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100 | gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.generate_config_set | def generate_config_set(self, config):
'''
Generates a list of magnitude frequency distributions and renders as
a tuple
:param dict/list config:
Configuration paramters of magnitude frequency distribution
'''
if isinstance(config, dict):
# Configuration list contains only one element
self.config = [(config, 1.0)]
elif isinstance(config, list):
# Multiple configurations with correscponding weights
total_weight = 0.
self.config = []
for params in config:
weight = params['Model_Weight']
total_weight += params['Model_Weight']
self.config.append((params, weight))
if fabs(total_weight - 1.0) > 1E-7:
raise ValueError('MFD config weights do not sum to 1.0 for '
'fault %s' % self.id)
else:
raise ValueError('MFD config must be input as dictionary or list!') | python | def generate_config_set(self, config):
'''
Generates a list of magnitude frequency distributions and renders as
a tuple
:param dict/list config:
Configuration paramters of magnitude frequency distribution
'''
if isinstance(config, dict):
# Configuration list contains only one element
self.config = [(config, 1.0)]
elif isinstance(config, list):
# Multiple configurations with correscponding weights
total_weight = 0.
self.config = []
for params in config:
weight = params['Model_Weight']
total_weight += params['Model_Weight']
self.config.append((params, weight))
if fabs(total_weight - 1.0) > 1E-7:
raise ValueError('MFD config weights do not sum to 1.0 for '
'fault %s' % self.id)
else:
raise ValueError('MFD config must be input as dictionary or list!') | [
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:param dict/list config:
Configuration paramters of magnitude frequency distribution | [
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101 | gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.collapse_branches | def collapse_branches(self, mmin, bin_width, mmax):
'''
Collapse the logic tree branches into a single IncrementalMFD
:param float mmin:
Minimum magnitude of reference mfd
:param float bin_width:
Bin width of reference mfd
:param float mmax:
Maximum magnitude of reference mfd
:returns:
:class: openquake.hmtk.models.IncrementalMFD
'''
master_mags = np.arange(mmin, mmax + (bin_width / 2.), bin_width)
master_rates = np.zeros(len(master_mags), dtype=float)
for model in self.mfd_models:
id0 = np.logical_and(
master_mags >= np.min(model.magnitudes) - 1E-9,
master_mags <= np.max(model.magnitudes) + 1E-9)
# Use interpolation in log10-y values
yvals = np.log10(model.recurrence.occur_rates)
interp_y = np.interp(master_mags[id0],
model.magnitudes,
yvals)
master_rates[id0] = master_rates[id0] + (model.weight *
10. ** interp_y)
return IncrementalMFD(mmin, bin_width, master_rates) | python | def collapse_branches(self, mmin, bin_width, mmax):
'''
Collapse the logic tree branches into a single IncrementalMFD
:param float mmin:
Minimum magnitude of reference mfd
:param float bin_width:
Bin width of reference mfd
:param float mmax:
Maximum magnitude of reference mfd
:returns:
:class: openquake.hmtk.models.IncrementalMFD
'''
master_mags = np.arange(mmin, mmax + (bin_width / 2.), bin_width)
master_rates = np.zeros(len(master_mags), dtype=float)
for model in self.mfd_models:
id0 = np.logical_and(
master_mags >= np.min(model.magnitudes) - 1E-9,
master_mags <= np.max(model.magnitudes) + 1E-9)
# Use interpolation in log10-y values
yvals = np.log10(model.recurrence.occur_rates)
interp_y = np.interp(master_mags[id0],
model.magnitudes,
yvals)
master_rates[id0] = master_rates[id0] + (model.weight *
10. ** interp_y)
return IncrementalMFD(mmin, bin_width, master_rates) | [
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:param float bin_width:
Bin width of reference mfd
:param float mmax:
Maximum magnitude of reference mfd
:returns:
:class: openquake.hmtk.models.IncrementalMFD | [
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102 | gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.generate_fault_source_model | def generate_fault_source_model(self):
'''
Creates a resulting `openquake.hmtk` fault source set.
:returns:
source_model - list of instances of either the :class:
`openquake.hmtk.sources.simple_fault_source.mtkSimpleFaultSource`
or :class:
`openquake.hmtk.sources.complex_fault_source.mtkComplexFaultSource`
model_weight - Corresponding weights for each source model
'''
source_model = []
model_weight = []
for iloc in range(0, self.get_number_mfd_models()):
model_mfd = EvenlyDiscretizedMFD(
self.mfd[0][iloc].min_mag,
self.mfd[0][iloc].bin_width,
self.mfd[0][iloc].occur_rates.tolist())
if isinstance(self.geometry, ComplexFaultGeometry):
# Complex fault class
source = mtkComplexFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_edges = self.geometry.trace
else:
# Simple Fault source
source = mtkSimpleFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.geometry.dip,
self.geometry.upper_depth,
self.geometry.lower_depth,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_trace = self.geometry.trace
source_model.append(source)
model_weight.append(self.mfd[1][iloc])
return source_model, model_weight | python | def generate_fault_source_model(self):
'''
Creates a resulting `openquake.hmtk` fault source set.
:returns:
source_model - list of instances of either the :class:
`openquake.hmtk.sources.simple_fault_source.mtkSimpleFaultSource`
or :class:
`openquake.hmtk.sources.complex_fault_source.mtkComplexFaultSource`
model_weight - Corresponding weights for each source model
'''
source_model = []
model_weight = []
for iloc in range(0, self.get_number_mfd_models()):
model_mfd = EvenlyDiscretizedMFD(
self.mfd[0][iloc].min_mag,
self.mfd[0][iloc].bin_width,
self.mfd[0][iloc].occur_rates.tolist())
if isinstance(self.geometry, ComplexFaultGeometry):
# Complex fault class
source = mtkComplexFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_edges = self.geometry.trace
else:
# Simple Fault source
source = mtkSimpleFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.geometry.dip,
self.geometry.upper_depth,
self.geometry.lower_depth,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_trace = self.geometry.trace
source_model.append(source)
model_weight.append(self.mfd[1][iloc])
return source_model, model_weight | [
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:returns:
source_model - list of instances of either the :class:
`openquake.hmtk.sources.simple_fault_source.mtkSimpleFaultSource`
or :class:
`openquake.hmtk.sources.complex_fault_source.mtkComplexFaultSource`
model_weight - Corresponding weights for each source model | [
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103 | gem/oq-engine | openquake/hmtk/models.py | SeismicSource.attrib | def attrib(self):
"""
General XML element attributes for a seismic source, as a dict.
"""
return dict([
('id', str(self.id)),
('name', str(self.name)),
('tectonicRegion', str(self.trt)),
]) | python | def attrib(self):
"""
General XML element attributes for a seismic source, as a dict.
"""
return dict([
('id', str(self.id)),
('name', str(self.name)),
('tectonicRegion', str(self.trt)),
]) | [
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104 | gem/oq-engine | openquake/hmtk/models.py | TGRMFD.attrib | def attrib(self):
"""
An dict of XML element attributes for this MFD.
"""
return dict([
('aValue', str(self.a_val)),
('bValue', str(self.b_val)),
('minMag', str(self.min_mag)),
('maxMag', str(self.max_mag)),
]) | python | def attrib(self):
"""
An dict of XML element attributes for this MFD.
"""
return dict([
('aValue', str(self.a_val)),
('bValue', str(self.b_val)),
('minMag', str(self.min_mag)),
('maxMag', str(self.max_mag)),
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105 | gem/oq-engine | openquake/hmtk/models.py | NodalPlane.attrib | def attrib(self):
"""
A dict of XML element attributes for this NodalPlane.
"""
return dict([
('probability', str(self.probability)),
('strike', str(self.strike)),
('dip', str(self.dip)),
('rake', str(self.rake)),
]) | python | def attrib(self):
"""
A dict of XML element attributes for this NodalPlane.
"""
return dict([
('probability', str(self.probability)),
('strike', str(self.strike)),
('dip', str(self.dip)),
('rake', str(self.rake)),
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106 | gem/oq-engine | openquake/hazardlib/correlation.py | jbcorrelation | def jbcorrelation(sites_or_distances, imt, vs30_clustering=False):
"""
Returns the Jayaram-Baker correlation model.
:param sites_or_distances:
SiteCollection instance o ristance matrix
:param imt:
Intensity Measure Type (PGA or SA)
:param vs30_clustering:
flag, defalt false
"""
if hasattr(sites_or_distances, 'mesh'):
distances = sites_or_distances.mesh.get_distance_matrix()
else:
distances = sites_or_distances
# formulae are from page 1700
if imt.period < 1:
if not vs30_clustering:
# case 1, eq. (17)
b = 8.5 + 17.2 * imt.period
else:
# case 2, eq. (18)
b = 40.7 - 15.0 * imt.period
else:
# both cases, eq. (19)
b = 22.0 + 3.7 * imt.period
# eq. (20)
return numpy.exp((- 3.0 / b) * distances) | python | def jbcorrelation(sites_or_distances, imt, vs30_clustering=False):
"""
Returns the Jayaram-Baker correlation model.
:param sites_or_distances:
SiteCollection instance o ristance matrix
:param imt:
Intensity Measure Type (PGA or SA)
:param vs30_clustering:
flag, defalt false
"""
if hasattr(sites_or_distances, 'mesh'):
distances = sites_or_distances.mesh.get_distance_matrix()
else:
distances = sites_or_distances
# formulae are from page 1700
if imt.period < 1:
if not vs30_clustering:
# case 1, eq. (17)
b = 8.5 + 17.2 * imt.period
else:
# case 2, eq. (18)
b = 40.7 - 15.0 * imt.period
else:
# both cases, eq. (19)
b = 22.0 + 3.7 * imt.period
# eq. (20)
return numpy.exp((- 3.0 / b) * distances) | [
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:param imt:
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:param vs30_clustering:
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107 | gem/oq-engine | openquake/hazardlib/correlation.py | hmcorrelation | def hmcorrelation(sites_or_distances, imt, uncertainty_multiplier=0):
"""
Returns the Heresi-Miranda correlation model.
:param sites_or_distances:
SiteCollection instance o distance matrix
:param imt:
Intensity Measure Type (PGA or SA)
:param uncertainty_multiplier:
Value to be multiplied by the uncertainty in the correlation parameter
beta. If uncertainty_multiplier = 0 (default), the median value is
used as a constant value.
"""
if hasattr(sites_or_distances, 'mesh'):
distances = sites_or_distances.mesh.get_distance_matrix()
else:
distances = sites_or_distances
period = imt.period
# Eq. (9)
if period < 1.37:
Med_b = 4.231 * period * period - 5.180 * period + 13.392
else:
Med_b = 0.140 * period * period - 2.249 * period + 17.050
# Eq. (10)
Std_b = (4.63e-3 * period*period + 0.028 * period + 0.713)
# Obtain realization of b
if uncertainty_multiplier == 0:
beta = Med_b
else:
beta = numpy.random.lognormal(
numpy.log(Med_b), Std_b * uncertainty_multiplier)
# Eq. (8)
return numpy.exp(-numpy.power((distances / beta), 0.55)) | python | def hmcorrelation(sites_or_distances, imt, uncertainty_multiplier=0):
"""
Returns the Heresi-Miranda correlation model.
:param sites_or_distances:
SiteCollection instance o distance matrix
:param imt:
Intensity Measure Type (PGA or SA)
:param uncertainty_multiplier:
Value to be multiplied by the uncertainty in the correlation parameter
beta. If uncertainty_multiplier = 0 (default), the median value is
used as a constant value.
"""
if hasattr(sites_or_distances, 'mesh'):
distances = sites_or_distances.mesh.get_distance_matrix()
else:
distances = sites_or_distances
period = imt.period
# Eq. (9)
if period < 1.37:
Med_b = 4.231 * period * period - 5.180 * period + 13.392
else:
Med_b = 0.140 * period * period - 2.249 * period + 17.050
# Eq. (10)
Std_b = (4.63e-3 * period*period + 0.028 * period + 0.713)
# Obtain realization of b
if uncertainty_multiplier == 0:
beta = Med_b
else:
beta = numpy.random.lognormal(
numpy.log(Med_b), Std_b * uncertainty_multiplier)
# Eq. (8)
return numpy.exp(-numpy.power((distances / beta), 0.55)) | [
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108 | gem/oq-engine | openquake/hazardlib/correlation.py | JB2009CorrelationModel.get_lower_triangle_correlation_matrix | def get_lower_triangle_correlation_matrix(self, sites, imt):
"""
Get lower-triangle matrix as a result of Cholesky-decomposition
of correlation matrix.
The resulting matrix should have zeros on values above
the main diagonal.
The actual implementations of :class:`BaseCorrelationModel` interface
might calculate the matrix considering site collection and IMT (like
:class:`JB2009CorrelationModel` does) or might have it pre-constructed
for a specific site collection and IMT, in which case they will need
to make sure that parameters to this function match parameters that
were used to pre-calculate decomposed correlation matrix.
:param sites:
:class:`~openquake.hazardlib.site.SiteCollection` to create
correlation matrix for.
:param imt:
Intensity measure type object, see :mod:`openquake.hazardlib.imt`.
"""
return numpy.linalg.cholesky(self._get_correlation_matrix(sites, imt)) | python | def get_lower_triangle_correlation_matrix(self, sites, imt):
"""
Get lower-triangle matrix as a result of Cholesky-decomposition
of correlation matrix.
The resulting matrix should have zeros on values above
the main diagonal.
The actual implementations of :class:`BaseCorrelationModel` interface
might calculate the matrix considering site collection and IMT (like
:class:`JB2009CorrelationModel` does) or might have it pre-constructed
for a specific site collection and IMT, in which case they will need
to make sure that parameters to this function match parameters that
were used to pre-calculate decomposed correlation matrix.
:param sites:
:class:`~openquake.hazardlib.site.SiteCollection` to create
correlation matrix for.
:param imt:
Intensity measure type object, see :mod:`openquake.hazardlib.imt`.
"""
return numpy.linalg.cholesky(self._get_correlation_matrix(sites, imt)) | [
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109 | gem/oq-engine | openquake/calculators/ebrisk.py | start_ebrisk | def start_ebrisk(rupgetter, srcfilter, param, monitor):
"""
Launcher for ebrisk tasks
"""
with monitor('weighting ruptures'):
rupgetter.set_weights(srcfilter, param['num_taxonomies'])
if rupgetter.weights.sum() <= param['maxweight']:
yield ebrisk(rupgetter, srcfilter, param, monitor)
else:
for rgetter in rupgetter.split(param['maxweight']):
yield ebrisk, rgetter, srcfilter, param | python | def start_ebrisk(rupgetter, srcfilter, param, monitor):
"""
Launcher for ebrisk tasks
"""
with monitor('weighting ruptures'):
rupgetter.set_weights(srcfilter, param['num_taxonomies'])
if rupgetter.weights.sum() <= param['maxweight']:
yield ebrisk(rupgetter, srcfilter, param, monitor)
else:
for rgetter in rupgetter.split(param['maxweight']):
yield ebrisk, rgetter, srcfilter, param | [
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110 | gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD.get_min_max_mag | def get_min_max_mag(self):
"Return the minimum and maximum magnitudes"
mag, num_bins = self._get_min_mag_and_num_bins()
return mag, mag + self. bin_width * (num_bins - 1) | python | def get_min_max_mag(self):
"Return the minimum and maximum magnitudes"
mag, num_bins = self._get_min_mag_and_num_bins()
return mag, mag + self. bin_width * (num_bins - 1) | [
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111 | gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD._get_rate | def _get_rate(self, mag):
"""
Calculate and return the annual occurrence rate for a specific bin.
:param mag:
Magnitude value corresponding to the center of the bin of interest.
:returns:
Float number, the annual occurrence rate for the :param mag value.
"""
mag_lo = mag - self.bin_width / 2.0
mag_hi = mag + self.bin_width / 2.0
if mag >= self.min_mag and mag < self.char_mag - DELTA_CHAR / 2:
# return rate according to exponential distribution
return (10 ** (self.a_val - self.b_val * mag_lo)
- 10 ** (self.a_val - self.b_val * mag_hi))
else:
# return characteristic rate (distributed over the characteristic
# range) for the given bin width
return (self.char_rate / DELTA_CHAR) * self.bin_width | python | def _get_rate(self, mag):
"""
Calculate and return the annual occurrence rate for a specific bin.
:param mag:
Magnitude value corresponding to the center of the bin of interest.
:returns:
Float number, the annual occurrence rate for the :param mag value.
"""
mag_lo = mag - self.bin_width / 2.0
mag_hi = mag + self.bin_width / 2.0
if mag >= self.min_mag and mag < self.char_mag - DELTA_CHAR / 2:
# return rate according to exponential distribution
return (10 ** (self.a_val - self.b_val * mag_lo)
- 10 ** (self.a_val - self.b_val * mag_hi))
else:
# return characteristic rate (distributed over the characteristic
# range) for the given bin width
return (self.char_rate / DELTA_CHAR) * self.bin_width | [
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112 | gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD._get_min_mag_and_num_bins | def _get_min_mag_and_num_bins(self):
"""
Estimate the number of bins in the histogram and return it along with
the first bin center value.
Rounds ``min_mag`` and ``max_mag`` with respect to ``bin_width`` to
make the distance between them include integer number of bins.
:returns:
A tuple of 2 items: first bin center, and total number of bins.
"""
min_mag = round(self.min_mag / self.bin_width) * self.bin_width
max_mag = (round((self.char_mag + DELTA_CHAR / 2) /
self.bin_width) * self.bin_width)
min_mag += self.bin_width / 2.0
max_mag -= self.bin_width / 2.0
# here we use math round on the result of division and not just
# cast it to integer because for some magnitude values that can't
# be represented as an IEEE 754 double precisely the result can
# look like 7.999999999999 which would become 7 instead of 8
# being naively casted to int so we would lose the last bin.
num_bins = int(round((max_mag - min_mag) / self.bin_width)) + 1
return min_mag, num_bins | python | def _get_min_mag_and_num_bins(self):
"""
Estimate the number of bins in the histogram and return it along with
the first bin center value.
Rounds ``min_mag`` and ``max_mag`` with respect to ``bin_width`` to
make the distance between them include integer number of bins.
:returns:
A tuple of 2 items: first bin center, and total number of bins.
"""
min_mag = round(self.min_mag / self.bin_width) * self.bin_width
max_mag = (round((self.char_mag + DELTA_CHAR / 2) /
self.bin_width) * self.bin_width)
min_mag += self.bin_width / 2.0
max_mag -= self.bin_width / 2.0
# here we use math round on the result of division and not just
# cast it to integer because for some magnitude values that can't
# be represented as an IEEE 754 double precisely the result can
# look like 7.999999999999 which would become 7 instead of 8
# being naively casted to int so we would lose the last bin.
num_bins = int(round((max_mag - min_mag) / self.bin_width)) + 1
return min_mag, num_bins | [
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113 | gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD.get_annual_occurrence_rates | def get_annual_occurrence_rates(self):
"""
Calculate and return the annual occurrence rates histogram.
:returns:
See :meth:
`openquake.hazardlib.mfd.base.BaseMFD.get_annual_occurrence_rates`.
"""
mag, num_bins = self._get_min_mag_and_num_bins()
rates = []
for i in range(num_bins):
rate = self._get_rate(mag)
rates.append((mag, rate))
mag += self.bin_width
return rates | python | def get_annual_occurrence_rates(self):
"""
Calculate and return the annual occurrence rates histogram.
:returns:
See :meth:
`openquake.hazardlib.mfd.base.BaseMFD.get_annual_occurrence_rates`.
"""
mag, num_bins = self._get_min_mag_and_num_bins()
rates = []
for i in range(num_bins):
rate = self._get_rate(mag)
rates.append((mag, rate))
mag += self.bin_width
return rates | [
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114 | gem/oq-engine | openquake/hmtk/sources/area_source.py | mtkAreaSource.create_geometry | def create_geometry(self, input_geometry, upper_depth, lower_depth):
'''
If geometry is defined as a numpy array then create instance of
nhlib.geo.polygon.Polygon class, otherwise if already instance of class
accept class
:param input_geometry:
Input geometry (polygon) as either
i) instance of nhlib.geo.polygon.Polygon class
ii) numpy.ndarray [Longitude, Latitude]
:param float upper_depth:
Upper seismogenic depth (km)
:param float lower_depth:
Lower seismogenic depth (km)
'''
self._check_seismogenic_depths(upper_depth, lower_depth)
# Check/create the geometry class
if not isinstance(input_geometry, Polygon):
if not isinstance(input_geometry, np.ndarray):
raise ValueError('Unrecognised or unsupported geometry '
'definition')
if np.shape(input_geometry)[0] < 3:
raise ValueError('Incorrectly formatted polygon geometry -'
' needs three or more vertices')
geometry = []
for row in input_geometry:
geometry.append(Point(row[0], row[1], self.upper_depth))
self.geometry = Polygon(geometry)
else:
self.geometry = input_geometry | python | def create_geometry(self, input_geometry, upper_depth, lower_depth):
'''
If geometry is defined as a numpy array then create instance of
nhlib.geo.polygon.Polygon class, otherwise if already instance of class
accept class
:param input_geometry:
Input geometry (polygon) as either
i) instance of nhlib.geo.polygon.Polygon class
ii) numpy.ndarray [Longitude, Latitude]
:param float upper_depth:
Upper seismogenic depth (km)
:param float lower_depth:
Lower seismogenic depth (km)
'''
self._check_seismogenic_depths(upper_depth, lower_depth)
# Check/create the geometry class
if not isinstance(input_geometry, Polygon):
if not isinstance(input_geometry, np.ndarray):
raise ValueError('Unrecognised or unsupported geometry '
'definition')
if np.shape(input_geometry)[0] < 3:
raise ValueError('Incorrectly formatted polygon geometry -'
' needs three or more vertices')
geometry = []
for row in input_geometry:
geometry.append(Point(row[0], row[1], self.upper_depth))
self.geometry = Polygon(geometry)
else:
self.geometry = input_geometry | [
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:param float upper_depth:
Upper seismogenic depth (km)
:param float lower_depth:
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115 | gem/oq-engine | openquake/hmtk/sources/area_source.py | mtkAreaSource.select_catalogue | def select_catalogue(self, selector, distance=None):
'''
Selects the catalogue of earthquakes attributable to the source
:param selector:
Populated instance of openquake.hmtk.seismicity.selector.CatalogueSelector
class
:param float distance:
Distance (in km) to extend or contract (if negative) the zone for
selecting events
'''
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
self.catalogue = selector.within_polygon(self.geometry,
distance,
upper_depth=self.upper_depth,
lower_depth=self.lower_depth)
if self.catalogue.get_number_events() < 5:
# Throw a warning regarding the small number of earthquakes in
# the source!
warnings.warn('Source %s (%s) has fewer than 5 events'
% (self.id, self.name)) | python | def select_catalogue(self, selector, distance=None):
'''
Selects the catalogue of earthquakes attributable to the source
:param selector:
Populated instance of openquake.hmtk.seismicity.selector.CatalogueSelector
class
:param float distance:
Distance (in km) to extend or contract (if negative) the zone for
selecting events
'''
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
self.catalogue = selector.within_polygon(self.geometry,
distance,
upper_depth=self.upper_depth,
lower_depth=self.lower_depth)
if self.catalogue.get_number_events() < 5:
# Throw a warning regarding the small number of earthquakes in
# the source!
warnings.warn('Source %s (%s) has fewer than 5 events'
% (self.id, self.name)) | [
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Distance (in km) to extend or contract (if negative) the zone for
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116 | gem/oq-engine | openquake/baselib/performance.py | Monitor.new | def new(self, operation='no operation', **kw):
"""
Return a copy of the monitor usable for a different operation.
"""
self_vars = vars(self).copy()
del self_vars['operation']
del self_vars['children']
del self_vars['counts']
del self_vars['_flush']
new = self.__class__(operation)
vars(new).update(self_vars)
vars(new).update(kw)
return new | python | def new(self, operation='no operation', **kw):
"""
Return a copy of the monitor usable for a different operation.
"""
self_vars = vars(self).copy()
del self_vars['operation']
del self_vars['children']
del self_vars['counts']
del self_vars['_flush']
new = self.__class__(operation)
vars(new).update(self_vars)
vars(new).update(kw)
return new | [
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117 | gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.from_shakemap | def from_shakemap(cls, shakemap_array):
"""
Build a site collection from a shakemap array
"""
self = object.__new__(cls)
self.complete = self
n = len(shakemap_array)
dtype = numpy.dtype([(p, site_param_dt[p])
for p in 'sids lon lat depth vs30'.split()])
self.array = arr = numpy.zeros(n, dtype)
arr['sids'] = numpy.arange(n, dtype=numpy.uint32)
arr['lon'] = shakemap_array['lon']
arr['lat'] = shakemap_array['lat']
arr['depth'] = numpy.zeros(n)
arr['vs30'] = shakemap_array['vs30']
arr.flags.writeable = False
return self | python | def from_shakemap(cls, shakemap_array):
"""
Build a site collection from a shakemap array
"""
self = object.__new__(cls)
self.complete = self
n = len(shakemap_array)
dtype = numpy.dtype([(p, site_param_dt[p])
for p in 'sids lon lat depth vs30'.split()])
self.array = arr = numpy.zeros(n, dtype)
arr['sids'] = numpy.arange(n, dtype=numpy.uint32)
arr['lon'] = shakemap_array['lon']
arr['lat'] = shakemap_array['lat']
arr['depth'] = numpy.zeros(n)
arr['vs30'] = shakemap_array['vs30']
arr.flags.writeable = False
return self | [
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118 | gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.from_points | def from_points(cls, lons, lats, depths=None, sitemodel=None,
req_site_params=()):
"""
Build the site collection from
:param lons:
a sequence of longitudes
:param lats:
a sequence of latitudes
:param depths:
a sequence of depths (or None)
:param sitemodel:
None or an object containing site parameters as attributes
:param req_site_params:
a sequence of required site parameters, possibly empty
"""
assert len(lons) < U32LIMIT, len(lons)
if depths is None:
depths = numpy.zeros(len(lons))
assert len(lons) == len(lats) == len(depths), (len(lons), len(lats),
len(depths))
self = object.__new__(cls)
self.complete = self
req = ['sids', 'lon', 'lat', 'depth'] + sorted(
par for par in req_site_params if par not in ('lon', 'lat'))
if 'vs30' in req and 'vs30measured' not in req:
req.append('vs30measured')
self.dtype = numpy.dtype([(p, site_param_dt[p]) for p in req])
self.array = arr = numpy.zeros(len(lons), self.dtype)
arr['sids'] = numpy.arange(len(lons), dtype=numpy.uint32)
arr['lon'] = fix_lon(numpy.array(lons))
arr['lat'] = numpy.array(lats)
arr['depth'] = numpy.array(depths)
if sitemodel is None:
pass
elif hasattr(sitemodel, 'reference_vs30_value'):
# sitemodel is actually an OqParam instance
self._set('vs30', sitemodel.reference_vs30_value)
self._set('vs30measured',
sitemodel.reference_vs30_type == 'measured')
self._set('z1pt0', sitemodel.reference_depth_to_1pt0km_per_sec)
self._set('z2pt5', sitemodel.reference_depth_to_2pt5km_per_sec)
self._set('siteclass', sitemodel.reference_siteclass)
else:
for name in sitemodel.dtype.names:
if name not in ('lon', 'lat'):
self._set(name, sitemodel[name])
return self | python | def from_points(cls, lons, lats, depths=None, sitemodel=None,
req_site_params=()):
"""
Build the site collection from
:param lons:
a sequence of longitudes
:param lats:
a sequence of latitudes
:param depths:
a sequence of depths (or None)
:param sitemodel:
None or an object containing site parameters as attributes
:param req_site_params:
a sequence of required site parameters, possibly empty
"""
assert len(lons) < U32LIMIT, len(lons)
if depths is None:
depths = numpy.zeros(len(lons))
assert len(lons) == len(lats) == len(depths), (len(lons), len(lats),
len(depths))
self = object.__new__(cls)
self.complete = self
req = ['sids', 'lon', 'lat', 'depth'] + sorted(
par for par in req_site_params if par not in ('lon', 'lat'))
if 'vs30' in req and 'vs30measured' not in req:
req.append('vs30measured')
self.dtype = numpy.dtype([(p, site_param_dt[p]) for p in req])
self.array = arr = numpy.zeros(len(lons), self.dtype)
arr['sids'] = numpy.arange(len(lons), dtype=numpy.uint32)
arr['lon'] = fix_lon(numpy.array(lons))
arr['lat'] = numpy.array(lats)
arr['depth'] = numpy.array(depths)
if sitemodel is None:
pass
elif hasattr(sitemodel, 'reference_vs30_value'):
# sitemodel is actually an OqParam instance
self._set('vs30', sitemodel.reference_vs30_value)
self._set('vs30measured',
sitemodel.reference_vs30_type == 'measured')
self._set('z1pt0', sitemodel.reference_depth_to_1pt0km_per_sec)
self._set('z2pt5', sitemodel.reference_depth_to_2pt5km_per_sec)
self._set('siteclass', sitemodel.reference_siteclass)
else:
for name in sitemodel.dtype.names:
if name not in ('lon', 'lat'):
self._set(name, sitemodel[name])
return self | [
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:param lons:
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:param lats:
a sequence of latitudes
:param depths:
a sequence of depths (or None)
:param sitemodel:
None or an object containing site parameters as attributes
:param req_site_params:
a sequence of required site parameters, possibly empty | [
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119 | gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.make_complete | def make_complete(self):
"""
Turns the site collection into a complete one, if needed
"""
# reset the site indices from 0 to N-1 and set self.complete to self
self.array['sids'] = numpy.arange(len(self), dtype=numpy.uint32)
self.complete = self | python | def make_complete(self):
"""
Turns the site collection into a complete one, if needed
"""
# reset the site indices from 0 to N-1 and set self.complete to self
self.array['sids'] = numpy.arange(len(self), dtype=numpy.uint32)
self.complete = self | [
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120 | gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.filter | def filter(self, mask):
"""
Create a SiteCollection with only a subset of sites.
:param mask:
Numpy array of boolean values of the same length as the site
collection. ``True`` values should indicate that site with that
index should be included into the filtered collection.
:returns:
A new :class:`SiteCollection` instance, unless all the
values in ``mask`` are ``True``, in which case this site collection
is returned, or if all the values in ``mask`` are ``False``,
in which case method returns ``None``. New collection has data
of only those sites that were marked for inclusion in the mask.
"""
assert len(mask) == len(self), (len(mask), len(self))
if mask.all():
# all sites satisfy the filter, return
# this collection unchanged
return self
if not mask.any():
# no sites pass the filter, return None
return None
# extract indices of Trues from the mask
indices, = mask.nonzero()
return self.filtered(indices) | python | def filter(self, mask):
"""
Create a SiteCollection with only a subset of sites.
:param mask:
Numpy array of boolean values of the same length as the site
collection. ``True`` values should indicate that site with that
index should be included into the filtered collection.
:returns:
A new :class:`SiteCollection` instance, unless all the
values in ``mask`` are ``True``, in which case this site collection
is returned, or if all the values in ``mask`` are ``False``,
in which case method returns ``None``. New collection has data
of only those sites that were marked for inclusion in the mask.
"""
assert len(mask) == len(self), (len(mask), len(self))
if mask.all():
# all sites satisfy the filter, return
# this collection unchanged
return self
if not mask.any():
# no sites pass the filter, return None
return None
# extract indices of Trues from the mask
indices, = mask.nonzero()
return self.filtered(indices) | [
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121 | gem/oq-engine | openquake/hazardlib/geo/point.py | Point.point_at | def point_at(self, horizontal_distance, vertical_increment, azimuth):
"""
Compute the point with given horizontal, vertical distances
and azimuth from this point.
:param horizontal_distance:
Horizontal distance, in km.
:type horizontal_distance:
float
:param vertical_increment:
Vertical increment, in km. When positive, the new point
has a greater depth. When negative, the new point
has a smaller depth.
:type vertical_increment:
float
:type azimuth:
Azimuth, in decimal degrees.
:type azimuth:
float
:returns:
The point at the given distances.
:rtype:
Instance of :class:`Point`
"""
lon, lat = geodetic.point_at(self.longitude, self.latitude,
azimuth, horizontal_distance)
return Point(lon, lat, self.depth + vertical_increment) | python | def point_at(self, horizontal_distance, vertical_increment, azimuth):
"""
Compute the point with given horizontal, vertical distances
and azimuth from this point.
:param horizontal_distance:
Horizontal distance, in km.
:type horizontal_distance:
float
:param vertical_increment:
Vertical increment, in km. When positive, the new point
has a greater depth. When negative, the new point
has a smaller depth.
:type vertical_increment:
float
:type azimuth:
Azimuth, in decimal degrees.
:type azimuth:
float
:returns:
The point at the given distances.
:rtype:
Instance of :class:`Point`
"""
lon, lat = geodetic.point_at(self.longitude, self.latitude,
azimuth, horizontal_distance)
return Point(lon, lat, self.depth + vertical_increment) | [
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Vertical increment, in km. When positive, the new point
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Azimuth, in decimal degrees.
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122 | gem/oq-engine | openquake/hazardlib/geo/point.py | Point.equally_spaced_points | def equally_spaced_points(self, point, distance):
"""
Compute the set of points equally spaced between this point
and the given point.
:param point:
Destination point.
:type point:
Instance of :class:`Point`
:param distance:
Distance between points (in km).
:type distance:
float
:returns:
The list of equally spaced points.
:rtype:
list of :class:`Point` instances
"""
lons, lats, depths = geodetic.intervals_between(
self.longitude, self.latitude, self.depth,
point.longitude, point.latitude, point.depth,
distance)
return [Point(lons[i], lats[i], depths[i]) for i in range(len(lons))] | python | def equally_spaced_points(self, point, distance):
"""
Compute the set of points equally spaced between this point
and the given point.
:param point:
Destination point.
:type point:
Instance of :class:`Point`
:param distance:
Distance between points (in km).
:type distance:
float
:returns:
The list of equally spaced points.
:rtype:
list of :class:`Point` instances
"""
lons, lats, depths = geodetic.intervals_between(
self.longitude, self.latitude, self.depth,
point.longitude, point.latitude, point.depth,
distance)
return [Point(lons[i], lats[i], depths[i]) for i in range(len(lons))] | [
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Distance between points (in km).
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123 | gem/oq-engine | openquake/hazardlib/geo/point.py | Point.to_polygon | def to_polygon(self, radius):
"""
Create a circular polygon with specified radius centered in the point.
:param radius:
Required radius of a new polygon, in km.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon` that
approximates a circle around the point with specified radius.
"""
assert radius > 0
# avoid circular imports
from openquake.hazardlib.geo.polygon import Polygon
# get a projection that is centered in the point
proj = geo_utils.OrthographicProjection(
self.longitude, self.longitude, self.latitude, self.latitude)
# create a shapely object from a projected point coordinates,
# which are supposedly (0, 0)
point = shapely.geometry.Point(*proj(self.longitude, self.latitude))
# extend the point to a shapely polygon using buffer()
# and create openquake.hazardlib.geo.polygon.Polygon object from it
return Polygon._from_2d(point.buffer(radius), proj) | python | def to_polygon(self, radius):
"""
Create a circular polygon with specified radius centered in the point.
:param radius:
Required radius of a new polygon, in km.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon` that
approximates a circle around the point with specified radius.
"""
assert radius > 0
# avoid circular imports
from openquake.hazardlib.geo.polygon import Polygon
# get a projection that is centered in the point
proj = geo_utils.OrthographicProjection(
self.longitude, self.longitude, self.latitude, self.latitude)
# create a shapely object from a projected point coordinates,
# which are supposedly (0, 0)
point = shapely.geometry.Point(*proj(self.longitude, self.latitude))
# extend the point to a shapely polygon using buffer()
# and create openquake.hazardlib.geo.polygon.Polygon object from it
return Polygon._from_2d(point.buffer(radius), proj) | [
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124 | gem/oq-engine | openquake/hazardlib/geo/point.py | Point.closer_than | def closer_than(self, mesh, radius):
"""
Check for proximity of points in the ``mesh``.
:param mesh:
:class:`openquake.hazardlib.geo.mesh.Mesh` instance.
:param radius:
Proximity measure in km.
:returns:
Numpy array of boolean values in the same shape as the mesh
coordinate arrays with ``True`` on indexes of points that
are not further than ``radius`` km from this point. Function
:func:`~openquake.hazardlib.geo.geodetic.distance` is used to
calculate distances to points of the mesh. Points of the mesh that
lie exactly ``radius`` km away from this point also have
``True`` in their indices.
"""
dists = geodetic.distance(self.longitude, self.latitude, self.depth,
mesh.lons, mesh.lats,
0 if mesh.depths is None else mesh.depths)
return dists <= radius | python | def closer_than(self, mesh, radius):
"""
Check for proximity of points in the ``mesh``.
:param mesh:
:class:`openquake.hazardlib.geo.mesh.Mesh` instance.
:param radius:
Proximity measure in km.
:returns:
Numpy array of boolean values in the same shape as the mesh
coordinate arrays with ``True`` on indexes of points that
are not further than ``radius`` km from this point. Function
:func:`~openquake.hazardlib.geo.geodetic.distance` is used to
calculate distances to points of the mesh. Points of the mesh that
lie exactly ``radius`` km away from this point also have
``True`` in their indices.
"""
dists = geodetic.distance(self.longitude, self.latitude, self.depth,
mesh.lons, mesh.lats,
0 if mesh.depths is None else mesh.depths)
return dists <= radius | [
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125 | gem/oq-engine | openquake/commands/info.py | print_csm_info | def print_csm_info(fname):
"""
Parse the composite source model without instantiating the sources and
prints information about its composition and the full logic tree
"""
oqparam = readinput.get_oqparam(fname)
csm = readinput.get_composite_source_model(oqparam, in_memory=False)
print(csm.info)
print('See http://docs.openquake.org/oq-engine/stable/'
'effective-realizations.html for an explanation')
rlzs_assoc = csm.info.get_rlzs_assoc()
print(rlzs_assoc)
dupl = [(srcs[0]['id'], len(srcs)) for srcs in csm.check_dupl_sources()]
if dupl:
print(rst_table(dupl, ['source_id', 'multiplicity']))
tot, pairs = get_pickled_sizes(rlzs_assoc)
print(rst_table(pairs, ['attribute', 'nbytes'])) | python | def print_csm_info(fname):
"""
Parse the composite source model without instantiating the sources and
prints information about its composition and the full logic tree
"""
oqparam = readinput.get_oqparam(fname)
csm = readinput.get_composite_source_model(oqparam, in_memory=False)
print(csm.info)
print('See http://docs.openquake.org/oq-engine/stable/'
'effective-realizations.html for an explanation')
rlzs_assoc = csm.info.get_rlzs_assoc()
print(rlzs_assoc)
dupl = [(srcs[0]['id'], len(srcs)) for srcs in csm.check_dupl_sources()]
if dupl:
print(rst_table(dupl, ['source_id', 'multiplicity']))
tot, pairs = get_pickled_sizes(rlzs_assoc)
print(rst_table(pairs, ['attribute', 'nbytes'])) | [
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126 | gem/oq-engine | openquake/commands/info.py | do_build_reports | def do_build_reports(directory):
"""
Walk the directory and builds pre-calculation reports for all the
job.ini files found.
"""
for cwd, dirs, files in os.walk(directory):
for f in sorted(files):
if f in ('job.ini', 'job_h.ini', 'job_haz.ini', 'job_hazard.ini'):
job_ini = os.path.join(cwd, f)
logging.info(job_ini)
try:
reportwriter.build_report(job_ini, cwd)
except Exception as e:
logging.error(str(e)) | python | def do_build_reports(directory):
"""
Walk the directory and builds pre-calculation reports for all the
job.ini files found.
"""
for cwd, dirs, files in os.walk(directory):
for f in sorted(files):
if f in ('job.ini', 'job_h.ini', 'job_haz.ini', 'job_hazard.ini'):
job_ini = os.path.join(cwd, f)
logging.info(job_ini)
try:
reportwriter.build_report(job_ini, cwd)
except Exception as e:
logging.error(str(e)) | [
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127 | gem/oq-engine | openquake/commands/info.py | info | def info(calculators, gsims, views, exports, extracts, parameters,
report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if extracts:
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
if parameters:
params = []
for val in vars(OqParam).values():
if hasattr(val, 'name'):
params.append(val)
params.sort(key=lambda x: x.name)
for param in params:
print(param.name)
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
if node['xmlns'].endswith('nrml/0.4'):
raise InvalidFile(
'%s is in NRML 0.4 format, please run the following '
'command:\noq upgrade_nrml %s' % (
input_file, os.path.dirname(input_file) or '.'))
print(source_model_info([node[0]]))
elif node[0].tag.endswith('logicTree'):
nodes = [nrml.read(sm_path)[0]
for sm_path in logictree.collect_info(input_file).smpaths]
print(source_model_info(nodes))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file) | python | def info(calculators, gsims, views, exports, extracts, parameters,
report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if extracts:
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
if parameters:
params = []
for val in vars(OqParam).values():
if hasattr(val, 'name'):
params.append(val)
params.sort(key=lambda x: x.name)
for param in params:
print(param.name)
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
if node['xmlns'].endswith('nrml/0.4'):
raise InvalidFile(
'%s is in NRML 0.4 format, please run the following '
'command:\noq upgrade_nrml %s' % (
input_file, os.path.dirname(input_file) or '.'))
print(source_model_info([node[0]]))
elif node[0].tag.endswith('logicTree'):
nodes = [nrml.read(sm_path)[0]
for sm_path in logictree.collect_info(input_file).smpaths]
print(source_model_info(nodes))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file) | [
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128 | gem/oq-engine | openquake/calculators/classical.py | classical_split_filter | def classical_split_filter(srcs, srcfilter, gsims, params, monitor):
"""
Split the given sources, filter the subsources and the compute the
PoEs. Yield back subtasks if the split sources contain more than
maxweight ruptures.
"""
# first check if we are sampling the sources
ss = int(os.environ.get('OQ_SAMPLE_SOURCES', 0))
if ss:
splits, stime = split_sources(srcs)
srcs = readinput.random_filtered_sources(splits, srcfilter, ss)
yield classical(srcs, srcfilter, gsims, params, monitor)
return
sources = []
with monitor("filtering/splitting sources"):
for src, _sites in srcfilter(srcs):
if src.num_ruptures >= params['maxweight']:
splits, stime = split_sources([src])
sources.extend(srcfilter.filter(splits))
else:
sources.append(src)
blocks = list(block_splitter(sources, params['maxweight'],
operator.attrgetter('num_ruptures')))
if blocks:
# yield the first blocks (if any) and compute the last block in core
# NB: the last block is usually the smallest one
for block in blocks[:-1]:
yield classical, block, srcfilter, gsims, params
yield classical(blocks[-1], srcfilter, gsims, params, monitor) | python | def classical_split_filter(srcs, srcfilter, gsims, params, monitor):
"""
Split the given sources, filter the subsources and the compute the
PoEs. Yield back subtasks if the split sources contain more than
maxweight ruptures.
"""
# first check if we are sampling the sources
ss = int(os.environ.get('OQ_SAMPLE_SOURCES', 0))
if ss:
splits, stime = split_sources(srcs)
srcs = readinput.random_filtered_sources(splits, srcfilter, ss)
yield classical(srcs, srcfilter, gsims, params, monitor)
return
sources = []
with monitor("filtering/splitting sources"):
for src, _sites in srcfilter(srcs):
if src.num_ruptures >= params['maxweight']:
splits, stime = split_sources([src])
sources.extend(srcfilter.filter(splits))
else:
sources.append(src)
blocks = list(block_splitter(sources, params['maxweight'],
operator.attrgetter('num_ruptures')))
if blocks:
# yield the first blocks (if any) and compute the last block in core
# NB: the last block is usually the smallest one
for block in blocks[:-1]:
yield classical, block, srcfilter, gsims, params
yield classical(blocks[-1], srcfilter, gsims, params, monitor) | [
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129 | gem/oq-engine | openquake/hmtk/seismicity/gcmt_utils.py | get_azimuth_plunge | def get_azimuth_plunge(vect, degrees=True):
'''
For a given vector in USE format, retrieve the azimuth and plunge
'''
if vect[0] > 0:
vect = -1. * np.copy(vect)
vect_hor = sqrt(vect[1] ** 2. + vect[2] ** 2.)
plunge = atan2(-vect[0], vect_hor)
azimuth = atan2(vect[2], -vect[1])
if degrees:
icr = 180. / pi
return icr * azimuth % 360., icr * plunge
else:
return azimuth % (2. * pi), plunge | python | def get_azimuth_plunge(vect, degrees=True):
'''
For a given vector in USE format, retrieve the azimuth and plunge
'''
if vect[0] > 0:
vect = -1. * np.copy(vect)
vect_hor = sqrt(vect[1] ** 2. + vect[2] ** 2.)
plunge = atan2(-vect[0], vect_hor)
azimuth = atan2(vect[2], -vect[1])
if degrees:
icr = 180. / pi
return icr * azimuth % 360., icr * plunge
else:
return azimuth % (2. * pi), plunge | [
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130 | gem/oq-engine | openquake/hmtk/seismicity/gcmt_utils.py | use_to_ned | def use_to_ned(tensor):
'''
Converts a tensor in USE coordinate sytem to NED
'''
return np.array(ROT_NED_USE.T * np.matrix(tensor) * ROT_NED_USE) | python | def use_to_ned(tensor):
'''
Converts a tensor in USE coordinate sytem to NED
'''
return np.array(ROT_NED_USE.T * np.matrix(tensor) * ROT_NED_USE) | [
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131 | gem/oq-engine | openquake/hmtk/seismicity/gcmt_utils.py | ned_to_use | def ned_to_use(tensor):
'''
Converts a tensor in NED coordinate sytem to USE
'''
return np.array(ROT_NED_USE * np.matrix(tensor) * ROT_NED_USE.T) | python | def ned_to_use(tensor):
'''
Converts a tensor in NED coordinate sytem to USE
'''
return np.array(ROT_NED_USE * np.matrix(tensor) * ROT_NED_USE.T) | [
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132 | gem/oq-engine | openquake/hazardlib/gsim/boore_atkinson_2008.py | Atkinson2010Hawaii.get_mean_and_stddevs | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Using a frequency dependent correction for the mean ground motion.
Standard deviation is fixed.
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists,
imt, stddev_types)
# Defining frequency
if imt == PGA():
freq = 50.0
elif imt == PGV():
freq = 2.0
else:
freq = 1./imt.period
# Equation 3 of Atkinson (2010)
x1 = np.min([-0.18+0.17*np.log10(freq), 0])
# Equation 4 a-b-c of Atkinson (2010)
if rup.hypo_depth < 20.0:
x0 = np.max([0.217-0.321*np.log10(freq), 0])
elif rup.hypo_depth > 35.0:
x0 = np.min([0.263+0.0924*np.log10(freq), 0.35])
else:
x0 = 0.2
# Limiting calculation distance to 1km
# (as suggested by C. Bruce Worden)
rjb = [d if d > 1 else 1 for d in dists.rjb]
# Equation 2 and 5 of Atkinson (2010)
mean += (x0 + x1*np.log10(rjb))/np.log10(np.e)
return mean, stddevs | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Using a frequency dependent correction for the mean ground motion.
Standard deviation is fixed.
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists,
imt, stddev_types)
# Defining frequency
if imt == PGA():
freq = 50.0
elif imt == PGV():
freq = 2.0
else:
freq = 1./imt.period
# Equation 3 of Atkinson (2010)
x1 = np.min([-0.18+0.17*np.log10(freq), 0])
# Equation 4 a-b-c of Atkinson (2010)
if rup.hypo_depth < 20.0:
x0 = np.max([0.217-0.321*np.log10(freq), 0])
elif rup.hypo_depth > 35.0:
x0 = np.min([0.263+0.0924*np.log10(freq), 0.35])
else:
x0 = 0.2
# Limiting calculation distance to 1km
# (as suggested by C. Bruce Worden)
rjb = [d if d > 1 else 1 for d in dists.rjb]
# Equation 2 and 5 of Atkinson (2010)
mean += (x0 + x1*np.log10(rjb))/np.log10(np.e)
return mean, stddevs | [
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133 | gem/oq-engine | openquake/commonlib/rlzs_assoc.py | RlzsAssoc.get_rlz | def get_rlz(self, rlzstr):
r"""
Get a Realization instance for a string of the form 'rlz-\d+'
"""
mo = re.match(r'rlz-(\d+)', rlzstr)
if not mo:
return
return self.realizations[int(mo.group(1))] | python | def get_rlz(self, rlzstr):
r"""
Get a Realization instance for a string of the form 'rlz-\d+'
"""
mo = re.match(r'rlz-(\d+)', rlzstr)
if not mo:
return
return self.realizations[int(mo.group(1))] | [
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Get a Realization instance for a string of the form 'rlz-\d+' | [
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134 | gem/oq-engine | openquake/commands/export.py | export | def export(datastore_key, calc_id=-1, exports='csv', export_dir='.'):
"""
Export an output from the datastore.
"""
dstore = util.read(calc_id)
parent_id = dstore['oqparam'].hazard_calculation_id
if parent_id:
dstore.parent = util.read(parent_id)
dstore.export_dir = export_dir
with performance.Monitor('export', measuremem=True) as mon:
for fmt in exports.split(','):
fnames = export_((datastore_key, fmt), dstore)
nbytes = sum(os.path.getsize(f) for f in fnames)
print('Exported %s in %s' % (general.humansize(nbytes), fnames))
if mon.duration > 1:
print(mon)
dstore.close() | python | def export(datastore_key, calc_id=-1, exports='csv', export_dir='.'):
"""
Export an output from the datastore.
"""
dstore = util.read(calc_id)
parent_id = dstore['oqparam'].hazard_calculation_id
if parent_id:
dstore.parent = util.read(parent_id)
dstore.export_dir = export_dir
with performance.Monitor('export', measuremem=True) as mon:
for fmt in exports.split(','):
fnames = export_((datastore_key, fmt), dstore)
nbytes = sum(os.path.getsize(f) for f in fnames)
print('Exported %s in %s' % (general.humansize(nbytes), fnames))
if mon.duration > 1:
print(mon)
dstore.close() | [
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135 | gem/oq-engine | openquake/calculators/ucerf_base.py | convert_UCERFSource | def convert_UCERFSource(self, node):
"""
Converts the Ucerf Source node into an SES Control object
"""
dirname = os.path.dirname(self.fname) # where the source_model_file is
source_file = os.path.join(dirname, node["filename"])
if "startDate" in node.attrib and "investigationTime" in node.attrib:
# Is a time-dependent model - even if rates were originally
# poissonian
# Verify that the source time span is the same as the TOM time span
inv_time = float(node["investigationTime"])
if inv_time != self.investigation_time:
raise ValueError("Source investigation time (%s) is not "
"equal to configuration investigation time "
"(%s)" % (inv_time, self.investigation_time))
start_date = datetime.strptime(node["startDate"], "%d/%m/%Y")
else:
start_date = None
return UCERFSource(
source_file,
self.investigation_time,
start_date,
float(node["minMag"]),
npd=self.convert_npdist(node),
hdd=self.convert_hpdist(node),
aspect=~node.ruptAspectRatio,
upper_seismogenic_depth=~node.pointGeometry.upperSeismoDepth,
lower_seismogenic_depth=~node.pointGeometry.lowerSeismoDepth,
msr=valid.SCALEREL[~node.magScaleRel](),
mesh_spacing=self.rupture_mesh_spacing,
trt=node["tectonicRegion"]) | python | def convert_UCERFSource(self, node):
"""
Converts the Ucerf Source node into an SES Control object
"""
dirname = os.path.dirname(self.fname) # where the source_model_file is
source_file = os.path.join(dirname, node["filename"])
if "startDate" in node.attrib and "investigationTime" in node.attrib:
# Is a time-dependent model - even if rates were originally
# poissonian
# Verify that the source time span is the same as the TOM time span
inv_time = float(node["investigationTime"])
if inv_time != self.investigation_time:
raise ValueError("Source investigation time (%s) is not "
"equal to configuration investigation time "
"(%s)" % (inv_time, self.investigation_time))
start_date = datetime.strptime(node["startDate"], "%d/%m/%Y")
else:
start_date = None
return UCERFSource(
source_file,
self.investigation_time,
start_date,
float(node["minMag"]),
npd=self.convert_npdist(node),
hdd=self.convert_hpdist(node),
aspect=~node.ruptAspectRatio,
upper_seismogenic_depth=~node.pointGeometry.upperSeismoDepth,
lower_seismogenic_depth=~node.pointGeometry.lowerSeismoDepth,
msr=valid.SCALEREL[~node.magScaleRel](),
mesh_spacing=self.rupture_mesh_spacing,
trt=node["tectonicRegion"]) | [
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136 | gem/oq-engine | openquake/calculators/ucerf_base.py | build_idx_set | def build_idx_set(branch_id, start_date):
"""
Builds a dictionary of keys based on the branch code
"""
code_set = branch_id.split("/")
code_set.insert(3, "Rates")
idx_set = {
"sec": "/".join([code_set[0], code_set[1], "Sections"]),
"mag": "/".join([code_set[0], code_set[1], code_set[2], "Magnitude"])}
idx_set["rate"] = "/".join(code_set)
idx_set["rake"] = "/".join([code_set[0], code_set[1], "Rake"])
idx_set["msr"] = "-".join(code_set[:3])
idx_set["geol"] = code_set[0]
if start_date: # time-dependent source
idx_set["grid_key"] = "_".join(
branch_id.replace("/", "_").split("_")[:-1])
else: # time-independent source
idx_set["grid_key"] = branch_id.replace("/", "_")
idx_set["total_key"] = branch_id.replace("/", "|")
return idx_set | python | def build_idx_set(branch_id, start_date):
"""
Builds a dictionary of keys based on the branch code
"""
code_set = branch_id.split("/")
code_set.insert(3, "Rates")
idx_set = {
"sec": "/".join([code_set[0], code_set[1], "Sections"]),
"mag": "/".join([code_set[0], code_set[1], code_set[2], "Magnitude"])}
idx_set["rate"] = "/".join(code_set)
idx_set["rake"] = "/".join([code_set[0], code_set[1], "Rake"])
idx_set["msr"] = "-".join(code_set[:3])
idx_set["geol"] = code_set[0]
if start_date: # time-dependent source
idx_set["grid_key"] = "_".join(
branch_id.replace("/", "_").split("_")[:-1])
else: # time-independent source
idx_set["grid_key"] = branch_id.replace("/", "_")
idx_set["total_key"] = branch_id.replace("/", "|")
return idx_set | [
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137 | gem/oq-engine | openquake/calculators/ucerf_base.py | get_rupture_surface | def get_rupture_surface(mag, nodal_plane, hypocenter, msr,
rupture_aspect_ratio, upper_seismogenic_depth,
lower_seismogenic_depth, mesh_spacing=1.0):
"""
Create and return rupture surface object with given properties.
:param mag:
Magnitude value, used to calculate rupture dimensions,
see :meth:`_get_rupture_dimensions`.
:param nodal_plane:
Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane`
describing the rupture orientation.
:param hypocenter:
Point representing rupture's hypocenter.
:returns:
Instance of
:class:`~openquake.hazardlib.geo.surface.planar.PlanarSurface`.
"""
assert (upper_seismogenic_depth <= hypocenter.depth
and lower_seismogenic_depth >= hypocenter.depth)
rdip = math.radians(nodal_plane.dip)
# precalculated azimuth values for horizontal-only and vertical-only
# moves from one point to another on the plane defined by strike
# and dip:
azimuth_right = nodal_plane.strike
azimuth_down = (azimuth_right + 90) % 360
azimuth_left = (azimuth_down + 90) % 360
azimuth_up = (azimuth_left + 90) % 360
rup_length, rup_width = get_rupture_dimensions(
mag, nodal_plane, msr, rupture_aspect_ratio, upper_seismogenic_depth,
lower_seismogenic_depth)
# calculate the height of the rupture being projected
# on the vertical plane:
rup_proj_height = rup_width * math.sin(rdip)
# and it's width being projected on the horizontal one:
rup_proj_width = rup_width * math.cos(rdip)
# half height of the vertical component of rupture width
# is the vertical distance between the rupture geometrical
# center and it's upper and lower borders:
hheight = rup_proj_height / 2
# calculate how much shallower the upper border of the rupture
# is than the upper seismogenic depth:
vshift = upper_seismogenic_depth - hypocenter.depth + hheight
# if it is shallower (vshift > 0) than we need to move the rupture
# by that value vertically.
if vshift < 0:
# the top edge is below upper seismogenic depth. now we need
# to check that we do not cross the lower border.
vshift = lower_seismogenic_depth - hypocenter.depth - hheight
if vshift > 0:
# the bottom edge of the rupture is above the lower sesmogenic
# depth. that means that we don't need to move the rupture
# as it fits inside seismogenic layer.
vshift = 0
# if vshift < 0 than we need to move the rupture up by that value.
# now we need to find the position of rupture's geometrical center.
# in any case the hypocenter point must lie on the surface, however
# the rupture center might be off (below or above) along the dip.
rupture_center = hypocenter
if vshift != 0:
# we need to move the rupture center to make the rupture fit
# inside the seismogenic layer.
hshift = abs(vshift / math.tan(rdip))
rupture_center = rupture_center.point_at(
horizontal_distance=hshift, vertical_increment=vshift,
azimuth=(azimuth_up if vshift < 0 else azimuth_down))
# from the rupture center we can now compute the coordinates of the
# four coorners by moving along the diagonals of the plane. This seems
# to be better then moving along the perimeter, because in this case
# errors are accumulated that induce distorsions in the shape with
# consequent raise of exceptions when creating PlanarSurface objects
# theta is the angle between the diagonal of the surface projection
# and the line passing through the rupture center and parallel to the
# top and bottom edges. Theta is zero for vertical ruptures (because
# rup_proj_width is zero)
theta = math.degrees(
math.atan((rup_proj_width / 2.) / (rup_length / 2.)))
hor_dist = math.sqrt(
(rup_length / 2.) ** 2 + (rup_proj_width / 2.) ** 2)
left_top = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=-rup_proj_height / 2,
azimuth=(nodal_plane.strike + 180 + theta) % 360)
right_top = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=-rup_proj_height / 2,
azimuth=(nodal_plane.strike - theta) % 360)
left_bottom = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=rup_proj_height / 2,
azimuth=(nodal_plane.strike + 180 - theta) % 360)
right_bottom = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=rup_proj_height / 2,
azimuth=(nodal_plane.strike + theta) % 360)
return PlanarSurface(nodal_plane.strike, nodal_plane.dip,
left_top, right_top, right_bottom, left_bottom) | python | def get_rupture_surface(mag, nodal_plane, hypocenter, msr,
rupture_aspect_ratio, upper_seismogenic_depth,
lower_seismogenic_depth, mesh_spacing=1.0):
"""
Create and return rupture surface object with given properties.
:param mag:
Magnitude value, used to calculate rupture dimensions,
see :meth:`_get_rupture_dimensions`.
:param nodal_plane:
Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane`
describing the rupture orientation.
:param hypocenter:
Point representing rupture's hypocenter.
:returns:
Instance of
:class:`~openquake.hazardlib.geo.surface.planar.PlanarSurface`.
"""
assert (upper_seismogenic_depth <= hypocenter.depth
and lower_seismogenic_depth >= hypocenter.depth)
rdip = math.radians(nodal_plane.dip)
# precalculated azimuth values for horizontal-only and vertical-only
# moves from one point to another on the plane defined by strike
# and dip:
azimuth_right = nodal_plane.strike
azimuth_down = (azimuth_right + 90) % 360
azimuth_left = (azimuth_down + 90) % 360
azimuth_up = (azimuth_left + 90) % 360
rup_length, rup_width = get_rupture_dimensions(
mag, nodal_plane, msr, rupture_aspect_ratio, upper_seismogenic_depth,
lower_seismogenic_depth)
# calculate the height of the rupture being projected
# on the vertical plane:
rup_proj_height = rup_width * math.sin(rdip)
# and it's width being projected on the horizontal one:
rup_proj_width = rup_width * math.cos(rdip)
# half height of the vertical component of rupture width
# is the vertical distance between the rupture geometrical
# center and it's upper and lower borders:
hheight = rup_proj_height / 2
# calculate how much shallower the upper border of the rupture
# is than the upper seismogenic depth:
vshift = upper_seismogenic_depth - hypocenter.depth + hheight
# if it is shallower (vshift > 0) than we need to move the rupture
# by that value vertically.
if vshift < 0:
# the top edge is below upper seismogenic depth. now we need
# to check that we do not cross the lower border.
vshift = lower_seismogenic_depth - hypocenter.depth - hheight
if vshift > 0:
# the bottom edge of the rupture is above the lower sesmogenic
# depth. that means that we don't need to move the rupture
# as it fits inside seismogenic layer.
vshift = 0
# if vshift < 0 than we need to move the rupture up by that value.
# now we need to find the position of rupture's geometrical center.
# in any case the hypocenter point must lie on the surface, however
# the rupture center might be off (below or above) along the dip.
rupture_center = hypocenter
if vshift != 0:
# we need to move the rupture center to make the rupture fit
# inside the seismogenic layer.
hshift = abs(vshift / math.tan(rdip))
rupture_center = rupture_center.point_at(
horizontal_distance=hshift, vertical_increment=vshift,
azimuth=(azimuth_up if vshift < 0 else azimuth_down))
# from the rupture center we can now compute the coordinates of the
# four coorners by moving along the diagonals of the plane. This seems
# to be better then moving along the perimeter, because in this case
# errors are accumulated that induce distorsions in the shape with
# consequent raise of exceptions when creating PlanarSurface objects
# theta is the angle between the diagonal of the surface projection
# and the line passing through the rupture center and parallel to the
# top and bottom edges. Theta is zero for vertical ruptures (because
# rup_proj_width is zero)
theta = math.degrees(
math.atan((rup_proj_width / 2.) / (rup_length / 2.)))
hor_dist = math.sqrt(
(rup_length / 2.) ** 2 + (rup_proj_width / 2.) ** 2)
left_top = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=-rup_proj_height / 2,
azimuth=(nodal_plane.strike + 180 + theta) % 360)
right_top = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=-rup_proj_height / 2,
azimuth=(nodal_plane.strike - theta) % 360)
left_bottom = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=rup_proj_height / 2,
azimuth=(nodal_plane.strike + 180 - theta) % 360)
right_bottom = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=rup_proj_height / 2,
azimuth=(nodal_plane.strike + theta) % 360)
return PlanarSurface(nodal_plane.strike, nodal_plane.dip,
left_top, right_top, right_bottom, left_bottom) | [
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138 | gem/oq-engine | openquake/calculators/ucerf_base.py | UCERFSource.get_ridx | def get_ridx(self, iloc):
"""List of rupture indices for the given iloc"""
with h5py.File(self.source_file, "r") as hdf5:
return hdf5[self.idx_set["geol"] + "/RuptureIndex"][iloc] | python | def get_ridx(self, iloc):
"""List of rupture indices for the given iloc"""
with h5py.File(self.source_file, "r") as hdf5:
return hdf5[self.idx_set["geol"] + "/RuptureIndex"][iloc] | [
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139 | gem/oq-engine | openquake/calculators/ucerf_base.py | UCERFSource.get_background_sids | def get_background_sids(self, src_filter):
"""
We can apply the filtering of the background sites as a pre-processing
step - this is done here rather than in the sampling of the ruptures
themselves
"""
branch_key = self.idx_set["grid_key"]
idist = src_filter.integration_distance(DEFAULT_TRT)
with h5py.File(self.source_file, 'r') as hdf5:
bg_locations = hdf5["Grid/Locations"].value
distances = min_geodetic_distance(
src_filter.sitecol.xyz,
(bg_locations[:, 0], bg_locations[:, 1]))
# Add buffer equal to half of length of median area from Mmax
mmax_areas = self.msr.get_median_area(
hdf5["/".join(["Grid", branch_key, "MMax"])].value, 0.0)
# for instance hdf5['Grid/FM0_0_MEANFS_MEANMSR/MMax']
mmax_lengths = numpy.sqrt(mmax_areas / self.aspect)
ok = distances <= (0.5 * mmax_lengths + idist)
# get list of indices from array of booleans
return numpy.where(ok)[0].tolist() | python | def get_background_sids(self, src_filter):
"""
We can apply the filtering of the background sites as a pre-processing
step - this is done here rather than in the sampling of the ruptures
themselves
"""
branch_key = self.idx_set["grid_key"]
idist = src_filter.integration_distance(DEFAULT_TRT)
with h5py.File(self.source_file, 'r') as hdf5:
bg_locations = hdf5["Grid/Locations"].value
distances = min_geodetic_distance(
src_filter.sitecol.xyz,
(bg_locations[:, 0], bg_locations[:, 1]))
# Add buffer equal to half of length of median area from Mmax
mmax_areas = self.msr.get_median_area(
hdf5["/".join(["Grid", branch_key, "MMax"])].value, 0.0)
# for instance hdf5['Grid/FM0_0_MEANFS_MEANMSR/MMax']
mmax_lengths = numpy.sqrt(mmax_areas / self.aspect)
ok = distances <= (0.5 * mmax_lengths + idist)
# get list of indices from array of booleans
return numpy.where(ok)[0].tolist() | [
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140 | gem/oq-engine | openquake/calculators/ucerf_base.py | UCERFSource.iter_ruptures | def iter_ruptures(self):
"""
Yield ruptures for the current set of indices
"""
assert self.orig, '%s is not fully initialized' % self
for ridx in range(self.start, self.stop):
if self.orig.rate[ridx]: # ruptures may have have zero rate
rup = self.get_ucerf_rupture(ridx, self.src_filter)
if rup:
yield rup | python | def iter_ruptures(self):
"""
Yield ruptures for the current set of indices
"""
assert self.orig, '%s is not fully initialized' % self
for ridx in range(self.start, self.stop):
if self.orig.rate[ridx]: # ruptures may have have zero rate
rup = self.get_ucerf_rupture(ridx, self.src_filter)
if rup:
yield rup | [
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141 | gem/oq-engine | openquake/calculators/ucerf_base.py | UCERFSource.get_background_sources | def get_background_sources(self, src_filter, sample_factor=None):
"""
Turn the background model of a given branch into a set of point sources
:param src_filter:
SourceFilter instance
:param sample_factor:
Used to reduce the sources if OQ_SAMPLE_SOURCES is set
"""
background_sids = self.get_background_sids(src_filter)
if sample_factor is not None: # hack for use in the mosaic
background_sids = random_filter(
background_sids, sample_factor, seed=42)
with h5py.File(self.source_file, "r") as hdf5:
grid_loc = "/".join(["Grid", self.idx_set["grid_key"]])
# for instance Grid/FM0_0_MEANFS_MEANMSR_MeanRates
mags = hdf5[grid_loc + "/Magnitude"].value
mmax = hdf5[grid_loc + "/MMax"][background_sids]
rates = hdf5[grid_loc + "/RateArray"][background_sids, :]
locations = hdf5["Grid/Locations"][background_sids, :]
sources = []
for i, bg_idx in enumerate(background_sids):
src_id = "_".join([self.idx_set["grid_key"], str(bg_idx)])
src_name = "|".join([self.idx_set["total_key"], str(bg_idx)])
mag_idx = (self.min_mag <= mags) & (mags < mmax[i])
src_mags = mags[mag_idx]
src_mfd = EvenlyDiscretizedMFD(
src_mags[0],
src_mags[1] - src_mags[0],
rates[i, mag_idx].tolist())
ps = PointSource(
src_id, src_name, self.tectonic_region_type, src_mfd,
self.mesh_spacing, self.msr, self.aspect, self.tom,
self.usd, self.lsd,
Point(locations[i, 0], locations[i, 1]),
self.npd, self.hdd)
ps.id = self.id
ps.src_group_id = self.src_group_id
ps.num_ruptures = ps.count_ruptures()
sources.append(ps)
return sources | python | def get_background_sources(self, src_filter, sample_factor=None):
"""
Turn the background model of a given branch into a set of point sources
:param src_filter:
SourceFilter instance
:param sample_factor:
Used to reduce the sources if OQ_SAMPLE_SOURCES is set
"""
background_sids = self.get_background_sids(src_filter)
if sample_factor is not None: # hack for use in the mosaic
background_sids = random_filter(
background_sids, sample_factor, seed=42)
with h5py.File(self.source_file, "r") as hdf5:
grid_loc = "/".join(["Grid", self.idx_set["grid_key"]])
# for instance Grid/FM0_0_MEANFS_MEANMSR_MeanRates
mags = hdf5[grid_loc + "/Magnitude"].value
mmax = hdf5[grid_loc + "/MMax"][background_sids]
rates = hdf5[grid_loc + "/RateArray"][background_sids, :]
locations = hdf5["Grid/Locations"][background_sids, :]
sources = []
for i, bg_idx in enumerate(background_sids):
src_id = "_".join([self.idx_set["grid_key"], str(bg_idx)])
src_name = "|".join([self.idx_set["total_key"], str(bg_idx)])
mag_idx = (self.min_mag <= mags) & (mags < mmax[i])
src_mags = mags[mag_idx]
src_mfd = EvenlyDiscretizedMFD(
src_mags[0],
src_mags[1] - src_mags[0],
rates[i, mag_idx].tolist())
ps = PointSource(
src_id, src_name, self.tectonic_region_type, src_mfd,
self.mesh_spacing, self.msr, self.aspect, self.tom,
self.usd, self.lsd,
Point(locations[i, 0], locations[i, 1]),
self.npd, self.hdd)
ps.id = self.id
ps.src_group_id = self.src_group_id
ps.num_ruptures = ps.count_ruptures()
sources.append(ps)
return sources | [
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:param sample_factor:
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142 | gem/oq-engine | openquake/hazardlib/source/rupture_collection.py | split | def split(src, chunksize=MINWEIGHT):
"""
Split a complex fault source in chunks
"""
for i, block in enumerate(block_splitter(src.iter_ruptures(), chunksize,
key=operator.attrgetter('mag'))):
rup = block[0]
source_id = '%s:%d' % (src.source_id, i)
amfd = mfd.ArbitraryMFD([rup.mag], [rup.mag_occ_rate])
rcs = RuptureCollectionSource(
source_id, src.name, src.tectonic_region_type, amfd, block)
yield rcs | python | def split(src, chunksize=MINWEIGHT):
"""
Split a complex fault source in chunks
"""
for i, block in enumerate(block_splitter(src.iter_ruptures(), chunksize,
key=operator.attrgetter('mag'))):
rup = block[0]
source_id = '%s:%d' % (src.source_id, i)
amfd = mfd.ArbitraryMFD([rup.mag], [rup.mag_occ_rate])
rcs = RuptureCollectionSource(
source_id, src.name, src.tectonic_region_type, amfd, block)
yield rcs | [
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143 | gem/oq-engine | openquake/hazardlib/source/rupture_collection.py | RuptureCollectionSource.get_bounding_box | def get_bounding_box(self, maxdist):
"""
Bounding box containing all the hypocenters, enlarged by the
maximum distance
"""
locations = [rup.hypocenter for rup in self.ruptures]
return get_bounding_box(locations, maxdist) | python | def get_bounding_box(self, maxdist):
"""
Bounding box containing all the hypocenters, enlarged by the
maximum distance
"""
locations = [rup.hypocenter for rup in self.ruptures]
return get_bounding_box(locations, maxdist) | [
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144 | gem/oq-engine | openquake/commands/show_attrs.py | show_attrs | def show_attrs(key, calc_id=-1):
"""
Show the attributes of a HDF5 dataset in the datastore.
"""
ds = util.read(calc_id)
try:
attrs = h5py.File.__getitem__(ds.hdf5, key).attrs
except KeyError:
print('%r is not in %s' % (key, ds))
else:
if len(attrs) == 0:
print('%s has no attributes' % key)
for name, value in attrs.items():
print(name, value)
finally:
ds.close() | python | def show_attrs(key, calc_id=-1):
"""
Show the attributes of a HDF5 dataset in the datastore.
"""
ds = util.read(calc_id)
try:
attrs = h5py.File.__getitem__(ds.hdf5, key).attrs
except KeyError:
print('%r is not in %s' % (key, ds))
else:
if len(attrs) == 0:
print('%s has no attributes' % key)
for name, value in attrs.items():
print(name, value)
finally:
ds.close() | [
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145 | gem/oq-engine | utils/compare_mean_curves.py | compare_mean_curves | def compare_mean_curves(calc_ref, calc, nsigma=3):
"""
Compare the hazard curves coming from two different calculations.
"""
dstore_ref = datastore.read(calc_ref)
dstore = datastore.read(calc)
imtls = dstore_ref['oqparam'].imtls
if dstore['oqparam'].imtls != imtls:
raise RuntimeError('The IMTs and levels are different between '
'calculation %d and %d' % (calc_ref, calc))
sitecol_ref = dstore_ref['sitecol']
sitecol = dstore['sitecol']
site_id_ref = {(lon, lat): sid for sid, lon, lat in zip(
sitecol_ref.sids, sitecol_ref.lons, sitecol_ref.lats)}
site_id = {(lon, lat): sid for sid, lon, lat in zip(
sitecol.sids, sitecol.lons, sitecol.lats)}
common = set(site_id_ref) & set(site_id)
if not common:
raise RuntimeError('There are no common sites between calculation '
'%d and %d' % (calc_ref, calc))
pmap_ref = PmapGetter(dstore_ref, sids=[site_id_ref[lonlat]
for lonlat in common]).get_mean()
pmap = PmapGetter(dstore, sids=[site_id[lonlat]
for lonlat in common]).get_mean()
for lonlat in common:
mean, std = pmap[site_id[lonlat]].array.T # shape (2, N)
mean_ref, std_ref = pmap_ref[site_id_ref[lonlat]].array.T
err = numpy.sqrt(std**2 + std_ref**2)
for imt in imtls:
sl = imtls(imt)
ok = (numpy.abs(mean[sl] - mean_ref[sl]) < nsigma * err[sl]).all()
if not ok:
md = (numpy.abs(mean[sl] - mean_ref[sl])).max()
plt.title('point=%s, imt=%s, maxdiff=%.2e' % (lonlat, imt, md))
plt.loglog(imtls[imt], mean_ref[sl] + std_ref[sl],
label=str(calc_ref), color='black')
plt.loglog(imtls[imt], mean_ref[sl] - std_ref[sl],
color='black')
plt.loglog(imtls[imt], mean[sl] + std[sl], label=str(calc),
color='red')
plt.loglog(imtls[imt], mean[sl] - std[sl], color='red')
plt.legend()
plt.show() | python | def compare_mean_curves(calc_ref, calc, nsigma=3):
"""
Compare the hazard curves coming from two different calculations.
"""
dstore_ref = datastore.read(calc_ref)
dstore = datastore.read(calc)
imtls = dstore_ref['oqparam'].imtls
if dstore['oqparam'].imtls != imtls:
raise RuntimeError('The IMTs and levels are different between '
'calculation %d and %d' % (calc_ref, calc))
sitecol_ref = dstore_ref['sitecol']
sitecol = dstore['sitecol']
site_id_ref = {(lon, lat): sid for sid, lon, lat in zip(
sitecol_ref.sids, sitecol_ref.lons, sitecol_ref.lats)}
site_id = {(lon, lat): sid for sid, lon, lat in zip(
sitecol.sids, sitecol.lons, sitecol.lats)}
common = set(site_id_ref) & set(site_id)
if not common:
raise RuntimeError('There are no common sites between calculation '
'%d and %d' % (calc_ref, calc))
pmap_ref = PmapGetter(dstore_ref, sids=[site_id_ref[lonlat]
for lonlat in common]).get_mean()
pmap = PmapGetter(dstore, sids=[site_id[lonlat]
for lonlat in common]).get_mean()
for lonlat in common:
mean, std = pmap[site_id[lonlat]].array.T # shape (2, N)
mean_ref, std_ref = pmap_ref[site_id_ref[lonlat]].array.T
err = numpy.sqrt(std**2 + std_ref**2)
for imt in imtls:
sl = imtls(imt)
ok = (numpy.abs(mean[sl] - mean_ref[sl]) < nsigma * err[sl]).all()
if not ok:
md = (numpy.abs(mean[sl] - mean_ref[sl])).max()
plt.title('point=%s, imt=%s, maxdiff=%.2e' % (lonlat, imt, md))
plt.loglog(imtls[imt], mean_ref[sl] + std_ref[sl],
label=str(calc_ref), color='black')
plt.loglog(imtls[imt], mean_ref[sl] - std_ref[sl],
color='black')
plt.loglog(imtls[imt], mean[sl] + std[sl], label=str(calc),
color='red')
plt.loglog(imtls[imt], mean[sl] - std[sl], color='red')
plt.legend()
plt.show() | [
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146 | gem/oq-engine | openquake/hazardlib/gsim/chiou_youngs_2014.py | ChiouYoungs2014PEER._get_stddevs | def _get_stddevs(self, sites, rup, C, stddev_types, ln_y_ref, exp1, exp2):
"""
Returns the standard deviation, which is fixed at 0.65 for every site
"""
ret = []
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
# eq. 13
ret.append(0.65 * np.ones_like(sites.vs30))
return ret | python | def _get_stddevs(self, sites, rup, C, stddev_types, ln_y_ref, exp1, exp2):
"""
Returns the standard deviation, which is fixed at 0.65 for every site
"""
ret = []
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
# eq. 13
ret.append(0.65 * np.ones_like(sites.vs30))
return ret | [
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147 | gem/oq-engine | openquake/risklib/scientific.py | build_imls | def build_imls(ff, continuous_fragility_discretization,
steps_per_interval=0):
"""
Build intensity measure levels from a fragility function. If the function
is continuous, they are produced simply as a linear space between minIML
and maxIML. If the function is discrete, they are generated with a
complex logic depending on the noDamageLimit and the parameter
steps per interval.
:param ff: a fragility function object
:param continuous_fragility_discretization: .ini file parameter
:param steps_per_interval: .ini file parameter
:returns: generated imls
"""
if ff.format == 'discrete':
imls = ff.imls
if ff.nodamage and ff.nodamage < imls[0]:
imls = [ff.nodamage] + imls
if steps_per_interval > 1:
gen_imls = fine_graining(imls, steps_per_interval)
else:
gen_imls = imls
else: # continuous
gen_imls = numpy.linspace(ff.minIML, ff.maxIML,
continuous_fragility_discretization)
return gen_imls | python | def build_imls(ff, continuous_fragility_discretization,
steps_per_interval=0):
"""
Build intensity measure levels from a fragility function. If the function
is continuous, they are produced simply as a linear space between minIML
and maxIML. If the function is discrete, they are generated with a
complex logic depending on the noDamageLimit and the parameter
steps per interval.
:param ff: a fragility function object
:param continuous_fragility_discretization: .ini file parameter
:param steps_per_interval: .ini file parameter
:returns: generated imls
"""
if ff.format == 'discrete':
imls = ff.imls
if ff.nodamage and ff.nodamage < imls[0]:
imls = [ff.nodamage] + imls
if steps_per_interval > 1:
gen_imls = fine_graining(imls, steps_per_interval)
else:
gen_imls = imls
else: # continuous
gen_imls = numpy.linspace(ff.minIML, ff.maxIML,
continuous_fragility_discretization)
return gen_imls | [
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148 | gem/oq-engine | openquake/risklib/scientific.py | insured_loss_curve | def insured_loss_curve(curve, deductible, insured_limit):
"""
Compute an insured loss ratio curve given a loss ratio curve
:param curve: an array 2 x R (where R is the curve resolution)
:param float deductible: the deductible limit in fraction form
:param float insured_limit: the insured limit in fraction form
>>> losses = numpy.array([3, 20, 101])
>>> poes = numpy.array([0.9, 0.5, 0.1])
>>> insured_loss_curve(numpy.array([losses, poes]), 5, 100)
array([[ 3. , 20. ],
[ 0.85294118, 0.5 ]])
"""
losses, poes = curve[:, curve[0] <= insured_limit]
limit_poe = interpolate.interp1d(
*curve, bounds_error=False, fill_value=1)(deductible)
return numpy.array([
losses,
numpy.piecewise(poes, [poes > limit_poe], [limit_poe, lambda x: x])]) | python | def insured_loss_curve(curve, deductible, insured_limit):
"""
Compute an insured loss ratio curve given a loss ratio curve
:param curve: an array 2 x R (where R is the curve resolution)
:param float deductible: the deductible limit in fraction form
:param float insured_limit: the insured limit in fraction form
>>> losses = numpy.array([3, 20, 101])
>>> poes = numpy.array([0.9, 0.5, 0.1])
>>> insured_loss_curve(numpy.array([losses, poes]), 5, 100)
array([[ 3. , 20. ],
[ 0.85294118, 0.5 ]])
"""
losses, poes = curve[:, curve[0] <= insured_limit]
limit_poe = interpolate.interp1d(
*curve, bounds_error=False, fill_value=1)(deductible)
return numpy.array([
losses,
numpy.piecewise(poes, [poes > limit_poe], [limit_poe, lambda x: x])]) | [
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149 | gem/oq-engine | openquake/risklib/scientific.py | bcr | def bcr(eal_original, eal_retrofitted, interest_rate,
asset_life_expectancy, asset_value, retrofitting_cost):
"""
Compute the Benefit-Cost Ratio.
BCR = (EALo - EALr)(1-exp(-r*t))/(r*C)
Where:
* BCR -- Benefit cost ratio
* EALo -- Expected annual loss for original asset
* EALr -- Expected annual loss for retrofitted asset
* r -- Interest rate
* t -- Life expectancy of the asset
* C -- Retrofitting cost
"""
return ((eal_original - eal_retrofitted) * asset_value *
(1 - numpy.exp(- interest_rate * asset_life_expectancy)) /
(interest_rate * retrofitting_cost)) | python | def bcr(eal_original, eal_retrofitted, interest_rate,
asset_life_expectancy, asset_value, retrofitting_cost):
"""
Compute the Benefit-Cost Ratio.
BCR = (EALo - EALr)(1-exp(-r*t))/(r*C)
Where:
* BCR -- Benefit cost ratio
* EALo -- Expected annual loss for original asset
* EALr -- Expected annual loss for retrofitted asset
* r -- Interest rate
* t -- Life expectancy of the asset
* C -- Retrofitting cost
"""
return ((eal_original - eal_retrofitted) * asset_value *
(1 - numpy.exp(- interest_rate * asset_life_expectancy)) /
(interest_rate * retrofitting_cost)) | [
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150 | gem/oq-engine | openquake/risklib/scientific.py | pairwise_mean | def pairwise_mean(values):
"Averages between a value and the next value in a sequence"
return numpy.array([numpy.mean(pair) for pair in pairwise(values)]) | python | def pairwise_mean(values):
"Averages between a value and the next value in a sequence"
return numpy.array([numpy.mean(pair) for pair in pairwise(values)]) | [
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151 | gem/oq-engine | openquake/risklib/scientific.py | pairwise_diff | def pairwise_diff(values):
"Differences between a value and the next value in a sequence"
return numpy.array([x - y for x, y in pairwise(values)]) | python | def pairwise_diff(values):
"Differences between a value and the next value in a sequence"
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152 | gem/oq-engine | openquake/risklib/scientific.py | mean_std | def mean_std(fractions):
"""
Given an N x M matrix, returns mean and std computed on the rows,
i.e. two M-dimensional vectors.
"""
n = fractions.shape[0]
if n == 1: # avoid warnings when computing the stddev
return fractions[0], numpy.ones_like(fractions[0]) * numpy.nan
return numpy.mean(fractions, axis=0), numpy.std(fractions, axis=0, ddof=1) | python | def mean_std(fractions):
"""
Given an N x M matrix, returns mean and std computed on the rows,
i.e. two M-dimensional vectors.
"""
n = fractions.shape[0]
if n == 1: # avoid warnings when computing the stddev
return fractions[0], numpy.ones_like(fractions[0]) * numpy.nan
return numpy.mean(fractions, axis=0), numpy.std(fractions, axis=0, ddof=1) | [
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153 | gem/oq-engine | openquake/risklib/scientific.py | broadcast | def broadcast(func, composite_array, *args):
"""
Broadcast an array function over a composite array
"""
dic = {}
dtypes = []
for name in composite_array.dtype.names:
dic[name] = func(composite_array[name], *args)
dtypes.append((name, dic[name].dtype))
res = numpy.zeros(dic[name].shape, numpy.dtype(dtypes))
for name in dic:
res[name] = dic[name]
return res | python | def broadcast(func, composite_array, *args):
"""
Broadcast an array function over a composite array
"""
dic = {}
dtypes = []
for name in composite_array.dtype.names:
dic[name] = func(composite_array[name], *args)
dtypes.append((name, dic[name].dtype))
res = numpy.zeros(dic[name].shape, numpy.dtype(dtypes))
for name in dic:
res[name] = dic[name]
return res | [
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154 | gem/oq-engine | openquake/risklib/scientific.py | average_loss | def average_loss(lc):
"""
Given a loss curve array with `poe` and `loss` fields,
computes the average loss on a period of time.
:note: As the loss curve is supposed to be piecewise linear as it
is a result of a linear interpolation, we compute an exact
integral by using the trapeizodal rule with the width given by the
loss bin width.
"""
losses, poes = (lc['loss'], lc['poe']) if lc.dtype.names else lc
return -pairwise_diff(losses) @ pairwise_mean(poes) | python | def average_loss(lc):
"""
Given a loss curve array with `poe` and `loss` fields,
computes the average loss on a period of time.
:note: As the loss curve is supposed to be piecewise linear as it
is a result of a linear interpolation, we compute an exact
integral by using the trapeizodal rule with the width given by the
loss bin width.
"""
losses, poes = (lc['loss'], lc['poe']) if lc.dtype.names else lc
return -pairwise_diff(losses) @ pairwise_mean(poes) | [
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155 | gem/oq-engine | openquake/risklib/scientific.py | normalize_curves_eb | def normalize_curves_eb(curves):
"""
A more sophisticated version of normalize_curves, used in the event
based calculator.
:param curves: a list of pairs (losses, poes)
:returns: first losses, all_poes
"""
# we assume non-decreasing losses, so losses[-1] is the maximum loss
non_zero_curves = [(losses, poes)
for losses, poes in curves if losses[-1] > 0]
if not non_zero_curves: # no damage. all zero curves
return curves[0][0], numpy.array([poes for _losses, poes in curves])
else: # standard case
max_losses = [losses[-1] for losses, _poes in non_zero_curves]
reference_curve = non_zero_curves[numpy.argmax(max_losses)]
loss_ratios = reference_curve[0]
curves_poes = [interpolate.interp1d(
losses, poes, bounds_error=False, fill_value=0)(loss_ratios)
for losses, poes in curves]
# fix degenerated case with flat curve
for cp in curves_poes:
if numpy.isnan(cp[0]):
cp[0] = 0
return loss_ratios, numpy.array(curves_poes) | python | def normalize_curves_eb(curves):
"""
A more sophisticated version of normalize_curves, used in the event
based calculator.
:param curves: a list of pairs (losses, poes)
:returns: first losses, all_poes
"""
# we assume non-decreasing losses, so losses[-1] is the maximum loss
non_zero_curves = [(losses, poes)
for losses, poes in curves if losses[-1] > 0]
if not non_zero_curves: # no damage. all zero curves
return curves[0][0], numpy.array([poes for _losses, poes in curves])
else: # standard case
max_losses = [losses[-1] for losses, _poes in non_zero_curves]
reference_curve = non_zero_curves[numpy.argmax(max_losses)]
loss_ratios = reference_curve[0]
curves_poes = [interpolate.interp1d(
losses, poes, bounds_error=False, fill_value=0)(loss_ratios)
for losses, poes in curves]
# fix degenerated case with flat curve
for cp in curves_poes:
if numpy.isnan(cp[0]):
cp[0] = 0
return loss_ratios, numpy.array(curves_poes) | [
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156 | gem/oq-engine | openquake/risklib/scientific.py | VulnerabilityFunction.sample | def sample(self, means, covs, idxs, epsilons=None):
"""
Sample the epsilons and apply the corrections to the means.
This method is called only if there are nonzero covs.
:param means:
array of E' loss ratios
:param covs:
array of E' floats
:param idxs:
array of E booleans with E >= E'
:param epsilons:
array of E floats (or None)
:returns:
array of E' loss ratios
"""
if epsilons is None:
return means
self.set_distribution(epsilons)
res = self.distribution.sample(means, covs, means * covs, idxs)
return res | python | def sample(self, means, covs, idxs, epsilons=None):
"""
Sample the epsilons and apply the corrections to the means.
This method is called only if there are nonzero covs.
:param means:
array of E' loss ratios
:param covs:
array of E' floats
:param idxs:
array of E booleans with E >= E'
:param epsilons:
array of E floats (or None)
:returns:
array of E' loss ratios
"""
if epsilons is None:
return means
self.set_distribution(epsilons)
res = self.distribution.sample(means, covs, means * covs, idxs)
return res | [
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157 | gem/oq-engine | openquake/risklib/scientific.py | VulnerabilityFunction.mean_loss_ratios_with_steps | def mean_loss_ratios_with_steps(self, steps):
"""
Split the mean loss ratios, producing a new set of loss ratios. The new
set of loss ratios always includes 0.0 and 1.0
:param int steps:
the number of steps we make to go from one loss
ratio to the next. For example, if we have [0.5, 0.7]::
steps = 1 produces [0.0, 0.5, 0.7, 1]
steps = 2 produces [0.0, 0.25, 0.5, 0.6, 0.7, 0.85, 1]
steps = 3 produces [0.0, 0.17, 0.33, 0.5, 0.57, 0.63,
0.7, 0.8, 0.9, 1]
"""
loss_ratios = self.mean_loss_ratios
if min(loss_ratios) > 0.0:
# prepend with a zero
loss_ratios = numpy.concatenate([[0.0], loss_ratios])
if max(loss_ratios) < 1.0:
# append a 1.0
loss_ratios = numpy.concatenate([loss_ratios, [1.0]])
return fine_graining(loss_ratios, steps) | python | def mean_loss_ratios_with_steps(self, steps):
"""
Split the mean loss ratios, producing a new set of loss ratios. The new
set of loss ratios always includes 0.0 and 1.0
:param int steps:
the number of steps we make to go from one loss
ratio to the next. For example, if we have [0.5, 0.7]::
steps = 1 produces [0.0, 0.5, 0.7, 1]
steps = 2 produces [0.0, 0.25, 0.5, 0.6, 0.7, 0.85, 1]
steps = 3 produces [0.0, 0.17, 0.33, 0.5, 0.57, 0.63,
0.7, 0.8, 0.9, 1]
"""
loss_ratios = self.mean_loss_ratios
if min(loss_ratios) > 0.0:
# prepend with a zero
loss_ratios = numpy.concatenate([[0.0], loss_ratios])
if max(loss_ratios) < 1.0:
# append a 1.0
loss_ratios = numpy.concatenate([loss_ratios, [1.0]])
return fine_graining(loss_ratios, steps) | [
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158 | gem/oq-engine | openquake/risklib/scientific.py | VulnerabilityFunctionWithPMF.sample | def sample(self, probs, _covs, idxs, epsilons):
"""
Sample the .loss_ratios with the given probabilities.
:param probs:
array of E' floats
:param _covs:
ignored, it is there only for API consistency
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array of E booleans with E >= E'
:param epsilons:
array of E floats
:returns:
array of E' probabilities
"""
self.set_distribution(epsilons)
return self.distribution.sample(self.loss_ratios, probs) | python | def sample(self, probs, _covs, idxs, epsilons):
"""
Sample the .loss_ratios with the given probabilities.
:param probs:
array of E' floats
:param _covs:
ignored, it is there only for API consistency
:param idxs:
array of E booleans with E >= E'
:param epsilons:
array of E floats
:returns:
array of E' probabilities
"""
self.set_distribution(epsilons)
return self.distribution.sample(self.loss_ratios, probs) | [
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159 | gem/oq-engine | openquake/risklib/scientific.py | FragilityModel.build | def build(self, continuous_fragility_discretization, steps_per_interval):
"""
Return a new FragilityModel instance, in which the values have been
replaced with FragilityFunctionList instances.
:param continuous_fragility_discretization:
configuration parameter
:param steps_per_interval:
configuration parameter
"""
newfm = copy.copy(self)
for key, ffl in self.items():
newfm[key] = ffl.build(self.limitStates,
continuous_fragility_discretization,
steps_per_interval)
return newfm | python | def build(self, continuous_fragility_discretization, steps_per_interval):
"""
Return a new FragilityModel instance, in which the values have been
replaced with FragilityFunctionList instances.
:param continuous_fragility_discretization:
configuration parameter
:param steps_per_interval:
configuration parameter
"""
newfm = copy.copy(self)
for key, ffl in self.items():
newfm[key] = ffl.build(self.limitStates,
continuous_fragility_discretization,
steps_per_interval)
return newfm | [
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160 | gem/oq-engine | openquake/calculators/event_based.py | compute_gmfs | def compute_gmfs(rupgetter, srcfilter, param, monitor):
"""
Compute GMFs and optionally hazard curves
"""
getter = GmfGetter(rupgetter, srcfilter, param['oqparam'])
with monitor('getting ruptures'):
getter.init()
return getter.compute_gmfs_curves(monitor) | python | def compute_gmfs(rupgetter, srcfilter, param, monitor):
"""
Compute GMFs and optionally hazard curves
"""
getter = GmfGetter(rupgetter, srcfilter, param['oqparam'])
with monitor('getting ruptures'):
getter.init()
return getter.compute_gmfs_curves(monitor) | [
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161 | gem/oq-engine | openquake/hmtk/sources/complex_fault_source.py | mtkComplexFaultSource._get_minmax_edges | def _get_minmax_edges(self, edge):
'''
Updates the upper and lower depths based on the input edges
'''
if isinstance(edge, Line):
# For instance of line class need to loop over values
depth_vals = np.array([node.depth for node in edge.points])
else:
depth_vals = edge[:, 2]
temp_upper_depth = np.min(depth_vals)
if not self.upper_depth:
self.upper_depth = temp_upper_depth
else:
if temp_upper_depth < self.upper_depth:
self.upper_depth = temp_upper_depth
temp_lower_depth = np.max(depth_vals)
if not self.lower_depth:
self.lower_depth = temp_lower_depth
else:
if temp_lower_depth > self.lower_depth:
self.lower_depth = temp_lower_depth | python | def _get_minmax_edges(self, edge):
'''
Updates the upper and lower depths based on the input edges
'''
if isinstance(edge, Line):
# For instance of line class need to loop over values
depth_vals = np.array([node.depth for node in edge.points])
else:
depth_vals = edge[:, 2]
temp_upper_depth = np.min(depth_vals)
if not self.upper_depth:
self.upper_depth = temp_upper_depth
else:
if temp_upper_depth < self.upper_depth:
self.upper_depth = temp_upper_depth
temp_lower_depth = np.max(depth_vals)
if not self.lower_depth:
self.lower_depth = temp_lower_depth
else:
if temp_lower_depth > self.lower_depth:
self.lower_depth = temp_lower_depth | [
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162 | gem/oq-engine | openquake/hazardlib/gsim/kotha_2016.py | KothaEtAl2016._get_magnitude_term | def _get_magnitude_term(self, C, mag):
"""
Returns the magnitude scaling term - equation 3
"""
if mag >= self.CONSTS["Mh"]:
return C["e1"] + C["b3"] * (mag - self.CONSTS["Mh"])
else:
return C["e1"] + (C["b1"] * (mag - self.CONSTS["Mh"])) +\
(C["b2"] * (mag - self.CONSTS["Mh"]) ** 2.) | python | def _get_magnitude_term(self, C, mag):
"""
Returns the magnitude scaling term - equation 3
"""
if mag >= self.CONSTS["Mh"]:
return C["e1"] + C["b3"] * (mag - self.CONSTS["Mh"])
else:
return C["e1"] + (C["b1"] * (mag - self.CONSTS["Mh"])) +\
(C["b2"] * (mag - self.CONSTS["Mh"]) ** 2.) | [
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163 | gem/oq-engine | openquake/hazardlib/gsim/kotha_2016.py | KothaEtAl2016._get_distance_term | def _get_distance_term(self, C, rjb, mag):
"""
Returns the general distance scaling term - equation 2
"""
c_3 = self._get_anelastic_coeff(C)
rval = np.sqrt(rjb ** 2. + C["h"] ** 2.)
return (C["c1"] + C["c2"] * (mag - self.CONSTS["Mref"])) *\
np.log(rval / self.CONSTS["Rref"]) +\
c_3 * (rval - self.CONSTS["Rref"]) | python | def _get_distance_term(self, C, rjb, mag):
"""
Returns the general distance scaling term - equation 2
"""
c_3 = self._get_anelastic_coeff(C)
rval = np.sqrt(rjb ** 2. + C["h"] ** 2.)
return (C["c1"] + C["c2"] * (mag - self.CONSTS["Mref"])) *\
np.log(rval / self.CONSTS["Rref"]) +\
c_3 * (rval - self.CONSTS["Rref"]) | [
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164 | gem/oq-engine | openquake/hazardlib/gsim/kotha_2016.py | KothaEtAl2016._get_site_term | def _get_site_term(self, C, vs30):
"""
Returns only a linear site amplification term
"""
dg1, dg2 = self._get_regional_site_term(C)
return (C["g1"] + dg1) + (C["g2"] + dg2) * np.log(vs30) | python | def _get_site_term(self, C, vs30):
"""
Returns only a linear site amplification term
"""
dg1, dg2 = self._get_regional_site_term(C)
return (C["g1"] + dg1) + (C["g2"] + dg2) * np.log(vs30) | [
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165 | gem/oq-engine | openquake/hazardlib/gsim/tusa_langer_2016.py | TusaLanger2016RepiBA08SE._get_stddevs | def _get_stddevs(self, C, stddev_types, num_sites):
"""
Return standard deviations as defined in tables below
"""
assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
for stddev_type in stddev_types)
stddevs = [np.zeros(num_sites) + C['SigmaTot'] for _ in stddev_types]
return stddevs | python | def _get_stddevs(self, C, stddev_types, num_sites):
"""
Return standard deviations as defined in tables below
"""
assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
for stddev_type in stddev_types)
stddevs = [np.zeros(num_sites) + C['SigmaTot'] for _ in stddev_types]
return stddevs | [
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166 | gem/oq-engine | openquake/hazardlib/gsim/dowrickrhoades_2005.py | DowrickRhoades2005Asc._compute_mean | def _compute_mean(self, C, mag, rrup, hypo_depth, delta_R, delta_S,
delta_V, delta_I, vs30):
"""
Compute MMI Intensity Value as per Equation in Table 5 and
Table 7 pag 198.
"""
# mean is calculated for all the 4 classes using the same equation.
# For DowrickRhoades2005SSlab, the coefficients which don't appear in
# Model 3 equationare assigned to zero
mean = (C['A1'] + (C['A2'] + C['A2R'] * delta_R + C['A2V'] * delta_V) *
mag + (C['A3'] + C['A3S'] * delta_S + C['A3V'] * delta_V) *
np.log10(np.power((rrup**3 + C['d']**3), 1.0 / 3.0)) +
C['A4'] * hypo_depth + C['A5'] * delta_I)
# Get S site class term
S = self._get_site_class(vs30, mean)
# Add S amplification term to mean value
mean = mean + S
return mean | python | def _compute_mean(self, C, mag, rrup, hypo_depth, delta_R, delta_S,
delta_V, delta_I, vs30):
"""
Compute MMI Intensity Value as per Equation in Table 5 and
Table 7 pag 198.
"""
# mean is calculated for all the 4 classes using the same equation.
# For DowrickRhoades2005SSlab, the coefficients which don't appear in
# Model 3 equationare assigned to zero
mean = (C['A1'] + (C['A2'] + C['A2R'] * delta_R + C['A2V'] * delta_V) *
mag + (C['A3'] + C['A3S'] * delta_S + C['A3V'] * delta_V) *
np.log10(np.power((rrup**3 + C['d']**3), 1.0 / 3.0)) +
C['A4'] * hypo_depth + C['A5'] * delta_I)
# Get S site class term
S = self._get_site_class(vs30, mean)
# Add S amplification term to mean value
mean = mean + S
return mean | [
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167 | gem/oq-engine | openquake/hazardlib/gsim/dowrickrhoades_2005.py | DowrickRhoades2005Asc._get_stddevs | def _get_stddevs(self, C, stddev_types, num_sites):
"""
Return total standard deviation as described in paragraph 5.2 pag 200.
"""
# interevent stddev
sigma_inter = C['tau'] + np.zeros(num_sites)
# intraevent std
sigma_intra = C['sigma'] + np.zeros(num_sites)
std = []
for stddev_type in stddev_types:
if stddev_type == const.StdDev.TOTAL:
# equation in section 5.2 page 200
std += [np.sqrt(sigma_intra**2 + sigma_inter**2)]
elif stddev_type == const.StdDev.INTRA_EVENT:
std.append(sigma_intra)
elif stddev_type == const.StdDev.INTER_EVENT:
std.append(sigma_inter)
return std | python | def _get_stddevs(self, C, stddev_types, num_sites):
"""
Return total standard deviation as described in paragraph 5.2 pag 200.
"""
# interevent stddev
sigma_inter = C['tau'] + np.zeros(num_sites)
# intraevent std
sigma_intra = C['sigma'] + np.zeros(num_sites)
std = []
for stddev_type in stddev_types:
if stddev_type == const.StdDev.TOTAL:
# equation in section 5.2 page 200
std += [np.sqrt(sigma_intra**2 + sigma_inter**2)]
elif stddev_type == const.StdDev.INTRA_EVENT:
std.append(sigma_intra)
elif stddev_type == const.StdDev.INTER_EVENT:
std.append(sigma_inter)
return std | [
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168 | gem/oq-engine | openquake/commands/plot_assets.py | plot_assets | def plot_assets(calc_id=-1, site_model=False):
"""
Plot the sites and the assets
"""
# NB: matplotlib is imported inside since it is a costly import
import matplotlib.pyplot as p
from openquake.hmtk.plotting.patch import PolygonPatch
dstore = util.read(calc_id)
try:
region = dstore['oqparam'].region
except KeyError:
region = None
sitecol = dstore['sitecol']
try:
assetcol = dstore['assetcol'].value
except AttributeError:
assetcol = dstore['assetcol'].array
fig = p.figure()
ax = fig.add_subplot(111)
if region:
pp = PolygonPatch(shapely.wkt.loads(region), alpha=0.1)
ax.add_patch(pp)
ax.grid(True)
if site_model and 'site_model' in dstore:
sm = dstore['site_model']
sm_lons, sm_lats = sm['lon'], sm['lat']
if len(sm_lons) > 1 and cross_idl(*sm_lons):
sm_lons %= 360
p.scatter(sm_lons, sm_lats, marker='.', color='orange')
p.scatter(sitecol.complete.lons, sitecol.complete.lats, marker='.',
color='gray')
p.scatter(assetcol['lon'], assetcol['lat'], marker='.', color='green')
p.scatter(sitecol.lons, sitecol.lats, marker='+', color='black')
if 'discarded' in dstore:
disc = numpy.unique(dstore['discarded'].value[['lon', 'lat']])
p.scatter(disc['lon'], disc['lat'], marker='x', color='red')
p.show() | python | def plot_assets(calc_id=-1, site_model=False):
"""
Plot the sites and the assets
"""
# NB: matplotlib is imported inside since it is a costly import
import matplotlib.pyplot as p
from openquake.hmtk.plotting.patch import PolygonPatch
dstore = util.read(calc_id)
try:
region = dstore['oqparam'].region
except KeyError:
region = None
sitecol = dstore['sitecol']
try:
assetcol = dstore['assetcol'].value
except AttributeError:
assetcol = dstore['assetcol'].array
fig = p.figure()
ax = fig.add_subplot(111)
if region:
pp = PolygonPatch(shapely.wkt.loads(region), alpha=0.1)
ax.add_patch(pp)
ax.grid(True)
if site_model and 'site_model' in dstore:
sm = dstore['site_model']
sm_lons, sm_lats = sm['lon'], sm['lat']
if len(sm_lons) > 1 and cross_idl(*sm_lons):
sm_lons %= 360
p.scatter(sm_lons, sm_lats, marker='.', color='orange')
p.scatter(sitecol.complete.lons, sitecol.complete.lats, marker='.',
color='gray')
p.scatter(assetcol['lon'], assetcol['lat'], marker='.', color='green')
p.scatter(sitecol.lons, sitecol.lats, marker='+', color='black')
if 'discarded' in dstore:
disc = numpy.unique(dstore['discarded'].value[['lon', 'lat']])
p.scatter(disc['lon'], disc['lat'], marker='x', color='red')
p.show() | [
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169 | gem/oq-engine | openquake/hmtk/seismicity/smoothing/smoothed_seismicity.py | _get_adjustment | def _get_adjustment(mag, year, mmin, completeness_year, t_f, mag_inc=0.1):
'''
If the magnitude is greater than the minimum in the completeness table
and the year is greater than the corresponding completeness year then
return the Weichert factor
:param float mag:
Magnitude of an earthquake
:param float year:
Year of earthquake
:param np.ndarray completeness_table:
Completeness table
:param float mag_inc:
Magnitude increment
:param float t_f:
Weichert adjustment factor
:returns:
Weichert adjustment factor is event is in complete part of catalogue
(0.0 otherwise)
'''
if len(completeness_year) == 1:
if (mag >= mmin) and (year >= completeness_year[0]):
# No adjustment needed - event weight == 1
return 1.0
else:
# Event should not be counted
return False
kval = int(((mag - mmin) / mag_inc)) + 1
if (kval >= 1) and (year >= completeness_year[kval - 1]):
return t_f
else:
return False | python | def _get_adjustment(mag, year, mmin, completeness_year, t_f, mag_inc=0.1):
'''
If the magnitude is greater than the minimum in the completeness table
and the year is greater than the corresponding completeness year then
return the Weichert factor
:param float mag:
Magnitude of an earthquake
:param float year:
Year of earthquake
:param np.ndarray completeness_table:
Completeness table
:param float mag_inc:
Magnitude increment
:param float t_f:
Weichert adjustment factor
:returns:
Weichert adjustment factor is event is in complete part of catalogue
(0.0 otherwise)
'''
if len(completeness_year) == 1:
if (mag >= mmin) and (year >= completeness_year[0]):
# No adjustment needed - event weight == 1
return 1.0
else:
# Event should not be counted
return False
kval = int(((mag - mmin) / mag_inc)) + 1
if (kval >= 1) and (year >= completeness_year[kval - 1]):
return t_f
else:
return False | [
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170 | gem/oq-engine | openquake/hmtk/seismicity/smoothing/smoothed_seismicity.py | get_catalogue_bounding_polygon | def get_catalogue_bounding_polygon(catalogue):
'''
Returns a polygon containing the bounding box of the catalogue
'''
upper_lon = np.max(catalogue.data['longitude'])
upper_lat = np.max(catalogue.data['latitude'])
lower_lon = np.min(catalogue.data['longitude'])
lower_lat = np.min(catalogue.data['latitude'])
return Polygon([Point(lower_lon, upper_lat), Point(upper_lon, upper_lat),
Point(upper_lon, lower_lat), Point(lower_lon, lower_lat)]) | python | def get_catalogue_bounding_polygon(catalogue):
'''
Returns a polygon containing the bounding box of the catalogue
'''
upper_lon = np.max(catalogue.data['longitude'])
upper_lat = np.max(catalogue.data['latitude'])
lower_lon = np.min(catalogue.data['longitude'])
lower_lat = np.min(catalogue.data['latitude'])
return Polygon([Point(lower_lon, upper_lat), Point(upper_lon, upper_lat),
Point(upper_lon, lower_lat), Point(lower_lon, lower_lat)]) | [
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171 | gem/oq-engine | openquake/hmtk/seismicity/smoothing/smoothed_seismicity.py | Grid.make_from_catalogue | def make_from_catalogue(cls, catalogue, spacing, dilate):
'''
Defines the grid on the basis of the catalogue
'''
new = cls()
cat_bbox = get_catalogue_bounding_polygon(catalogue)
if dilate > 0:
cat_bbox = cat_bbox.dilate(dilate)
# Define Grid spacing
new.update({'xmin': np.min(cat_bbox.lons),
'xmax': np.max(cat_bbox.lons),
'xspc': spacing,
'ymin': np.min(cat_bbox.lats),
'ymax': np.max(cat_bbox.lats),
'yspc': spacing,
'zmin': 0.,
'zmax': np.max(catalogue.data['depth']),
'zspc': np.max(catalogue.data['depth'])})
if new['zmin'] == new['zmax'] == new['zspc'] == 0:
new['zmax'] = new['zspc'] = 1
return new | python | def make_from_catalogue(cls, catalogue, spacing, dilate):
'''
Defines the grid on the basis of the catalogue
'''
new = cls()
cat_bbox = get_catalogue_bounding_polygon(catalogue)
if dilate > 0:
cat_bbox = cat_bbox.dilate(dilate)
# Define Grid spacing
new.update({'xmin': np.min(cat_bbox.lons),
'xmax': np.max(cat_bbox.lons),
'xspc': spacing,
'ymin': np.min(cat_bbox.lats),
'ymax': np.max(cat_bbox.lats),
'yspc': spacing,
'zmin': 0.,
'zmax': np.max(catalogue.data['depth']),
'zspc': np.max(catalogue.data['depth'])})
if new['zmin'] == new['zmax'] == new['zspc'] == 0:
new['zmax'] = new['zspc'] = 1
return new | [
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172 | gem/oq-engine | openquake/hmtk/seismicity/smoothing/smoothed_seismicity.py | SmoothedSeismicity.write_to_csv | def write_to_csv(self, filename):
'''
Exports to simple csv
:param str filename:
Path to file for export
'''
fid = open(filename, 'wt')
# Create header list
header_info = ['Longitude', 'Latitude', 'Depth', 'Observed Count',
'Smoothed Rate', 'b-value']
writer = csv.DictWriter(fid, fieldnames=header_info)
headers = dict((name0, name0) for name0 in header_info)
# Write to file
writer.writerow(headers)
for row in self.data:
# institute crude compression by omitting points with no seismicity
# and taking advantage of the %g format
if row[4] == 0:
continue
row_dict = {'Longitude': '%g' % row[0],
'Latitude': '%g' % row[1],
'Depth': '%g' % row[2],
'Observed Count': '%d' % row[3],
'Smoothed Rate': '%.6g' % row[4],
'b-value': '%g' % self.bval}
writer.writerow(row_dict)
fid.close() | python | def write_to_csv(self, filename):
'''
Exports to simple csv
:param str filename:
Path to file for export
'''
fid = open(filename, 'wt')
# Create header list
header_info = ['Longitude', 'Latitude', 'Depth', 'Observed Count',
'Smoothed Rate', 'b-value']
writer = csv.DictWriter(fid, fieldnames=header_info)
headers = dict((name0, name0) for name0 in header_info)
# Write to file
writer.writerow(headers)
for row in self.data:
# institute crude compression by omitting points with no seismicity
# and taking advantage of the %g format
if row[4] == 0:
continue
row_dict = {'Longitude': '%g' % row[0],
'Latitude': '%g' % row[1],
'Depth': '%g' % row[2],
'Observed Count': '%d' % row[3],
'Smoothed Rate': '%.6g' % row[4],
'b-value': '%g' % self.bval}
writer.writerow(row_dict)
fid.close() | [
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173 | gem/oq-engine | openquake/commonlib/hazard_writers.py | _validate_hazard_metadata | def _validate_hazard_metadata(md):
"""
Validate metadata `dict` of attributes, which are more or less the same for
hazard curves, hazard maps, and disaggregation histograms.
:param dict md:
`dict` which can contain the following keys:
* statistics
* gsimlt_path
* smlt_path
* imt
* sa_period
* sa_damping
:raises:
:exc:`ValueError` if the metadata is not valid.
"""
if (md.get('statistics') is not None and (
md.get('smlt_path') is not None or
md.get('gsimlt_path') is not None)):
raise ValueError('Cannot specify both `statistics` and logic tree '
'paths')
if md.get('statistics') is not None:
# make sure only valid statistics types are specified
if md.get('statistics') not in ('mean', 'max', 'quantile', 'std'):
raise ValueError('`statistics` must be either `mean`, `max`, or '
'`quantile`')
else:
# must specify both logic tree paths
if md.get('smlt_path') is None or md.get('gsimlt_path') is None:
raise ValueError('Both logic tree paths are required for '
'non-statistical results')
if md.get('statistics') == 'quantile':
if md.get('quantile_value') is None:
raise ValueError('quantile stastics results require a quantile'
' value to be specified')
if not md.get('statistics') == 'quantile':
if md.get('quantile_value') is not None:
raise ValueError('Quantile value must be specified with '
'quantile statistics')
if md.get('imt') == 'SA':
if md.get('sa_period') is None:
raise ValueError('`sa_period` is required for IMT == `SA`')
if md.get('sa_damping') is None:
raise ValueError('`sa_damping` is required for IMT == `SA`') | python | def _validate_hazard_metadata(md):
"""
Validate metadata `dict` of attributes, which are more or less the same for
hazard curves, hazard maps, and disaggregation histograms.
:param dict md:
`dict` which can contain the following keys:
* statistics
* gsimlt_path
* smlt_path
* imt
* sa_period
* sa_damping
:raises:
:exc:`ValueError` if the metadata is not valid.
"""
if (md.get('statistics') is not None and (
md.get('smlt_path') is not None or
md.get('gsimlt_path') is not None)):
raise ValueError('Cannot specify both `statistics` and logic tree '
'paths')
if md.get('statistics') is not None:
# make sure only valid statistics types are specified
if md.get('statistics') not in ('mean', 'max', 'quantile', 'std'):
raise ValueError('`statistics` must be either `mean`, `max`, or '
'`quantile`')
else:
# must specify both logic tree paths
if md.get('smlt_path') is None or md.get('gsimlt_path') is None:
raise ValueError('Both logic tree paths are required for '
'non-statistical results')
if md.get('statistics') == 'quantile':
if md.get('quantile_value') is None:
raise ValueError('quantile stastics results require a quantile'
' value to be specified')
if not md.get('statistics') == 'quantile':
if md.get('quantile_value') is not None:
raise ValueError('Quantile value must be specified with '
'quantile statistics')
if md.get('imt') == 'SA':
if md.get('sa_period') is None:
raise ValueError('`sa_period` is required for IMT == `SA`')
if md.get('sa_damping') is None:
raise ValueError('`sa_damping` is required for IMT == `SA`') | [
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] | Validate metadata `dict` of attributes, which are more or less the same for
hazard curves, hazard maps, and disaggregation histograms.
:param dict md:
`dict` which can contain the following keys:
* statistics
* gsimlt_path
* smlt_path
* imt
* sa_period
* sa_damping
:raises:
:exc:`ValueError` if the metadata is not valid. | [
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174 | gem/oq-engine | openquake/commonlib/hazard_writers.py | _set_metadata | def _set_metadata(element, metadata, attr_map, transform=str):
"""
Set metadata attributes on a given ``element``.
:param element:
:class:`xml.etree.ElementTree.Element` instance
:param metadata:
Dictionary of metadata items containing attribute data for ``element``.
:param attr_map:
Dictionary mapping of metadata key->attribute name.
:param transform:
A function accepting and returning a single value to be applied to each
attribute value. Defaults to `str`.
"""
for kw, attr in attr_map.items():
value = metadata.get(kw)
if value is not None:
element.set(attr, transform(value)) | python | def _set_metadata(element, metadata, attr_map, transform=str):
"""
Set metadata attributes on a given ``element``.
:param element:
:class:`xml.etree.ElementTree.Element` instance
:param metadata:
Dictionary of metadata items containing attribute data for ``element``.
:param attr_map:
Dictionary mapping of metadata key->attribute name.
:param transform:
A function accepting and returning a single value to be applied to each
attribute value. Defaults to `str`.
"""
for kw, attr in attr_map.items():
value = metadata.get(kw)
if value is not None:
element.set(attr, transform(value)) | [
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175 | gem/oq-engine | openquake/commonlib/hazard_writers.py | HazardCurveXMLWriter.serialize | def serialize(self, data):
"""
Write a sequence of hazard curves to the specified file.
:param data:
Iterable of hazard curve data. Each datum must be an object with
the following attributes:
* poes: A list of probability of exceedence values (floats).
* location: An object representing the location of the curve; must
have `x` and `y` to represent lon and lat, respectively.
"""
with open(self.dest, 'wb') as fh:
root = et.Element('nrml')
self.add_hazard_curves(root, self.metadata, data)
nrml.write(list(root), fh) | python | def serialize(self, data):
"""
Write a sequence of hazard curves to the specified file.
:param data:
Iterable of hazard curve data. Each datum must be an object with
the following attributes:
* poes: A list of probability of exceedence values (floats).
* location: An object representing the location of the curve; must
have `x` and `y` to represent lon and lat, respectively.
"""
with open(self.dest, 'wb') as fh:
root = et.Element('nrml')
self.add_hazard_curves(root, self.metadata, data)
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176 | gem/oq-engine | openquake/commonlib/hazard_writers.py | HazardCurveXMLWriter.add_hazard_curves | def add_hazard_curves(self, root, metadata, data):
"""
Add hazard curves stored into `data` as child of the `root`
element with `metadata`. See the documentation of the method
`serialize` and the constructor for a description of `data`
and `metadata`, respectively.
"""
hazard_curves = et.SubElement(root, 'hazardCurves')
_set_metadata(hazard_curves, metadata, _ATTR_MAP)
imls_elem = et.SubElement(hazard_curves, 'IMLs')
imls_elem.text = ' '.join(map(scientificformat, metadata['imls']))
gml_ns = nrml.SERIALIZE_NS_MAP['gml']
for hc in data:
hc_elem = et.SubElement(hazard_curves, 'hazardCurve')
gml_point = et.SubElement(hc_elem, '{%s}Point' % gml_ns)
gml_pos = et.SubElement(gml_point, '{%s}pos' % gml_ns)
gml_pos.text = '%s %s' % (hc.location.x, hc.location.y)
poes_elem = et.SubElement(hc_elem, 'poEs')
poes_elem.text = ' '.join(map(scientificformat, hc.poes)) | python | def add_hazard_curves(self, root, metadata, data):
"""
Add hazard curves stored into `data` as child of the `root`
element with `metadata`. See the documentation of the method
`serialize` and the constructor for a description of `data`
and `metadata`, respectively.
"""
hazard_curves = et.SubElement(root, 'hazardCurves')
_set_metadata(hazard_curves, metadata, _ATTR_MAP)
imls_elem = et.SubElement(hazard_curves, 'IMLs')
imls_elem.text = ' '.join(map(scientificformat, metadata['imls']))
gml_ns = nrml.SERIALIZE_NS_MAP['gml']
for hc in data:
hc_elem = et.SubElement(hazard_curves, 'hazardCurve')
gml_point = et.SubElement(hc_elem, '{%s}Point' % gml_ns)
gml_pos = et.SubElement(gml_point, '{%s}pos' % gml_ns)
gml_pos.text = '%s %s' % (hc.location.x, hc.location.y)
poes_elem = et.SubElement(hc_elem, 'poEs')
poes_elem.text = ' '.join(map(scientificformat, hc.poes)) | [
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177 | gem/oq-engine | openquake/commonlib/hazard_writers.py | EventBasedGMFXMLWriter.serialize | def serialize(self, data, fmt='%10.7E'):
"""
Serialize a collection of ground motion fields to XML.
:param data:
An iterable of "GMF set" objects.
Each "GMF set" object should:
* have an `investigation_time` attribute
* have an `stochastic_event_set_id` attribute
* be iterable, yielding a sequence of "GMF" objects
Each "GMF" object should:
* have an `imt` attribute
* have an `sa_period` attribute (only if `imt` is 'SA')
* have an `sa_damping` attribute (only if `imt` is 'SA')
* have a `event_id` attribute (to indicate which rupture
contributed to this gmf)
* be iterable, yielding a sequence of "GMF node" objects
Each "GMF node" object should have:
* a `gmv` attribute (to indicate the ground motion value
* `lon` and `lat` attributes (to indicate the geographical location
of the ground motion field)
"""
gmf_set_nodes = []
for gmf_set in data:
gmf_set_node = Node('gmfSet')
if gmf_set.investigation_time:
gmf_set_node['investigationTime'] = str(
gmf_set.investigation_time)
gmf_set_node['stochasticEventSetId'] = str(
gmf_set.stochastic_event_set_id)
gmf_set_node.nodes = gen_gmfs(gmf_set)
gmf_set_nodes.append(gmf_set_node)
gmf_container = Node('gmfCollection')
gmf_container[SM_TREE_PATH] = self.sm_lt_path
gmf_container[GSIM_TREE_PATH] = self.gsim_lt_path
gmf_container.nodes = gmf_set_nodes
with open(self.dest, 'wb') as dest:
nrml.write([gmf_container], dest, fmt) | python | def serialize(self, data, fmt='%10.7E'):
"""
Serialize a collection of ground motion fields to XML.
:param data:
An iterable of "GMF set" objects.
Each "GMF set" object should:
* have an `investigation_time` attribute
* have an `stochastic_event_set_id` attribute
* be iterable, yielding a sequence of "GMF" objects
Each "GMF" object should:
* have an `imt` attribute
* have an `sa_period` attribute (only if `imt` is 'SA')
* have an `sa_damping` attribute (only if `imt` is 'SA')
* have a `event_id` attribute (to indicate which rupture
contributed to this gmf)
* be iterable, yielding a sequence of "GMF node" objects
Each "GMF node" object should have:
* a `gmv` attribute (to indicate the ground motion value
* `lon` and `lat` attributes (to indicate the geographical location
of the ground motion field)
"""
gmf_set_nodes = []
for gmf_set in data:
gmf_set_node = Node('gmfSet')
if gmf_set.investigation_time:
gmf_set_node['investigationTime'] = str(
gmf_set.investigation_time)
gmf_set_node['stochasticEventSetId'] = str(
gmf_set.stochastic_event_set_id)
gmf_set_node.nodes = gen_gmfs(gmf_set)
gmf_set_nodes.append(gmf_set_node)
gmf_container = Node('gmfCollection')
gmf_container[SM_TREE_PATH] = self.sm_lt_path
gmf_container[GSIM_TREE_PATH] = self.gsim_lt_path
gmf_container.nodes = gmf_set_nodes
with open(self.dest, 'wb') as dest:
nrml.write([gmf_container], dest, fmt) | [
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* have an `stochastic_event_set_id` attribute
* be iterable, yielding a sequence of "GMF" objects
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* have an `imt` attribute
* have an `sa_period` attribute (only if `imt` is 'SA')
* have an `sa_damping` attribute (only if `imt` is 'SA')
* have a `event_id` attribute (to indicate which rupture
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178 | gem/oq-engine | openquake/commonlib/hazard_writers.py | SESXMLWriter.serialize | def serialize(self, data, investigation_time):
"""
Serialize a collection of stochastic event sets to XML.
:param data:
A dictionary src_group_id -> list of
:class:`openquake.commonlib.calc.Rupture` objects.
Each Rupture should have the following attributes:
* `rupid`
* `events_by_ses`
* `magnitude`
* `strike`
* `dip`
* `rake`
* `tectonic_region_type`
* `is_from_fault_source` (a `bool`)
* `is_multi_surface` (a `bool`)
* `lons`
* `lats`
* `depths`
If `is_from_fault_source` is `True`, the rupture originated from a
simple or complex fault sources. In this case, `lons`, `lats`, and
`depths` should all be 2D arrays (of uniform shape). These
coordinate triples represent nodes of the rupture mesh.
If `is_from_fault_source` is `False`, the rupture originated from a
point or area source. In this case, the rupture is represented by a
quadrilateral planar surface. This planar surface is defined by 3D
vertices. In this case, the rupture should have the following
attributes:
* `top_left_corner`
* `top_right_corner`
* `bottom_right_corner`
* `bottom_left_corner`
Each of these should be a triple of `lon`, `lat`, `depth`.
If `is_multi_surface` is `True`, the rupture originated from a
multi-surface source. In this case, `lons`, `lats`, and `depths`
should have uniform length. The length should be a multiple of 4,
where each segment of 4 represents the corner points of a planar
surface in the following order:
* top left
* top right
* bottom left
* bottom right
Each of these should be a triple of `lon`, `lat`, `depth`.
:param investigation_time:
Investigation time parameter specified in the job.ini
"""
with open(self.dest, 'wb') as fh:
root = et.Element('nrml')
ses_container = et.SubElement(root, 'ruptureCollection')
ses_container.set('investigationTime', str(investigation_time))
for grp_id in sorted(data):
attrs = dict(
id=grp_id,
tectonicRegion=data[grp_id][0].tectonic_region_type)
sg = et.SubElement(ses_container, 'ruptureGroup', attrs)
for rupture in data[grp_id]:
rupture_to_element(rupture, sg)
nrml.write(list(root), fh) | python | def serialize(self, data, investigation_time):
"""
Serialize a collection of stochastic event sets to XML.
:param data:
A dictionary src_group_id -> list of
:class:`openquake.commonlib.calc.Rupture` objects.
Each Rupture should have the following attributes:
* `rupid`
* `events_by_ses`
* `magnitude`
* `strike`
* `dip`
* `rake`
* `tectonic_region_type`
* `is_from_fault_source` (a `bool`)
* `is_multi_surface` (a `bool`)
* `lons`
* `lats`
* `depths`
If `is_from_fault_source` is `True`, the rupture originated from a
simple or complex fault sources. In this case, `lons`, `lats`, and
`depths` should all be 2D arrays (of uniform shape). These
coordinate triples represent nodes of the rupture mesh.
If `is_from_fault_source` is `False`, the rupture originated from a
point or area source. In this case, the rupture is represented by a
quadrilateral planar surface. This planar surface is defined by 3D
vertices. In this case, the rupture should have the following
attributes:
* `top_left_corner`
* `top_right_corner`
* `bottom_right_corner`
* `bottom_left_corner`
Each of these should be a triple of `lon`, `lat`, `depth`.
If `is_multi_surface` is `True`, the rupture originated from a
multi-surface source. In this case, `lons`, `lats`, and `depths`
should have uniform length. The length should be a multiple of 4,
where each segment of 4 represents the corner points of a planar
surface in the following order:
* top left
* top right
* bottom left
* bottom right
Each of these should be a triple of `lon`, `lat`, `depth`.
:param investigation_time:
Investigation time parameter specified in the job.ini
"""
with open(self.dest, 'wb') as fh:
root = et.Element('nrml')
ses_container = et.SubElement(root, 'ruptureCollection')
ses_container.set('investigationTime', str(investigation_time))
for grp_id in sorted(data):
attrs = dict(
id=grp_id,
tectonicRegion=data[grp_id][0].tectonic_region_type)
sg = et.SubElement(ses_container, 'ruptureGroup', attrs)
for rupture in data[grp_id]:
rupture_to_element(rupture, sg)
nrml.write(list(root), fh) | [
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:param data:
A dictionary src_group_id -> list of
:class:`openquake.commonlib.calc.Rupture` objects.
Each Rupture should have the following attributes:
* `rupid`
* `events_by_ses`
* `magnitude`
* `strike`
* `dip`
* `rake`
* `tectonic_region_type`
* `is_from_fault_source` (a `bool`)
* `is_multi_surface` (a `bool`)
* `lons`
* `lats`
* `depths`
If `is_from_fault_source` is `True`, the rupture originated from a
simple or complex fault sources. In this case, `lons`, `lats`, and
`depths` should all be 2D arrays (of uniform shape). These
coordinate triples represent nodes of the rupture mesh.
If `is_from_fault_source` is `False`, the rupture originated from a
point or area source. In this case, the rupture is represented by a
quadrilateral planar surface. This planar surface is defined by 3D
vertices. In this case, the rupture should have the following
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* `top_left_corner`
* `top_right_corner`
* `bottom_right_corner`
* `bottom_left_corner`
Each of these should be a triple of `lon`, `lat`, `depth`.
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multi-surface source. In this case, `lons`, `lats`, and `depths`
should have uniform length. The length should be a multiple of 4,
where each segment of 4 represents the corner points of a planar
surface in the following order:
* top left
* top right
* bottom left
* bottom right
Each of these should be a triple of `lon`, `lat`, `depth`.
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Investigation time parameter specified in the job.ini | [
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179 | gem/oq-engine | openquake/commonlib/hazard_writers.py | HazardMapXMLWriter.serialize | def serialize(self, data):
"""
Serialize hazard map data to XML.
See :meth:`HazardMapWriter.serialize` for details about the expected
input.
"""
with open(self.dest, 'wb') as fh:
root = et.Element('nrml')
hazard_map = et.SubElement(root, 'hazardMap')
_set_metadata(hazard_map, self.metadata, _ATTR_MAP)
for lon, lat, iml in data:
node = et.SubElement(hazard_map, 'node')
node.set('lon', str(lon))
node.set('lat', str(lat))
node.set('iml', str(iml))
nrml.write(list(root), fh) | python | def serialize(self, data):
"""
Serialize hazard map data to XML.
See :meth:`HazardMapWriter.serialize` for details about the expected
input.
"""
with open(self.dest, 'wb') as fh:
root = et.Element('nrml')
hazard_map = et.SubElement(root, 'hazardMap')
_set_metadata(hazard_map, self.metadata, _ATTR_MAP)
for lon, lat, iml in data:
node = et.SubElement(hazard_map, 'node')
node.set('lon', str(lon))
node.set('lat', str(lat))
node.set('iml', str(iml))
nrml.write(list(root), fh) | [
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180 | gem/oq-engine | openquake/commonlib/hazard_writers.py | UHSXMLWriter.serialize | def serialize(self, data):
"""
Write a sequence of uniform hazard spectra to the specified file.
:param data:
Iterable of UHS data. Each datum must be an object with the
following attributes:
* imls: A sequence of Intensity Measure Levels
* location: An object representing the location of the curve; must
have `x` and `y` to represent lon and lat, respectively.
"""
gml_ns = nrml.SERIALIZE_NS_MAP['gml']
with open(self.dest, 'wb') as fh:
root = et.Element('nrml')
uh_spectra = et.SubElement(root, 'uniformHazardSpectra')
_set_metadata(uh_spectra, self.metadata, _ATTR_MAP)
periods_elem = et.SubElement(uh_spectra, 'periods')
periods_elem.text = ' '.join([str(x)
for x in self.metadata['periods']])
for uhs in data:
uhs_elem = et.SubElement(uh_spectra, 'uhs')
gml_point = et.SubElement(uhs_elem, '{%s}Point' % gml_ns)
gml_pos = et.SubElement(gml_point, '{%s}pos' % gml_ns)
gml_pos.text = '%s %s' % (uhs.location.x, uhs.location.y)
imls_elem = et.SubElement(uhs_elem, 'IMLs')
imls_elem.text = ' '.join(['%10.7E' % x for x in uhs.imls])
nrml.write(list(root), fh) | python | def serialize(self, data):
"""
Write a sequence of uniform hazard spectra to the specified file.
:param data:
Iterable of UHS data. Each datum must be an object with the
following attributes:
* imls: A sequence of Intensity Measure Levels
* location: An object representing the location of the curve; must
have `x` and `y` to represent lon and lat, respectively.
"""
gml_ns = nrml.SERIALIZE_NS_MAP['gml']
with open(self.dest, 'wb') as fh:
root = et.Element('nrml')
uh_spectra = et.SubElement(root, 'uniformHazardSpectra')
_set_metadata(uh_spectra, self.metadata, _ATTR_MAP)
periods_elem = et.SubElement(uh_spectra, 'periods')
periods_elem.text = ' '.join([str(x)
for x in self.metadata['periods']])
for uhs in data:
uhs_elem = et.SubElement(uh_spectra, 'uhs')
gml_point = et.SubElement(uhs_elem, '{%s}Point' % gml_ns)
gml_pos = et.SubElement(gml_point, '{%s}pos' % gml_ns)
gml_pos.text = '%s %s' % (uhs.location.x, uhs.location.y)
imls_elem = et.SubElement(uhs_elem, 'IMLs')
imls_elem.text = ' '.join(['%10.7E' % x for x in uhs.imls])
nrml.write(list(root), fh) | [
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* location: An object representing the location of the curve; must
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181 | gem/oq-engine | openquake/hmtk/seismicity/max_magnitude/kijko_sellevol_bayes.py | check_config | def check_config(config, data):
'''Check config file inputs
:param dict config:
Configuration settings for the function
'''
essential_keys = ['input_mmin', 'b-value', 'sigma-b']
for key in essential_keys:
if not key in config.keys():
raise ValueError('For KijkoSellevolBayes the key %s needs to '
'be set in the configuation' % key)
if 'tolerance' not in config.keys() or not config['tolerance']:
config['tolerance'] = 1E-5
if not config.get('maximum_iterations', False):
config['maximum_iterations'] = 1000
if config['input_mmin'] < np.min(data['magnitude']):
config['input_mmin'] = np.min(data['magnitude'])
if fabs(config['sigma-b'] < 1E-15):
raise ValueError('Sigma-b must be greater than zero!')
return config | python | def check_config(config, data):
'''Check config file inputs
:param dict config:
Configuration settings for the function
'''
essential_keys = ['input_mmin', 'b-value', 'sigma-b']
for key in essential_keys:
if not key in config.keys():
raise ValueError('For KijkoSellevolBayes the key %s needs to '
'be set in the configuation' % key)
if 'tolerance' not in config.keys() or not config['tolerance']:
config['tolerance'] = 1E-5
if not config.get('maximum_iterations', False):
config['maximum_iterations'] = 1000
if config['input_mmin'] < np.min(data['magnitude']):
config['input_mmin'] = np.min(data['magnitude'])
if fabs(config['sigma-b'] < 1E-15):
raise ValueError('Sigma-b must be greater than zero!')
return config | [
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182 | gem/oq-engine | openquake/hazardlib/gsim/toro_1997.py | ToroEtAl1997MblgNSHMP2008._compute_mean | def _compute_mean(self, C, mag, rjb):
"""
Compute ground motion mean value.
"""
# line 1686 in hazgridXnga2.f
ffc = self._compute_finite_fault_correction(mag)
d = np.sqrt(rjb ** 2 + (C['c7'] ** 2) * (ffc ** 2))
# lines 1663, 1694-1696 in hazgridXnga2.f
mean = (
C['c1'] + C['c2'] * (mag - 6.) +
C['c3'] * ((mag - 6.) ** 2) -
C['c4'] * np.log(d) - C['c6'] * d
)
factor = np.log(rjb / 100.)
idx = factor > 0
mean[idx] -= (C['c5'] - C['c4']) * factor[idx]
return mean | python | def _compute_mean(self, C, mag, rjb):
"""
Compute ground motion mean value.
"""
# line 1686 in hazgridXnga2.f
ffc = self._compute_finite_fault_correction(mag)
d = np.sqrt(rjb ** 2 + (C['c7'] ** 2) * (ffc ** 2))
# lines 1663, 1694-1696 in hazgridXnga2.f
mean = (
C['c1'] + C['c2'] * (mag - 6.) +
C['c3'] * ((mag - 6.) ** 2) -
C['c4'] * np.log(d) - C['c6'] * d
)
factor = np.log(rjb / 100.)
idx = factor > 0
mean[idx] -= (C['c5'] - C['c4']) * factor[idx]
return mean | [
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183 | gem/oq-engine | openquake/hazardlib/gsim/toro_1997.py | ToroEtAl1997MblgNSHMP2008._compute_finite_fault_correction | def _compute_finite_fault_correction(self, mag):
"""
Compute finite fault correction term as geometric mean of correction
terms obtained from Mw values calculated with Johnston 1996 and
Atkinson and Boore 1987 conversion equations.
Implement equations as in lines 1653 - 1658 in hazgridXnga2.f
"""
mw_j96 = mblg_to_mw_johnston_96(mag)
mw_ab87 = mblg_to_mw_atkinson_boore_87(mag)
t1 = np.exp(-1.25 + 0.227 * mw_j96)
t2 = np.exp(-1.25 + 0.227 * mw_ab87)
return np.sqrt(t1 * t2) | python | def _compute_finite_fault_correction(self, mag):
"""
Compute finite fault correction term as geometric mean of correction
terms obtained from Mw values calculated with Johnston 1996 and
Atkinson and Boore 1987 conversion equations.
Implement equations as in lines 1653 - 1658 in hazgridXnga2.f
"""
mw_j96 = mblg_to_mw_johnston_96(mag)
mw_ab87 = mblg_to_mw_atkinson_boore_87(mag)
t1 = np.exp(-1.25 + 0.227 * mw_j96)
t2 = np.exp(-1.25 + 0.227 * mw_ab87)
return np.sqrt(t1 * t2) | [
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terms obtained from Mw values calculated with Johnston 1996 and
Atkinson and Boore 1987 conversion equations.
Implement equations as in lines 1653 - 1658 in hazgridXnga2.f | [
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184 | gem/oq-engine | openquake/commands/upgrade_nrml.py | get_vulnerability_functions_04 | def get_vulnerability_functions_04(fname):
"""
Parse the vulnerability model in NRML 0.4 format.
:param fname:
path of the vulnerability file
:returns:
a dictionary imt, taxonomy -> vulnerability function + vset
"""
categories = dict(assetCategory=set(), lossCategory=set(),
vulnerabilitySetID=set())
imts = set()
taxonomies = set()
vf_dict = {} # imt, taxonomy -> vulnerability function
for vset in nrml.read(fname).vulnerabilityModel:
categories['assetCategory'].add(vset['assetCategory'])
categories['lossCategory'].add(vset['lossCategory'])
categories['vulnerabilitySetID'].add(vset['vulnerabilitySetID'])
IML = vset.IML
imt_str = IML['IMT']
imls = ~IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
categories['id'] = '_'.join(sorted(categories['vulnerabilitySetID']))
del categories['vulnerabilitySetID']
return vf_dict, categories | python | def get_vulnerability_functions_04(fname):
"""
Parse the vulnerability model in NRML 0.4 format.
:param fname:
path of the vulnerability file
:returns:
a dictionary imt, taxonomy -> vulnerability function + vset
"""
categories = dict(assetCategory=set(), lossCategory=set(),
vulnerabilitySetID=set())
imts = set()
taxonomies = set()
vf_dict = {} # imt, taxonomy -> vulnerability function
for vset in nrml.read(fname).vulnerabilityModel:
categories['assetCategory'].add(vset['assetCategory'])
categories['lossCategory'].add(vset['lossCategory'])
categories['vulnerabilitySetID'].add(vset['vulnerabilitySetID'])
IML = vset.IML
imt_str = IML['IMT']
imls = ~IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
categories['id'] = '_'.join(sorted(categories['vulnerabilitySetID']))
del categories['vulnerabilitySetID']
return vf_dict, categories | [
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:param fname:
path of the vulnerability file
:returns:
a dictionary imt, taxonomy -> vulnerability function + vset | [
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185 | gem/oq-engine | openquake/commands/upgrade_nrml.py | upgrade_file | def upgrade_file(path, multipoint):
"""Upgrade to the latest NRML version"""
node0 = nrml.read(path, chatty=False)[0]
shutil.copy(path, path + '.bak') # make a backup of the original file
tag = striptag(node0.tag)
gml = True
if tag == 'vulnerabilityModel':
vf_dict, cat_dict = get_vulnerability_functions_04(path)
# below I am converting into a NRML 0.5 vulnerabilityModel
node0 = Node(
'vulnerabilityModel', cat_dict,
nodes=[obj_to_node(val) for val in vf_dict.values()])
gml = False
elif tag == 'fragilityModel':
node0 = read_nrml.convert_fragility_model_04(
nrml.read(path)[0], path)
gml = False
elif tag == 'sourceModel':
node0 = nrml.read(path)[0]
dic = groupby(node0.nodes, operator.itemgetter('tectonicRegion'))
node0.nodes = [Node('sourceGroup',
dict(tectonicRegion=trt, name="group %s" % i),
nodes=srcs)
for i, (trt, srcs) in enumerate(dic.items(), 1)]
if multipoint:
sourceconverter.update_source_model(node0, path + '.bak')
with open(path, 'wb') as f:
nrml.write([node0], f, gml=gml) | python | def upgrade_file(path, multipoint):
"""Upgrade to the latest NRML version"""
node0 = nrml.read(path, chatty=False)[0]
shutil.copy(path, path + '.bak') # make a backup of the original file
tag = striptag(node0.tag)
gml = True
if tag == 'vulnerabilityModel':
vf_dict, cat_dict = get_vulnerability_functions_04(path)
# below I am converting into a NRML 0.5 vulnerabilityModel
node0 = Node(
'vulnerabilityModel', cat_dict,
nodes=[obj_to_node(val) for val in vf_dict.values()])
gml = False
elif tag == 'fragilityModel':
node0 = read_nrml.convert_fragility_model_04(
nrml.read(path)[0], path)
gml = False
elif tag == 'sourceModel':
node0 = nrml.read(path)[0]
dic = groupby(node0.nodes, operator.itemgetter('tectonicRegion'))
node0.nodes = [Node('sourceGroup',
dict(tectonicRegion=trt, name="group %s" % i),
nodes=srcs)
for i, (trt, srcs) in enumerate(dic.items(), 1)]
if multipoint:
sourceconverter.update_source_model(node0, path + '.bak')
with open(path, 'wb') as f:
nrml.write([node0], f, gml=gml) | [
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186 | gem/oq-engine | openquake/hazardlib/gsim/faccioli_2010.py | FaccioliEtAl2010._compute_term_3 | def _compute_term_3(self, C, rrup, mag):
"""
This computes the third term in equation 2, page 2.
"""
return (C['a3'] *
np.log10(rrup + C['a4'] * np.power(10, C['a5'] * mag))) | python | def _compute_term_3(self, C, rrup, mag):
"""
This computes the third term in equation 2, page 2.
"""
return (C['a3'] *
np.log10(rrup + C['a4'] * np.power(10, C['a5'] * mag))) | [
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187 | gem/oq-engine | openquake/hmtk/sources/source_conversion_utils.py | mag_scale_rel_to_hazardlib | def mag_scale_rel_to_hazardlib(mag_scale_rel, use_default=False):
"""
Returns the magnitude scaling relation in a format readable by
openquake.hazardlib
"""
if isinstance(mag_scale_rel, BaseMSR):
return mag_scale_rel
elif isinstance(mag_scale_rel, str):
if not mag_scale_rel in SCALE_RELS.keys():
raise ValueError('Magnitude scaling relation %s not supported!'
% mag_scale_rel)
else:
return SCALE_RELS[mag_scale_rel]()
else:
if use_default:
# Returns the Wells and Coppersmith string
return WC1994()
else:
raise ValueError('Magnitude Scaling Relation Not Defined!') | python | def mag_scale_rel_to_hazardlib(mag_scale_rel, use_default=False):
"""
Returns the magnitude scaling relation in a format readable by
openquake.hazardlib
"""
if isinstance(mag_scale_rel, BaseMSR):
return mag_scale_rel
elif isinstance(mag_scale_rel, str):
if not mag_scale_rel in SCALE_RELS.keys():
raise ValueError('Magnitude scaling relation %s not supported!'
% mag_scale_rel)
else:
return SCALE_RELS[mag_scale_rel]()
else:
if use_default:
# Returns the Wells and Coppersmith string
return WC1994()
else:
raise ValueError('Magnitude Scaling Relation Not Defined!') | [
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188 | gem/oq-engine | openquake/hmtk/sources/source_conversion_utils.py | npd_to_pmf | def npd_to_pmf(nodal_plane_dist, use_default=False):
"""
Returns the nodal plane distribution as an instance of the PMF class
"""
if isinstance(nodal_plane_dist, PMF):
# Aready in PMF format - return
return nodal_plane_dist
else:
if use_default:
return PMF([(1.0, NodalPlane(0.0, 90.0, 0.0))])
else:
raise ValueError('Nodal Plane distribution not defined') | python | def npd_to_pmf(nodal_plane_dist, use_default=False):
"""
Returns the nodal plane distribution as an instance of the PMF class
"""
if isinstance(nodal_plane_dist, PMF):
# Aready in PMF format - return
return nodal_plane_dist
else:
if use_default:
return PMF([(1.0, NodalPlane(0.0, 90.0, 0.0))])
else:
raise ValueError('Nodal Plane distribution not defined') | [
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189 | gem/oq-engine | openquake/commands/engine.py | run_job | def run_job(job_ini, log_level='info', log_file=None, exports='',
username=getpass.getuser(), **kw):
"""
Run a job using the specified config file and other options.
:param str job_ini:
Path to calculation config (INI-style) files.
:param str log_level:
'debug', 'info', 'warn', 'error', or 'critical'
:param str log_file:
Path to log file.
:param exports:
A comma-separated string of export types requested by the user.
:param username:
Name of the user running the job
:param kw:
Extra parameters like hazard_calculation_id and calculation_mode
"""
job_id = logs.init('job', getattr(logging, log_level.upper()))
with logs.handle(job_id, log_level, log_file):
job_ini = os.path.abspath(job_ini)
oqparam = eng.job_from_file(job_ini, job_id, username, **kw)
kw['username'] = username
eng.run_calc(job_id, oqparam, exports, **kw)
for line in logs.dbcmd('list_outputs', job_id, False):
safeprint(line)
return job_id | python | def run_job(job_ini, log_level='info', log_file=None, exports='',
username=getpass.getuser(), **kw):
"""
Run a job using the specified config file and other options.
:param str job_ini:
Path to calculation config (INI-style) files.
:param str log_level:
'debug', 'info', 'warn', 'error', or 'critical'
:param str log_file:
Path to log file.
:param exports:
A comma-separated string of export types requested by the user.
:param username:
Name of the user running the job
:param kw:
Extra parameters like hazard_calculation_id and calculation_mode
"""
job_id = logs.init('job', getattr(logging, log_level.upper()))
with logs.handle(job_id, log_level, log_file):
job_ini = os.path.abspath(job_ini)
oqparam = eng.job_from_file(job_ini, job_id, username, **kw)
kw['username'] = username
eng.run_calc(job_id, oqparam, exports, **kw)
for line in logs.dbcmd('list_outputs', job_id, False):
safeprint(line)
return job_id | [
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:param str job_ini:
Path to calculation config (INI-style) files.
:param str log_level:
'debug', 'info', 'warn', 'error', or 'critical'
:param str log_file:
Path to log file.
:param exports:
A comma-separated string of export types requested by the user.
:param username:
Name of the user running the job
:param kw:
Extra parameters like hazard_calculation_id and calculation_mode | [
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190 | gem/oq-engine | openquake/commands/engine.py | run_tile | def run_tile(job_ini, sites_slice):
"""
Used in tiling calculations
"""
return run_job(job_ini, sites_slice=(sites_slice.start, sites_slice.stop)) | python | def run_tile(job_ini, sites_slice):
"""
Used in tiling calculations
"""
return run_job(job_ini, sites_slice=(sites_slice.start, sites_slice.stop)) | [
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191 | gem/oq-engine | openquake/commands/engine.py | del_calculation | def del_calculation(job_id, confirmed=False):
"""
Delete a calculation and all associated outputs.
"""
if logs.dbcmd('get_job', job_id) is None:
print('There is no job %d' % job_id)
return
if confirmed or confirm(
'Are you sure you want to (abort and) delete this calculation and '
'all associated outputs?\nThis action cannot be undone. (y/n): '):
try:
abort(job_id)
resp = logs.dbcmd('del_calc', job_id, getpass.getuser())
except RuntimeError as err:
safeprint(err)
else:
if 'success' in resp:
print('Removed %d' % job_id)
else:
print(resp['error']) | python | def del_calculation(job_id, confirmed=False):
"""
Delete a calculation and all associated outputs.
"""
if logs.dbcmd('get_job', job_id) is None:
print('There is no job %d' % job_id)
return
if confirmed or confirm(
'Are you sure you want to (abort and) delete this calculation and '
'all associated outputs?\nThis action cannot be undone. (y/n): '):
try:
abort(job_id)
resp = logs.dbcmd('del_calc', job_id, getpass.getuser())
except RuntimeError as err:
safeprint(err)
else:
if 'success' in resp:
print('Removed %d' % job_id)
else:
print(resp['error']) | [
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192 | gem/oq-engine | openquake/commands/engine.py | smart_run | def smart_run(job_ini, oqparam, log_level, log_file, exports, reuse_hazard):
"""
Run calculations by storing their hazard checksum and reusing previous
calculations if requested.
"""
haz_checksum = readinput.get_checksum32(oqparam, hazard=True)
# retrieve an old calculation with the right checksum, if any
job = logs.dbcmd('get_job_from_checksum', haz_checksum)
reuse = reuse_hazard and job and os.path.exists(job.ds_calc_dir + '.hdf5')
# recompute the hazard and store the checksum
ebr = (oqparam.calculation_mode == 'event_based_risk' and
'gmfs' not in oqparam.inputs)
if ebr:
kw = dict(calculation_mode='event_based')
if (oqparam.sites or 'sites' in oqparam.inputs or
'site_model' in oqparam.inputs):
# remove exposure from the hazard
kw['exposure_file'] = ''
else:
kw = {}
if not reuse:
hc_id = run_job(job_ini, log_level, log_file, exports, **kw)
if job is None:
logs.dbcmd('add_checksum', hc_id, haz_checksum)
elif not reuse_hazard or not os.path.exists(job.ds_calc_dir + '.hdf5'):
logs.dbcmd('update_job_checksum', hc_id, haz_checksum)
if ebr:
run_job(job_ini, log_level, log_file,
exports, hazard_calculation_id=hc_id)
else:
hc_id = job.id
logging.info('Reusing job #%d', job.id)
run_job(job_ini, log_level, log_file,
exports, hazard_calculation_id=hc_id) | python | def smart_run(job_ini, oqparam, log_level, log_file, exports, reuse_hazard):
"""
Run calculations by storing their hazard checksum and reusing previous
calculations if requested.
"""
haz_checksum = readinput.get_checksum32(oqparam, hazard=True)
# retrieve an old calculation with the right checksum, if any
job = logs.dbcmd('get_job_from_checksum', haz_checksum)
reuse = reuse_hazard and job and os.path.exists(job.ds_calc_dir + '.hdf5')
# recompute the hazard and store the checksum
ebr = (oqparam.calculation_mode == 'event_based_risk' and
'gmfs' not in oqparam.inputs)
if ebr:
kw = dict(calculation_mode='event_based')
if (oqparam.sites or 'sites' in oqparam.inputs or
'site_model' in oqparam.inputs):
# remove exposure from the hazard
kw['exposure_file'] = ''
else:
kw = {}
if not reuse:
hc_id = run_job(job_ini, log_level, log_file, exports, **kw)
if job is None:
logs.dbcmd('add_checksum', hc_id, haz_checksum)
elif not reuse_hazard or not os.path.exists(job.ds_calc_dir + '.hdf5'):
logs.dbcmd('update_job_checksum', hc_id, haz_checksum)
if ebr:
run_job(job_ini, log_level, log_file,
exports, hazard_calculation_id=hc_id)
else:
hc_id = job.id
logging.info('Reusing job #%d', job.id)
run_job(job_ini, log_level, log_file,
exports, hazard_calculation_id=hc_id) | [
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193 | gem/oq-engine | openquake/hazardlib/gsim/campbell_bozorgnia_2008.py | CampbellBozorgnia2008._get_stddevs | def _get_stddevs(self, C, sites, pga1100, sigma_pga, stddev_types):
"""
Returns the standard deviations as described in the "ALEATORY
UNCERTAINTY MODEL" section of the paper. Equations 13 to 19, pages 147
to 151
"""
std_intra = self._compute_intra_event_std(C,
sites.vs30,
pga1100,
sigma_pga)
std_inter = C['t_lny'] * np.ones_like(sites.vs30)
stddevs = []
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(self._get_total_sigma(C, std_intra, std_inter))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(std_intra)
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(std_inter)
return stddevs | python | def _get_stddevs(self, C, sites, pga1100, sigma_pga, stddev_types):
"""
Returns the standard deviations as described in the "ALEATORY
UNCERTAINTY MODEL" section of the paper. Equations 13 to 19, pages 147
to 151
"""
std_intra = self._compute_intra_event_std(C,
sites.vs30,
pga1100,
sigma_pga)
std_inter = C['t_lny'] * np.ones_like(sites.vs30)
stddevs = []
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(self._get_total_sigma(C, std_intra, std_inter))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(std_intra)
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(std_inter)
return stddevs | [
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194 | gem/oq-engine | openquake/hazardlib/gsim/campbell_bozorgnia_2008.py | CampbellBozorgnia2008._compute_intra_event_std | def _compute_intra_event_std(self, C, vs30, pga1100, sigma_pga):
"""
Returns the intra-event standard deviation at the site, as defined in
equation 15, page 147
"""
# Get intra-event standard deviation at the base of the site profile
sig_lnyb = np.sqrt(C['s_lny'] ** 2. - C['s_lnAF'] ** 2.)
sig_lnab = np.sqrt(sigma_pga ** 2. - C['s_lnAF'] ** 2.)
# Get linearised relationship between f_site and ln PGA
alpha = self._compute_intra_event_alpha(C, vs30, pga1100)
return np.sqrt(
(sig_lnyb ** 2.) +
(C['s_lnAF'] ** 2.) +
((alpha ** 2.) * (sig_lnab ** 2.)) +
(2.0 * alpha * C['rho'] * sig_lnyb * sig_lnab)) | python | def _compute_intra_event_std(self, C, vs30, pga1100, sigma_pga):
"""
Returns the intra-event standard deviation at the site, as defined in
equation 15, page 147
"""
# Get intra-event standard deviation at the base of the site profile
sig_lnyb = np.sqrt(C['s_lny'] ** 2. - C['s_lnAF'] ** 2.)
sig_lnab = np.sqrt(sigma_pga ** 2. - C['s_lnAF'] ** 2.)
# Get linearised relationship between f_site and ln PGA
alpha = self._compute_intra_event_alpha(C, vs30, pga1100)
return np.sqrt(
(sig_lnyb ** 2.) +
(C['s_lnAF'] ** 2.) +
((alpha ** 2.) * (sig_lnab ** 2.)) +
(2.0 * alpha * C['rho'] * sig_lnyb * sig_lnab)) | [
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equation 15, page 147 | [
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195 | gem/oq-engine | openquake/hazardlib/gsim/campbell_bozorgnia_2008.py | CampbellBozorgnia2008._compute_intra_event_alpha | def _compute_intra_event_alpha(self, C, vs30, pga1100):
"""
Returns the linearised functional relationship between fsite and
pga1100, determined from the partial derivative defined on equation 17
on page 148
"""
alpha = np.zeros_like(vs30, dtype=float)
idx = vs30 < C['k1']
if np.any(idx):
temp1 = (pga1100[idx] +
C['c'] * (vs30[idx] / C['k1']) ** C['n']) ** -1.
temp1 = temp1 - ((pga1100[idx] + C['c']) ** -1.)
alpha[idx] = C['k2'] * pga1100[idx] * temp1
return alpha | python | def _compute_intra_event_alpha(self, C, vs30, pga1100):
"""
Returns the linearised functional relationship between fsite and
pga1100, determined from the partial derivative defined on equation 17
on page 148
"""
alpha = np.zeros_like(vs30, dtype=float)
idx = vs30 < C['k1']
if np.any(idx):
temp1 = (pga1100[idx] +
C['c'] * (vs30[idx] / C['k1']) ** C['n']) ** -1.
temp1 = temp1 - ((pga1100[idx] + C['c']) ** -1.)
alpha[idx] = C['k2'] * pga1100[idx] * temp1
return alpha | [
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196 | gem/oq-engine | openquake/hazardlib/gsim/campbell_bozorgnia_2008.py | CampbellBozorgnia2008Arbitrary._get_total_sigma | def _get_total_sigma(self, C, std_intra, std_inter):
"""
Returns the total sigma term for the arbitrary horizontal component of
ground motion defined by equation 18, page 150
"""
return np.sqrt(std_intra ** 2. + std_inter ** 2. + C['c_lny'] ** 2.) | python | def _get_total_sigma(self, C, std_intra, std_inter):
"""
Returns the total sigma term for the arbitrary horizontal component of
ground motion defined by equation 18, page 150
"""
return np.sqrt(std_intra ** 2. + std_inter ** 2. + C['c_lny'] ** 2.) | [
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197 | gem/oq-engine | openquake/calculators/ucerf_event_based.py | generate_event_set | def generate_event_set(ucerf, background_sids, src_filter, ses_idx, seed):
"""
Generates the event set corresponding to a particular branch
"""
serial = seed + ses_idx * TWO16
# get rates from file
with h5py.File(ucerf.source_file, 'r') as hdf5:
occurrences = ucerf.tom.sample_number_of_occurrences(ucerf.rate, seed)
indices, = numpy.where(occurrences)
logging.debug(
'Considering "%s", %d ruptures', ucerf.source_id, len(indices))
# get ruptures from the indices
ruptures = []
rupture_occ = []
for iloc, n_occ in zip(indices, occurrences[indices]):
ucerf_rup = ucerf.get_ucerf_rupture(iloc, src_filter)
if ucerf_rup:
ucerf_rup.serial = serial
serial += 1
ruptures.append(ucerf_rup)
rupture_occ.append(n_occ)
# sample background sources
background_ruptures, background_n_occ = sample_background_model(
hdf5, ucerf.idx_set["grid_key"], ucerf.tom, seed,
background_sids, ucerf.min_mag, ucerf.npd, ucerf.hdd, ucerf.usd,
ucerf.lsd, ucerf.msr, ucerf.aspect, ucerf.tectonic_region_type)
for i, brup in enumerate(background_ruptures):
brup.serial = serial
serial += 1
ruptures.append(brup)
rupture_occ.extend(background_n_occ)
assert len(ruptures) < TWO16, len(ruptures) # < 2^16 ruptures per SES
return ruptures, rupture_occ | python | def generate_event_set(ucerf, background_sids, src_filter, ses_idx, seed):
"""
Generates the event set corresponding to a particular branch
"""
serial = seed + ses_idx * TWO16
# get rates from file
with h5py.File(ucerf.source_file, 'r') as hdf5:
occurrences = ucerf.tom.sample_number_of_occurrences(ucerf.rate, seed)
indices, = numpy.where(occurrences)
logging.debug(
'Considering "%s", %d ruptures', ucerf.source_id, len(indices))
# get ruptures from the indices
ruptures = []
rupture_occ = []
for iloc, n_occ in zip(indices, occurrences[indices]):
ucerf_rup = ucerf.get_ucerf_rupture(iloc, src_filter)
if ucerf_rup:
ucerf_rup.serial = serial
serial += 1
ruptures.append(ucerf_rup)
rupture_occ.append(n_occ)
# sample background sources
background_ruptures, background_n_occ = sample_background_model(
hdf5, ucerf.idx_set["grid_key"], ucerf.tom, seed,
background_sids, ucerf.min_mag, ucerf.npd, ucerf.hdd, ucerf.usd,
ucerf.lsd, ucerf.msr, ucerf.aspect, ucerf.tectonic_region_type)
for i, brup in enumerate(background_ruptures):
brup.serial = serial
serial += 1
ruptures.append(brup)
rupture_occ.extend(background_n_occ)
assert len(ruptures) < TWO16, len(ruptures) # < 2^16 ruptures per SES
return ruptures, rupture_occ | [
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198 | gem/oq-engine | openquake/calculators/ucerf_event_based.py | sample_background_model | def sample_background_model(
hdf5, branch_key, tom, seed, filter_idx, min_mag, npd, hdd,
upper_seismogenic_depth, lower_seismogenic_depth, msr=WC1994(),
aspect=1.5, trt=DEFAULT_TRT):
"""
Generates a rupture set from a sample of the background model
:param branch_key:
Key to indicate the branch for selecting the background model
:param tom:
Temporal occurrence model as instance of :class:
openquake.hazardlib.tom.TOM
:param seed:
Random seed to use in the call to tom.sample_number_of_occurrences
:param filter_idx:
Sites for consideration (can be None!)
:param float min_mag:
Minimim magnitude for consideration of background sources
:param npd:
Nodal plane distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param hdd:
Hypocentral depth distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param float aspect:
Aspect ratio
:param float upper_seismogenic_depth:
Upper seismogenic depth (km)
:param float lower_seismogenic_depth:
Lower seismogenic depth (km)
:param msr:
Magnitude scaling relation
:param float integration_distance:
Maximum distance from rupture to site for consideration
"""
bg_magnitudes = hdf5["/".join(["Grid", branch_key, "Magnitude"])].value
# Select magnitudes above the minimum magnitudes
mag_idx = bg_magnitudes >= min_mag
mags = bg_magnitudes[mag_idx]
rates = hdf5["/".join(["Grid", branch_key, "RateArray"])][filter_idx, :]
rates = rates[:, mag_idx]
valid_locs = hdf5["Grid/Locations"][filter_idx, :]
# Sample remaining rates
sampler = tom.sample_number_of_occurrences(rates, seed)
background_ruptures = []
background_n_occ = []
for i, mag in enumerate(mags):
rate_idx = numpy.where(sampler[:, i])[0]
rate_cnt = sampler[rate_idx, i]
occurrence = rates[rate_idx, i]
locations = valid_locs[rate_idx, :]
ruptures = generate_background_ruptures(
tom, locations, occurrence,
mag, npd, hdd, upper_seismogenic_depth,
lower_seismogenic_depth, msr, aspect, trt)
background_ruptures.extend(ruptures)
background_n_occ.extend(rate_cnt.tolist())
return background_ruptures, background_n_occ | python | def sample_background_model(
hdf5, branch_key, tom, seed, filter_idx, min_mag, npd, hdd,
upper_seismogenic_depth, lower_seismogenic_depth, msr=WC1994(),
aspect=1.5, trt=DEFAULT_TRT):
"""
Generates a rupture set from a sample of the background model
:param branch_key:
Key to indicate the branch for selecting the background model
:param tom:
Temporal occurrence model as instance of :class:
openquake.hazardlib.tom.TOM
:param seed:
Random seed to use in the call to tom.sample_number_of_occurrences
:param filter_idx:
Sites for consideration (can be None!)
:param float min_mag:
Minimim magnitude for consideration of background sources
:param npd:
Nodal plane distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param hdd:
Hypocentral depth distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param float aspect:
Aspect ratio
:param float upper_seismogenic_depth:
Upper seismogenic depth (km)
:param float lower_seismogenic_depth:
Lower seismogenic depth (km)
:param msr:
Magnitude scaling relation
:param float integration_distance:
Maximum distance from rupture to site for consideration
"""
bg_magnitudes = hdf5["/".join(["Grid", branch_key, "Magnitude"])].value
# Select magnitudes above the minimum magnitudes
mag_idx = bg_magnitudes >= min_mag
mags = bg_magnitudes[mag_idx]
rates = hdf5["/".join(["Grid", branch_key, "RateArray"])][filter_idx, :]
rates = rates[:, mag_idx]
valid_locs = hdf5["Grid/Locations"][filter_idx, :]
# Sample remaining rates
sampler = tom.sample_number_of_occurrences(rates, seed)
background_ruptures = []
background_n_occ = []
for i, mag in enumerate(mags):
rate_idx = numpy.where(sampler[:, i])[0]
rate_cnt = sampler[rate_idx, i]
occurrence = rates[rate_idx, i]
locations = valid_locs[rate_idx, :]
ruptures = generate_background_ruptures(
tom, locations, occurrence,
mag, npd, hdd, upper_seismogenic_depth,
lower_seismogenic_depth, msr, aspect, trt)
background_ruptures.extend(ruptures)
background_n_occ.extend(rate_cnt.tolist())
return background_ruptures, background_n_occ | [
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Key to indicate the branch for selecting the background model
:param tom:
Temporal occurrence model as instance of :class:
openquake.hazardlib.tom.TOM
:param seed:
Random seed to use in the call to tom.sample_number_of_occurrences
:param filter_idx:
Sites for consideration (can be None!)
:param float min_mag:
Minimim magnitude for consideration of background sources
:param npd:
Nodal plane distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param hdd:
Hypocentral depth distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param float aspect:
Aspect ratio
:param float upper_seismogenic_depth:
Upper seismogenic depth (km)
:param float lower_seismogenic_depth:
Lower seismogenic depth (km)
:param msr:
Magnitude scaling relation
:param float integration_distance:
Maximum distance from rupture to site for consideration | [
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199 | gem/oq-engine | openquake/hazardlib/scalerel/wc1994.py | WC1994.get_median_area | def get_median_area(self, mag, rake):
"""
The values are a function of both magnitude and rake.
Setting the rake to ``None`` causes their "All" rupture-types
to be applied.
"""
assert rake is None or -180 <= rake <= 180
if rake is None:
# their "All" case
return 10.0 ** (-3.49 + 0.91 * mag)
elif (-45 <= rake <= 45) or (rake >= 135) or (rake <= -135):
# strike slip
return 10.0 ** (-3.42 + 0.90 * mag)
elif rake > 0:
# thrust/reverse
return 10.0 ** (-3.99 + 0.98 * mag)
else:
# normal
return 10.0 ** (-2.87 + 0.82 * mag) | python | def get_median_area(self, mag, rake):
"""
The values are a function of both magnitude and rake.
Setting the rake to ``None`` causes their "All" rupture-types
to be applied.
"""
assert rake is None or -180 <= rake <= 180
if rake is None:
# their "All" case
return 10.0 ** (-3.49 + 0.91 * mag)
elif (-45 <= rake <= 45) or (rake >= 135) or (rake <= -135):
# strike slip
return 10.0 ** (-3.42 + 0.90 * mag)
elif rake > 0:
# thrust/reverse
return 10.0 ** (-3.99 + 0.98 * mag)
else:
# normal
return 10.0 ** (-2.87 + 0.82 * mag) | [
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] | The values are a function of both magnitude and rake.
Setting the rake to ``None`` causes their "All" rupture-types
to be applied. | [
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] | 8294553a0b8aba33fd96437a35065d03547d0040 | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/scalerel/wc1994.py#L33-L52 |