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import copy
import numpy as num
from pyrocko import gf
from pyrocko.guts_array import Array
from pyrocko.guts import Object, Float, Dict
from grond.analysers.base import AnalyserResult
from grond.meta import has_get_plot_classes
guts_prefix = 'grond'
class TargetGroup(Object):
normalisation_family = gf.StringID.T(
optional=True,
help='Group with common misfit normalisation')
path = gf.StringID.T(
optional=True,
help='Targets.id will be prefixed with this path')
weight = Float.T(
default=1.0,
help='Additional manual weight of the target group')
interpolation = gf.InterpolationMethod.T(
default='nearest_neighbor',
help='Interpolation from pre-calculated GF store.')
store_id = gf.StringID.T(
optional=True,
help='ID of the Green\'s function store for this TargetGroup.')
def get_targets(self, ds, event, default_path='none'):
if not self._targets:
raise NotImplementedError()
class MisfitResult(Object):
misfits = Array.T(
shape=(None, 2),
dtype=num.float)
class MisfitConfig(Object):
pass
@has_get_plot_classes
class MisfitTarget(Object):
manual_weight = Float.T(
default=1.0,
help='Relative weight of this target')
analyser_results = Dict.T(
gf.StringID.T(),
AnalyserResult.T(),
help='Dictionary of analyser results')
normalisation_family = gf.StringID.T(
optional=True,
help='Normalisation family of this misfit target')
path = gf.StringID.T(
help='A path identifier used for plotting')
misfit_config = MisfitConfig.T(
default=MisfitConfig.D(),
help='Misfit configuration')
bootstrap_weights = Array.T(
dtype=num.float,
serialize_as='base64',
optional=True)
bootstrap_residuals = Array.T(
dtype=num.float,
serialize_as='base64',
optional=True)
can_bootstrap_weights = False
can_bootstrap_residuals = False
plot_misfits_cumulative = True
def __init__(self, **kwargs):
Object.__init__(self, **kwargs)
self.parameters = []
self._ds = None
self._result_mode = 'sparse'
self._combined_weight = None
self._target_parameters = None
self._target_ranges = None
self._combined_weight = None
@classmethod
def get_plot_classes(cls):
return []
def set_dataset(self, ds):
self._ds = ds
def get_dataset(self):
return self._ds
def string_id(self):
return str(self.path)
def misfits_string_ids(self):
raise NotImplementedError('%s does not implement misfits_string_id'
% self.__class__.__name__)
@property
def nmisfits(self):
return 1
def noise_weight_matrix(self):
return num.array([[1]])
@property
def nparameters(self):
if self._target_parameters is None:
return 0
return len(self._target_parameters)
@property
def target_parameters(self):
if self._target_parameters is None:
self._target_parameters = copy.deepcopy(self.parameters)
for p in self._target_parameters:
p.set_groups([self.string_id()])
return self._target_parameters
@property
def target_ranges(self):
return {}
def set_parameter_values(self, model):
for i, p in enumerate(self.parameters):
self.parameter_values[p.name_nogroups] = model[i]
def set_result_mode(self, result_mode):
self._result_mode = result_mode
def post_process(self, engine, source, statics):
raise NotImplementedError()
def get_combined_weight(self):
if self._combined_weight is None:
self._combined_weight = num.ones(1, dtype=num.float)
return self._combined_weight
def get_correlated_weights(self, nthreads=0):
pass
def set_bootstrap_weights(self, weights):
self.bootstrap_weights = weights
def get_bootstrap_weights(self):
if self.bootstrap_weights is None:
raise Exception('Bootstrap weights have not been set!')
nbootstraps = self.bootstrap_weights.size // self.nmisfits
return self.bootstrap_weights.reshape(nbootstraps, self.nmisfits)
def init_bootstrap_residuals(self, nbootstrap, rstate=None, nthreads=0):
raise NotImplementedError()
def set_bootstrap_residuals(self, residuals):
self.bootstrap_residuals = residuals
def get_bootstrap_residuals(self):
if self.bootstrap_residuals is None:
raise Exception('Bootstrap residuals have not been set!')
nbootstraps = self.bootstrap_residuals.size // self.nmisfits
return self.bootstrap_residuals.reshape(nbootstraps, self.nmisfits)
def prepare_modelling(self, engine, source, targets):
''' Prepare modelling target
This function shall return a list of :class:`pyrocko.gf.Target`
for forward modelling in the :class:`pyrocko.gf.LocalEngine`.
'''
return [self]
def finalize_modelling(
self, engine, source, modelling_targets, modelling_results):
''' Manipulate modelling before misfit calculation
This function can be overloaded interact with the modelling results.
'''
return modelling_results[0]
__all__ = '''
TargetGroup
MisfitTarget
MisfitResult
'''.split()
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