arr['lon'] = shakemap_array['lon']
arr['lat'] = shakemap_array['lat']
arr['depth'] = numpy.zeros(n)
arr['vs30'] = shakemap_array['vs30']
return self
[docs] @classmethod # this is the method used by the engine
def from_points(cls, lons, lats, depths=None, sitemodel=None,
req_site_params=()):
"""
Build the site collection from
:param lons:
a sequence of longitudes
:param lats:
a sequence of latitudes
:param depths:
a sequence of depths (or None)
:param sitemodel:
None or an object containing site parameters as attributes
:param req_site_params:
a sequence of required site parameters, possibly empty
"""
assert len(lons) < U32LIMIT, len(lons)
if depths is None:
depths = numpy.zeros(len(lons))
assert len(lons) == len(lats) == len(depths), (len(lons), len(lats),
len(depths))
self = object.__new__(cls)
self.complete = self
req = ['sids', 'lon', 'lat', 'depth'] + sorted(
par for par in req_site_params if par not in ('lon', 'lat'))
if 'vs30' in req and 'vs30measured' not in req:
req.append('vs30measured')
dtype = numpy.dtype([(p, site_param_dt[p]) for p in req])
self.array = arr = numpy.zeros(len(lons), dtype)
arr['sids'] = numpy.arange(len(lons), dtype=numpy.uint32)
arr['lon'] = fix_lon(numpy.array(lons))
arr['lat'] = numpy.array(lats)
arr['depth'] = numpy.array(depths)
if sitemodel is None:
pass
elif hasattr(sitemodel, 'reference_vs30_value'):
# sitemodel is actually an OqParam instance
self._set('vs30', sitemodel.reference_vs30_value)
self._set('vs30measured',
sitemodel.reference_vs30_type == 'measured')
if 'z1pt0' in req_site_params:
self._set('z1pt0', sitemodel.reference_depth_to_1pt0km_per_sec)
if 'z2pt5' in req_site_params:
self._set('z2pt5', sitemodel.reference_depth_to_2pt5km_per_sec)
if 'backarc' in req_site_params:
self._set('backarc', sitemodel.reference_backarc)
else:
for name in sitemodel.dtype.names:
if name not in ('lon', 'lat'):
self._set(name, sitemodel[name])
dupl = get_duplicates(self.array, 'lon', 'lat')
if dupl:
# raise a decent error message displaying only the first 9
# duplicates (there could be millions)
n = len(dupl)
dots = ' ...' if n > 9 else ''
items = list(dupl.items())[:9]
raise ValueError('There are %d duplicate sites %s%s' %
(n, items, dots))
return self
def _set(self, param, value):
if param not in self.array.dtype.names:
self.add_col(param, site_param_dt[param])
self.array[param] = value
xyz = Mesh.xyz
[docs] def filtered(self, indices):
"""
:param indices:
a subset of indices in the range [0 .. tot_sites - 1]
:returns:
a filtered SiteCollection instance if `indices` is a proper subset
of the available indices, otherwise returns the full SiteCollection
"""
if indices is None or len(indices) == len(self):
return self
new = object.__new__(self.__class__)
indices = numpy.uint32(indices)
new.array = self.array[indices]
new.complete = self.complete
return new
[docs] def reduce(self, nsites):
"""
:returns: a filtered SiteCollection with around nsites (if nsites<=N)
"""
N = len(self.complete)
n = N // nsites
if n <= 1:
return self
sids, = numpy.where(self.complete.sids % n == 0)
return self.filtered(sids)
[docs] def add_col(self, colname, dtype, values=None):
"""
Add a column to the underlying array
"""
names = self.array.dtype.names
dtlist = [(name, self.array.dtype[name]) for name in names]
dtlist.append((colname, dtype))
arr = numpy.zeros(len(self), dtlist)
for name in names:
arr[name] = self.array[name]
if values is not None:
arr[colname] = values
self.array = arr
[docs] def make_complete(self):
"""
Turns the site collection into a complete one, if needed
"""
# reset the site indices from 0 to N-1 and set self.complete to self
self.array['sids'] = numpy.arange(len(self), dtype=numpy.uint32)
self.complete = self
[docs] def one(self):
"""
:returns: a SiteCollection with a site of the minimal vs30
"""
if 'vs30' in self.array.dtype.names:
idx = self.array['vs30'].argmin()
else:
idx = 0
return self.filtered([self.sids[idx]])
# used in preclassical
[docs] def get_cdist(self, rec_or_loc):
"""
:param rec_or_loc: a record with field 'hypo' or a Point instance
:returns: array of N euclidean distances from rec['hypo']
"""
try:
lon, lat, dep = rec_or_loc['hypo']
except TypeError:
lon, lat, dep = rec_or_loc.x, rec_or_loc.y, rec_or_loc.z
xyz = spherical_to_cartesian(lon, lat, dep).reshape(1, 3)
return distance.cdist(self.xyz, xyz)[:, 0]
def __init__(self, sites):
"""
Build a complete SiteCollection from a list of Site objects
"""
extra = [(p, site_param_dt[p]) for p in sorted(vars(sites[0]))
if p in site_param_dt]
dtlist = [(p, site_param_dt[p])
for p in ('sids', 'lon', 'lat', 'depth')] + extra
self.array = arr = numpy.zeros(len(sites), dtlist)
self.complete = self
for i in range(len(arr)):
arr['sids'][i] = i
arr['lon'][i] = sites[i].location.longitude
arr['lat'][i] = sites[i].location.latitude
arr['depth'][i] = sites[i].location.depth
for p, dt in extra:
arr[p][i] = getattr(sites[i], p)
# NB: in test_correlation.py we define a SiteCollection with
# non-unique sites, so we cannot do an
# assert len(numpy.unique(self[['lon', 'lat']])) == len(self)
def __eq__(self, other):
return not self.__ne__(other)
def __ne__(self, other):
return not_equal(self.array, other.array)
def __toh5__(self):
names = self.array.dtype.names
cols = ' '.join(names)
return {n: self.array[n] for n in names}, {'__pdcolumns__': cols}
def __fromh5__(self, dic, attrs):
if isinstance(dic, dict): # engine >= 3.11
params = attrs['__pdcolumns__'].split()
dtype = numpy.dtype([(p, site_param_dt[p]) for p in params])
self.array = numpy.zeros(len(dic['sids']), dtype)
for p in dic:
self.array[p] = dic[p][()]
else: # old engine, dic is actually a structured array
self.array = dic
self.complete = self
@property
def mesh(self):
"""Return a mesh with the given lons, lats, and depths"""
return Mesh(self['lon'], self['lat'], self['depth'])
[docs] def at_sea_level(self):
"""True if all depths are zero"""
return (self.depths == 0).all()
# used in the engine
[docs] def split_max(self, max_sites):
"""
Split a SiteCollection into SiteCollection instances
"""
return self.split(numpy.ceil(len(self) / max_sites))
[docs] def split(self, ntiles):
"""
:param ntiles: number of tiles to generate (rounded if float)
:returns: self if there are <=1 tiles, otherwise the tiles
"""
ntiles = min(int(numpy.ceil(ntiles)), len(self))
if ntiles <= 1:
return [self]
tiles = []
for i in range(ntiles):
sc = SiteCollection.__new__(SiteCollection)
# smart trick to split in "homogenous" tiles
sc.array = self.array[self.sids % ntiles == i]
sc.complete = self
tiles.append(sc)
return tiles
[docs] def split_in_tiles(self, hint):
"""
Split a SiteCollection into a set of tiles with contiguous site IDs
"""
if hint > len(self):
hint = len(self)
tiles = []
for sids in numpy.array_split(self.sids, hint):
assert len(sids), 'Cannot split %s in %d tiles' % (self, hint)
sc = SiteCollection.__new__(SiteCollection)
sc.array = self.complete.array[sids]
sc.complete = self.complete
tiles.append(sc)
return tiles
[docs] def count_close(self, location, distance):
"""
:returns: the number of sites within the distance from the location
"""
return (self.get_cdist(location) < distance).sum()
def __iter__(self):
"""
Iterate through all :class:`sites <Site>` in the collection, yielding
one at a time.
"""
params = self.array.dtype.names[4:] # except sids, lons, lats, depths
sids = self.sids
for i, location in enumerate(self.mesh):
kw = {p: self.array[i][p] for p in params}
s = Site(location, **kw)
s.id = sids[i]
yield s
[docs] def filter(self, mask):
"""
Create a SiteCollection with only a subset of sites.
:param mask:
Numpy array of boolean values of the same length as the site
collection. ``True`` values should indicate that site with that
index should be included into the filtered collection.
:returns:
A new :class:`SiteCollection` instance, unless all the
values in ``mask`` are ``True``, in which case this site collection
is returned, or if all the values in ``mask`` are ``False``,
in which case method returns ``None``. New collection has data
of only those sites that were marked for inclusion in the mask.
"""
assert len(mask) == len(self), (len(mask), len(self))
if mask.all():
# all sites satisfy the filter, return
# this collection unchanged
return self
if not mask.any():
# no sites pass the filter, return None
return None
# extract indices of Trues from the mask
indices, = mask.nonzero()
return self.filtered(indices)
[docs] def assoc(self, site_model, assoc_dist, ignore=()):
"""
Associate the `site_model` parameters to the sites.
Log a warning if the site parameters are more distant than
`assoc_dist`.
:returns: the site model array reduced to the hazard sites
"""
m1, m2 = site_model[['lon', 'lat']], self[['lon', 'lat']]
if len(m1) != len(m2) or (m1 != m2).any(): # associate
_sitecol, site_model, _discarded = _GeographicObjects(
site_model).assoc(self, assoc_dist, 'warn')
ok = set(self.array.dtype.names) & set(site_model.dtype.names) - set(
ignore) - {'lon', 'lat', 'depth'}
for name in ok:
self._set(name, site_model[name])
for name in set(self.array.dtype.names) - set(site_model.dtype.names):
if name == 'vs30measured':
self._set(name, 0) # default
# NB: by default reference_vs30_type == 'measured' is 1
# but vs30measured is 0 (the opposite!!)
return site_model
[docs] def within(self, region):
"""
:param region: a shapely polygon
:returns: a filtered SiteCollection of sites within the region
"""
mask = numpy.array([
geometry.Point(rec['lon'], rec['lat']).within(region)
for rec in self.array])
return self.filter(mask)
[docs] def within_bbox(self, bbox):
"""
:param bbox:
a quartet (min_lon, min_lat, max_lon, max_lat)
:returns:
site IDs within the bounding box
"""
min_lon, min_lat, max_lon, max_lat = bbox
lons, lats = self['lon'], self['lat']
if cross_idl(lons.min(), lons.max(), min_lon, max_lon):
lons = lons % 360
min_lon, max_lon = min_lon % 360, max_lon % 360
mask = (min_lon < lons) * (lons < max_lon) * \
(min_lat < lats) * (lats < max_lat)
return mask.nonzero()[0]
[docs] def geohash(self, length):
"""
:param length: length of the geohash in the range 1..8
:returns: an array of N geohashes, one per site
"""
lst = [geohash(lon, lat, length)
for lon, lat in zip(self['lon'], self['lat'])]
return numpy.array(lst, (numpy.string_, length))
[docs] def num_geohashes(self, length):
"""
:param length: length of the geohash in the range 1..8
:returns: number of distinct geohashes in the site collection
"""
return len(numpy.unique(self.geohash(length)))
def __getstate__(self):
return dict(array=self.array, complete=self.complete)
def __getitem__(self, sid):
"""
Return a site record
"""
return self.array[sid]
def __getattr__(self, name):
if name in ('lons', 'lats', 'depths'): # legacy names
return self.array[name[:-1]]
if name not in site_param_dt:
raise AttributeError(name)
return self.array[name]
def __len__(self):
"""
Return the number of sites in the collection.
"""
return len(self.array)
def __repr__(self):
total_sites = len(self.complete.array)
return '<SiteCollection with %d/%d sites>' % (
len(self), total_sites)