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0 | gem/oq-engine | openquake/calculators/export/hazard.py | export_hmaps_csv | def export_hmaps_csv(key, dest, sitemesh, array, comment):
"""
Export the hazard maps of the given realization into CSV.
:param key: output_type and export_type
:param dest: name of the exported file
:param sitemesh: site collection
:param array: a composite array of dtype hmap_dt
:param comment: comment to use as header of the exported CSV file
"""
curves = util.compose_arrays(sitemesh, array)
writers.write_csv(dest, curves, comment=comment)
return [dest] | python | def export_hmaps_csv(key, dest, sitemesh, array, comment):
"""
Export the hazard maps of the given realization into CSV.
:param key: output_type and export_type
:param dest: name of the exported file
:param sitemesh: site collection
:param array: a composite array of dtype hmap_dt
:param comment: comment to use as header of the exported CSV file
"""
curves = util.compose_arrays(sitemesh, array)
writers.write_csv(dest, curves, comment=comment)
return [dest] | [
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:param key: output_type and export_type
:param dest: name of the exported file
:param sitemesh: site collection
:param array: a composite array of dtype hmap_dt
:param comment: comment to use as header of the exported CSV file | [
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1 | gem/oq-engine | openquake/calculators/export/hazard.py | export_hcurves_by_imt_csv | def export_hcurves_by_imt_csv(
key, kind, rlzs_assoc, fname, sitecol, array, oq, checksum):
"""
Export the curves of the given realization into CSV.
:param key: output_type and export_type
:param kind: a string with the kind of output (realization or statistics)
:param rlzs_assoc: a :class:`openquake.commonlib.source.RlzsAssoc` instance
:param fname: name of the exported file
:param sitecol: site collection
:param array: an array of shape (N, L) and dtype numpy.float32
:param oq: job.ini parameters
"""
nsites = len(sitecol)
fnames = []
for imt, imls in oq.imtls.items():
slc = oq.imtls(imt)
dest = add_imt(fname, imt)
lst = [('lon', F32), ('lat', F32), ('depth', F32)]
for iml in imls:
lst.append(('poe-%s' % iml, F32))
hcurves = numpy.zeros(nsites, lst)
for sid, lon, lat, dep in zip(
range(nsites), sitecol.lons, sitecol.lats, sitecol.depths):
hcurves[sid] = (lon, lat, dep) + tuple(array[sid, slc])
fnames.append(writers.write_csv(dest, hcurves, comment=_comment(
rlzs_assoc, kind, oq.investigation_time) + (
', imt="%s", checksum=%d' % (imt, checksum)
), header=[name for (name, dt) in lst]))
return fnames | python | def export_hcurves_by_imt_csv(
key, kind, rlzs_assoc, fname, sitecol, array, oq, checksum):
"""
Export the curves of the given realization into CSV.
:param key: output_type and export_type
:param kind: a string with the kind of output (realization or statistics)
:param rlzs_assoc: a :class:`openquake.commonlib.source.RlzsAssoc` instance
:param fname: name of the exported file
:param sitecol: site collection
:param array: an array of shape (N, L) and dtype numpy.float32
:param oq: job.ini parameters
"""
nsites = len(sitecol)
fnames = []
for imt, imls in oq.imtls.items():
slc = oq.imtls(imt)
dest = add_imt(fname, imt)
lst = [('lon', F32), ('lat', F32), ('depth', F32)]
for iml in imls:
lst.append(('poe-%s' % iml, F32))
hcurves = numpy.zeros(nsites, lst)
for sid, lon, lat, dep in zip(
range(nsites), sitecol.lons, sitecol.lats, sitecol.depths):
hcurves[sid] = (lon, lat, dep) + tuple(array[sid, slc])
fnames.append(writers.write_csv(dest, hcurves, comment=_comment(
rlzs_assoc, kind, oq.investigation_time) + (
', imt="%s", checksum=%d' % (imt, checksum)
), header=[name for (name, dt) in lst]))
return fnames | [
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:param key: output_type and export_type
:param kind: a string with the kind of output (realization or statistics)
:param rlzs_assoc: a :class:`openquake.commonlib.source.RlzsAssoc` instance
:param fname: name of the exported file
:param sitecol: site collection
:param array: an array of shape (N, L) and dtype numpy.float32
:param oq: job.ini parameters | [
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2 | gem/oq-engine | openquake/calculators/export/hazard.py | export_hcurves_csv | def export_hcurves_csv(ekey, dstore):
"""
Exports the hazard curves into several .csv files
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = dstore['oqparam']
info = get_info(dstore)
rlzs_assoc = dstore['csm_info'].get_rlzs_assoc()
R = len(rlzs_assoc.realizations)
sitecol = dstore['sitecol']
sitemesh = get_mesh(sitecol)
key, kind, fmt = get_kkf(ekey)
fnames = []
checksum = dstore.get_attr('/', 'checksum32')
hmap_dt = oq.hmap_dt()
for kind in oq.get_kinds(kind, R):
fname = hazard_curve_name(dstore, (key, fmt), kind, rlzs_assoc)
comment = _comment(rlzs_assoc, kind, oq.investigation_time)
if (key in ('hmaps', 'uhs') and oq.uniform_hazard_spectra or
oq.hazard_maps):
hmap = extract(dstore, 'hmaps?kind=' + kind)[kind]
if key == 'uhs' and oq.poes and oq.uniform_hazard_spectra:
uhs_curves = calc.make_uhs(hmap, info)
writers.write_csv(
fname, util.compose_arrays(sitemesh, uhs_curves),
comment=comment + ', checksum=%d' % checksum)
fnames.append(fname)
elif key == 'hmaps' and oq.poes and oq.hazard_maps:
fnames.extend(
export_hmaps_csv(ekey, fname, sitemesh,
hmap.flatten().view(hmap_dt),
comment + ', checksum=%d' % checksum))
elif key == 'hcurves':
hcurves = extract(dstore, 'hcurves?kind=' + kind)[kind]
fnames.extend(
export_hcurves_by_imt_csv(
ekey, kind, rlzs_assoc, fname, sitecol, hcurves, oq,
checksum))
return sorted(fnames) | python | def export_hcurves_csv(ekey, dstore):
"""
Exports the hazard curves into several .csv files
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = dstore['oqparam']
info = get_info(dstore)
rlzs_assoc = dstore['csm_info'].get_rlzs_assoc()
R = len(rlzs_assoc.realizations)
sitecol = dstore['sitecol']
sitemesh = get_mesh(sitecol)
key, kind, fmt = get_kkf(ekey)
fnames = []
checksum = dstore.get_attr('/', 'checksum32')
hmap_dt = oq.hmap_dt()
for kind in oq.get_kinds(kind, R):
fname = hazard_curve_name(dstore, (key, fmt), kind, rlzs_assoc)
comment = _comment(rlzs_assoc, kind, oq.investigation_time)
if (key in ('hmaps', 'uhs') and oq.uniform_hazard_spectra or
oq.hazard_maps):
hmap = extract(dstore, 'hmaps?kind=' + kind)[kind]
if key == 'uhs' and oq.poes and oq.uniform_hazard_spectra:
uhs_curves = calc.make_uhs(hmap, info)
writers.write_csv(
fname, util.compose_arrays(sitemesh, uhs_curves),
comment=comment + ', checksum=%d' % checksum)
fnames.append(fname)
elif key == 'hmaps' and oq.poes and oq.hazard_maps:
fnames.extend(
export_hmaps_csv(ekey, fname, sitemesh,
hmap.flatten().view(hmap_dt),
comment + ', checksum=%d' % checksum))
elif key == 'hcurves':
hcurves = extract(dstore, 'hcurves?kind=' + kind)[kind]
fnames.extend(
export_hcurves_by_imt_csv(
ekey, kind, rlzs_assoc, fname, sitecol, hcurves, oq,
checksum))
return sorted(fnames) | [
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3 | gem/oq-engine | openquake/calculators/export/hazard.py | save_disagg_to_csv | def save_disagg_to_csv(metadata, matrices):
"""
Save disaggregation matrices to multiple .csv files.
"""
skip_keys = ('Mag', 'Dist', 'Lon', 'Lat', 'Eps', 'TRT')
base_header = ','.join(
'%s=%s' % (key, value) for key, value in metadata.items()
if value is not None and key not in skip_keys)
for disag_tup, (poe, iml, matrix, fname) in matrices.items():
header = '%s,poe=%.7f,iml=%.7e\n' % (base_header, poe, iml)
if disag_tup == ('Mag', 'Lon', 'Lat'):
matrix = numpy.swapaxes(matrix, 0, 1)
matrix = numpy.swapaxes(matrix, 1, 2)
disag_tup = ('Lon', 'Lat', 'Mag')
axis = [metadata[v] for v in disag_tup]
header += ','.join(v for v in disag_tup)
header += ',poe'
# compute axis mid points
axis = [(ax[: -1] + ax[1:]) / 2. if ax.dtype == float
else ax for ax in axis]
values = None
if len(axis) == 1:
values = numpy.array([axis[0], matrix.flatten()]).T
else:
grids = numpy.meshgrid(*axis, indexing='ij')
values = [g.flatten() for g in grids]
values.append(matrix.flatten())
values = numpy.array(values).T
writers.write_csv(fname, values, comment=header, fmt='%.5E') | python | def save_disagg_to_csv(metadata, matrices):
"""
Save disaggregation matrices to multiple .csv files.
"""
skip_keys = ('Mag', 'Dist', 'Lon', 'Lat', 'Eps', 'TRT')
base_header = ','.join(
'%s=%s' % (key, value) for key, value in metadata.items()
if value is not None and key not in skip_keys)
for disag_tup, (poe, iml, matrix, fname) in matrices.items():
header = '%s,poe=%.7f,iml=%.7e\n' % (base_header, poe, iml)
if disag_tup == ('Mag', 'Lon', 'Lat'):
matrix = numpy.swapaxes(matrix, 0, 1)
matrix = numpy.swapaxes(matrix, 1, 2)
disag_tup = ('Lon', 'Lat', 'Mag')
axis = [metadata[v] for v in disag_tup]
header += ','.join(v for v in disag_tup)
header += ',poe'
# compute axis mid points
axis = [(ax[: -1] + ax[1:]) / 2. if ax.dtype == float
else ax for ax in axis]
values = None
if len(axis) == 1:
values = numpy.array([axis[0], matrix.flatten()]).T
else:
grids = numpy.meshgrid(*axis, indexing='ij')
values = [g.flatten() for g in grids]
values.append(matrix.flatten())
values = numpy.array(values).T
writers.write_csv(fname, values, comment=header, fmt='%.5E') | [
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4 | gem/oq-engine | openquake/hazardlib/gsim/bradley_2013b.py | Bradley2013bChchCBD._interp_function | def _interp_function(self, y_ip1, y_i, t_ip1, t_i, imt_per):
"""
Generic interpolation function used in equation 19 of 2013 report.
"""
return y_i + (y_ip1 - y_i) / (t_ip1 - t_i) * (imt_per - t_i) | python | def _interp_function(self, y_ip1, y_i, t_ip1, t_i, imt_per):
"""
Generic interpolation function used in equation 19 of 2013 report.
"""
return y_i + (y_ip1 - y_i) / (t_ip1 - t_i) * (imt_per - t_i) | [
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5 | gem/oq-engine | openquake/hazardlib/gsim/bradley_2013b.py | Bradley2013bChchCBD._get_SRF_tau | def _get_SRF_tau(self, imt_per):
"""
Table 6 and equation 19 of 2013 report.
"""
if imt_per < 1:
srf = 0.87
elif 1 <= imt_per < 5:
srf = self._interp_function(0.58, 0.87, 5, 1, imt_per)
elif 5 <= imt_per <= 10:
srf = 0.58
else:
srf = 1
return srf | python | def _get_SRF_tau(self, imt_per):
"""
Table 6 and equation 19 of 2013 report.
"""
if imt_per < 1:
srf = 0.87
elif 1 <= imt_per < 5:
srf = self._interp_function(0.58, 0.87, 5, 1, imt_per)
elif 5 <= imt_per <= 10:
srf = 0.58
else:
srf = 1
return srf | [
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6 | gem/oq-engine | openquake/hazardlib/gsim/bradley_2013b.py | Bradley2013bChchCBD._get_SRF_phi | def _get_SRF_phi(self, imt_per):
"""
Table 7 and equation 19 of 2013 report. NB change in notation,
2013 report calls this term 'sigma' but it is referred to here
as phi.
"""
if imt_per < 0.6:
srf = 0.8
elif 0.6 <= imt_per < 1:
srf = self._interp_function(0.7, 0.8, 1, 0.6, imt_per)
elif 1 <= imt_per <= 10:
srf = self._interp_function(0.6, 0.7, 10, 1, imt_per)
else:
srf = 1
return srf | python | def _get_SRF_phi(self, imt_per):
"""
Table 7 and equation 19 of 2013 report. NB change in notation,
2013 report calls this term 'sigma' but it is referred to here
as phi.
"""
if imt_per < 0.6:
srf = 0.8
elif 0.6 <= imt_per < 1:
srf = self._interp_function(0.7, 0.8, 1, 0.6, imt_per)
elif 1 <= imt_per <= 10:
srf = self._interp_function(0.6, 0.7, 10, 1, imt_per)
else:
srf = 1
return srf | [
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7 | gem/oq-engine | openquake/hazardlib/gsim/bradley_2013b.py | Bradley2013bChchCBD._get_SRF_sigma | def _get_SRF_sigma(self, imt_per):
"""
Table 8 and equation 19 of 2013 report. NB change in notation,
2013 report calls this term 'sigma_t' but it is referred to
here as sigma. Note that Table 8 is identical to Table 7 in
the 2013 report.
"""
if imt_per < 0.6:
srf = 0.8
elif 0.6 <= imt_per < 1:
srf = self._interp_function(0.7, 0.8, 1, 0.6, imt_per)
elif 1 <= imt_per <= 10:
srf = self._interp_function(0.6, 0.7, 10, 1, imt_per)
else:
srf = 1
return srf | python | def _get_SRF_sigma(self, imt_per):
"""
Table 8 and equation 19 of 2013 report. NB change in notation,
2013 report calls this term 'sigma_t' but it is referred to
here as sigma. Note that Table 8 is identical to Table 7 in
the 2013 report.
"""
if imt_per < 0.6:
srf = 0.8
elif 0.6 <= imt_per < 1:
srf = self._interp_function(0.7, 0.8, 1, 0.6, imt_per)
elif 1 <= imt_per <= 10:
srf = self._interp_function(0.6, 0.7, 10, 1, imt_per)
else:
srf = 1
return srf | [
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8 | gem/oq-engine | openquake/hazardlib/gsim/bradley_2013b.py | Bradley2013bChchCBD._get_dL2L | def _get_dL2L(self, imt_per):
"""
Table 3 and equation 19 of 2013 report.
"""
if imt_per < 0.18:
dL2L = -0.06
elif 0.18 <= imt_per < 0.35:
dL2L = self._interp_function(0.12, -0.06, 0.35, 0.18, imt_per)
elif 0.35 <= imt_per <= 10:
dL2L = self._interp_function(0.65, 0.12, 10, 0.35, imt_per)
else:
dL2L = 0
return dL2L | python | def _get_dL2L(self, imt_per):
"""
Table 3 and equation 19 of 2013 report.
"""
if imt_per < 0.18:
dL2L = -0.06
elif 0.18 <= imt_per < 0.35:
dL2L = self._interp_function(0.12, -0.06, 0.35, 0.18, imt_per)
elif 0.35 <= imt_per <= 10:
dL2L = self._interp_function(0.65, 0.12, 10, 0.35, imt_per)
else:
dL2L = 0
return dL2L | [
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9 | gem/oq-engine | openquake/hazardlib/gsim/bradley_2013b.py | Bradley2013bChchCBD._get_dS2S | def _get_dS2S(self, imt_per):
"""
Table 4 of 2013 report
"""
if imt_per == 0:
dS2S = 0.05
elif 0 < imt_per < 0.15:
dS2S = self._interp_function(-0.15, 0.05, 0.15, 0, imt_per)
elif 0.15 <= imt_per < 0.45:
dS2S = self._interp_function(0.4, -0.15, 0.45, 0.15, imt_per)
elif 0.45 <= imt_per < 3.2:
dS2S = 0.4
elif 3.2 <= imt_per < 5:
dS2S = self._interp_function(0.08, 0.4, 5, 3.2, imt_per)
elif 5 <= imt_per <= 10:
dS2S = 0.08
else:
dS2S = 0
return dS2S | python | def _get_dS2S(self, imt_per):
"""
Table 4 of 2013 report
"""
if imt_per == 0:
dS2S = 0.05
elif 0 < imt_per < 0.15:
dS2S = self._interp_function(-0.15, 0.05, 0.15, 0, imt_per)
elif 0.15 <= imt_per < 0.45:
dS2S = self._interp_function(0.4, -0.15, 0.45, 0.15, imt_per)
elif 0.45 <= imt_per < 3.2:
dS2S = 0.4
elif 3.2 <= imt_per < 5:
dS2S = self._interp_function(0.08, 0.4, 5, 3.2, imt_per)
elif 5 <= imt_per <= 10:
dS2S = 0.08
else:
dS2S = 0
return dS2S | [
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10 | gem/oq-engine | openquake/hazardlib/calc/filters.py | context | 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) | python | 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) | [
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11 | gem/oq-engine | openquake/hazardlib/calc/filters.py | IntegrationDistance.get_bounding_box | 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 | python | 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 | [
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:param trt: tectonic region type, possibly None
:param mag: magnitude, possibly None
:returns: min_lon, min_lat, max_lon, max_lat | [
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12 | gem/oq-engine | openquake/hazardlib/calc/filters.py | IntegrationDistance.get_affected_box | 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]) | python | 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]) | [
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13 | gem/oq-engine | openquake/hazardlib/calc/filters.py | SourceFilter.sitecol | def sitecol(self):
"""
Read the site collection from .filename and cache it
"""
if 'sitecol' in vars(self):
return self.__dict__['sitecol']
if self.filename is None or not os.path.exists(self.filename):
# case of nofilter/None sitecol
return
with hdf5.File(self.filename, 'r') as h5:
self.__dict__['sitecol'] = sc = h5.get('sitecol')
return sc | python | def sitecol(self):
"""
Read the site collection from .filename and cache it
"""
if 'sitecol' in vars(self):
return self.__dict__['sitecol']
if self.filename is None or not os.path.exists(self.filename):
# case of nofilter/None sitecol
return
with hdf5.File(self.filename, 'r') as h5:
self.__dict__['sitecol'] = sc = h5.get('sitecol')
return sc | [
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14 | gem/oq-engine | openquake/hazardlib/geo/surface/simple_fault.py | SimpleFaultSurface.hypocentre_patch_index | def hypocentre_patch_index(cls, hypocentre, rupture_top_edge,
upper_seismogenic_depth,
lower_seismogenic_depth, dip):
"""
This methods finds the index of the fault patch including
the hypocentre.
:param hypocentre:
:class:`~openquake.hazardlib.geo.point.Point` object
representing the location of hypocentre.
:param rupture_top_edge:
A instances of :class:`openquake.hazardlib.geo.line.Line`
representing the rupture surface's top edge.
:param upper_seismo_depth:
Minimum depth ruptures can reach, in km (i.e. depth
to fault's top edge).
:param lower_seismo_depth:
Maximum depth ruptures can reach, in km (i.e. depth
to fault's bottom edge).
:param dip:
Dip angle (i.e. angle between fault surface
and earth surface), in degrees.
:return:
An integer corresponding to the index of the fault patch which
contains the hypocentre.
"""
totaln_patch = len(rupture_top_edge)
indexlist = []
dist_list = []
for i, index in enumerate(range(1, totaln_patch)):
p0, p1, p2, p3 = cls.get_fault_patch_vertices(
rupture_top_edge, upper_seismogenic_depth,
lower_seismogenic_depth, dip, index_patch=index)
[normal, dist_to_plane] = get_plane_equation(p0, p1, p2,
hypocentre)
indexlist.append(index)
dist_list.append(dist_to_plane)
if numpy.allclose(dist_to_plane, 0., atol=25., rtol=0.):
return index
break
index = indexlist[numpy.argmin(dist_list)]
return index | python | def hypocentre_patch_index(cls, hypocentre, rupture_top_edge,
upper_seismogenic_depth,
lower_seismogenic_depth, dip):
"""
This methods finds the index of the fault patch including
the hypocentre.
:param hypocentre:
:class:`~openquake.hazardlib.geo.point.Point` object
representing the location of hypocentre.
:param rupture_top_edge:
A instances of :class:`openquake.hazardlib.geo.line.Line`
representing the rupture surface's top edge.
:param upper_seismo_depth:
Minimum depth ruptures can reach, in km (i.e. depth
to fault's top edge).
:param lower_seismo_depth:
Maximum depth ruptures can reach, in km (i.e. depth
to fault's bottom edge).
:param dip:
Dip angle (i.e. angle between fault surface
and earth surface), in degrees.
:return:
An integer corresponding to the index of the fault patch which
contains the hypocentre.
"""
totaln_patch = len(rupture_top_edge)
indexlist = []
dist_list = []
for i, index in enumerate(range(1, totaln_patch)):
p0, p1, p2, p3 = cls.get_fault_patch_vertices(
rupture_top_edge, upper_seismogenic_depth,
lower_seismogenic_depth, dip, index_patch=index)
[normal, dist_to_plane] = get_plane_equation(p0, p1, p2,
hypocentre)
indexlist.append(index)
dist_list.append(dist_to_plane)
if numpy.allclose(dist_to_plane, 0., atol=25., rtol=0.):
return index
break
index = indexlist[numpy.argmin(dist_list)]
return index | [
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:class:`~openquake.hazardlib.geo.point.Point` object
representing the location of hypocentre.
:param rupture_top_edge:
A instances of :class:`openquake.hazardlib.geo.line.Line`
representing the rupture surface's top edge.
:param upper_seismo_depth:
Minimum depth ruptures can reach, in km (i.e. depth
to fault's top edge).
:param lower_seismo_depth:
Maximum depth ruptures can reach, in km (i.e. depth
to fault's bottom edge).
:param dip:
Dip angle (i.e. angle between fault surface
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An integer corresponding to the index of the fault patch which
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15 | gem/oq-engine | openquake/hazardlib/geo/surface/simple_fault.py | SimpleFaultSurface.get_surface_vertexes | def get_surface_vertexes(cls, fault_trace,
upper_seismogenic_depth,
lower_seismogenic_depth, dip):
"""
Get surface main vertexes.
Parameters are the same as for :meth:`from_fault_data`, excluding
mesh spacing.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon`
describing the surface projection of the simple fault with
specified parameters.
"""
# Similar to :meth:`from_fault_data`, we just don't resample edges
dip_tan = math.tan(math.radians(dip))
hdist_top = upper_seismogenic_depth / dip_tan
hdist_bottom = lower_seismogenic_depth / dip_tan
strike = fault_trace[0].azimuth(fault_trace[-1])
azimuth = (strike + 90.0) % 360
# Collect coordinates of vertices on the top and bottom edge
lons = []
lats = []
for point in fault_trace.points:
top_edge_point = point.point_at(hdist_top, 0, azimuth)
bottom_edge_point = point.point_at(hdist_bottom, 0, azimuth)
lons.append(top_edge_point.longitude)
lats.append(top_edge_point.latitude)
lons.append(bottom_edge_point.longitude)
lats.append(bottom_edge_point.latitude)
lons = numpy.array(lons, float)
lats = numpy.array(lats, float)
return lons, lats | python | def get_surface_vertexes(cls, fault_trace,
upper_seismogenic_depth,
lower_seismogenic_depth, dip):
"""
Get surface main vertexes.
Parameters are the same as for :meth:`from_fault_data`, excluding
mesh spacing.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon`
describing the surface projection of the simple fault with
specified parameters.
"""
# Similar to :meth:`from_fault_data`, we just don't resample edges
dip_tan = math.tan(math.radians(dip))
hdist_top = upper_seismogenic_depth / dip_tan
hdist_bottom = lower_seismogenic_depth / dip_tan
strike = fault_trace[0].azimuth(fault_trace[-1])
azimuth = (strike + 90.0) % 360
# Collect coordinates of vertices on the top and bottom edge
lons = []
lats = []
for point in fault_trace.points:
top_edge_point = point.point_at(hdist_top, 0, azimuth)
bottom_edge_point = point.point_at(hdist_bottom, 0, azimuth)
lons.append(top_edge_point.longitude)
lats.append(top_edge_point.latitude)
lons.append(bottom_edge_point.longitude)
lats.append(bottom_edge_point.latitude)
lons = numpy.array(lons, float)
lats = numpy.array(lats, float)
return lons, lats | [
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16 | gem/oq-engine | openquake/hazardlib/geo/surface/simple_fault.py | SimpleFaultSurface.surface_projection_from_fault_data | def surface_projection_from_fault_data(cls, fault_trace,
upper_seismogenic_depth,
lower_seismogenic_depth, dip):
"""
Get a surface projection of the simple fault surface.
Parameters are the same as for :meth:`from_fault_data`, excluding
mesh spacing.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon`
describing the surface projection of the simple fault with
specified parameters.
"""
lons, lats = cls.get_surface_vertexes(fault_trace,
upper_seismogenic_depth,
lower_seismogenic_depth, dip)
return Mesh(lons, lats, depths=None).get_convex_hull() | python | def surface_projection_from_fault_data(cls, fault_trace,
upper_seismogenic_depth,
lower_seismogenic_depth, dip):
"""
Get a surface projection of the simple fault surface.
Parameters are the same as for :meth:`from_fault_data`, excluding
mesh spacing.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon`
describing the surface projection of the simple fault with
specified parameters.
"""
lons, lats = cls.get_surface_vertexes(fault_trace,
upper_seismogenic_depth,
lower_seismogenic_depth, dip)
return Mesh(lons, lats, depths=None).get_convex_hull() | [
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17 | gem/oq-engine | openquake/hazardlib/gsim/zhao_2006.py | ZhaoEtAl2006Asc._compute_distance_term | def _compute_distance_term(self, C, mag, rrup):
"""
Compute second and third terms in equation 1, p. 901.
"""
term1 = C['b'] * rrup
term2 = - np.log(rrup + C['c'] * np.exp(C['d'] * mag))
return term1 + term2 | python | def _compute_distance_term(self, C, mag, rrup):
"""
Compute second and third terms in equation 1, p. 901.
"""
term1 = C['b'] * rrup
term2 = - np.log(rrup + C['c'] * np.exp(C['d'] * mag))
return term1 + term2 | [
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18 | gem/oq-engine | openquake/hazardlib/gsim/zhao_2006.py | ZhaoEtAl2006Asc._compute_focal_depth_term | def _compute_focal_depth_term(self, C, hypo_depth):
"""
Compute fourth term in equation 1, p. 901.
"""
# p. 901. "(i.e, depth is capped at 125 km)".
focal_depth = hypo_depth
if focal_depth > 125.0:
focal_depth = 125.0
# p. 902. "We used the value of 15 km for the
# depth coefficient hc ...".
hc = 15.0
# p. 901. "When h is larger than hc, the depth terms takes
# effect ...". The next sentence specifies h>=hc.
return float(focal_depth >= hc) * C['e'] * (focal_depth - hc) | python | def _compute_focal_depth_term(self, C, hypo_depth):
"""
Compute fourth term in equation 1, p. 901.
"""
# p. 901. "(i.e, depth is capped at 125 km)".
focal_depth = hypo_depth
if focal_depth > 125.0:
focal_depth = 125.0
# p. 902. "We used the value of 15 km for the
# depth coefficient hc ...".
hc = 15.0
# p. 901. "When h is larger than hc, the depth terms takes
# effect ...". The next sentence specifies h>=hc.
return float(focal_depth >= hc) * C['e'] * (focal_depth - hc) | [
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19 | gem/oq-engine | openquake/hazardlib/gsim/zhao_2006.py | ZhaoEtAl2006Asc._compute_site_class_term | def _compute_site_class_term(self, C, vs30):
"""
Compute nine-th term in equation 1, p. 901.
"""
# map vs30 value to site class, see table 2, p. 901.
site_term = np.zeros(len(vs30))
# hard rock
site_term[vs30 > 1100.0] = C['CH']
# rock
site_term[(vs30 > 600) & (vs30 <= 1100)] = C['C1']
# hard soil
site_term[(vs30 > 300) & (vs30 <= 600)] = C['C2']
# medium soil
site_term[(vs30 > 200) & (vs30 <= 300)] = C['C3']
# soft soil
site_term[vs30 <= 200] = C['C4']
return site_term | python | def _compute_site_class_term(self, C, vs30):
"""
Compute nine-th term in equation 1, p. 901.
"""
# map vs30 value to site class, see table 2, p. 901.
site_term = np.zeros(len(vs30))
# hard rock
site_term[vs30 > 1100.0] = C['CH']
# rock
site_term[(vs30 > 600) & (vs30 <= 1100)] = C['C1']
# hard soil
site_term[(vs30 > 300) & (vs30 <= 600)] = C['C2']
# medium soil
site_term[(vs30 > 200) & (vs30 <= 300)] = C['C3']
# soft soil
site_term[vs30 <= 200] = C['C4']
return site_term | [
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20 | gem/oq-engine | openquake/hazardlib/gsim/zhao_2006.py | ZhaoEtAl2006Asc._compute_magnitude_squared_term | def _compute_magnitude_squared_term(self, P, M, Q, W, mag):
"""
Compute magnitude squared term, equation 5, p. 909.
"""
return P * (mag - M) + Q * (mag - M) ** 2 + W | python | def _compute_magnitude_squared_term(self, P, M, Q, W, mag):
"""
Compute magnitude squared term, equation 5, p. 909.
"""
return P * (mag - M) + Q * (mag - M) ** 2 + W | [
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21 | gem/oq-engine | openquake/hazardlib/gsim/zhao_2006.py | ZhaoEtAl2006SSlab._compute_slab_correction_term | def _compute_slab_correction_term(self, C, rrup):
"""
Compute path modification term for slab events, that is
the 8-th term in equation 1, p. 901.
"""
slab_term = C['SSL'] * np.log(rrup)
return slab_term | python | def _compute_slab_correction_term(self, C, rrup):
"""
Compute path modification term for slab events, that is
the 8-th term in equation 1, p. 901.
"""
slab_term = C['SSL'] * np.log(rrup)
return slab_term | [
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22 | gem/oq-engine | openquake/hazardlib/gsim/zhao_2006.py | ZhaoEtAl2006AscSGS.get_mean_and_stddevs | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Using a minimum distance of 5km for the calculation.
"""
dists_mod = copy.deepcopy(dists)
dists_mod.rrup[dists.rrup <= 5.] = 5.
return super().get_mean_and_stddevs(
sites, rup, dists_mod, imt, stddev_types) | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Using a minimum distance of 5km for the calculation.
"""
dists_mod = copy.deepcopy(dists)
dists_mod.rrup[dists.rrup <= 5.] = 5.
return super().get_mean_and_stddevs(
sites, rup, dists_mod, imt, stddev_types) | [
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23 | gem/oq-engine | openquake/engine/utils/__init__.py | confirm | def confirm(prompt):
"""
Ask for confirmation, given a ``prompt`` and return a boolean value.
"""
while True:
try:
answer = input(prompt)
except KeyboardInterrupt:
# the user presses ctrl+c, just say 'no'
return False
answer = answer.strip().lower()
if answer not in ('y', 'n'):
print('Please enter y or n')
continue
return answer == 'y' | python | def confirm(prompt):
"""
Ask for confirmation, given a ``prompt`` and return a boolean value.
"""
while True:
try:
answer = input(prompt)
except KeyboardInterrupt:
# the user presses ctrl+c, just say 'no'
return False
answer = answer.strip().lower()
if answer not in ('y', 'n'):
print('Please enter y or n')
continue
return answer == 'y' | [
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24 | gem/oq-engine | openquake/risklib/asset.py | Exposure._csv_header | def _csv_header(self):
"""
Extract the expected CSV header from the exposure metadata
"""
fields = ['id', 'number', 'taxonomy', 'lon', 'lat']
for name in self.cost_types['name']:
fields.append(name)
if 'per_area' in self.cost_types['type']:
fields.append('area')
if self.occupancy_periods:
fields.extend(self.occupancy_periods.split())
fields.extend(self.tagcol.tagnames)
return set(fields) | python | def _csv_header(self):
"""
Extract the expected CSV header from the exposure metadata
"""
fields = ['id', 'number', 'taxonomy', 'lon', 'lat']
for name in self.cost_types['name']:
fields.append(name)
if 'per_area' in self.cost_types['type']:
fields.append('area')
if self.occupancy_periods:
fields.extend(self.occupancy_periods.split())
fields.extend(self.tagcol.tagnames)
return set(fields) | [
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25 | gem/oq-engine | openquake/risklib/riskmodels.py | build_vf_node | def build_vf_node(vf):
"""
Convert a VulnerabilityFunction object into a Node suitable
for XML conversion.
"""
nodes = [Node('imls', {'imt': vf.imt}, vf.imls),
Node('meanLRs', {}, vf.mean_loss_ratios),
Node('covLRs', {}, vf.covs)]
return Node(
'vulnerabilityFunction',
{'id': vf.id, 'dist': vf.distribution_name}, nodes=nodes) | python | def build_vf_node(vf):
"""
Convert a VulnerabilityFunction object into a Node suitable
for XML conversion.
"""
nodes = [Node('imls', {'imt': vf.imt}, vf.imls),
Node('meanLRs', {}, vf.mean_loss_ratios),
Node('covLRs', {}, vf.covs)]
return Node(
'vulnerabilityFunction',
{'id': vf.id, 'dist': vf.distribution_name}, nodes=nodes) | [
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26 | gem/oq-engine | openquake/risklib/riskmodels.py | get_riskmodel | def get_riskmodel(taxonomy, oqparam, **extra):
"""
Return an instance of the correct riskmodel class, depending on the
attribute `calculation_mode` of the object `oqparam`.
:param taxonomy:
a taxonomy string
:param oqparam:
an object containing the parameters needed by the riskmodel class
:param extra:
extra parameters to pass to the riskmodel class
"""
riskmodel_class = registry[oqparam.calculation_mode]
# arguments needed to instantiate the riskmodel class
argnames = inspect.getfullargspec(riskmodel_class.__init__).args[3:]
# arguments extracted from oqparam
known_args = set(name for name, value in
inspect.getmembers(oqparam.__class__)
if isinstance(value, valid.Param))
all_args = {}
for argname in argnames:
if argname in known_args:
all_args[argname] = getattr(oqparam, argname)
if 'hazard_imtls' in argnames: # special case
all_args['hazard_imtls'] = oqparam.imtls
all_args.update(extra)
missing = set(argnames) - set(all_args)
if missing:
raise TypeError('Missing parameter: %s' % ', '.join(missing))
return riskmodel_class(taxonomy, **all_args) | python | def get_riskmodel(taxonomy, oqparam, **extra):
"""
Return an instance of the correct riskmodel class, depending on the
attribute `calculation_mode` of the object `oqparam`.
:param taxonomy:
a taxonomy string
:param oqparam:
an object containing the parameters needed by the riskmodel class
:param extra:
extra parameters to pass to the riskmodel class
"""
riskmodel_class = registry[oqparam.calculation_mode]
# arguments needed to instantiate the riskmodel class
argnames = inspect.getfullargspec(riskmodel_class.__init__).args[3:]
# arguments extracted from oqparam
known_args = set(name for name, value in
inspect.getmembers(oqparam.__class__)
if isinstance(value, valid.Param))
all_args = {}
for argname in argnames:
if argname in known_args:
all_args[argname] = getattr(oqparam, argname)
if 'hazard_imtls' in argnames: # special case
all_args['hazard_imtls'] = oqparam.imtls
all_args.update(extra)
missing = set(argnames) - set(all_args)
if missing:
raise TypeError('Missing parameter: %s' % ', '.join(missing))
return riskmodel_class(taxonomy, **all_args) | [
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:param taxonomy:
a taxonomy string
:param oqparam:
an object containing the parameters needed by the riskmodel class
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27 | gem/oq-engine | openquake/hmtk/plotting/beachball.py | Beachball | def Beachball(fm, linewidth=2, facecolor='b', bgcolor='w', edgecolor='k',
alpha=1.0, xy=(0, 0), width=200, size=100, nofill=False,
zorder=100, outfile=None, format=None, fig=None):
"""
Draws a beach ball diagram of an earthquake focal mechanism.
S1, D1, and R1, the strike, dip and rake of one of the focal planes, can
be vectors of multiple focal mechanisms.
:param fm: Focal mechanism that is either number of mechanisms (NM) by 3
(strike, dip, and rake) or NM x 6 (M11, M22, M33, M12, M13, M23 - the
six independent components of the moment tensor, where the coordinate
system is 1,2,3 = Up,South,East which equals r,theta,phi). The strike
is of the first plane, clockwise relative to north.
The dip is of the first plane, defined clockwise and perpendicular to
strike, relative to horizontal such that 0 is horizontal and 90 is
vertical. The rake is of the first focal plane solution. 90 moves the
hanging wall up-dip (thrust), 0 moves it in the strike direction
(left-lateral), -90 moves it down-dip (normal), and 180 moves it
opposite to strike (right-lateral).
:param facecolor: Color to use for quadrants of tension; can be a string,
e.g. ``'r'``, ``'b'`` or three component color vector, [R G B].
Defaults to ``'b'`` (blue).
:param bgcolor: The background color. Defaults to ``'w'`` (white).
:param edgecolor: Color of the edges. Defaults to ``'k'`` (black).
:param alpha: The alpha level of the beach ball. Defaults to ``1.0``
(opaque).
:param xy: Origin position of the beach ball as tuple. Defaults to
``(0, 0)``.
:type width: int
:param width: Symbol size of beach ball. Defaults to ``200``.
:param size: Controls the number of interpolation points for the
curves. Minimum is automatically set to ``100``.
:param nofill: Do not fill the beach ball, but only plot the planes.
:param zorder: Set zorder. Artists with lower zorder values are drawn
first.
:param outfile: Output file string. Also used to automatically
determine the output format. Supported file formats depend on your
matplotlib backend. Most backends support png, pdf, ps, eps and
svg. Defaults to ``None``.
:param format: Format of the graph picture. If no format is given the
outfile parameter will be used to try to automatically determine
the output format. If no format is found it defaults to png output.
If no outfile is specified but a format is, than a binary
imagestring will be returned.
Defaults to ``None``.
:param fig: Give an existing figure instance to plot into. New Figure if
set to ``None``.
"""
plot_width = width * 0.95
# plot the figure
if not fig:
fig = plt.figure(figsize=(3, 3), dpi=100)
fig.subplots_adjust(left=0, bottom=0, right=1, top=1)
fig.set_figheight(width // 100)
fig.set_figwidth(width // 100)
ax = fig.add_subplot(111, aspect='equal')
# hide axes + ticks
ax.axison = False
# plot the collection
collection = Beach(fm, linewidth=linewidth, facecolor=facecolor,
edgecolor=edgecolor, bgcolor=bgcolor,
alpha=alpha, nofill=nofill, xy=xy,
width=plot_width, size=size, zorder=zorder)
ax.add_collection(collection)
ax.autoscale_view(tight=False, scalex=True, scaley=True)
# export
if outfile:
if format:
fig.savefig(outfile, dpi=100, transparent=True, format=format)
else:
fig.savefig(outfile, dpi=100, transparent=True)
elif format and not outfile:
imgdata = compatibility.BytesIO()
fig.savefig(imgdata, format=format, dpi=100, transparent=True)
imgdata.seek(0)
return imgdata.read()
else:
plt.show()
return fig | python | def Beachball(fm, linewidth=2, facecolor='b', bgcolor='w', edgecolor='k',
alpha=1.0, xy=(0, 0), width=200, size=100, nofill=False,
zorder=100, outfile=None, format=None, fig=None):
"""
Draws a beach ball diagram of an earthquake focal mechanism.
S1, D1, and R1, the strike, dip and rake of one of the focal planes, can
be vectors of multiple focal mechanisms.
:param fm: Focal mechanism that is either number of mechanisms (NM) by 3
(strike, dip, and rake) or NM x 6 (M11, M22, M33, M12, M13, M23 - the
six independent components of the moment tensor, where the coordinate
system is 1,2,3 = Up,South,East which equals r,theta,phi). The strike
is of the first plane, clockwise relative to north.
The dip is of the first plane, defined clockwise and perpendicular to
strike, relative to horizontal such that 0 is horizontal and 90 is
vertical. The rake is of the first focal plane solution. 90 moves the
hanging wall up-dip (thrust), 0 moves it in the strike direction
(left-lateral), -90 moves it down-dip (normal), and 180 moves it
opposite to strike (right-lateral).
:param facecolor: Color to use for quadrants of tension; can be a string,
e.g. ``'r'``, ``'b'`` or three component color vector, [R G B].
Defaults to ``'b'`` (blue).
:param bgcolor: The background color. Defaults to ``'w'`` (white).
:param edgecolor: Color of the edges. Defaults to ``'k'`` (black).
:param alpha: The alpha level of the beach ball. Defaults to ``1.0``
(opaque).
:param xy: Origin position of the beach ball as tuple. Defaults to
``(0, 0)``.
:type width: int
:param width: Symbol size of beach ball. Defaults to ``200``.
:param size: Controls the number of interpolation points for the
curves. Minimum is automatically set to ``100``.
:param nofill: Do not fill the beach ball, but only plot the planes.
:param zorder: Set zorder. Artists with lower zorder values are drawn
first.
:param outfile: Output file string. Also used to automatically
determine the output format. Supported file formats depend on your
matplotlib backend. Most backends support png, pdf, ps, eps and
svg. Defaults to ``None``.
:param format: Format of the graph picture. If no format is given the
outfile parameter will be used to try to automatically determine
the output format. If no format is found it defaults to png output.
If no outfile is specified but a format is, than a binary
imagestring will be returned.
Defaults to ``None``.
:param fig: Give an existing figure instance to plot into. New Figure if
set to ``None``.
"""
plot_width = width * 0.95
# plot the figure
if not fig:
fig = plt.figure(figsize=(3, 3), dpi=100)
fig.subplots_adjust(left=0, bottom=0, right=1, top=1)
fig.set_figheight(width // 100)
fig.set_figwidth(width // 100)
ax = fig.add_subplot(111, aspect='equal')
# hide axes + ticks
ax.axison = False
# plot the collection
collection = Beach(fm, linewidth=linewidth, facecolor=facecolor,
edgecolor=edgecolor, bgcolor=bgcolor,
alpha=alpha, nofill=nofill, xy=xy,
width=plot_width, size=size, zorder=zorder)
ax.add_collection(collection)
ax.autoscale_view(tight=False, scalex=True, scaley=True)
# export
if outfile:
if format:
fig.savefig(outfile, dpi=100, transparent=True, format=format)
else:
fig.savefig(outfile, dpi=100, transparent=True)
elif format and not outfile:
imgdata = compatibility.BytesIO()
fig.savefig(imgdata, format=format, dpi=100, transparent=True)
imgdata.seek(0)
return imgdata.read()
else:
plt.show()
return fig | [
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S1, D1, and R1, the strike, dip and rake of one of the focal planes, can
be vectors of multiple focal mechanisms.
:param fm: Focal mechanism that is either number of mechanisms (NM) by 3
(strike, dip, and rake) or NM x 6 (M11, M22, M33, M12, M13, M23 - the
six independent components of the moment tensor, where the coordinate
system is 1,2,3 = Up,South,East which equals r,theta,phi). The strike
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strike, relative to horizontal such that 0 is horizontal and 90 is
vertical. The rake is of the first focal plane solution. 90 moves the
hanging wall up-dip (thrust), 0 moves it in the strike direction
(left-lateral), -90 moves it down-dip (normal), and 180 moves it
opposite to strike (right-lateral).
:param facecolor: Color to use for quadrants of tension; can be a string,
e.g. ``'r'``, ``'b'`` or three component color vector, [R G B].
Defaults to ``'b'`` (blue).
:param bgcolor: The background color. Defaults to ``'w'`` (white).
:param edgecolor: Color of the edges. Defaults to ``'k'`` (black).
:param alpha: The alpha level of the beach ball. Defaults to ``1.0``
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:param xy: Origin position of the beach ball as tuple. Defaults to
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:type width: int
:param width: Symbol size of beach ball. Defaults to ``200``.
:param size: Controls the number of interpolation points for the
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:param nofill: Do not fill the beach ball, but only plot the planes.
:param zorder: Set zorder. Artists with lower zorder values are drawn
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:param outfile: Output file string. Also used to automatically
determine the output format. Supported file formats depend on your
matplotlib backend. Most backends support png, pdf, ps, eps and
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:param format: Format of the graph picture. If no format is given the
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the output format. If no format is found it defaults to png output.
If no outfile is specified but a format is, than a binary
imagestring will be returned.
Defaults to ``None``.
:param fig: Give an existing figure instance to plot into. New Figure if
set to ``None``. | [
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28 | gem/oq-engine | openquake/hmtk/plotting/beachball.py | StrikeDip | def StrikeDip(n, e, u):
"""
Finds strike and dip of plane given normal vector having components n, e,
and u.
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd.
"""
r2d = 180 / np.pi
if u < 0:
n = -n
e = -e
u = -u
strike = np.arctan2(e, n) * r2d
strike = strike - 90
while strike >= 360:
strike = strike - 360
while strike < 0:
strike = strike + 360
x = np.sqrt(np.power(n, 2) + np.power(e, 2))
dip = np.arctan2(x, u) * r2d
return (strike, dip) | python | def StrikeDip(n, e, u):
"""
Finds strike and dip of plane given normal vector having components n, e,
and u.
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd.
"""
r2d = 180 / np.pi
if u < 0:
n = -n
e = -e
u = -u
strike = np.arctan2(e, n) * r2d
strike = strike - 90
while strike >= 360:
strike = strike - 360
while strike < 0:
strike = strike + 360
x = np.sqrt(np.power(n, 2) + np.power(e, 2))
dip = np.arctan2(x, u) * r2d
return (strike, dip) | [
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29 | gem/oq-engine | openquake/hmtk/plotting/beachball.py | AuxPlane | def AuxPlane(s1, d1, r1):
"""
Get Strike and dip of second plane.
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd.
"""
r2d = 180 / np.pi
z = (s1 + 90) / r2d
z2 = d1 / r2d
z3 = r1 / r2d
# slick vector in plane 1
sl1 = -np.cos(z3) * np.cos(z) - np.sin(z3) * np.sin(z) * np.cos(z2)
sl2 = np.cos(z3) * np.sin(z) - np.sin(z3) * np.cos(z) * np.cos(z2)
sl3 = np.sin(z3) * np.sin(z2)
(strike, dip) = StrikeDip(sl2, sl1, sl3)
n1 = np.sin(z) * np.sin(z2) # normal vector to plane 1
n2 = np.cos(z) * np.sin(z2)
h1 = -sl2 # strike vector of plane 2
h2 = sl1
# note h3=0 always so we leave it out
# n3 = np.cos(z2)
z = h1 * n1 + h2 * n2
z = z / np.sqrt(h1 * h1 + h2 * h2)
z = np.arccos(z)
rake = 0
if sl3 > 0:
rake = z * r2d
if sl3 <= 0:
rake = -z * r2d
return (strike, dip, rake) | python | def AuxPlane(s1, d1, r1):
"""
Get Strike and dip of second plane.
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd.
"""
r2d = 180 / np.pi
z = (s1 + 90) / r2d
z2 = d1 / r2d
z3 = r1 / r2d
# slick vector in plane 1
sl1 = -np.cos(z3) * np.cos(z) - np.sin(z3) * np.sin(z) * np.cos(z2)
sl2 = np.cos(z3) * np.sin(z) - np.sin(z3) * np.cos(z) * np.cos(z2)
sl3 = np.sin(z3) * np.sin(z2)
(strike, dip) = StrikeDip(sl2, sl1, sl3)
n1 = np.sin(z) * np.sin(z2) # normal vector to plane 1
n2 = np.cos(z) * np.sin(z2)
h1 = -sl2 # strike vector of plane 2
h2 = sl1
# note h3=0 always so we leave it out
# n3 = np.cos(z2)
z = h1 * n1 + h2 * n2
z = z / np.sqrt(h1 * h1 + h2 * h2)
z = np.arccos(z)
rake = 0
if sl3 > 0:
rake = z * r2d
if sl3 <= 0:
rake = -z * r2d
return (strike, dip, rake) | [
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30 | gem/oq-engine | openquake/hmtk/plotting/beachball.py | MT2Plane | def MT2Plane(mt):
"""
Calculates a nodal plane of a given moment tensor.
:param mt: :class:`~MomentTensor`
:return: :class:`~NodalPlane`
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd.
"""
(d, v) = np.linalg.eig(mt.mt)
D = np.array([d[1], d[0], d[2]])
V = np.array([[v[1, 1], -v[1, 0], -v[1, 2]],
[v[2, 1], -v[2, 0], -v[2, 2]],
[-v[0, 1], v[0, 0], v[0, 2]]])
IMAX = D.argmax()
IMIN = D.argmin()
AE = (V[:, IMAX] + V[:, IMIN]) / np.sqrt(2.0)
AN = (V[:, IMAX] - V[:, IMIN]) / np.sqrt(2.0)
AER = np.sqrt(np.power(AE[0], 2) + np.power(AE[1], 2) + np.power(AE[2], 2))
ANR = np.sqrt(np.power(AN[0], 2) + np.power(AN[1], 2) + np.power(AN[2], 2))
AE = AE / AER
if not ANR:
AN = np.array([np.nan, np.nan, np.nan])
else:
AN = AN / ANR
if AN[2] <= 0.:
AN1 = AN
AE1 = AE
else:
AN1 = -AN
AE1 = -AE
(ft, fd, fl) = TDL(AN1, AE1)
return NodalPlane(360 - ft, fd, 180 - fl) | python | def MT2Plane(mt):
"""
Calculates a nodal plane of a given moment tensor.
:param mt: :class:`~MomentTensor`
:return: :class:`~NodalPlane`
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd.
"""
(d, v) = np.linalg.eig(mt.mt)
D = np.array([d[1], d[0], d[2]])
V = np.array([[v[1, 1], -v[1, 0], -v[1, 2]],
[v[2, 1], -v[2, 0], -v[2, 2]],
[-v[0, 1], v[0, 0], v[0, 2]]])
IMAX = D.argmax()
IMIN = D.argmin()
AE = (V[:, IMAX] + V[:, IMIN]) / np.sqrt(2.0)
AN = (V[:, IMAX] - V[:, IMIN]) / np.sqrt(2.0)
AER = np.sqrt(np.power(AE[0], 2) + np.power(AE[1], 2) + np.power(AE[2], 2))
ANR = np.sqrt(np.power(AN[0], 2) + np.power(AN[1], 2) + np.power(AN[2], 2))
AE = AE / AER
if not ANR:
AN = np.array([np.nan, np.nan, np.nan])
else:
AN = AN / ANR
if AN[2] <= 0.:
AN1 = AN
AE1 = AE
else:
AN1 = -AN
AE1 = -AE
(ft, fd, fl) = TDL(AN1, AE1)
return NodalPlane(360 - ft, fd, 180 - fl) | [
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:param mt: :class:`~MomentTensor`
:return: :class:`~NodalPlane`
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd. | [
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31 | gem/oq-engine | openquake/hmtk/plotting/beachball.py | TDL | def TDL(AN, BN):
"""
Helper function for MT2Plane.
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd.
"""
XN = AN[0]
YN = AN[1]
ZN = AN[2]
XE = BN[0]
YE = BN[1]
ZE = BN[2]
AAA = 1.0 / (1000000)
CON = 57.2957795
if np.fabs(ZN) < AAA:
FD = 90.
AXN = np.fabs(XN)
if AXN > 1.0:
AXN = 1.0
FT = np.arcsin(AXN) * CON
ST = -XN
CT = YN
if ST >= 0. and CT < 0:
FT = 180. - FT
if ST < 0. and CT <= 0:
FT = 180. + FT
if ST < 0. and CT > 0:
FT = 360. - FT
FL = np.arcsin(abs(ZE)) * CON
SL = -ZE
if np.fabs(XN) < AAA:
CL = XE / YN
else:
CL = -YE / XN
if SL >= 0. and CL < 0:
FL = 180. - FL
if SL < 0. and CL <= 0:
FL = FL - 180.
if SL < 0. and CL > 0:
FL = -FL
else:
if - ZN > 1.0:
ZN = -1.0
FDH = np.arccos(-ZN)
FD = FDH * CON
SD = np.sin(FDH)
if SD == 0:
return
ST = -XN / SD
CT = YN / SD
SX = np.fabs(ST)
if SX > 1.0:
SX = 1.0
FT = np.arcsin(SX) * CON
if ST >= 0. and CT < 0:
FT = 180. - FT
if ST < 0. and CT <= 0:
FT = 180. + FT
if ST < 0. and CT > 0:
FT = 360. - FT
SL = -ZE / SD
SX = np.fabs(SL)
if SX > 1.0:
SX = 1.0
FL = np.arcsin(SX) * CON
if ST == 0:
CL = XE / CT
else:
XXX = YN * ZN * ZE / SD / SD + YE
CL = -SD * XXX / XN
if CT == 0:
CL = YE / ST
if SL >= 0. and CL < 0:
FL = 180. - FL
if SL < 0. and CL <= 0:
FL = FL - 180.
if SL < 0. and CL > 0:
FL = -FL
return (FT, FD, FL) | python | def TDL(AN, BN):
"""
Helper function for MT2Plane.
Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd.
"""
XN = AN[0]
YN = AN[1]
ZN = AN[2]
XE = BN[0]
YE = BN[1]
ZE = BN[2]
AAA = 1.0 / (1000000)
CON = 57.2957795
if np.fabs(ZN) < AAA:
FD = 90.
AXN = np.fabs(XN)
if AXN > 1.0:
AXN = 1.0
FT = np.arcsin(AXN) * CON
ST = -XN
CT = YN
if ST >= 0. and CT < 0:
FT = 180. - FT
if ST < 0. and CT <= 0:
FT = 180. + FT
if ST < 0. and CT > 0:
FT = 360. - FT
FL = np.arcsin(abs(ZE)) * CON
SL = -ZE
if np.fabs(XN) < AAA:
CL = XE / YN
else:
CL = -YE / XN
if SL >= 0. and CL < 0:
FL = 180. - FL
if SL < 0. and CL <= 0:
FL = FL - 180.
if SL < 0. and CL > 0:
FL = -FL
else:
if - ZN > 1.0:
ZN = -1.0
FDH = np.arccos(-ZN)
FD = FDH * CON
SD = np.sin(FDH)
if SD == 0:
return
ST = -XN / SD
CT = YN / SD
SX = np.fabs(ST)
if SX > 1.0:
SX = 1.0
FT = np.arcsin(SX) * CON
if ST >= 0. and CT < 0:
FT = 180. - FT
if ST < 0. and CT <= 0:
FT = 180. + FT
if ST < 0. and CT > 0:
FT = 360. - FT
SL = -ZE / SD
SX = np.fabs(SL)
if SX > 1.0:
SX = 1.0
FL = np.arcsin(SX) * CON
if ST == 0:
CL = XE / CT
else:
XXX = YN * ZN * ZE / SD / SD + YE
CL = -SD * XXX / XN
if CT == 0:
CL = YE / ST
if SL >= 0. and CL < 0:
FL = 180. - FL
if SL < 0. and CL <= 0:
FL = FL - 180.
if SL < 0. and CL > 0:
FL = -FL
return (FT, FD, FL) | [
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Adapted from MATLAB script
`bb.m <http://www.ceri.memphis.edu/people/olboyd/Software/Software.html>`_
written by Andy Michael and Oliver Boyd. | [
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32 | gem/oq-engine | openquake/hmtk/plotting/beachball.py | MT2Axes | def MT2Axes(mt):
"""
Calculates the principal axes of a given moment tensor.
:param mt: :class:`~MomentTensor`
:return: tuple of :class:`~PrincipalAxis` T, N and P
Adapted from ps_tensor / utilmeca.c /
`Generic Mapping Tools (GMT) <http://gmt.soest.hawaii.edu>`_.
"""
(D, V) = np.linalg.eigh(mt.mt)
pl = np.arcsin(-V[0])
az = np.arctan2(V[2], -V[1])
for i in range(0, 3):
if pl[i] <= 0:
pl[i] = -pl[i]
az[i] += np.pi
if az[i] < 0:
az[i] += 2 * np.pi
if az[i] > 2 * np.pi:
az[i] -= 2 * np.pi
pl *= R2D
az *= R2D
T = PrincipalAxis(D[2], az[2], pl[2])
N = PrincipalAxis(D[1], az[1], pl[1])
P = PrincipalAxis(D[0], az[0], pl[0])
return (T, N, P) | python | def MT2Axes(mt):
"""
Calculates the principal axes of a given moment tensor.
:param mt: :class:`~MomentTensor`
:return: tuple of :class:`~PrincipalAxis` T, N and P
Adapted from ps_tensor / utilmeca.c /
`Generic Mapping Tools (GMT) <http://gmt.soest.hawaii.edu>`_.
"""
(D, V) = np.linalg.eigh(mt.mt)
pl = np.arcsin(-V[0])
az = np.arctan2(V[2], -V[1])
for i in range(0, 3):
if pl[i] <= 0:
pl[i] = -pl[i]
az[i] += np.pi
if az[i] < 0:
az[i] += 2 * np.pi
if az[i] > 2 * np.pi:
az[i] -= 2 * np.pi
pl *= R2D
az *= R2D
T = PrincipalAxis(D[2], az[2], pl[2])
N = PrincipalAxis(D[1], az[1], pl[1])
P = PrincipalAxis(D[0], az[0], pl[0])
return (T, N, P) | [
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33 | gem/oq-engine | openquake/hmtk/strain/strain_utils.py | tapered_gutenberg_richter_cdf | def tapered_gutenberg_richter_cdf(moment, moment_threshold, beta,
corner_moment):
'''
Tapered Gutenberg Richter Cumulative Density Function
:param float or numpy.ndarray moment:
Moment for calculation of rate
:param float or numpy.ndarray moment_threshold:
Threshold Moment of the distribution (moment rate essentially!)
:param float beta:
Beta value (b * ln(10.)) of the Tapered Gutenberg-Richter Function
:param float corner_momnet:
Corner moment of the Tapered Gutenberg-Richter Function
:returns:
Cumulative probability of moment release > moment
'''
cdf = np.exp((moment_threshold - moment) / corner_moment)
return ((moment / moment_threshold) ** (-beta)) * cdf | python | def tapered_gutenberg_richter_cdf(moment, moment_threshold, beta,
corner_moment):
'''
Tapered Gutenberg Richter Cumulative Density Function
:param float or numpy.ndarray moment:
Moment for calculation of rate
:param float or numpy.ndarray moment_threshold:
Threshold Moment of the distribution (moment rate essentially!)
:param float beta:
Beta value (b * ln(10.)) of the Tapered Gutenberg-Richter Function
:param float corner_momnet:
Corner moment of the Tapered Gutenberg-Richter Function
:returns:
Cumulative probability of moment release > moment
'''
cdf = np.exp((moment_threshold - moment) / corner_moment)
return ((moment / moment_threshold) ** (-beta)) * cdf | [
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34 | gem/oq-engine | openquake/hmtk/strain/strain_utils.py | tapered_gutenberg_richter_pdf | def tapered_gutenberg_richter_pdf(moment, moment_threshold, beta,
corner_moment):
'''
Tapered Gutenberg-Richter Probability Density Function
:param float or numpy.ndarray moment:
Moment for calculation of rate
:param float or numpy.ndarray moment_threshold:
Threshold Moment of the distribution (moment rate essentially!)
:param float beta:
Beta value (b * ln(10.)) of the Tapered Gutenberg-Richter Function
:param float corner_momnet:
Corner moment of the Tapered Gutenberg-Richter Function
:returns:
Absolute probability of moment release > moment
'''
return ((beta / moment + 1. / corner_moment) *
tapered_gutenberg_richter_cdf(moment, moment_threshold, beta,
corner_moment)) | python | def tapered_gutenberg_richter_pdf(moment, moment_threshold, beta,
corner_moment):
'''
Tapered Gutenberg-Richter Probability Density Function
:param float or numpy.ndarray moment:
Moment for calculation of rate
:param float or numpy.ndarray moment_threshold:
Threshold Moment of the distribution (moment rate essentially!)
:param float beta:
Beta value (b * ln(10.)) of the Tapered Gutenberg-Richter Function
:param float corner_momnet:
Corner moment of the Tapered Gutenberg-Richter Function
:returns:
Absolute probability of moment release > moment
'''
return ((beta / moment + 1. / corner_moment) *
tapered_gutenberg_richter_cdf(moment, moment_threshold, beta,
corner_moment)) | [
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35 | gem/oq-engine | openquake/engine/export/core.py | makedirs | def makedirs(path):
"""
Make all of the directories in the ``path`` using `os.makedirs`.
"""
if os.path.exists(path):
if not os.path.isdir(path):
# If it's not a directory, we can't do anything.
# This is a problem
raise RuntimeError('%s already exists and is not a directory.'
% path)
else:
os.makedirs(path) | python | def makedirs(path):
"""
Make all of the directories in the ``path`` using `os.makedirs`.
"""
if os.path.exists(path):
if not os.path.isdir(path):
# If it's not a directory, we can't do anything.
# This is a problem
raise RuntimeError('%s already exists and is not a directory.'
% path)
else:
os.makedirs(path) | [
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36 | gem/oq-engine | openquake/hmtk/seismicity/max_magnitude/base.py | _get_observed_mmax | def _get_observed_mmax(catalogue, config):
'''Check see if observed mmax values are input, if not then take
from the catalogue'''
if config['input_mmax']:
obsmax = config['input_mmax']
if config['input_mmax_uncertainty']:
return config['input_mmax'], config['input_mmax_uncertainty']
else:
raise ValueError('Input mmax uncertainty must be specified!')
max_location = np.argmax(catalogue['magnitude'])
obsmax = catalogue['magnitude'][max_location]
cond = isinstance(catalogue['sigmaMagnitude'], np.ndarray) and \
len(catalogue['sigmaMagnitude']) > 0 and not \
np.all(np.isnan(catalogue['sigmaMagnitude']))
if cond:
if not np.isnan(catalogue['sigmaMagnitude'][max_location]):
return obsmax, catalogue['sigmaMagnitude'][max_location]
else:
print('Uncertainty not given on observed Mmax\n'
'Taking largest magnitude uncertainty found in catalogue')
return obsmax, np.nanmax(catalogue['sigmaMagnitude'])
elif config['input_mmax_uncertainty']:
return obsmax, config['input_mmax_uncertainty']
else:
raise ValueError('Input mmax uncertainty must be specified!') | python | def _get_observed_mmax(catalogue, config):
'''Check see if observed mmax values are input, if not then take
from the catalogue'''
if config['input_mmax']:
obsmax = config['input_mmax']
if config['input_mmax_uncertainty']:
return config['input_mmax'], config['input_mmax_uncertainty']
else:
raise ValueError('Input mmax uncertainty must be specified!')
max_location = np.argmax(catalogue['magnitude'])
obsmax = catalogue['magnitude'][max_location]
cond = isinstance(catalogue['sigmaMagnitude'], np.ndarray) and \
len(catalogue['sigmaMagnitude']) > 0 and not \
np.all(np.isnan(catalogue['sigmaMagnitude']))
if cond:
if not np.isnan(catalogue['sigmaMagnitude'][max_location]):
return obsmax, catalogue['sigmaMagnitude'][max_location]
else:
print('Uncertainty not given on observed Mmax\n'
'Taking largest magnitude uncertainty found in catalogue')
return obsmax, np.nanmax(catalogue['sigmaMagnitude'])
elif config['input_mmax_uncertainty']:
return obsmax, config['input_mmax_uncertainty']
else:
raise ValueError('Input mmax uncertainty must be specified!') | [
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37 | gem/oq-engine | openquake/hmtk/seismicity/max_magnitude/base.py | _get_magnitude_vector_properties | def _get_magnitude_vector_properties(catalogue, config):
'''If an input minimum magnitude is given then consider catalogue
only above the minimum magnitude - returns corresponding properties'''
mmin = config.get('input_mmin', np.min(catalogue['magnitude']))
neq = np.float(np.sum(catalogue['magnitude'] >= mmin - 1.E-7))
return neq, mmin | python | def _get_magnitude_vector_properties(catalogue, config):
'''If an input minimum magnitude is given then consider catalogue
only above the minimum magnitude - returns corresponding properties'''
mmin = config.get('input_mmin', np.min(catalogue['magnitude']))
neq = np.float(np.sum(catalogue['magnitude'] >= mmin - 1.E-7))
return neq, mmin | [
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38 | gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.get_dip | def get_dip(self):
"""
Return the fault dip as the average dip over the mesh.
The average dip is defined as the weighted mean inclination
of all the mesh cells. See
:meth:`openquake.hazardlib.geo.mesh.RectangularMesh.get_mean_inclination_and_azimuth`
:returns:
The average dip, in decimal degrees.
"""
# uses the same approach as in simple fault surface
if self.dip is None:
mesh = self.mesh
self.dip, self.strike = mesh.get_mean_inclination_and_azimuth()
return self.dip | python | def get_dip(self):
"""
Return the fault dip as the average dip over the mesh.
The average dip is defined as the weighted mean inclination
of all the mesh cells. See
:meth:`openquake.hazardlib.geo.mesh.RectangularMesh.get_mean_inclination_and_azimuth`
:returns:
The average dip, in decimal degrees.
"""
# uses the same approach as in simple fault surface
if self.dip is None:
mesh = self.mesh
self.dip, self.strike = mesh.get_mean_inclination_and_azimuth()
return self.dip | [
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39 | gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.check_surface_validity | def check_surface_validity(cls, edges):
"""
Check validity of the surface.
Project edge points to vertical plane anchored to surface upper left
edge and with strike equal to top edge strike. Check that resulting
polygon is valid.
This method doesn't have to be called by hands before creating the
surface object, because it is called from :meth:`from_fault_data`.
"""
# extract coordinates of surface boundary (as defined from edges)
full_boundary = []
left_boundary = []
right_boundary = []
for i in range(1, len(edges) - 1):
left_boundary.append(edges[i].points[0])
right_boundary.append(edges[i].points[-1])
full_boundary.extend(edges[0].points)
full_boundary.extend(right_boundary)
full_boundary.extend(edges[-1].points[::-1])
full_boundary.extend(left_boundary[::-1])
lons = [p.longitude for p in full_boundary]
lats = [p.latitude for p in full_boundary]
depths = [p.depth for p in full_boundary]
# define reference plane. Corner points are separated by an arbitrary
# distance of 10 km. The mesh spacing is set to 2 km. Both corner
# distance and mesh spacing values do not affect the algorithm results.
ul = edges[0].points[0]
strike = ul.azimuth(edges[0].points[-1])
dist = 10.
ur = ul.point_at(dist, 0, strike)
bl = Point(ul.longitude, ul.latitude, ul.depth + dist)
br = bl.point_at(dist, 0, strike)
# project surface boundary to reference plane and check for
# validity.
ref_plane = PlanarSurface.from_corner_points(ul, ur, br, bl)
_, xx, yy = ref_plane._project(
spherical_to_cartesian(lons, lats, depths))
coords = [(x, y) for x, y in zip(xx, yy)]
p = shapely.geometry.Polygon(coords)
if not p.is_valid:
raise ValueError('Edges points are not in the right order') | python | def check_surface_validity(cls, edges):
"""
Check validity of the surface.
Project edge points to vertical plane anchored to surface upper left
edge and with strike equal to top edge strike. Check that resulting
polygon is valid.
This method doesn't have to be called by hands before creating the
surface object, because it is called from :meth:`from_fault_data`.
"""
# extract coordinates of surface boundary (as defined from edges)
full_boundary = []
left_boundary = []
right_boundary = []
for i in range(1, len(edges) - 1):
left_boundary.append(edges[i].points[0])
right_boundary.append(edges[i].points[-1])
full_boundary.extend(edges[0].points)
full_boundary.extend(right_boundary)
full_boundary.extend(edges[-1].points[::-1])
full_boundary.extend(left_boundary[::-1])
lons = [p.longitude for p in full_boundary]
lats = [p.latitude for p in full_boundary]
depths = [p.depth for p in full_boundary]
# define reference plane. Corner points are separated by an arbitrary
# distance of 10 km. The mesh spacing is set to 2 km. Both corner
# distance and mesh spacing values do not affect the algorithm results.
ul = edges[0].points[0]
strike = ul.azimuth(edges[0].points[-1])
dist = 10.
ur = ul.point_at(dist, 0, strike)
bl = Point(ul.longitude, ul.latitude, ul.depth + dist)
br = bl.point_at(dist, 0, strike)
# project surface boundary to reference plane and check for
# validity.
ref_plane = PlanarSurface.from_corner_points(ul, ur, br, bl)
_, xx, yy = ref_plane._project(
spherical_to_cartesian(lons, lats, depths))
coords = [(x, y) for x, y in zip(xx, yy)]
p = shapely.geometry.Polygon(coords)
if not p.is_valid:
raise ValueError('Edges points are not in the right order') | [
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polygon is valid.
This method doesn't have to be called by hands before creating the
surface object, because it is called from :meth:`from_fault_data`. | [
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40 | gem/oq-engine | openquake/hazardlib/geo/surface/complex_fault.py | ComplexFaultSurface.surface_projection_from_fault_data | def surface_projection_from_fault_data(cls, edges):
"""
Get a surface projection of the complex fault surface.
:param edges:
A list of horizontal edges of the surface as instances
of :class:`openquake.hazardlib.geo.line.Line`.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon`
describing the surface projection of the complex fault.
"""
# collect lons and lats of all the vertices of all the edges
lons = []
lats = []
for edge in edges:
for point in edge:
lons.append(point.longitude)
lats.append(point.latitude)
lons = numpy.array(lons, dtype=float)
lats = numpy.array(lats, dtype=float)
return Mesh(lons, lats, depths=None).get_convex_hull() | python | def surface_projection_from_fault_data(cls, edges):
"""
Get a surface projection of the complex fault surface.
:param edges:
A list of horizontal edges of the surface as instances
of :class:`openquake.hazardlib.geo.line.Line`.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon`
describing the surface projection of the complex fault.
"""
# collect lons and lats of all the vertices of all the edges
lons = []
lats = []
for edge in edges:
for point in edge:
lons.append(point.longitude)
lats.append(point.latitude)
lons = numpy.array(lons, dtype=float)
lats = numpy.array(lats, dtype=float)
return Mesh(lons, lats, depths=None).get_convex_hull() | [
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41 | gem/oq-engine | openquake/calculators/base.py | check_time_event | def check_time_event(oqparam, occupancy_periods):
"""
Check the `time_event` parameter in the datastore, by comparing
with the periods found in the exposure.
"""
time_event = oqparam.time_event
if time_event and time_event not in occupancy_periods:
raise ValueError(
'time_event is %s in %s, but the exposure contains %s' %
(time_event, oqparam.inputs['job_ini'],
', '.join(occupancy_periods))) | python | def check_time_event(oqparam, occupancy_periods):
"""
Check the `time_event` parameter in the datastore, by comparing
with the periods found in the exposure.
"""
time_event = oqparam.time_event
if time_event and time_event not in occupancy_periods:
raise ValueError(
'time_event is %s in %s, but the exposure contains %s' %
(time_event, oqparam.inputs['job_ini'],
', '.join(occupancy_periods))) | [
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42 | gem/oq-engine | openquake/calculators/base.py | get_idxs | def get_idxs(data, eid2idx):
"""
Convert from event IDs to event indices.
:param data: an array with a field eid
:param eid2idx: a dictionary eid -> idx
:returns: the array of event indices
"""
uniq, inv = numpy.unique(data['eid'], return_inverse=True)
idxs = numpy.array([eid2idx[eid] for eid in uniq])[inv]
return idxs | python | def get_idxs(data, eid2idx):
"""
Convert from event IDs to event indices.
:param data: an array with a field eid
:param eid2idx: a dictionary eid -> idx
:returns: the array of event indices
"""
uniq, inv = numpy.unique(data['eid'], return_inverse=True)
idxs = numpy.array([eid2idx[eid] for eid in uniq])[inv]
return idxs | [
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43 | gem/oq-engine | openquake/calculators/base.py | import_gmfs | def import_gmfs(dstore, fname, sids):
"""
Import in the datastore a ground motion field CSV file.
:param dstore: the datastore
:param fname: the CSV file
:param sids: the site IDs (complete)
:returns: event_ids, num_rlzs
"""
array = writers.read_composite_array(fname).array
# has header rlzi, sid, eid, gmv_PGA, ...
imts = [name[4:] for name in array.dtype.names[3:]]
n_imts = len(imts)
gmf_data_dt = numpy.dtype(
[('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (n_imts,)))])
# store the events
eids = numpy.unique(array['eid'])
eids.sort()
E = len(eids)
eid2idx = dict(zip(eids, range(E)))
events = numpy.zeros(E, rupture.events_dt)
events['eid'] = eids
dstore['events'] = events
# store the GMFs
dic = general.group_array(array.view(gmf_data_dt), 'sid')
lst = []
offset = 0
for sid in sids:
n = len(dic.get(sid, []))
lst.append((offset, offset + n))
if n:
offset += n
gmvs = dic[sid]
gmvs['eid'] = get_idxs(gmvs, eid2idx)
gmvs['rlzi'] = 0 # effectively there is only 1 realization
dstore.extend('gmf_data/data', gmvs)
dstore['gmf_data/indices'] = numpy.array(lst, U32)
dstore['gmf_data/imts'] = ' '.join(imts)
sig_eps_dt = [('eid', U64), ('sig', (F32, n_imts)), ('eps', (F32, n_imts))]
dstore['gmf_data/sigma_epsilon'] = numpy.zeros(0, sig_eps_dt)
dstore['weights'] = numpy.ones((1, n_imts))
return eids | python | def import_gmfs(dstore, fname, sids):
"""
Import in the datastore a ground motion field CSV file.
:param dstore: the datastore
:param fname: the CSV file
:param sids: the site IDs (complete)
:returns: event_ids, num_rlzs
"""
array = writers.read_composite_array(fname).array
# has header rlzi, sid, eid, gmv_PGA, ...
imts = [name[4:] for name in array.dtype.names[3:]]
n_imts = len(imts)
gmf_data_dt = numpy.dtype(
[('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (n_imts,)))])
# store the events
eids = numpy.unique(array['eid'])
eids.sort()
E = len(eids)
eid2idx = dict(zip(eids, range(E)))
events = numpy.zeros(E, rupture.events_dt)
events['eid'] = eids
dstore['events'] = events
# store the GMFs
dic = general.group_array(array.view(gmf_data_dt), 'sid')
lst = []
offset = 0
for sid in sids:
n = len(dic.get(sid, []))
lst.append((offset, offset + n))
if n:
offset += n
gmvs = dic[sid]
gmvs['eid'] = get_idxs(gmvs, eid2idx)
gmvs['rlzi'] = 0 # effectively there is only 1 realization
dstore.extend('gmf_data/data', gmvs)
dstore['gmf_data/indices'] = numpy.array(lst, U32)
dstore['gmf_data/imts'] = ' '.join(imts)
sig_eps_dt = [('eid', U64), ('sig', (F32, n_imts)), ('eps', (F32, n_imts))]
dstore['gmf_data/sigma_epsilon'] = numpy.zeros(0, sig_eps_dt)
dstore['weights'] = numpy.ones((1, n_imts))
return eids | [
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44 | gem/oq-engine | openquake/calculators/base.py | BaseCalculator.save_params | def save_params(self, **kw):
"""
Update the current calculation parameters and save engine_version
"""
if ('hazard_calculation_id' in kw and
kw['hazard_calculation_id'] is None):
del kw['hazard_calculation_id']
vars(self.oqparam).update(**kw)
self.datastore['oqparam'] = self.oqparam # save the updated oqparam
attrs = self.datastore['/'].attrs
attrs['engine_version'] = engine_version
attrs['date'] = datetime.now().isoformat()[:19]
if 'checksum32' not in attrs:
attrs['checksum32'] = readinput.get_checksum32(self.oqparam)
self.datastore.flush() | python | def save_params(self, **kw):
"""
Update the current calculation parameters and save engine_version
"""
if ('hazard_calculation_id' in kw and
kw['hazard_calculation_id'] is None):
del kw['hazard_calculation_id']
vars(self.oqparam).update(**kw)
self.datastore['oqparam'] = self.oqparam # save the updated oqparam
attrs = self.datastore['/'].attrs
attrs['engine_version'] = engine_version
attrs['date'] = datetime.now().isoformat()[:19]
if 'checksum32' not in attrs:
attrs['checksum32'] = readinput.get_checksum32(self.oqparam)
self.datastore.flush() | [
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45 | gem/oq-engine | openquake/calculators/base.py | BaseCalculator.run | def run(self, pre_execute=True, concurrent_tasks=None, close=True, **kw):
"""
Run the calculation and return the exported outputs.
"""
with self._monitor:
self._monitor.username = kw.get('username', '')
self._monitor.hdf5 = self.datastore.hdf5
if concurrent_tasks is None: # use the job.ini parameter
ct = self.oqparam.concurrent_tasks
else: # used the parameter passed in the command-line
ct = concurrent_tasks
if ct == 0: # disable distribution temporarily
oq_distribute = os.environ.get('OQ_DISTRIBUTE')
os.environ['OQ_DISTRIBUTE'] = 'no'
if ct != self.oqparam.concurrent_tasks:
# save the used concurrent_tasks
self.oqparam.concurrent_tasks = ct
self.save_params(**kw)
try:
if pre_execute:
self.pre_execute()
self.result = self.execute()
if self.result is not None:
self.post_execute(self.result)
self.before_export()
self.export(kw.get('exports', ''))
except Exception:
if kw.get('pdb'): # post-mortem debug
tb = sys.exc_info()[2]
traceback.print_tb(tb)
pdb.post_mortem(tb)
else:
logging.critical('', exc_info=True)
raise
finally:
# cleanup globals
if ct == 0: # restore OQ_DISTRIBUTE
if oq_distribute is None: # was not set
del os.environ['OQ_DISTRIBUTE']
else:
os.environ['OQ_DISTRIBUTE'] = oq_distribute
readinput.pmap = None
readinput.exposure = None
readinput.gmfs = None
readinput.eids = None
self._monitor.flush()
if close: # in the engine we close later
self.result = None
try:
self.datastore.close()
except (RuntimeError, ValueError):
# sometimes produces errors but they are difficult to
# reproduce
logging.warning('', exc_info=True)
return getattr(self, 'exported', {}) | python | def run(self, pre_execute=True, concurrent_tasks=None, close=True, **kw):
"""
Run the calculation and return the exported outputs.
"""
with self._monitor:
self._monitor.username = kw.get('username', '')
self._monitor.hdf5 = self.datastore.hdf5
if concurrent_tasks is None: # use the job.ini parameter
ct = self.oqparam.concurrent_tasks
else: # used the parameter passed in the command-line
ct = concurrent_tasks
if ct == 0: # disable distribution temporarily
oq_distribute = os.environ.get('OQ_DISTRIBUTE')
os.environ['OQ_DISTRIBUTE'] = 'no'
if ct != self.oqparam.concurrent_tasks:
# save the used concurrent_tasks
self.oqparam.concurrent_tasks = ct
self.save_params(**kw)
try:
if pre_execute:
self.pre_execute()
self.result = self.execute()
if self.result is not None:
self.post_execute(self.result)
self.before_export()
self.export(kw.get('exports', ''))
except Exception:
if kw.get('pdb'): # post-mortem debug
tb = sys.exc_info()[2]
traceback.print_tb(tb)
pdb.post_mortem(tb)
else:
logging.critical('', exc_info=True)
raise
finally:
# cleanup globals
if ct == 0: # restore OQ_DISTRIBUTE
if oq_distribute is None: # was not set
del os.environ['OQ_DISTRIBUTE']
else:
os.environ['OQ_DISTRIBUTE'] = oq_distribute
readinput.pmap = None
readinput.exposure = None
readinput.gmfs = None
readinput.eids = None
self._monitor.flush()
if close: # in the engine we close later
self.result = None
try:
self.datastore.close()
except (RuntimeError, ValueError):
# sometimes produces errors but they are difficult to
# reproduce
logging.warning('', exc_info=True)
return getattr(self, 'exported', {}) | [
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46 | gem/oq-engine | openquake/calculators/base.py | BaseCalculator.export | def export(self, exports=None):
"""
Export all the outputs in the datastore in the given export formats.
Individual outputs are not exported if there are multiple realizations.
"""
self.exported = getattr(self.precalc, 'exported', {})
if isinstance(exports, tuple):
fmts = exports
elif exports: # is a string
fmts = exports.split(',')
elif isinstance(self.oqparam.exports, tuple):
fmts = self.oqparam.exports
else: # is a string
fmts = self.oqparam.exports.split(',')
keys = set(self.datastore)
has_hcurves = ('hcurves-stats' in self.datastore or
'hcurves-rlzs' in self.datastore)
if has_hcurves:
keys.add('hcurves')
for fmt in fmts:
if not fmt:
continue
for key in sorted(keys): # top level keys
if 'rlzs' in key and self.R > 1:
continue # skip individual curves
self._export((key, fmt))
if has_hcurves and self.oqparam.hazard_maps:
self._export(('hmaps', fmt))
if has_hcurves and self.oqparam.uniform_hazard_spectra:
self._export(('uhs', fmt)) | python | def export(self, exports=None):
"""
Export all the outputs in the datastore in the given export formats.
Individual outputs are not exported if there are multiple realizations.
"""
self.exported = getattr(self.precalc, 'exported', {})
if isinstance(exports, tuple):
fmts = exports
elif exports: # is a string
fmts = exports.split(',')
elif isinstance(self.oqparam.exports, tuple):
fmts = self.oqparam.exports
else: # is a string
fmts = self.oqparam.exports.split(',')
keys = set(self.datastore)
has_hcurves = ('hcurves-stats' in self.datastore or
'hcurves-rlzs' in self.datastore)
if has_hcurves:
keys.add('hcurves')
for fmt in fmts:
if not fmt:
continue
for key in sorted(keys): # top level keys
if 'rlzs' in key and self.R > 1:
continue # skip individual curves
self._export((key, fmt))
if has_hcurves and self.oqparam.hazard_maps:
self._export(('hmaps', fmt))
if has_hcurves and self.oqparam.uniform_hazard_spectra:
self._export(('uhs', fmt)) | [
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Individual outputs are not exported if there are multiple realizations. | [
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47 | gem/oq-engine | openquake/calculators/base.py | BaseCalculator.before_export | def before_export(self):
"""
Set the attributes nbytes
"""
# sanity check that eff_ruptures have been set, i.e. are not -1
try:
csm_info = self.datastore['csm_info']
except KeyError:
csm_info = self.datastore['csm_info'] = self.csm.info
for sm in csm_info.source_models:
for sg in sm.src_groups:
assert sg.eff_ruptures != -1, sg
for key in self.datastore:
self.datastore.set_nbytes(key)
self.datastore.flush() | python | def before_export(self):
"""
Set the attributes nbytes
"""
# sanity check that eff_ruptures have been set, i.e. are not -1
try:
csm_info = self.datastore['csm_info']
except KeyError:
csm_info = self.datastore['csm_info'] = self.csm.info
for sm in csm_info.source_models:
for sg in sm.src_groups:
assert sg.eff_ruptures != -1, sg
for key in self.datastore:
self.datastore.set_nbytes(key)
self.datastore.flush() | [
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48 | gem/oq-engine | openquake/calculators/base.py | HazardCalculator.read_inputs | def read_inputs(self):
"""
Read risk data and sources if any
"""
oq = self.oqparam
self._read_risk_data()
self.check_overflow() # check if self.sitecol is too large
if ('source_model_logic_tree' in oq.inputs and
oq.hazard_calculation_id is None):
self.csm = readinput.get_composite_source_model(
oq, self.monitor(), srcfilter=self.src_filter)
self.init() | python | def read_inputs(self):
"""
Read risk data and sources if any
"""
oq = self.oqparam
self._read_risk_data()
self.check_overflow() # check if self.sitecol is too large
if ('source_model_logic_tree' in oq.inputs and
oq.hazard_calculation_id is None):
self.csm = readinput.get_composite_source_model(
oq, self.monitor(), srcfilter=self.src_filter)
self.init() | [
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49 | gem/oq-engine | openquake/calculators/base.py | HazardCalculator.pre_execute | def pre_execute(self):
"""
Check if there is a previous calculation ID.
If yes, read the inputs by retrieving the previous calculation;
if not, read the inputs directly.
"""
oq = self.oqparam
if 'gmfs' in oq.inputs or 'multi_peril' in oq.inputs:
# read hazard from files
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with gmfs_file')
self.read_inputs()
if 'gmfs' in oq.inputs:
save_gmfs(self)
else:
self.save_multi_peril()
elif 'hazard_curves' in oq.inputs: # read hazard from file
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with hazard_curves')
haz_sitecol = readinput.get_site_collection(oq)
# NB: horrible: get_site_collection calls get_pmap_from_nrml
# that sets oq.investigation_time, so it must be called first
self.load_riskmodel() # must be after get_site_collection
self.read_exposure(haz_sitecol) # define .assets_by_site
self.datastore['poes/grp-00'] = fix_ones(readinput.pmap)
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc()
elif oq.hazard_calculation_id:
parent = util.read(oq.hazard_calculation_id)
self.check_precalc(parent['oqparam'].calculation_mode)
self.datastore.parent = parent
# copy missing parameters from the parent
params = {name: value for name, value in
vars(parent['oqparam']).items()
if name not in vars(self.oqparam)}
self.save_params(**params)
self.read_inputs()
oqp = parent['oqparam']
if oqp.investigation_time != oq.investigation_time:
raise ValueError(
'The parent calculation was using investigation_time=%s'
' != %s' % (oqp.investigation_time, oq.investigation_time))
if oqp.minimum_intensity != oq.minimum_intensity:
raise ValueError(
'The parent calculation was using minimum_intensity=%s'
' != %s' % (oqp.minimum_intensity, oq.minimum_intensity))
missing_imts = set(oq.risk_imtls) - set(oqp.imtls)
if missing_imts:
raise ValueError(
'The parent calculation is missing the IMT(s) %s' %
', '.join(missing_imts))
elif self.__class__.precalc:
calc = calculators[self.__class__.precalc](
self.oqparam, self.datastore.calc_id)
calc.run()
self.param = calc.param
self.sitecol = calc.sitecol
self.assetcol = calc.assetcol
self.riskmodel = calc.riskmodel
if hasattr(calc, 'rlzs_assoc'):
self.rlzs_assoc = calc.rlzs_assoc
else:
# this happens for instance for a scenario_damage without
# rupture, gmfs, multi_peril
raise InvalidFile(
'%(job_ini)s: missing gmfs_csv, multi_peril_csv' %
oq.inputs)
if hasattr(calc, 'csm'): # no scenario
self.csm = calc.csm
else:
self.read_inputs()
if self.riskmodel:
self.save_riskmodel() | python | def pre_execute(self):
"""
Check if there is a previous calculation ID.
If yes, read the inputs by retrieving the previous calculation;
if not, read the inputs directly.
"""
oq = self.oqparam
if 'gmfs' in oq.inputs or 'multi_peril' in oq.inputs:
# read hazard from files
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with gmfs_file')
self.read_inputs()
if 'gmfs' in oq.inputs:
save_gmfs(self)
else:
self.save_multi_peril()
elif 'hazard_curves' in oq.inputs: # read hazard from file
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with hazard_curves')
haz_sitecol = readinput.get_site_collection(oq)
# NB: horrible: get_site_collection calls get_pmap_from_nrml
# that sets oq.investigation_time, so it must be called first
self.load_riskmodel() # must be after get_site_collection
self.read_exposure(haz_sitecol) # define .assets_by_site
self.datastore['poes/grp-00'] = fix_ones(readinput.pmap)
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc()
elif oq.hazard_calculation_id:
parent = util.read(oq.hazard_calculation_id)
self.check_precalc(parent['oqparam'].calculation_mode)
self.datastore.parent = parent
# copy missing parameters from the parent
params = {name: value for name, value in
vars(parent['oqparam']).items()
if name not in vars(self.oqparam)}
self.save_params(**params)
self.read_inputs()
oqp = parent['oqparam']
if oqp.investigation_time != oq.investigation_time:
raise ValueError(
'The parent calculation was using investigation_time=%s'
' != %s' % (oqp.investigation_time, oq.investigation_time))
if oqp.minimum_intensity != oq.minimum_intensity:
raise ValueError(
'The parent calculation was using minimum_intensity=%s'
' != %s' % (oqp.minimum_intensity, oq.minimum_intensity))
missing_imts = set(oq.risk_imtls) - set(oqp.imtls)
if missing_imts:
raise ValueError(
'The parent calculation is missing the IMT(s) %s' %
', '.join(missing_imts))
elif self.__class__.precalc:
calc = calculators[self.__class__.precalc](
self.oqparam, self.datastore.calc_id)
calc.run()
self.param = calc.param
self.sitecol = calc.sitecol
self.assetcol = calc.assetcol
self.riskmodel = calc.riskmodel
if hasattr(calc, 'rlzs_assoc'):
self.rlzs_assoc = calc.rlzs_assoc
else:
# this happens for instance for a scenario_damage without
# rupture, gmfs, multi_peril
raise InvalidFile(
'%(job_ini)s: missing gmfs_csv, multi_peril_csv' %
oq.inputs)
if hasattr(calc, 'csm'): # no scenario
self.csm = calc.csm
else:
self.read_inputs()
if self.riskmodel:
self.save_riskmodel() | [
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50 | gem/oq-engine | openquake/calculators/base.py | HazardCalculator.init | def init(self):
"""
To be overridden to initialize the datasets needed by the calculation
"""
oq = self.oqparam
if not oq.risk_imtls:
if self.datastore.parent:
oq.risk_imtls = (
self.datastore.parent['oqparam'].risk_imtls)
if 'precalc' in vars(self):
self.rlzs_assoc = self.precalc.rlzs_assoc
elif 'csm_info' in self.datastore:
csm_info = self.datastore['csm_info']
if oq.hazard_calculation_id and 'gsim_logic_tree' in oq.inputs:
# redefine the realizations by reading the weights from the
# gsim_logic_tree_file that could be different from the parent
csm_info.gsim_lt = logictree.GsimLogicTree(
oq.inputs['gsim_logic_tree'], set(csm_info.trts))
self.rlzs_assoc = csm_info.get_rlzs_assoc()
elif hasattr(self, 'csm'):
self.check_floating_spinning()
self.rlzs_assoc = self.csm.info.get_rlzs_assoc()
else: # build a fake; used by risk-from-file calculators
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc() | python | def init(self):
"""
To be overridden to initialize the datasets needed by the calculation
"""
oq = self.oqparam
if not oq.risk_imtls:
if self.datastore.parent:
oq.risk_imtls = (
self.datastore.parent['oqparam'].risk_imtls)
if 'precalc' in vars(self):
self.rlzs_assoc = self.precalc.rlzs_assoc
elif 'csm_info' in self.datastore:
csm_info = self.datastore['csm_info']
if oq.hazard_calculation_id and 'gsim_logic_tree' in oq.inputs:
# redefine the realizations by reading the weights from the
# gsim_logic_tree_file that could be different from the parent
csm_info.gsim_lt = logictree.GsimLogicTree(
oq.inputs['gsim_logic_tree'], set(csm_info.trts))
self.rlzs_assoc = csm_info.get_rlzs_assoc()
elif hasattr(self, 'csm'):
self.check_floating_spinning()
self.rlzs_assoc = self.csm.info.get_rlzs_assoc()
else: # build a fake; used by risk-from-file calculators
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc() | [
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51 | gem/oq-engine | openquake/calculators/base.py | HazardCalculator.read_exposure | def read_exposure(self, haz_sitecol=None): # after load_risk_model
"""
Read the exposure, the riskmodel and update the attributes
.sitecol, .assetcol
"""
with self.monitor('reading exposure', autoflush=True):
self.sitecol, self.assetcol, discarded = (
readinput.get_sitecol_assetcol(
self.oqparam, haz_sitecol, self.riskmodel.loss_types))
if len(discarded):
self.datastore['discarded'] = discarded
if hasattr(self, 'rup'):
# this is normal for the case of scenario from rupture
logging.info('%d assets were discarded because too far '
'from the rupture; use `oq show discarded` '
'to show them and `oq plot_assets` to plot '
'them' % len(discarded))
elif not self.oqparam.discard_assets: # raise an error
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(
'%d assets were discarded; use `oq show discarded` to'
' show them and `oq plot_assets` to plot them' %
len(discarded))
# reduce the riskmodel to the relevant taxonomies
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
if len(self.riskmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.riskmodel.taxonomies), len(taxonomies))
self.riskmodel = self.riskmodel.reduce(taxonomies)
return readinput.exposure | python | def read_exposure(self, haz_sitecol=None): # after load_risk_model
"""
Read the exposure, the riskmodel and update the attributes
.sitecol, .assetcol
"""
with self.monitor('reading exposure', autoflush=True):
self.sitecol, self.assetcol, discarded = (
readinput.get_sitecol_assetcol(
self.oqparam, haz_sitecol, self.riskmodel.loss_types))
if len(discarded):
self.datastore['discarded'] = discarded
if hasattr(self, 'rup'):
# this is normal for the case of scenario from rupture
logging.info('%d assets were discarded because too far '
'from the rupture; use `oq show discarded` '
'to show them and `oq plot_assets` to plot '
'them' % len(discarded))
elif not self.oqparam.discard_assets: # raise an error
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(
'%d assets were discarded; use `oq show discarded` to'
' show them and `oq plot_assets` to plot them' %
len(discarded))
# reduce the riskmodel to the relevant taxonomies
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
if len(self.riskmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.riskmodel.taxonomies), len(taxonomies))
self.riskmodel = self.riskmodel.reduce(taxonomies)
return readinput.exposure | [
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52 | gem/oq-engine | openquake/calculators/base.py | HazardCalculator.save_riskmodel | def save_riskmodel(self):
"""
Save the risk models in the datastore
"""
self.datastore['risk_model'] = rm = self.riskmodel
self.datastore['taxonomy_mapping'] = self.riskmodel.tmap
attrs = self.datastore.getitem('risk_model').attrs
attrs['min_iml'] = hdf5.array_of_vstr(sorted(rm.min_iml.items()))
self.datastore.set_nbytes('risk_model') | python | def save_riskmodel(self):
"""
Save the risk models in the datastore
"""
self.datastore['risk_model'] = rm = self.riskmodel
self.datastore['taxonomy_mapping'] = self.riskmodel.tmap
attrs = self.datastore.getitem('risk_model').attrs
attrs['min_iml'] = hdf5.array_of_vstr(sorted(rm.min_iml.items()))
self.datastore.set_nbytes('risk_model') | [
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53 | gem/oq-engine | openquake/calculators/base.py | HazardCalculator.store_rlz_info | def store_rlz_info(self, eff_ruptures=None):
"""
Save info about the composite source model inside the csm_info dataset
"""
if hasattr(self, 'csm'): # no scenario
self.csm.info.update_eff_ruptures(eff_ruptures)
self.rlzs_assoc = self.csm.info.get_rlzs_assoc(
self.oqparam.sm_lt_path)
if not self.rlzs_assoc:
raise RuntimeError('Empty logic tree: too much filtering?')
self.datastore['csm_info'] = self.csm.info
R = len(self.rlzs_assoc.realizations)
logging.info('There are %d realization(s)', R)
if self.oqparam.imtls:
self.datastore['weights'] = arr = build_weights(
self.rlzs_assoc.realizations, self.oqparam.imt_dt())
self.datastore.set_attrs('weights', nbytes=arr.nbytes)
if hasattr(self, 'hdf5cache'): # no scenario
with hdf5.File(self.hdf5cache, 'r+') as cache:
cache['weights'] = arr
if 'event_based' in self.oqparam.calculation_mode and R >= TWO16:
# rlzi is 16 bit integer in the GMFs, so there is hard limit or R
raise ValueError(
'The logic tree has %d realizations, the maximum '
'is %d' % (R, TWO16))
elif R > 10000:
logging.warning(
'The logic tree has %d realizations(!), please consider '
'sampling it', R)
self.datastore.flush() | python | def store_rlz_info(self, eff_ruptures=None):
"""
Save info about the composite source model inside the csm_info dataset
"""
if hasattr(self, 'csm'): # no scenario
self.csm.info.update_eff_ruptures(eff_ruptures)
self.rlzs_assoc = self.csm.info.get_rlzs_assoc(
self.oqparam.sm_lt_path)
if not self.rlzs_assoc:
raise RuntimeError('Empty logic tree: too much filtering?')
self.datastore['csm_info'] = self.csm.info
R = len(self.rlzs_assoc.realizations)
logging.info('There are %d realization(s)', R)
if self.oqparam.imtls:
self.datastore['weights'] = arr = build_weights(
self.rlzs_assoc.realizations, self.oqparam.imt_dt())
self.datastore.set_attrs('weights', nbytes=arr.nbytes)
if hasattr(self, 'hdf5cache'): # no scenario
with hdf5.File(self.hdf5cache, 'r+') as cache:
cache['weights'] = arr
if 'event_based' in self.oqparam.calculation_mode and R >= TWO16:
# rlzi is 16 bit integer in the GMFs, so there is hard limit or R
raise ValueError(
'The logic tree has %d realizations, the maximum '
'is %d' % (R, TWO16))
elif R > 10000:
logging.warning(
'The logic tree has %d realizations(!), please consider '
'sampling it', R)
self.datastore.flush() | [
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54 | gem/oq-engine | openquake/calculators/base.py | RiskCalculator.read_shakemap | def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
extra = self.riskmodel.get_extra_imts(oq.risk_imtls)
if extra:
logging.warning('There are risk functions for not available IMTs '
'which will be ignored: %s' % extra)
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'],
oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol = assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(
shakemap, oq.spatial_correlation, oq.cross_correlation,
oq.site_effects, oq.truncation_level, E, oq.random_seed,
oq.imtls)
save_gmf_data(self.datastore, sitecol, gmfs, imts)
return sitecol, assetcol | python | def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
extra = self.riskmodel.get_extra_imts(oq.risk_imtls)
if extra:
logging.warning('There are risk functions for not available IMTs '
'which will be ignored: %s' % extra)
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'],
oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol = assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(
shakemap, oq.spatial_correlation, oq.cross_correlation,
oq.site_effects, oq.truncation_level, E, oq.random_seed,
oq.imtls)
save_gmf_data(self.datastore, sitecol, gmfs, imts)
return sitecol, assetcol | [
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55 | gem/oq-engine | openquake/baselib/zeromq.py | bind | def bind(end_point, socket_type):
"""
Bind to a zmq URL; raise a proper error if the URL is invalid; return
a zmq socket.
"""
sock = context.socket(socket_type)
try:
sock.bind(end_point)
except zmq.error.ZMQError as exc:
sock.close()
raise exc.__class__('%s: %s' % (exc, end_point))
return sock | python | def bind(end_point, socket_type):
"""
Bind to a zmq URL; raise a proper error if the URL is invalid; return
a zmq socket.
"""
sock = context.socket(socket_type)
try:
sock.bind(end_point)
except zmq.error.ZMQError as exc:
sock.close()
raise exc.__class__('%s: %s' % (exc, end_point))
return sock | [
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56 | gem/oq-engine | openquake/baselib/zeromq.py | Socket.send | def send(self, obj):
"""
Send an object to the remote server; block and return the reply
if the socket type is REQ.
:param obj:
the Python object to send
"""
self.zsocket.send_pyobj(obj)
self.num_sent += 1
if self.socket_type == zmq.REQ:
return self.zsocket.recv_pyobj() | python | def send(self, obj):
"""
Send an object to the remote server; block and return the reply
if the socket type is REQ.
:param obj:
the Python object to send
"""
self.zsocket.send_pyobj(obj)
self.num_sent += 1
if self.socket_type == zmq.REQ:
return self.zsocket.recv_pyobj() | [
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57 | gem/oq-engine | openquake/hazardlib/geo/utils.py | angular_distance | def angular_distance(km, lat, lat2=None):
"""
Return the angular distance of two points at the given latitude.
>>> '%.3f' % angular_distance(100, lat=40)
'1.174'
>>> '%.3f' % angular_distance(100, lat=80)
'5.179'
"""
if lat2 is not None:
# use the largest latitude to compute the angular distance
lat = max(abs(lat), abs(lat2))
return km * KM_TO_DEGREES / math.cos(lat * DEGREES_TO_RAD) | python | def angular_distance(km, lat, lat2=None):
"""
Return the angular distance of two points at the given latitude.
>>> '%.3f' % angular_distance(100, lat=40)
'1.174'
>>> '%.3f' % angular_distance(100, lat=80)
'5.179'
"""
if lat2 is not None:
# use the largest latitude to compute the angular distance
lat = max(abs(lat), abs(lat2))
return km * KM_TO_DEGREES / math.cos(lat * DEGREES_TO_RAD) | [
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58 | gem/oq-engine | openquake/hazardlib/geo/utils.py | assoc | def assoc(objects, sitecol, assoc_dist, mode, asset_refs=()):
"""
Associate geographic objects to a site collection.
:param objects:
something with .lons, .lats or ['lon'] ['lat'], or a list of lists
of objects with a .location attribute (i.e. assets_by_site)
:param assoc_dist:
the maximum distance for association
:param mode:
if 'strict' fail if at least one site is not associated
if 'error' fail if all sites are not associated
:returns: (filtered site collection, filtered objects)
"""
if isinstance(objects, numpy.ndarray) or hasattr(objects, 'lons'):
# objects is a geo array with lon, lat fields or a mesh-like instance
return _GeographicObjects(objects).assoc(sitecol, assoc_dist, mode)
else: # objects is the list assets_by_site
return _GeographicObjects(sitecol).assoc2(
objects, assoc_dist, mode, asset_refs) | python | def assoc(objects, sitecol, assoc_dist, mode, asset_refs=()):
"""
Associate geographic objects to a site collection.
:param objects:
something with .lons, .lats or ['lon'] ['lat'], or a list of lists
of objects with a .location attribute (i.e. assets_by_site)
:param assoc_dist:
the maximum distance for association
:param mode:
if 'strict' fail if at least one site is not associated
if 'error' fail if all sites are not associated
:returns: (filtered site collection, filtered objects)
"""
if isinstance(objects, numpy.ndarray) or hasattr(objects, 'lons'):
# objects is a geo array with lon, lat fields or a mesh-like instance
return _GeographicObjects(objects).assoc(sitecol, assoc_dist, mode)
else: # objects is the list assets_by_site
return _GeographicObjects(sitecol).assoc2(
objects, assoc_dist, mode, asset_refs) | [
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59 | gem/oq-engine | openquake/hazardlib/geo/utils.py | line_intersects_itself | def line_intersects_itself(lons, lats, closed_shape=False):
"""
Return ``True`` if line of points intersects itself.
Line with the last point repeating the first one considered
intersecting itself.
The line is defined by lists (or numpy arrays) of points'
longitudes and latitudes (depth is not taken into account).
:param closed_shape:
If ``True`` the line will be checked twice: first time with
its original shape and second time with the points sequence
being shifted by one point (the last point becomes first,
the first turns second and so on). This is useful for
checking that the sequence of points defines a valid
:class:`~openquake.hazardlib.geo.polygon.Polygon`.
"""
assert len(lons) == len(lats)
if len(lons) <= 3:
# line can not intersect itself unless there are
# at least four points
return False
west, east, north, south = get_spherical_bounding_box(lons, lats)
proj = OrthographicProjection(west, east, north, south)
xx, yy = proj(lons, lats)
if not shapely.geometry.LineString(list(zip(xx, yy))).is_simple:
return True
if closed_shape:
xx, yy = proj(numpy.roll(lons, 1), numpy.roll(lats, 1))
if not shapely.geometry.LineString(list(zip(xx, yy))).is_simple:
return True
return False | python | def line_intersects_itself(lons, lats, closed_shape=False):
"""
Return ``True`` if line of points intersects itself.
Line with the last point repeating the first one considered
intersecting itself.
The line is defined by lists (or numpy arrays) of points'
longitudes and latitudes (depth is not taken into account).
:param closed_shape:
If ``True`` the line will be checked twice: first time with
its original shape and second time with the points sequence
being shifted by one point (the last point becomes first,
the first turns second and so on). This is useful for
checking that the sequence of points defines a valid
:class:`~openquake.hazardlib.geo.polygon.Polygon`.
"""
assert len(lons) == len(lats)
if len(lons) <= 3:
# line can not intersect itself unless there are
# at least four points
return False
west, east, north, south = get_spherical_bounding_box(lons, lats)
proj = OrthographicProjection(west, east, north, south)
xx, yy = proj(lons, lats)
if not shapely.geometry.LineString(list(zip(xx, yy))).is_simple:
return True
if closed_shape:
xx, yy = proj(numpy.roll(lons, 1), numpy.roll(lats, 1))
if not shapely.geometry.LineString(list(zip(xx, yy))).is_simple:
return True
return False | [
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longitudes and latitudes (depth is not taken into account).
:param closed_shape:
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60 | gem/oq-engine | openquake/hazardlib/geo/utils.py | get_bounding_box | def get_bounding_box(obj, maxdist):
"""
Return the dilated bounding box of a geometric object.
:param obj:
an object with method .get_bounding_box, or with an attribute .polygon
or a list of locations
:param maxdist: maximum distance in km
"""
if hasattr(obj, 'get_bounding_box'):
return obj.get_bounding_box(maxdist)
elif hasattr(obj, 'polygon'):
bbox = obj.polygon.get_bbox()
else:
if isinstance(obj, list): # a list of locations
lons = numpy.array([loc.longitude for loc in obj])
lats = numpy.array([loc.latitude for loc in obj])
else: # assume an array with fields lon, lat
lons, lats = obj['lon'], obj['lat']
min_lon, max_lon = lons.min(), lons.max()
if cross_idl(min_lon, max_lon):
lons %= 360
bbox = lons.min(), lats.min(), lons.max(), lats.max()
a1 = min(maxdist * KM_TO_DEGREES, 90)
a2 = min(angular_distance(maxdist, bbox[1], bbox[3]), 180)
return bbox[0] - a2, bbox[1] - a1, bbox[2] + a2, bbox[3] + a1 | python | def get_bounding_box(obj, maxdist):
"""
Return the dilated bounding box of a geometric object.
:param obj:
an object with method .get_bounding_box, or with an attribute .polygon
or a list of locations
:param maxdist: maximum distance in km
"""
if hasattr(obj, 'get_bounding_box'):
return obj.get_bounding_box(maxdist)
elif hasattr(obj, 'polygon'):
bbox = obj.polygon.get_bbox()
else:
if isinstance(obj, list): # a list of locations
lons = numpy.array([loc.longitude for loc in obj])
lats = numpy.array([loc.latitude for loc in obj])
else: # assume an array with fields lon, lat
lons, lats = obj['lon'], obj['lat']
min_lon, max_lon = lons.min(), lons.max()
if cross_idl(min_lon, max_lon):
lons %= 360
bbox = lons.min(), lats.min(), lons.max(), lats.max()
a1 = min(maxdist * KM_TO_DEGREES, 90)
a2 = min(angular_distance(maxdist, bbox[1], bbox[3]), 180)
return bbox[0] - a2, bbox[1] - a1, bbox[2] + a2, bbox[3] + a1 | [
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61 | gem/oq-engine | openquake/hazardlib/geo/utils.py | get_spherical_bounding_box | def get_spherical_bounding_box(lons, lats):
"""
Given a collection of points find and return the bounding box,
as a pair of longitudes and a pair of latitudes.
Parameters define longitudes and latitudes of a point collection
respectively in a form of lists or numpy arrays.
:return:
A tuple of four items. These items represent western, eastern,
northern and southern borders of the bounding box respectively.
Values are floats in decimal degrees.
:raises ValueError:
If points collection has the longitudinal extent of more than
180 degrees (it is impossible to define a single hemisphere
bound to poles that would contain the whole collection).
"""
north, south = numpy.max(lats), numpy.min(lats)
west, east = numpy.min(lons), numpy.max(lons)
assert (-180 <= west <= 180) and (-180 <= east <= 180), (west, east)
if get_longitudinal_extent(west, east) < 0:
# points are lying on both sides of the international date line
# (meridian 180). the actual west longitude is the lowest positive
# longitude and east one is the highest negative.
if hasattr(lons, 'flatten'):
# fixes test_surface_crossing_international_date_line
lons = lons.flatten()
west = min(lon for lon in lons if lon > 0)
east = max(lon for lon in lons if lon < 0)
if not all((get_longitudinal_extent(west, lon) >= 0
and get_longitudinal_extent(lon, east) >= 0)
for lon in lons):
raise ValueError('points collection has longitudinal extent '
'wider than 180 deg')
return SphericalBB(west, east, north, south) | python | def get_spherical_bounding_box(lons, lats):
"""
Given a collection of points find and return the bounding box,
as a pair of longitudes and a pair of latitudes.
Parameters define longitudes and latitudes of a point collection
respectively in a form of lists or numpy arrays.
:return:
A tuple of four items. These items represent western, eastern,
northern and southern borders of the bounding box respectively.
Values are floats in decimal degrees.
:raises ValueError:
If points collection has the longitudinal extent of more than
180 degrees (it is impossible to define a single hemisphere
bound to poles that would contain the whole collection).
"""
north, south = numpy.max(lats), numpy.min(lats)
west, east = numpy.min(lons), numpy.max(lons)
assert (-180 <= west <= 180) and (-180 <= east <= 180), (west, east)
if get_longitudinal_extent(west, east) < 0:
# points are lying on both sides of the international date line
# (meridian 180). the actual west longitude is the lowest positive
# longitude and east one is the highest negative.
if hasattr(lons, 'flatten'):
# fixes test_surface_crossing_international_date_line
lons = lons.flatten()
west = min(lon for lon in lons if lon > 0)
east = max(lon for lon in lons if lon < 0)
if not all((get_longitudinal_extent(west, lon) >= 0
and get_longitudinal_extent(lon, east) >= 0)
for lon in lons):
raise ValueError('points collection has longitudinal extent '
'wider than 180 deg')
return SphericalBB(west, east, north, south) | [
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62 | gem/oq-engine | openquake/hazardlib/geo/utils.py | get_middle_point | def get_middle_point(lon1, lat1, lon2, lat2):
"""
Given two points return the point exactly in the middle lying on the same
great circle arc.
Parameters are point coordinates in degrees.
:returns:
Tuple of longitude and latitude of the point in the middle.
"""
if lon1 == lon2 and lat1 == lat2:
return lon1, lat1
dist = geodetic.geodetic_distance(lon1, lat1, lon2, lat2)
azimuth = geodetic.azimuth(lon1, lat1, lon2, lat2)
return geodetic.point_at(lon1, lat1, azimuth, dist / 2.0) | python | def get_middle_point(lon1, lat1, lon2, lat2):
"""
Given two points return the point exactly in the middle lying on the same
great circle arc.
Parameters are point coordinates in degrees.
:returns:
Tuple of longitude and latitude of the point in the middle.
"""
if lon1 == lon2 and lat1 == lat2:
return lon1, lat1
dist = geodetic.geodetic_distance(lon1, lat1, lon2, lat2)
azimuth = geodetic.azimuth(lon1, lat1, lon2, lat2)
return geodetic.point_at(lon1, lat1, azimuth, dist / 2.0) | [
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63 | gem/oq-engine | openquake/hazardlib/geo/utils.py | cartesian_to_spherical | def cartesian_to_spherical(vectors):
"""
Return the spherical coordinates for coordinates in Cartesian space.
This function does an opposite to :func:`spherical_to_cartesian`.
:param vectors:
Array of 3d vectors in Cartesian space of shape (..., 3)
:returns:
Tuple of three arrays of the same shape as ``vectors`` representing
longitude (decimal degrees), latitude (decimal degrees) and depth (km)
in specified order.
"""
rr = numpy.sqrt(numpy.sum(vectors * vectors, axis=-1))
xx, yy, zz = vectors.T
lats = numpy.degrees(numpy.arcsin((zz / rr).clip(-1., 1.)))
lons = numpy.degrees(numpy.arctan2(yy, xx))
depths = EARTH_RADIUS - rr
return lons.T, lats.T, depths | python | def cartesian_to_spherical(vectors):
"""
Return the spherical coordinates for coordinates in Cartesian space.
This function does an opposite to :func:`spherical_to_cartesian`.
:param vectors:
Array of 3d vectors in Cartesian space of shape (..., 3)
:returns:
Tuple of three arrays of the same shape as ``vectors`` representing
longitude (decimal degrees), latitude (decimal degrees) and depth (km)
in specified order.
"""
rr = numpy.sqrt(numpy.sum(vectors * vectors, axis=-1))
xx, yy, zz = vectors.T
lats = numpy.degrees(numpy.arcsin((zz / rr).clip(-1., 1.)))
lons = numpy.degrees(numpy.arctan2(yy, xx))
depths = EARTH_RADIUS - rr
return lons.T, lats.T, depths | [
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64 | gem/oq-engine | openquake/hazardlib/geo/utils.py | triangle_area | def triangle_area(e1, e2, e3):
"""
Get the area of triangle formed by three vectors.
Parameters are three three-dimensional numpy arrays representing
vectors of triangle's edges in Cartesian space.
:returns:
Float number, the area of the triangle in squared units of coordinates,
or numpy array of shape of edges with one dimension less.
Uses Heron formula, see http://mathworld.wolfram.com/HeronsFormula.html.
"""
# calculating edges length
e1_length = numpy.sqrt(numpy.sum(e1 * e1, axis=-1))
e2_length = numpy.sqrt(numpy.sum(e2 * e2, axis=-1))
e3_length = numpy.sqrt(numpy.sum(e3 * e3, axis=-1))
# calculating half perimeter
s = (e1_length + e2_length + e3_length) / 2.0
# applying Heron's formula
return numpy.sqrt(s * (s - e1_length) * (s - e2_length) * (s - e3_length)) | python | def triangle_area(e1, e2, e3):
"""
Get the area of triangle formed by three vectors.
Parameters are three three-dimensional numpy arrays representing
vectors of triangle's edges in Cartesian space.
:returns:
Float number, the area of the triangle in squared units of coordinates,
or numpy array of shape of edges with one dimension less.
Uses Heron formula, see http://mathworld.wolfram.com/HeronsFormula.html.
"""
# calculating edges length
e1_length = numpy.sqrt(numpy.sum(e1 * e1, axis=-1))
e2_length = numpy.sqrt(numpy.sum(e2 * e2, axis=-1))
e3_length = numpy.sqrt(numpy.sum(e3 * e3, axis=-1))
# calculating half perimeter
s = (e1_length + e2_length + e3_length) / 2.0
# applying Heron's formula
return numpy.sqrt(s * (s - e1_length) * (s - e2_length) * (s - e3_length)) | [
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65 | gem/oq-engine | openquake/hazardlib/geo/utils.py | normalized | def normalized(vector):
"""
Get unit vector for a given one.
:param vector:
Numpy vector as coordinates in Cartesian space, or an array of such.
:returns:
Numpy array of the same shape and structure where all vectors are
normalized. That is, each coordinate component is divided by its
vector's length.
"""
length = numpy.sum(vector * vector, axis=-1)
length = numpy.sqrt(length.reshape(length.shape + (1, )))
return vector / length | python | def normalized(vector):
"""
Get unit vector for a given one.
:param vector:
Numpy vector as coordinates in Cartesian space, or an array of such.
:returns:
Numpy array of the same shape and structure where all vectors are
normalized. That is, each coordinate component is divided by its
vector's length.
"""
length = numpy.sum(vector * vector, axis=-1)
length = numpy.sqrt(length.reshape(length.shape + (1, )))
return vector / length | [
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66 | gem/oq-engine | openquake/hazardlib/geo/utils.py | point_to_polygon_distance | def point_to_polygon_distance(polygon, pxx, pyy):
"""
Calculate the distance to polygon for each point of the collection
on the 2d Cartesian plane.
:param polygon:
Shapely "Polygon" geometry object.
:param pxx:
List or numpy array of abscissae values of points to calculate
the distance from.
:param pyy:
Same structure as ``pxx``, but with ordinate values.
:returns:
Numpy array of distances in units of coordinate system. Points
that lie inside the polygon have zero distance.
"""
pxx = numpy.array(pxx)
pyy = numpy.array(pyy)
assert pxx.shape == pyy.shape
if pxx.ndim == 0:
pxx = pxx.reshape((1, ))
pyy = pyy.reshape((1, ))
result = numpy.array([
polygon.distance(shapely.geometry.Point(pxx.item(i), pyy.item(i)))
for i in range(pxx.size)
])
return result.reshape(pxx.shape) | python | def point_to_polygon_distance(polygon, pxx, pyy):
"""
Calculate the distance to polygon for each point of the collection
on the 2d Cartesian plane.
:param polygon:
Shapely "Polygon" geometry object.
:param pxx:
List or numpy array of abscissae values of points to calculate
the distance from.
:param pyy:
Same structure as ``pxx``, but with ordinate values.
:returns:
Numpy array of distances in units of coordinate system. Points
that lie inside the polygon have zero distance.
"""
pxx = numpy.array(pxx)
pyy = numpy.array(pyy)
assert pxx.shape == pyy.shape
if pxx.ndim == 0:
pxx = pxx.reshape((1, ))
pyy = pyy.reshape((1, ))
result = numpy.array([
polygon.distance(shapely.geometry.Point(pxx.item(i), pyy.item(i)))
for i in range(pxx.size)
])
return result.reshape(pxx.shape) | [
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List or numpy array of abscissae values of points to calculate
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67 | gem/oq-engine | openquake/hazardlib/geo/utils.py | cross_idl | def cross_idl(lon1, lon2, *lons):
"""
Return True if two longitude values define line crossing international date
line.
>>> cross_idl(-45, 45)
False
>>> cross_idl(-180, -179)
False
>>> cross_idl(180, 179)
False
>>> cross_idl(45, -45)
False
>>> cross_idl(0, 0)
False
>>> cross_idl(-170, 170)
True
>>> cross_idl(170, -170)
True
>>> cross_idl(-180, 180)
True
"""
lons = (lon1, lon2) + lons
l1, l2 = min(lons), max(lons)
# a line crosses the international date line if the end positions
# have different sign and they are more than 180 degrees longitude apart
return l1 * l2 < 0 and abs(l1 - l2) > 180 | python | def cross_idl(lon1, lon2, *lons):
"""
Return True if two longitude values define line crossing international date
line.
>>> cross_idl(-45, 45)
False
>>> cross_idl(-180, -179)
False
>>> cross_idl(180, 179)
False
>>> cross_idl(45, -45)
False
>>> cross_idl(0, 0)
False
>>> cross_idl(-170, 170)
True
>>> cross_idl(170, -170)
True
>>> cross_idl(-180, 180)
True
"""
lons = (lon1, lon2) + lons
l1, l2 = min(lons), max(lons)
# a line crosses the international date line if the end positions
# have different sign and they are more than 180 degrees longitude apart
return l1 * l2 < 0 and abs(l1 - l2) > 180 | [
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False
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False
>>> cross_idl(180, 179)
False
>>> cross_idl(45, -45)
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68 | gem/oq-engine | openquake/hazardlib/geo/utils.py | normalize_lons | def normalize_lons(l1, l2):
"""
An international date line safe way of returning a range of longitudes.
>>> normalize_lons(20, 30) # no IDL within the range
[(20, 30)]
>>> normalize_lons(-17, +17) # no IDL within the range
[(-17, 17)]
>>> normalize_lons(-178, +179)
[(-180, -178), (179, 180)]
>>> normalize_lons(178, -179)
[(-180, -179), (178, 180)]
>>> normalize_lons(179, -179)
[(-180, -179), (179, 180)]
>>> normalize_lons(177, -176)
[(-180, -176), (177, 180)]
"""
if l1 > l2: # exchange lons
l1, l2 = l2, l1
delta = l2 - l1
if l1 < 0 and l2 > 0 and delta > 180:
return [(-180, l1), (l2, 180)]
elif l1 > 0 and l2 > 180 and delta < 180:
return [(l1, 180), (-180, l2 - 360)]
elif l1 < -180 and l2 < 0 and delta < 180:
return [(l1 + 360, 180), (l2, -180)]
return [(l1, l2)] | python | def normalize_lons(l1, l2):
"""
An international date line safe way of returning a range of longitudes.
>>> normalize_lons(20, 30) # no IDL within the range
[(20, 30)]
>>> normalize_lons(-17, +17) # no IDL within the range
[(-17, 17)]
>>> normalize_lons(-178, +179)
[(-180, -178), (179, 180)]
>>> normalize_lons(178, -179)
[(-180, -179), (178, 180)]
>>> normalize_lons(179, -179)
[(-180, -179), (179, 180)]
>>> normalize_lons(177, -176)
[(-180, -176), (177, 180)]
"""
if l1 > l2: # exchange lons
l1, l2 = l2, l1
delta = l2 - l1
if l1 < 0 and l2 > 0 and delta > 180:
return [(-180, l1), (l2, 180)]
elif l1 > 0 and l2 > 180 and delta < 180:
return [(l1, 180), (-180, l2 - 360)]
elif l1 < -180 and l2 < 0 and delta < 180:
return [(l1 + 360, 180), (l2, -180)]
return [(l1, l2)] | [
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>>> normalize_lons(-17, +17) # no IDL within the range
[(-17, 17)]
>>> normalize_lons(-178, +179)
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[(-180, -179), (179, 180)]
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[(-180, -176), (177, 180)] | [
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69 | gem/oq-engine | openquake/hazardlib/geo/utils.py | _GeographicObjects.get_closest | def get_closest(self, lon, lat, depth=0):
"""
Get the closest object to the given longitude and latitude
and its distance.
:param lon: longitude in degrees
:param lat: latitude in degrees
:param depth: depth in km (default 0)
:returns: (object, distance)
"""
xyz = spherical_to_cartesian(lon, lat, depth)
min_dist, idx = self.kdtree.query(xyz)
return self.objects[idx], min_dist | python | def get_closest(self, lon, lat, depth=0):
"""
Get the closest object to the given longitude and latitude
and its distance.
:param lon: longitude in degrees
:param lat: latitude in degrees
:param depth: depth in km (default 0)
:returns: (object, distance)
"""
xyz = spherical_to_cartesian(lon, lat, depth)
min_dist, idx = self.kdtree.query(xyz)
return self.objects[idx], min_dist | [
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70 | gem/oq-engine | openquake/hazardlib/geo/utils.py | _GeographicObjects.assoc2 | def assoc2(self, assets_by_site, assoc_dist, mode, asset_refs):
"""
Associated a list of assets by site to the site collection used
to instantiate GeographicObjects.
:param assets_by_sites: a list of lists of assets
:param assoc_dist: the maximum distance for association
:param mode: 'strict', 'warn' or 'filter'
:param asset_ref: ID of the assets are a list of strings
:returns: filtered site collection, filtered assets by site, discarded
"""
assert mode in 'strict filter', mode
self.objects.filtered # self.objects must be a SiteCollection
asset_dt = numpy.dtype(
[('asset_ref', vstr), ('lon', F32), ('lat', F32)])
assets_by_sid = collections.defaultdict(list)
discarded = []
for assets in assets_by_site:
lon, lat = assets[0].location
obj, distance = self.get_closest(lon, lat)
if distance <= assoc_dist:
# keep the assets, otherwise discard them
assets_by_sid[obj['sids']].extend(assets)
elif mode == 'strict':
raise SiteAssociationError(
'There is nothing closer than %s km '
'to site (%s %s)' % (assoc_dist, lon, lat))
else:
discarded.extend(assets)
sids = sorted(assets_by_sid)
if not sids:
raise SiteAssociationError(
'Could not associate any site to any assets within the '
'asset_hazard_distance of %s km' % assoc_dist)
assets_by_site = [
sorted(assets_by_sid[sid], key=operator.attrgetter('ordinal'))
for sid in sids]
data = [(asset_refs[asset.ordinal],) + asset.location
for asset in discarded]
discarded = numpy.array(data, asset_dt)
return self.objects.filtered(sids), assets_by_site, discarded | python | def assoc2(self, assets_by_site, assoc_dist, mode, asset_refs):
"""
Associated a list of assets by site to the site collection used
to instantiate GeographicObjects.
:param assets_by_sites: a list of lists of assets
:param assoc_dist: the maximum distance for association
:param mode: 'strict', 'warn' or 'filter'
:param asset_ref: ID of the assets are a list of strings
:returns: filtered site collection, filtered assets by site, discarded
"""
assert mode in 'strict filter', mode
self.objects.filtered # self.objects must be a SiteCollection
asset_dt = numpy.dtype(
[('asset_ref', vstr), ('lon', F32), ('lat', F32)])
assets_by_sid = collections.defaultdict(list)
discarded = []
for assets in assets_by_site:
lon, lat = assets[0].location
obj, distance = self.get_closest(lon, lat)
if distance <= assoc_dist:
# keep the assets, otherwise discard them
assets_by_sid[obj['sids']].extend(assets)
elif mode == 'strict':
raise SiteAssociationError(
'There is nothing closer than %s km '
'to site (%s %s)' % (assoc_dist, lon, lat))
else:
discarded.extend(assets)
sids = sorted(assets_by_sid)
if not sids:
raise SiteAssociationError(
'Could not associate any site to any assets within the '
'asset_hazard_distance of %s km' % assoc_dist)
assets_by_site = [
sorted(assets_by_sid[sid], key=operator.attrgetter('ordinal'))
for sid in sids]
data = [(asset_refs[asset.ordinal],) + asset.location
for asset in discarded]
discarded = numpy.array(data, asset_dt)
return self.objects.filtered(sids), assets_by_site, discarded | [
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to instantiate GeographicObjects.
:param assets_by_sites: a list of lists of assets
:param assoc_dist: the maximum distance for association
:param mode: 'strict', 'warn' or 'filter'
:param asset_ref: ID of the assets are a list of strings
:returns: filtered site collection, filtered assets by site, discarded | [
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71 | gem/oq-engine | openquake/risklib/read_nrml.py | ffconvert | def ffconvert(fname, limit_states, ff, min_iml=1E-10):
"""
Convert a fragility function into a numpy array plus a bunch
of attributes.
:param fname: path to the fragility model file
:param limit_states: expected limit states
:param ff: fragility function node
:returns: a pair (array, dictionary)
"""
with context(fname, ff):
ffs = ff[1:]
imls = ff.imls
nodamage = imls.attrib.get('noDamageLimit')
if nodamage == 0:
# use a cutoff to avoid log(0) in GMPE.to_distribution_values
logging.warning('Found a noDamageLimit=0 in %s, line %s, '
'using %g instead', fname, ff.lineno, min_iml)
nodamage = min_iml
with context(fname, imls):
attrs = dict(format=ff['format'],
imt=imls['imt'],
id=ff['id'],
nodamage=nodamage)
LS = len(limit_states)
if LS != len(ffs):
with context(fname, ff):
raise InvalidFile('expected %d limit states, found %d' %
(LS, len(ffs)))
if ff['format'] == 'continuous':
minIML = float(imls['minIML'])
if minIML == 0:
# use a cutoff to avoid log(0) in GMPE.to_distribution_values
logging.warning('Found minIML=0 in %s, line %s, using %g instead',
fname, imls.lineno, min_iml)
minIML = min_iml
attrs['minIML'] = minIML
attrs['maxIML'] = float(imls['maxIML'])
array = numpy.zeros(LS, [('mean', F64), ('stddev', F64)])
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
if ls != node['ls']:
with context(fname, node):
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
array['mean'][i] = node['mean']
array['stddev'][i] = node['stddev']
elif ff['format'] == 'discrete':
attrs['imls'] = ~imls
valid.check_levels(attrs['imls'], attrs['imt'], min_iml)
num_poes = len(attrs['imls'])
array = numpy.zeros((LS, num_poes))
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
with context(fname, node):
if ls != node['ls']:
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
poes = (~node if isinstance(~node, list)
else valid.probabilities(~node))
if len(poes) != num_poes:
raise InvalidFile('expected %s, found' %
(num_poes, len(poes)))
array[i, :] = poes
# NB: the format is constrained in nrml.FragilityNode to be either
# discrete or continuous, there is no third option
return array, attrs | python | def ffconvert(fname, limit_states, ff, min_iml=1E-10):
"""
Convert a fragility function into a numpy array plus a bunch
of attributes.
:param fname: path to the fragility model file
:param limit_states: expected limit states
:param ff: fragility function node
:returns: a pair (array, dictionary)
"""
with context(fname, ff):
ffs = ff[1:]
imls = ff.imls
nodamage = imls.attrib.get('noDamageLimit')
if nodamage == 0:
# use a cutoff to avoid log(0) in GMPE.to_distribution_values
logging.warning('Found a noDamageLimit=0 in %s, line %s, '
'using %g instead', fname, ff.lineno, min_iml)
nodamage = min_iml
with context(fname, imls):
attrs = dict(format=ff['format'],
imt=imls['imt'],
id=ff['id'],
nodamage=nodamage)
LS = len(limit_states)
if LS != len(ffs):
with context(fname, ff):
raise InvalidFile('expected %d limit states, found %d' %
(LS, len(ffs)))
if ff['format'] == 'continuous':
minIML = float(imls['minIML'])
if minIML == 0:
# use a cutoff to avoid log(0) in GMPE.to_distribution_values
logging.warning('Found minIML=0 in %s, line %s, using %g instead',
fname, imls.lineno, min_iml)
minIML = min_iml
attrs['minIML'] = minIML
attrs['maxIML'] = float(imls['maxIML'])
array = numpy.zeros(LS, [('mean', F64), ('stddev', F64)])
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
if ls != node['ls']:
with context(fname, node):
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
array['mean'][i] = node['mean']
array['stddev'][i] = node['stddev']
elif ff['format'] == 'discrete':
attrs['imls'] = ~imls
valid.check_levels(attrs['imls'], attrs['imt'], min_iml)
num_poes = len(attrs['imls'])
array = numpy.zeros((LS, num_poes))
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
with context(fname, node):
if ls != node['ls']:
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
poes = (~node if isinstance(~node, list)
else valid.probabilities(~node))
if len(poes) != num_poes:
raise InvalidFile('expected %s, found' %
(num_poes, len(poes)))
array[i, :] = poes
# NB: the format is constrained in nrml.FragilityNode to be either
# discrete or continuous, there is no third option
return array, attrs | [
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:param fname: path to the fragility model file
:param limit_states: expected limit states
:param ff: fragility function node
:returns: a pair (array, dictionary) | [
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] | 8294553a0b8aba33fd96437a35065d03547d0040 | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/read_nrml.py#L152-L217 |
72 | gem/oq-engine | openquake/risklib/read_nrml.py | taxonomy | def taxonomy(value):
"""
Any ASCII character goes into a taxonomy, except spaces.
"""
try:
value.encode('ascii')
except UnicodeEncodeError:
raise ValueError('tag %r is not ASCII' % value)
if re.search(r'\s', value):
raise ValueError('The taxonomy %r contains whitespace chars' % value)
return value | python | def taxonomy(value):
"""
Any ASCII character goes into a taxonomy, except spaces.
"""
try:
value.encode('ascii')
except UnicodeEncodeError:
raise ValueError('tag %r is not ASCII' % value)
if re.search(r'\s', value):
raise ValueError('The taxonomy %r contains whitespace chars' % value)
return value | [
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73 | gem/oq-engine | openquake/risklib/read_nrml.py | update_validators | def update_validators():
"""
Call this to updade the global nrml.validators
"""
validators.update({
'fragilityFunction.id': valid.utf8, # taxonomy
'vulnerabilityFunction.id': valid.utf8, # taxonomy
'consequenceFunction.id': valid.utf8, # taxonomy
'asset.id': valid.asset_id,
'costType.name': valid.cost_type,
'costType.type': valid.cost_type_type,
'cost.type': valid.cost_type,
'area.type': valid.name,
'isAbsolute': valid.boolean,
'insuranceLimit': valid.positivefloat,
'deductible': valid.positivefloat,
'occupants': valid.positivefloat,
'value': valid.positivefloat,
'retrofitted': valid.positivefloat,
'number': valid.compose(valid.positivefloat, valid.nonzero),
'vulnerabilitySetID': str, # any ASCII string is fine
'vulnerabilityFunctionID': str, # any ASCII string is fine
'lossCategory': valid.utf8, # a description field
'lr': valid.probability,
'lossRatio': valid.positivefloats,
'coefficientsVariation': valid.positivefloats,
'probabilisticDistribution': valid.Choice('LN', 'BT'),
'dist': valid.Choice('LN', 'BT', 'PM'),
'meanLRs': valid.positivefloats,
'covLRs': valid.positivefloats,
'format': valid.ChoiceCI('discrete', 'continuous'),
'mean': valid.positivefloat,
'stddev': valid.positivefloat,
'minIML': valid.positivefloat,
'maxIML': valid.positivefloat,
'limitStates': valid.namelist,
'noDamageLimit': valid.NoneOr(valid.positivefloat),
'loss_type': valid_loss_types,
'losses': valid.positivefloats,
'averageLoss': valid.positivefloat,
'stdDevLoss': valid.positivefloat,
'ffs.type': valid.ChoiceCI('lognormal'),
'assetLifeExpectancy': valid.positivefloat,
'interestRate': valid.positivefloat,
'lossType': valid_loss_types,
'aalOrig': valid.positivefloat,
'aalRetr': valid.positivefloat,
'ratio': valid.positivefloat,
'cf': asset_mean_stddev,
'damage': damage_triple,
'damageStates': valid.namelist,
'taxonomy': taxonomy,
'tagNames': valid.namelist,
}) | python | def update_validators():
"""
Call this to updade the global nrml.validators
"""
validators.update({
'fragilityFunction.id': valid.utf8, # taxonomy
'vulnerabilityFunction.id': valid.utf8, # taxonomy
'consequenceFunction.id': valid.utf8, # taxonomy
'asset.id': valid.asset_id,
'costType.name': valid.cost_type,
'costType.type': valid.cost_type_type,
'cost.type': valid.cost_type,
'area.type': valid.name,
'isAbsolute': valid.boolean,
'insuranceLimit': valid.positivefloat,
'deductible': valid.positivefloat,
'occupants': valid.positivefloat,
'value': valid.positivefloat,
'retrofitted': valid.positivefloat,
'number': valid.compose(valid.positivefloat, valid.nonzero),
'vulnerabilitySetID': str, # any ASCII string is fine
'vulnerabilityFunctionID': str, # any ASCII string is fine
'lossCategory': valid.utf8, # a description field
'lr': valid.probability,
'lossRatio': valid.positivefloats,
'coefficientsVariation': valid.positivefloats,
'probabilisticDistribution': valid.Choice('LN', 'BT'),
'dist': valid.Choice('LN', 'BT', 'PM'),
'meanLRs': valid.positivefloats,
'covLRs': valid.positivefloats,
'format': valid.ChoiceCI('discrete', 'continuous'),
'mean': valid.positivefloat,
'stddev': valid.positivefloat,
'minIML': valid.positivefloat,
'maxIML': valid.positivefloat,
'limitStates': valid.namelist,
'noDamageLimit': valid.NoneOr(valid.positivefloat),
'loss_type': valid_loss_types,
'losses': valid.positivefloats,
'averageLoss': valid.positivefloat,
'stdDevLoss': valid.positivefloat,
'ffs.type': valid.ChoiceCI('lognormal'),
'assetLifeExpectancy': valid.positivefloat,
'interestRate': valid.positivefloat,
'lossType': valid_loss_types,
'aalOrig': valid.positivefloat,
'aalRetr': valid.positivefloat,
'ratio': valid.positivefloat,
'cf': asset_mean_stddev,
'damage': damage_triple,
'damageStates': valid.namelist,
'taxonomy': taxonomy,
'tagNames': valid.namelist,
}) | [
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74 | gem/oq-engine | openquake/calculators/extract.py | barray | def barray(iterlines):
"""
Array of bytes
"""
lst = [line.encode('utf-8') for line in iterlines]
arr = numpy.array(lst)
return arr | python | def barray(iterlines):
"""
Array of bytes
"""
lst = [line.encode('utf-8') for line in iterlines]
arr = numpy.array(lst)
return arr | [
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75 | gem/oq-engine | openquake/calculators/extract.py | losses_by_tag | def losses_by_tag(dstore, tag):
"""
Statistical average losses by tag. For instance call
$ oq extract losses_by_tag/occupancy
"""
dt = [(tag, vstr)] + dstore['oqparam'].loss_dt_list()
aids = dstore['assetcol/array'][tag]
dset, stats = _get(dstore, 'avg_losses')
arr = dset.value
tagvalues = dstore['assetcol/tagcol/' + tag][1:] # except tagvalue="?"
for s, stat in enumerate(stats):
out = numpy.zeros(len(tagvalues), dt)
for li, (lt, lt_dt) in enumerate(dt[1:]):
for i, tagvalue in enumerate(tagvalues):
out[i][tag] = tagvalue
counts = arr[aids == i + 1, s, li].sum()
if counts:
out[i][lt] = counts
yield stat, out | python | def losses_by_tag(dstore, tag):
"""
Statistical average losses by tag. For instance call
$ oq extract losses_by_tag/occupancy
"""
dt = [(tag, vstr)] + dstore['oqparam'].loss_dt_list()
aids = dstore['assetcol/array'][tag]
dset, stats = _get(dstore, 'avg_losses')
arr = dset.value
tagvalues = dstore['assetcol/tagcol/' + tag][1:] # except tagvalue="?"
for s, stat in enumerate(stats):
out = numpy.zeros(len(tagvalues), dt)
for li, (lt, lt_dt) in enumerate(dt[1:]):
for i, tagvalue in enumerate(tagvalues):
out[i][tag] = tagvalue
counts = arr[aids == i + 1, s, li].sum()
if counts:
out[i][lt] = counts
yield stat, out | [
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76 | gem/oq-engine | openquake/calculators/extract.py | WebExtractor.dump | def dump(self, fname):
"""
Dump the remote datastore on a local path.
"""
url = '%s/v1/calc/%d/datastore' % (self.server, self.calc_id)
resp = self.sess.get(url, stream=True)
down = 0
with open(fname, 'wb') as f:
logging.info('Saving %s', fname)
for chunk in resp.iter_content(CHUNKSIZE):
f.write(chunk)
down += len(chunk)
println('Downloaded {:,} bytes'.format(down))
print() | python | def dump(self, fname):
"""
Dump the remote datastore on a local path.
"""
url = '%s/v1/calc/%d/datastore' % (self.server, self.calc_id)
resp = self.sess.get(url, stream=True)
down = 0
with open(fname, 'wb') as f:
logging.info('Saving %s', fname)
for chunk in resp.iter_content(CHUNKSIZE):
f.write(chunk)
down += len(chunk)
println('Downloaded {:,} bytes'.format(down))
print() | [
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77 | gem/oq-engine | openquake/hazardlib/gsim/utils_swiss_gmpe.py | _compute_small_mag_correction_term | def _compute_small_mag_correction_term(C, mag, rhypo):
"""
small magnitude correction applied to the median values
"""
if mag >= 3.00 and mag < 5.5:
min_term = np.minimum(rhypo, C['Rm'])
max_term = np.maximum(min_term, 10)
term_ln = np.log(max_term / 20)
term_ratio = ((5.50 - mag) / C['a1'])
temp = (term_ratio) ** C['a2'] * (C['b1'] + C['b2'] * term_ln)
return 1 / np.exp(temp)
else:
return 1 | python | def _compute_small_mag_correction_term(C, mag, rhypo):
"""
small magnitude correction applied to the median values
"""
if mag >= 3.00 and mag < 5.5:
min_term = np.minimum(rhypo, C['Rm'])
max_term = np.maximum(min_term, 10)
term_ln = np.log(max_term / 20)
term_ratio = ((5.50 - mag) / C['a1'])
temp = (term_ratio) ** C['a2'] * (C['b1'] + C['b2'] * term_ln)
return 1 / np.exp(temp)
else:
return 1 | [
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78 | gem/oq-engine | openquake/hazardlib/gsim/utils_swiss_gmpe.py | _apply_adjustments | def _apply_adjustments(COEFFS, C_ADJ, tau_ss, mean, stddevs, sites, rup, dists,
imt, stddev_types, log_phi_ss, NL=None, tau_value=None):
"""
This method applies adjustments to the mean and standard deviation.
The small-magnitude adjustments are applied to mean, whereas the
embeded single station sigma logic tree is applied to the
total standard deviation.
"""
c1_dists = _compute_C1_term(C_ADJ, dists)
phi_ss = _compute_phi_ss(
C_ADJ, rup.mag, c1_dists, log_phi_ss, C_ADJ['mean_phi_ss']
)
mean_corr = np.exp(mean) * C_ADJ['k_adj'] * \
_compute_small_mag_correction_term(C_ADJ, rup.mag, dists)
mean_corr = np.log(mean_corr)
std_corr = _get_corr_stddevs(COEFFS[imt], tau_ss, stddev_types,
len(sites.vs30), phi_ss, NL, tau_value)
stddevs = np.array(std_corr)
return mean_corr, stddevs | python | def _apply_adjustments(COEFFS, C_ADJ, tau_ss, mean, stddevs, sites, rup, dists,
imt, stddev_types, log_phi_ss, NL=None, tau_value=None):
"""
This method applies adjustments to the mean and standard deviation.
The small-magnitude adjustments are applied to mean, whereas the
embeded single station sigma logic tree is applied to the
total standard deviation.
"""
c1_dists = _compute_C1_term(C_ADJ, dists)
phi_ss = _compute_phi_ss(
C_ADJ, rup.mag, c1_dists, log_phi_ss, C_ADJ['mean_phi_ss']
)
mean_corr = np.exp(mean) * C_ADJ['k_adj'] * \
_compute_small_mag_correction_term(C_ADJ, rup.mag, dists)
mean_corr = np.log(mean_corr)
std_corr = _get_corr_stddevs(COEFFS[imt], tau_ss, stddev_types,
len(sites.vs30), phi_ss, NL, tau_value)
stddevs = np.array(std_corr)
return mean_corr, stddevs | [
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79 | gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_info | def get_info(self, sm_id):
"""
Extract a CompositionInfo instance containing the single
model of index `sm_id`.
"""
sm = self.source_models[sm_id]
num_samples = sm.samples if self.num_samples else 0
return self.__class__(
self.gsim_lt, self.seed, num_samples, [sm], self.tot_weight) | python | def get_info(self, sm_id):
"""
Extract a CompositionInfo instance containing the single
model of index `sm_id`.
"""
sm = self.source_models[sm_id]
num_samples = sm.samples if self.num_samples else 0
return self.__class__(
self.gsim_lt, self.seed, num_samples, [sm], self.tot_weight) | [
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80 | gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_source_model | def get_source_model(self, src_group_id):
"""
Return the source model for the given src_group_id
"""
for smodel in self.source_models:
for src_group in smodel.src_groups:
if src_group.id == src_group_id:
return smodel | python | def get_source_model(self, src_group_id):
"""
Return the source model for the given src_group_id
"""
for smodel in self.source_models:
for src_group in smodel.src_groups:
if src_group.id == src_group_id:
return smodel | [
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81 | gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_model | def get_model(self, sm_id):
"""
Extract a CompositeSourceModel instance containing the single
model of index `sm_id`.
"""
sm = self.source_models[sm_id]
if self.source_model_lt.num_samples:
self.source_model_lt.num_samples = sm.samples
new = self.__class__(self.gsim_lt, self.source_model_lt, [sm],
self.optimize_same_id)
new.sm_id = sm_id
return new | python | def get_model(self, sm_id):
"""
Extract a CompositeSourceModel instance containing the single
model of index `sm_id`.
"""
sm = self.source_models[sm_id]
if self.source_model_lt.num_samples:
self.source_model_lt.num_samples = sm.samples
new = self.__class__(self.gsim_lt, self.source_model_lt, [sm],
self.optimize_same_id)
new.sm_id = sm_id
return new | [
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82 | gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.new | def new(self, sources_by_grp):
"""
Generate a new CompositeSourceModel from the given dictionary.
:param sources_by_group: a dictionary grp_id -> sources
:returns: a new CompositeSourceModel instance
"""
source_models = []
for sm in self.source_models:
src_groups = []
for src_group in sm.src_groups:
sg = copy.copy(src_group)
sg.sources = sorted(sources_by_grp.get(sg.id, []),
key=operator.attrgetter('id'))
src_groups.append(sg)
newsm = logictree.LtSourceModel(
sm.names, sm.weight, sm.path, src_groups,
sm.num_gsim_paths, sm.ordinal, sm.samples)
source_models.append(newsm)
new = self.__class__(self.gsim_lt, self.source_model_lt, source_models,
self.optimize_same_id)
new.info.update_eff_ruptures(new.get_num_ruptures())
new.info.tot_weight = new.get_weight()
return new | python | def new(self, sources_by_grp):
"""
Generate a new CompositeSourceModel from the given dictionary.
:param sources_by_group: a dictionary grp_id -> sources
:returns: a new CompositeSourceModel instance
"""
source_models = []
for sm in self.source_models:
src_groups = []
for src_group in sm.src_groups:
sg = copy.copy(src_group)
sg.sources = sorted(sources_by_grp.get(sg.id, []),
key=operator.attrgetter('id'))
src_groups.append(sg)
newsm = logictree.LtSourceModel(
sm.names, sm.weight, sm.path, src_groups,
sm.num_gsim_paths, sm.ordinal, sm.samples)
source_models.append(newsm)
new = self.__class__(self.gsim_lt, self.source_model_lt, source_models,
self.optimize_same_id)
new.info.update_eff_ruptures(new.get_num_ruptures())
new.info.tot_weight = new.get_weight()
return new | [
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83 | gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.check_dupl_sources | def check_dupl_sources(self): # used in print_csm_info
"""
Extracts duplicated sources, i.e. sources with the same source_id in
different source groups. Raise an exception if there are sources with
the same ID which are not duplicated.
:returns: a list of list of sources, ordered by source_id
"""
dd = collections.defaultdict(list)
for src_group in self.src_groups:
for src in src_group:
try:
srcid = src.source_id
except AttributeError: # src is a Node object
srcid = src['id']
dd[srcid].append(src)
dupl = []
for srcid, srcs in sorted(dd.items()):
if len(srcs) > 1:
_assert_equal_sources(srcs)
dupl.append(srcs)
return dupl | python | def check_dupl_sources(self): # used in print_csm_info
"""
Extracts duplicated sources, i.e. sources with the same source_id in
different source groups. Raise an exception if there are sources with
the same ID which are not duplicated.
:returns: a list of list of sources, ordered by source_id
"""
dd = collections.defaultdict(list)
for src_group in self.src_groups:
for src in src_group:
try:
srcid = src.source_id
except AttributeError: # src is a Node object
srcid = src['id']
dd[srcid].append(src)
dupl = []
for srcid, srcs in sorted(dd.items()):
if len(srcs) > 1:
_assert_equal_sources(srcs)
dupl.append(srcs)
return dupl | [
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84 | gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_sources | def get_sources(self, kind='all'):
"""
Extract the sources contained in the source models by optionally
filtering and splitting them, depending on the passed parameter.
"""
assert kind in ('all', 'indep', 'mutex'), kind
sources = []
for sm in self.source_models:
for src_group in sm.src_groups:
if kind in ('all', src_group.src_interdep):
for src in src_group:
if sm.samples > 1:
src.samples = sm.samples
sources.append(src)
return sources | python | def get_sources(self, kind='all'):
"""
Extract the sources contained in the source models by optionally
filtering and splitting them, depending on the passed parameter.
"""
assert kind in ('all', 'indep', 'mutex'), kind
sources = []
for sm in self.source_models:
for src_group in sm.src_groups:
if kind in ('all', src_group.src_interdep):
for src in src_group:
if sm.samples > 1:
src.samples = sm.samples
sources.append(src)
return sources | [
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85 | gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.init_serials | def init_serials(self, ses_seed):
"""
Generate unique seeds for each rupture with numpy.arange.
This should be called only in event based calculators
"""
sources = self.get_sources()
serial = ses_seed
for src in sources:
nr = src.num_ruptures
src.serial = serial
serial += nr | python | def init_serials(self, ses_seed):
"""
Generate unique seeds for each rupture with numpy.arange.
This should be called only in event based calculators
"""
sources = self.get_sources()
serial = ses_seed
for src in sources:
nr = src.num_ruptures
src.serial = serial
serial += nr | [
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86 | gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_maxweight | def get_maxweight(self, weight, concurrent_tasks, minweight=MINWEIGHT):
"""
Return an appropriate maxweight for use in the block_splitter
"""
totweight = self.get_weight(weight)
ct = concurrent_tasks or 1
mw = math.ceil(totweight / ct)
return max(mw, minweight) | python | def get_maxweight(self, weight, concurrent_tasks, minweight=MINWEIGHT):
"""
Return an appropriate maxweight for use in the block_splitter
"""
totweight = self.get_weight(weight)
ct = concurrent_tasks or 1
mw = math.ceil(totweight / ct)
return max(mw, minweight) | [
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87 | gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | weight_list_to_tuple | def weight_list_to_tuple(data, attr_name):
'''
Converts a list of values and corresponding weights to a tuple of values
'''
if len(data['Value']) != len(data['Weight']):
raise ValueError('Number of weights do not correspond to number of '
'attributes in %s' % attr_name)
weight = np.array(data['Weight'])
if fabs(np.sum(weight) - 1.) > 1E-7:
raise ValueError('Weights do not sum to 1.0 in %s' % attr_name)
data_tuple = []
for iloc, value in enumerate(data['Value']):
data_tuple.append((value, weight[iloc]))
return data_tuple | python | def weight_list_to_tuple(data, attr_name):
'''
Converts a list of values and corresponding weights to a tuple of values
'''
if len(data['Value']) != len(data['Weight']):
raise ValueError('Number of weights do not correspond to number of '
'attributes in %s' % attr_name)
weight = np.array(data['Weight'])
if fabs(np.sum(weight) - 1.) > 1E-7:
raise ValueError('Weights do not sum to 1.0 in %s' % attr_name)
data_tuple = []
for iloc, value in enumerate(data['Value']):
data_tuple.append((value, weight[iloc]))
return data_tuple | [
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88 | gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | parse_tect_region_dict_to_tuples | def parse_tect_region_dict_to_tuples(region_dict):
'''
Parses the tectonic regionalisation dictionary attributes to tuples
'''
output_region_dict = []
tuple_keys = ['Displacement_Length_Ratio', 'Shear_Modulus']
# Convert MSR string name to openquake.hazardlib.scalerel object
for region in region_dict:
for val_name in tuple_keys:
region[val_name] = weight_list_to_tuple(region[val_name],
val_name)
# MSR works differently - so call get_scaling_relation_tuple
region['Magnitude_Scaling_Relation'] = weight_list_to_tuple(
region['Magnitude_Scaling_Relation'],
'Magnitude Scaling Relation')
output_region_dict.append(region)
return output_region_dict | python | def parse_tect_region_dict_to_tuples(region_dict):
'''
Parses the tectonic regionalisation dictionary attributes to tuples
'''
output_region_dict = []
tuple_keys = ['Displacement_Length_Ratio', 'Shear_Modulus']
# Convert MSR string name to openquake.hazardlib.scalerel object
for region in region_dict:
for val_name in tuple_keys:
region[val_name] = weight_list_to_tuple(region[val_name],
val_name)
# MSR works differently - so call get_scaling_relation_tuple
region['Magnitude_Scaling_Relation'] = weight_list_to_tuple(
region['Magnitude_Scaling_Relation'],
'Magnitude Scaling Relation')
output_region_dict.append(region)
return output_region_dict | [
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89 | gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | get_scaling_relation_tuple | def get_scaling_relation_tuple(msr_dict):
'''
For a dictionary of scaling relation values convert string list to
object list and then to tuple
'''
# Convert MSR string name to openquake.hazardlib.scalerel object
for iloc, value in enumerate(msr_dict['Value']):
if not value in SCALE_REL_MAP.keys():
raise ValueError('Scaling relation %s not supported!' % value)
msr_dict['Value'][iloc] = SCALE_REL_MAP[value]()
return weight_list_to_tuple(msr_dict,
'Magnitude Scaling Relation') | python | def get_scaling_relation_tuple(msr_dict):
'''
For a dictionary of scaling relation values convert string list to
object list and then to tuple
'''
# Convert MSR string name to openquake.hazardlib.scalerel object
for iloc, value in enumerate(msr_dict['Value']):
if not value in SCALE_REL_MAP.keys():
raise ValueError('Scaling relation %s not supported!' % value)
msr_dict['Value'][iloc] = SCALE_REL_MAP[value]()
return weight_list_to_tuple(msr_dict,
'Magnitude Scaling Relation') | [
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90 | gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | FaultYmltoSource.read_file | def read_file(self, mesh_spacing=1.0):
'''
Reads the file and returns an instance of the FaultSource class.
:param float mesh_spacing:
Fault mesh spacing (km)
'''
# Process the tectonic regionalisation
tectonic_reg = self.process_tectonic_regionalisation()
model = mtkActiveFaultModel(self.data['Fault_Model_ID'],
self.data['Fault_Model_Name'])
for fault in self.data['Fault_Model']:
fault_geometry = self.read_fault_geometry(fault['Fault_Geometry'],
mesh_spacing)
if fault['Shear_Modulus']:
fault['Shear_Modulus'] = weight_list_to_tuple(
fault['Shear_Modulus'], '%s Shear Modulus' % fault['ID'])
if fault['Displacement_Length_Ratio']:
fault['Displacement_Length_Ratio'] = weight_list_to_tuple(
fault['Displacement_Length_Ratio'],
'%s Displacement to Length Ratio' % fault['ID'])
fault_source = mtkActiveFault(
fault['ID'],
fault['Fault_Name'],
fault_geometry,
weight_list_to_tuple(fault['Slip'], '%s - Slip' % fault['ID']),
float(fault['Rake']),
fault['Tectonic_Region'],
float(fault['Aseismic']),
weight_list_to_tuple(
fault['Scaling_Relation_Sigma'],
'%s Scaling_Relation_Sigma' % fault['ID']),
neotectonic_fault=None,
scale_rel=get_scaling_relation_tuple(
fault['Magnitude_Scaling_Relation']),
aspect_ratio=fault['Aspect_Ratio'],
shear_modulus=fault['Shear_Modulus'],
disp_length_ratio=fault['Displacement_Length_Ratio'])
if tectonic_reg:
fault_source.get_tectonic_regionalisation(
tectonic_reg,
fault['Tectonic_Region'])
assert isinstance(fault['MFD_Model'], list)
fault_source.generate_config_set(fault['MFD_Model'])
model.faults.append(fault_source)
return model, tectonic_reg | python | def read_file(self, mesh_spacing=1.0):
'''
Reads the file and returns an instance of the FaultSource class.
:param float mesh_spacing:
Fault mesh spacing (km)
'''
# Process the tectonic regionalisation
tectonic_reg = self.process_tectonic_regionalisation()
model = mtkActiveFaultModel(self.data['Fault_Model_ID'],
self.data['Fault_Model_Name'])
for fault in self.data['Fault_Model']:
fault_geometry = self.read_fault_geometry(fault['Fault_Geometry'],
mesh_spacing)
if fault['Shear_Modulus']:
fault['Shear_Modulus'] = weight_list_to_tuple(
fault['Shear_Modulus'], '%s Shear Modulus' % fault['ID'])
if fault['Displacement_Length_Ratio']:
fault['Displacement_Length_Ratio'] = weight_list_to_tuple(
fault['Displacement_Length_Ratio'],
'%s Displacement to Length Ratio' % fault['ID'])
fault_source = mtkActiveFault(
fault['ID'],
fault['Fault_Name'],
fault_geometry,
weight_list_to_tuple(fault['Slip'], '%s - Slip' % fault['ID']),
float(fault['Rake']),
fault['Tectonic_Region'],
float(fault['Aseismic']),
weight_list_to_tuple(
fault['Scaling_Relation_Sigma'],
'%s Scaling_Relation_Sigma' % fault['ID']),
neotectonic_fault=None,
scale_rel=get_scaling_relation_tuple(
fault['Magnitude_Scaling_Relation']),
aspect_ratio=fault['Aspect_Ratio'],
shear_modulus=fault['Shear_Modulus'],
disp_length_ratio=fault['Displacement_Length_Ratio'])
if tectonic_reg:
fault_source.get_tectonic_regionalisation(
tectonic_reg,
fault['Tectonic_Region'])
assert isinstance(fault['MFD_Model'], list)
fault_source.generate_config_set(fault['MFD_Model'])
model.faults.append(fault_source)
return model, tectonic_reg | [
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:param float mesh_spacing:
Fault mesh spacing (km) | [
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91 | gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | FaultYmltoSource.process_tectonic_regionalisation | def process_tectonic_regionalisation(self):
'''
Processes the tectonic regionalisation from the yaml file
'''
if 'tectonic_regionalisation' in self.data.keys():
tectonic_reg = TectonicRegionalisation()
tectonic_reg.populate_regions(
parse_tect_region_dict_to_tuples(
self.data['tectonic_regionalisation']))
else:
tectonic_reg = None
return tectonic_reg | python | def process_tectonic_regionalisation(self):
'''
Processes the tectonic regionalisation from the yaml file
'''
if 'tectonic_regionalisation' in self.data.keys():
tectonic_reg = TectonicRegionalisation()
tectonic_reg.populate_regions(
parse_tect_region_dict_to_tuples(
self.data['tectonic_regionalisation']))
else:
tectonic_reg = None
return tectonic_reg | [
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92 | gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | FaultYmltoSource.read_fault_geometry | def read_fault_geometry(self, geo_dict, mesh_spacing=1.0):
'''
Creates the fault geometry from the parameters specified in the
dictionary.
:param dict geo_dict:
Sub-dictionary of main fault dictionary containing only
the geometry attributes
:param float mesh_spacing:
Fault mesh spacing (km)
:returns:
Instance of SimpleFaultGeometry or ComplexFaultGeometry, depending
on typology
'''
if geo_dict['Fault_Typology'] == 'Simple':
# Simple fault geometry
raw_trace = geo_dict['Fault_Trace']
trace = Line([Point(raw_trace[ival], raw_trace[ival + 1])
for ival in range(0, len(raw_trace), 2)])
geometry = SimpleFaultGeometry(trace,
geo_dict['Dip'],
geo_dict['Upper_Depth'],
geo_dict['Lower_Depth'],
mesh_spacing)
elif geo_dict['Fault_Typology'] == 'Complex':
# Complex Fault Typology
trace = []
for raw_trace in geo_dict['Fault_Trace']:
fault_edge = Line(
[Point(raw_trace[ival], raw_trace[ival + 1],
raw_trace[ival + 2]) for ival in range(0, len(raw_trace),
3)])
trace.append(fault_edge)
geometry = ComplexFaultGeometry(trace, mesh_spacing)
else:
raise ValueError('Unrecognised or unsupported fault geometry!')
return geometry | python | def read_fault_geometry(self, geo_dict, mesh_spacing=1.0):
'''
Creates the fault geometry from the parameters specified in the
dictionary.
:param dict geo_dict:
Sub-dictionary of main fault dictionary containing only
the geometry attributes
:param float mesh_spacing:
Fault mesh spacing (km)
:returns:
Instance of SimpleFaultGeometry or ComplexFaultGeometry, depending
on typology
'''
if geo_dict['Fault_Typology'] == 'Simple':
# Simple fault geometry
raw_trace = geo_dict['Fault_Trace']
trace = Line([Point(raw_trace[ival], raw_trace[ival + 1])
for ival in range(0, len(raw_trace), 2)])
geometry = SimpleFaultGeometry(trace,
geo_dict['Dip'],
geo_dict['Upper_Depth'],
geo_dict['Lower_Depth'],
mesh_spacing)
elif geo_dict['Fault_Typology'] == 'Complex':
# Complex Fault Typology
trace = []
for raw_trace in geo_dict['Fault_Trace']:
fault_edge = Line(
[Point(raw_trace[ival], raw_trace[ival + 1],
raw_trace[ival + 2]) for ival in range(0, len(raw_trace),
3)])
trace.append(fault_edge)
geometry = ComplexFaultGeometry(trace, mesh_spacing)
else:
raise ValueError('Unrecognised or unsupported fault geometry!')
return geometry | [
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93 | gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014._get_distance_scaling_term | def _get_distance_scaling_term(self, C, mag, rrup):
"""
Returns the distance scaling parameter
"""
return (C["r1"] + C["r2"] * mag) * np.log10(rrup + C["r3"]) | python | def _get_distance_scaling_term(self, C, mag, rrup):
"""
Returns the distance scaling parameter
"""
return (C["r1"] + C["r2"] * mag) * np.log10(rrup + C["r3"]) | [
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94 | gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014._get_style_of_faulting_term | def _get_style_of_faulting_term(self, C, rake):
"""
Returns the style of faulting term. Cauzzi et al. determind SOF from
the plunge of the B-, T- and P-axes. For consistency with existing
GMPEs the Wells & Coppersmith model is preferred
"""
if rake > -150.0 and rake <= -30.0:
return C['fN']
elif rake > 30.0 and rake <= 150.0:
return C['fR']
else:
return C['fSS'] | python | def _get_style_of_faulting_term(self, C, rake):
"""
Returns the style of faulting term. Cauzzi et al. determind SOF from
the plunge of the B-, T- and P-axes. For consistency with existing
GMPEs the Wells & Coppersmith model is preferred
"""
if rake > -150.0 and rake <= -30.0:
return C['fN']
elif rake > 30.0 and rake <= 150.0:
return C['fR']
else:
return C['fSS'] | [
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95 | gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014Eurocode8._get_site_amplification_term | def _get_site_amplification_term(self, C, vs30):
"""
Returns the site amplification term on the basis of Eurocode 8
site class
"""
s_b, s_c, s_d = self._get_site_dummy_variables(vs30)
return (C["sB"] * s_b) + (C["sC"] * s_c) + (C["sD"] * s_d) | python | def _get_site_amplification_term(self, C, vs30):
"""
Returns the site amplification term on the basis of Eurocode 8
site class
"""
s_b, s_c, s_d = self._get_site_dummy_variables(vs30)
return (C["sB"] * s_b) + (C["sC"] * s_c) + (C["sD"] * s_d) | [
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96 | gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014Eurocode8._get_site_dummy_variables | def _get_site_dummy_variables(self, vs30):
"""
Returns the Eurocode 8 site class dummy variable
"""
s_b = np.zeros_like(vs30)
s_c = np.zeros_like(vs30)
s_d = np.zeros_like(vs30)
s_b[np.logical_and(vs30 >= 360., vs30 < 800.)] = 1.0
s_c[np.logical_and(vs30 >= 180., vs30 < 360.)] = 1.0
s_d[vs30 < 180] = 1.0
return s_b, s_c, s_d | python | def _get_site_dummy_variables(self, vs30):
"""
Returns the Eurocode 8 site class dummy variable
"""
s_b = np.zeros_like(vs30)
s_c = np.zeros_like(vs30)
s_d = np.zeros_like(vs30)
s_b[np.logical_and(vs30 >= 360., vs30 < 800.)] = 1.0
s_c[np.logical_and(vs30 >= 180., vs30 < 360.)] = 1.0
s_d[vs30 < 180] = 1.0
return s_b, s_c, s_d | [
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97 | gem/oq-engine | openquake/hmtk/faults/fault_models.py | RecurrenceBranch.get_recurrence | def get_recurrence(self, config):
'''
Calculates the recurrence model for the given settings as
an instance of the openquake.hmtk.models.IncrementalMFD
:param dict config:
Configuration settings of the magnitude frequency distribution.
'''
model = MFD_MAP[config['Model_Name']]()
model.setUp(config)
model.get_mmax(config, self.msr, self.rake, self.area)
model.mmax = model.mmax + (self.msr_sigma * model.mmax_sigma)
# As the Anderson & Luco arbitrary model requires the input of the
# displacement to length ratio
if 'AndersonLucoAreaMmax' in config['Model_Name']:
if not self.disp_length_ratio:
# If not defined then default to 1.25E-5
self.disp_length_ratio = 1.25E-5
min_mag, bin_width, occur_rates = model.get_mfd(
self.slip,
self.area, self.shear_modulus, self.disp_length_ratio)
else:
min_mag, bin_width, occur_rates = model.get_mfd(self.slip,
self.area,
self.shear_modulus)
self.recurrence = IncrementalMFD(min_mag, bin_width, occur_rates)
self.magnitudes = min_mag + np.cumsum(
bin_width *
np.ones(len(occur_rates), dtype=float)) - bin_width
self.max_mag = np.max(self.magnitudes) | python | def get_recurrence(self, config):
'''
Calculates the recurrence model for the given settings as
an instance of the openquake.hmtk.models.IncrementalMFD
:param dict config:
Configuration settings of the magnitude frequency distribution.
'''
model = MFD_MAP[config['Model_Name']]()
model.setUp(config)
model.get_mmax(config, self.msr, self.rake, self.area)
model.mmax = model.mmax + (self.msr_sigma * model.mmax_sigma)
# As the Anderson & Luco arbitrary model requires the input of the
# displacement to length ratio
if 'AndersonLucoAreaMmax' in config['Model_Name']:
if not self.disp_length_ratio:
# If not defined then default to 1.25E-5
self.disp_length_ratio = 1.25E-5
min_mag, bin_width, occur_rates = model.get_mfd(
self.slip,
self.area, self.shear_modulus, self.disp_length_ratio)
else:
min_mag, bin_width, occur_rates = model.get_mfd(self.slip,
self.area,
self.shear_modulus)
self.recurrence = IncrementalMFD(min_mag, bin_width, occur_rates)
self.magnitudes = min_mag + np.cumsum(
bin_width *
np.ones(len(occur_rates), dtype=float)) - bin_width
self.max_mag = np.max(self.magnitudes) | [
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:param dict config:
Configuration settings of the magnitude frequency distribution. | [
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98 | gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.get_tectonic_regionalisation | def get_tectonic_regionalisation(self, regionalisation, region_type=None):
'''
Defines the tectonic region and updates the shear modulus,
magnitude scaling relation and displacement to length ratio using
the regional values, if not previously defined for the fault
:param regionalistion:
Instance of the :class:
openquake.hmtk.faults.tectonic_regionalisaion.TectonicRegionalisation
:param str region_type:
Name of the region type - if not in regionalisation an error will
be raised
'''
if region_type:
self.trt = region_type
if not self.trt in regionalisation.key_list:
raise ValueError('Tectonic region classification missing or '
'not defined in regionalisation')
for iloc, key_val in enumerate(regionalisation.key_list):
if self.trt in key_val:
self.regionalisation = regionalisation.regionalisation[iloc]
# Update undefined shear modulus from tectonic regionalisation
if not self.shear_modulus:
self.shear_modulus = self.regionalisation.shear_modulus
# Update undefined scaling relation from tectonic
# regionalisation
if not self.msr:
self.msr = self.regionalisation.scaling_rel
# Update undefined displacement to length ratio from tectonic
# regionalisation
if not self.disp_length_ratio:
self.disp_length_ratio = \
self.regionalisation.disp_length_ratio
break
return | python | def get_tectonic_regionalisation(self, regionalisation, region_type=None):
'''
Defines the tectonic region and updates the shear modulus,
magnitude scaling relation and displacement to length ratio using
the regional values, if not previously defined for the fault
:param regionalistion:
Instance of the :class:
openquake.hmtk.faults.tectonic_regionalisaion.TectonicRegionalisation
:param str region_type:
Name of the region type - if not in regionalisation an error will
be raised
'''
if region_type:
self.trt = region_type
if not self.trt in regionalisation.key_list:
raise ValueError('Tectonic region classification missing or '
'not defined in regionalisation')
for iloc, key_val in enumerate(regionalisation.key_list):
if self.trt in key_val:
self.regionalisation = regionalisation.regionalisation[iloc]
# Update undefined shear modulus from tectonic regionalisation
if not self.shear_modulus:
self.shear_modulus = self.regionalisation.shear_modulus
# Update undefined scaling relation from tectonic
# regionalisation
if not self.msr:
self.msr = self.regionalisation.scaling_rel
# Update undefined displacement to length ratio from tectonic
# regionalisation
if not self.disp_length_ratio:
self.disp_length_ratio = \
self.regionalisation.disp_length_ratio
break
return | [
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:param str region_type:
Name of the region type - if not in regionalisation an error will
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99 | gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.select_catalogue | def select_catalogue(self, selector, distance, distance_metric="rupture",
upper_eq_depth=None, lower_eq_depth=None):
"""
Select earthquakes within a specied distance of the fault
"""
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
# rupture metric is selected
if ('rupture' in distance_metric):
# Use rupture distance
self.catalogue = selector.within_rupture_distance(
self.geometry.surface,
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.surface,
distance,
upper_depth=upper_eq_depth,
lower_depth=lower_eq_depth) | python | def select_catalogue(self, selector, distance, distance_metric="rupture",
upper_eq_depth=None, lower_eq_depth=None):
"""
Select earthquakes within a specied distance of the fault
"""
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
# rupture metric is selected
if ('rupture' in distance_metric):
# Use rupture distance
self.catalogue = selector.within_rupture_distance(
self.geometry.surface,
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.surface,
distance,
upper_depth=upper_eq_depth,
lower_depth=lower_eq_depth) | [
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