class QSeisConfigFull(QSeisConfig):
time_start = Float.T(default=0.0)
time_reduction_velocity = Float.T(default=0.0)
time_window = Float.T(default=900.0)
source_depth = Float.T(default=10.0)
source_mech = QSeisSourceMech.T(optional=True,
default=QSeisSourceMechMT.D())
receiver_depth = Float.T(default=0.0)
receiver_distances = List.T(Float.T())
nsamples = Int.T(default=256)
gf_sw_source_types = Tuple.T(6, Int.T(), default=(1, 1, 1, 1, 0, 0))
gf_filenames = Tuple.T(6, String.T(), default=qseis_greenf_names)
seismogram_filename = String.T(default='seis')
receiver_azimuths = List.T(Float.T())
earthmodel_1d = gf.meta.Earthmodel1D.T(optional=True)
earthmodel_receiver_1d = gf.meta.Earthmodel1D.T(optional=True)
@staticmethod
def example():
conf = QSeisConfigFull()
conf.receiver_distances = [2000.]
conf.receiver_azimuths = [0.]
conf.time_start = -10.0
conf.time_reduction_velocity = 15.0
conf.earthmodel_1d = cake.load_model().extract(depth_max='cmb')
conf.earthmodel_receiver_1d = None
conf.sw_flat_earth_transform = 1
return conf
def get_output_filenames(self, rundir):
return [
pjoin(rundir, self.seismogram_filename + '.t' + c)
for c in qseis_components
]
def get_output_filenames_gf(self, rundir):
return [
pjoin(rundir, fn + '.t' + c) for fn in self.gf_filenames
for c in qseis_components
]
def string_for_config(self):
def aggregate(l):
return len(l), '\n'.join([''] + [x.string_for_config() for x in l])
assert len(self.receiver_distances) > 0
assert len(self.receiver_distances) == len(self.receiver_azimuths)
assert self.earthmodel_1d is not None
d = self.__dict__.copy()
# fixing these switches here to reduce the amount of wrapper code
d['sw_distance_unit'] = 1 # always give distances in [km]
d['sw_t_reduce'] = 1 # time reduction always as velocity [km/s]
d['sw_equidistant'] = 0 # always give all distances and azimuths
d['sw_irregular_azimuths'] = 1
d['n_distances'] = len(self.receiver_distances)
d['str_distances'] = str_float_vals(self.receiver_distances)
d['str_azimuths'] = str_float_vals(self.receiver_azimuths)
model_str, nlines = cake_model_to_config(self.earthmodel_1d)
d['n_model_lines'] = nlines
d['model_lines'] = model_str
if self.earthmodel_receiver_1d:
model_str, nlines = cake_model_to_config(
self.earthmodel_receiver_1d)
else:
model_str = "# no receiver side model"
nlines = 0
d['n_model_receiver_lines'] = nlines
d['model_receiver_lines'] = model_str
d['str_slowness_window'] = str_float_vals(self.slowness_window)
d['n_depth_ranges'], d['str_depth_ranges'] = \
aggregate(self.propagation_filters)
if self.wavelet_type == 0: # user wavelet
d['str_w_samples'] = '\n' \
+ '%i\n' % len(self.user_wavelet_samples) \
+ str_float_vals(self.user_wavelet_samples)
else:
d['str_w_samples'] = ''
if self.receiver_filter:
d['str_receiver_filter'] = self.receiver_filter.string_for_config(
self.qseis_version)
else:
if self.qseis_version == '2006a':
d['str_receiver_filter'] = '(1.0,0.0)\n0\n#\n0'
else:
d['str_receiver_filter'] = '1.0\n0\n#\n0'
d['str_gf_sw_source_types'] = str_int_vals(self.gf_sw_source_types)
d['str_gf_filenames'] = str_str_vals(self.gf_filenames)
if self.source_mech:
d['str_source'] = '%s \'%s\'' % (
self.source_mech.string_for_config(), self.seismogram_filename)
else:
d['str_source'] = '0'
template = '''# autogenerated QSEIS input by qseis.py
#
# This is the input file of FORTRAN77 program "qseis06" for calculation of
# synthetic seismograms based on a layered halfspace earth model.
#
# by
# Rongjiang Wang <*****@*****.**>
# GeoForschungsZentrum Potsdam
# Telegrafenberg, D-14473 Potsdam, Germany
#
# Last modified: Potsdam, Nov., 2006
#
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
# If not specified, SI Unit System is used overall!
#
# Coordinate systems:
# cylindrical (z,r,t) with z = downward,
# r = from source outward,
# t = azmuth angle from north to east;
# cartesian (x,y,z) with x = north,
# y = east,
# z = downward;
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
#
# SOURCE PARAMETERS
# =================
# 1. source depth [km]
#------------------------------------------------------------------------------
%(source_depth)e |dble: source_depth;
#------------------------------------------------------------------------------
#
# RECEIVER PARAMETERS
# ===================
# 1. receiver depth [km]
# 2. switch for distance sampling role (1/0 = equidistant/irregular); switch
# for unit used (1/0 = km/deg)
# 3. number of distance samples
# 4. if equidistant, then start and end trace distance (> 0); else distance
# list (please order the receiver distances from small to large)
# 5. (reduced) time begin [sec] & length of time window [sec], number of time
# samples (<= 2*nfmax in qsglobal.h)
# 6. switch for unit of the following time reduction parameter: 1 = velocity
# [km/sec], 0 = slowness [sec/deg]; time reduction parameter
#------------------------------------------------------------------------------
%(receiver_depth)e |dble: receiver_depth;
%(sw_equidistant)i %(sw_distance_unit)i |int: sw_equidistant, sw_d_unit;
%(n_distances)i |int: no_distances;
%(str_distances)s |dble: d_1,d_n; or d_1,d_2, ...(no comments in between!);
%(time_start)e %(time_window)e %(nsamples)i |dble: t_start,t_window; int: no_t_samples;
%(sw_t_reduce)i %(time_reduction_velocity)e |int: sw_t_reduce; dble: t_reduce;
#------------------------------------------------------------------------------
#
# WAVENUMBER INTEGRATION PARAMETERS
# =================================
# 1. select slowness integration algorithm (0 = suggested for full wave-field
# modelling; 1 or 2 = suggested when using a slowness window with narrow
# taper range - a technique for suppressing space-domain aliasing);
# 2. 4 parameters for low and high slowness (Note 1) cut-offs [s/km] with
# tapering: 0 < slw1 < slw2 defining cosine taper at the lower end, and 0 <
# slw3 < slw4 defining the cosine taper at the higher end. default values
# will be used in case of inconsistent input of the cut-offs (possibly with
# much more computational effort);
# 3. parameter for sampling rate of the wavenumber integration (1 = sampled
# with the spatial Nyquist frequency, 2 = sampled with twice higher than
# the Nyquist, and so on: the larger this parameter, the smaller the space-
# domain aliasing effect, but also the more computation effort);
# 4. the factor for suppressing time domain aliasing (> 0 and <= 1) (Note 2).
#------------------------------------------------------------------------------
%(sw_algorithm)i |int: sw_algorithm;
%(str_slowness_window)s |dble: slw(1-4);
%(wavenumber_sampling)e |dble: sample_rate;
%(aliasing_suppression_factor)e |dble: supp_factor;
#------------------------------------------------------------------------------
#
# OPTIONS FOR PARTIAL SOLUTIONS
# (only applied to the source-site structure)
# ===========================================
#
# 1. switch for filtering free surface effects (0 = with free surface, i.e.,
# do not select this filter; 1 = without free surface; 2 = without free
# surface but with correction on amplitude and wave form. Note switch 2
# can only be used for receivers at the surface)
# 2. switch for filtering waves with a shallow penetration depth (concerning
# their whole trace from source to receiver), penetration depth limit [km]
#
# if this option is selected, waves whose travel path never exceeds the
# given depth limit will be filtered ("seismic nuting"). the condition for
# selecting this filter is that the given shallow path depth limit should
# be larger than both source and receiver depth.
#
# 3. number of depth ranges where the following selected up/down-sp2oing P or
# SV waves should be filtered
# 4. the 1. depth range: upper and lower depth [km], switch for filtering P
# or SV wave in this depth range:
#
# switch no: 1 2 3 4 other
# filtered phase: P(up) P(down) SV(up) SV(down) Error
#
# 5. the 2. ...
#
# The partial solution options are useful tools to increase the numerical
# significance of desired wave phases. Especially when the desired phases
# are smaller than the undesired phases, these options should be selected
# and carefully combined.
#------------------------------------------------------------------------------
%(filter_surface_effects)i |int: isurf;
%(filter_shallow_paths)i %(filter_shallow_paths_depth)e |int: sw_path_filter; dble:shallow_depth_limit;
%(n_depth_ranges)i %(str_depth_ranges)s
#------------------------------------------------------------------------------
#
# SOURCE TIME FUNCTION (WAVELET) PARAMETERS (Note 3)
# ==================================================
# 1. wavelet duration [unit = time sample rather than sec!], that is about
# equal to the half-amplitude cut-off period of the wavelet (> 0. if <= 0,
# then default value = 2 time samples will be used), and switch for the
# wavelet form (0 = user's own wavelet; 1 = default wavelet: normalized
# square half-sinusoid for simulating a physical delta impulse; 2 = tapered
# Heaviside wavelet, i.e. integral of wavelet 1)
# 2. IF user's own wavelet is selected, then number of the wavelet time samples
# (<= 1024), and followed by
# 3. equidistant wavelet time samples
# 4 ...(continue) (! no comment lines allowed between the time sample list!)
# IF default, delete line 2, 3, 4 ... or comment them out!
#------------------------------------------------------------------------------
%(wavelet_duration_samples)e %(wavelet_type)i%(str_w_samples)s
#------------------------------------------------------------------------------
#
# FILTER PARAMETERS OF RECEIVERS (SEISMOMETERS OR HYDROPHONES)
# ============================================================
# 1. constant coefficient (normalization factor)
# 2. number of roots (<= nrootmax in qsglobal.h)
# 3. list of the root positions in the complex format (Re,Im). If no roots,
# comment out this line
# 4. number of poles (<= npolemax in qsglobal.h)
# 5. list of the pole positions in the complex format (Re,Im). If no poles,
# comment out this line
#------------------------------------------------------------------------------
%(str_receiver_filter)s
#------------------------------------------------------------------------------
#
# OUTPUT FILES FOR GREEN'S FUNCTIONS (Note 4)
# ===========================================
# 1. selections of source types (yes/no = 1/0)
# 2. file names of Green's functions (please give the names without extensions,
# which will be appended by the program automatically: *.tz, *.tr, *.tt
# and *.tv are for the vertical, radial, tangential, and volume change (for
# hydrophones) components, respectively)
#------------------------------------------------------------------------------
# explosion strike-slip dip-slip clvd single_f_v single_f_h
#------------------------------------------------------------------------------
%(str_gf_sw_source_types)s
%(str_gf_filenames)s
#------------------------------------------------------------------------------
# OUTPUT FILES FOR AN ARBITRARY POINT DISLOCATION SOURCE
# (for applications to earthquakes)
# ======================================================
# 1. selection (0 = not selected; 1 or 2 = selected), if (selection = 1), then
# the 6 moment tensor elements [N*m]: Mxx, Myy, Mzz, Mxy, Myz, Mzx (x is
# northward, y is eastward and z is downard); else if (selection = 2), then
# Mis [N*m] = isotropic moment part = (MT+MN+MP)/3, Mcl = CLVD moment part
# = (2/3)(MT+MP-2*MN), Mdc = double-couple moment part = MT-MN, Strike [deg],
# Dip [deg] and Rake [deg].
#
# Note: to use this option, the Green's functions above should be computed
# (selection = 1) if they do not exist already.
#
# north(x)
# /
# /\ strike
# *-----------------------> east(y)
# |\ \
# |-\ \
# | \ fault plane \
# |90 \ \
# |-dip\ \
# | \ \
# | \ \
# downward(z) \-----------------------\\
#
# 2. switch for azimuth distribution of the stations (0 = uniform azimuth,
# else = irregular azimuth angles)
# 3. list of the azimuth angles [deg] for all stations given above (if the
# uniform azimuth is selected, then only one azimuth angle is required)
#
#------------------------------------------------------------------------------
# Mis Mcl Mdc Strike Dip Rake File
#------------------------------------------------------------------------------
# 2 0.00 1.00 6.0E+19 120.0 30.0 25.0 'seis'
#------------------------------------------------------------------------------
# Mxx Myy Mzz Mxy Myz Mzx File
#------------------------------------------------------------------------------
%(str_source)s
%(sw_irregular_azimuths)i
%(str_azimuths)s
#------------------------------------------------------------------------------
#
# GLOBAL MODEL PARAMETERS (Note 5)
# ================================
# 1. switch for flat-earth-transform
# 2. gradient resolution [%%] of vp, vs, and ro (density), if <= 0, then default
# values (depending on wave length at cut-off frequency) will be used
#------------------------------------------------------------------------------
%(sw_flat_earth_transform)i |int: sw_flat_earth_transform;
%(gradient_resolution_vp)e %(gradient_resolution_vs)e %(gradient_resolution_density)e |dble: vp_res, vs_res, ro_res;
#------------------------------------------------------------------------------
#
# LAYERED EARTH MODEL
# (SHALLOW SOURCE + UNIFORM DEEP SOURCE/RECEIVER STRUCTURE)
# =========================================================
# 1. number of data lines of the layered model (source site)
#------------------------------------------------------------------------------
%(n_model_lines)i |int: no_model_lines;
#------------------------------------------------------------------------------
#
# MULTILAYERED MODEL PARAMETERS (source site)
# ===========================================
# no depth[km] vp[km/s] vs[km/s] ro[g/cm^3] qp qs
#------------------------------------------------------------------------------
%(model_lines)s
#------------------------------------------------------------------------------
#
# LAYERED EARTH MODEL
# (ONLY THE SHALLOW RECEIVER STRUCTURE)
# =====================================
# 1. number of data lines of the layered model
#
# Note: if the number = 0, then the receiver site is the same as the
# source site, else different receiver-site structure is considered.
# please be sure that the lowest interface of the receiver-site
# structure given given below can be found within the source-site
# structure, too.
#
#------------------------------------------------------------------------------
%(n_model_receiver_lines)i |int: no_model_lines;
#------------------------------------------------------------------------------
#
# MULTILAYERED MODEL PARAMETERS (shallow receiver-site structure)
# ===============================================================
# no depth[km] vp[km/s] vs[km/s] ro[g/cm^3] qp qs
#------------------------------------------------------------------------------
%(model_receiver_lines)s
#---------------------------------end of all inputs----------------------------
Note 1:
The slowness is defined by inverse value of apparent wave velocity = sin(i)/v
with i = incident angle and v = true wave velocity.
Note 2:
The suppression of the time domain aliasing is achieved by using the complex
frequency technique. The suppression factor should be a value between 0 and 1.
If this factor is set to 0.1, for example, the aliasing phase at the reduced
time begin is suppressed to 10%%.
Note 3:
The default basic wavelet function (option 1) is (2/tau)*sin^2(pi*t/tau),
for 0 < t < tau, simulating physical delta impuls. Its half-amplitude cut-off
frequency is 1/tau. To avoid high-frequency noise, tau should not be smaller
than 4-5 time samples.
Note 4:
Double-Couple m11/ m22/ m33/ m12/ m23/ m31 Azimuth_Factor_(tz,tr,tv)/(tt)
============================================================================
explosion 1.0/ 1.0/ 1.0/ -- / -- / -- 1.0 / 0.0
strike-slip -- / -- / -- / 1.0/ -- / -- sin(2*azi) / cos(2*azi)
1.0/-1.0/ -- / -- / -- / -- cos(2*azi) / -sin(2*azi)
dip-slip -- / -- / -- / -- / -- / 1.0 cos(azi) / sin(azi)
-- / -- / -- / -- / 1.0/ -- sin(azi) / -cos(azi)
clvd -0.5/-0.5/ 1.0/ -- / -- / -- 1.0 / 0.0
============================================================================
Single-Force fx / fy / fz Azimuth_Factor_(tz,tr,tv)/(tt)
============================================================================
fz -- / -- / 1.0 1.0 / 0.0
fx 1.0/ -- / -- cos(azi) / sin(azi)
fy -- / 1.0/ -- sin(azi) / -cos(azi)
============================================================================
Note 5:
Layers with a constant gradient will be discretized with a number of homogeneous
sublayers. The gradient resolutions are then used to determine the maximum
allowed thickness of the sublayers. If the resolutions of Vp, Vs and Rho
(density) require different thicknesses, the smallest is first chosen. If this
is even smaller than 1%% of the characteristic wavelength, then the latter is
taken finally for the sublayer thickness.
''' # noqa
return (template % d).encode('ascii')
class Parameter(Object):
name__ = String.T()
unit = Unicode.T(optional=True)
scale_factor = Float.T(default=1., optional=True)
scale_unit = Unicode.T(optional=True)
label = Unicode.T(optional=True)
optional = Bool.T(default=True, optional=True)
def __init__(self, *args, **kwargs):
if len(args) >= 1:
kwargs['name'] = args[0]
if len(args) >= 2:
kwargs['unit'] = newstr(args[1])
self.groups = [None]
self._name = None
Object.__init__(self, **kwargs)
def get_label(self, with_unit=True):
lbl = [self.label or self.name]
if with_unit:
unit = self.get_unit_label()
if unit:
lbl.append('[%s]' % unit)
return ' '.join(lbl)
def set_groups(self, groups):
if not isinstance(groups, list):
raise AttributeError('Groups must be a list of strings.')
self.groups = groups
def _get_name(self):
if None not in self.groups:
return '%s.%s' % ('.'.join(self.groups), self._name)
return self._name
def _set_name(self, value):
self._name = value
name = property(_get_name, _set_name)
@property
def name_nogroups(self):
return self._name
def get_value_label(self, value, format='%(value)g%(unit)s'):
value = self.scaled(value)
unit = self.get_unit_suffix()
return format % dict(value=value, unit=unit)
def get_unit_label(self):
if self.scale_unit is not None:
return self.scale_unit
elif self.unit:
return self.unit
else:
return None
def get_unit_suffix(self):
unit = self.get_unit_label()
if not unit:
return ''
else:
return ' %s' % unit
def scaled(self, x):
if isinstance(x, tuple):
return tuple(v / self.scale_factor for v in x)
if isinstance(x, list):
return list(v / self.scale_factor for v in x)
else:
return x / self.scale_factor
def inv_scaled(self, x):
if isinstance(x, tuple):
return tuple(v * self.scale_factor for v in x)
if isinstance(x, list):
return list(v * self.scale_factor for v in x)
else:
return x * self.scale_factor
class GNSSCampaignMisfitTarget(gf.GNSSCampaignTarget, MisfitTarget):
"""Handles and carries out operations related to the objective functions.
The objective function is here the weighted misfit between observed
and predicted surface displacements.
"""
campaign_name = String.T()
misfit_config = GNSSCampaignMisfitConfig.T()
can_bootstrap_weights = True
can_bootstrap_residuals = True
plot_misfits_cumulative = False
def __init__(self, **kwargs):
gf.GNSSCampaignTarget.__init__(self, **kwargs)
MisfitTarget.__init__(self, **kwargs)
self._obs_data = None
self._sigma = None
self._weights = None
self._station_component_mask = None
@property
def id(self):
return self.campaign_name
def string_id(self):
return self.campaign_name
def misfits_string_ids(self):
return [
'%s.%s' % (self.path, station.code)
for station in self.campaign.stations
]
@property
def station_names(self):
return ['%s' % (station.code) for station in self.campaign.stations]
@property
def nmisfits(self):
return self.lats.size
@property
def nstations(self):
return self.nmisfits
def set_dataset(self, ds):
MisfitTarget.set_dataset(self, ds)
@property
def campaign(self):
return self._ds.get_gnss_campaign(self.campaign_name)
@property
def obs_data(self):
if self._obs_data is None:
self._obs_data = num.concatenate(
[s.get_displacement_data() for s in self.campaign.stations])
return self._obs_data
@property
def obs_sigma(self):
if self._sigma is None:
self._sigma = num.array([
[s.north.sigma for s in self.campaign.stations],
[s.east.sigma for s in self.campaign.stations],
[s.up.sigma for s in self.campaign.stations]])\
.ravel(order='F')
return self._sigma
@property
def weights(self):
"""Weights are the inverse of the data error variance-covariance.
The single component variances, and if provided the component
covariances, are used to build a data variance matrix or
variance-covariance matrix. Correlations between stations are
not implemented.
"""
if self._weights is None:
covar = self.campaign.get_covariance_matrix()
if not num.any(covar.diagonal()):
logger.warning('GNSS Stations have an empty covariance matrix.'
' Weights will be all equal.')
num.fill_diagonal(covar, 1.)
self._weights = num.asmatrix(covar).I
return self._weights
@property
def station_component_mask(self):
if self._station_component_mask is None:
self._station_component_mask = self.campaign.get_component_mask()
return self._station_component_mask
@property
def station_weights(self):
weights = num.diag(self.weights)
return num.mean([weights[0::3], weights[1::3], weights[2::3]], axis=0)
def post_process(self, engine, source, statics):
"""Applies the objective function.
As a result the weighted misfits are given and the observed and
synthetic data.
"""
obs = self.obs_data
weights = self.weights
nstations = self.campaign.nstations
misfit_value = num.zeros(num.shape(self.station_component_mask))
misfit_norm = num.zeros(num.shape(self.station_component_mask))
# All data is ordered in vectors as
# S1_n, S1_e, S1_u, ..., Sn_n, Sn_e, Sn_u. Hence (.ravel(order='F'))
syn = num.array([
statics['displacement.n'],
statics['displacement.e'],
-statics['displacement.d']])\
.ravel(order='F')
syn = syn[self.station_component_mask]
res = num.abs(obs - syn)
misfit_value0 = res * weights
misfit_norm0 = obs * weights
i_truecomponents = num.where(self.station_component_mask)
misfit_value[i_truecomponents] = misfit_value0
misfit_norm[i_truecomponents] = misfit_norm0
misfit_value = num.sum(misfit_value.reshape((nstations, 3)), axis=1)
misfit_norm = num.sum(misfit_norm.reshape((nstations, 3)), axis=1)
misfit_value = misfit_value.reshape(nstations, 1)
misfit_norm = misfit_norm.reshape(nstations, 1)
mf = num.hstack((misfit_value, misfit_norm))
result = GNSSCampaignMisfitResult(misfits=mf)
if self._result_mode == 'full':
result.statics_syn = statics
result.statics_obs = obs
return result
def get_combined_weight(self):
"""A given manual weight in the configuration is applied."""
if self._combined_weight is None:
self._combined_weight = num.full(self.nmisfits, self.manual_weight)
return self._combined_weight
def prepare_modelling(self, engine, source, targets):
return [self]
def init_bootstrap_residuals(self, nbootstraps, rstate=None):
logger.info('GNSS campaign %s, bootstrapping residuals'
' from measurement uncertainties ...' % self.campaign.name)
if rstate is None:
rstate = num.random.RandomState()
campaign = self.campaign
bootstraps = num.empty((nbootstraps, campaign.nstations))
sigmas = num.array([])
for s in campaign.stations:
if s.north:
sigmas = num.hstack((sigmas, s.north.sigma))
if s.east:
sigmas = num.hstack((sigmas, s.east.sigma))
if s.up:
sigmas = num.hstack((sigmas, s.up.sigma))
#sigmas = num.array([(s.north.sigma, s.east.sigma, s.up.sigma)
# for s in campaign.stations])
sigmas = num.abs(sigmas)
if not num.all(sigmas):
logger.warning('Bootstrapping GNSS stations is meaningless,'
' all station\'s sigma are 0.0!')
for ibs in range(nbootstraps):
syn_noise = num.zeros(num.shape(self.station_component_mask))
i_truecomponents = num.where(self.station_component_mask)
syn_noise0 = rstate.normal(scale=sigmas.ravel())
syn_noise[i_truecomponents] = syn_noise0
syn_noise = syn_noise.reshape(campaign.nstations, 3) \
.sum(axis=1)
bootstraps[ibs, :] = syn_noise
self.set_bootstrap_residuals(bootstraps)
def finalize_modelling(self, engine, source, modelling_targets,
modelling_results):
return modelling_results[0]
@classmethod
def get_plot_classes(cls):
from . import plot
plots = super(GNSSCampaignMisfitTarget, cls).get_plot_classes()
plots.extend(plot.get_plot_classes())
return plots
class Response(Object):
resource_id = String.T(optional=True, xmlstyle='attribute')
instrument_sensitivity = Sensitivity.T(optional=True,
xmltagname='InstrumentSensitivity')
instrument_polynomial = Polynomial.T(optional=True,
xmltagname='InstrumentPolynomial')
stage_list = List.T(ResponseStage.T(xmltagname='Stage'))
def get_pyrocko_response(self, nslc, fake_input_units=None):
responses = []
for stage in self.stage_list:
responses.extend(stage.get_pyrocko_response(nslc))
if not self.stage_list and self.instrument_sensitivity:
responses.append(
trace.PoleZeroResponse(
constant=self.instrument_sensitivity.value))
if fake_input_units is not None:
if not self.instrument_sensitivity or \
self.instrument_sensitivity.input_units is None:
raise NoResponseInformation('no input units given')
input_units = self.instrument_sensitivity.input_units.name
try:
conresp = conversion[
fake_input_units.upper(), input_units.upper()]
except KeyError:
raise NoResponseInformation(
'cannot convert between units: %s, %s'
% (fake_input_units, input_units))
if conresp is not None:
responses.append(conresp)
return trace.MultiplyResponse(responses)
@classmethod
def from_pyrocko_pz_response(cls, presponse, input_unit, output_unit,
normalization_frequency=1.0):
norm_factor = 1.0/float(abs(
presponse.evaluate(num.array([normalization_frequency]))[0]
/ presponse.constant))
pzs = PolesZeros(
pz_transfer_function_type='LAPLACE (RADIANS/SECOND)',
normalization_factor=norm_factor,
normalization_frequency=Frequency(normalization_frequency),
zero_list=[PoleZero(real=FloatNoUnit(z.real),
imaginary=FloatNoUnit(z.imag))
for z in presponse.zeros],
pole_list=[PoleZero(real=FloatNoUnit(z.real),
imaginary=FloatNoUnit(z.imag))
for z in presponse.poles])
pzs.validate()
stage = ResponseStage(
number=1,
poles_zeros_list=[pzs],
stage_gain=Gain(float(abs(presponse.constant))/norm_factor))
resp = Response(
instrument_sensitivity=Sensitivity(
value=stage.stage_gain.value,
input_units=Units(input_unit),
output_units=Units(output_unit)),
stage_list=[stage])
return resp
class Event(Object):
'''Seismic event representation
:param lat: latitude of hypocenter (default 0.0)
:param lon: longitude of hypocenter (default 0.0)
:param time: origin time as float in seconds after '1970-01-01 00:00:00
:param name: event identifier as string (optional)
:param depth: source depth (optional)
:param magnitude: magnitude of event (optional)
:param region: source region (optional)
:param catalog: name of catalog that lists this event (optional)
:param moment_tensor: moment tensor as
:py:class:`moment_tensor.MomentTensor` instance (optional)
:param duration: source duration as float (optional)
'''
lat = Float.T(default=0.0)
lon = Float.T(default=0.0)
time = Timestamp.T(default=util.str_to_time('1970-01-01 00:00:00'))
name = String.T(default='', optional=True)
depth = Float.T(optional=True)
magnitude = Float.T(optional=True)
magnitude_type = String.T(optional=True)
region = Unicode.T(optional=True)
catalog = String.T(optional=True)
moment_tensor = moment_tensor.MomentTensor.T(optional=True)
duration = Float.T(optional=True)
def __init__(self,
lat=0.,
lon=0.,
time=0.,
name='',
depth=None,
magnitude=None,
magnitude_type=None,
region=None,
load=None,
loadf=None,
catalog=None,
moment_tensor=None,
duration=None):
vals = None
if load is not None:
vals = Event.oldload(load)
elif loadf is not None:
vals = Event.oldloadf(loadf)
if vals:
lat, lon, time, name, depth, magnitude, magnitude_type, region, \
catalog, moment_tensor, duration = vals
Object.__init__(self,
lat=lat,
lon=lon,
time=time,
name=name,
depth=depth,
magnitude=magnitude,
magnitude_type=magnitude_type,
region=region,
catalog=catalog,
moment_tensor=moment_tensor,
duration=duration)
def time_as_string(self):
return util.time_to_str(self.time)
def set_name(self, name):
self.name = name
def olddump(self, filename):
file = open(filename, 'w')
self.olddumpf(file)
file.close()
def olddumpf(self, file):
file.write('name = %s\n' % self.name)
file.write('time = %s\n' % util.time_to_str(self.time))
if self.lat is not None:
file.write('latitude = %.12g\n' % self.lat)
if self.lon is not None:
file.write('longitude = %.12g\n' % self.lon)
if self.magnitude is not None:
file.write('magnitude = %g\n' % self.magnitude)
file.write('moment = %g\n' %
moment_tensor.magnitude_to_moment(self.magnitude))
if self.magnitude_type is not None:
file.write('magnitude_type = %s\n' % self.magnitude_type)
if self.depth is not None:
file.write('depth = %.10g\n' % self.depth)
if self.region is not None:
file.write('region = %s\n' % self.region)
if self.catalog is not None:
file.write('catalog = %s\n' % self.catalog)
if self.moment_tensor is not None:
m = self.moment_tensor.m()
sdr1, sdr2 = self.moment_tensor.both_strike_dip_rake()
file.write(
('mnn = %g\nmee = %g\nmdd = %g\nmne = %g\nmnd = %g\nmed = %g\n'
'strike1 = %g\ndip1 = %g\nrake1 = %g\n'
'strike2 = %g\ndip2 = %g\nrake2 = %g\n') %
((m[0, 0], m[1, 1], m[2, 2], m[0, 1], m[0, 2], m[1, 2]) +
sdr1 + sdr2))
if self.duration is not None:
file.write('duration = %g\n' % self.duration)
@staticmethod
def unique(events,
deltat=10.,
group_cmp=(lambda a, b: cmp(a.catalog, b.catalog))):
groups = Event.grouped(events, deltat)
events = []
for group in groups:
if group:
group.sort(group_cmp)
events.append(group[-1])
return events
@staticmethod
def grouped(events, deltat=10.):
events = list(events)
groups = []
for ia, a in enumerate(events):
groups.append([])
haveit = False
for ib, b in enumerate(events[:ia]):
if abs(b.time - a.time) < deltat:
groups[ib].append(a)
haveit = True
break
if not haveit:
groups[ia].append(a)
groups = [g for g in groups if g]
groups.sort(key=lambda g: sum(e.time for e in g) // len(g))
return groups
@staticmethod
def dump_catalog(events, filename=None, stream=None):
if filename is not None:
file = open(filename, 'w')
else:
file = stream
try:
i = 0
for ev in events:
ev.olddumpf(file)
file.write('--------------------------------------------\n')
i += 1
finally:
if filename is not None:
file.close()
@staticmethod
def oldload(filename):
with open(filename, 'r') as file:
return Event.oldloadf(file)
@staticmethod
def oldloadf(file):
d = {}
try:
for line in file:
if line.lstrip().startswith('#'):
continue
toks = line.split(' = ', 1)
if len(toks) == 2:
k, v = toks[0].strip(), toks[1].strip()
if k in ('name', 'region', 'catalog', 'magnitude_type'):
d[k] = v
if k in (('latitude longitude magnitude depth duration '
'mnn mee mdd mne mnd med strike1 dip1 rake1 '
'strike2 dip2 rake2 duration').split()):
d[k] = float(v)
if k == 'time':
d[k] = util.str_to_time(v)
if line.startswith('---'):
d['have_separator'] = True
break
except Exception as e:
raise FileParseError(e)
if not d:
raise EOF()
if 'have_separator' in d and len(d) == 1:
raise EmptyEvent()
mt = None
m6 = [d[x] for x in 'mnn mee mdd mne mnd med'.split() if x in d]
if len(m6) == 6:
mt = moment_tensor.MomentTensor(m=moment_tensor.symmat6(*m6))
else:
sdr = [d[x] for x in 'strike1 dip1 rake1'.split() if x in d]
if len(sdr) == 3:
moment = 1.0
if 'moment' in d:
moment = d['moment']
elif 'magnitude' in d:
moment = moment_tensor.magnitude_to_moment(d['magnitude'])
mt = moment_tensor.MomentTensor(strike=sdr[0],
dip=sdr[1],
rake=sdr[2],
scalar_moment=moment)
return (d.get('latitude', 0.0), d.get('longitude', 0.0),
d.get('time', 0.0), d.get('name', ''), d.get('depth', None),
d.get('magnitude', None), d.get('magnitude_type',
None), d.get('region', None),
d.get('catalog', None), mt, d.get('duration', None))
@staticmethod
def load_catalog(filename):
file = open(filename, 'r')
try:
while True:
try:
ev = Event(loadf=file)
yield ev
except EmptyEvent:
pass
except EOF:
pass
file.close()
def get_hash(self):
e = self
if isinstance(e.time, util.hpfloat):
stime = util.time_to_str(e.time, format='%Y-%m-%d %H:%M:%S.6FRAC')
else:
stime = util.time_to_str(e.time, format='%Y-%m-%d %H:%M:%S.3FRAC')
s = float_or_none_to_str
return ehash(', '.join(
(stime, s(e.lat), s(e.lon), s(e.depth), s(e.magnitude),
str(e.catalog), str(e.name), str(e.region))))
def human_str(self):
s = [
'Latitude [deg]: %g' % self.lat,
'Longitude [deg]: %g' % self.lon,
'Time [UTC]: %s' % util.time_to_str(self.time)
]
if self.name:
s.append('Name: %s' % self.name)
if self.depth is not None:
s.append('Depth [km]: %g' % (self.depth / 1000.))
if self.magnitude is not None:
s.append('Magnitude [%s]: %3.1f' %
(self.magnitude_type or 'M?', self.magnitude))
if self.region:
s.append('Region: %s' % self.region)
if self.catalog:
s.append('Catalog: %s' % self.catalog)
if self.moment_tensor:
s.append(str(self.moment_tensor))
return '\n'.join(s)
class Frequency(FloatWithUnit):
unit = String.T(default='HERTZ', optional=True, xmlstyle='attribute')
class Longitude(FloatWithUnit):
'''Type for longitude coordinate.'''
unit = String.T(default='DEGREES', optional=True, xmlstyle='attribute')
# fixed unit
datum = String.T(default='WGS84', optional=True, xmlstyle='attribute')
class SeismosizerData(DataGenerator):
fn_sources = String.T(
help='filename containing pyrocko.gf.seismosizer.Source instances')
fn_targets = String.T(
help='filename containing pyrocko.gf.seismosizer.Target instances',
optional=True)
fn_stations = String.T(
help='filename containing pyrocko.model.Station instances. Will be\
converted to Target instances',
optional=True)
store_id = String.T(optional=True)
center_sources = Bool.T(
default=False,
help='Transform the center of sources to the center of stations')
engine = LocalEngine.T()
onset_phase = String.T(default='first(p|P)')
def setup(self):
self.sources = guts.load(filename=self.fn_sources)
self.targets = []
if self.fn_targets:
self.targets.extend(guts.load(filename=self.fn_targets))
if self.fn_stations:
stats = load_stations(self.fn_stations)
self.targets.extend(self.cast_stations_to_targets(stats))
if self.store_id:
for t in self.targets:
t.store_id = self.store_id
if self.center_sources:
self.move_sources_to_station_center()
self.config.channels = [t.codes for t in self.targets]
store_ids = [t.store_id for t in self.targets]
store_id = set(store_ids)
assert len(store_id) == 1, 'More than one store used. Not \
implemented yet'
self.store = self.engine.get_store(store_id.pop())
self.sources = filter_oob(self.sources, self.targets, self.store.config)
dt = self.config.deltat_want or self.store.config.deltat
self.n_samples = int((self.config.sample_length + self.config.tpad) / dt)
def move_sources_to_station_center(self):
'''Transform the center of sources to the center of stations.'''
lat, lon = orthodrome.geographic_midpoint_locations(self.targets)
for s in self.sources:
s.lat = lat
s.lon = lon
def cast_stations_to_targets(self, stations):
targets = []
channels = 'ENZ'
for s in stations:
targets.extend(
[Target(codes=(s.network, s.station, s.location, c),
lat=s.lat, lon=s.lon, elevation=s.elevation,) for c in
channels])
return targets
def extract_labels(self, source):
if not self.labeled:
return UNLABELED
return (source.north_shift, source.east_shift, source.depth)
def iter_examples_and_labels(self):
ensure_list(self.sources)
ensure_list(self.targets)
response = self.engine.process(
sources=self.sources,
targets=self.targets)
for isource, source in enumerate(response.request.sources):
traces = [x.trace.pyrocko_trace() for x in \
response.results_list[isource]]
for tr in traces:
self.preprocess(tr)
arrivals = [self.store.t(self.onset_phase,
(source.depth, source.distance_to(t))) for t in self.targets]
tref = min([a for a in arrivals if a is not None])
chunk = self.get_raw_data_chunk(self.tensor_shape)
self.fit_data_into_chunk(traces, chunk=chunk, tref=tref+source.time)
label = self.extract_labels(source)
yield chunk, label
class TestResult(Object):
package = String.T()
branch = String.T(optional=True)
box = String.T()
py_version = String.T(optional=True)
prerequisite_versions = Dict.T(String.T(),
String.T(),
optional=True,
default={})
log = String.T(optional=True, yamlstyle='|')
result = String.T(optional=True)
errors = List.T(String.T(), optional=True, default=[], yamlstyle='block')
fails = List.T(String.T(), optional=True, default=[], yamlstyle='block')
skips = List.T(String.T(), optional=True, default=[], yamlstyle='block')
class DataGeneratorBase(Object):
'''This is the base class for all generators.
This class to dump and load data to/from all subclasses into
TFRecordDatasets.
'''
fn_tfrecord = String.T(optional=True)
noise = Noise.T(optional=True, help='Add noise to feature')
station_dropout_rate = Float.T(default=0.,
help='Rate by which to mask all channels of station')
station_dropout_distribution = Bool.T(default=True,
help='If *true*, station dropout will be drawn from a uniform '
'distribution limited by this station_dropout.')
nmax = Int.T(optional=True)
labeled = Bool.T(default=True)
blacklist = List.T(optional=True, help='List of indices to ignore.')
random_seed = Int.T(default=0)
def __init__(self, *args, **kwargs):
self.config = kwargs.pop('config', None)
super().__init__(**kwargs)
self.blacklist = set() if not self.blacklist else set(self.blacklist)
self.n_classes = self.config.n_classes
self.evolution = 0
def normalize_label(self, label):
if self.labeled:
return self.config.normalize_label(label)
return label
def set_config(self, pinky_config):
self.config = pinky_config
self.setup()
def setup(self):
...
def reset(self):
self.evolution = 0
@property
def tensor_shape(self):
return self.config.tensor_shape
@property
def n_samples(self):
return self.config._n_samples
@n_samples.setter
def n_samples(self, v):
self.config._n_samples = v
@property
@lru_cache(maxsize=1)
def nsl_to_indices(self):
''' Returns a dictionary which maps nsl codes to indexing arrays.'''
indices = OrderedDict()
for nslc, index in self.nslc_to_index.items():
key = nslc[:3]
_v = indices.get(key, [])
_v.append(index)
indices[key] = _v
for k in indices.keys():
indices[k] = num.array(indices[k])
return indices
@property
@lru_cache(maxsize=1)
def nsl_indices(self):
''' Returns a 2D array of indices of channels belonging to one station.'''
return [v for v in self.nsl_to_indices.values()]
@property
def nslc_to_index(self):
''' Returns a dictionary which maps nslc codes to trace indices.'''
d = OrderedDict()
idx = 0
for nslc in self.config.channels:
if not util.match_nslc(self.config.blacklist, nslc):
d[nslc] = idx
idx += 1
return d
def reject_blacklisted(self, tr):
'''returns `False` if nslc codes of `tr` match any of the blacklisting
patters. Otherwise returns `True`'''
return not util.match_nslc(self.config.blacklist, tr.nslc_id)
def filter_iter(self, iterator):
'''Apply *blacklist*ing by example indices
:param iterator: producing iterator
'''
for i, item in enumerate(iterator):
if i not in self.blacklist:
yield i, item
@property
def generate_output_types(self):
'''Return data types of features and labels'''
return tf.float32, tf.float32
def unpack_examples(self, record_iterator):
'''Parse examples stored in TFRecordData to `tf.train.Example`'''
for string_record in record_iterator:
example = tf.train.Example()
example.ParseFromString(string_record)
chunk = example.features.feature['data'].bytes_list.value[0]
label = example.features.feature['label'].bytes_list.value[0]
chunk = num.fromstring(chunk, dtype=num.float32)
chunk = chunk.reshape((self.config.n_channels, -1))
label = num.fromstring(label, dtype=num.float32)
yield chunk, label
@property
def tstart_data(self):
return None
def iter_chunked(self, tinc):
# if data has been written to tf records:
return self.iter_examples_and_labels()
def iter_examples_and_labels(self):
'''Subclass this method!
Yields: feature, label
Chunks that are all NAN will be skipped.
'''
record_iterator = tf.python_io.tf_record_iterator(
path=self.fn_tfrecord)
for chunk, label in self.unpack_examples(record_iterator):
if all_NAN(chunk):
logger.debug('all NAN. skipping...')
continue
yield chunk, label
def generate_chunked(self, tinc=1):
'''Takes the output of `iter_examples_and_labels` and applies post
processing (see: `process_chunk`).
'''
for i, (chunk, label) in self.filter_iter(self.iter_chunked(tinc)):
yield self.process_chunk(chunk), self.normalize_label(label)
def generate(self, return_gaps=False):
'''Takes the output of `iter_examples_and_labels` and applies post
processing (see: `process_chunk`).
'''
self.evolution += 1
num.random.seed(self.random_seed + self.evolution)
for i, (chunk, label) in self.filter_iter(
self.iter_examples_and_labels()):
yield self.process_chunk(chunk, return_gaps=return_gaps), self.normalize_label(label)
def extract_labels(self):
'''Overwrite this method!'''
if not self.labeled:
return UNLABELED
def iter_labels(self):
'''Iterate through labels.'''
for i, (_, label) in self.filter_iter(
self.iter_examples_and_labels()):
yield label
@property
def text_labels(self):
'''Returns a list of strings to identify the labels.
Overwrite this method for more meaningfull identifiers.'''
return ['%i' % (i) for i, d in
self.filter_iter(self.iter_examples_and_labels())]
def gaps(self):
'''Returns a list containing the gaps of each example'''
gaps = []
for (_, gap), _ in self.generate(return_gaps=True):
gaps.append(gap)
return gaps
def snrs(self, split_factor):
snrs = []
for chunk, _ in self.generate():
snrs.append(snr(chunk, split_factor))
return snrs
@property
def output_shapes(self):
return (self.config.output_shapes)
def get_dataset(self):
return tf.data.Dataset.from_generator(
self.generate,
self.generate_output_types,
output_shapes=self.output_shapes)
def get_chunked_dataset(self, tinc=1.):
gen = partial(self.generate_chunked, tinc=tinc)
return tf.data.Dataset.from_generator(
gen,
self.generate_output_types,
output_shapes=self.output_shapes)
def get_raw_data_chunk(self, shape):
'''Return an array of size (Nchannels x Nsamples_max) filled with
NANs.'''
empty_array = num.empty(shape, dtype=num.float32)
empty_array.fill(num.nan)
return empty_array
def pack_examples(self):
'''Serialize Examples to strings.'''
for ydata, label in self.iter_examples_and_labels():
yield tf.train.Example(
features=tf.train.Features(
feature={
'data': _BytesFeature(ydata.tobytes()),
'label': _BytesFeature(num.array(
label, dtype=num.float32).tobytes()),
}))
def mask(self, chunk, rate):
'''For data augmentation: Mask traces in chunks with NaNs.
NaNs will be filled by the imputation method provided by the config
file.
:param rate: probability with which traces are NaN-ed
'''
# print(rate)
indices = self.nsl_indices
a = num.random.random(len(indices))
i = num.where(a < rate)[0]
for ii in i:
chunk[indices[ii], :] = num.nan
def random_trim(self, chunk, margin):
'''For data augmentation: Randomly trim examples in time domain with
*margin* seconds.'''
sample_margin = int(margin / self.config.effective_deltat)
nstart = num.random.randint(low=0, high=sample_margin)
_, n_samples = self.config.tensor_shape
nstop = nstart + n_samples
chunk[:, :nstart] = 0.
chunk[:, nstop:] = 0.
def process_chunk(self, chunk, return_gaps=False):
'''Performs preprocessing of data chunks.'''
if self.config.t_translation_max:
self.random_trim(chunk, self.config.t_translation_max)
# add noise
if self.noise:
self.noise(chunk)
# apply normalization
self.config.normalization(chunk)
# apply station dropout
if self.station_dropout_rate:
if self.station_dropout_distribution:
self.mask(chunk, num.random.uniform(
high=self.station_dropout_rate))
else:
self.mask(chunk, self.station_dropout_rate)
# fill gaps
if self.config.imputation:
gaps = num.isnan(chunk)
chunk[gaps] = self.config.imputation(chunk)
if not return_gaps:
return chunk
else:
return chunk, gaps
def write(self, directory):
'''Write example data to TFRecordDataset using `self.writer`.'''
logger.debug('writing TFRecordDataset: %s' % directory)
writer = tf.python_io.TFRecordWriter(directory)
for ex in self.pack_examples():
writer.write(ex.SerializeToString())
def cleanup(self):
'''Remove remaining folders'''
delete_if_exists(self.fn_tfrecord)
class PileData(DataGenerator):
'''Data generator for locally saved data.'''
fn_stations = String.T()
data_paths = List.T(String.T())
data_format = String.T(default='detect')
fn_markers = String.T()
fn_events = String.T(optional=True)
sort_markers = Bool.T(default=False,
help= 'Sorting markers speeds up data io. Shuffled markers \
improve generalization')
align_phase = String.T(default='P')
tstart = String.T(optional=True)
tstop = String.T(optional=True)
def setup(self):
self.data_pile = pile.make_pile(
self.data_paths, fileformat=self.data_format)
if self.data_pile.is_empty():
sys.exit('Data pile is empty!')
self.deltat_want = self.config.deltat_want or \
min(self.data_pile.deltats.keys())
self.n_samples = int(
(self.config.sample_length + self.config.tpad) / self.deltat_want)
logger.debug('loading marker file %s' % self.fn_markers)
# loads just plain markers:
markers = marker.load_markers(self.fn_markers)
if self.fn_events:
markers.extend(
[marker.EventMarker(e) for e in
load_events(self.fn_events)])
if self.sort_markers:
logger.info('sorting markers!')
markers.sort(key=lambda x: x.tmin)
marker.associate_phases_to_events(markers)
markers_by_nsl = {}
for m in markers:
if not m.match_nsl(self.config.reference_target.codes[:3]):
continue
if m.get_phasename().upper() != self.align_phase:
continue
markers_by_nsl.setdefault(m.one_nslc()[:3], []).append(m)
assert(len(markers_by_nsl) == 1)
# filter markers that do not have an event assigned:
self.markers = list(markers_by_nsl.values())[0]
if not self.labeled:
dummy_event = Event(lat=0., lon=0., depth=0.)
for m in self.markers:
if not m.get_event():
m.set_event(dummy_event)
self.markers = [m for m in self.markers if m.get_event() is not None]
if not len(self.markers):
raise Exception('No markers left in dataset')
self.config.channels = list(self.data_pile.nslc_ids.keys())
self.config.channels.sort()
def check_inputs(self):
if len(self.data_pile.deltats()) > 1:
logger.warn(
'Different sampling rates in dataset. Preprocessing slow')
def extract_labels(self, marker):
if not self.labeled:
return UNLABELED
source = marker.get_event()
n, e = orthodrome.latlon_to_ne(
self.config.reference_target.lat, self.config.reference_target.lon,
source.lat, source.lon)
return (n, e, source.depth)
@property
def tstart_data(self):
'''Returns start point of data returned by generator.'''
return util.stt(self.tstart) if self.tstart else self.data_pile.tmin
def iter_chunked(self, tinc):
tr_len = self.n_samples * self.deltat_want
nslc_to_index = self.nslc_to_index
tpad = self.config.effective_tpad
tstart = util.stt(self.tstart) if self.tstart else None
tstop = util.stt(self.tstop) if self.tstop else None
logger.debug('START')
for trs in self.data_pile.chopper(
tinc=tinc, tmin=tstart, tmax=tstop, tpad=tpad,
keep_current_files_open=True, want_incomplete=False,
trace_selector=self.reject_blacklisted):
chunk = self.get_raw_data_chunk(self.tensor_shape)
if not trs:
yield chunk, UNLABELED
continue
for tr in trs:
self.preprocess(tr)
indices = [nslc_to_index[tr.nslc_id] for tr in trs]
self.fit_data_into_chunk(trs, chunk=chunk, indices=indices,
tref=trs[0].tmin)
if all_NAN(chunk):
logger.debug('all NAN. skipping...')
continue
yield chunk, UNLABELED
def iter_labels(self):
for m in self.markers:
yield self.extract_labels(m)
def iter_examples_and_labels(self):
tr_len = self.n_samples * self.deltat_want
nslc_to_index = self.nslc_to_index
tpad = self.config.effective_tpad
for i_m, m in enumerate(self.markers):
logger.debug('processig marker %s / %s' % (i_m, len(self.markers)))
for trs in self.data_pile.chopper(
tmin=m.tmin-tpad, tmax=m.tmax+tr_len+tpad,
keep_current_files_open=True,
want_incomplete=False,
trace_selector=self.reject_blacklisted):
for tr in trs:
self.preprocess(tr)
indices = [nslc_to_index[tr.nslc_id] for tr in trs]
chunk = self.get_raw_data_chunk(self.tensor_shape)
self.fit_data_into_chunk(trs, chunk=chunk, indices=indices, tref=m.tmin)
if all_NAN(chunk):
logger.debug('all NAN. skipping...')
continue
label = self.extract_labels(m)
yield chunk, label
class FitsWaveformEnsemblePlot(PlotConfig):
''' Plot showing all waveform fits for the ensemble of solutions'''
name = 'fits_waveform_ensemble'
size_cm = Tuple.T(2,
Float.T(),
default=(9., 5.),
help='width and length of the figure in cm')
nx = Int.T(default=1, help='horizontal number of subplots on every page')
ny = Int.T(default=1, help='vertical number of subplots on every page')
misfit_cutoff = Float.T(
optional=True, help='Plot fits for models up to this misfit value')
color_parameter = String.T(
default='misfit',
help='Choice of value to color, options: dist and misfit')
font_size = Float.T(default=8, help='Font Size of all fonts, except title')
font_size_title = Float.T(default=10, help='Font size of title')
def make(self, environ):
cm = environ.get_plot_collection_manager()
mpl_init(fontsize=self.font_size)
environ.setup_modelling()
ds = environ.get_dataset()
history = environ.get_history(subset='harvest')
cm.create_group_mpl(self,
self.draw_figures(ds, history),
title=u'Waveform fits for the ensemble',
section='fits',
feather_icon='activity',
description=u'''
Plot showing waveform (attribute) fits for the ensemble of solutions.
Waveform fits for every nth model in the ensemble of bootstrap solutions.
Depending on the target configuration different types of comparisons are
possible: (i) time domain waveform differences, (ii) amplitude spectra, (iii)
envelopes, (iv) cross correlation functions. Each waveform plot gives a number
of details:
1) Target information (left side, from top to bottom) gives station name with
component, distance to source, azimuth of station with respect to source,
target weight, target misfit and starting time of the waveform relative to the
origin time.
2) The background gray area shows the applied taper function.
3) The waveforms shown are: the restituted and filtered observed trace without
tapering (light grey) and the same trace with tapering and processing (dark
gray), the synthetic trace (light red) and the filtered, tapered and (if
enabled) shifted and processed synthetic trace (colored). The colors of the
synthetic traces indicate how well the corresponding models fit in the global
weighting scheme (when all bootstrap weights are equal), from better fit (red)
to worse fit (blue). The amplitudes of the traces are scaled according to the
target weight (small weight, small amplitude) and normed relative to the
maximum amplitude of the targets of the corresponding normalisation family.
4) The bottom panel shows, depending on the type of comparison, sample-wise
residuals for time domain comparisons (red filled), spectra of observed and
synthetic traces for amplitude spectrum comparisons, or cross correlation
traces.''')
def draw_figures(self, ds, history):
color_parameter = self.color_parameter
misfit_cutoff = self.misfit_cutoff
fontsize = self.font_size
fontsize_title = self.font_size_title
nxmax = self.nx
nymax = self.ny
problem = history.problem
for target in problem.targets:
target.set_dataset(ds)
target_index = {}
i = 0
for target in problem.targets:
target_index[target] = i, i + target.nmisfits
i += target.nmisfits
gms = problem.combine_misfits(history.misfits)
isort = num.argsort(gms)[::-1]
gms = gms[isort]
models = history.models[isort, :]
if misfit_cutoff is not None:
ibest = gms < misfit_cutoff
gms = gms[ibest]
models = models[ibest]
gms = gms[::10]
models = models[::10]
nmodels = models.shape[0]
if color_parameter == 'dist':
mx = num.mean(models, axis=0)
cov = num.cov(models.T)
mdists = core.mahalanobis_distance(models, mx, cov)
icolor = meta.ordersort(mdists)
elif color_parameter == 'misfit':
iorder = num.arange(nmodels)
icolor = iorder
elif color_parameter in problem.parameter_names:
ind = problem.name_to_index(color_parameter)
icolor = problem.extract(models, ind)
target_to_results = defaultdict(list)
all_syn_trs = []
dtraces = []
for imodel in range(nmodels):
model = models[imodel, :]
source = problem.get_source(model)
results = problem.evaluate(model)
dtraces.append([])
for target, result in zip(problem.targets, results):
if isinstance(result, gf.SeismosizerError):
dtraces[-1].append(None)
continue
if not isinstance(target, WaveformMisfitTarget):
dtraces[-1].append(None)
continue
itarget, itarget_end = target_index[target]
assert itarget_end == itarget + 1
w = target.get_combined_weight()
if target.misfit_config.domain == 'cc_max_norm':
tref = (
result.filtered_obs.tmin + result.filtered_obs.tmax) \
* 0.5
for tr_filt, tr_proc, tshift in ((result.filtered_obs,
result.processed_obs,
0.),
(result.filtered_syn,
result.processed_syn,
result.tshift)):
norm = num.sum(num.abs(tr_proc.ydata)) \
/ tr_proc.data_len()
tr_filt.ydata /= norm
tr_proc.ydata /= norm
tr_filt.shift(tshift)
tr_proc.shift(tshift)
ctr = result.cc
ctr.shift(tref)
dtrace = ctr
else:
for tr in (result.filtered_obs, result.filtered_syn,
result.processed_obs, result.processed_syn):
tr.ydata *= w
if result.tshift is not None and result.tshift != 0.0:
# result.filtered_syn.shift(result.tshift)
result.processed_syn.shift(result.tshift)
dtrace = make_norm_trace(result.processed_syn,
result.processed_obs,
problem.norm_exponent)
target_to_results[target].append(result)
dtrace.meta = dict(
normalisation_family=target.normalisation_family,
path=target.path)
dtraces[-1].append(dtrace)
result.processed_syn.meta = dict(
normalisation_family=target.normalisation_family,
path=target.path)
all_syn_trs.append(result.processed_syn)
if not all_syn_trs:
logger.warn('No traces to show!')
return
def skey(tr):
return tr.meta['normalisation_family'], tr.meta['path']
trace_minmaxs = trace.minmax(all_syn_trs, skey)
dtraces_all = []
for dtraces_group in dtraces:
dtraces_all.extend(dtraces_group)
dminmaxs = trace.minmax(
[dtrace_ for dtrace_ in dtraces_all if dtrace_ is not None], skey)
for tr in dtraces_all:
if tr:
dmin, dmax = dminmaxs[skey(tr)]
tr.ydata /= max(abs(dmin), abs(dmax))
cg_to_targets = meta.gather(problem.waveform_targets,
lambda t: (t.path, t.codes[3]),
filter=lambda t: t in target_to_results)
cgs = sorted(cg_to_targets.keys())
from matplotlib import colors
cmap = cm.ScalarMappable(norm=colors.Normalize(vmin=num.min(icolor),
vmax=num.max(icolor)),
cmap=plt.get_cmap('coolwarm'))
imodel_to_color = []
for imodel in range(nmodels):
imodel_to_color.append(cmap.to_rgba(icolor[imodel]))
for cg in cgs:
targets = cg_to_targets[cg]
frame_to_target, nx, ny, nxx, nyy = layout(source, targets, nxmax,
nymax)
figures = {}
for iy in range(ny):
for ix in range(nx):
if (iy, ix) not in frame_to_target:
continue
ixx = ix // nxmax
iyy = iy // nymax
if (iyy, ixx) not in figures:
title = '_'.join(x for x in cg if x)
item = PlotItem(name='fig_%s_%i_%i' %
(title, ixx, iyy))
item.attributes['targets'] = []
figures[iyy,
ixx] = (item,
plt.figure(figsize=self.size_inch))
figures[iyy, ixx][1].subplots_adjust(left=0.03,
right=1.0 - 0.03,
bottom=0.03,
top=1.0 - 0.06,
wspace=0.2,
hspace=0.2)
item, fig = figures[iyy, ixx]
target = frame_to_target[iy, ix]
item.attributes['targets'].append(target.string_id())
amin, amax = trace_minmaxs[target.normalisation_family,
target.path]
absmax = max(abs(amin), abs(amax))
ny_this = nymax # min(ny, nymax)
nx_this = nxmax # min(nx, nxmax)
i_this = (iy % ny_this) * nx_this + (ix % nx_this) + 1
axes2 = fig.add_subplot(ny_this, nx_this, i_this)
space = 0.5
space_factor = 1.0 + space
axes2.set_axis_off()
axes2.set_ylim(-1.05 * space_factor, 1.05)
axes = axes2.twinx()
axes.set_axis_off()
if target.misfit_config.domain == 'cc_max_norm':
axes.set_ylim(-10. * space_factor, 10.)
else:
axes.set_ylim(-absmax * 1.33 * space_factor,
absmax * 1.33)
itarget, itarget_end = target_index[target]
assert itarget_end == itarget + 1
for imodel, result in enumerate(target_to_results[target]):
syn_color = imodel_to_color[imodel]
dtrace = dtraces[imodel][itarget]
tap_color_annot = (0.35, 0.35, 0.25)
tap_color_edge = (0.85, 0.85, 0.80)
tap_color_fill = (0.95, 0.95, 0.90)
plot_taper(axes2,
result.processed_obs.get_xdata(),
result.taper,
fc=tap_color_fill,
ec=tap_color_edge,
alpha=0.2)
obs_color = mpl_color('aluminium5')
obs_color_light = light(obs_color, 0.5)
plot_dtrace(axes2,
dtrace,
space,
0.,
1.,
fc='none',
ec=syn_color)
# plot_trace(
# axes, result.filtered_syn,
# color=syn_color_light, lw=1.0)
if imodel == 0:
plot_trace(axes,
result.filtered_obs,
color=obs_color_light,
lw=0.75)
plot_trace(axes,
result.processed_syn,
color=syn_color,
lw=1.0,
alpha=0.3)
plot_trace(axes,
result.processed_obs,
color=obs_color,
lw=0.75,
alpha=0.3)
if imodel != 0:
continue
xdata = result.filtered_obs.get_xdata()
axes.set_xlim(xdata[0], xdata[-1])
tmarks = [
result.processed_obs.tmin,
result.processed_obs.tmax
]
for tmark in tmarks:
axes2.plot([tmark, tmark], [-0.9, 0.1],
color=tap_color_annot)
dur = tmarks[1] - tmarks[0]
for tmark, text, ha in [
(tmarks[0], '$\\,$ ' +
meta.str_duration(tmarks[0] - source.time),
'left'),
(tmarks[1], '$\\Delta$ ' + meta.str_duration(dur),
'right')
]:
axes2.annotate(
text,
xy=(tmark, -0.9),
xycoords='data',
xytext=(fontsize * 0.4 * [-1, 1][ha == 'left'],
fontsize * 0.2),
textcoords='offset points',
ha=ha,
va='bottom',
color=tap_color_annot,
fontsize=fontsize)
axes2.set_xlim(tmarks[0] - dur * 0.1,
tmarks[1] + dur * 0.1)
scale_string = None
if target.misfit_config.domain == 'cc_max_norm':
scale_string = 'Syn/obs scales differ!'
infos = []
if scale_string:
infos.append(scale_string)
if self.nx == 1 and self.ny == 1:
infos.append(target.string_id())
else:
infos.append('.'.join(x for x in target.codes if x))
dist = source.distance_to(target)
azi = source.azibazi_to(target)[0]
infos.append(meta.str_dist(dist))
infos.append(u'%.0f\u00B0' % azi)
axes2.annotate('\n'.join(infos),
xy=(0., 1.),
xycoords='axes fraction',
xytext=(2., 2.),
textcoords='offset points',
ha='left',
va='top',
fontsize=fontsize,
fontstyle='normal')
if (self.nx == 1 and self.ny == 1):
yield item, fig
del figures[iyy, ixx]
if not (self.nx == 1 and self.ny == 1):
for (iyy, ixx), (_, fig) in figures.items():
title = '.'.join(x for x in cg if x)
if len(figures) > 1:
title += ' (%i/%i, %i/%i)' % (iyy + 1, nyy, ixx + 1,
nxx)
fig.suptitle(title, fontsize=fontsize_title)
for item, fig in figures.values():
yield item, fig
class DirContextEntry(Object):
path = String.T()
tstart = Timestamp.T()
ifile = Int.T()
class SatelliteTargetDisplacement(PlotConfig):
''' Maps showing surface displacements from satellite and modelled data '''
name = 'satellite'
dpi = Int.T(default=250)
size_cm = Tuple.T(2, Float.T(), default=(22., 12.))
colormap = String.T(default='RdBu',
help='Colormap for the surface displacements')
relative_coordinates = Bool.T(
default=False,
help='Show relative coordinates, initial location centered at 0N, 0E')
def make(self, environ):
cm = environ.get_plot_collection_manager()
history = environ.get_history(subset='harvest')
optimiser = environ.get_optimiser()
ds = environ.get_dataset()
environ.setup_modelling()
cm.create_group_mpl(self,
self.draw_static_fits(ds, history, optimiser),
title=u'InSAR Displacements',
section='fits',
feather_icon='navigation',
description=u'''
Maps showing subsampled surface displacements as observed, modelled and the
residual (observed minus modelled).
The displacement values predicted by the orbit-ambiguity ramps are added to the
modelled displacements (middle panels). The color shows the LOS displacement
values associated with, and the extent of, every quadtree box. The light grey
dots show the focal point of pixels combined in the quadtree box. This point
corresponds to the position of the modelled data point.
The large dark grey dot shows the reference source position. The grey filled
box shows the surface projection of the modelled source, with the thick-lined
edge marking the upper fault edge. Complete data extent is shown.
''')
def draw_static_fits(self, ds, history, optimiser, closeup=False):
from pyrocko.orthodrome import latlon_to_ne_numpy
problem = history.problem
sat_targets = problem.satellite_targets
for target in sat_targets:
target.set_dataset(ds)
source = history.get_best_source()
best_model = history.get_best_model()
results = problem.evaluate(best_model, targets=sat_targets)
def initAxes(ax, scene, title, last_axes=False):
ax.set_title(title)
ax.tick_params(length=2)
if scene.frame.isMeter():
ax.set_xlabel('Easting [km]')
scale_x = {'scale': 1. / km}
scale_y = {'scale': 1. / km}
if not self.relative_coordinates:
import utm
utm_E, utm_N, utm_zone, utm_zone_letter =\
utm.from_latlon(source.effective_lat,
source.effective_lon)
scale_x['offset'] = utm_E
scale_y['offset'] = utm_N
if last_axes:
ax.text(0.975,
0.025,
'UTM Zone %d%s' % (utm_zone, utm_zone_letter),
va='bottom',
ha='right',
fontsize=8,
alpha=.7,
transform=ax.transAxes)
ax.set_aspect('equal')
elif scene.frame.isDegree():
ax.set_xlabel('Lon [°]')
scale_x = {'scale': 1.}
scale_y = {'scale': 1.}
if not self.relative_coordinates:
scale_x['offset'] = source.effective_lon
scale_y['offset'] = source.effective_lat
ax.set_aspect(1. / num.cos(source.effective_lat * d2r))
scale_axes(ax.get_xaxis(), **scale_x)
scale_axes(ax.get_yaxis(), **scale_y)
def drawSource(ax, scene):
if scene.frame.isMeter():
fn, fe = source.outline(cs='xy').T
fn -= fn.mean()
fe -= fe.mean()
elif scene.frame.isDegree():
fn, fe = source.outline(cs='latlon').T
fn -= source.effective_lat
fe -= source.effective_lon
# source is centered
ax.scatter(0., 0., color='black', s=3, alpha=.5, marker='o')
ax.fill(fe,
fn,
edgecolor=(0., 0., 0.),
facecolor=(.5, .5, .5),
alpha=0.7)
ax.plot(fe[0:2], fn[0:2], 'k', linewidth=1.3)
def mapDisplacementGrid(displacements, scene):
arr = num.full_like(scene.displacement, fill_value=num.nan)
qt = scene.quadtree
for syn_v, l in zip(displacements, qt.leaves):
arr[l._slice_rows, l._slice_cols] = syn_v
arr[scene.displacement_mask] = num.nan
return arr
def drawLeaves(ax, scene, offset_e=0., offset_n=0.):
rects = scene.quadtree.getMPLRectangles()
for r in rects:
r.set_edgecolor((.4, .4, .4))
r.set_linewidth(.5)
r.set_facecolor('none')
r.set_x(r.get_x() - offset_e)
r.set_y(r.get_y() - offset_n)
map(ax.add_artist, rects)
ax.scatter(scene.quadtree.leaf_coordinates[:, 0] - offset_e,
scene.quadtree.leaf_coordinates[:, 1] - offset_n,
s=.25,
c='black',
alpha=.1)
def addArrow(ax, scene):
phi = num.nanmean(scene.phi)
los_dx = num.cos(phi + num.pi) * .0625
los_dy = num.sin(phi + num.pi) * .0625
az_dx = num.cos(phi - num.pi / 2) * .125
az_dy = num.sin(phi - num.pi / 2) * .125
anchor_x = .9 if los_dx < 0 else .1
anchor_y = .85 if los_dx < 0 else .975
az_arrow = patches.FancyArrow(x=anchor_x - az_dx,
y=anchor_y - az_dy,
dx=az_dx,
dy=az_dy,
head_width=.025,
alpha=.5,
fc='k',
head_starts_at_zero=False,
length_includes_head=True,
transform=ax.transAxes)
los_arrow = patches.FancyArrow(x=anchor_x - az_dx / 2,
y=anchor_y - az_dy / 2,
dx=los_dx,
dy=los_dy,
head_width=.02,
alpha=.5,
fc='k',
head_starts_at_zero=False,
length_includes_head=True,
transform=ax.transAxes)
ax.add_artist(az_arrow)
ax.add_artist(los_arrow)
urE, urN, llE, llN = (0., 0., 0., 0.)
for target in sat_targets:
if target.scene.frame.isMeter():
off_n, off_e = map(
float,
latlon_to_ne_numpy(target.scene.frame.llLat,
target.scene.frame.llLon,
source.effective_lat,
source.effective_lon))
if target.scene.frame.isDegree():
off_n = source.effective_lat - target.scene.frame.llLat
off_e = source.effective_lon - target.scene.frame.llLon
turE, turN, tllE, tllN = zip(
*[(l.gridE.max() - off_e, l.gridN.max() - off_n,
l.gridE.min() - off_e, l.gridN.min() - off_n)
for l in target.scene.quadtree.leaves])
turE, turN = map(max, (turE, turN))
tllE, tllN = map(min, (tllE, tllN))
urE, urN = map(max, ((turE, urE), (urN, turN)))
llE, llN = map(min, ((tllE, llE), (llN, tllN)))
def generate_plot(sat_target, result, ifig):
scene = sat_target.scene
fig = plt.figure()
fig.set_size_inches(*self.size_inch)
gs = gridspec.GridSpec(2,
3,
wspace=.15,
hspace=.2,
left=.1,
right=.975,
top=.95,
height_ratios=[12, 1])
item = PlotItem(name='fig_%i' % ifig,
attributes={'targets': [sat_target.path]},
title=u'Satellite Surface Displacements - %s' %
scene.meta.scene_title,
description=u'''
Surface displacements derived from satellite data.
(Left) the input data, (center) the modelled
data and (right) the model residual.
'''.format(meta=scene.meta))
stat_obs = result.statics_obs
stat_syn = result.statics_syn['displacement.los']
res = stat_obs - stat_syn
if scene.frame.isMeter():
offset_n, offset_e = map(
float,
latlon_to_ne_numpy(scene.frame.llLat, scene.frame.llLon,
source.effective_lat,
source.effective_lon))
elif scene.frame.isDegree():
offset_n = source.effective_lat - scene.frame.llLat
offset_e = source.effective_lon - scene.frame.llLon
im_extent = (scene.frame.E.min() - offset_e, scene.frame.E.max() -
offset_e, scene.frame.N.min() - offset_n,
scene.frame.N.max() - offset_n)
abs_displ = num.abs([
stat_obs.min(),
stat_obs.max(),
stat_syn.min(),
stat_syn.max(),
res.min(),
res.max()
]).max()
cmw = cm.ScalarMappable(cmap=self.colormap)
cmw.set_clim(vmin=-abs_displ, vmax=abs_displ)
cmw.set_array(stat_obs)
axes = [
fig.add_subplot(gs[0, 0]),
fig.add_subplot(gs[0, 1]),
fig.add_subplot(gs[0, 2])
]
ax = axes[0]
ax.imshow(mapDisplacementGrid(stat_obs, scene),
extent=im_extent,
cmap=self.colormap,
vmin=-abs_displ,
vmax=abs_displ,
origin='lower')
drawLeaves(ax, scene, offset_e, offset_n)
drawSource(ax, scene)
addArrow(ax, scene)
initAxes(ax, scene, 'Observed')
ax.text(.025,
.025,
'Scene ID: %s' % scene.meta.scene_id,
fontsize=8,
alpha=.7,
va='bottom',
transform=ax.transAxes)
if scene.frame.isDegree():
ax.set_ylabel('Lat [°]')
elif scene.frame.isMeter():
ax.set_ylabel('Northing [km]')
ax = axes[1]
ax.imshow(mapDisplacementGrid(stat_syn, scene),
extent=im_extent,
cmap=self.colormap,
vmin=-abs_displ,
vmax=abs_displ,
origin='lower')
drawLeaves(ax, scene, offset_e, offset_n)
drawSource(ax, scene)
addArrow(ax, scene)
initAxes(ax, scene, 'Model')
ax.get_yaxis().set_visible(False)
ax = axes[2]
ax.imshow(mapDisplacementGrid(res, scene),
extent=im_extent,
cmap=self.colormap,
vmin=-abs_displ,
vmax=abs_displ,
origin='lower')
drawLeaves(ax, scene, offset_e, offset_n)
drawSource(ax, scene)
addArrow(ax, scene)
initAxes(ax, scene, 'Residual', last_axes=True)
ax.get_yaxis().set_visible(False)
for ax in axes:
ax.set_xlim(llE, urE)
ax.set_ylim(llN, urN)
if closeup:
if scene.frame.isMeter():
fn, fe = source.outline(cs='xy').T
elif scene.frame.isDegree():
fn, fe = source.outline(cs='latlon').T
fn -= source.effective_lat
fe -= source.effective_lon
if fn.size > 1:
off_n = (fn[0] + fn[1]) / 2
off_e = (fe[0] + fe[1]) / 2
else:
off_n = fn[0]
off_e = fe[0]
fault_size = 2 * num.sqrt(
max(abs(fn - off_n))**2 + max(abs(fe - off_e))**2)
fault_size *= self.map_scale
if fault_size == 0.0:
extent = (scene.frame.N[-1] + scene.frame.E[-1]) / 2
fault_size = extent * .25
for ax in axes:
ax.set_xlim(-fault_size / 2 + off_e,
fault_size / 2 + off_e)
ax.set_ylim(-fault_size / 2 + off_n,
fault_size / 2 + off_n)
cax = fig.add_subplot(gs[1, :])
cbar = fig.colorbar(cmw,
cax=cax,
orientation='horizontal',
use_gridspec=True)
cbar.set_label('LOS Displacement [m]')
return (item, fig)
for ifig, (sat_target, result) in enumerate(zip(sat_targets, results)):
yield generate_plot(sat_target, result, ifig)
class Dip(FloatWithUnit):
'''Instrument dip in degrees down from horizontal. Together
azimuth and dip describe the direction of the sensitive axis of
the instrument.'''
unit = String.T(default='DEGREES', optional=True, xmlstyle='attribute')
class ExternalReference(Object):
'''This type contains a URI and description for external data that
users may want to reference in StationXML.'''
uri = String.T(xmltagname='URI')
description = Unicode.T(xmltagname='Description')
class Distance(FloatWithUnit):
'''Extension of FloatWithUnit for distances, elevations, and depths.'''
unit = String.T(default='METERS', optional=True, xmlstyle='attribute')
class FloatWithUnit(FloatNoUnit):
unit = String.T(optional=True, xmlstyle='attribute')
class SampleRate(FloatWithUnit):
'''Sample rate in samples per second.'''
unit = String.T(default='SAMPLES/S', optional=True, xmlstyle='attribute')
class ClockDrift(FloatWithUnit):
unit = String.T(default='SECONDS/SAMPLE', optional=True,
xmlstyle='attribute') # fixed
class Operator(Object):
agency_list = List.T(Unicode.T(xmltagname='Agency'))
contact_list = List.T(Person.T(xmltagname='Contact'))
web_site = String.T(optional=True, xmltagname='WebSite')
class Second(FloatWithUnit):
'''A time value in seconds.'''
unit = String.T(default='SECONDS', optional=True, xmlstyle='attribute')
class FDSNStationXML(Object):
'''Top-level type for Station XML. Required field are Source
(network ID of the institution sending the message) and one or
more Network containers or one or more Station containers.'''
schema_version = Float.T(default=1.0, xmlstyle='attribute')
source = String.T(xmltagname='Source')
sender = String.T(optional=True, xmltagname='Sender')
module = String.T(optional=True, xmltagname='Module')
module_uri = String.T(optional=True, xmltagname='ModuleURI')
created = Timestamp.T(xmltagname='Created')
network_list = List.T(Network.T(xmltagname='Network'))
xmltagname = 'FDSNStationXML'
guessable_xmlns = [guts_xmlns]
def get_pyrocko_stations(self, nslcs=None, nsls=None,
time=None, timespan=None,
inconsistencies='warn'):
assert inconsistencies in ('raise', 'warn')
if nslcs is not None:
nslcs = set(nslcs)
if nsls is not None:
nsls = set(nsls)
tt = ()
if time is not None:
tt = (time,)
elif timespan is not None:
tt = timespan
pstations = []
for network in self.network_list:
if not network.spans(*tt):
continue
for station in network.station_list:
if not station.spans(*tt):
continue
if station.channel_list:
loc_to_channels = {}
for channel in station.channel_list:
if not channel.spans(*tt):
continue
loc = channel.location_code.strip()
if loc not in loc_to_channels:
loc_to_channels[loc] = []
loc_to_channels[loc].append(channel)
for loc in sorted(loc_to_channels.keys()):
channels = loc_to_channels[loc]
if nslcs is not None:
channels = [channel for channel in channels
if (network.code, station.code, loc,
channel.code) in nslcs]
if not channels:
continue
nsl = network.code, station.code, loc
if nsls is not None and nsl not in nsls:
continue
pstations.append(
pyrocko_station_from_channels(
nsl,
channels,
inconsistencies=inconsistencies))
else:
pstations.append(pyrocko.model.Station(
network.code, station.code, '*',
lat=station.latitude.value,
lon=station.longitude.value,
elevation=value_or_none(station.elevation),
name=station.description or ''))
return pstations
@classmethod
def from_pyrocko_stations(
cls, pyrocko_stations, add_flat_responses_from=None):
''' Generate :py:class:`FDSNStationXML` from list of
:py:class;`pyrocko.model.Station` instances.
:param pyrocko_stations: list of :py:class;`pyrocko.model.Station`
instances.
:param add_flat_responses_from: unit, 'M', 'M/S' or 'M/S**2'
'''
from collections import defaultdict
network_dict = defaultdict(list)
if add_flat_responses_from:
assert add_flat_responses_from in ('M', 'M/S', 'M/S**2')
extra = dict(
response=Response(
instrument_sensitivity=Sensitivity(
value=1.0,
frequency=1.0,
input_units=Units(name=add_flat_responses_from))))
else:
extra = {}
for s in pyrocko_stations:
network, station, location = s.nsl()
channel_list = []
for c in s.channels:
channel_list.append(
Channel(
location_code=location,
code=c.name,
latitude=Latitude(value=s.lat),
longitude=Longitude(value=s.lon),
elevation=Distance(value=s.elevation),
depth=Distance(value=s.depth),
azimuth=Azimuth(value=c.azimuth),
dip=Dip(value=c.dip),
**extra
)
)
network_dict[network].append(
Station(
code=station,
latitude=Latitude(value=s.lat),
longitude=Longitude(value=s.lon),
elevation=Distance(value=s.elevation),
channel_list=channel_list)
)
timestamp = time.time()
network_list = []
for k, station_list in network_dict.items():
network_list.append(
Network(
code=k, station_list=station_list,
total_number_stations=len(station_list)))
sxml = FDSNStationXML(
source='from pyrocko stations list', created=timestamp,
network_list=network_list)
sxml.validate()
return sxml
def iter_network_stations(
self, net=None, sta=None, time=None, timespan=None):
tt = ()
if time is not None:
tt = (time,)
elif timespan is not None:
tt = timespan
for network in self.network_list:
if not network.spans(*tt) or (
net is not None and network.code != net):
continue
for station in network.station_list:
if not station.spans(*tt) or (
sta is not None and station.code != sta):
continue
yield (network, station)
def iter_network_station_channels(
self, net=None, sta=None, loc=None, cha=None,
time=None, timespan=None):
if loc is not None:
loc = loc.strip()
tt = ()
if time is not None:
tt = (time,)
elif timespan is not None:
tt = timespan
for network in self.network_list:
if not network.spans(*tt) or (
net is not None and network.code != net):
continue
for station in network.station_list:
if not station.spans(*tt) or (
sta is not None and station.code != sta):
continue
if station.channel_list:
for channel in station.channel_list:
if (not channel.spans(*tt) or
(cha is not None and channel.code != cha) or
(loc is not None and
channel.location_code.strip() != loc)):
continue
yield (network, station, channel)
def get_channel_groups(self, net=None, sta=None, loc=None, cha=None,
time=None, timespan=None):
groups = {}
for network, station, channel in self.iter_network_station_channels(
net, sta, loc, cha, time=time, timespan=timespan):
net = network.code
sta = station.code
cha = channel.code
loc = channel.location_code.strip()
if len(cha) == 3:
bic = cha[:2] # band and intrument code according to SEED
elif len(cha) == 1:
bic = ''
else:
bic = cha
if channel.response and \
channel.response.instrument_sensitivity and \
channel.response.instrument_sensitivity.input_units:
unit = channel.response.instrument_sensitivity.input_units.name
else:
unit = None
bic = (bic, unit)
k = net, sta, loc
if k not in groups:
groups[k] = {}
if bic not in groups[k]:
groups[k][bic] = []
groups[k][bic].append(channel)
for nsl, bic_to_channels in groups.items():
bad_bics = []
for bic, channels in bic_to_channels.items():
sample_rates = []
for channel in channels:
sample_rates.append(channel.sample_rate.value)
if not same(sample_rates):
scs = ','.join(channel.code for channel in channels)
srs = ', '.join('%e' % x for x in sample_rates)
err = 'ignoring channels with inconsistent sampling ' + \
'rates (%s.%s.%s.%s: %s)' % (nsl + (scs, srs))
logger.warn(err)
bad_bics.append(bic)
for bic in bad_bics:
del bic_to_channels[bic]
return groups
def choose_channels(
self,
target_sample_rate=None,
priority_band_code=['H', 'B', 'M', 'L', 'V', 'E', 'S'],
priority_units=['M/S', 'M/S**2'],
priority_instrument_code=['H', 'L'],
time=None,
timespan=None):
nslcs = {}
for nsl, bic_to_channels in self.get_channel_groups(
time=time, timespan=timespan).items():
useful_bics = []
for bic, channels in bic_to_channels.items():
rate = channels[0].sample_rate.value
if target_sample_rate is not None and \
rate < target_sample_rate*0.99999:
continue
if len(bic[0]) == 2:
if bic[0][0] not in priority_band_code:
continue
if bic[0][1] not in priority_instrument_code:
continue
unit = bic[1]
prio_unit = len(priority_units)
try:
prio_unit = priority_units.index(unit)
except ValueError:
pass
prio_inst = len(priority_instrument_code)
prio_band = len(priority_band_code)
if len(channels[0].code) == 3:
try:
prio_inst = priority_instrument_code.index(
channels[0].code[1])
except ValueError:
pass
try:
prio_band = priority_band_code.index(
channels[0].code[0])
except ValueError:
pass
if target_sample_rate is None:
rate = -rate
useful_bics.append((-len(channels), prio_band, rate, prio_unit,
prio_inst, bic))
useful_bics.sort()
for _, _, rate, _, _, bic in useful_bics:
channels = sorted(
bic_to_channels[bic],
key=lambda channel: channel.code)
if channels:
for channel in channels:
nslcs[nsl + (channel.code,)] = channel
break
return nslcs
def get_pyrocko_response(
self, nslc, time=None, timespan=None, fake_input_units=None):
net, sta, loc, cha = nslc
resps = []
for _, _, channel in self.iter_network_station_channels(
net, sta, loc, cha, time=time, timespan=timespan):
resp = channel.response
if resp:
resps.append(resp.get_pyrocko_response(
nslc, fake_input_units=fake_input_units))
if not resps:
raise NoResponseInformation('%s.%s.%s.%s' % nslc)
elif len(resps) > 1:
raise MultipleResponseInformation('%s.%s.%s.%s' % nslc)
return resps[0]
@property
def n_code_list(self):
return sorted(set(x.code for x in self.network_list))
@property
def ns_code_list(self):
nss = set()
for network in self.network_list:
for station in network.station_list:
nss.add((network.code, station.code))
return sorted(nss)
@property
def nsl_code_list(self):
nsls = set()
for network in self.network_list:
for station in network.station_list:
for channel in station.channel_list:
nsls.add(
(network.code, station.code, channel.location_code))
return sorted(nsls)
@property
def nslc_code_list(self):
nslcs = set()
for network in self.network_list:
for station in network.station_list:
for channel in station.channel_list:
nslcs.add(
(network.code, station.code, channel.location_code,
channel.code))
return sorted(nslcs)
def summary(self):
lst = [
'number of n codes: %i' % len(self.n_code_list),
'number of ns codes: %i' % len(self.ns_code_list),
'number of nsl codes: %i' % len(self.nsl_code_list),
'number of nslc codes: %i' % len(self.nslc_code_list)
]
return '\n'.join(lst)
class Voltage(FloatWithUnit):
unit = String.T(default='VOLTS', optional=True, xmlstyle='attribute')
class Station(Location):
network = String.T()
station = String.T()
location = String.T()
name = String.T(default='')
channels = List.T(Channel.T())
def __init__(self,
network='',
station='',
location='',
lat=0.0,
lon=0.0,
elevation=0.0,
depth=0.0,
name='',
channels=None):
Location.__init__(self,
network=network,
station=station,
location=location,
lat=float(lat),
lon=float(lon),
elevation=elevation and float(elevation) or 0.0,
depth=depth and float(depth) or 0.0,
name=name or '',
channels=channels or [])
self.dist_deg = None
self.dist_m = None
self.azimuth = None
self.backazimuth = None
def copy(self):
return copy.deepcopy(self)
def set_event_relative_data(self, event, distance_3d=False):
surface_dist = orthodrome.distance_accurate50m(event, self)
if distance_3d:
dd = event.depth - self.depth
self.dist_m = math.sqrt(dd**2 + surface_dist**2)
else:
self.dist_m = surface_dist
self.dist_deg = surface_dist / orthodrome.earthradius_equator * \
orthodrome.r2d
self.azimuth = orthodrome.azimuth(event, self)
self.backazimuth = orthodrome.azimuth(self, event)
def set_channels_by_name(self, *args):
self.set_channels([])
for name in args:
self.add_channel(Channel(name))
def set_channels(self, channels):
self.channels = []
for ch in channels:
self.add_channel(ch)
def get_channels(self):
return list(self.channels)
def get_channel_names(self):
return set(ch.name for ch in self.channels)
def remove_channel_by_name(self, name):
todel = [ch for ch in self.channels if ch.name == name]
for ch in todel:
self.channels.remove(ch)
def add_channel(self, channel):
self.remove_channel_by_name(channel.name)
self.channels.append(channel)
self.channels.sort(key=lambda ch: ch.name)
def get_channel(self, name):
for ch in self.channels:
if ch.name == name:
return ch
return None
def rotation_ne_to_rt(self, in_channel_names, out_channel_names):
angle = wrap(self.backazimuth + 180., -180., 180.)
in_channels = [self.get_channel(name) for name in in_channel_names]
out_channels = [
Channel(out_channel_names[0],
wrap(self.backazimuth + 180., -180., 180.), 0., 1.),
Channel(out_channel_names[1],
wrap(self.backazimuth + 270., -180., 180.), 0., 1.)
]
return angle, in_channels, out_channels
def _projection_to(self,
to,
in_channel_names,
out_channel_names,
use_gains=False):
in_channels = [self.get_channel(name) for name in in_channel_names]
# create orthogonal vectors for missing components, such that this
# won't break projections when components are missing.
vecs = []
for ch in in_channels:
if ch is None:
vecs.append(None)
else:
vec = getattr(ch, to)
if use_gains:
vec /= ch.gain
vecs.append(vec)
fill_orthogonal(vecs)
if not are_orthogonal(vecs):
raise ChannelsNotOrthogonal(
'components are not orthogonal: station %s.%s.%s, '
'channels %s, %s, %s' % (self.nsl() + tuple(in_channel_names)))
m = num.hstack([vec2[:, num.newaxis] for vec2 in vecs])
m = num.where(num.abs(m) < num.max(num.abs(m)) * 1e-16, 0., m)
if to == 'ned':
out_channels = [
Channel(out_channel_names[0], 0., 0., 1.),
Channel(out_channel_names[1], 90., 0., 1.),
Channel(out_channel_names[2], 0., 90., 1.)
]
elif to == 'enu':
out_channels = [
Channel(out_channel_names[0], 90., 0., 1.),
Channel(out_channel_names[1], 0., 0., 1.),
Channel(out_channel_names[2], 0., -90., 1.)
]
return m, in_channels, out_channels
def guess_channel_groups(self):
cg = {}
for channel in self.get_channels():
if len(channel.name) >= 1:
kind = channel.name[:-1]
if kind not in cg:
cg[kind] = []
cg[kind].append(channel.name[-1])
def allin(a, b):
return all(x in b for x in a)
out_groups = []
for kind, components in cg.items():
for sys in ('ENZ', '12Z', 'XYZ', 'RTZ'):
if allin(sys, components):
out_groups.append(tuple([kind + c for c in sys]))
return out_groups
def guess_projections_to_enu(self, out_channels=('E', 'N', 'U'), **kwargs):
proj = []
for cg in self.guess_channel_groups():
try:
proj.append(
self.projection_to_enu(cg,
out_channels=out_channels,
**kwargs))
except ChannelsNotOrthogonal as e:
logger.warning(str(e))
return proj
def guess_projections_to_rtu(self,
out_channels=('R', 'T', 'U'),
backazimuth=None,
**kwargs):
if backazimuth is None:
backazimuth = self.backazimuth
out_channels_ = [
Channel(out_channels[0], wrap(backazimuth + 180., -180., 180.), 0.,
1.),
Channel(out_channels[1], wrap(backazimuth + 270., -180., 180.), 0.,
1.),
Channel(out_channels[2], 0., -90., 1.)
]
proj = []
for (m, in_channels, _) in self.guess_projections_to_enu(**kwargs):
phi = (backazimuth + 180.) * d2r
r = num.array([[math.sin(phi), math.cos(phi), 0.0],
[math.cos(phi), -math.sin(phi), 0.0],
[0.0, 0.0, 1.0]])
proj.append((num.dot(r, m), in_channels, out_channels_))
return proj
def projection_to_enu(self,
in_channels,
out_channels=('E', 'N', 'U'),
**kwargs):
return self._projection_to('enu', in_channels, out_channels, **kwargs)
def projection_to_ned(self,
in_channels,
out_channels=('N', 'E', 'D'),
**kwargs):
return self._projection_to('ned', in_channels, out_channels, **kwargs)
def projection_from_enu(self,
in_channels=('E', 'N', 'U'),
out_channels=('X', 'Y', 'Z'),
**kwargs):
m, out_channels, in_channels = self._projection_to(
'enu', out_channels, in_channels, **kwargs)
return num.linalg.inv(m), in_channels, out_channels
def projection_from_ned(self,
in_channels=('N', 'E', 'D'),
out_channels=('X', 'Y', 'Z'),
**kwargs):
m, out_channels, in_channels = self._projection_to(
'ned', out_channels, in_channels, **kwargs)
return num.linalg.inv(m), in_channels, out_channels
def nsl_string(self):
return '.'.join((self.network, self.station, self.location))
def nsl(self):
return self.network, self.station, self.location
def oldstr(self):
nsl = '%s.%s.%s' % (self.network, self.station, self.location)
s = '%-15s %14.5f %14.5f %14.1f %14.1f %s' % (
nsl, self.lat, self.lon, self.elevation, self.depth, self.name)
return s
class Angle(FloatWithUnit):
unit = String.T(default='DEGREES', optional=True, xmlstyle='attribute')
class PsGrnConfigFull(PsGrnConfig):
earthmodel_1d = gf.meta.Earthmodel1D.T(optional=True)
psgrn_outdir = String.T(default='psgrn_green/')
sampling_interval = Float.T(default=1.0) # 1.0 for equidistant
sw_source_regime = Int.T(default=1) # 1-continental, 0-ocean
sw_gravity = Int.T(default=0)
accuracy_wavenumber_integration = Float.T(default=0.025)
displ_filenames = Tuple.T(3, String.T(), default=psgrn_displ_names)
stress_filenames = Tuple.T(6, String.T(), default=psgrn_stress_names)
tilt_filenames = Tuple.T(3, String.T(), psgrn_tilt_names)
gravity_filenames = Tuple.T(2, String.T(), psgrn_gravity_names)
@staticmethod
def example():
conf = PsGrnConfigFull()
conf.earthmodel_1d = cake.load_model().extract(depth_max=100 * km)
conf.psgrn_outdir = 'TEST_psgrn_functions/'
return conf
def string_for_config(self):
assert self.earthmodel_1d is not None
d = self.__dict__.copy()
model_str, nlines = cake_model_to_config(self.earthmodel_1d)
d['n_model_lines'] = nlines
d['model_lines'] = model_str
d['str_psgrn_outdir'] = "'%s'" % './'
d['str_displ_filenames'] = str_str_vals(self.displ_filenames)
d['str_stress_filenames'] = str_str_vals(self.stress_filenames)
d['str_tilt_filenames'] = str_str_vals(self.tilt_filenames)
d['str_gravity_filenames'] = str_str_vals(self.gravity_filenames)
d['str_distance_grid'] = self.distance_grid.string_for_config()
d['str_depth_grid'] = self.depth_grid.string_for_config()
template = '''# autogenerated PSGRN input by psgrn.py
#=============================================================================
# This is input file of FORTRAN77 program "psgrn08a" for computing responses
# (Green's functions) of a multi-layered viscoelastic halfspace to point
# dislocation sources buried at different depths. All results will be stored in
# the given directory and provide the necessary data base for the program
# "pscmp07a" for computing time-dependent deformation, geoid and gravity changes
# induced by an earthquake with extended fault planes via linear superposition.
# For more details, please read the accompanying READ.ME file.
#
# written by Rongjiang Wang
# GeoForschungsZentrum Potsdam
# e-mail: [email protected]
# phone +49 331 2881209
# fax +49 331 2881204
#
# Last modified: Potsdam, Jan, 2008
#
#################################################################
## ##
## Cylindrical coordinates (Z positive downwards!) are used. ##
## ##
## If not specified otherwise, SI Unit System is used overall! ##
## ##
#################################################################
#
#------------------------------------------------------------------------------
#
# PARAMETERS FOR SOURCE-OBSERVATION CONFIGURATIONS
# ================================================
# 1. the uniform depth of the observation points [km], switch for oceanic (0)
# or continental(1) earthquakes;
# 2. number of (horizontal) observation distances (> 1 and <= nrmax defined in
# psgglob.h), start and end distances [km], ratio (>= 1.0) between max. and
# min. sampling interval (1.0 for equidistant sampling);
# 3. number of equidistant source depths (>= 1 and <= nzsmax defined in
# psgglob.h), start and end source depths [km];
#
# r1,r2 = minimum and maximum horizontal source-observation
# distances (r2 > r1).
# zs1,zs2 = minimum and maximum source depths (zs2 >= zs1 > 0).
#
# Note that the same sampling rates dr_min and dzs will be used later by the
# program "pscmp07a" for discretizing the finite source planes to a 2D grid
# of point sources.
#------------------------------------------------------------------------------
%(observation_depth)e %(sw_source_regime)i
%(str_distance_grid)s %(sampling_interval)e
%(str_depth_grid)s
#------------------------------------------------------------------------------
#
# PARAMETERS FOR TIME SAMPLING
# ============================
# 1. number of time samples (<= ntmax def. in psgglob.h) and time window [days].
#
# Note that nt (> 0) should be power of 2 (the fft-rule). If nt = 1, the
# coseismic (t = 0) changes will be computed; If nt = 2, the coseismic
# (t = 0) and steady-state (t -> infinity) changes will be computed;
# Otherwise, time series for the given time samples will be computed.
#
#------------------------------------------------------------------------------
%(n_snapshots)i %(max_time)f
#------------------------------------------------------------------------------
#
# PARAMETERS FOR WAVENUMBER INTEGRATION
# =====================================
# 1. relative accuracy of the wave-number integration (suggested: 0.1 - 0.01)
# 2. factor (> 0 and < 1) for including influence of earth's gravity on the
# deformation field (e.g. 0/1 = without / with 100percent gravity effect).
#------------------------------------------------------------------------------
%(accuracy_wavenumber_integration)e
%(sw_gravity)i
#------------------------------------------------------------------------------
#
# PARAMETERS FOR OUTPUT FILES
# ===========================
#
# 1. output directory
# 2. file names for 3 displacement components (uz, ur, ut)
# 3. file names for 6 stress components (szz, srr, stt, szr, srt, stz)
# 4. file names for radial and tangential tilt components (as measured by a
# borehole tiltmeter), rigid rotation of horizontal plane, geoid and gravity
# changes (tr, tt, rot, gd, gr)
#
# Note that all file or directory names should not be longer than 80
# characters. Directory and subdirectoy names must be separated and ended
# by / (unix) or \ (dos)! All file names should be given without extensions
# that will be appended automatically by ".ep" for the explosion (inflation)
# source, ".ss" for the strike-slip source, ".ds" for the dip-slip source,
# and ".cl" for the compensated linear vector dipole source)
#
#------------------------------------------------------------------------------
%(str_psgrn_outdir)s
%(str_displ_filenames)s
%(str_stress_filenames)s
%(str_tilt_filenames)s %(str_gravity_filenames)s
#------------------------------------------------------------------------------
#
# GLOBAL MODEL PARAMETERS
# =======================
# 1. number of data lines of the layered model (<= lmax as defined in psgglob.h)
#
# The surface and the upper boundary of the half-space as well as the
# interfaces at which the viscoelastic parameters are continuous, are all
# defined by a single data line; All other interfaces, at which the
# viscoelastic parameters are discontinuous, are all defined by two data
# lines (upper-side and lower-side values). This input format could also be
# used for a graphic plot of the layered model. Layers which have different
# parameter values at top and bottom, will be treated as layers with a
# constant gradient, and will be discretised to a number of homogeneous
# sublayers. Errors due to the discretisation are limited within about
# 5percent (changeable, see psgglob.h).
#
# 2.... parameters of the multilayered model
#
# Burgers rheology (a Kelvin-Voigt body and a Maxwell body in series
# connection) for relaxation of shear modulus is implemented. No relaxation
# of compressional modulus is considered.
#
# eta1 = transient viscosity (dashpot of the Kelvin-Voigt body; <= 0 means
# infinity value)
# eta2 = steady-state viscosity (dashpot of the Maxwell body; <= 0 means
# infinity value)
# alpha = ratio between the effective and the unrelaxed shear modulus
# = mu1/(mu1+mu2) (> 0 and <= 1)
#
# Special cases:
# (1) Elastic: eta1 and eta2 <= 0 (i.e. infinity); alpha meaningless
# (2) Maxwell body: eta1 <= 0 (i.e. eta1 = infinity)
# or alpha = 1 (i.e. mu1 = infinity)
# (3) Standard-Linear-Solid: eta2 <= 0 (i.e. infinity)
#------------------------------------------------------------------------------
%(n_model_lines)i |int: no_model_lines;
#------------------------------------------------------------------------------
# no depth[km] vp[km/s] vs[km/s] rho[kg/m^3] eta1[Pa*s] eta2[Pa*s] alpha
#------------------------------------------------------------------------------
%(model_lines)s
#=======================end of input===========================================
''' # noqa
return template % d
class Azimuth(FloatWithUnit):
'''Instrument azimuth, degrees clockwise from North.'''
unit = String.T(default='DEGREES', optional=True, xmlstyle='attribute')
class Problem(Object):
'''
Base class for objective function setup.
Defines the *problem* to be solved by the optimiser.
'''
name = String.T()
ranges = Dict.T(String.T(), gf.Range.T())
dependants = List.T(Parameter.T())
norm_exponent = Int.T(default=2)
base_source = gf.Source.T(optional=True)
targets = List.T(MisfitTarget.T())
target_groups = List.T(TargetGroup.T())
grond_version = String.T(optional=True)
nthreads = Int.T(default=1)
def __init__(self, **kwargs):
Object.__init__(self, **kwargs)
if self.grond_version is None:
self.grond_version = __version__
self._target_weights = None
self._engine = None
self._family_mask = None
if hasattr(self, 'problem_waveform_parameters') and self.has_waveforms:
self.problem_parameters =\
self.problem_parameters + self.problem_waveform_parameters
self.check()
@classmethod
def get_plot_classes(cls):
from . import plot
return plot.get_plot_classes()
def check(self):
paths = set()
for grp in self.target_groups:
if grp.path == 'all':
continue
if grp.path in paths:
raise ValueError('Path %s defined more than once! In %s' %
(grp.path, grp.__class__.__name__))
paths.add(grp.path)
logger.debug('TargetGroup check OK.')
def copy(self):
o = copy.copy(self)
o._target_weights = None
return o
def set_target_parameter_values(self, x):
nprob = len(self.problem_parameters)
for target in self.targets:
target.set_parameter_values(x[nprob:nprob + target.nparameters])
nprob += target.nparameters
def get_parameter_dict(self, model, group=None):
params = []
for ip, p in enumerate(self.parameters):
if group in p.groups or group is None:
params.append((p.name, model[ip]))
return ADict(params)
def get_parameter_array(self, d):
arr = num.zeros(self.nparameters, dtype=num.float)
for ip, p in enumerate(self.parameters):
if p.name in d.keys():
arr[ip] = d[p.name]
return arr
def dump_problem_info(self, dirname):
fn = op.join(dirname, 'problem.yaml')
util.ensuredirs(fn)
guts.dump(self, filename=fn)
def dump_problem_data(self,
dirname,
x,
misfits,
bootstraps=None,
sampler_context=None):
fn = op.join(dirname, 'models')
if not isinstance(x, num.ndarray):
x = num.array(x)
with open(fn, 'ab') as f:
x.astype('<f8').tofile(f)
fn = op.join(dirname, 'misfits')
with open(fn, 'ab') as f:
misfits.astype('<f8').tofile(f)
if bootstraps is not None:
fn = op.join(dirname, 'bootstraps')
with open(fn, 'ab') as f:
bootstraps.astype('<f8').tofile(f)
if sampler_context is not None:
fn = op.join(dirname, 'choices')
with open(fn, 'ab') as f:
num.array(sampler_context, dtype='<i8').tofile(f)
def name_to_index(self, name):
pnames = [p.name for p in self.combined]
return pnames.index(name)
@property
def parameters(self):
target_parameters = []
for target in self.targets:
target_parameters.extend(target.target_parameters)
return self.problem_parameters + target_parameters
@property
def parameter_names(self):
return [p.name for p in self.combined]
@property
def dependant_names(self):
return [p.name for p in self.dependants]
@property
def nparameters(self):
return len(self.parameters)
@property
def ntargets(self):
return len(self.targets)
@property
def nwaveform_targets(self):
return len(self.waveform_targets)
@property
def nsatellite_targets(self):
return len(self.satellite_targets)
@property
def ngnss_targets(self):
return len(self.gnss_targets)
@property
def nmisfits(self):
nmisfits = 0
for target in self.targets:
nmisfits += target.nmisfits
return nmisfits
@property
def ndependants(self):
return len(self.dependants)
@property
def ncombined(self):
return len(self.parameters) + len(self.dependants)
@property
def combined(self):
return self.parameters + self.dependants
@property
def satellite_targets(self):
return [
t for t in self.targets if isinstance(t, SatelliteMisfitTarget)
]
@property
def gnss_targets(self):
return [
t for t in self.targets if isinstance(t, GNSSCampaignMisfitTarget)
]
@property
def waveform_targets(self):
return [t for t in self.targets if isinstance(t, WaveformMisfitTarget)]
@property
def has_satellite(self):
if self.satellite_targets:
return True
return False
@property
def has_waveforms(self):
if self.waveform_targets:
return True
return False
def set_engine(self, engine):
self._engine = engine
def get_engine(self):
return self._engine
def get_gf_store(self, target):
if self.get_engine() is None:
raise GrondError('Cannot get GF Store, modelling is not set up!')
return self.get_engine().get_store(target.store_id)
def random_uniform(self, xbounds, rstate):
x = rstate.uniform(0., 1., self.nparameters)
x *= (xbounds[:, 1] - xbounds[:, 0])
x += xbounds[:, 0]
return x
def preconstrain(self, x):
return x
def extract(self, xs, i):
if xs.ndim == 1:
return self.extract(xs[num.newaxis, :], i)[0]
if i < self.nparameters:
return xs[:, i]
else:
return self.make_dependant(
xs, self.dependants[i - self.nparameters].name)
def get_target_weights(self):
if self._target_weights is None:
self._target_weights = num.concatenate(
[target.get_combined_weight() for target in self.targets])
return self._target_weights
def get_target_residuals(self):
pass
def inter_family_weights(self, ns):
exp, root = self.get_norm_functions()
family, nfamilies = self.get_family_mask()
ws = num.zeros(self.nmisfits)
for ifamily in range(nfamilies):
mask = family == ifamily
ws[mask] = 1.0 / root(num.nansum(exp(ns[mask])))
return ws
def inter_family_weights2(self, ns):
'''
:param ns: 2D array with normalization factors ``ns[imodel, itarget]``
:returns: 2D array ``weights[imodel, itarget]``
'''
exp, root = self.get_norm_functions()
family, nfamilies = self.get_family_mask()
ws = num.zeros(ns.shape)
for ifamily in range(nfamilies):
mask = family == ifamily
ws[:, mask] = (
1.0 / root(num.nansum(exp(ns[:, mask]), axis=1)))[:,
num.newaxis]
return ws
def get_reference_model(self, expand=False):
if expand:
src_params = self.pack(self.base_source)
ref = num.zeros(self.nparameters)
ref[:src_params.size] = src_params
else:
ref = self.pack(self.base_source)
return ref
def get_parameter_bounds(self):
out = []
for p in self.problem_parameters:
r = self.ranges[p.name]
out.append((r.start, r.stop))
for target in self.targets:
for p in target.target_parameters:
r = target.target_ranges[p.name_nogroups]
out.append((r.start, r.stop))
return num.array(out, dtype=num.float)
def get_dependant_bounds(self):
return num.zeros((0, 2))
def get_combined_bounds(self):
return num.vstack(
(self.get_parameter_bounds(), self.get_dependant_bounds()))
def raise_invalid_norm_exponent(self):
raise GrondError('Invalid norm exponent: %f' % self.norm_exponent)
def get_norm_functions(self):
if self.norm_exponent == 2:
def sqr(x):
return x**2
return sqr, num.sqrt
elif self.norm_exponent == 1:
def noop(x):
return x
return noop, num.abs
else:
self.raise_invalid_norm_exponent()
def combine_misfits(self,
misfits,
extra_weights=None,
extra_residuals=None,
get_contributions=False):
'''
Combine misfit contributions (residuals) to global or bootstrap misfits
:param misfits: 3D array ``misfits[imodel, iresidual, 0]`` are the
misfit contributions (residuals) ``misfits[imodel, iresidual, 1]``
are the normalisation contributions. It is also possible to give
the misfit and normalisation contributions for a single model as
``misfits[iresidual, 0]`` and misfits[iresidual, 1]`` in which
case, the first dimension (imodel) of the result will be stipped
off.
:param extra_weights: if given, 2D array of extra weights to be applied
to the contributions, indexed as
``extra_weights[ibootstrap, iresidual]``.
:param extra_residuals: if given, 2D array of perturbations to be added
to the residuals, indexed as
``extra_residuals[ibootstrap, iresidual]``.
:param get_contributions: get the weighted and perturbed contributions
(don't do the sum).
:returns: if no *extra_weights* or *extra_residuals* are given, a 1D
array indexed as ``misfits[imodel]`` containing the global misfit
for each model is returned, otherwise a 2D array
``misfits[imodel, ibootstrap]`` with the misfit for every model and
weighting/residual set is returned.
'''
exp, root = self.get_norm_functions()
if misfits.ndim == 2:
misfits = misfits[num.newaxis, :, :]
return self.combine_misfits(misfits, extra_weights,
extra_residuals,
get_contributions)[0, ...]
assert misfits.ndim == 3
assert extra_weights is None or extra_weights.ndim == 2
assert extra_residuals is None or extra_residuals.ndim == 2
if extra_weights is not None or extra_residuals is not None:
if extra_weights is not None:
w = extra_weights[num.newaxis, :, :] \
* self.get_target_weights()[num.newaxis, num.newaxis, :] \
* self.inter_family_weights2(
misfits[:, :, 1])[:, num.newaxis, :]
else:
w = 1.0
if extra_residuals is not None:
r = extra_residuals[num.newaxis, :, :]
else:
r = 0.0
if get_contributions:
return exp(w*(misfits[:, num.newaxis, :, 0]+r)) \
/ num.nansum(
exp(w*misfits[:, num.newaxis, :, 1]),
axis=2)[:, :, num.newaxis]
res = root(
num.nansum(exp(w * (misfits[:, num.newaxis, :, 0] + r)),
axis=2) /
num.nansum(exp(w * (misfits[:, num.newaxis, :, 1])), axis=2))
assert res[res < 0].size == 0
return res
else:
w = self.get_target_weights()[num.newaxis, :] \
* self.inter_family_weights2(misfits[:, :, 1])
r = self.get_target_weights()[num.newaxis, :] \
* self.inter_family_weights2(misfits[:, :, 1])
if get_contributions:
return exp(w*misfits[:, :, 0]) \
/ num.nansum(
exp(w*misfits[:, :, 1]),
axis=1)[:, num.newaxis]
return root(
num.nansum(exp(w * misfits[:, :, 0]), axis=1) /
num.nansum(exp(w * misfits[:, :, 1]), axis=1))
def make_family_mask(self):
family_names = set()
families = num.zeros(self.nmisfits, dtype=num.int)
idx = 0
for itarget, target in enumerate(self.targets):
family_names.add(target.normalisation_family)
families[idx:idx + target.nmisfits] = len(family_names) - 1
idx += target.nmisfits
return families, len(family_names)
def get_family_mask(self):
if self._family_mask is None:
self._family_mask = self.make_family_mask()
return self._family_mask
def evaluate(self, x, mask=None, result_mode='full', targets=None):
source = self.get_source(x)
engine = self.get_engine()
self.set_target_parameter_values(x)
if mask is not None and targets is not None:
raise ValueError('Mask cannot be defined with targets set.')
targets = targets if targets is not None else self.targets
for target in targets:
target.set_result_mode(result_mode)
modelling_targets = []
t2m_map = {}
for itarget, target in enumerate(targets):
t2m_map[target] = target.prepare_modelling(engine, source, targets)
if mask is None or mask[itarget]:
modelling_targets.extend(t2m_map[target])
u2m_map = {}
for imtarget, mtarget in enumerate(modelling_targets):
if mtarget not in u2m_map:
u2m_map[mtarget] = []
u2m_map[mtarget].append(imtarget)
modelling_targets_unique = list(u2m_map.keys())
resp = engine.process(source,
modelling_targets_unique,
nthreads=self.nthreads)
modelling_results_unique = list(resp.results_list[0])
modelling_results = [None] * len(modelling_targets)
for mtarget, mresult in zip(modelling_targets_unique,
modelling_results_unique):
for itarget in u2m_map[mtarget]:
modelling_results[itarget] = mresult
imt = 0
results = []
for itarget, target in enumerate(targets):
nmt_this = len(t2m_map[target])
if mask is None or mask[itarget]:
result = target.finalize_modelling(
engine, source, t2m_map[target],
modelling_results[imt:imt + nmt_this])
imt += nmt_this
else:
result = gf.SeismosizerError(
'target was excluded from modelling')
results.append(result)
return results
def misfits(self, x, mask=None):
results = self.evaluate(x, mask=mask, result_mode='sparse')
misfits = num.full((self.nmisfits, 2), num.nan)
imisfit = 0
for target, result in zip(self.targets, results):
if isinstance(result, MisfitResult):
misfits[imisfit:imisfit + target.nmisfits, :] = result.misfits
imisfit += target.nmisfits
return misfits
def forward(self, x):
source = self.get_source(x)
engine = self.get_engine()
plain_targets = []
for target in self.targets:
plain_targets.extend(target.get_plain_targets(engine, source))
resp = engine.process(source, plain_targets)
results = []
for target, result in zip(plain_targets, resp.results_list[0]):
if isinstance(result, gf.SeismosizerError):
logger.debug('%s.%s.%s.%s: %s' % (target.codes +
(str(result), )))
else:
results.append(result)
return results
def get_random_model(self, ntries_limit=100):
xbounds = self.get_parameter_bounds()
for _ in range(ntries_limit):
x = self.random_uniform(xbounds, rstate=g_rstate)
try:
return self.preconstrain(x)
except Forbidden:
pass
raise GrondError(
'Could not find any suitable candidate sample within %i tries' %
(ntries_limit))
def testOptionalDefault(self):
from pyrocko.guts_array import Array, array_equal
import numpy as num
assert_ae = num.testing.assert_almost_equal
def array_equal_noneaware(a, b):
if a is None:
return b is None
elif b is None:
return a is None
else:
return array_equal(a, b)
data = [
('a', Int.T(),
[None, 0, 1, 2],
['aerr', 0, 1, 2]),
('b', Int.T(optional=True),
[None, 0, 1, 2],
[None, 0, 1, 2]),
('c', Int.T(default=1),
[None, 0, 1, 2],
[1, 0, 1, 2]),
('d', Int.T(default=1, optional=True),
[None, 0, 1, 2],
[1, 0, 1, 2]),
('e', List.T(Int.T()),
[None, [], [1], [2]],
[[], [], [1], [2]]),
('f', List.T(Int.T(), optional=True),
[None, [], [1], [2]],
[None, [], [1], [2]]),
('g', List.T(Int.T(), default=[1]), [
None, [], [1], [2]],
[[1], [], [1], [2]]),
('h', List.T(Int.T(), default=[1], optional=True),
[None, [], [1], [2]],
[[1], [], [1], [2]]),
('i', Tuple.T(2, Int.T()),
[None, (1, 2)],
['err', (1, 2)]),
('j', Tuple.T(2, Int.T(), optional=True),
[None, (1, 2)],
[None, (1, 2)]),
('k', Tuple.T(2, Int.T(), default=(1, 2)),
[None, (1, 2), (3, 4)],
[(1, 2), (1, 2), (3, 4)]),
('l', Tuple.T(2, Int.T(), default=(1, 2), optional=True),
[None, (1, 2), (3, 4)],
[(1, 2), (1, 2), (3, 4)]),
('i2', Tuple.T(None, Int.T()),
[None, (1, 2)],
[(), (1, 2)]),
('j2', Tuple.T(None, Int.T(), optional=True),
[None, (), (3, 4)],
[None, (), (3, 4)]),
('k2', Tuple.T(None, Int.T(), default=(1,)),
[None, (), (3, 4)],
[(1,), (), (3, 4)]),
('l2', Tuple.T(None, Int.T(), default=(1,), optional=True),
[None, (), (3, 4)],
[(1,), (), (3, 4)]),
('m', Array.T(shape=(None,), dtype=num.int, serialize_as='list'),
[num.arange(0), num.arange(2)],
[num.arange(0), num.arange(2)]),
('n', Array.T(shape=(None,), dtype=num.int, serialize_as='list',
optional=True),
[None, num.arange(0), num.arange(2)],
[None, num.arange(0), num.arange(2)]),
('o', Array.T(shape=(None,), dtype=num.int, serialize_as='list',
default=num.arange(2)),
[None, num.arange(0), num.arange(2), num.arange(3)],
[num.arange(2), num.arange(0), num.arange(2), num.arange(3)]),
('p', Array.T(shape=(None,), dtype=num.int, serialize_as='list',
default=num.arange(2), optional=True),
[None, num.arange(0), num.arange(2), num.arange(3)],
[num.arange(2), num.arange(0), num.arange(2), num.arange(3)]),
('q', Dict.T(String.T(), Int.T()),
[None, {}, {'a': 1}],
[{}, {}, {'a': 1}]),
('r', Dict.T(String.T(), Int.T(), optional=True),
[None, {}, {'a': 1}],
[None, {}, {'a': 1}]),
('s', Dict.T(String.T(), Int.T(), default={'a': 1}),
[None, {}, {'a': 1}],
[{'a': 1}, {}, {'a': 1}]),
('t', Dict.T(String.T(), Int.T(), default={'a': 1}, optional=True),
[None, {}, {'a': 1}],
[{'a': 1}, {}, {'a': 1}]),
]
for k, t, vals, exp, in data:
last = [None]
class A(Object):
def __init__(self, **kwargs):
last[0] = len(kwargs)
Object.__init__(self, **kwargs)
v = t
A.T.class_signature()
for v, e in zip(vals, exp):
if isinstance(e, str) and e == 'aerr':
with self.assertRaises(ArgumentError):
if v is not None:
a1 = A(v=v)
else:
a1 = A()
continue
else:
if v is not None:
a1 = A(v=v)
else:
a1 = A()
if isinstance(e, str) and e == 'err':
with self.assertRaises(ValidationError):
a1.validate()
else:
a1.validate()
a2 = load_string(dump(a1))
if isinstance(e, num.ndarray):
assert last[0] == int(
not (array_equal_noneaware(t.default(), a1.v)
and t.optional))
assert_ae(a1.v, e)
assert_ae(a1.v, e)
else:
assert last[0] == int(
not(t.default() == a1.v and t.optional))
self.assertEqual(a1.v, e)
self.assertEqual(a2.v, e)
