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gem/oq-engine | openquake/calculators/export/hazard.py | export_ruptures_csv | def export_ruptures_csv(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = dstore['oqparam']
if 'scenario' in oq.calculation_mode:
return []
dest = dstore.export_path('ruptures.csv')
header = ('rupid multiplicity mag centroid_lon centroid_lat '
'centroid_depth trt strike dip rake boundary').split()
rows = []
for rgetter in gen_rupture_getters(dstore):
rups = rgetter.get_ruptures()
rup_data = calc.RuptureData(rgetter.trt, rgetter.rlzs_by_gsim)
for r in rup_data.to_array(rups):
rows.append(
(r['rup_id'], r['multiplicity'], r['mag'],
r['lon'], r['lat'], r['depth'],
rgetter.trt, r['strike'], r['dip'], r['rake'],
r['boundary']))
rows.sort() # by rupture serial
comment = 'investigation_time=%s, ses_per_logic_tree_path=%s' % (
oq.investigation_time, oq.ses_per_logic_tree_path)
writers.write_csv(dest, rows, header=header, sep='\t', comment=comment)
return [dest] | python | def export_ruptures_csv(ekey, dstore):
oq = dstore['oqparam']
if 'scenario' in oq.calculation_mode:
return []
dest = dstore.export_path('ruptures.csv')
header = ('rupid multiplicity mag centroid_lon centroid_lat '
'centroid_depth trt strike dip rake boundary').split()
rows = []
for rgetter in gen_rupture_getters(dstore):
rups = rgetter.get_ruptures()
rup_data = calc.RuptureData(rgetter.trt, rgetter.rlzs_by_gsim)
for r in rup_data.to_array(rups):
rows.append(
(r['rup_id'], r['multiplicity'], r['mag'],
r['lon'], r['lat'], r['depth'],
rgetter.trt, r['strike'], r['dip'], r['rake'],
r['boundary']))
rows.sort()
comment = 'investigation_time=%s, ses_per_logic_tree_path=%s' % (
oq.investigation_time, oq.ses_per_logic_tree_path)
writers.write_csv(dest, rows, header=header, sep='\t', comment=comment)
return [dest] | [
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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):
curves = util.compose_arrays(sitemesh, array)
writers.write_csv(dest, curves, comment=comment)
return [dest] | [
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gem/oq-engine | openquake/calculators/export/hazard.py | add_imt | def add_imt(fname, imt):
"""
>>> add_imt('/path/to/hcurve_23.csv', 'SA(0.1)')
'/path/to/hcurve-SA(0.1)_23.csv'
"""
name = os.path.basename(fname)
newname = re.sub(r'(_\d+\.)', '-%s\\1' % imt, name)
return os.path.join(os.path.dirname(fname), newname) | python | def add_imt(fname, imt):
name = os.path.basename(fname)
newname = re.sub(r'(_\d+\.)', '-%s\\1' % imt, name)
return os.path.join(os.path.dirname(fname), newname) | [
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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):
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 sitecol: site collection
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gem/oq-engine | openquake/calculators/export/hazard.py | hazard_curve_name | def hazard_curve_name(dstore, ekey, kind, rlzs_assoc):
"""
:param calc_id: the calculation ID
:param ekey: the export key
:param kind: the kind of key
:param rlzs_assoc: a RlzsAssoc instance
"""
key, fmt = ekey
prefix = {'hcurves': 'hazard_curve', 'hmaps': 'hazard_map',
'uhs': 'hazard_uhs'}[key]
if kind.startswith('quantile-'): # strip the 7 characters 'hazard_'
fname = dstore.build_fname('quantile_' + prefix[7:], kind[9:], fmt)
else:
fname = dstore.build_fname(prefix, kind, fmt)
return fname | python | def hazard_curve_name(dstore, ekey, kind, rlzs_assoc):
key, fmt = ekey
prefix = {'hcurves': 'hazard_curve', 'hmaps': 'hazard_map',
'uhs': 'hazard_uhs'}[key]
if kind.startswith('quantile-'):
fname = dstore.build_fname('quantile_' + prefix[7:], kind[9:], fmt)
else:
fname = dstore.build_fname(prefix, kind, fmt)
return fname | [
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gem/oq-engine | openquake/calculators/export/hazard.py | get_kkf | def get_kkf(ekey):
"""
:param ekey: export key, for instance ('uhs/rlz-1', 'xml')
:returns: key, kind and fmt from the export key, i.e. 'uhs', 'rlz-1', 'xml'
"""
key, fmt = ekey
if '/' in key:
key, kind = key.split('/', 1)
else:
kind = ''
return key, kind, fmt | python | def get_kkf(ekey):
key, fmt = ekey
if '/' in key:
key, kind = key.split('/', 1)
else:
kind = ''
return key, kind, fmt | [
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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):
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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gem/oq-engine | openquake/calculators/export/hazard.py | get_metadata | def get_metadata(realizations, kind):
"""
:param list realizations:
realization objects
:param str kind:
kind of data, i.e. a key in the datastore
:returns:
a dictionary with smlt_path, gsimlt_path, statistics, quantile_value
"""
metadata = {}
if kind.startswith('rlz-'):
rlz = realizations[int(kind[4:])]
metadata['smlt_path'] = '_'.join(rlz.sm_lt_path)
metadata['gsimlt_path'] = rlz.gsim_rlz.uid
elif kind.startswith('quantile-'):
metadata['statistics'] = 'quantile'
metadata['quantile_value'] = float(kind[9:])
elif kind == 'mean':
metadata['statistics'] = 'mean'
elif kind == 'max':
metadata['statistics'] = 'max'
elif kind == 'std':
metadata['statistics'] = 'std'
return metadata | python | def get_metadata(realizations, kind):
metadata = {}
if kind.startswith('rlz-'):
rlz = realizations[int(kind[4:])]
metadata['smlt_path'] = '_'.join(rlz.sm_lt_path)
metadata['gsimlt_path'] = rlz.gsim_rlz.uid
elif kind.startswith('quantile-'):
metadata['statistics'] = 'quantile'
metadata['quantile_value'] = float(kind[9:])
elif kind == 'mean':
metadata['statistics'] = 'mean'
elif kind == 'max':
metadata['statistics'] = 'max'
elif kind == 'std':
metadata['statistics'] = 'std'
return metadata | [
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gem/oq-engine | openquake/calculators/export/hazard.py | export_gmf | def export_gmf(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = dstore['oqparam']
if not oq.calculation_mode.startswith('scenario'):
return []
sitecol = dstore['sitecol']
investigation_time = (None if oq.calculation_mode == 'scenario'
else oq.investigation_time)
fmt = ekey[-1]
gmf_data = dstore['gmf_data']
nbytes = gmf_data.attrs['nbytes']
logging.info('Internal size of the GMFs: %s', humansize(nbytes))
if nbytes > GMF_MAX_SIZE:
logging.warning(GMF_WARNING, dstore.filename)
data = gmf_data['data'].value
ses_idx = 1 # for scenario only
events = []
for eid, gmfa in group_array(data, 'eid').items():
rup = Event(eid, ses_idx, sorted(set(gmfa['sid'])), gmfa)
events.append(rup)
fname = dstore.build_fname('gmf', 'scenario', fmt)
writer = hazard_writers.EventBasedGMFXMLWriter(
fname, sm_lt_path='', gsim_lt_path='')
writer.serialize(
GmfCollection(sitecol, oq.imtls, events, investigation_time))
return [fname] | python | def export_gmf(ekey, dstore):
oq = dstore['oqparam']
if not oq.calculation_mode.startswith('scenario'):
return []
sitecol = dstore['sitecol']
investigation_time = (None if oq.calculation_mode == 'scenario'
else oq.investigation_time)
fmt = ekey[-1]
gmf_data = dstore['gmf_data']
nbytes = gmf_data.attrs['nbytes']
logging.info('Internal size of the GMFs: %s', humansize(nbytes))
if nbytes > GMF_MAX_SIZE:
logging.warning(GMF_WARNING, dstore.filename)
data = gmf_data['data'].value
ses_idx = 1
events = []
for eid, gmfa in group_array(data, 'eid').items():
rup = Event(eid, ses_idx, sorted(set(gmfa['sid'])), gmfa)
events.append(rup)
fname = dstore.build_fname('gmf', 'scenario', fmt)
writer = hazard_writers.EventBasedGMFXMLWriter(
fname, sm_lt_path='', gsim_lt_path='')
writer.serialize(
GmfCollection(sitecol, oq.imtls, events, investigation_time))
return [fname] | [
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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):
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'
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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gem/oq-engine | openquake/hazardlib/gsim/bradley_2013b.py | Bradley2013bChchCBD.get_mean_and_stddevs | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extracting dictionary of coefficients specific to required
# intensity measure type.
C = self.COEFFS[imt]
if isinstance(imt, PGA):
imt_per = 0.0
else:
imt_per = imt.period
# Fix site parameters for consistent dS2S application.
sites.vs30 = np.array([250])
sites.z1pt0 = np.array([330])
# intensity on a reference soil is used for both mean
# and stddev calculations.
ln_y_ref = self._get_ln_y_ref(rup, dists, C)
# exp1 and exp2 are parts of eq. 7
exp1 = np.exp(C['phi3'] * (sites.vs30.clip(-np.inf, 1130) - 360))
exp2 = np.exp(C['phi3'] * (1130 - 360))
# v1 is the period dependent site term. The Vs30 above which, the
# amplification is constant
v1 = self._get_v1(imt)
# Get log-mean from regular unadjusted model
b13a_mean = self._get_mean(sites, C, ln_y_ref, exp1, exp2, v1)
# Adjust mean and standard deviation
mean = b13a_mean + self._get_dL2L(imt_per) + self._get_dS2S(imt_per)
mean += convert_to_LHC(imt)
stddevs = self._get_adjusted_stddevs(sites, rup, C, stddev_types,
ln_y_ref, exp1, exp2, imt_per)
return mean, stddevs | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
C = self.COEFFS[imt]
if isinstance(imt, PGA):
imt_per = 0.0
else:
imt_per = imt.period
sites.vs30 = np.array([250])
sites.z1pt0 = np.array([330])
ln_y_ref = self._get_ln_y_ref(rup, dists, C)
exp1 = np.exp(C['phi3'] * (sites.vs30.clip(-np.inf, 1130) - 360))
exp2 = np.exp(C['phi3'] * (1130 - 360))
v1 = self._get_v1(imt)
b13a_mean = self._get_mean(sites, C, ln_y_ref, exp1, exp2, v1)
mean = b13a_mean + self._get_dL2L(imt_per) + self._get_dS2S(imt_per)
mean += convert_to_LHC(imt)
stddevs = self._get_adjusted_stddevs(sites, rup, C, stddev_types,
ln_y_ref, exp1, exp2, imt_per)
return mean, stddevs | [
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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):
return y_i + (y_ip1 - y_i) / (t_ip1 - t_i) * (imt_per - t_i) | [
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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):
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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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):
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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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:
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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):
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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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):
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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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):
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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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):
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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Typically the operation is filtering a source, that can fail for
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gem/oq-engine | openquake/hazardlib/calc/filters.py | split_sources | def split_sources(srcs):
"""
:param srcs: sources
:returns: a pair (split sources, split time) or just the split_sources
"""
from openquake.hazardlib.source import splittable
sources = []
split_time = {} # src.id -> time
for src in srcs:
t0 = time.time()
mag_a, mag_b = src.get_min_max_mag()
min_mag = src.min_mag
if mag_b < min_mag: # discard the source completely
continue
has_serial = hasattr(src, 'serial')
if has_serial:
src.serial = numpy.arange(
src.serial, src.serial + src.num_ruptures)
if not splittable(src):
sources.append(src)
split_time[src.id] = time.time() - t0
continue
if min_mag:
splits = []
for s in src:
s.min_mag = min_mag
mag_a, mag_b = s.get_min_max_mag()
if mag_b < min_mag:
continue
s.num_ruptures = s.count_ruptures()
if s.num_ruptures:
splits.append(s)
else:
splits = list(src)
split_time[src.id] = time.time() - t0
sources.extend(splits)
has_samples = hasattr(src, 'samples')
if len(splits) > 1:
start = 0
for i, split in enumerate(splits):
split.source_id = '%s:%s' % (src.source_id, i)
split.src_group_id = src.src_group_id
split.id = src.id
if has_serial:
nr = split.num_ruptures
split.serial = src.serial[start:start + nr]
start += nr
if has_samples:
split.samples = src.samples
elif splits: # single source
splits[0].id = src.id
if has_serial:
splits[0].serial = src.serial
if has_samples:
splits[0].samples = src.samples
return sources, split_time | python | def split_sources(srcs):
from openquake.hazardlib.source import splittable
sources = []
split_time = {}
for src in srcs:
t0 = time.time()
mag_a, mag_b = src.get_min_max_mag()
min_mag = src.min_mag
if mag_b < min_mag:
continue
has_serial = hasattr(src, 'serial')
if has_serial:
src.serial = numpy.arange(
src.serial, src.serial + src.num_ruptures)
if not splittable(src):
sources.append(src)
split_time[src.id] = time.time() - t0
continue
if min_mag:
splits = []
for s in src:
s.min_mag = min_mag
mag_a, mag_b = s.get_min_max_mag()
if mag_b < min_mag:
continue
s.num_ruptures = s.count_ruptures()
if s.num_ruptures:
splits.append(s)
else:
splits = list(src)
split_time[src.id] = time.time() - t0
sources.extend(splits)
has_samples = hasattr(src, 'samples')
if len(splits) > 1:
start = 0
for i, split in enumerate(splits):
split.source_id = '%s:%s' % (src.source_id, i)
split.src_group_id = src.src_group_id
split.id = src.id
if has_serial:
nr = split.num_ruptures
split.serial = src.serial[start:start + nr]
start += nr
if has_samples:
split.samples = src.samples
elif splits:
splits[0].id = src.id
if has_serial:
splits[0].serial = src.serial
if has_samples:
splits[0].samples = src.samples
return sources, split_time | [
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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):
if trt is None:
maxdist = max(self(trt, mag) for trt in self.dic)
else:
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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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):
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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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):
if 'sitecol' in vars(self):
return self.__dict__['sitecol']
if self.filename is None or not os.path.exists(self.filename):
return
with hdf5.File(self.filename, 'r') as h5:
self.__dict__['sitecol'] = sc = h5.get('sitecol')
return sc | [
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gem/oq-engine | openquake/hazardlib/calc/filters.py | SourceFilter.get_rectangle | def get_rectangle(self, src):
"""
:param src: a source object
:returns: ((min_lon, min_lat), width, height), useful for plotting
"""
min_lon, min_lat, max_lon, max_lat = (
self.integration_distance.get_affected_box(src))
return (min_lon, min_lat), (max_lon - min_lon) % 360, max_lat - min_lat | python | def get_rectangle(self, src):
min_lon, min_lat, max_lon, max_lat = (
self.integration_distance.get_affected_box(src))
return (min_lon, min_lat), (max_lon - min_lon) % 360, max_lat - min_lat | [
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gem/oq-engine | openquake/hazardlib/calc/filters.py | SourceFilter.get_bounding_boxes | def get_bounding_boxes(self, trt=None, mag=None):
"""
:param trt: a tectonic region type (used for the integration distance)
:param mag: a magnitude (used for the integration distance)
:returns: a list of bounding boxes, one per site
"""
bbs = []
for site in self.sitecol:
bb = self.integration_distance.get_bounding_box(
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bbs.append(bb)
return bbs | python | def get_bounding_boxes(self, trt=None, mag=None):
bbs = []
for site in self.sitecol:
bb = self.integration_distance.get_bounding_box(
site.location.longitude, site.location.latitude, trt, mag)
bbs.append(bb)
return bbs | [
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gem/oq-engine | openquake/hazardlib/calc/filters.py | SourceFilter.close_sids | def close_sids(self, rec, trt, mag):
"""
:param rec:
a record with fields minlon, minlat, maxlon, maxlat
:param trt:
tectonic region type string
:param mag:
magnitude
:returns:
the site indices within the bounding box enlarged by the integration
distance for the given TRT and magnitude
"""
if self.sitecol is None:
return []
elif not self.integration_distance: # do not filter
return self.sitecol.sids
if hasattr(rec, 'dtype'):
bbox = rec['minlon'], rec['minlat'], rec['maxlon'], rec['maxlat']
else:
bbox = rec # assume it is a 4-tuple
maxdist = self.integration_distance(trt, mag)
a1 = min(maxdist * KM_TO_DEGREES, 90)
a2 = min(angular_distance(maxdist, bbox[1], bbox[3]), 180)
bb = bbox[0] - a2, bbox[1] - a1, bbox[2] + a2, bbox[3] + a1
if hasattr(self, 'index'): # RtreeFilter
return within(bb, self.index)
return self.sitecol.within_bbox(bb) | python | def close_sids(self, rec, trt, mag):
if self.sitecol is None:
return []
elif not self.integration_distance:
return self.sitecol.sids
if hasattr(rec, 'dtype'):
bbox = rec['minlon'], rec['minlat'], rec['maxlon'], rec['maxlat']
else:
bbox = rec
maxdist = self.integration_distance(trt, mag)
a1 = min(maxdist * KM_TO_DEGREES, 90)
a2 = min(angular_distance(maxdist, bbox[1], bbox[3]), 180)
bb = bbox[0] - a2, bbox[1] - a1, bbox[2] + a2, bbox[3] + a1
if hasattr(self, 'index'):
return within(bb, self.index)
return self.sitecol.within_bbox(bb) | [
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a record with fields minlon, minlat, maxlon, maxlat
:param trt:
tectonic region type string
:param mag:
magnitude
:returns:
the site indices within the bounding box enlarged by the integration
distance for the given TRT and magnitude | [
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gem/oq-engine | openquake/hazardlib/calc/filters.py | SourceFilter.filter | def filter(self, sources):
"""
:param sources: a sequence of sources
:yields: sources with .indices
"""
for src in sources:
if hasattr(src, 'indices'): # already filtered
yield src
continue
box = self.integration_distance.get_affected_box(src)
indices = self.sitecol.within_bbox(box)
if len(indices):
src.indices = indices
yield src | python | def filter(self, sources):
for src in sources:
if hasattr(src, 'indices'):
yield src
continue
box = self.integration_distance.get_affected_box(src)
indices = self.sitecol.within_bbox(box)
if len(indices):
src.indices = indices
yield src | [
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gem/oq-engine | openquake/hazardlib/calc/filters.py | RtreeFilter.filter | def filter(self, sources):
"""
:param sources: a sequence of sources
:yields: rtree-filtered sources
"""
if self.sitecol is None: # do not filter
yield from sources
return
for src in sources:
box = self.integration_distance.get_affected_box(src)
indices = within(box, self.index)
if len(indices):
src.indices = indices
yield src | python | def filter(self, sources):
if self.sitecol is None:
yield from sources
return
for src in sources:
box = self.integration_distance.get_affected_box(src)
indices = within(box, self.index)
if len(indices):
src.indices = indices
yield src | [
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gem/oq-engine | openquake/commands/to_shapefile.py | to_shapefile | def to_shapefile(output, input_nrml_file, validate):
"""
Convert a NRML source model file to ESRI Shapefile(s).
For each type of source geometry defined in the NRML file (point, area,
simple fault, complex fault, planar) a separate shapefile is created. Each
shapefile is differentiated by a specific ending('_point', '_area',
'_simple', '_complex', '_planar').
NB: nonparametric sources are not supported.
"""
input_parser = shapefileparser.SourceModelParser()
source_model = input_parser.read(input_nrml_file, validate)
if not output:
output = os.path.splitext(input_nrml_file)[0]
print('Extracting %s_ files' % output)
shapefileparser.ShapefileParser().write(output, source_model) | python | def to_shapefile(output, input_nrml_file, validate):
input_parser = shapefileparser.SourceModelParser()
source_model = input_parser.read(input_nrml_file, validate)
if not output:
output = os.path.splitext(input_nrml_file)[0]
print('Extracting %s_ files' % output)
shapefileparser.ShapefileParser().write(output, source_model) | [
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gem/oq-engine | openquake/hazardlib/geo/surface/simple_fault.py | simple_fault_node | def simple_fault_node(fault_trace, dip, upper_depth, lower_depth):
"""
:param fault_trace: an object with an attribute .points
:param dip: dip parameter
:param upper_depth: upper seismogenic depth
:param lower_depth: lower seismogenic depth
:returns: a Node of kind simpleFaultGeometry
"""
node = Node('simpleFaultGeometry')
line = []
for p in fault_trace.points:
line.append(p.longitude)
line.append(p.latitude)
node.append(Node('gml:LineString', nodes=[Node('gml:posList', {}, line)]))
node.append(Node('dip', {}, dip))
node.append(Node('upperSeismoDepth', {}, upper_depth))
node.append(Node('lowerSeismoDepth', {}, lower_depth))
return node | python | def simple_fault_node(fault_trace, dip, upper_depth, lower_depth):
node = Node('simpleFaultGeometry')
line = []
for p in fault_trace.points:
line.append(p.longitude)
line.append(p.latitude)
node.append(Node('gml:LineString', nodes=[Node('gml:posList', {}, line)]))
node.append(Node('dip', {}, dip))
node.append(Node('upperSeismoDepth', {}, upper_depth))
node.append(Node('lowerSeismoDepth', {}, lower_depth))
return node | [
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gem/oq-engine | openquake/hazardlib/geo/surface/simple_fault.py | SimpleFaultSurface.check_fault_data | def check_fault_data(cls, fault_trace, upper_seismogenic_depth,
lower_seismogenic_depth, dip, mesh_spacing):
"""
Verify the fault data and raise ``ValueError`` if anything is wrong.
This method doesn't have to be called by hands before creating the
surface object, because it is called from :meth:`from_fault_data`.
"""
if not len(fault_trace) >= 2:
raise ValueError("the fault trace must have at least two points")
if not fault_trace.horizontal():
raise ValueError("the fault trace must be horizontal")
tlats = [point.latitude for point in fault_trace.points]
tlons = [point.longitude for point in fault_trace.points]
if geo_utils.line_intersects_itself(tlons, tlats):
raise ValueError("fault trace intersects itself")
if not 0.0 < dip <= 90.0:
raise ValueError("dip must be between 0.0 and 90.0")
if not lower_seismogenic_depth > upper_seismogenic_depth:
raise ValueError("lower seismogenic depth must be greater than "
"upper seismogenic depth")
if not upper_seismogenic_depth >= fault_trace[0].depth:
raise ValueError("upper seismogenic depth must be greater than "
"or equal to depth of fault trace")
if not mesh_spacing > 0.0:
raise ValueError("mesh spacing must be positive") | python | def check_fault_data(cls, fault_trace, upper_seismogenic_depth,
lower_seismogenic_depth, dip, mesh_spacing):
if not len(fault_trace) >= 2:
raise ValueError("the fault trace must have at least two points")
if not fault_trace.horizontal():
raise ValueError("the fault trace must be horizontal")
tlats = [point.latitude for point in fault_trace.points]
tlons = [point.longitude for point in fault_trace.points]
if geo_utils.line_intersects_itself(tlons, tlats):
raise ValueError("fault trace intersects itself")
if not 0.0 < dip <= 90.0:
raise ValueError("dip must be between 0.0 and 90.0")
if not lower_seismogenic_depth > upper_seismogenic_depth:
raise ValueError("lower seismogenic depth must be greater than "
"upper seismogenic depth")
if not upper_seismogenic_depth >= fault_trace[0].depth:
raise ValueError("upper seismogenic depth must be greater than "
"or equal to depth of fault trace")
if not mesh_spacing > 0.0:
raise ValueError("mesh spacing must be positive") | [
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gem/oq-engine | openquake/hazardlib/geo/surface/simple_fault.py | SimpleFaultSurface.from_fault_data | def from_fault_data(cls, fault_trace, upper_seismogenic_depth,
lower_seismogenic_depth, dip, mesh_spacing):
"""
Create and return a fault surface using fault source data.
:param openquake.hazardlib.geo.line.Line fault_trace:
Geographical line representing the intersection between the fault
surface and the earth surface. The line must be horizontal (i.e.
all depth values must be equal). If the depths are not given, they
are assumed to be zero, meaning the trace intersects the surface at
sea level, e.g. fault_trace = Line([Point(1, 1), Point(1, 2)]).
: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.
:param mesh_spacing:
Distance between two subsequent points in a mesh, in km.
:returns:
An instance of :class:`SimpleFaultSurface` created using that data.
Uses :meth:`check_fault_data` for checking parameters.
"""
cls.check_fault_data(fault_trace, upper_seismogenic_depth,
lower_seismogenic_depth, dip, mesh_spacing)
# Loops over points in the top edge, for each point
# on the top edge compute corresponding point on the bottom edge, then
# computes equally spaced points between top and bottom points.
vdist_top = upper_seismogenic_depth - fault_trace[0].depth
vdist_bottom = lower_seismogenic_depth - fault_trace[0].depth
hdist_top = vdist_top / math.tan(math.radians(dip))
hdist_bottom = vdist_bottom / math.tan(math.radians(dip))
strike = fault_trace[0].azimuth(fault_trace[-1])
azimuth = (strike + 90.0) % 360
mesh = []
for point in fault_trace.resample(mesh_spacing):
top = point.point_at(hdist_top, vdist_top, azimuth)
bottom = point.point_at(hdist_bottom, vdist_bottom, azimuth)
mesh.append(top.equally_spaced_points(bottom, mesh_spacing))
# number of rows corresponds to number of points along dip
# number of columns corresponds to number of points along strike
surface_points = numpy.array(mesh).transpose().tolist()
mesh = RectangularMesh.from_points_list(surface_points)
assert 1 not in mesh.shape, (
"Mesh must have at least 2 nodes along both length and width."
" Possible cause: Mesh spacing could be too large with respect to"
" the fault length and width."
)
self = cls(mesh)
self.surface_nodes = [simple_fault_node(
fault_trace, dip,
upper_seismogenic_depth, lower_seismogenic_depth)]
return self | python | def from_fault_data(cls, fault_trace, upper_seismogenic_depth,
lower_seismogenic_depth, dip, mesh_spacing):
cls.check_fault_data(fault_trace, upper_seismogenic_depth,
lower_seismogenic_depth, dip, mesh_spacing)
vdist_top = upper_seismogenic_depth - fault_trace[0].depth
vdist_bottom = lower_seismogenic_depth - fault_trace[0].depth
hdist_top = vdist_top / math.tan(math.radians(dip))
hdist_bottom = vdist_bottom / math.tan(math.radians(dip))
strike = fault_trace[0].azimuth(fault_trace[-1])
azimuth = (strike + 90.0) % 360
mesh = []
for point in fault_trace.resample(mesh_spacing):
top = point.point_at(hdist_top, vdist_top, azimuth)
bottom = point.point_at(hdist_bottom, vdist_bottom, azimuth)
mesh.append(top.equally_spaced_points(bottom, mesh_spacing))
surface_points = numpy.array(mesh).transpose().tolist()
mesh = RectangularMesh.from_points_list(surface_points)
assert 1 not in mesh.shape, (
"Mesh must have at least 2 nodes along both length and width."
" Possible cause: Mesh spacing could be too large with respect to"
" the fault length and width."
)
self = cls(mesh)
self.surface_nodes = [simple_fault_node(
fault_trace, dip,
upper_seismogenic_depth, lower_seismogenic_depth)]
return self | [
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:param openquake.hazardlib.geo.line.Line fault_trace:
Geographical line representing the intersection between the fault
surface and the earth surface. The line must be horizontal (i.e.
all depth values must be equal). If the depths are not given, they
are assumed to be zero, meaning the trace intersects the surface at
sea level, e.g. fault_trace = Line([Point(1, 1), Point(1, 2)]).
: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.
:param mesh_spacing:
Distance between two subsequent points in a mesh, in km.
:returns:
An instance of :class:`SimpleFaultSurface` created using that data.
Uses :meth:`check_fault_data` for checking parameters. | [
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gem/oq-engine | openquake/hazardlib/geo/surface/simple_fault.py | SimpleFaultSurface.get_fault_patch_vertices | def get_fault_patch_vertices(cls, rupture_top_edge,
upper_seismogenic_depth,
lower_seismogenic_depth, dip, index_patch=1):
"""
Get surface main vertices.
Parameters are the same as for :meth:`from_fault_data`, excluding
fault_trace, and mesh spacing.
:param rupture_top_edge:
A instances of :class:`openquake.hazardlib.geo.line.Line`
representing the rupture surface's top edge.
:param index_patch:
Indicate the patch of the fault in order to output the vertices.
The fault patch numbering follows the same logic of the right-hand
rule i.e. patch with index 1 is the first patch along the trace.
:returns:
Four :class:~openquake.hazardlib.geo.point.Point objects
representing the four vertices of the target patch.
"""
# Similar to :meth:`from_fault_data`, we just don't resample edges
dip_tan = math.tan(math.radians(dip))
hdist_bottom = (
lower_seismogenic_depth - upper_seismogenic_depth) / dip_tan
strike = rupture_top_edge[0].azimuth(rupture_top_edge[-1])
azimuth = (strike + 90.0) % 360
# Collect coordinates of vertices on the top and bottom edge
lons = []
lats = []
deps = []
t_lon = []
t_lat = []
t_dep = []
for point in rupture_top_edge.points:
top_edge_point = point
bottom_edge_point = point.point_at(hdist_bottom, 0, azimuth)
lons.append(top_edge_point.longitude)
lats.append(top_edge_point.latitude)
deps.append(upper_seismogenic_depth)
t_lon.append(bottom_edge_point.longitude)
t_lat.append(bottom_edge_point.latitude)
t_dep.append(lower_seismogenic_depth)
all_lons = numpy.array(lons + list(reversed(t_lon)), float)
all_lats = numpy.array(lats + list(reversed(t_lat)), float)
all_deps = numpy.array(deps + list(reversed(t_dep)), float)
index1 = int(index_patch - 1)
index2 = int(index_patch)
index3 = int(2 * len(rupture_top_edge) - (index_patch + 1))
index4 = int(2 * len(rupture_top_edge) - index_patch)
p0 = Point(all_lons[index1], all_lats[index1], all_deps[index1])
p1 = Point(all_lons[index2], all_lats[index2], all_deps[index2])
p2 = Point(all_lons[index3], all_lats[index3], all_deps[index3])
p3 = Point(all_lons[index4], all_lats[index4], all_deps[index4])
return p0, p1, p2, p3 | python | def get_fault_patch_vertices(cls, rupture_top_edge,
upper_seismogenic_depth,
lower_seismogenic_depth, dip, index_patch=1):
dip_tan = math.tan(math.radians(dip))
hdist_bottom = (
lower_seismogenic_depth - upper_seismogenic_depth) / dip_tan
strike = rupture_top_edge[0].azimuth(rupture_top_edge[-1])
azimuth = (strike + 90.0) % 360
lons = []
lats = []
deps = []
t_lon = []
t_lat = []
t_dep = []
for point in rupture_top_edge.points:
top_edge_point = point
bottom_edge_point = point.point_at(hdist_bottom, 0, azimuth)
lons.append(top_edge_point.longitude)
lats.append(top_edge_point.latitude)
deps.append(upper_seismogenic_depth)
t_lon.append(bottom_edge_point.longitude)
t_lat.append(bottom_edge_point.latitude)
t_dep.append(lower_seismogenic_depth)
all_lons = numpy.array(lons + list(reversed(t_lon)), float)
all_lats = numpy.array(lats + list(reversed(t_lat)), float)
all_deps = numpy.array(deps + list(reversed(t_dep)), float)
index1 = int(index_patch - 1)
index2 = int(index_patch)
index3 = int(2 * len(rupture_top_edge) - (index_patch + 1))
index4 = int(2 * len(rupture_top_edge) - index_patch)
p0 = Point(all_lons[index1], all_lats[index1], all_deps[index1])
p1 = Point(all_lons[index2], all_lats[index2], all_deps[index2])
p2 = Point(all_lons[index3], all_lats[index3], all_deps[index3])
p3 = Point(all_lons[index4], all_lats[index4], all_deps[index4])
return p0, p1, p2, p3 | [
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Parameters are the same as for :meth:`from_fault_data`, excluding
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:param rupture_top_edge:
A instances of :class:`openquake.hazardlib.geo.line.Line`
representing the rupture surface's top edge.
:param index_patch:
Indicate the patch of the fault in order to output the vertices.
The fault patch numbering follows the same logic of the right-hand
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:returns:
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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):
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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:param rupture_top_edge:
A instances of :class:`openquake.hazardlib.geo.line.Line`
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Minimum depth ruptures can reach, in km (i.e. depth
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Maximum depth ruptures can reach, in km (i.e. depth
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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):
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
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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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
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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):
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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gem/oq-engine | openquake/hazardlib/gsim/zhao_2006.py | ZhaoEtAl2006Asc.get_mean_and_stddevs | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extracting dictionary of coefficients specific to required
# intensity measure type.
C = self.COEFFS_ASC[imt]
# mean value as given by equation 1, p. 901, without considering the
# interface and intraslab terms (that is SI, SS, SSL = 0) and the
# inter and intra event terms, plus the magnitude-squared term
# correction factor (equation 5 p. 909).
mean = self._compute_magnitude_term(C, rup.mag) +\
self._compute_distance_term(C, rup.mag, dists.rrup) +\
self._compute_focal_depth_term(C, rup.hypo_depth) +\
self._compute_faulting_style_term(C, rup.rake) +\
self._compute_site_class_term(C, sites.vs30) +\
self._compute_magnitude_squared_term(P=0.0, M=6.3, Q=C['QC'],
W=C['WC'], mag=rup.mag)
# convert from cm/s**2 to g
mean = np.log(np.exp(mean) * 1e-2 / g)
stddevs = self._get_stddevs(C['sigma'], C['tauC'], stddev_types,
num_sites=len(sites.vs30))
return mean, stddevs | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
C = self.COEFFS_ASC[imt]
mean = self._compute_magnitude_term(C, rup.mag) +\
self._compute_distance_term(C, rup.mag, dists.rrup) +\
self._compute_focal_depth_term(C, rup.hypo_depth) +\
self._compute_faulting_style_term(C, rup.rake) +\
self._compute_site_class_term(C, sites.vs30) +\
self._compute_magnitude_squared_term(P=0.0, M=6.3, Q=C['QC'],
W=C['WC'], mag=rup.mag)
mean = np.log(np.exp(mean) * 1e-2 / g)
stddevs = self._get_stddevs(C['sigma'], C['tauC'], stddev_types,
num_sites=len(sites.vs30))
return mean, stddevs | [
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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):
term1 = C['b'] * rrup
term2 = - np.log(rrup + C['c'] * np.exp(C['d'] * mag))
return term1 + term2 | [
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gem/oq-engine | openquake/hazardlib/gsim/zhao_2006.py | ZhaoEtAl2006Asc._compute_focal_depth_term | def _compute_focal_depth_term(self, C, hypo_depth):
"""
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"""
# 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
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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):
focal_depth = hypo_depth
if focal_depth > 125.0:
focal_depth = 125.0
hc = 15.0
return float(focal_depth >= hc) * C['e'] * (focal_depth - hc) | [
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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):
site_term = np.zeros(len(vs30))
site_term[vs30 > 1100.0] = C['CH']
site_term[(vs30 > 600) & (vs30 <= 1100)] = C['C1']
site_term[(vs30 > 300) & (vs30 <= 600)] = C['C2']
site_term[(vs30 > 200) & (vs30 <= 300)] = C['C3']
site_term[vs30 <= 200] = C['C4']
return site_term | [
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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):
return P * (mag - M) + Q * (mag - M) ** 2 + W | [
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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):
slab_term = C['SSL'] * np.log(rrup)
return slab_term | [
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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):
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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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):
while True:
try:
answer = input(prompt)
except KeyboardInterrupt:
return False
answer = answer.strip().lower()
if answer not in ('y', 'n'):
print('Please enter y or n')
continue
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gem/oq-engine | openquake/risklib/asset.py | build_asset_array | def build_asset_array(assets_by_site, tagnames=(), time_event=None):
"""
:param assets_by_site: a list of lists of assets
:param tagnames: a list of tag names
:returns: an array `assetcol`
"""
for assets in assets_by_site:
if len(assets):
first_asset = assets[0]
break
else: # no break
raise ValueError('There are no assets!')
loss_types = []
occupancy_periods = []
for name in sorted(first_asset.values):
if name.startswith('occupants_'):
period = name.split('_', 1)[1]
if period != 'None':
# see scenario_risk test_case_2d
occupancy_periods.append(period)
loss_types.append(name)
# discard occupants for different time periods
else:
loss_types.append('value-' + name)
# loss_types can be ['value-business_interruption', 'value-contents',
# 'value-nonstructural', 'occupants_None', 'occupants_day',
# 'occupants_night', 'occupants_transit']
deductible_d = first_asset.deductibles or {}
limit_d = first_asset.insurance_limits or {}
if deductible_d or limit_d:
logging.warning('Exposures with insuranceLimit/deductible fields are '
'deprecated and may be removed in the future')
retro = ['retrofitted'] if first_asset._retrofitted else []
float_fields = loss_types + retro
int_fields = [(str(name), U16) for name in tagnames]
tagi = {str(name): i for i, name in enumerate(tagnames)}
asset_dt = numpy.dtype(
[('ordinal', U32), ('lon', F32), ('lat', F32), ('site_id', U32),
('number', F32), ('area', F32)] + [
(str(name), float) for name in float_fields] + int_fields)
num_assets = sum(len(assets) for assets in assets_by_site)
assetcol = numpy.zeros(num_assets, asset_dt)
asset_ordinal = 0
fields = set(asset_dt.fields)
for sid, assets_ in enumerate(assets_by_site):
for asset in assets_:
asset.ordinal = asset_ordinal
record = assetcol[asset_ordinal]
asset_ordinal += 1
for field in fields:
if field == 'ordinal':
value = asset.ordinal
elif field == 'number':
value = asset.number
elif field == 'area':
value = asset.area
elif field == 'site_id':
value = sid
elif field == 'lon':
value = asset.location[0]
elif field == 'lat':
value = asset.location[1]
elif field.startswith('occupants_'):
value = asset.values[field]
elif field == 'retrofitted':
value = asset.retrofitted()
elif field in tagnames:
value = asset.tagidxs[tagi[field]]
else:
name, lt = field.split('-')
value = asset.value(lt, time_event)
record[field] = value
return assetcol, ' '.join(occupancy_periods) | python | def build_asset_array(assets_by_site, tagnames=(), time_event=None):
for assets in assets_by_site:
if len(assets):
first_asset = assets[0]
break
else:
raise ValueError('There are no assets!')
loss_types = []
occupancy_periods = []
for name in sorted(first_asset.values):
if name.startswith('occupants_'):
period = name.split('_', 1)[1]
if period != 'None':
occupancy_periods.append(period)
loss_types.append(name)
else:
loss_types.append('value-' + name)
deductible_d = first_asset.deductibles or {}
limit_d = first_asset.insurance_limits or {}
if deductible_d or limit_d:
logging.warning('Exposures with insuranceLimit/deductible fields are '
'deprecated and may be removed in the future')
retro = ['retrofitted'] if first_asset._retrofitted else []
float_fields = loss_types + retro
int_fields = [(str(name), U16) for name in tagnames]
tagi = {str(name): i for i, name in enumerate(tagnames)}
asset_dt = numpy.dtype(
[('ordinal', U32), ('lon', F32), ('lat', F32), ('site_id', U32),
('number', F32), ('area', F32)] + [
(str(name), float) for name in float_fields] + int_fields)
num_assets = sum(len(assets) for assets in assets_by_site)
assetcol = numpy.zeros(num_assets, asset_dt)
asset_ordinal = 0
fields = set(asset_dt.fields)
for sid, assets_ in enumerate(assets_by_site):
for asset in assets_:
asset.ordinal = asset_ordinal
record = assetcol[asset_ordinal]
asset_ordinal += 1
for field in fields:
if field == 'ordinal':
value = asset.ordinal
elif field == 'number':
value = asset.number
elif field == 'area':
value = asset.area
elif field == 'site_id':
value = sid
elif field == 'lon':
value = asset.location[0]
elif field == 'lat':
value = asset.location[1]
elif field.startswith('occupants_'):
value = asset.values[field]
elif field == 'retrofitted':
value = asset.retrofitted()
elif field in tagnames:
value = asset.tagidxs[tagi[field]]
else:
name, lt = field.split('-')
value = asset.value(lt, time_event)
record[field] = value
return assetcol, ' '.join(occupancy_periods) | [
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] | :param assets_by_site: a list of lists of assets
:param tagnames: a list of tag names
:returns: an array `assetcol` | [
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gem/oq-engine | openquake/risklib/asset.py | _get_exposure | def _get_exposure(fname, stop=None):
"""
:param fname:
path of the XML file containing the exposure
:param stop:
node at which to stop parsing (or None)
:returns:
a pair (Exposure instance, list of asset nodes)
"""
[exposure] = nrml.read(fname, stop=stop)
if not exposure.tag.endswith('exposureModel'):
raise InvalidFile('%s: expected exposureModel, got %s' %
(fname, exposure.tag))
description = exposure.description
try:
conversions = exposure.conversions
except AttributeError:
conversions = Node('conversions', nodes=[Node('costTypes', [])])
try:
inslimit = conversions.insuranceLimit
except AttributeError:
inslimit = Node('insuranceLimit', text=True)
try:
deductible = conversions.deductible
except AttributeError:
deductible = Node('deductible', text=True)
try:
area = conversions.area
except AttributeError:
# NB: the area type cannot be an empty string because when sending
# around the CostCalculator object we would run into this numpy bug
# about pickling dictionaries with empty strings:
# https://github.com/numpy/numpy/pull/5475
area = Node('area', dict(type='?'))
try:
occupancy_periods = exposure.occupancyPeriods.text or ''
except AttributeError:
occupancy_periods = ''
try:
tagNames = exposure.tagNames
except AttributeError:
tagNames = Node('tagNames', text='')
tagnames = ~tagNames or []
if set(tagnames) & {'taxonomy', 'exposure', 'country'}:
raise InvalidFile('taxonomy, exposure and country are reserved names '
'you cannot use it in <tagNames>: %s' % fname)
tagnames.insert(0, 'taxonomy')
# read the cost types and make some check
cost_types = []
retrofitted = False
for ct in conversions.costTypes:
with context(fname, ct):
ctname = ct['name']
if ctname == 'structural' and 'retrofittedType' in ct.attrib:
if ct['retrofittedType'] != ct['type']:
raise ValueError(
'The retrofittedType %s is different from the type'
'%s' % (ct['retrofittedType'], ct['type']))
if ct['retrofittedUnit'] != ct['unit']:
raise ValueError(
'The retrofittedUnit %s is different from the unit'
'%s' % (ct['retrofittedUnit'], ct['unit']))
retrofitted = True
cost_types.append(
(ctname, valid.cost_type_type(ct['type']), ct['unit']))
if 'occupants' in cost_types:
cost_types.append(('occupants', 'per_area', 'people'))
cost_types.sort(key=operator.itemgetter(0))
cost_types = numpy.array(cost_types, cost_type_dt)
insurance_limit_is_absolute = il = inslimit.get('isAbsolute')
deductible_is_absolute = de = deductible.get('isAbsolute')
cc = CostCalculator(
{}, {}, {},
True if de is None else de,
True if il is None else il,
{name: i for i, name in enumerate(tagnames)},
)
for ct in cost_types:
name = ct['name'] # structural, nonstructural, ...
cc.cost_types[name] = ct['type'] # aggregated, per_asset, per_area
cc.area_types[name] = area['type']
cc.units[name] = ct['unit']
assets = []
asset_refs = []
exp = Exposure(
exposure['id'], exposure['category'],
description.text, cost_types, occupancy_periods,
insurance_limit_is_absolute, deductible_is_absolute, retrofitted,
area.attrib, assets, asset_refs, cc, TagCollection(tagnames))
assets_text = exposure.assets.text.strip()
if assets_text:
# the <assets> tag contains a list of file names
dirname = os.path.dirname(fname)
exp.datafiles = [os.path.join(dirname, f) for f in assets_text.split()]
else:
exp.datafiles = []
return exp, exposure.assets | python | def _get_exposure(fname, stop=None):
[exposure] = nrml.read(fname, stop=stop)
if not exposure.tag.endswith('exposureModel'):
raise InvalidFile('%s: expected exposureModel, got %s' %
(fname, exposure.tag))
description = exposure.description
try:
conversions = exposure.conversions
except AttributeError:
conversions = Node('conversions', nodes=[Node('costTypes', [])])
try:
inslimit = conversions.insuranceLimit
except AttributeError:
inslimit = Node('insuranceLimit', text=True)
try:
deductible = conversions.deductible
except AttributeError:
deductible = Node('deductible', text=True)
try:
area = conversions.area
except AttributeError:
area = Node('area', dict(type='?'))
try:
occupancy_periods = exposure.occupancyPeriods.text or ''
except AttributeError:
occupancy_periods = ''
try:
tagNames = exposure.tagNames
except AttributeError:
tagNames = Node('tagNames', text='')
tagnames = ~tagNames or []
if set(tagnames) & {'taxonomy', 'exposure', 'country'}:
raise InvalidFile('taxonomy, exposure and country are reserved names '
'you cannot use it in <tagNames>: %s' % fname)
tagnames.insert(0, 'taxonomy')
cost_types = []
retrofitted = False
for ct in conversions.costTypes:
with context(fname, ct):
ctname = ct['name']
if ctname == 'structural' and 'retrofittedType' in ct.attrib:
if ct['retrofittedType'] != ct['type']:
raise ValueError(
'The retrofittedType %s is different from the type'
'%s' % (ct['retrofittedType'], ct['type']))
if ct['retrofittedUnit'] != ct['unit']:
raise ValueError(
'The retrofittedUnit %s is different from the unit'
'%s' % (ct['retrofittedUnit'], ct['unit']))
retrofitted = True
cost_types.append(
(ctname, valid.cost_type_type(ct['type']), ct['unit']))
if 'occupants' in cost_types:
cost_types.append(('occupants', 'per_area', 'people'))
cost_types.sort(key=operator.itemgetter(0))
cost_types = numpy.array(cost_types, cost_type_dt)
insurance_limit_is_absolute = il = inslimit.get('isAbsolute')
deductible_is_absolute = de = deductible.get('isAbsolute')
cc = CostCalculator(
{}, {}, {},
True if de is None else de,
True if il is None else il,
{name: i for i, name in enumerate(tagnames)},
)
for ct in cost_types:
name = ct['name']
cc.cost_types[name] = ct['type']
cc.area_types[name] = area['type']
cc.units[name] = ct['unit']
assets = []
asset_refs = []
exp = Exposure(
exposure['id'], exposure['category'],
description.text, cost_types, occupancy_periods,
insurance_limit_is_absolute, deductible_is_absolute, retrofitted,
area.attrib, assets, asset_refs, cc, TagCollection(tagnames))
assets_text = exposure.assets.text.strip()
if assets_text:
dirname = os.path.dirname(fname)
exp.datafiles = [os.path.join(dirname, f) for f in assets_text.split()]
else:
exp.datafiles = []
return exp, exposure.assets | [
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gem/oq-engine | openquake/risklib/asset.py | CostCalculator.get_units | def get_units(self, loss_types):
"""
:param: a list of loss types
:returns: an array of units as byte strings, suitable for HDF5
"""
lst = []
for lt in loss_types:
if lt.endswith('_ins'):
lt = lt[:-4]
if lt == 'occupants':
unit = 'people'
else:
unit = self.units[lt]
lst.append(encode(unit))
return numpy.array(lst) | python | def get_units(self, loss_types):
lst = []
for lt in loss_types:
if lt.endswith('_ins'):
lt = lt[:-4]
if lt == 'occupants':
unit = 'people'
else:
unit = self.units[lt]
lst.append(encode(unit))
return numpy.array(lst) | [
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gem/oq-engine | openquake/risklib/asset.py | Asset.value | def value(self, loss_type, time_event=None):
"""
:returns: the total asset value for `loss_type`
"""
if loss_type == 'occupants':
return self.values['occupants_' + str(time_event)]
try: # extract from the cache
val = self._cost[loss_type]
except KeyError: # compute
val = self.calc(loss_type, self.values, self.area, self.number)
self._cost[loss_type] = val
return val | python | def value(self, loss_type, time_event=None):
if loss_type == 'occupants':
return self.values['occupants_' + str(time_event)]
try:
val = self._cost[loss_type]
except KeyError:
val = self.calc(loss_type, self.values, self.area, self.number)
self._cost[loss_type] = val
return val | [
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gem/oq-engine | openquake/risklib/asset.py | Asset.deductible | def deductible(self, loss_type, dummy=None):
"""
:returns: the deductible fraction of the asset cost for `loss_type`
"""
val = self.calc(loss_type, self.deductibles, self.area, self.number)
if self.calc.deduct_abs: # convert to relative value
return val / self.calc(loss_type, self.values,
self.area, self.number)
else:
return val | python | def deductible(self, loss_type, dummy=None):
val = self.calc(loss_type, self.deductibles, self.area, self.number)
if self.calc.deduct_abs:
return val / self.calc(loss_type, self.values,
self.area, self.number)
else:
return val | [
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gem/oq-engine | openquake/risklib/asset.py | Asset.insurance_limit | def insurance_limit(self, loss_type, dummy=None):
"""
:returns: the limit fraction of the asset cost for `loss_type`
"""
val = self.calc(loss_type, self.insurance_limits, self.area,
self.number)
if self.calc.limit_abs: # convert to relative value
return val / self.calc(loss_type, self.values,
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else:
return val | python | def insurance_limit(self, loss_type, dummy=None):
val = self.calc(loss_type, self.insurance_limits, self.area,
self.number)
if self.calc.limit_abs:
return val / self.calc(loss_type, self.values,
self.area, self.number)
else:
return val | [
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gem/oq-engine | openquake/risklib/asset.py | Asset.retrofitted | def retrofitted(self):
"""
:returns: the asset retrofitted value
"""
return self.calc('structural', {'structural': self._retrofitted},
self.area, self.number) | python | def retrofitted(self):
return self.calc('structural', {'structural': self._retrofitted},
self.area, self.number) | [
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gem/oq-engine | openquake/risklib/asset.py | Asset.tagmask | def tagmask(self, tags):
"""
:returns: a boolean array with True where the assets has tags
"""
mask = numpy.zeros(len(tags), bool)
for t, tag in enumerate(tags):
tagname, tagvalue = tag.split('=')
mask[t] = self.tagvalue(tagname) == tagvalue
return mask | python | def tagmask(self, tags):
mask = numpy.zeros(len(tags), bool)
for t, tag in enumerate(tags):
tagname, tagvalue = tag.split('=')
mask[t] = self.tagvalue(tagname) == tagvalue
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gem/oq-engine | openquake/risklib/asset.py | TagCollection.add | def add(self, tagname, tagvalue):
"""
:returns: numeric index associated to the tag
"""
dic = getattr(self, tagname + '_idx')
try:
return dic[tagvalue]
except KeyError:
dic[tagvalue] = idx = len(dic)
getattr(self, tagname).append(tagvalue)
if idx > TWO16:
raise InvalidFile('contains more then %d tags' % TWO16)
return idx | python | def add(self, tagname, tagvalue):
dic = getattr(self, tagname + '_idx')
try:
return dic[tagvalue]
except KeyError:
dic[tagvalue] = idx = len(dic)
getattr(self, tagname).append(tagvalue)
if idx > TWO16:
raise InvalidFile('contains more then %d tags' % TWO16)
return idx | [
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gem/oq-engine | openquake/risklib/asset.py | TagCollection.add_tags | def add_tags(self, dic, prefix):
"""
:param dic: a dictionary tagname -> tagvalue
:returns: a list of tag indices, one per tagname
"""
# fill missing tagvalues with "?", raise an error for unknown tagnames
idxs = []
for tagname in self.tagnames:
if tagname in ('exposure', 'country'):
idxs.append(self.add(tagname, prefix))
continue
try:
tagvalue = dic.pop(tagname)
except KeyError:
tagvalue = '?'
else:
if tagvalue in '?*':
raise ValueError(
'Invalid tagvalue="%s"' % tagvalue)
idxs.append(self.add(tagname, tagvalue))
if dic:
raise ValueError(
'Unknown tagname %s or <tagNames> not '
'specified in the exposure' % ', '.join(dic))
return idxs | python | def add_tags(self, dic, prefix):
idxs = []
for tagname in self.tagnames:
if tagname in ('exposure', 'country'):
idxs.append(self.add(tagname, prefix))
continue
try:
tagvalue = dic.pop(tagname)
except KeyError:
tagvalue = '?'
else:
if tagvalue in '?*':
raise ValueError(
'Invalid tagvalue="%s"' % tagvalue)
idxs.append(self.add(tagname, tagvalue))
if dic:
raise ValueError(
'Unknown tagname %s or <tagNames> not '
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return idxs | [
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gem/oq-engine | openquake/risklib/asset.py | TagCollection.get_tag | def get_tag(self, tagname, tagidx):
"""
:returns: the tag associated to the given tagname and tag index
"""
return '%s=%s' % (tagname, decode(getattr(self, tagname)[tagidx])) | python | def get_tag(self, tagname, tagidx):
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gem/oq-engine | openquake/risklib/asset.py | TagCollection.get_tagvalues | def get_tagvalues(self, tagnames, tagidxs):
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"""
values = tuple(getattr(self, tagname)[tagidx + 1]
for tagidx, tagname in zip(tagidxs, tagnames))
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values = tuple(getattr(self, tagname)[tagidx + 1]
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gem/oq-engine | openquake/risklib/asset.py | TagCollection.gen_tags | def gen_tags(self, tagname):
"""
:yields: the tags associated to the given tagname
"""
for tagvalue in getattr(self, tagname):
yield '%s=%s' % (tagname, decode(tagvalue)) | python | def gen_tags(self, tagname):
for tagvalue in getattr(self, tagname):
yield '%s=%s' % (tagname, decode(tagvalue)) | [
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gem/oq-engine | openquake/risklib/asset.py | TagCollection.agg_shape | def agg_shape(self, shp, aggregate_by):
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:returns: a shape shp + (T, ...) depending on the tagnames
"""
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len(getattr(self, tagname)) - 1 for tagname in aggregate_by) | python | def agg_shape(self, shp, aggregate_by):
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gem/oq-engine | openquake/risklib/asset.py | AssetCollection.num_taxonomies_by_site | def num_taxonomies_by_site(self):
"""
:returns: an array with the number of assets per each site
"""
dic = general.group_array(self.array, 'site_id')
num_taxonomies = numpy.zeros(self.tot_sites, U32)
for sid, arr in dic.items():
num_taxonomies[sid] = len(numpy.unique(arr['taxonomy']))
return num_taxonomies | python | def num_taxonomies_by_site(self):
dic = general.group_array(self.array, 'site_id')
num_taxonomies = numpy.zeros(self.tot_sites, U32)
for sid, arr in dic.items():
num_taxonomies[sid] = len(numpy.unique(arr['taxonomy']))
return num_taxonomies | [
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gem/oq-engine | openquake/risklib/asset.py | AssetCollection.get_aids_by_tag | def get_aids_by_tag(self):
"""
:returns: dict tag -> asset ordinals
"""
aids_by_tag = general.AccumDict(accum=set())
for aid, ass in enumerate(self):
for tagname in self.tagnames:
tag = self.tagcol.get_tag(tagname, ass[tagname])
aids_by_tag[tag].add(aid)
return aids_by_tag | python | def get_aids_by_tag(self):
aids_by_tag = general.AccumDict(accum=set())
for aid, ass in enumerate(self):
for tagname in self.tagnames:
tag = self.tagcol.get_tag(tagname, ass[tagname])
aids_by_tag[tag].add(aid)
return aids_by_tag | [
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gem/oq-engine | openquake/risklib/asset.py | AssetCollection.assets_by_site | def assets_by_site(self):
"""
:returns: numpy array of lists with the assets by each site
"""
assets_by_site = [[] for sid in range(self.tot_sites)]
for i, ass in enumerate(self.array):
assets_by_site[ass['site_id']].append(self[i])
return numpy.array(assets_by_site) | python | def assets_by_site(self):
assets_by_site = [[] for sid in range(self.tot_sites)]
for i, ass in enumerate(self.array):
assets_by_site[ass['site_id']].append(self[i])
return numpy.array(assets_by_site) | [
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gem/oq-engine | openquake/risklib/asset.py | AssetCollection.aggregate_by | def aggregate_by(self, tagnames, array):
"""
:param tagnames: a list of valid tag names
:param array: an array with the same length as the asset collection
:returns: an array of aggregate values with the proper shape
"""
missing = set(tagnames) - set(self.tagcol.tagnames)
if missing:
raise ValueError('Unknown tagname(s) %s' % missing)
A, *shp = array.shape
if A != len(self):
raise ValueError('The array must have length %d, got %d' %
(len(self), A))
if not tagnames:
return array.sum(axis=0)
shape = [len(getattr(self.tagcol, tagname))-1 for tagname in tagnames]
acc = numpy.zeros(shape, (F32, shp) if shp else F32)
for asset, row in zip(self.array, array):
acc[tuple(idx - 1 for idx in asset[tagnames])] += row
return acc | python | def aggregate_by(self, tagnames, array):
missing = set(tagnames) - set(self.tagcol.tagnames)
if missing:
raise ValueError('Unknown tagname(s) %s' % missing)
A, *shp = array.shape
if A != len(self):
raise ValueError('The array must have length %d, got %d' %
(len(self), A))
if not tagnames:
return array.sum(axis=0)
shape = [len(getattr(self.tagcol, tagname))-1 for tagname in tagnames]
acc = numpy.zeros(shape, (F32, shp) if shp else F32)
for asset, row in zip(self.array, array):
acc[tuple(idx - 1 for idx in asset[tagnames])] += row
return acc | [
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gem/oq-engine | openquake/risklib/asset.py | AssetCollection.agg_value | def agg_value(self, *tagnames):
"""
:param tagnames:
tagnames of lengths T1, T2, ... respectively
:returns:
the values of the exposure aggregated by tagnames as an array
of shape (T1, T2, ..., L)
"""
aval = numpy.zeros((len(self), len(self.loss_types)), F32) # (A, L)
for asset in self:
for lti, lt in enumerate(self.loss_types):
if lt == 'occupants':
aval[asset['ordinal'], lti] = asset[lt + '_None']
else:
aval[asset['ordinal'], lti] = asset['value-' + lt]
return self.aggregate_by(list(tagnames), aval) | python | def agg_value(self, *tagnames):
aval = numpy.zeros((len(self), len(self.loss_types)), F32)
for asset in self:
for lti, lt in enumerate(self.loss_types):
if lt == 'occupants':
aval[asset['ordinal'], lti] = asset[lt + '_None']
else:
aval[asset['ordinal'], lti] = asset['value-' + lt]
return self.aggregate_by(list(tagnames), aval) | [
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gem/oq-engine | openquake/risklib/asset.py | AssetCollection.reduce | def reduce(self, sitecol):
"""
:returns: a reduced AssetCollection on the given sitecol
"""
ok_indices = numpy.sum(
[self.array['site_id'] == sid for sid in sitecol.sids],
axis=0, dtype=bool)
new = object.__new__(self.__class__)
vars(new).update(vars(self))
new.array = self.array[ok_indices]
new.array['ordinal'] = numpy.arange(len(new.array))
new.asset_refs = self.asset_refs[ok_indices]
return new | python | def reduce(self, sitecol):
ok_indices = numpy.sum(
[self.array['site_id'] == sid for sid in sitecol.sids],
axis=0, dtype=bool)
new = object.__new__(self.__class__)
vars(new).update(vars(self))
new.array = self.array[ok_indices]
new.array['ordinal'] = numpy.arange(len(new.array))
new.asset_refs = self.asset_refs[ok_indices]
return new | [
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gem/oq-engine | openquake/risklib/asset.py | AssetCollection.reduce_also | def reduce_also(self, sitecol):
"""
:returns: a reduced AssetCollection on the given sitecol
NB: diffently from .reduce, also the SiteCollection is reduced
and turned into a complete site collection.
"""
array = []
asset_refs = []
for idx, sid in enumerate(sitecol.sids):
mask = self.array['site_id'] == sid
arr = self.array[mask]
arr['site_id'] = idx
array.append(arr)
asset_refs.append(self.asset_refs[mask])
new = object.__new__(self.__class__)
vars(new).update(vars(self))
new.tot_sites = len(sitecol)
new.array = numpy.concatenate(array)
new.array['ordinal'] = numpy.arange(len(new.array))
new.asset_refs = numpy.concatenate(asset_refs)
sitecol.make_complete()
return new | python | def reduce_also(self, sitecol):
array = []
asset_refs = []
for idx, sid in enumerate(sitecol.sids):
mask = self.array['site_id'] == sid
arr = self.array[mask]
arr['site_id'] = idx
array.append(arr)
asset_refs.append(self.asset_refs[mask])
new = object.__new__(self.__class__)
vars(new).update(vars(self))
new.tot_sites = len(sitecol)
new.array = numpy.concatenate(array)
new.array['ordinal'] = numpy.arange(len(new.array))
new.asset_refs = numpy.concatenate(asset_refs)
sitecol.make_complete()
return new | [
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gem/oq-engine | openquake/risklib/asset.py | Exposure.read | def read(fnames, calculation_mode='', region_constraint='',
ignore_missing_costs=(), asset_nodes=False, check_dupl=True,
tagcol=None, by_country=False):
"""
Call `Exposure.read(fname)` to get an :class:`Exposure` instance
keeping all the assets in memory or
`Exposure.read(fname, asset_nodes=True)` to get an iterator over
Node objects (one Node for each asset).
"""
if by_country: # E??_ -> countrycode
prefix2cc = countries.from_exposures(
os.path.basename(f) for f in fnames)
else:
prefix = ''
allargs = []
tagcol = _minimal_tagcol(fnames, by_country)
for i, fname in enumerate(fnames, 1):
if by_country and len(fnames) > 1:
prefix = prefix2cc['E%02d_' % i] + '_'
elif len(fnames) > 1:
prefix = 'E%02d_' % i
else:
prefix = ''
allargs.append((fname, calculation_mode, region_constraint,
ignore_missing_costs, asset_nodes, check_dupl,
prefix, tagcol))
exp = None
for exposure in parallel.Starmap(
Exposure.read_exp, allargs, distribute='no'):
if exp is None: # first time
exp = exposure
exp.description = 'Composite exposure[%d]' % len(fnames)
else:
assert exposure.cost_types == exp.cost_types
assert exposure.occupancy_periods == exp.occupancy_periods
assert (exposure.insurance_limit_is_absolute ==
exp.insurance_limit_is_absolute)
assert exposure.retrofitted == exp.retrofitted
assert exposure.area == exp.area
exp.assets.extend(exposure.assets)
exp.asset_refs.extend(exposure.asset_refs)
exp.tagcol.extend(exposure.tagcol)
exp.exposures = [os.path.splitext(os.path.basename(f))[0]
for f in fnames]
return exp | python | def read(fnames, calculation_mode='', region_constraint='',
ignore_missing_costs=(), asset_nodes=False, check_dupl=True,
tagcol=None, by_country=False):
if by_country:
prefix2cc = countries.from_exposures(
os.path.basename(f) for f in fnames)
else:
prefix = ''
allargs = []
tagcol = _minimal_tagcol(fnames, by_country)
for i, fname in enumerate(fnames, 1):
if by_country and len(fnames) > 1:
prefix = prefix2cc['E%02d_' % i] + '_'
elif len(fnames) > 1:
prefix = 'E%02d_' % i
else:
prefix = ''
allargs.append((fname, calculation_mode, region_constraint,
ignore_missing_costs, asset_nodes, check_dupl,
prefix, tagcol))
exp = None
for exposure in parallel.Starmap(
Exposure.read_exp, allargs, distribute='no'):
if exp is None:
exp = exposure
exp.description = 'Composite exposure[%d]' % len(fnames)
else:
assert exposure.cost_types == exp.cost_types
assert exposure.occupancy_periods == exp.occupancy_periods
assert (exposure.insurance_limit_is_absolute ==
exp.insurance_limit_is_absolute)
assert exposure.retrofitted == exp.retrofitted
assert exposure.area == exp.area
exp.assets.extend(exposure.assets)
exp.asset_refs.extend(exposure.asset_refs)
exp.tagcol.extend(exposure.tagcol)
exp.exposures = [os.path.splitext(os.path.basename(f))[0]
for f in fnames]
return exp | [
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keeping all the assets in memory or
`Exposure.read(fname, asset_nodes=True)` to get an iterator over
Node objects (one Node for each asset). | [
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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):
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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gem/oq-engine | openquake/risklib/asset.py | Exposure._read_csv | def _read_csv(self):
"""
:yields: asset nodes
"""
expected_header = self._csv_header()
for fname in self.datafiles:
with open(fname, encoding='utf-8') as f:
fields = next(csv.reader(f))
header = set(fields)
if len(header) < len(fields):
raise InvalidFile(
'%s: The header %s contains a duplicated field' %
(fname, header))
elif expected_header - header - {'exposure', 'country'}:
raise InvalidFile(
'Unexpected header in %s\nExpected: %s\nGot: %s' %
(fname, sorted(expected_header), sorted(header)))
occupancy_periods = self.occupancy_periods.split()
for fname in self.datafiles:
with open(fname, encoding='utf-8') as f:
for i, dic in enumerate(csv.DictReader(f), 1):
asset = Node('asset', lineno=i)
with context(fname, asset):
asset['id'] = dic['id']
asset['number'] = valid.positivefloat(dic['number'])
asset['taxonomy'] = dic['taxonomy']
if 'area' in dic: # optional attribute
asset['area'] = dic['area']
loc = Node('location',
dict(lon=valid.longitude(dic['lon']),
lat=valid.latitude(dic['lat'])))
costs = Node('costs')
for cost in self.cost_types['name']:
a = dict(type=cost, value=dic[cost])
if 'retrofitted' in dic:
a['retrofitted'] = dic['retrofitted']
costs.append(Node('cost', a))
occupancies = Node('occupancies')
for period in occupancy_periods:
a = dict(occupants=float(dic[period]),
period=period)
occupancies.append(Node('occupancy', a))
tags = Node('tags')
for tagname in self.tagcol.tagnames:
if tagname not in (
'taxonomy', 'exposure', 'country'):
tags.attrib[tagname] = dic[tagname]
asset.nodes.extend([loc, costs, occupancies, tags])
yield asset | python | def _read_csv(self):
expected_header = self._csv_header()
for fname in self.datafiles:
with open(fname, encoding='utf-8') as f:
fields = next(csv.reader(f))
header = set(fields)
if len(header) < len(fields):
raise InvalidFile(
'%s: The header %s contains a duplicated field' %
(fname, header))
elif expected_header - header - {'exposure', 'country'}:
raise InvalidFile(
'Unexpected header in %s\nExpected: %s\nGot: %s' %
(fname, sorted(expected_header), sorted(header)))
occupancy_periods = self.occupancy_periods.split()
for fname in self.datafiles:
with open(fname, encoding='utf-8') as f:
for i, dic in enumerate(csv.DictReader(f), 1):
asset = Node('asset', lineno=i)
with context(fname, asset):
asset['id'] = dic['id']
asset['number'] = valid.positivefloat(dic['number'])
asset['taxonomy'] = dic['taxonomy']
if 'area' in dic:
asset['area'] = dic['area']
loc = Node('location',
dict(lon=valid.longitude(dic['lon']),
lat=valid.latitude(dic['lat'])))
costs = Node('costs')
for cost in self.cost_types['name']:
a = dict(type=cost, value=dic[cost])
if 'retrofitted' in dic:
a['retrofitted'] = dic['retrofitted']
costs.append(Node('cost', a))
occupancies = Node('occupancies')
for period in occupancy_periods:
a = dict(occupants=float(dic[period]),
period=period)
occupancies.append(Node('occupancy', a))
tags = Node('tags')
for tagname in self.tagcol.tagnames:
if tagname not in (
'taxonomy', 'exposure', 'country'):
tags.attrib[tagname] = dic[tagname]
asset.nodes.extend([loc, costs, occupancies, tags])
yield asset | [
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gem/oq-engine | openquake/risklib/asset.py | Exposure.get_mesh_assets_by_site | def get_mesh_assets_by_site(self):
"""
:returns: (Mesh instance, assets_by_site list)
"""
assets_by_loc = general.groupby(self, key=lambda a: a.location)
mesh = geo.Mesh.from_coords(list(assets_by_loc))
assets_by_site = [
assets_by_loc[lonlat] for lonlat in zip(mesh.lons, mesh.lats)]
return mesh, assets_by_site | python | def get_mesh_assets_by_site(self):
assets_by_loc = general.groupby(self, key=lambda a: a.location)
mesh = geo.Mesh.from_coords(list(assets_by_loc))
assets_by_site = [
assets_by_loc[lonlat] for lonlat in zip(mesh.lons, mesh.lats)]
return mesh, assets_by_site | [
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gem/oq-engine | openquake/risklib/riskmodels.py | get_risk_files | def get_risk_files(inputs):
"""
:param inputs: a dictionary key -> path name
:returns: a pair (file_type, {risk_type: path})
"""
rfs = {}
job_ini = inputs['job_ini']
for key in inputs:
if key == 'fragility':
# backward compatibily for .ini files with key fragility_file
# instead of structural_fragility_file
rfs['fragility/structural'] = inputs[
'structural_fragility'] = inputs[key]
del inputs['fragility']
elif key.endswith(('_fragility', '_vulnerability', '_consequence')):
match = RISK_TYPE_REGEX.match(key)
if match and 'retrofitted' not in key and 'consequence' not in key:
rfs['%s/%s' % (match.group(2), match.group(1))] = inputs[key]
elif match is None:
raise ValueError('Invalid key in %s: %s_file' % (job_ini, key))
return rfs | python | def get_risk_files(inputs):
rfs = {}
job_ini = inputs['job_ini']
for key in inputs:
if key == 'fragility':
rfs['fragility/structural'] = inputs[
'structural_fragility'] = inputs[key]
del inputs['fragility']
elif key.endswith(('_fragility', '_vulnerability', '_consequence')):
match = RISK_TYPE_REGEX.match(key)
if match and 'retrofitted' not in key and 'consequence' not in key:
rfs['%s/%s' % (match.group(2), match.group(1))] = inputs[key]
elif match is None:
raise ValueError('Invalid key in %s: %s_file' % (job_ini, key))
return rfs | [
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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):
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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gem/oq-engine | openquake/risklib/riskmodels.py | get_risk_models | def get_risk_models(oqparam, kind='vulnerability vulnerability_retrofitted '
'fragility consequence'):
"""
:param oqparam:
an OqParam instance
:param kind:
a space-separated string with the kinds of risk models to read
:returns:
a dictionary riskid -> loss_type, kind -> function
"""
kinds = kind.split()
rmodels = AccumDict()
for kind in kinds:
for key in sorted(oqparam.inputs):
mo = re.match('(occupants|%s)_%s$' % (COST_TYPE_REGEX, kind), key)
if mo:
loss_type = mo.group(1) # the cost_type in the key
# can be occupants, structural, nonstructural, ...
rmodel = nrml.to_python(oqparam.inputs[key])
if len(rmodel) == 0:
raise InvalidFile('%s is empty!' % oqparam.inputs[key])
rmodels[loss_type, kind] = rmodel
if rmodel.lossCategory is None: # NRML 0.4
continue
cost_type = str(rmodel.lossCategory)
rmodel_kind = rmodel.__class__.__name__
kind_ = kind.replace('_retrofitted', '') # strip retrofitted
if not rmodel_kind.lower().startswith(kind_):
raise ValueError(
'Error in the file "%s_file=%s": is '
'of kind %s, expected %s' % (
key, oqparam.inputs[key], rmodel_kind,
kind.capitalize() + 'Model'))
if cost_type != loss_type:
raise ValueError(
'Error in the file "%s_file=%s": lossCategory is of '
'type "%s", expected "%s"' %
(key, oqparam.inputs[key],
rmodel.lossCategory, loss_type))
rdict = AccumDict(accum={})
rdict.limit_states = []
for (loss_type, kind), rm in sorted(rmodels.items()):
if kind == 'fragility':
# build a copy of the FragilityModel with different IM levels
newfm = rm.build(oqparam.continuous_fragility_discretization,
oqparam.steps_per_interval)
for (imt, riskid), ffl in newfm.items():
if not rdict.limit_states:
rdict.limit_states.extend(rm.limitStates)
# we are rejecting the case of loss types with different
# limit states; this may change in the future
assert rdict.limit_states == rm.limitStates, (
rdict.limit_states, rm.limitStates)
rdict[riskid][loss_type, kind] = ffl
# TODO: see if it is possible to remove the attribute
# below, used in classical_damage
ffl.steps_per_interval = oqparam.steps_per_interval
elif kind == 'consequence':
for riskid, cf in rm.items():
rdict[riskid][loss_type, kind] = cf
else: # vulnerability
cl_risk = oqparam.calculation_mode in (
'classical', 'classical_risk')
# only for classical_risk reduce the loss_ratios
# to make sure they are strictly increasing
for (imt, riskid), rf in rm.items():
rdict[riskid][loss_type, kind] = (
rf.strictly_increasing() if cl_risk else rf)
return rdict | python | def get_risk_models(oqparam, kind='vulnerability vulnerability_retrofitted '
'fragility consequence'):
kinds = kind.split()
rmodels = AccumDict()
for kind in kinds:
for key in sorted(oqparam.inputs):
mo = re.match('(occupants|%s)_%s$' % (COST_TYPE_REGEX, kind), key)
if mo:
loss_type = mo.group(1)
rmodel = nrml.to_python(oqparam.inputs[key])
if len(rmodel) == 0:
raise InvalidFile('%s is empty!' % oqparam.inputs[key])
rmodels[loss_type, kind] = rmodel
if rmodel.lossCategory is None:
continue
cost_type = str(rmodel.lossCategory)
rmodel_kind = rmodel.__class__.__name__
kind_ = kind.replace('_retrofitted', '')
if not rmodel_kind.lower().startswith(kind_):
raise ValueError(
'Error in the file "%s_file=%s": is '
'of kind %s, expected %s' % (
key, oqparam.inputs[key], rmodel_kind,
kind.capitalize() + 'Model'))
if cost_type != loss_type:
raise ValueError(
'Error in the file "%s_file=%s": lossCategory is of '
'type "%s", expected "%s"' %
(key, oqparam.inputs[key],
rmodel.lossCategory, loss_type))
rdict = AccumDict(accum={})
rdict.limit_states = []
for (loss_type, kind), rm in sorted(rmodels.items()):
if kind == 'fragility':
newfm = rm.build(oqparam.continuous_fragility_discretization,
oqparam.steps_per_interval)
for (imt, riskid), ffl in newfm.items():
if not rdict.limit_states:
rdict.limit_states.extend(rm.limitStates)
assert rdict.limit_states == rm.limitStates, (
rdict.limit_states, rm.limitStates)
rdict[riskid][loss_type, kind] = ffl
ffl.steps_per_interval = oqparam.steps_per_interval
elif kind == 'consequence':
for riskid, cf in rm.items():
rdict[riskid][loss_type, kind] = cf
else:
cl_risk = oqparam.calculation_mode in (
'classical', 'classical_risk')
for (imt, riskid), rf in rm.items():
rdict[riskid][loss_type, kind] = (
rf.strictly_increasing() if cl_risk else rf)
return rdict | [
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:param kind:
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:returns:
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gem/oq-engine | openquake/risklib/riskmodels.py | rescale | def rescale(curves, values):
"""
Multiply the losses in each curve of kind (losses, poes) by the
corresponding value.
:param curves: an array of shape (A, 2, C)
:param values: an array of shape (A,)
"""
A, _, C = curves.shape
assert A == len(values), (A, len(values))
array = numpy.zeros((A, C), loss_poe_dt)
array['loss'] = [c * v for c, v in zip(curves[:, 0], values)]
array['poe'] = curves[:, 1]
return array | python | def rescale(curves, values):
A, _, C = curves.shape
assert A == len(values), (A, len(values))
array = numpy.zeros((A, C), loss_poe_dt)
array['loss'] = [c * v for c, v in zip(curves[:, 0], values)]
array['poe'] = curves[:, 1]
return array | [
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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__)
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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):
riskmodel_class = registry[oqparam.calculation_mode]
argnames = inspect.getfullargspec(riskmodel_class.__init__).args[3:]
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:
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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extra parameters to pass to the riskmodel class | [
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gem/oq-engine | openquake/risklib/riskmodels.py | RiskModel.get_loss_types | def get_loss_types(self, imt):
"""
:param imt: Intensity Measure Type string
:returns: loss types with risk functions of the given imt
"""
return [lt for lt in self.loss_types
if self.risk_functions[lt].imt == imt] | python | def get_loss_types(self, imt):
return [lt for lt in self.loss_types
if self.risk_functions[lt].imt == imt] | [
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gem/oq-engine | openquake/hmtk/plotting/beachball.py | Beach | def Beach(fm, linewidth=2, facecolor='b', bgcolor='w', edgecolor='k',
alpha=1.0, xy=(0, 0), width=200, size=100, nofill=False,
zorder=100, axes=None):
"""
Return a beach ball as a collection which can be connected to an
current matplotlib axes instance (ax.add_collection).
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 or tuple
:param width: Symbol size of beach ball, or tuple for elliptically
shaped patches. Defaults to size ``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.
:type axes: :class:`matplotlib.axes.Axes`
:param axes: Used to make beach balls circular on non-scaled axes. Also
maintains the aspect ratio when resizing the figure. Will not add
the returned collection to the axes instance.
"""
# check if one or two widths are specified (Circle or Ellipse)
try:
assert(len(width) == 2)
except TypeError:
width = (width, width)
mt = None
np1 = None
if isinstance(fm, MomentTensor):
mt = fm
np1 = MT2Plane(mt)
elif isinstance(fm, NodalPlane):
np1 = fm
elif len(fm) == 6:
mt = MomentTensor(fm[0], fm[1], fm[2], fm[3], fm[4], fm[5], 0)
np1 = MT2Plane(mt)
elif len(fm) == 3:
np1 = NodalPlane(fm[0], fm[1], fm[2])
else:
raise TypeError("Wrong input value for 'fm'.")
# Only at least size 100, i.e. 100 points in the matrix are allowed
if size < 100:
size = 100
# Return as collection
if mt:
(T, N, P) = MT2Axes(mt)
if np.fabs(N.val) < EPSILON and np.fabs(T.val + P.val) < EPSILON:
colors, p = plotDC(np1, size, xy=xy, width=width)
else:
colors, p = plotMT(T, N, P, size,
plot_zerotrace=True, xy=xy, width=width)
else:
colors, p = plotDC(np1, size=size, xy=xy, width=width)
if nofill:
# XXX: not tested with plotMT
col = collections.PatchCollection([p[1]], match_original=False)
col.set_facecolor('none')
else:
col = collections.PatchCollection(p, match_original=False)
# Replace color dummies 'b' and 'w' by face and bgcolor
fc = [facecolor if c == 'b' else bgcolor for c in colors]
col.set_facecolors(fc)
# Use the given axes to maintain the aspect ratio of beachballs on figure
# resize.
if axes is not None:
# This is what holds the aspect ratio (but breaks the positioning)
col.set_transform(transforms.IdentityTransform())
# Next is a dirty hack to fix the positioning:
# 1. Need to bring the all patches to the origin (0, 0).
for p in col._paths:
p.vertices -= xy
# 2. Then use the offset property of the collection to position the
# patches
col.set_offsets(xy)
col._transOffset = axes.transData
col.set_edgecolor(edgecolor)
col.set_alpha(alpha)
col.set_linewidth(linewidth)
col.set_zorder(zorder)
return col | python | def Beach(fm, linewidth=2, facecolor='b', bgcolor='w', edgecolor='k',
alpha=1.0, xy=(0, 0), width=200, size=100, nofill=False,
zorder=100, axes=None):
try:
assert(len(width) == 2)
except TypeError:
width = (width, width)
mt = None
np1 = None
if isinstance(fm, MomentTensor):
mt = fm
np1 = MT2Plane(mt)
elif isinstance(fm, NodalPlane):
np1 = fm
elif len(fm) == 6:
mt = MomentTensor(fm[0], fm[1], fm[2], fm[3], fm[4], fm[5], 0)
np1 = MT2Plane(mt)
elif len(fm) == 3:
np1 = NodalPlane(fm[0], fm[1], fm[2])
else:
raise TypeError("Wrong input value for 'fm'.")
if size < 100:
size = 100
if mt:
(T, N, P) = MT2Axes(mt)
if np.fabs(N.val) < EPSILON and np.fabs(T.val + P.val) < EPSILON:
colors, p = plotDC(np1, size, xy=xy, width=width)
else:
colors, p = plotMT(T, N, P, size,
plot_zerotrace=True, xy=xy, width=width)
else:
colors, p = plotDC(np1, size=size, xy=xy, width=width)
if nofill:
col = collections.PatchCollection([p[1]], match_original=False)
col.set_facecolor('none')
else:
col = collections.PatchCollection(p, match_original=False)
fc = [facecolor if c == 'b' else bgcolor for c in colors]
col.set_facecolors(fc)
if axes is not None:
col.set_transform(transforms.IdentityTransform())
for p in col._paths:
p.vertices -= xy
col.set_offsets(xy)
col._transOffset = axes.transData
col.set_edgecolor(edgecolor)
col.set_alpha(alpha)
col.set_linewidth(linewidth)
col.set_zorder(zorder)
return col | [
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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
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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 or tuple
:param width: Symbol size of beach ball, or tuple for elliptically
shaped patches. Defaults to size ``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.
:type axes: :class:`matplotlib.axes.Axes`
:param axes: Used to make beach balls circular on non-scaled axes. Also
maintains the aspect ratio when resizing the figure. Will not add
the returned collection to the axes instance. | [
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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):
plot_width = width * 0.95
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')
ax.axison = False
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)
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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] | 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``. | [
"Draws",
"a",
"beach",
"ball",
"diagram",
"of",
"an",
"earthquake",
"focal",
"mechanism",
"."
] | train | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/beachball.py#L147-L230 |
gem/oq-engine | openquake/hmtk/plotting/beachball.py | plotMT | def plotMT(T, N, P, size=200, plot_zerotrace=True,
x0=0, y0=0, xy=(0, 0), width=200):
"""
Uses a principal axis T, N and P to draw a beach ball plot.
:param ax: axis object of a matplotlib figure
:param T: :class:`~PrincipalAxis`
:param N: :class:`~PrincipalAxis`
:param P: :class:`~PrincipalAxis`
Adapted from ps_tensor / utilmeca.c / `Generic Mapping Tools (GMT)`_.
.. _`Generic Mapping Tools (GMT)`: http://gmt.soest.hawaii.edu
"""
# check if one or two widths are specified (Circle or Ellipse)
try:
assert(len(width) == 2)
except TypeError:
width = (width, width)
collect = []
colors = []
res = [value / float(size) for value in width]
b = 1
big_iso = 0
j = 1
j2 = 0
j3 = 0
n = 0
azi = np.zeros((3, 2))
x = np.zeros(400)
y = np.zeros(400)
x2 = np.zeros(400)
y2 = np.zeros(400)
x3 = np.zeros(400)
y3 = np.zeros(400)
xp1 = np.zeros(800)
yp1 = np.zeros(800)
xp2 = np.zeros(400)
yp2 = np.zeros(400)
a = np.zeros(3)
p = np.zeros(3)
v = np.zeros(3)
a[0] = T.strike
a[1] = N.strike
a[2] = P.strike
p[0] = T.dip
p[1] = N.dip
p[2] = P.dip
v[0] = T.val
v[1] = N.val
v[2] = P.val
vi = (v[0] + v[1] + v[2]) / 3.
for i in range(0, 3):
v[i] = v[i] - vi
radius_size = size * 0.5
if np.fabs(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]) < EPSILON:
# pure implosion-explosion
if vi > 0.:
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append('b')
if vi < 0.:
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append('w')
return colors, collect
if np.fabs(v[0]) >= np.fabs(v[2]):
d = 0
m = 2
else:
d = 2
m = 0
if (plot_zerotrace):
vi = 0.
f = -v[1] / float(v[d])
iso = vi / float(v[d])
# Cliff Frohlich, Seismological Research letters,
# Vol 7, Number 1, January-February, 1996
# Unless the isotropic parameter lies in the range
# between -1 and 1 - f there will be no nodes whatsoever
if iso < -1:
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append('w')
return colors, collect
elif iso > 1 - f:
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append('b')
return colors, collect
spd = np.sin(p[d] * D2R)
cpd = np.cos(p[d] * D2R)
spb = np.sin(p[b] * D2R)
cpb = np.cos(p[b] * D2R)
spm = np.sin(p[m] * D2R)
cpm = np.cos(p[m] * D2R)
sad = np.sin(a[d] * D2R)
cad = np.cos(a[d] * D2R)
sab = np.sin(a[b] * D2R)
cab = np.cos(a[b] * D2R)
sam = np.sin(a[m] * D2R)
cam = np.cos(a[m] * D2R)
for i in range(0, 360):
fir = i * D2R
s2alphan = (2. + 2. * iso) / \
float(3. + (1. - 2. * f) * np.cos(2. * fir))
if s2alphan > 1.:
big_iso += 1
else:
alphan = np.arcsin(np.sqrt(s2alphan))
sfi = np.sin(fir)
cfi = np.cos(fir)
san = np.sin(alphan)
can = np.cos(alphan)
xz = can * spd + san * sfi * spb + san * cfi * spm
xn = can * cpd * cad + san * sfi * cpb * cab + \
san * cfi * cpm * cam
xe = can * cpd * sad + san * sfi * cpb * sab + \
san * cfi * cpm * sam
if np.fabs(xn) < EPSILON and np.fabs(xe) < EPSILON:
takeoff = 0.
az = 0.
else:
az = np.arctan2(xe, xn)
if az < 0.:
az += np.pi * 2.
takeoff = np.arccos(xz / float(np.sqrt(xz * xz + xn * xn +
xe * xe)))
if takeoff > np.pi / 2.:
takeoff = np.pi - takeoff
az += np.pi
if az > np.pi * 2.:
az -= np.pi * 2.
r = np.sqrt(2) * np.sin(takeoff / 2.)
si = np.sin(az)
co = np.cos(az)
if i == 0:
azi[i][0] = az
x[i] = x0 + radius_size * r * si
y[i] = y0 + radius_size * r * co
azp = az
else:
if np.fabs(np.fabs(az - azp) - np.pi) < D2R * 10.:
azi[n][1] = azp
n += 1
azi[n][0] = az
if np.fabs(np.fabs(az - azp) - np.pi * 2.) < D2R * 2.:
if azp < az:
azi[n][0] += np.pi * 2.
else:
azi[n][0] -= np.pi * 2.
if n == 0:
x[j] = x0 + radius_size * r * si
y[j] = y0 + radius_size * r * co
j += 1
elif n == 1:
x2[j2] = x0 + radius_size * r * si
y2[j2] = y0 + radius_size * r * co
j2 += 1
elif n == 2:
x3[j3] = x0 + radius_size * r * si
y3[j3] = y0 + radius_size * r * co
j3 += 1
azp = az
azi[n][1] = az
if v[1] < 0.:
rgb1 = 'b'
rgb2 = 'w'
else:
rgb1 = 'w'
rgb2 = 'b'
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append(rgb2)
if n == 0:
collect.append(xy2patch(x[0:360], y[0:360], res, xy))
colors.append(rgb1)
return colors, collect
elif n == 1:
for i in range(0, j):
xp1[i] = x[i]
yp1[i] = y[i]
if azi[0][0] - azi[0][1] > np.pi:
azi[0][0] -= np.pi * 2.
elif azi[0][1] - azi[0][0] > np.pi:
azi[0][0] += np.pi * 2.
if azi[0][0] < azi[0][1]:
az = azi[0][1] - D2R
while az > azi[0][0]:
si = np.sin(az)
co = np.cos(az)
xp1[i] = x0 + radius_size * si
yp1[i] = y0 + radius_size * co
i += 1
az -= D2R
else:
az = azi[0][1] + D2R
while az < azi[0][0]:
si = np.sin(az)
co = np.cos(az)
xp1[i] = x0 + radius_size * si
yp1[i] = y0 + radius_size * co
i += 1
az += D2R
collect.append(xy2patch(xp1[0:i], yp1[0:i], res, xy))
colors.append(rgb1)
for i in range(0, j2):
xp2[i] = x2[i]
yp2[i] = y2[i]
if azi[1][0] - azi[1][1] > np.pi:
azi[1][0] -= np.pi * 2.
elif azi[1][1] - azi[1][0] > np.pi:
azi[1][0] += np.pi * 2.
if azi[1][0] < azi[1][1]:
az = azi[1][1] - D2R
while az > azi[1][0]:
si = np.sin(az)
co = np.cos(az)
xp2[i] = x0 + radius_size * si
i += 1
yp2[i] = y0 + radius_size * co
az -= D2R
else:
az = azi[1][1] + D2R
while az < azi[1][0]:
si = np.sin(az)
co = np.cos(az)
xp2[i] = x0 + radius_size * si
i += 1
yp2[i] = y0 + radius_size * co
az += D2R
collect.append(xy2patch(xp2[0:i], yp2[0:i], res, xy))
colors.append(rgb1)
return colors, collect
elif n == 2:
for i in range(0, j3):
xp1[i] = x3[i]
yp1[i] = y3[i]
for ii in range(0, j):
xp1[i] = x[ii]
i += 1
yp1[i] = y[ii]
if big_iso:
ii = j2 - 1
while ii >= 0:
xp1[i] = x2[ii]
i += 1
yp1[i] = y2[ii]
ii -= 1
collect.append(xy2patch(xp1[0:i], yp1[0:i], res, xy))
colors.append(rgb1)
return colors, collect
if azi[2][0] - azi[0][1] > np.pi:
azi[2][0] -= np.pi * 2.
elif azi[0][1] - azi[2][0] > np.pi:
azi[2][0] += np.pi * 2.
if azi[2][0] < azi[0][1]:
az = azi[0][1] - D2R
while az > azi[2][0]:
si = np.sin(az)
co = np.cos(az)
xp1[i] = x0 + radius_size * si
i += 1
yp1[i] = y0 + radius_size * co
az -= D2R
else:
az = azi[0][1] + D2R
while az < azi[2][0]:
si = np.sin(az)
co = np.cos(az)
xp1[i] = x0 + radius_size * si
i += 1
yp1[i] = y0 + radius_size * co
az += D2R
collect.append(xy2patch(xp1[0:i], yp1[0:i], res, xy))
colors.append(rgb1)
for i in range(0, j2):
xp2[i] = x2[i]
yp2[i] = y2[i]
if azi[1][0] - azi[1][1] > np.pi:
azi[1][0] -= np.pi * 2.
elif azi[1][1] - azi[1][0] > np.pi:
azi[1][0] += np.pi * 2.
if azi[1][0] < azi[1][1]:
az = azi[1][1] - D2R
while az > azi[1][0]:
si = np.sin(az)
co = np.cos(az)
xp2[i] = x0 + radius_size * si
i += 1
yp2[i] = y0 + radius_size * co
az -= D2R
else:
az = azi[1][1] + D2R
while az < azi[1][0]:
si = np.sin(az)
co = np.cos(az)
xp2[i] = x0 + radius_size * si
i += 1
yp2[i] = y0 + radius_size * co
az += D2R
collect.append(xy2patch(xp2[0:i], yp2[0:i], res, xy))
colors.append(rgb1)
return colors, collect | python | def plotMT(T, N, P, size=200, plot_zerotrace=True,
x0=0, y0=0, xy=(0, 0), width=200):
try:
assert(len(width) == 2)
except TypeError:
width = (width, width)
collect = []
colors = []
res = [value / float(size) for value in width]
b = 1
big_iso = 0
j = 1
j2 = 0
j3 = 0
n = 0
azi = np.zeros((3, 2))
x = np.zeros(400)
y = np.zeros(400)
x2 = np.zeros(400)
y2 = np.zeros(400)
x3 = np.zeros(400)
y3 = np.zeros(400)
xp1 = np.zeros(800)
yp1 = np.zeros(800)
xp2 = np.zeros(400)
yp2 = np.zeros(400)
a = np.zeros(3)
p = np.zeros(3)
v = np.zeros(3)
a[0] = T.strike
a[1] = N.strike
a[2] = P.strike
p[0] = T.dip
p[1] = N.dip
p[2] = P.dip
v[0] = T.val
v[1] = N.val
v[2] = P.val
vi = (v[0] + v[1] + v[2]) / 3.
for i in range(0, 3):
v[i] = v[i] - vi
radius_size = size * 0.5
if np.fabs(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]) < EPSILON:
if vi > 0.:
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append('b')
if vi < 0.:
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append('w')
return colors, collect
if np.fabs(v[0]) >= np.fabs(v[2]):
d = 0
m = 2
else:
d = 2
m = 0
if (plot_zerotrace):
vi = 0.
f = -v[1] / float(v[d])
iso = vi / float(v[d])
if iso < -1:
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append('w')
return colors, collect
elif iso > 1 - f:
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append('b')
return colors, collect
spd = np.sin(p[d] * D2R)
cpd = np.cos(p[d] * D2R)
spb = np.sin(p[b] * D2R)
cpb = np.cos(p[b] * D2R)
spm = np.sin(p[m] * D2R)
cpm = np.cos(p[m] * D2R)
sad = np.sin(a[d] * D2R)
cad = np.cos(a[d] * D2R)
sab = np.sin(a[b] * D2R)
cab = np.cos(a[b] * D2R)
sam = np.sin(a[m] * D2R)
cam = np.cos(a[m] * D2R)
for i in range(0, 360):
fir = i * D2R
s2alphan = (2. + 2. * iso) / \
float(3. + (1. - 2. * f) * np.cos(2. * fir))
if s2alphan > 1.:
big_iso += 1
else:
alphan = np.arcsin(np.sqrt(s2alphan))
sfi = np.sin(fir)
cfi = np.cos(fir)
san = np.sin(alphan)
can = np.cos(alphan)
xz = can * spd + san * sfi * spb + san * cfi * spm
xn = can * cpd * cad + san * sfi * cpb * cab + \
san * cfi * cpm * cam
xe = can * cpd * sad + san * sfi * cpb * sab + \
san * cfi * cpm * sam
if np.fabs(xn) < EPSILON and np.fabs(xe) < EPSILON:
takeoff = 0.
az = 0.
else:
az = np.arctan2(xe, xn)
if az < 0.:
az += np.pi * 2.
takeoff = np.arccos(xz / float(np.sqrt(xz * xz + xn * xn +
xe * xe)))
if takeoff > np.pi / 2.:
takeoff = np.pi - takeoff
az += np.pi
if az > np.pi * 2.:
az -= np.pi * 2.
r = np.sqrt(2) * np.sin(takeoff / 2.)
si = np.sin(az)
co = np.cos(az)
if i == 0:
azi[i][0] = az
x[i] = x0 + radius_size * r * si
y[i] = y0 + radius_size * r * co
azp = az
else:
if np.fabs(np.fabs(az - azp) - np.pi) < D2R * 10.:
azi[n][1] = azp
n += 1
azi[n][0] = az
if np.fabs(np.fabs(az - azp) - np.pi * 2.) < D2R * 2.:
if azp < az:
azi[n][0] += np.pi * 2.
else:
azi[n][0] -= np.pi * 2.
if n == 0:
x[j] = x0 + radius_size * r * si
y[j] = y0 + radius_size * r * co
j += 1
elif n == 1:
x2[j2] = x0 + radius_size * r * si
y2[j2] = y0 + radius_size * r * co
j2 += 1
elif n == 2:
x3[j3] = x0 + radius_size * r * si
y3[j3] = y0 + radius_size * r * co
j3 += 1
azp = az
azi[n][1] = az
if v[1] < 0.:
rgb1 = 'b'
rgb2 = 'w'
else:
rgb1 = 'w'
rgb2 = 'b'
cir = patches.Ellipse(xy, width=width[0], height=width[1])
collect.append(cir)
colors.append(rgb2)
if n == 0:
collect.append(xy2patch(x[0:360], y[0:360], res, xy))
colors.append(rgb1)
return colors, collect
elif n == 1:
for i in range(0, j):
xp1[i] = x[i]
yp1[i] = y[i]
if azi[0][0] - azi[0][1] > np.pi:
azi[0][0] -= np.pi * 2.
elif azi[0][1] - azi[0][0] > np.pi:
azi[0][0] += np.pi * 2.
if azi[0][0] < azi[0][1]:
az = azi[0][1] - D2R
while az > azi[0][0]:
si = np.sin(az)
co = np.cos(az)
xp1[i] = x0 + radius_size * si
yp1[i] = y0 + radius_size * co
i += 1
az -= D2R
else:
az = azi[0][1] + D2R
while az < azi[0][0]:
si = np.sin(az)
co = np.cos(az)
xp1[i] = x0 + radius_size * si
yp1[i] = y0 + radius_size * co
i += 1
az += D2R
collect.append(xy2patch(xp1[0:i], yp1[0:i], res, xy))
colors.append(rgb1)
for i in range(0, j2):
xp2[i] = x2[i]
yp2[i] = y2[i]
if azi[1][0] - azi[1][1] > np.pi:
azi[1][0] -= np.pi * 2.
elif azi[1][1] - azi[1][0] > np.pi:
azi[1][0] += np.pi * 2.
if azi[1][0] < azi[1][1]:
az = azi[1][1] - D2R
while az > azi[1][0]:
si = np.sin(az)
co = np.cos(az)
xp2[i] = x0 + radius_size * si
i += 1
yp2[i] = y0 + radius_size * co
az -= D2R
else:
az = azi[1][1] + D2R
while az < azi[1][0]:
si = np.sin(az)
co = np.cos(az)
xp2[i] = x0 + radius_size * si
i += 1
yp2[i] = y0 + radius_size * co
az += D2R
collect.append(xy2patch(xp2[0:i], yp2[0:i], res, xy))
colors.append(rgb1)
return colors, collect
elif n == 2:
for i in range(0, j3):
xp1[i] = x3[i]
yp1[i] = y3[i]
for ii in range(0, j):
xp1[i] = x[ii]
i += 1
yp1[i] = y[ii]
if big_iso:
ii = j2 - 1
while ii >= 0:
xp1[i] = x2[ii]
i += 1
yp1[i] = y2[ii]
ii -= 1
collect.append(xy2patch(xp1[0:i], yp1[0:i], res, xy))
colors.append(rgb1)
return colors, collect
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azi[2][0] -= np.pi * 2.
elif azi[0][1] - azi[2][0] > np.pi:
azi[2][0] += np.pi * 2.
if azi[2][0] < azi[0][1]:
az = azi[0][1] - D2R
while az > azi[2][0]:
si = np.sin(az)
co = np.cos(az)
xp1[i] = x0 + radius_size * si
i += 1
yp1[i] = y0 + radius_size * co
az -= D2R
else:
az = azi[0][1] + D2R
while az < azi[2][0]:
si = np.sin(az)
co = np.cos(az)
xp1[i] = x0 + radius_size * si
i += 1
yp1[i] = y0 + radius_size * co
az += D2R
collect.append(xy2patch(xp1[0:i], yp1[0:i], res, xy))
colors.append(rgb1)
for i in range(0, j2):
xp2[i] = x2[i]
yp2[i] = y2[i]
if azi[1][0] - azi[1][1] > np.pi:
azi[1][0] -= np.pi * 2.
elif azi[1][1] - azi[1][0] > np.pi:
azi[1][0] += np.pi * 2.
if azi[1][0] < azi[1][1]:
az = azi[1][1] - D2R
while az > azi[1][0]:
si = np.sin(az)
co = np.cos(az)
xp2[i] = x0 + radius_size * si
i += 1
yp2[i] = y0 + radius_size * co
az -= D2R
else:
az = azi[1][1] + D2R
while az < azi[1][0]:
si = np.sin(az)
co = np.cos(az)
xp2[i] = x0 + radius_size * si
i += 1
yp2[i] = y0 + radius_size * co
az += D2R
collect.append(xy2patch(xp2[0:i], yp2[0:i], res, xy))
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] | Uses a principal axis T, N and P to draw a beach ball plot.
:param ax: axis object of a matplotlib figure
:param T: :class:`~PrincipalAxis`
:param N: :class:`~PrincipalAxis`
:param P: :class:`~PrincipalAxis`
Adapted from ps_tensor / utilmeca.c / `Generic Mapping Tools (GMT)`_.
.. _`Generic Mapping Tools (GMT)`: http://gmt.soest.hawaii.edu | [
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] | train | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/beachball.py#L233-L553 |
gem/oq-engine | openquake/hmtk/plotting/beachball.py | plotDC | def plotDC(np1, size=200, xy=(0, 0), width=200):
"""
Uses one nodal plane of a double couple to draw a beach ball plot.
:param ax: axis object of a matplotlib figure
:param np1: :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.
"""
# check if one or two widths are specified (Circle or Ellipse)
try:
assert(len(width) == 2)
except TypeError:
width = (width, width)
S1 = np1.strike
D1 = np1.dip
R1 = np1.rake
M = 0
if R1 > 180:
R1 -= 180
M = 1
if R1 < 0:
R1 += 180
M = 1
# Get azimuth and dip of second plane
(S2, D2, _R2) = AuxPlane(S1, D1, R1)
D = size / 2
if D1 >= 90:
D1 = 89.9999
if D2 >= 90:
D2 = 89.9999
# arange checked for numerical stablility, np.pi is not multiple of 0.1
phi = np.arange(0, np.pi, .01)
l1 = np.sqrt(
np.power(90 - D1, 2) / (
np.power(np.sin(phi), 2) +
np.power(np.cos(phi), 2) * np.power(90 - D1, 2) / np.power(90, 2)))
l2 = np.sqrt(
np.power(90 - D2, 2) / (
np.power(np.sin(phi), 2) + np.power(np.cos(phi), 2) *
np.power(90 - D2, 2) / np.power(90, 2)))
inc = 1
(X1, Y1) = Pol2Cart(phi + S1 * D2R, l1)
if M == 1:
lo = S1 - 180
hi = S2
if lo > hi:
inc = -1
th1 = np.arange(S1 - 180, S2, inc)
(Xs1, Ys1) = Pol2Cart(th1 * D2R, 90 * np.ones((1, len(th1))))
(X2, Y2) = Pol2Cart(phi + S2 * D2R, l2)
th2 = np.arange(S2 + 180, S1, -inc)
else:
hi = S1 - 180
lo = S2 - 180
if lo > hi:
inc = -1
th1 = np.arange(hi, lo, -inc)
(Xs1, Ys1) = Pol2Cart(th1 * D2R, 90 * np.ones((1, len(th1))))
(X2, Y2) = Pol2Cart(phi + S2 * D2R, l2)
X2 = X2[::-1]
Y2 = Y2[::-1]
th2 = np.arange(S2, S1, inc)
(Xs2, Ys2) = Pol2Cart(th2 * D2R, 90 * np.ones((1, len(th2))))
X = np.concatenate((X1, Xs1[0], X2, Xs2[0]))
Y = np.concatenate((Y1, Ys1[0], Y2, Ys2[0]))
X = X * D / 90
Y = Y * D / 90
# calculate resolution
res = [value / float(size) for value in width]
# construct the patches
collect = [patches.Ellipse(xy, width=width[0], height=width[1])]
collect.append(xy2patch(Y, X, res, xy))
return ['b', 'w'], collect | python | def plotDC(np1, size=200, xy=(0, 0), width=200):
try:
assert(len(width) == 2)
except TypeError:
width = (width, width)
S1 = np1.strike
D1 = np1.dip
R1 = np1.rake
M = 0
if R1 > 180:
R1 -= 180
M = 1
if R1 < 0:
R1 += 180
M = 1
(S2, D2, _R2) = AuxPlane(S1, D1, R1)
D = size / 2
if D1 >= 90:
D1 = 89.9999
if D2 >= 90:
D2 = 89.9999
phi = np.arange(0, np.pi, .01)
l1 = np.sqrt(
np.power(90 - D1, 2) / (
np.power(np.sin(phi), 2) +
np.power(np.cos(phi), 2) * np.power(90 - D1, 2) / np.power(90, 2)))
l2 = np.sqrt(
np.power(90 - D2, 2) / (
np.power(np.sin(phi), 2) + np.power(np.cos(phi), 2) *
np.power(90 - D2, 2) / np.power(90, 2)))
inc = 1
(X1, Y1) = Pol2Cart(phi + S1 * D2R, l1)
if M == 1:
lo = S1 - 180
hi = S2
if lo > hi:
inc = -1
th1 = np.arange(S1 - 180, S2, inc)
(Xs1, Ys1) = Pol2Cart(th1 * D2R, 90 * np.ones((1, len(th1))))
(X2, Y2) = Pol2Cart(phi + S2 * D2R, l2)
th2 = np.arange(S2 + 180, S1, -inc)
else:
hi = S1 - 180
lo = S2 - 180
if lo > hi:
inc = -1
th1 = np.arange(hi, lo, -inc)
(Xs1, Ys1) = Pol2Cart(th1 * D2R, 90 * np.ones((1, len(th1))))
(X2, Y2) = Pol2Cart(phi + S2 * D2R, l2)
X2 = X2[::-1]
Y2 = Y2[::-1]
th2 = np.arange(S2, S1, inc)
(Xs2, Ys2) = Pol2Cart(th2 * D2R, 90 * np.ones((1, len(th2))))
X = np.concatenate((X1, Xs1[0], X2, Xs2[0]))
Y = np.concatenate((Y1, Ys1[0], Y2, Ys2[0]))
X = X * D / 90
Y = Y * D / 90
res = [value / float(size) for value in width]
collect = [patches.Ellipse(xy, width=width[0], height=width[1])]
collect.append(xy2patch(Y, X, res, xy))
return ['b', 'w'], collect | [
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:param ax: axis object of a matplotlib figure
:param np1: :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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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):
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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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):
r2d = 180 / np.pi
z = (s1 + 90) / r2d
z2 = d1 / r2d
z3 = r1 / r2d
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)
n2 = np.cos(z) * np.sin(z2)
h1 = -sl2
h2 = sl1
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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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):
(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>`_
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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):
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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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):
(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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gem/oq-engine | openquake/hmtk/strain/strain_utils.py | calculate_taper_function | def calculate_taper_function(obs_threshold_moment, sel_threshold_moment,
corner_moment, beta):
'''
Calculates the tapering function of the tapered Gutenberg & Richter model:
as described in Bird & Liu (2007)::
taper_function = (M_0(M_T) / M_0(M_T^{CMT}))^-beta x exp((M_0(m_T^CMT) -
M_0(m_T)) / M_0(m_c))
:param numpy.ndarray obs_threshold_moment:
Moment of the threshold magnitude of the observed earthquake catalogue
:param numpy.ndarray sel_threshold_moment:
Moment of the target magnitude
:param float corner_momnet:
Corner moment of the Tapered Gutenberg-Richter Function
:param float beta:
Beta value (b * ln(10.)) of the Tapered Gutenberg-Richter Function
:returns:
Relative moment rate
'''
argument = (obs_threshold_moment - sel_threshold_moment) /\
corner_moment
if argument < -100.0:
g_function = 0.0
else:
g_function = ((sel_threshold_moment / obs_threshold_moment) **
-beta) * exp(argument)
return g_function | python | def calculate_taper_function(obs_threshold_moment, sel_threshold_moment,
corner_moment, beta):
argument = (obs_threshold_moment - sel_threshold_moment) /\
corner_moment
if argument < -100.0:
g_function = 0.0
else:
g_function = ((sel_threshold_moment / obs_threshold_moment) **
-beta) * exp(argument)
return g_function | [
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Moment of the threshold magnitude of the observed earthquake catalogue
:param numpy.ndarray sel_threshold_moment:
Moment of the target magnitude
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Corner moment of the Tapered Gutenberg-Richter Function
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Beta value (b * ln(10.)) of the Tapered Gutenberg-Richter Function
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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):
cdf = np.exp((moment_threshold - moment) / corner_moment)
return ((moment / moment_threshold) ** (-beta)) * cdf | [
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Moment for calculation of rate
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Threshold Moment of the distribution (moment rate essentially!)
:param float beta:
Beta value (b * ln(10.)) of the Tapered Gutenberg-Richter Function
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Corner moment of the Tapered Gutenberg-Richter Function
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Cumulative probability of moment release > moment | [
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] | train | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/strain/strain_utils.py#L111-L134 |
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):
return ((beta / moment + 1. / corner_moment) *
tapered_gutenberg_richter_cdf(moment, moment_threshold, beta,
corner_moment)) | [
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gem/oq-engine | openquake/engine/export/core.py | check_version | def check_version(dstore):
"""
:param dstore: a DataStore instance
:returns:
a message if the stored version is different from the current version
"""
ds_version = dstore.hdf5.attrs['engine_version']
if ds_version != __version__:
return (': the datastore is at version %s, but the exporter at '
'version %s' % (ds_version, __version__))
else:
return '' | python | def check_version(dstore):
ds_version = dstore.hdf5.attrs['engine_version']
if ds_version != __version__:
return (': the datastore is at version %s, but the exporter at '
'version %s' % (ds_version, __version__))
else:
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gem/oq-engine | openquake/engine/export/core.py | export_from_db | def export_from_db(output_key, calc_id, datadir, target):
"""
:param output_key: a pair (ds_key, fmt)
:param calc_id: calculation ID
:param datadir: directory containing the datastore
:param target: directory, temporary when called from the engine server
:returns: the list of exported path names
"""
makedirs(target)
export.from_db = True
ds_key, fmt = output_key
with datastore.read(calc_id, datadir=datadir) as dstore:
dstore.export_dir = target
try:
exported = export(output_key, dstore)
except Exception:
etype, err, tb = sys.exc_info()
tb_str = ''.join(traceback.format_tb(tb))
version = check_version(dstore)
raise DataStoreExportError(
'Could not export %s in %s%s\n%s%s' %
(output_key + (version, tb_str, err)))
return exported | python | def export_from_db(output_key, calc_id, datadir, target):
makedirs(target)
export.from_db = True
ds_key, fmt = output_key
with datastore.read(calc_id, datadir=datadir) as dstore:
dstore.export_dir = target
try:
exported = export(output_key, dstore)
except Exception:
etype, err, tb = sys.exc_info()
tb_str = ''.join(traceback.format_tb(tb))
version = check_version(dstore)
raise DataStoreExportError(
'Could not export %s in %s%s\n%s%s' %
(output_key + (version, tb_str, err)))
return exported | [
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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):
if os.path.exists(path):
if not os.path.isdir(path):
raise RuntimeError('%s already exists and is not a directory.'
% path)
else:
os.makedirs(path) | [
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gem/oq-engine | openquake/engine/export/core.py | get_outkey | def get_outkey(dskey, export_types):
"""
Extract the first pair (dskey, exptype) found in export
"""
for exptype in export_types:
if (dskey, exptype) in export:
return (dskey, exptype) | python | def get_outkey(dskey, export_types):
for exptype in export_types:
if (dskey, exptype) in export:
return (dskey, exptype) | [
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gem/oq-engine | openquake/engine/export/core.py | export_output | def export_output(dskey, calc_id, datadir, target_dir, export_types):
"""
Simple UI wrapper around
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return
yield from export_from_db(outkey, calc_id, datadir, target_dir) | python | def export_output(dskey, calc_id, datadir, target_dir, export_types):
outkey = get_outkey(dskey, export_types.split(','))
if export_types and not outkey:
yield 'There is no exporter for %s, %s' % (dskey, export_types)
return
yield from export_from_db(outkey, calc_id, datadir, target_dir) | [
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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):
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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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):
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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gem/oq-engine | openquake/hazardlib/gsim/campbell_1997.py | Campbell1997.get_mean_and_stddevs | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
R = (dists.rrup)
M = rup.mag
# get constants
Ssr = self.get_Ssr_term(sites.vs30)
Shr = self.get_Shr_term(sites.vs30)
rake = rup.rake
F = self.get_fault_term(rake)
# compute mean
mean = -3.512 + (0.904 * M) - (1.328 * np.log(np.sqrt(R**2
+ (0.149 * np.exp(0.647 * M))**2))) \
+ (1.125 - 0.112 * np.log(R) - 0.0957 * M) * F \
+ (0.440 - 0.171 * np.log(R)) * Ssr \
+ (0.405 - 0.222 * np.log(R)) * Shr
stddevs = self.get_stddevs(mean, stddev_types)
return mean, stddevs | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
R = (dists.rrup)
M = rup.mag
Ssr = self.get_Ssr_term(sites.vs30)
Shr = self.get_Shr_term(sites.vs30)
rake = rup.rake
F = self.get_fault_term(rake)
mean = -3.512 + (0.904 * M) - (1.328 * np.log(np.sqrt(R**2
+ (0.149 * np.exp(0.647 * M))**2))) \
+ (1.125 - 0.112 * np.log(R) - 0.0957 * M) * F \
+ (0.440 - 0.171 * np.log(R)) * Ssr \
+ (0.405 - 0.222 * np.log(R)) * Shr
stddevs = self.get_stddevs(mean, stddev_types)
return mean, stddevs | [
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gem/oq-engine | openquake/hazardlib/gsim/campbell_1997.py | Campbell1997.get_fault_term | def get_fault_term(self, rake):
"""
Returns coefficient for faulting style (pg 156)
"""
rake = rake + 360 if rake < 0 else rake
if (rake >= 45) & (rake <= 135):
f = 1.
elif (rake >= 225) & (rake <= 315):
f = 0.5
else:
f = 0.
return f | python | def get_fault_term(self, rake):
rake = rake + 360 if rake < 0 else rake
if (rake >= 45) & (rake <= 135):
f = 1.
elif (rake >= 225) & (rake <= 315):
f = 0.5
else:
f = 0.
return f | [
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gem/oq-engine | openquake/hazardlib/gsim/campbell_1997.py | Campbell1997.get_stddevs | def get_stddevs(self, mean, stddev_types):
"""
Returns the standard deviations from mean (pg 164; more robust than
estimate using magnitude)
"""
mean = np.exp(mean)
sigma = 0.39 + np.zeros(mean.shape)
sigma[mean < 0.068] = 0.55
idx = np.logical_and(mean >= 0.068, mean <= 0.21)
sigma[idx] = 0.173- 0.140 * np.log(mean[idx])
stddevs = []
for stddev in stddev_types:
if stddev == const.StdDev.TOTAL:
stddevs.append(sigma)
return stddevs | python | def get_stddevs(self, mean, stddev_types):
mean = np.exp(mean)
sigma = 0.39 + np.zeros(mean.shape)
sigma[mean < 0.068] = 0.55
idx = np.logical_and(mean >= 0.068, mean <= 0.21)
sigma[idx] = 0.173- 0.140 * np.log(mean[idx])
stddevs = []
for stddev in stddev_types:
if stddev == const.StdDev.TOTAL:
stddevs.append(sigma)
return stddevs | [
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gem/oq-engine | openquake/hazardlib/gsim/tavakoli_pezeshk_2005.py | TavakoliPezeshk2005.get_mean_and_stddevs | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extracting dictionary of coefficients
C = self.COEFFS[imt]
mag = self._convert_magnitude(rup.mag)
# computing the magnitude term. Equation 19, page 2291
f1 = self._compute_magnitude_scaling_term(C, mag)
# computing the geometrical spreading term. Equation 20, page 2291
f2 = self._compute_geometrical_spreading_term(C, dists.rrup)
# computing the anelastic attenuation term. Equation 21, page 2291
f3 = self._compute_anelastic_attenuation_term(C, dists.rrup, mag)
# computing the mean ln(IMT) using equation 18 at page 2290
mean = f1 + f2 + f3
mean = self._clip_mean(imt, mean)
# computing the total standard deviation
stddevs = self._get_stddevs(C, stddev_types, num_sites=len(dists.rrup),
mag=mag)
return mean, stddevs | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
C = self.COEFFS[imt]
mag = self._convert_magnitude(rup.mag)
f1 = self._compute_magnitude_scaling_term(C, mag)
f2 = self._compute_geometrical_spreading_term(C, dists.rrup)
f3 = self._compute_anelastic_attenuation_term(C, dists.rrup, mag)
mean = f1 + f2 + f3
mean = self._clip_mean(imt, mean)
stddevs = self._get_stddevs(C, stddev_types, num_sites=len(dists.rrup),
mag=mag)
return mean, stddevs | [
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gem/oq-engine | openquake/hazardlib/gsim/tavakoli_pezeshk_2005.py | TavakoliPezeshk2005._get_stddevs | def _get_stddevs(self, C, stddev_types, num_sites, mag):
"""
Returns standard deviation as defined in equation 23, page 2291
(Tavakoli and Pezeshk, 2005)
"""
stddevs = []
sigma = (C['c14'] + C['c15'] * mag) if mag < 7.2 else C['c16']
vals = sigma * np.ones((num_sites))
for _ in stddev_types:
stddevs.append(vals)
return stddevs | python | def _get_stddevs(self, C, stddev_types, num_sites, mag):
stddevs = []
sigma = (C['c14'] + C['c15'] * mag) if mag < 7.2 else C['c16']
vals = sigma * np.ones((num_sites))
for _ in stddev_types:
stddevs.append(vals)
return stddevs | [
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gem/oq-engine | openquake/hazardlib/gsim/tavakoli_pezeshk_2005.py | TavakoliPezeshk2005._compute_magnitude_scaling_term | def _compute_magnitude_scaling_term(self, C, mag):
"""
Compute magnitude scaling term as defined in equation 19, page 2291
(Tavakoli and Pezeshk, 2005)
"""
assert mag <= 8.5
return C['c1'] + C['c2'] * mag + C['c3'] * (8.5 - mag) ** 2.5 | python | def _compute_magnitude_scaling_term(self, C, mag):
assert mag <= 8.5
return C['c1'] + C['c2'] * mag + C['c3'] * (8.5 - mag) ** 2.5 | [
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gem/oq-engine | openquake/hazardlib/gsim/tavakoli_pezeshk_2005.py | TavakoliPezeshk2005._compute_geometrical_spreading_term | def _compute_geometrical_spreading_term(self, C, rrup):
"""
Compute magnitude scaling term as defined in equation 19, page 2291
(Tavakoli and Pezeshk, 2005)
"""
f2 = np.ones_like(rrup)
idx1 = np.nonzero(rrup <= 70.)
idx2 = np.nonzero((rrup > 70.) & (rrup <= 130.))
idx3 = np.nonzero(rrup > 130.)
f2[idx1] = (C['c9'] * np.log(rrup[idx1] + 4.5))
f2[idx2] = (C['c10'] * np.log(rrup[idx2]/70.) +
C['c9'] * np.log(rrup[idx2] + 4.5))
f2[idx3] = (C['c11'] * np.log(rrup[idx3]/130.) +
C['c10'] * np.log(rrup[idx3]/70.) +
C['c9'] * np.log(rrup[idx3] + 4.5))
return f2 | python | def _compute_geometrical_spreading_term(self, C, rrup):
f2 = np.ones_like(rrup)
idx1 = np.nonzero(rrup <= 70.)
idx2 = np.nonzero((rrup > 70.) & (rrup <= 130.))
idx3 = np.nonzero(rrup > 130.)
f2[idx1] = (C['c9'] * np.log(rrup[idx1] + 4.5))
f2[idx2] = (C['c10'] * np.log(rrup[idx2]/70.) +
C['c9'] * np.log(rrup[idx2] + 4.5))
f2[idx3] = (C['c11'] * np.log(rrup[idx3]/130.) +
C['c10'] * np.log(rrup[idx3]/70.) +
C['c9'] * np.log(rrup[idx3] + 4.5))
return f2 | [
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