# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2018 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/>.
import sys
import time
import operator
import collections
from contextlib import contextmanager
import numpy
import rtree
from scipy.interpolate import interp1d
from openquake.baselib import hdf5, config
from openquake.baselib.general import gettemp
from openquake.baselib.python3compat import raise_
from openquake.hazardlib.geo.utils import (
KM_TO_DEGREES, angular_distance, within, fix_lon, get_bounding_box)
MAX_DISTANCE = 2000 # km, ultra big distance used if there is no filter
src_group_id = operator.attrgetter('src_group_id')
[docs]@contextmanager
def context(src):
"""
Used to add the source_id to the error message. To be used as
with context(src):
operation_with(src)
Typically the operation is filtering a source, that can fail for
tricky geometries.
"""
try:
yield
except Exception:
etype, err, tb = sys.exc_info()
msg = 'An error occurred with source id=%s. Error: %s'
msg %= (src.source_id, err)
raise_(etype, msg, tb)
[docs]def getdefault(dic_with_default, key):
"""
:param dic_with_default: a dictionary with a 'default' key
:param key: a key that may be present in the dictionary or not
:returns: the value associated to the key, or to 'default'
"""
try:
return dic_with_default[key]
except KeyError:
return dic_with_default['default']
[docs]class Piecewise(object):
"""
Given two arrays x and y of non-decreasing values, build a piecewise
function associating to each x the corresponding y. If x is smaller
then the minimum x, the minimum y is returned; if x is larger than the
maximum x, the maximum y is returned.
"""
def __init__(self, x, y):
self.y = numpy.array(y)
# interpolating from x values to indices in the range [0: len(x)]
self.piecewise = interp1d(x, range(len(x)), bounds_error=False,
fill_value=(0, len(x) - 1))
def __call__(self, x):
idx = numpy.int64(numpy.ceil(self.piecewise(x)))
return self.y[idx]
[docs]class IntegrationDistance(collections.Mapping):
"""
Pickleable object wrapping a dictionary of integration distances per
tectonic region type. The integration distances can be scalars or
list of pairs (magnitude, distance). Here is an example using 'default'
as tectonic region type, so that the same values will be used for all
tectonic region types:
>>> maxdist = IntegrationDistance({'default': [
... (3, 30), (4, 40), (5, 100), (6, 200), (7, 300), (8, 400)]})
>>> maxdist('Some TRT', mag=2.5)
30
>>> maxdist('Some TRT', mag=3)
30
>>> maxdist('Some TRT', mag=3.1)
40
>>> maxdist('Some TRT', mag=8)
400
>>> maxdist('Some TRT', mag=8.5) # 2000 km are used above the maximum
2000
It has also a method `.get_closest(sites, rupture)` returning the closest
sites to the rupture and their distances. The integration distance can be
missing if the sites have been already filtered (empty dictionary): in
that case the method returns all the sites and all the distances.
"""
def __init__(self, dic):
self.dic = dic or {} # TRT -> float or list of pairs
self.magdist = {} # TRT -> (magnitudes, distances)
for trt, value in self.dic.items():
if isinstance(value, list): # assume a list of pairs (mag, dist)
self.magdist[trt] = value
else:
self.dic[trt] = float(value)
def __call__(self, trt, mag=None):
value = getdefault(self.dic, trt)
if isinstance(value, float): # scalar maximum distance
return value
elif mag is None: # get the maximum distance
return MAX_DISTANCE
elif not hasattr(self, 'piecewise'):
self.piecewise = {} # function cache
try:
md = self.piecewise[trt] # retrieve from the cache
except KeyError: # fill the cache
mags, dists = zip(*getdefault(self.magdist, trt))
if mags[-1] < 11: # use 2000 km for mag > mags[-1]
mags = numpy.concatenate([mags, [11]])
dists = numpy.concatenate([dists, [MAX_DISTANCE]])
md = self.piecewise[trt] = Piecewise(mags, dists)
return md(mag)
[docs] def get_bounding_box(self, lon, lat, trt=None, mag=None):
"""
Build a bounding box around the given lon, lat by computing the
maximum_distance at the given tectonic region type and magnitude.
:param lon: longitude
:param lat: latitude
:param trt: tectonic region type, possibly None
:param mag: magnitude, possibly None
:returns: min_lon, min_lat, max_lon, max_lat
"""
if trt is None: # take the greatest integration distance
maxdist = max(self(trt, mag) for trt in self.dic)
else: # get the integration distance for the given TRT
maxdist = self(trt, mag)
a1 = min(maxdist * KM_TO_DEGREES, 90)
a2 = min(angular_distance(maxdist, lat), 180)
return lon - a2, lat - a1, lon + a2, lat + a1
[docs] def get_affected_box(self, src):
"""
Get the enlarged bounding box of a source.
:param src: a source object
:returns: a bounding box (min_lon, min_lat, max_lon, max_lat)
"""
mag = src.get_min_max_mag()[1]
maxdist = self(src.tectonic_region_type, mag)
bbox = get_bounding_box(src, maxdist)
return (fix_lon(bbox[0]), bbox[1], fix_lon(bbox[2]), bbox[3])
def __getstate__(self):
# otherwise is not pickleable due to .piecewise
return dict(dic=self.dic, magdist=self.magdist)
def __getitem__(self, trt):
return self(trt)
def __iter__(self):
return iter(self.dic)
def __len__(self):
return len(self.dic)
def __repr__(self):
return repr(self.dic)
[docs]def split_sources(srcs):
"""
:param srcs: sources
:returns: a pair (split sources, split time)
"""
from openquake.hazardlib.source import splittable
sources = []
split_time = {} # src.id -> time
for src in srcs:
t0 = time.time()
mag_a, mag_b = src.get_min_max_mag()
min_mag = src.min_mag
if mag_b < min_mag: # discard the source completely
continue
has_serial = hasattr(src, 'serial')
if has_serial:
src.serial = numpy.arange(
src.serial, src.serial + src.num_ruptures)
if not splittable(src):
sources.append(src)
split_time[src.id] = time.time() - t0
continue
if min_mag:
splits = []
for s in src:
s.min_mag = min_mag
mag_a, mag_b = s.get_min_max_mag()
if mag_b < min_mag:
continue
s.num_ruptures = s.count_ruptures()
if s.num_ruptures:
splits.append(s)
else:
splits = list(src)
split_time[src.id] = time.time() - t0
sources.extend(splits)
has_samples = hasattr(src, 'samples')
if len(splits) > 1:
start = 0
for i, split in enumerate(splits):
split.source_id = '%s:%s' % (src.source_id, i)
split.src_group_id = src.src_group_id
split.id = src.id
if has_serial:
nr = split.num_ruptures
split.serial = src.serial[start:start + nr]
start += nr
if has_samples:
split.samples = src.samples
elif splits: # single source
splits[0].id = src.id
if has_serial:
splits[0].serial = src.serial
if has_samples:
splits[0].samples = src.samples
return sources, split_time
[docs]class SourceFilter(object):
"""
Filter objects have a .filter method yielding filtered sources,
i.e. sources with an attribute .indices, containg the IDs of the sites
within the given maximum distance. There is also a .new method
that filters the sources in parallel and returns a dictionary
src_group_id -> filtered sources.
Filter the sources by using `self.sitecol.within_bbox` which is
based on numpy.
"""
def __init__(self, sitecol, integration_distance, hdf5path=None):
if sitecol is not None and len(sitecol) < len(sitecol.complete):
raise ValueError('%s is not complete!' % sitecol)
elif sitecol is None:
integration_distance = {}
self.hdf5path = hdf5path
self.integration_distance = (
IntegrationDistance(integration_distance)
if isinstance(integration_distance, dict)
else integration_distance)
if hdf5path and (
config.distribution.oq_distribute in ('no', 'processpool') or
config.directory.shared_dir): # store the sitecol
with hdf5.File(hdf5path, 'w') as h5:
if sitecol is not None:
h5['sitecol'] = sitecol
else: # keep the sitecol in memory
self.__dict__['sitecol'] = sitecol
@property
def sitecol(self):
"""
Read the site collection from .hdf5path and cache it
"""
if 'sitecol' in vars(self):
return self.__dict__['sitecol']
with hdf5.File(self.hdf5path, 'r') as h5:
self.__dict__['sitecol'] = sc = h5.get('sitecol')
return sc
[docs] def get_rectangle(self, src):
"""
:param src: a source object
:returns: ((min_lon, min_lat), width, height), useful for plotting
"""
min_lon, min_lat, max_lon, max_lat = (
self.integration_distance.get_affected_box(src))
return (min_lon, min_lat), (max_lon - min_lon) % 360, max_lat - min_lat
[docs] def get_close_sites(self, source):
"""
Returns the sites within the integration distance from the source,
or None.
"""
source_sites = list(self([source]))
if source_sites:
return source_sites[0][1]
def __call__(self, sources):
"""
:yields: pairs (src, sites)
"""
if not self.integration_distance: # do not filter
for src in sources:
yield src, self.sitecol
return
for src in self.filter(sources):
yield src, self.sitecol.filtered(src.indices)
# used in the disaggregation calculator
[docs] def get_bounding_boxes(self, trt=None, mag=None):
"""
:param trt: a tectonic region type (used for the integration distance)
:param mag: a magnitude (used for the integration distance)
:returns: a list of bounding boxes, one per site
"""
bbs = []
for site in self.sitecol:
bb = self.integration_distance.get_bounding_box(
site.location.longitude, site.location.latitude, trt, mag)
bbs.append(bb)
return bbs
[docs] def get_sids_within(self, bbox, trt, mag):
"""
:returns:
the site indices within the bounding box enlarged by the integration
distance for the given TRT and magnitude
"""
if not self.integration_distance: # do not filter
return self.sitecol.sids
maxdist = self.integration_distance(trt, mag)
a1 = min(maxdist * KM_TO_DEGREES, 90)
a2 = min(angular_distance(maxdist, bbox[1], bbox[3]), 180)
bb = bbox[0] - a2, bbox[1] - a1, bbox[2] + a2, bbox[3] + a1
return self.sitecol.within_bbox(bb)
[docs] def filter(self, sources):
"""
:param sources: a sequence of sources
:yields: sources with .indices
"""
for src in sources:
if hasattr(src, 'indices'): # already filtered
yield src
continue
box = self.integration_distance.get_affected_box(src)
indices = self.sitecol.within_bbox(box)
if len(indices):
src.indices = indices
yield src
[docs]class RtreeFilter(SourceFilter):
"""
The RtreeFilter uses the rtree library. The index is generated at
instantiation time and stored in a temporary file. The filter should be
instantiated only once per calculation, after the site collection is
known. It should be used as follows::
rfilter = RtreeFilter(sitecol, integration_distance)
for src, sites in rfilter(sources):
do_something(...)
As a side effect, sets the `.indices` attribute of the source, i.e. the
number of sites within the integration distance. Notice that
libspatialindex indices cannot be properly pickled
(https://github.com/Toblerity/rtree/issues/65) this is why they must
be saved on the file system where they can be read from the workers.
NB: an RtreeFilter has an .indexpath attribute, but not a .sitecol
attribute nor an .index attribute, so it can be pickled and transferred
easily.
:param sitecol:
:class:`openquake.hazardlib.site.SiteCollection` instance
:param integration_distance:
Integration distance dictionary (TRT -> distance in km)
"""
def __init__(self, sitecol, integration_distance, hdf5path=None):
super().__init__(sitecol, integration_distance, hdf5path)
self.indexpath = gettemp()
index = rtree.index.Index(self.indexpath)
if sitecol:
lonlats = zip(sitecol.lons, sitecol.lats)
for i, (lon, lat) in enumerate(lonlats):
index.insert(i, (lon, lat, lon, lat))
index.close()
[docs] def filter(self, sources):
"""
:param sources: a sequence of sources
:yields: rtree-filtered sources
"""
if self.sitecol is None: # do not filter
yield from sources
return
index = rtree.index.Index(self.indexpath)
try:
for src in sources:
box = self.integration_distance.get_affected_box(src)
indices = within(box, index)
if len(indices):
src.indices = indices
yield src
finally:
index.close()
source_site_noop_filter = SourceFilter(None, {})