def resampling(self, step):
"""
Rebuild the sample and its statistics sorted by the likelihood of the parameter values
"""
θOld = step.theta
priorOld = step.prior
dataOld = step.data
postOld = step.posterior
# allocate the new entities
θ = altar.matrix(shape=θOld.shape)
prior = altar.vector(shape=priorOld.shape)
data = altar.vector(shape=dataOld.shape)
posterior = altar.vector(shape=postOld.shape)
# build a histogram for the new samples and convert it into a vector
multi = self.computeSampleMultiplicities(step=step).values()
# print(" histogram as vector:")
# print(" counts: {}".format(tuple(multi)))
# unique samples count
unique_samples = 0
# sample count
index = 0
# indices for kept samples
indices = altar.vector(shape=multi.shape)
# record kept sample indices
for i in range(multi.shape):
count = int(multi[i])
# if count is zero, skip
if count == 0: continue
# add the unique samples count
unique_samples += 1
# duplicate indices
for ic in range(count):
indices[index] = i
index += 1
# shuffle the indices
indices.shuffle(rng=self.rng)
self.info.log(
f"resampling: unique samples {unique_samples} out of {multi.shape}"
)
# print(" kept sample indices: {}".format(tuple(indices[i] for i in range(indices.shape))))
# copy theta, (prior, data, posterior) over according to the indices
for i in range(indices.shape):
# get the index for old samples
old = int(indices[i])
# duplicate theta
for param in range(step.parameters):
θ[i, param] = θOld[old, param]
prior[i] = priorOld[old]
data[i] = dataOld[old]
posterior[i] = postOld[old]
# return the shuffled data
return θ, (prior, data, posterior)
def alloc(cls, samples, parameters):
"""
Allocate storage for the parts of a cooling step
"""
# allocate the initial sample set
theta = altar.matrix(shape=(samples, parameters)).zero()
# allocate the likelihood vectors
prior = altar.vector(shape=samples).zero()
data = altar.vector(shape=samples).zero()
posterior = altar.vector(shape=samples).zero()
# build one of my instances and return it
return cls(beta=0, theta=theta, likelihoods=(prior, data, posterior))
def rank(self, step):
"""
Rebuild the sample and its statistics sorted by the likelihood of the parameter values
"""
θOld = step.theta
priorOld = step.prior
dataOld = step.data
postOld = step.posterior
# allocate the new entities
θ = altar.matrix(shape=θOld.shape)
prior = altar.vector(shape=priorOld.shape)
data = altar.vector(shape=dataOld.shape)
posterior = altar.vector(shape=postOld.shape)
# build a histogram for the new samples and convert it into a vector
multi = self.computeSampleMultiplicities(step=step).values()
# print(" histogram as vector:")
# print(" counts: {}".format(tuple(multi)))
# compute the permutation that would sort the frequency table according to the sample
# multiplicity, in reverse order
p = multi.sortIndirect().reverse()
# print(" sorted: {}".format(tuple(p[i] for i in range(p.shape))))
# the number of samples we have processed
done = 0
# start moving stuff around until we have built a complete sample set
for i in range(p.shape):
# the old sample index
old = p[i]
# and its multiplicity
count = int(multi[old])
# if the count has dropped to zero, we are done
if count == 0: break
# otherwise, duplicate this sample {count} times
for dupl in range(count):
# update the samples
for param in range(step.parameters):
θ[done, param] = θOld[old, param]
# update the log-likelihoods
prior[done] = priorOld[old]
data[done] = dataOld[old]
posterior[done] = postOld[old]
# update the number of processed samples
done += 1
# print(i, old, count, done)
# return the shuffled data
return θ, (prior, data, posterior)
def computeCp(self, theta_mean):
"""
Calculate Cp
"""
# grab the samples shape=(samples, parameters)
parameters = self.parameters
observations = self.observations
nCmu = self.nCmu
Cp = altar.matrix(shape=(observations, observations))
# calculate
kv = altar.vector(shape=observations)
cmu = self.Cmu
kmu = self.Kmu
Kp = altar.matrix(shape=(observations, nCmu))
for i in range(nCmu):
# get kmu_i from list, shape=(observations, parameters)
kmu_i = kmu[i]
# kv = Kmu_i * thetha_mean
# dgemv y = alpha Op(A) x + beta y
altar.blas.dgemv(kmu_i.opNoTrans, 1.0, kmu_i, theta_mean, 0.0, kv)
Kp.setColumn(i, kv)
# KpC = Kp * Cmu
KpC = altar.matrix(shape=(observations, nCmu))
altar.blas.dsymm(cmu.sideRight, cmu.upperTriangular, 1.0, cmu, Kp, 0.0,
KpC)
# Cp = KpC*Kp
altar.blas.dgemm(KpC.opNoTrans, Kp.opTrans, 1.0, KpC, Kp, 0.0, Cp)
# all done
return Cp
def dataLikelihood(self, step):
"""
Fill {step.data} with the likelihoods of the samples in {step.theta} given the available
data. This is what is usually referred to as the "forward model"
"""
# cache the inverse of {σ}
σ_inv = self.σ_inv
# grab the portion of the sample that's mine
θ = self.restrict(theta=step.theta)
# and the storage for the data likelihoods
data = step.data
# find out how many samples in the set
samples = θ.rows
# for each sample in the sample set
for sample in range(samples):
# prepare vector with the sample difference from the mean
δ = θ.getRow(sample)
δ -= self.peak
# storage for {σ_inv . δ}
y = altar.vector(shape=δ.shape).zero()
# compute {σ_inv . δ} and store it in {y}
altar.blas.dsymv(σ_inv.upperTriangular, 1.0, σ_inv, δ, 0.0, y)
# finally, form {δ^T . σ_inv . δ}
v = altar.blas.ddot(δ, y)
# compute and return the log-likelihood of the data given this sample
data[sample] += self.normalization - v / 2
# all done
return self
def displacements(self, locations, los):
"""
Compute the expected displacements from a point pressure source at a set of observation
locations
"""
# the location of the source
x_src = self.x
y_src = self.y
d_src = self.d
# its strength
dV = self.dV
# get the material properties
nu = self.nu
# allocate space for the result
u = altar.vector(shape=len(locations))
# go through each observation location
for index, (x_obs,y_obs) in enumerate(locations):
# compute displacements
x = x_src - x_obs
y = y_src - y_obs
d = d_src
# compute the distance to the point source
x2 = x**2
y2 = y**2
d2 = d**2
# intermediate values
C = (nu-1) * dV/π
R = sqrt(x2 + y2 + d2)
CR3 = C * R**-3
# store the expected displacement
u[index] = x*CR3 * los[index, 0] + y*CR3 * los[index, 1] - d*CR3 * los[index,2]
# all done
return u
def initializeSample(self, theta):
"""
Fill my portion of {theta} with initial random values from my distribution.
"""
# grab the portion of the sample that's mine
θ = self.restrict(theta=theta)
# grab the number of samples (rows of theta)
samples = θ.rows
# grab the number of patches/parameters
parameters = self.patches
# grab the area of patches
area_patches = self.area_patches
# create a gaussian distribution to generate Mw for each sample
gaussian_Mw = altar.pdf.gaussian(mean=self.Mw_mean, sigma=self.Mw_sigma, rng=self.rng)
# create a dirichlet distribution to generate displacements
alpha = altar.vector(shape=parameters).fill(1) # power 0, or (alpha_i = 1)
dirichlet_D = altar.pdf.dirichlet(alpha=alpha, rng=self.rng)
# create a tempory vector for theta of samples
theta_sample = altar.vector(shape=parameters)
# iterate through samples to initialize samples
for sample in range(samples):
# generate a Mw sample
Mw = gaussian_Mw.sample()
# Pentiar = M0/Mu = \sum (A_i D_i)
# 15 here is for GPa * Km^2, instead of Pa * m^2
Pentier = pow(10, 1.5*Mw + 9.1 - 15)/self.Mu
# generate a dirichlet sample \sum x_i = 1
dirichlet_D.vector(vector=theta_sample)
# D_i = P * x_i /A_i
for parameter in range (parameters):
theta_sample[parameter]*=Pentier/area_patches[parameter]
# set theta
θ.setRow(sample, theta_sample)
# all done and return
return self
def displacements(self, locations, los):
"""
Compute the expected displacements at a set of observation locations from a compound
(triaxial) dislocation source at depth.
"""
# the location of the source
x_src = self.x
y_src = self.y
d_src = self.d
# clockwise rotation angles about x, y, z axes
omegaX_src = self.omegaX
omegaY_src = self.omegaY
omegaZ_src = self.omegaZ
# semi-lengths
ax_src = self.ax
ay_src = self.ay
az_src = self.az
# opening
opening = self.opening
# get the material properties
v = self.v
# from locations, a vector of (x,y) tuples, create the flattened vectors Xf, Yf required by
# CDM
Xf = numpy.zeros(len(locations), dtype=float)
Yf = numpy.zeros(len(locations), dtype=float)
for i, loc in enumerate(locations):
Xf[i] = loc[0]
Yf[i] = loc[1]
# allocate space for the result
u = altar.vector(shape=len(locations))
# compute the displacements
ue, un, uv = CDM(X=Xf,
Y=Yf,
X0=x_src,
Y0=y_src,
depth=d_src,
ax=ax_src,
ay=ay_src,
az=az_src,
omegaX=omegaX_src,
omegaY=omegaY_src,
omegaZ=omegaZ_src,
opening=opening,
nu=v)
# go through each observation location
for idx, (ux, uy, uz) in enumerate(zip(ue, un, uv)):
# project the expected displacement along LOS and store
u[idx] = ux * los[idx, 0] + uy * los[idx, 1] + uz * los[idx, 2]
# all done
return u
def updateTemperature(self, step):
"""
Generate the next temperature increment
"""
# grab the data log-likelihood
dataLikelihood = step.data
# initialize the vector of weights
self.w = altar.vector(shape=step.samples).zero()
# compute {δβ} and the normalized {w}
β, self.cov = self.solver.solve(dataLikelihood, self.w)
# and return the new temperature
return β
def computeCovariance(self, step):
"""
Compute the parameter covariance Σ of the sample in {step}
Σ = c_m^2 \sum_{i \in samples} \tilde{w}_{i} θ_i θ_i^T} - \bar{θ} \bar{θ}^Τ
where
\bar{θ} = \sum_{i \in samples} \tilde{w}_{i} θ_{i}
The covariance Σ gets used to build a proposal pdf for the posterior
"""
# unpack what i need
w = self.w # w is assumed normalized
θ = step.theta # the current sample set
# extract the number of samples and number of parameters
samples = step.samples
parameters = step.parameters
# initialize the covariance matrix
Σ = altar.matrix(shape=(parameters, parameters)).zero()
# check the geometries
assert w.shape == samples
assert θ.shape == (samples, parameters)
assert Σ.shape == (parameters, parameters)
# calculate the weighted mean of every parameter across all samples
θbar = altar.vector(shape=parameters)
# for each parameter
for j in range(parameters):
# the jth column in θ has the value of this parameter in the various samples
θbar[j] = θ.getColumn(j).mean(weights=w)
# start filling out Σ
for i in range(samples):
# get the sample
sample = θ.getRow(i)
# form Σ += w[i] sample sample^T
altar.blas.dsyr(Σ.lowerTriangular, w[i], sample, Σ)
# subtract θbar θbar^T
altar.blas.dsyr(Σ.lowerTriangular, -1, θbar, Σ)
# fill the upper triangle
for i in range(parameters):
for j in range(i):
Σ[j, i] = Σ[i, j]
# condition the covariance matrix
if self.check_positive_definiteness:
self.conditionCovariance(Σ=Σ)
# all done
return Σ
def initialize(self, application):
"""
Initialize the state of the model given a {problem} specification
"""
# externals
from math import sin, cos
# chain up
super().initialize(application=application)
# initialize my parameter sets
self.initializeParameterSets()
# mount the directory with my input data
self.ifs = self.mountInputDataspace(pfs=application.pfs)
# load the data from the inputs into memory
displacements, self.cd = self.loadInputs()
# compute the normalization
self.normalization = self.computeNormalization()
# compute the inverse of the covariance matrix
self.cd_inv = self.computeCovarianceInverse()
# build the local representations
self.points = []
self.d = altar.vector(shape=self.observations)
self.los = altar.matrix(shape=(self.observations, 3))
self.oid = []
# populate them
for obs, record in enumerate(displacements):
# extract the observation id
self.oid.append(record.oid)
# extract the (x,y) coordinate of the observation point
self.points.append((record.x, record.y))
# extract the observed displacement
self.d[obs] = record.d
# get the LOS angles
theta = record.theta
phi = record.phi
# form the projection vectors and store them
self.los[obs, 0] = sin(theta) * cos(phi)
self.los[obs, 1] = sin(theta) * sin(phi)
self.los[obs, 2] = cos(theta)
# save the parameter meta data
self.meta()
# show me
# self.show(job=application.job, channel=self.info)
# all done
return self
def loadData(self):
"""
load data and covariance
"""
# grab the input dataspace
ifs = self.ifs
# next, the observations
try:
# get the path to the file
df = ifs[self.data_file]
# if the file doesn't exist
except ifs.NotFoundError:
# grab my error channel
channel = self.error
# complain
channel.log(f"missing observations: no '{self.data_file}' {ifs.path()}")
# and raise the exception again
raise
# if all goes well
else:
# allocate the vector
self.dataobs= altar.vector(shape=self.observations)
# and load the file contents into memory
self.dataobs.load(df.uri)
if self.cd_file is not None:
# finally, the data covariance
try:
# get the path to the file
cf = ifs[self.cd_file]
# if the file doesn't exist
except ifs.NotFoundError:
# grab my error channel
channel = self.error
# complain
channel.log(f"missing data covariance matrix: no '{self.cd_file}'")
# and raise the exception again
raise
# if all goes well
else:
# allocate the matrix
self.cd = altar.matrix(shape=(self.observations, self.observations))
# and load the file contents into memory
self.cd.load(cf.uri)
else:
# use a constant covariance
self.cd = self.cd_std
return
def initialize(self, rng):
"""
Initialize with the given random number generator
"""
# initialize the parent uniform distribution
super().initialize(rng=rng)
# initialize the area for each patches
self.patches = self.parameters
# by default, assign the constant patch_area to each patch
self.area_patches = altar.vector(shape=self.patches).fill(self.area)
# if a file is provided, load it
if self.area_patches_file is not None:
self.area_patches.load(self.area_patches_file.uri)
# all done
return self
def computeSampleMultiplicities(self, step):
"""
Prepare a frequency vector for the new samples given the scaled data log-likelihood in
{w} for this cooling step
"""
# print(" computing sample multiplicities:")
# unpack what we need
w = self.w
samples = step.samples
# build a vector of random numbers uniformly distributed in [0,1]
r = altar.vector(shape=samples).random(pdf=self.uniform)
# compute the bin edges in the range [0, 1]
ticks = tuple(self.buildHistogramRanges(w))
# build a histogram
h = altar.histogram(bins=samples).ranges(points=ticks).fill(r)
# and return it
return h
def updateTemperature(self, step):
"""
Generate the next temperature increment
"""
# grab the data log-likelihood
dataLikelihood = step.data
# initialize the vector of weights
self.w = altar.vector(shape=step.samples).zero()
# compute the median data log-likelihood; clone the source vector first, since the
# sorting happens in place
median = dataLikelihood.clone().sort().median()
# compute {δβ} and the normalized {w}
β, self.cov = altar.libaltar.dbeta(self.minimizer, dataLikelihood.data, median, self.w.data)
# and return the new temperature
return β
def __init__(self, **kwds):
# chain up
super().__init__(**kwds)
# local names for the math functions
log, π, cos, sin = math.log, math.pi, math.cos, math.sin
# the number of model parameters
dof = self.parameters
# convert the central value into a vector; allocate
peak = altar.vector(shape=dof)
# and populate
for index, value in enumerate(self.μ):
peak[index] = value
# the trigonometry
cos_φ = cos(self.φ)
sin_φ = sin(self.φ)
# the eigenvalues
λ0 = self.λ[0]
λ1 = self.λ[1]
# the eigenvalue inverses
λ0_inv = 1 / λ0
λ1_inv = 1 / λ1
# build the inverse of the covariance matrix
σ_inv = altar.matrix(shape=(dof, dof))
σ_inv[0, 0] = λ0_inv * cos_φ**2 + λ1_inv * sin_φ**2
σ_inv[1, 1] = λ1_inv * cos_φ**2 + λ0_inv * sin_φ**2
σ_inv[0, 1] = σ_inv[1, 0] = (λ1_inv - λ0_inv) * cos_φ * sin_φ
# compute its determinant and store it
σ_lndet = log(λ0 * λ1)
# attach the characteristics of my pdf
self.peak = peak
self.σ_inv = σ_inv
# the log-normalization
self.normalization = -.5 * (dof * log(2 * π) + σ_lndet)
# all done
return
def displacements(self, locations, los):
"""
Compute the expected displacements at a set of observation locations from a
two magma chamber (reverso) volcano model
"""
# the radius of the shallow reservoir
as_src = self.as
# the radius of the deep reservoir
ad_src = self.ad
# the radius of the connecting pipe between the two reservoirs
ac_src = self.ac
# depth of the shallow reservoir
hs_src = self.hs
# depth of the deep reservoir
hd_src = self.hd
# the basal magma inflow rate
q_src = self.q
# get the material properties
v = self.v
# from locations, a vector of (x,y) tuples, create the flattened vectors Xf, Yf required by
# CDM
Xf = numpy.zeros(len(locations), dtype=float)
Yf = numpy.zeros(len(locations), dtype=float)
for i, loc in enumerate(locations):
Xf[i] = loc[0]
Yf[i] = loc[1]
# allocate space for the result
u = altar.vector(shape=len(locations))
# compute the displacements
ue, un, uv = Reverso(X=Xf, Y=Yf, X0=x_src, Y0=y_src, depth=d_src,
ax=ax_src, ay=ay_src, az=az_src,
omegaX=omegaX_src, omegaY=omegaY_src, omegaZ=omegaZ_src,
opening=opening, nu=v)
# go through each observation location
for idx, (ux,uy,uz) in enumerate(zip(ue, un, uv)):
# project the expected displacement along LOS and store
u[idx] = ux * los[idx,0] + uy * los[idx,1] + uz * los[idx,2]
# all done
return u
def forwardModelBatched(self, theta, prediction):
"""
The forward model for a batch of theta: compute prediction from theta
also return {residual}=True, False if the difference between data and prediction is computed
"""
# The default method computes samples one by one
batch = self.samples
# create a prediction vector
prediction_sample = altar.vector(shape=self.observations)
# iterate over samples
for sample in range(batch):
# obtain the sample (one set of parameters)
theta_sample = theta.getRow(sample)
# call the forward model
self.forwardModel(theta=theta_sample, prediction=prediction_sample)
# copy to the prediction matrix
prediction.setRow(sample, prediction_sample)
# all done
return self
def walkChains(self, annealer, step):
"""
Run the Metropolis algorithm on the Markov chains
"""
# get the model
model = annealer.model
# and the event dispatcher
dispatcher = annealer.dispatcher
# unpack what i need from the cooling step
β = step.beta
θ = step.theta
prior = step.prior
data = step.data
posterior = step.posterior
# get the parameter covariance
Σ_chol = self.sigma_chol
# the sample geometry
samples = step.samples
parameters = step.parameters
# a couple of functions from the math module
exp = math.exp
log = math.log
# reset the accept/reject counters
accepted = rejected = unlikely = 0
# allocate some vectors that we use throughout the following
# candidate likelihoods
cprior = altar.vector(shape=samples)
cdata = altar.vector(shape=samples)
cpost = altar.vector(shape=samples)
# a fake covariance matrix for the candidate steps, just so we don't have to rebuild it
# every time
csigma = altar.matrix(shape=(parameters, parameters))
# the mask of samples rejected due to model constraint violations
rejects = altar.vector(shape=samples)
# and a vector with random numbers for the Metropolis acceptance
dice = altar.vector(shape=samples)
# step all chains together
for step in range(self.steps):
# notify we are advancing the chains
dispatcher.notify(event=dispatcher.chainAdvanceStart,
controller=annealer)
# initialize the candidate sample by randomly displacing the current one
cθ = self.displace(sample=θ)
# initialize the likelihoods
likelihoods = cprior.zero(), cdata.zero(), cpost.zero()
# and the covariance matrix
csigma.zero()
# build a candidate state
candidate = self.CoolingStep(beta=β,
theta=cθ,
likelihoods=likelihoods,
sigma=csigma)
# the random displacement may have generated candidates that are outside the
# support of the model, so we must give it an opportunity to reject them;
# notify we are starting the verification process
dispatcher.notify(event=dispatcher.verifyStart,
controller=annealer)
# reset the mask and ask the model to verify the sample validity
model.verify(step=candidate, mask=rejects.zero())
# make the candidate a consistent set by replacing the rejected samples with copies
# of the originals from {θ}
for index, flag in enumerate(rejects):
# if this sample was rejected
if flag:
# copy the corresponding row from {θ} into {candidate}
cθ.setRow(index, θ.getRow(index))
# notify that the verification process is finished
dispatcher.notify(event=dispatcher.verifyFinish,
controller=annealer)
# compute the likelihoods
model.likelihoods(annealer=annealer, step=candidate)
# build a vector to hold the difference of the two posterior likelihoods
diff = cpost.clone()
# subtract the previous posterior
diff -= posterior
# randomize the Metropolis acceptance vector
dice.random(self.uniform)
# notify we are starting accepting samples
dispatcher.notify(event=dispatcher.acceptStart,
controller=annealer)
# accept/reject: go through all the samples
for sample in range(samples):
# a candidate is rejected if the model considered it invalid
if rejects[sample]:
# nothing to do: θ, priorL, dataL, and postL contain the right statistics
# for this sample; just update the rejection count
rejected += 1
# and move on
continue
# a candidate is also rejected if the model considered it less likely than the
# original and it wasn't saved by the {dice}
if log(dice[sample]) > diff[sample]:
# nothing to do: θ, priorL, dataL, and postL contain the right statistics
# for this sample; just update the unlikely count
unlikely += 1
# and move on
continue
# otherwise, update the acceptance count
accepted += 1
# copy the candidate sample
θ.setRow(sample, cθ.getRow(sample))
# and its likelihoods
prior[sample] = cprior[sample]
data[sample] = cdata[sample]
posterior[sample] = cpost[sample]
# notify we are done accepting samples
dispatcher.notify(event=dispatcher.acceptFinish,
controller=annealer)
# notify we are done advancing the chains
dispatcher.notify(event=dispatcher.chainAdvanceFinish,
controller=annealer)
# all done
return accepted, rejected, unlikely
def cuInitialize(self, application):
"""
cuda interface of initialization
"""
# initialize the parent uniform distribution
super().cuInitialize(application=application)
# get the input path
ifs = application.pfs["inputs"]
# assign the rng
self.rng = application.rng.rng
# set the number of patches
self.patches = self.parameters
# initialize the area for each patch
if len(self.area) == 1:
# by default, assign the constant patch_area to each patch
self.area_patches = altar.vector(shape=self.patches).fill(
self.area[0])
elif len(self.area) != self.patches:
# if the size doesn't match
channel = self.error
raise channel.log(
"the size of area doesn't match the number of patches")
else:
#
self.area_patches = self.area
# if a file is provided, load it
if self.area_patch_file is not None:
try:
# get the path to the file
areafile = ifs[self.area_patch_file]
# if the file doesn't exist
except ifs.NotFoundError:
# grab my error channel
channel = self.error
# complain
channel.log(
f"missing area_patch_file: no '{self.area_patch_file}' {ifs.path()}"
)
# and raise the exception again
raise
# if all goes well
else:
# allocate the vector
self.area_patches = altar.vector(shape=self.patches)
# and load the file contents into memory
self.area_patches.load(self.areafile.uri)
# initialize the shear modulus for each patch
if len(self.Mu) == 1:
# by default, assign the constant to each patch
self.mu_patches = altar.vector(shape=self.patches).fill(self.Mu[0])
elif len(self.Mu) != self.patches:
# if the size doesn't match
channel = self.error
raise channel.log(
"the size of Mu doesn't match the number of patches")
else:
#
self.mu_patches = self.Mu
# all done
return self
def cuInitSample(self, theta):
"""
Fill my portion of {theta} with initial random values from my distribution.
"""
# use cpu to generate a batch of samples
samples = theta.shape[0]
parameters = self.parameters
θ = altar.matrix(shape=(samples, parameters))
# grab the references for area/shear modulus
area_patches = self.area_patches
mu_patches = self.mu_patches
# create a gaussian distribution to generate Mw for each sample
gaussian_Mw = altar.pdf.gaussian(mean=self.Mw_mean,
sigma=self.Mw_sigma,
rng=self.rng)
# create a dirichlet distribution to generate displacements
alpha = altar.vector(shape=parameters).fill(
1) # power 0, or (alpha_i = 1)
dirichlet_D = altar.pdf.dirichlet(alpha=alpha, rng=self.rng)
# create a tempory vector for theta of samples
theta_sample = altar.vector(shape=parameters)
# get the range
low, high = self.support
# iterate through samples to initialize samples
for sample in range(samples):
within_range = False
# iterate until all samples are within support
while within_range is False:
# assume within_range is true in the beginning
within_range = True
# generate a Mw sample
Mw = gaussian_Mw.sample()
# Pentiar = M0 = \sum (A_i D_i Mu_i)
# 15 here is for GPa * Km^2, instead of Pa * m^2
Pentier = pow(10, 1.5 * Mw + 9.1 - 15)
# if a negative sign is desired
if self.slip_sign == 'negative':
Pentier = -Pentier
# generate a dirichlet sample \sum x_i = 1
dirichlet_D.vector(vector=theta_sample)
# D_i = P * x_i /A_i
for patch in range(parameters):
theta_sample[patch] *= Pentier / (area_patches[patch] *
mu_patches[patch])
# check the range
if (theta_sample[patch] >= high
or theta_sample[patch] <= low):
within_range = False
break
# set theta
θ.setRow(sample, theta_sample)
# make a copy to gpu
gθ = altar.cuda.matrix(source=θ, dtype=self.precision)
# insert into theta according to the idx_range
theta.insert(src=gθ, start=(0, self.idx_range[0]))
# and return
return self
def initialize(self, application):
"""
Initialize the state of the model given a problem specification
"""
# chain up
super().initialize(application=application)
# initialize the parameter sets
self.initializeParameterSets()
# mount the workspace
self.ifs = self.mountInputDataspace(pfs=application.pfs)
# load the data
data = self.loadInputs()
# prep to swallow the inputs
self.ticks = []
self.d = altar.vector(shape=(3 * self.observations))
self.cd = altar.matrix(shape=(3 * self.observations,
3 * self.observations)).zero()
# go through the data records
for idx, rec in enumerate(data):
# save the (t,x,y) triplet
self.ticks.append((rec.t, rec.x, rec.y))
# save the three components of the displacements
self.d[3 * idx + 0] = rec.uE
self.d[3 * idx + 1] = rec.uN
self.d[3 * idx + 2] = rec.uZ
# populate the covariance matrix
self.cd[3 * idx + 0, 3 * idx + 0] = rec.σE
self.cd[3 * idx + 1, 3 * idx + 1] = rec.σN
self.cd[3 * idx + 2, 3 * idx + 2] = rec.σZ
# compute the normalization
self.normalization = self.computeNormalization()
# compute the inverse of the covariance matrix
self.cd_inv = self.computeCovarianceInverse()
# save the parameter meta data
self.meta()
# pick an implementation strategy
# if the user specified {fast} mode
if self.mode == "fast":
# attempt to
try:
# get the fast strategy that involves a CDM source implemented in C++
from .Fast import Fast as strategy
# if this fails
except ImportError:
# make channel
channel = application.error
# complain
raise channel.log("unable to find support for <fast> mode")
# otherwise
else:
# get the strategy implemented in pure python
from .Native import Native as strategy
# initialize it and save it
self.strategy = strategy().initialize(application=application,
model=self)
# show me
# self.show(job=application.job, channel=self.info)
# all done
return self
def loadInputsCp(self):
"""
Load the additional data (for Cp problem) in the input files into memory
"""
# grab the input dataspace
ifs = self.ifs
# the covariance/uncertainty for model parameter Cmu
try:
# get the path to the file
cmuf = ifs[self.cmu_file]
# if the file doesn't exist
except ifs.NotFoundError:
# grab my error channel
channel = self.error
# complain
channel.log(
f"missing data covariance matrix: no '{self.cmu_file}' in '{self.case}'"
)
# and raise the exception again
raise
# if all goes well
else:
# allocate the matrix
cmu = altar.matrix(shape=(self.nCmu, self.nCmu))
# and load the file contents into memory
cmu.load(cmuf.uri)
# the sensitivity kernel, Kmu ususally
nCmu = self.nCmu
prefix, suffix = self.kmu_file.split("[n]")
kmu = []
kmu_i = altar.matrix(shape=(self.observations, self.parameters))
for i in range(nCmu):
kmufn = prefix + str(i + 1) + suffix
try:
kmuf = ifs[kmufn]
except ifs.NotFoundErr:
channel.log(
f"missing sensitivity kernel: no '{kmufn}' in '{self.case}'"
)
raise
else:
kmu_i.load(kmuf.uri)
kmu.append(kmu_i)
# the initial model
try:
# get the path to the file
initModelf = ifs[self.initialModel_file]
# if the file doesn't exist
except ifs.NotFoundError:
channel.log(
f"missing initial model file: no '{initModelf}' in '{self.case}'"
)
raise
# if all goes well
else:
# and load the file contents into memory
initModel = altar.vector(shape=self.parameters)
initModel.load(initModelf.uri)
# all done
return kmu, cmu, initModel
def initialize(self, application):
"""
Initialize the state of the model given a {problem} specification
"""
# externals
from math import sin, cos
# chain up
super().initialize(application=application)
# initialize my parameter sets
self.initializeParameterSets()
# mount the directory with my input data
self.ifs = self.mountInputDataspace(pfs=application.pfs)
# load the data from the inputs into memory
displacements, self.cd = self.loadInputs()
# compute the normalization
self.normalization = self.computeNormalization()
# compute the inverse of the covariance matrix
self.cd_inv = self.computeCovarianceInverse()
# build the local representations
self.points = []
self.d = altar.vector(shape=self.observations)
self.los = altar.matrix(shape=(self.observations, 3))
self.oid = []
# populate them
for obs, record in enumerate(displacements):
# extract the observation id
self.oid.append(record.oid)
# extract the (x,y) coordinate of the observation point
self.points.append((record.x, record.y))
# extract the observed displacement
self.d[obs] = record.d
# get the LOS angles
theta = record.theta
phi = record.phi
# form the projection vectors and store them
self.los[obs, 0] = sin(theta) * cos(phi)
self.los[obs, 1] = sin(theta) * sin(phi)
self.los[obs, 2] = cos(theta)
# save the parameter meta data
self.meta()
# pick an implementation strategy
# if the user has asked for CUDA support
if application.job.gpus > 0:
# attempt to
try:
# use the CUDA implementation
from .CUDA import CUDA as strategy
# if this fails
except ImportError:
# make a channel
channel = application.error
# complain
raise channel.log("unable to find CUDA support")
# if the user specified {fast} mode
elif self.mode == "fast":
# attempt to
try:
# get the fast strategy that involves a Reverso source implemented in C++
from .Fast import Fast as strategy
# if this fails
except ImportError:
# make channel
channel = application.error
# complain
raise channel.log("unable to find support for <fast> mode")
# otherwise
else:
# get the strategy implemented in pure python
from .Native import Native as strategy
# initialize it and save it
self.strategy = strategy().initialize(application=application,
model=self)
# show me
# self.show(job=application.job, channel=self.info)
# all done
return self
def loadInputs(self):
"""
Load the data in the input files into memory
"""
# grab the input dataspace
ifs = self.ifs
# first the green functions
try:
# get the path to the file
gf = ifs[self.green]
# if the file doesn't exist
except ifs.NotFoundError:
# grab my error channel
channel = self.error
# complain
channel.log(
f"missing Green functions: no '{self.green}' in '{self.case}'")
# and raise the exception again
raise
# if all goes well
else:
# allocate the matrix
green = altar.matrix(shape=(self.observations, self.parameters))
# and load the file contents into memory
green.load(gf.uri)
# next, the observations
try:
# get the path to the file
df = ifs[self.data]
# if the file doesn't exist
except ifs.NotFoundError:
# grab my error channel
channel = self.error
# complain
channel.log(
f"missing observations: no '{self.data}' in '{self.case}'")
# and raise the exception again
raise
# if all goes well
else:
# allocate the vector
data = altar.vector(shape=self.observations)
# and load the file contents into memory
data.load(df.uri)
# finally, the data covariance
try:
# get the path to the file
cf = ifs[self.cd]
# if the file doesn't exist
except ifs.NotFoundError:
# grab my error channel
channel = self.error
# complain
channel.log(
f"missing data covariance matrix: no '{self.cd}' in '{self.case}'"
)
# and raise the exception again
raise
# if all goes well
else:
# allocate the matrix
cd = altar.matrix(shape=(self.observations, self.observations))
# and load the file contents into memory
cd.load(cf.uri)
# all done
return green, data, cd
def dataLikelihood(self, model, step):
"""
Fill {step.data} with the likelihoods of the samples in {step.theta} given the available
data.
"""
# get the norm
norm = model.norm
# grab the portion of the sample that belongs to this model
θ = model.restrict(theta=step.theta)
# the observed displacements
displacements = model.d
# the inverse of the data covariance matrix
cd_inv = model.cd_inv
# the normalization
normalization = model.normalization
# and the storage for the data likelihoods
dataLLK = step.data
# find out how many samples in the set
samples = θ.rows
# get the parameter sets
psets = model.psets
# get the offsets of the various parameter sets
QinIdx = model.Qin_idx
HsIdx = model.Hs_idx
HdIdx = model.Hd_idx
asIdx = model.as_idx
adIdx = model.ad_idx
acIdx = model.ac_idx
# get the locations and times of the observations
ticks = model.ticks
# initialize a vector to hold the expected displacements
u = altar.vector(shape=(3 * model.observations))
# for each sample in the sample set
for sample in range(samples):
# extract the parameters
parameters = θ.getRow(sample)
# get the flow rate
Qin = parameters[QinIdx]
# get the locations of the chambers
H_s = parameters[HsIdx]
H_d = parameters[HdIdx]
# get the sizes
a_s = parameters[asIdx]
a_d = parameters[adIdx]
a_c = parameters[acIdx]
# make a source using the sample parameters
reverso = source(H_s=H_s,
H_d=H_d,
a_s=a_s,
a_d=a_d,
a_c=a_c,
Qin=Qin,
G=model.G,
v=model.v,
mu=model.mu,
drho=model.drho,
g=model.g)
# prime the displacement calculator
predicted = reverso.displacements(locations=ticks)
# compute the displacements
for idx, ((t, x, y), (u_R,
u_Z)) in enumerate(zip(ticks, predicted)):
# find the polar angle of the vector to the observation location
phi = math.atan2(y, x)
# compute the E and N components
u_E = u_R * math.sin(phi)
u_N = u_R * math.cos(phi)
# save
u[3 * idx + 0] = u_E
u[3 * idx + 1] = u_N
u[3 * idx + 2] = u_Z
# subtract the observed displacements
u -= displacements
# compute the norm of the displacements
nrm = norm.eval(v=u, sigma_inv=cd_inv)
# normalize and store it as the data log likelihood
dataLLK[sample] = normalization - nrm**2 / 2
# all done
return self
