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# Copyright(C) 2013-2021 Max-Planck-Society
# SPDX-License-Identifier: GPL-2.0+ OR BSD-2-Clause

Demonstration of the non-parametric correlated field model in NIFTy.re#

The Model#

import jax
import matplotlib.pyplot as plt
from jax import numpy as jnp
from jax import random

import nifty8.re as jft

jax.config.update("jax_enable_x64", True)

seed = 42
key = random.PRNGKey(seed)

dims = (128, 128)

cf_zm = dict(offset_mean=0.0, offset_std=(1e-3, 1e-4))
cf_fl = dict(
    fluctuations=(1e-1, 5e-3),
    loglogavgslope=(-1.0, 1e-2),
    flexibility=(1e0, 5e-1),
    asperity=(5e-1, 5e-2),
)
cfm = jft.CorrelatedFieldMaker("cf")
cfm.set_amplitude_total_offset(**cf_zm)
cfm.add_fluctuations(
    dims, distances=1.0 / dims[0], **cf_fl, prefix="ax1", non_parametric_kind="power"
)
correlated_field = cfm.finalize()

scaling = jft.LogNormalPrior(3.0, 1.0, name="scaling", shape=(1,))


class Signal(jft.Model):
    def __init__(self, correlated_field, scaling):
        self.cf = correlated_field
        self.scaling = scaling
        # Init methods of the Correlated Field model and any prior model in
        # NIFTy.re are aware that their input is standard normal a priori.
        # The `domain` of a model does not know this. Thus, tracking the `init`
        # methods should be preferred over tracking the `domain`.
        super().__init__(init=self.cf.init | self.scaling.init)

    def __call__(self, x):
        # NOTE, think of `Model` as being just a plain function that takes some
        # input and performs all the necessary computation for your model.
        # Note, `scaling` here is completely degenarate with `offset_std` in the
        # likelihood but the priors for them are very different.
        return self.scaling(x) * jnp.exp(self.cf(x))


signal = Signal(correlated_field, scaling)

Matplotlib is building the font cache; this may take a moment.

NIFTy to NIFTy.re#

The equivalent model for the correlated field in numpy-based NIFTy reads

import nifty8 as ift

position_space = ift.RGSpace(dims, distances=1. / dims[0])
cf_fl_nft = {
    k: v
    for k, v in cf_fl.items() if k not in ("harmonic_domain_type", )
}
cfm_nft = ift.CorrelatedFieldMaker("cf")
cfm_nft.add_fluctuations(position_space, **cf_fl_nft, prefix="ax1")
cfm_nft.set_amplitude_total_offset(**cf_zm)
correlated_field_nft = cfm_nft.finalize()

For convience, NIFTy implements a method to translate numpy-based NIFTy operators to NIFTy.re. One can access the equivalent expression in JAX for a NIFTy model via the .jax_expr property of an operator. In addition, NIFTy features a method to additionally preserve the domain and target: ift.nifty2jax.convert translate NIFTy operators to jft.Model. NIFTy.re models feature .domain and .target properties but instead of yielding domains, they return [JAX PyTrees](TODO:cite PyTree docu) of shape-and-dtype objects.

# Convenience method to get JAX expression as NIFTy.re model which tracks
# domain and target
correlated_field_nft: jft.Model = ift.nifty2jax.convert(
    correlated_field_nft, float
)

Both expressions are identical up to floating point precision

import numpy as np

t = correlated_field_nft.init(random.PRNGKey(42))
np.testing.assert_allclose(
    correlated_field(t), correlated_field_nft(t), atol=1e-13, rtol=1e-13
)

Note, caution is advised when translating NIFTy models working on complex numbers. Numyp-based NIFTy models are not dtype aware and thus require more care when translating them to NIFTy.re/JAX which requires known dtypes.

Notes on Refinement Field#

The above could just as well be a refinement field e.g. on a HEALPix sphere with logarithmically spaced radial voxels. All of NIFTy.re is agnostic to the specifics of the forward model. The sampling and minimization always works the same.

def matern_kernel(distance, scale=1., cutoff=1., dof=1.5):
    if dof == 0.5:
        cov = scale**2 * jnp.exp(-distance / cutoff)
    elif dof == 1.5:
        reg_dist = jnp.sqrt(3) * distance / cutoff
        cov = scale**2 * (1 + reg_dist) * jnp.exp(-reg_dist)
    elif dof == 2.5:
        reg_dist = jnp.sqrt(5) * distance / cutoff
        cov = scale**2 * (1 + reg_dist + reg_dist**2 / 3) * jnp.exp(-reg_dist)
    else:
        raise NotImplementedError()
    # NOTE, this is not safe for differentiating because `cov` still may
    # contain NaNs
    return jnp.where(distance < 1e-8 * cutoff, scale**2, cov)

def rg2cart(x, idx0, scl):
    """Transforms regular, points from a Euclidean space to irregular points in
    an cartesian coordinate system in 1D."""
    return jnp.exp(scl * x[0] + idx0)[jnp.newaxis, ...]

def cart2rg(x, idx0, scl):
    """Inverse of `rg2cart`."""
    return ((jnp.log(x[0]) - idx0) / scl)[jnp.newaxis, ...]

cc = jft.HEALPixChart(
    min_shape=(12 * 32**2, 4),  # 32 (Nside) times (at least) 4 radial bins
    nonhp_rg2cart=partial(rg2cart, idx0=-0.27, scl=1.1),  # radial spacing
    nonhp_cart2rg=partial(cart2rg, idx0=-0.27, scl=1.1),
)
rf = jft.RefinementHPField(cc)
# Make the refinement fast by leaving the kernel fixed
rfm = rf.matrices(matern_kernel)
correlated_field = jft.Model(
    partial(rf, kernel=rfm), domain=rf.domain, init=rf.init
)

The likelihood#

signal_response = signal
noise_cov = lambda x: 0.1**2 * x
noise_cov_inv = lambda x: 0.1**-2 * x

# Create synthetic data
key, subkey = random.split(key)
pos_truth = jft.random_like(subkey, signal_response.domain)
signal_response_truth = signal_response(pos_truth)
key, subkey = random.split(key)
noise_truth = (
    (noise_cov(jft.ones_like(signal_response.target))) ** 0.5
) * jft.random_like(key, signal_response.target)
data = signal_response_truth + noise_truth

lh = jft.Gaussian(data, noise_cov_inv).amend(signal_response)

assuming a diagonal covariance matrix;
setting `std_inv` to `cov_inv(ones_like(data))**0.5`

The inference#

n_vi_iterations = 6
delta = 1e-4
n_samples = 4

key, k_i, k_o = random.split(key, 3)
# NOTE, changing the number of samples always triggers a resampling even if
# `resamples=False`, as more samples have to be drawn that did not exist before.
samples, state = jft.optimize_kl(
    lh,
    jft.Vector(lh.init(k_i)),
    n_total_iterations=n_vi_iterations,
    n_samples=lambda i: n_samples // 2 if i < 2 else n_samples,
    # Source for the stochasticity for sampling
    key=k_o,
    # Names of parameters that should not be sampled but still optimized
    # can be specified as point_estimates (effectively we are doing MAP for
    # these degrees of freedom).
    # point_estimates=("cfax1flexibility", "cfax1asperity"),
    # Arguments for the conjugate gradient method used to drawing samples from
    # an implicit covariance matrix
    draw_linear_kwargs=dict(
        cg_name="SL",
        cg_kwargs=dict(absdelta=delta * jft.size(lh.domain) / 10.0, maxiter=100),
    ),
    # Arguements for the minimizer in the nonlinear updating of the samples
    nonlinearly_update_kwargs=dict(
        minimize_kwargs=dict(
            name="SN",
            xtol=delta,
            cg_kwargs=dict(name=None),
            maxiter=5,
        )
    ),
    # Arguments for the minimizer of the KL-divergence cost potential
    kl_kwargs=dict(
        minimize_kwargs=dict(
            name="M", xtol=delta, cg_kwargs=dict(name=None), maxiter=35
        )
    ),
    sample_mode="nonlinear_resample",
    odir="results_intro",
    resume=False,
)

OPTIMIZE_KL: Starting 0001


SL: Iteration 0 ⛰:+4.0446e+04 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+4.0373e+04 Δ⛰:7.2899e+01 ➽:1.9628e-01


SL: Iteration 2 ⛰:+3.8579e+04 Δ⛰:1.7947e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+2.8716e+04 Δ⛰:9.8630e+03 ➽:1.9628e-01


SL: Iteration 4 ⛰:+1.6650e+04 Δ⛰:1.2066e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+1.1409e+04 Δ⛰:5.2410e+03 ➽:1.9628e-01


SL: Iteration 6 ⛰:+1.1405e+04 Δ⛰:3.5204e+00 ➽:1.9628e-01


SL: Iteration 7 ⛰:+3.9342e+03 Δ⛰:7.4712e+03 ➽:1.9628e-01


SL: Iteration 8 ⛰:-1.8404e+03 Δ⛰:5.7746e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-5.2324e+03 Δ⛰:3.3920e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-6.3975e+03 Δ⛰:1.1651e+03 ➽:1.9628e-01


SL: Iteration 11 ⛰:-6.9880e+03 Δ⛰:5.9044e+02 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.7904e+03 Δ⛰:8.0243e+02 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.6123e+03 Δ⛰:8.2190e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-9.0903e+03 Δ⛰:4.7804e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-9.0904e+03 Δ⛰:1.0592e-01 ➽:1.9628e-01


SL: Iteration 0 ⛰:+1.6457e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+4.4909e+04 Δ⛰:1.1966e+05 ➽:1.9628e-01


SL: Iteration 2 ⛰:+4.1599e+04 Δ⛰:3.3108e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+3.3463e+04 Δ⛰:8.1353e+03 ➽:1.9628e-01


SL: Iteration 4 ⛰:+2.7254e+04 Δ⛰:6.2089e+03 ➽:1.9628e-01


SL: Iteration 5 ⛰:+1.3327e+04 Δ⛰:1.3927e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+1.3265e+04 Δ⛰:6.1913e+01 ➽:1.9628e-01


SL: Iteration 7 ⛰:+4.9685e+03 Δ⛰:8.2970e+03 ➽:1.9628e-01


SL: Iteration 8 ⛰:-2.1617e+03 Δ⛰:7.1301e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-5.2462e+03 Δ⛰:3.0845e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-6.0932e+03 Δ⛰:8.4705e+02 ➽:1.9628e-01


SL: Iteration 11 ⛰:-6.8050e+03 Δ⛰:7.1180e+02 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.7508e+03 Δ⛰:9.4574e+02 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.5205e+03 Δ⛰:7.6968e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.9819e+03 Δ⛰:4.6145e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.9820e+03 Δ⛰:1.1742e-01 ➽:1.9628e-01


SN: →:1.0 ↺:False #∇²:06 |↘|:1.139915e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+9.091584e+03 Δ⛰:8.199126e+05


SN: →:1.0 ↺:False #∇²:12 |↘|:8.127232e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+5.788479e+03 Δ⛰:3.303105e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.308595e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+4.778153e+03 Δ⛰:1.010326e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:2.866287e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+4.237620e+03 Δ⛰:5.405330e+02


SN: →:1.0 ↺:False #∇²:30 |↘|:2.115670e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+3.846415e+03 Δ⛰:3.912057e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.400728e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+8.230964e+03 Δ⛰:9.736373e+05


SN: →:1.0 ↺:False #∇²:12 |↘|:6.480931e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+5.535063e+03 Δ⛰:2.695901e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:4.335553e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+4.330150e+03 Δ⛰:1.204913e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:2.877313e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+3.709676e+03 Δ⛰:6.204742e+02


SN: →:1.0 ↺:False #∇²:30 |↘|:2.100274e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+3.392564e+03 Δ⛰:3.171123e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.260299e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+8.890230e+03 Δ⛰:1.243685e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:7.044508e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+5.824211e+03 Δ⛰:3.066019e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.549644e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+4.780485e+03 Δ⛰:1.043726e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:2.536483e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+4.244810e+03 Δ⛰:5.356747e+02


SN: →:1.0 ↺:False #∇²:31 |↘|:2.217360e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+3.853784e+03 Δ⛰:3.910261e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.427780e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+8.597012e+03 Δ⛰:2.171729e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:5.748724e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+5.593473e+03 Δ⛰:3.003539e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.104933e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+4.708588e+03 Δ⛰:8.848857e+02


SN: →:1.0 ↺:False #∇²:24 |↘|:2.538736e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+4.194907e+03 Δ⛰:5.136803e+02


SN: →:1.0 ↺:False #∇²:30 |↘|:1.990905e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+3.851477e+03 Δ⛰:3.434302e+02


SN: Iteration Limit Reached


M: →:1.0 ↺:False #∇²:06 |↘|:5.060345e+03 ➽:1.962800e+00
M: Iteration 1 ⛰:+2.074281e+05 Δ⛰:9.205151e+05


M: →:0.5 ↺:False #∇²:12 |↘|:1.077665e+04 ➽:1.962800e+00
M: Iteration 2 ⛰:+1.146056e+05 Δ⛰:9.282251e+04


M: →:1.0 ↺:False #∇²:18 |↘|:2.059747e+03 ➽:1.962800e+00
M: Iteration 3 ⛰:+6.603196e+04 Δ⛰:4.857364e+04


M: →:1.0 ↺:False #∇²:24 |↘|:2.963930e+03 ➽:1.962800e+00
M: Iteration 4 ⛰:+4.110575e+04 Δ⛰:2.492621e+04


M: →:1.0 ↺:False #∇²:30 |↘|:3.248146e+03 ➽:1.962800e+00
M: Iteration 5 ⛰:+3.155918e+04 Δ⛰:9.546568e+03


M: →:1.0 ↺:False #∇²:36 |↘|:1.303948e+03 ➽:1.962800e+00
M: Iteration 6 ⛰:+2.971888e+04 Δ⛰:1.840307e+03


M: →:1.0 ↺:False #∇²:42 |↘|:6.600111e+02 ➽:1.962800e+00
M: Iteration 7 ⛰:+2.915802e+04 Δ⛰:5.608531e+02


M: →:1.0 ↺:False #∇²:48 |↘|:4.489161e+02 ➽:1.962800e+00
M: Iteration 8 ⛰:+2.892266e+04 Δ⛰:2.353673e+02


M: →:1.0 ↺:False #∇²:54 |↘|:2.909820e+02 ➽:1.962800e+00
M: Iteration 9 ⛰:+2.878839e+04 Δ⛰:1.342609e+02


M: →:1.0 ↺:False #∇²:60 |↘|:2.225802e+02 ➽:1.962800e+00
M: Iteration 10 ⛰:+2.869633e+04 Δ⛰:9.206812e+01


M: →:1.0 ↺:False #∇²:66 |↘|:1.766389e+02 ➽:1.962800e+00
M: Iteration 11 ⛰:+2.862540e+04 Δ⛰:7.093026e+01


M: →:1.0 ↺:False #∇²:72 |↘|:1.490391e+02 ➽:1.962800e+00
M: Iteration 12 ⛰:+2.856948e+04 Δ⛰:5.591193e+01


M: →:1.0 ↺:False #∇²:78 |↘|:1.266734e+02 ➽:1.962800e+00
M: Iteration 13 ⛰:+2.852393e+04 Δ⛰:4.555334e+01


M: →:1.0 ↺:False #∇²:84 |↘|:1.152120e+02 ➽:1.962800e+00
M: Iteration 14 ⛰:+2.848632e+04 Δ⛰:3.760777e+01


M: →:1.0 ↺:False #∇²:90 |↘|:9.988969e+01 ➽:1.962800e+00
M: Iteration 15 ⛰:+2.845396e+04 Δ⛰:3.235910e+01


M: →:1.0 ↺:False #∇²:96 |↘|:9.529676e+01 ➽:1.962800e+00
M: Iteration 16 ⛰:+2.842411e+04 Δ⛰:2.985659e+01


M: →:1.0 ↺:False #∇²:102 |↘|:8.900868e+01 ➽:1.962800e+00
M: Iteration 17 ⛰:+2.839428e+04 Δ⛰:2.983040e+01


M: →:1.0 ↺:False #∇²:108 |↘|:9.788325e+01 ➽:1.962800e+00
M: Iteration 18 ⛰:+2.835580e+04 Δ⛰:3.848115e+01


M: →:1.0 ↺:False #∇²:114 |↘|:1.295945e+02 ➽:1.962800e+00
M: Iteration 19 ⛰:+2.831242e+04 Δ⛰:4.337666e+01


M: →:1.0 ↺:False #∇²:120 |↘|:1.396440e+02 ➽:1.962800e+00
M: Iteration 20 ⛰:+2.826781e+04 Δ⛰:4.460681e+01


M: →:1.0 ↺:False #∇²:126 |↘|:1.470588e+02 ➽:1.962800e+00
M: Iteration 21 ⛰:+2.823762e+04 Δ⛰:3.019548e+01


M: →:1.0 ↺:False #∇²:132 |↘|:7.885238e+01 ➽:1.962800e+00
M: Iteration 22 ⛰:+2.821769e+04 Δ⛰:1.992293e+01


M: →:1.0 ↺:False #∇²:138 |↘|:1.127151e+02 ➽:1.962800e+00
M: Iteration 23 ⛰:+2.820160e+04 Δ⛰:1.608980e+01


M: →:1.0 ↺:False #∇²:144 |↘|:4.425735e+01 ➽:1.962800e+00
M: Iteration 24 ⛰:+2.819071e+04 Δ⛰:1.089588e+01


M: →:1.0 ↺:False #∇²:150 |↘|:5.348430e+01 ➽:1.962800e+00
M: Iteration 25 ⛰:+2.818397e+04 Δ⛰:6.734948e+00


M: →:1.0 ↺:False #∇²:156 |↘|:3.174689e+01 ➽:1.962800e+00
M: Iteration 26 ⛰:+2.817865e+04 Δ⛰:5.319147e+00


M: →:1.0 ↺:False #∇²:162 |↘|:4.424672e+01 ➽:1.962800e+00
M: Iteration 27 ⛰:+2.817376e+04 Δ⛰:4.891890e+00


M: →:1.0 ↺:False #∇²:168 |↘|:2.615404e+01 ➽:1.962800e+00
M: Iteration 28 ⛰:+2.816973e+04 Δ⛰:4.037107e+00


M: →:1.0 ↺:False #∇²:174 |↘|:6.292507e+01 ➽:1.962800e+00
M: Iteration 29 ⛰:+2.816422e+04 Δ⛰:5.508033e+00


M: →:1.0 ↺:False #∇²:180 |↘|:1.557728e+01 ➽:1.962800e+00
M: Iteration 30 ⛰:+2.816030e+04 Δ⛰:3.912017e+00


M: →:1.0 ↺:False #∇²:186 |↘|:3.198018e+01 ➽:1.962800e+00
M: Iteration 31 ⛰:+2.815791e+04 Δ⛰:2.398947e+00


M: →:1.0 ↺:False #∇²:192 |↘|:1.193483e+01 ➽:1.962800e+00
M: Iteration 32 ⛰:+2.815580e+04 Δ⛰:2.109055e+00


M: →:1.0 ↺:False #∇²:198 |↘|:2.395076e+01 ➽:1.962800e+00
M: Iteration 33 ⛰:+2.815410e+04 Δ⛰:1.699144e+00


M: →:1.0 ↺:False #∇²:204 |↘|:1.003602e+01 ➽:1.962800e+00
M: Iteration 34 ⛰:+2.815257e+04 Δ⛰:1.525714e+00


M: →:1.0 ↺:False #∇²:210 |↘|:1.999512e+01 ➽:1.962800e+00
M: Iteration 35 ⛰:+2.815125e+04 Δ⛰:1.323586e+00


M: Iteration Limit Reached


OPTIMIZE_KL: Iteration 0001 ⛰:+2.8151e+04
OPTIMIZE_KL: #(Nonlinear sampling steps) (5, 5, 5, 5)
OPTIMIZE_KL: #(KL minimization steps) 35
OPTIMIZE_KL: Likelihood residual(s):
reduced χ²:     1.6±   0.021, avg: +0.00036±  0.0065, #dof:  16384
OPTIMIZE_KL: Prior residual(s):
cfax1asperity         :: reduced χ²:    0.12±   0.093, avg:    +0.15±    0.31, #dof:      1
cfax1flexibility      :: reduced χ²:   1e+01±     2.9, avg:     -3.2±    0.45, #dof:      1
cfax1fluctuations     :: reduced χ²:     8.0±    0.87, avg:     -2.8±    0.15, #dof:      1
cfax1loglogavgslope   :: reduced χ²:   2e+01±     3.6, avg:     +4.4±    0.41, #dof:      1
cfax1spectrum         :: reduced χ²:    0.97±    0.02, avg:  +0.0087±    0.02, #dof:   3238
cfxi                  :: reduced χ²:     1.6±  0.0097, avg:  +0.0012± 0.00084, #dof:  16384
cfzeromode            :: reduced χ²:     3.0±     1.6, avg:    -0.48±     1.7, #dof:      1
scaling               :: reduced χ²:    0.38±  0.0055, avg:    +0.62±  0.0045, #dof:      1




OPTIMIZE_KL: Starting 0002


SL: Iteration 0 ⛰:+2.4082e+06 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+6.8415e+04 Δ⛰:2.3398e+06 ➽:1.9628e-01


SL: Iteration 2 ⛰:+5.9867e+04 Δ⛰:8.5480e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+3.9935e+04 Δ⛰:1.9932e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+2.5880e+04 Δ⛰:1.4055e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+2.5853e+04 Δ⛰:2.6890e+01 ➽:1.9628e-01


SL: Iteration 6 ⛰:+1.5891e+04 Δ⛰:9.9629e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+5.7789e+03 Δ⛰:1.0112e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:-2.0954e+03 Δ⛰:7.8743e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-5.4432e+03 Δ⛰:3.3478e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-5.4439e+03 Δ⛰:6.7962e-01 ➽:1.9628e-01


SL: Iteration 11 ⛰:-6.7897e+03 Δ⛰:1.3459e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-8.2018e+03 Δ⛰:1.4121e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.7970e+03 Δ⛰:5.9520e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.8020e+03 Δ⛰:4.9987e+00 ➽:1.9628e-01


SL: Iteration 15 ⛰:-9.0810e+03 Δ⛰:2.7901e+02 ➽:1.9628e-01


SL: Iteration 16 ⛰:-9.3263e+03 Δ⛰:2.4532e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.4862e+03 Δ⛰:1.5981e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.5813e+03 Δ⛰:9.5162e+01 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.5828e+03 Δ⛰:1.5027e+00 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.6250e+03 Δ⛰:4.2227e+01 ➽:1.9628e-01


SL: Iteration 21 ⛰:-9.6492e+03 Δ⛰:2.4199e+01 ➽:1.9628e-01


SL: Iteration 22 ⛰:-9.6693e+03 Δ⛰:2.0093e+01 ➽:1.9628e-01


SL: Iteration 23 ⛰:-9.6693e+03 Δ⛰:1.4931e-03 ➽:1.9628e-01


SL: Iteration 0 ⛰:+8.4314e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+6.4228e+04 Δ⛰:7.7891e+05 ➽:1.9628e-01


SL: Iteration 2 ⛰:+4.8255e+04 Δ⛰:1.5973e+04 ➽:1.9628e-01


SL: Iteration 3 ⛰:+3.2650e+04 Δ⛰:1.5605e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+1.7917e+04 Δ⛰:1.4733e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+1.7909e+04 Δ⛰:8.6216e+00 ➽:1.9628e-01


SL: Iteration 6 ⛰:+9.8654e+03 Δ⛰:8.0431e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+3.9605e+03 Δ⛰:5.9049e+03 ➽:1.9628e-01


SL: Iteration 8 ⛰:-2.6356e+03 Δ⛰:6.5961e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-5.8731e+03 Δ⛰:3.2375e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-5.9089e+03 Δ⛰:3.5794e+01 ➽:1.9628e-01


SL: Iteration 11 ⛰:-7.1202e+03 Δ⛰:1.2112e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-8.3361e+03 Δ⛰:1.2160e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.8754e+03 Δ⛰:5.3928e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.8758e+03 Δ⛰:4.1117e-01 ➽:1.9628e-01


SL: Iteration 15 ⛰:-9.1621e+03 Δ⛰:2.8625e+02 ➽:1.9628e-01


SL: Iteration 16 ⛰:-9.3301e+03 Δ⛰:1.6800e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.5064e+03 Δ⛰:1.7629e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.5815e+03 Δ⛰:7.5126e+01 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.5861e+03 Δ⛰:4.5511e+00 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.6205e+03 Δ⛰:3.4487e+01 ➽:1.9628e-01


SL: Iteration 21 ⛰:-9.6489e+03 Δ⛰:2.8371e+01 ➽:1.9628e-01


SL: Iteration 22 ⛰:-9.6655e+03 Δ⛰:1.6578e+01 ➽:1.9628e-01


SL: Iteration 23 ⛰:-9.6655e+03 Δ⛰:5.3087e-03 ➽:1.9628e-01


SN: →:1.0 ↺:False #∇²:06 |↘|:2.740806e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+3.636662e+02 Δ⛰:3.201396e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.167820e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.609019e+02 Δ⛰:1.027643e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:2.749164e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+3.715699e+02 Δ⛰:2.875251e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.086366e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.674801e+02 Δ⛰:1.040897e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:2.501262e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+4.204056e+02 Δ⛰:2.283138e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.369355e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.970791e+02 Δ⛰:1.233265e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:2.812642e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+4.229630e+02 Δ⛰:3.021697e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.354851e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.985343e+02 Δ⛰:1.244287e+02


M: →:1.0 ↺:False #∇²:13 |↘|:3.225276e+03 ➽:1.962800e+00
M: Iteration 1 ⛰:+2.298534e+04 Δ⛰:1.311247e+02


M: →:1.0 ↺:False #∇²:19 |↘|:3.277249e+02 ➽:1.962800e+00
M: Iteration 2 ⛰:+2.275635e+04 Δ⛰:2.289933e+02


M: →:1.0 ↺:False #∇²:25 |↘|:2.213629e+02 ➽:1.962800e+00
M: Iteration 3 ⛰:+2.264523e+04 Δ⛰:1.111168e+02


M: →:1.0 ↺:False #∇²:31 |↘|:1.768791e+02 ➽:1.962800e+00
M: Iteration 4 ⛰:+2.257501e+04 Δ⛰:7.022343e+01


M: →:1.0 ↺:False #∇²:42 |↘|:1.411035e+03 ➽:1.962800e+00
M: Iteration 5 ⛰:+2.247270e+04 Δ⛰:1.023067e+02


M: →:1.0 ↺:False #∇²:48 |↘|:1.364611e+02 ➽:1.962800e+00
M: Iteration 6 ⛰:+2.243300e+04 Δ⛰:3.970402e+01


M: →:1.0 ↺:False #∇²:54 |↘|:6.529795e+01 ➽:1.962800e+00
M: Iteration 7 ⛰:+2.241742e+04 Δ⛰:1.558158e+01


M: →:1.0 ↺:False #∇²:60 |↘|:7.039017e+01 ➽:1.962800e+00
M: Iteration 8 ⛰:+2.240954e+04 Δ⛰:7.879561e+00


M: →:1.0 ↺:False #∇²:66 |↘|:3.100916e+01 ➽:1.962800e+00
M: Iteration 9 ⛰:+2.240461e+04 Δ⛰:4.925526e+00


M: →:1.0 ↺:False #∇²:72 |↘|:5.081713e+01 ➽:1.962800e+00
M: Iteration 10 ⛰:+2.240131e+04 Δ⛰:3.297200e+00


M: →:1.0 ↺:False #∇²:83 |↘|:2.989775e+02 ➽:1.962800e+00
M: Iteration 11 ⛰:+2.239488e+04 Δ⛰:6.430717e+00


M: →:1.0 ↺:False #∇²:89 |↘|:3.827149e+01 ➽:1.962800e+00
M: Iteration 12 ⛰:+2.239331e+04 Δ⛰:1.570646e+00


M: →:1.0 ↺:False #∇²:95 |↘|:7.431186e+00 ➽:1.962800e+00
M: Iteration 13 ⛰:+2.239259e+04 Δ⛰:7.255611e-01


M: →:1.0 ↺:False #∇²:101 |↘|:2.068445e+01 ➽:1.962800e+00
M: Iteration 14 ⛰:+2.239223e+04 Δ⛰:3.602094e-01


M: →:1.0 ↺:False #∇²:107 |↘|:3.515207e+00 ➽:1.962800e+00
M: Iteration 15 ⛰:+2.239202e+04 Δ⛰:2.058704e-01


M: →:1.0 ↺:False #∇²:113 |↘|:1.356677e+01 ➽:1.962800e+00
M: Iteration 16 ⛰:+2.239190e+04 Δ⛰:1.198013e-01


M: →:1.0 ↺:False #∇²:119 |↘|:1.911848e+00 ➽:1.962800e+00
M: Iteration 17 ⛰:+2.239182e+04 Δ⛰:8.328632e-02


OPTIMIZE_KL: Iteration 0002 ⛰:+2.2392e+04
OPTIMIZE_KL: #(Nonlinear sampling steps) (2, 2, 2, 2)
OPTIMIZE_KL: #(KL minimization steps) 17
OPTIMIZE_KL: Likelihood residual(s):
reduced χ²:     1.3±  0.0041, avg: +7.6e-05±  0.0065, #dof:  16384
OPTIMIZE_KL: Prior residual(s):
cfax1asperity         :: reduced χ²:     1.8±    0.89, avg:    -0.21±     1.3, #dof:      1
cfax1flexibility      :: reduced χ²:     6.1±    0.55, avg:     -2.5±    0.11, #dof:      1
cfax1fluctuations     :: reduced χ²:   0.006±  0.0053, avg:   -0.063±   0.046, #dof:      1
cfax1loglogavgslope   :: reduced χ²:     2.2±     2.1, avg:    +0.79±     1.3, #dof:      1
cfax1spectrum         :: reduced χ²:    0.97±   0.012, avg:  +0.0062±  0.0094, #dof:   3238
cfxi                  :: reduced χ²:     1.3±  0.0058, avg: +0.00057±  0.0016, #dof:  16384
cfzeromode            :: reduced χ²:    0.46±    0.42, avg:    +0.11±    0.67, #dof:      1
scaling               :: reduced χ²:    0.38±  0.0024, avg:    +0.61±   0.002, #dof:      1




OPTIMIZE_KL: Starting 0003


SL: Iteration 0 ⛰:+3.7868e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+9.2440e+04 Δ⛰:2.8624e+05 ➽:1.9628e-01


SL: Iteration 2 ⛰:+7.7813e+04 Δ⛰:1.4627e+04 ➽:1.9628e-01


SL: Iteration 3 ⛰:+4.3188e+04 Δ⛰:3.4625e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+3.4446e+04 Δ⛰:8.7420e+03 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.3666e+04 Δ⛰:7.8025e+02 ➽:1.9628e-01


SL: Iteration 6 ⛰:+1.9552e+04 Δ⛰:1.4114e+04 ➽:1.9628e-01


SL: Iteration 7 ⛰:+9.2731e+03 Δ⛰:1.0279e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+2.2350e+03 Δ⛰:7.0381e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-2.8089e+03 Δ⛰:5.0440e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-2.8296e+03 Δ⛰:2.0660e+01 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.1547e+03 Δ⛰:2.3251e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.0248e+03 Δ⛰:1.8701e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.0158e+03 Δ⛰:9.9092e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.5420e+03 Δ⛰:5.2619e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.5425e+03 Δ⛰:5.4045e-01 ➽:1.9628e-01


SL: Iteration 16 ⛰:-9.0649e+03 Δ⛰:5.2237e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.3835e+03 Δ⛰:3.1865e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.5488e+03 Δ⛰:1.6526e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.6503e+03 Δ⛰:1.0150e+02 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.6527e+03 Δ⛰:2.4711e+00 ➽:1.9628e-01


SL: Iteration 21 ⛰:-9.7058e+03 Δ⛰:5.3025e+01 ➽:1.9628e-01


SL: Iteration 22 ⛰:-9.7650e+03 Δ⛰:5.9211e+01 ➽:1.9628e-01


SL: Iteration 23 ⛰:-9.7876e+03 Δ⛰:2.2593e+01 ➽:1.9628e-01


SL: Iteration 24 ⛰:-9.7901e+03 Δ⛰:2.5006e+00 ➽:1.9628e-01


SL: Iteration 25 ⛰:-9.7964e+03 Δ⛰:6.3445e+00 ➽:1.9628e-01


SL: Iteration 26 ⛰:-9.8050e+03 Δ⛰:8.6410e+00 ➽:1.9628e-01


SL: Iteration 27 ⛰:-9.8107e+03 Δ⛰:5.6343e+00 ➽:1.9628e-01


SL: Iteration 28 ⛰:-9.8107e+03 Δ⛰:3.7870e-02 ➽:1.9628e-01


SL: Iteration 0 ⛰:+9.2643e+04 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+9.2540e+04 Δ⛰:1.0306e+02 ➽:1.9628e-01


SL: Iteration 2 ⛰:+8.4051e+04 Δ⛰:8.4889e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+6.3668e+04 Δ⛰:2.0383e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+3.9138e+04 Δ⛰:2.4529e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+2.1658e+04 Δ⛰:1.7480e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.1654e+04 Δ⛰:3.7740e+00 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.1879e+04 Δ⛰:9.7754e+03 ➽:1.9628e-01


SL: Iteration 8 ⛰:+4.0825e+03 Δ⛰:7.7963e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-3.2794e+03 Δ⛰:7.3619e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-4.1604e+03 Δ⛰:8.8100e+02 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.1777e+03 Δ⛰:1.0172e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.1868e+03 Δ⛰:2.0092e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.1135e+03 Δ⛰:9.2667e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.6677e+03 Δ⛰:5.5420e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.6679e+03 Δ⛰:1.7116e-01 ➽:1.9628e-01


SL: Iteration 0 ⛰:+8.0575e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+9.1638e+04 Δ⛰:7.1411e+05 ➽:1.9628e-01


SL: Iteration 2 ⛰:+8.1961e+04 Δ⛰:9.6775e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+6.3065e+04 Δ⛰:1.8896e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+4.0749e+04 Δ⛰:2.2316e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.2832e+04 Δ⛰:7.9167e+03 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.4127e+04 Δ⛰:8.7052e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.3857e+04 Δ⛰:1.0270e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+4.2491e+03 Δ⛰:9.6075e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-3.2996e+03 Δ⛰:7.5487e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-3.3012e+03 Δ⛰:1.6636e+00 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.3036e+03 Δ⛰:2.0024e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-6.9673e+03 Δ⛰:1.6636e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.1769e+03 Δ⛰:1.2096e+03 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.6159e+03 Δ⛰:4.3906e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.6168e+03 Δ⛰:8.8525e-01 ➽:1.9628e-01


SL: Iteration 16 ⛰:-9.0732e+03 Δ⛰:4.5637e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.3137e+03 Δ⛰:2.4058e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.5313e+03 Δ⛰:2.1754e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.5830e+03 Δ⛰:5.1769e+01 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.6060e+03 Δ⛰:2.2951e+01 ➽:1.9628e-01


SL: Iteration 21 ⛰:-9.6721e+03 Δ⛰:6.6114e+01 ➽:1.9628e-01


SL: Iteration 22 ⛰:-9.7256e+03 Δ⛰:5.3464e+01 ➽:1.9628e-01


SL: Iteration 23 ⛰:-9.7326e+03 Δ⛰:7.0075e+00 ➽:1.9628e-01


SL: Iteration 24 ⛰:-9.7329e+03 Δ⛰:2.9055e-01 ➽:1.9628e-01


SL: Iteration 25 ⛰:-9.7498e+03 Δ⛰:1.6902e+01 ➽:1.9628e-01


SL: Iteration 26 ⛰:-9.7659e+03 Δ⛰:1.6095e+01 ➽:1.9628e-01


SL: Iteration 27 ⛰:-9.7748e+03 Δ⛰:8.9019e+00 ➽:1.9628e-01


SL: Iteration 28 ⛰:-9.7748e+03 Δ⛰:5.7309e-02 ➽:1.9628e-01


SL: Iteration 0 ⛰:+2.3086e+06 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+9.3624e+04 Δ⛰:2.2150e+06 ➽:1.9628e-01


SL: Iteration 2 ⛰:+8.5993e+04 Δ⛰:7.6309e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+6.3516e+04 Δ⛰:2.2477e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+3.9007e+04 Δ⛰:2.4509e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.6491e+04 Δ⛰:2.5164e+03 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.0426e+04 Δ⛰:1.6065e+04 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.1086e+04 Δ⛰:9.3398e+03 ➽:1.9628e-01


SL: Iteration 8 ⛰:+4.0895e+03 Δ⛰:6.9962e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-3.0261e+03 Δ⛰:7.1157e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-3.0268e+03 Δ⛰:6.3167e-01 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.6353e+03 Δ⛰:2.6086e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.2067e+03 Δ⛰:1.5713e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.0699e+03 Δ⛰:8.6325e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.7169e+03 Δ⛰:6.4695e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.7234e+03 Δ⛰:6.5159e+00 ➽:1.9628e-01


SL: Iteration 16 ⛰:-8.9934e+03 Δ⛰:2.7003e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.3124e+03 Δ⛰:3.1898e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.5258e+03 Δ⛰:2.1343e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.5367e+03 Δ⛰:1.0860e+01 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.5923e+03 Δ⛰:5.5677e+01 ➽:1.9628e-01


SL: Iteration 21 ⛰:-9.6424e+03 Δ⛰:5.0054e+01 ➽:1.9628e-01


SL: Iteration 22 ⛰:-9.6904e+03 Δ⛰:4.8014e+01 ➽:1.9628e-01


SL: Iteration 23 ⛰:-9.6991e+03 Δ⛰:8.7154e+00 ➽:1.9628e-01


SL: Iteration 24 ⛰:-9.6991e+03 Δ⛰:2.3500e-02 ➽:1.9628e-01


SN: →:1.0 ↺:False #∇²:06 |↘|:3.885805e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+3.556339e+02 Δ⛰:3.587346e+05


SN: →:1.0 ↺:False #∇²:12 |↘|:9.326122e-01 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.688227e+02 Δ⛰:8.681116e+01


SN: →:1.0 ↺:False #∇²:06 |↘|:4.398366e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+7.377969e+02 Δ⛰:5.320385e+05


SN: →:1.0 ↺:False #∇²:12 |↘|:1.155816e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+5.628367e+02 Δ⛰:1.749603e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:1.695721e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+1.727523e+04 Δ⛰:2.236396e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:6.846043e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.284631e+04 Δ⛰:4.428925e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.263621e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.142526e+04 Δ⛰:1.421048e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:2.898830e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.052836e+04 Δ⛰:8.968978e+02


SN: →:1.0 ↺:False #∇²:30 |↘|:2.144448e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+9.885241e+03 Δ⛰:6.431188e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.745531e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+1.882189e+04 Δ⛰:1.369479e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:5.801308e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.285847e+04 Δ⛰:5.963423e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.562132e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.121190e+04 Δ⛰:1.646562e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:2.899093e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.021275e+04 Δ⛰:9.991499e+02


SN: →:1.0 ↺:False #∇²:30 |↘|:2.278869e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+9.517248e+03 Δ⛰:6.955058e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:2.317318e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+3.459041e+02 Δ⛰:2.754994e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.106528e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.316095e+02 Δ⛰:1.142946e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:4.085871e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+5.159420e+02 Δ⛰:2.397646e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.300486e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+3.290076e+02 Δ⛰:1.869344e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:4.156637e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+1.031515e+03 Δ⛰:9.267988e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.851189e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+7.307217e+02 Δ⛰:3.007931e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:5.312414e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+1.032584e+03 Δ⛰:7.573564e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:2.046081e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+7.148678e+02 Δ⛰:3.177161e+02


SN: →:1.0 ↺:False #∇²:18 |↘|:8.973878e-01 ➽:1.962800e+00
SN: Iteration 3 ⛰:+6.165785e+02 Δ⛰:9.828929e+01


M: →:1.0 ↺:False #∇²:07 |↘|:3.605298e+02 ➽:1.962800e+00
M: Iteration 1 ⛰:+2.030500e+04 Δ⛰:9.432483e+01


M: →:1.0 ↺:False #∇²:13 |↘|:7.110066e+01 ➽:1.962800e+00
M: Iteration 2 ⛰:+2.028194e+04 Δ⛰:2.306123e+01


M: →:1.0 ↺:False #∇²:19 |↘|:7.335823e+01 ➽:1.962800e+00
M: Iteration 3 ⛰:+2.027394e+04 Δ⛰:7.993043e+00


M: →:1.0 ↺:False #∇²:25 |↘|:5.199207e+01 ➽:1.962800e+00
M: Iteration 4 ⛰:+2.026731e+04 Δ⛰:6.635302e+00


M: →:1.0 ↺:False #∇²:31 |↘|:5.415231e+01 ➽:1.962800e+00
M: Iteration 5 ⛰:+2.026238e+04 Δ⛰:4.930793e+00


M: →:1.0 ↺:False #∇²:37 |↘|:3.008476e+01 ➽:1.962800e+00
M: Iteration 6 ⛰:+2.025889e+04 Δ⛰:3.490533e+00


M: →:1.0 ↺:False #∇²:43 |↘|:4.778178e+01 ➽:1.962800e+00
M: Iteration 7 ⛰:+2.025609e+04 Δ⛰:2.799406e+00


M: →:1.0 ↺:False #∇²:49 |↘|:2.239365e+01 ➽:1.962800e+00
M: Iteration 8 ⛰:+2.025371e+04 Δ⛰:2.379299e+00


M: →:1.0 ↺:False #∇²:55 |↘|:4.724188e+01 ➽:1.962800e+00
M: Iteration 9 ⛰:+2.025171e+04 Δ⛰:1.994538e+00


M: →:1.0 ↺:False #∇²:61 |↘|:1.618721e+01 ➽:1.962800e+00
M: Iteration 10 ⛰:+2.024998e+04 Δ⛰:1.730039e+00


M: →:1.0 ↺:False #∇²:67 |↘|:5.809832e+01 ➽:1.962800e+00
M: Iteration 11 ⛰:+2.024840e+04 Δ⛰:1.582300e+00


M: →:1.0 ↺:False #∇²:73 |↘|:1.022797e+01 ➽:1.962800e+00
M: Iteration 12 ⛰:+2.024698e+04 Δ⛰:1.425776e+00


M: →:1.0 ↺:False #∇²:79 |↘|:6.730201e+01 ➽:1.962800e+00
M: Iteration 13 ⛰:+2.024601e+04 Δ⛰:9.698202e-01


M: →:1.0 ↺:False #∇²:85 |↘|:5.383902e+00 ➽:1.962800e+00
M: Iteration 14 ⛰:+2.024507e+04 Δ⛰:9.365765e-01


M: →:1.0 ↺:False #∇²:91 |↘|:3.791131e+01 ➽:1.962800e+00
M: Iteration 15 ⛰:+2.024477e+04 Δ⛰:2.993095e-01


M: →:1.0 ↺:False #∇²:97 |↘|:3.949289e+00 ➽:1.962800e+00
M: Iteration 16 ⛰:+2.024450e+04 Δ⛰:2.739300e-01


M: →:1.0 ↺:False #∇²:103 |↘|:3.332016e+00 ➽:1.962800e+00
M: Iteration 17 ⛰:+2.024445e+04 Δ⛰:4.282126e-02


M: →:1.0 ↺:False #∇²:109 |↘|:9.137824e-01 ➽:1.962800e+00
M: Iteration 18 ⛰:+2.024443e+04 Δ⛰:2.794811e-02


OPTIMIZE_KL: Iteration 0003 ⛰:+2.0244e+04
OPTIMIZE_KL: #(Nonlinear sampling steps) (2, 2, 5, 5, 2, 2, 2, 3)
OPTIMIZE_KL: #(KL minimization steps) 18
OPTIMIZE_KL: Likelihood residual(s):
reduced χ²:     1.1±   0.038, avg: +2.4e-05±  0.0064, #dof:  16384
OPTIMIZE_KL: Prior residual(s):
cfax1asperity         :: reduced χ²:     1.4±    0.79, avg:   -0.083±     1.2, #dof:      1
cfax1flexibility      :: reduced χ²:     5.0±     2.5, avg:     -2.2±    0.59, #dof:      1
cfax1fluctuations     :: reduced χ²:     1.1±    0.35, avg:     +1.1±    0.16, #dof:      1
cfax1loglogavgslope   :: reduced χ²:     1.3±     1.4, avg:    +0.25±     1.1, #dof:      1
cfax1spectrum         :: reduced χ²:    0.98±   0.019, avg:  +0.0052±   0.012, #dof:   3238
cfxi                  :: reduced χ²:     1.1±   0.021, avg: +0.00026±  0.0027, #dof:  16384
cfzeromode            :: reduced χ²:     2.8±     2.7, avg:    +0.12±     1.7, #dof:      1
scaling               :: reduced χ²:    0.38±  0.0039, avg:    +0.62±  0.0032, #dof:      1




OPTIMIZE_KL: Starting 0004


SL: Iteration 0 ⛰:+2.9285e+06 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.0199e+05 Δ⛰:2.8265e+06 ➽:1.9628e-01


SL: Iteration 2 ⛰:+8.9092e+04 Δ⛰:1.2897e+04 ➽:1.9628e-01


SL: Iteration 3 ⛰:+7.6784e+04 Δ⛰:1.2308e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+4.1372e+04 Δ⛰:3.5412e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.9356e+04 Δ⛰:2.0160e+03 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.3469e+04 Δ⛰:1.5887e+04 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.3116e+04 Δ⛰:1.0353e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+4.5437e+03 Δ⛰:8.5721e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-2.4966e+03 Δ⛰:7.0403e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-2.5075e+03 Δ⛰:1.0861e+01 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.1101e+03 Δ⛰:2.6026e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-6.6294e+03 Δ⛰:1.5193e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-7.9456e+03 Δ⛰:1.3163e+03 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.4910e+03 Δ⛰:5.4532e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.4911e+03 Δ⛰:1.9096e-01 ➽:1.9628e-01


SL: Iteration 0 ⛰:+2.0058e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.0517e+05 Δ⛰:9.5415e+04 ➽:1.9628e-01


SL: Iteration 2 ⛰:+9.7817e+04 Δ⛰:7.3509e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+7.7772e+04 Δ⛰:2.0045e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+4.2985e+04 Δ⛰:3.4787e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+2.6653e+04 Δ⛰:1.6332e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.5743e+04 Δ⛰:9.1031e+02 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.5048e+04 Δ⛰:1.0695e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+4.1476e+03 Δ⛰:1.0900e+04 ➽:1.9628e-01


SL: Iteration 9 ⛰:-1.0084e+03 Δ⛰:5.1560e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-1.6329e+03 Δ⛰:6.2458e+02 ➽:1.9628e-01


SL: Iteration 11 ⛰:-4.6384e+03 Δ⛰:3.0055e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-6.7697e+03 Δ⛰:2.1312e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-7.9856e+03 Δ⛰:1.2160e+03 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.4997e+03 Δ⛰:5.1412e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.5000e+03 Δ⛰:2.7626e-01 ➽:1.9628e-01


SL: Iteration 16 ⛰:-8.9633e+03 Δ⛰:4.6328e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.2853e+03 Δ⛰:3.2202e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.4420e+03 Δ⛰:1.5671e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.5470e+03 Δ⛰:1.0494e+02 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.5476e+03 Δ⛰:6.0624e-01 ➽:1.9628e-01


SL: Iteration 21 ⛰:-9.6478e+03 Δ⛰:1.0022e+02 ➽:1.9628e-01


SL: Iteration 22 ⛰:-9.6721e+03 Δ⛰:2.4309e+01 ➽:1.9628e-01


SL: Iteration 23 ⛰:-9.7071e+03 Δ⛰:3.5024e+01 ➽:1.9628e-01


SL: Iteration 24 ⛰:-9.7071e+03 Δ⛰:1.5231e-02 ➽:1.9628e-01


SL: Iteration 0 ⛰:+3.6268e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.0353e+05 Δ⛰:2.5915e+05 ➽:1.9628e-01


SL: Iteration 2 ⛰:+9.3154e+04 Δ⛰:1.0371e+04 ➽:1.9628e-01


SL: Iteration 3 ⛰:+8.0049e+04 Δ⛰:1.3104e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+4.2509e+04 Δ⛰:3.7541e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.7418e+04 Δ⛰:5.0910e+03 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.5725e+04 Δ⛰:1.1693e+04 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.4205e+04 Δ⛰:1.1520e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+4.4747e+03 Δ⛰:9.7299e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-5.8403e+02 Δ⛰:5.0588e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-6.1149e+02 Δ⛰:2.7456e+01 ➽:1.9628e-01


SL: Iteration 11 ⛰:-4.5902e+03 Δ⛰:3.9788e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-6.7964e+03 Δ⛰:2.2062e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-7.7016e+03 Δ⛰:9.0516e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.3948e+03 Δ⛰:6.9318e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.3949e+03 Δ⛰:1.6864e-01 ➽:1.9628e-01


SL: Iteration 0 ⛰:+1.0834e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.0138e+05 Δ⛰:6.9525e+03 ➽:1.9628e-01


SL: Iteration 2 ⛰:+9.0463e+04 Δ⛰:1.0921e+04 ➽:1.9628e-01


SL: Iteration 3 ⛰:+7.3188e+04 Δ⛰:1.7275e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+4.4620e+04 Δ⛰:2.8568e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+2.5758e+04 Δ⛰:1.8863e+04 ➽:1.9628e-01


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SL: Iteration 7 ⛰:+1.4528e+04 Δ⛰:1.1092e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+6.5586e+03 Δ⛰:7.9695e+03 ➽:1.9628e-01


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SL: Iteration 10 ⛰:-4.5992e+03 Δ⛰:1.7124e+03 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.2631e+03 Δ⛰:6.6392e+02 ➽:1.9628e-01


SL: Iteration 12 ⛰:-6.8621e+03 Δ⛰:1.5990e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.0267e+03 Δ⛰:1.1646e+03 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.6628e+03 Δ⛰:6.3608e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.6839e+03 Δ⛰:2.1138e+01 ➽:1.9628e-01


SL: Iteration 16 ⛰:-8.9761e+03 Δ⛰:2.9214e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.3313e+03 Δ⛰:3.5518e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.5079e+03 Δ⛰:1.7667e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.5962e+03 Δ⛰:8.8304e+01 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.5963e+03 Δ⛰:2.9439e-02 ➽:1.9628e-01


SN: →:1.0 ↺:False #∇²:06 |↘|:1.547169e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.556537e+04 Δ⛰:3.999612e+05


SN: →:1.0 ↺:False #∇²:12 |↘|:7.258810e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.899308e+04 Δ⛰:6.572294e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.803330e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.632555e+04 Δ⛰:2.667525e+03


SN: →:1.0 ↺:False #∇²:25 |↘|:3.410090e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.484398e+04 Δ⛰:1.481570e+03


SN: →:1.0 ↺:False #∇²:31 |↘|:2.360426e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+1.383530e+04 Δ⛰:1.008681e+03


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.751551e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.424208e+04 Δ⛰:4.519238e+05


SN: →:1.0 ↺:False #∇²:12 |↘|:9.075881e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.632958e+04 Δ⛰:7.912505e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.448495e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.432214e+04 Δ⛰:2.007441e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:3.512201e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.312756e+04 Δ⛰:1.194572e+03


SN: →:1.0 ↺:False #∇²:30 |↘|:2.315928e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+1.227076e+04 Δ⛰:8.568037e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:5.720859e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+1.515447e+03 Δ⛰:2.459444e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.592707e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.176665e+03 Δ⛰:3.387818e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:7.452809e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.090954e+03 Δ⛰:2.658049e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:2.560273e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.346390e+03 Δ⛰:7.445648e+02


SN: →:1.0 ↺:False #∇²:18 |↘|:1.282547e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.123806e+03 Δ⛰:2.225839e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:1.678704e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.655740e+04 Δ⛰:2.971010e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:7.602673e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.892441e+04 Δ⛰:7.632984e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:4.402405e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.615082e+04 Δ⛰:2.773590e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:3.148851e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.468411e+04 Δ⛰:1.466717e+03


SN: →:1.0 ↺:False #∇²:30 |↘|:2.735328e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+1.368266e+04 Δ⛰:1.001448e+03


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.733714e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.302534e+04 Δ⛰:3.785132e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:6.066924e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.807958e+04 Δ⛰:4.945761e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:4.122067e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.585236e+04 Δ⛰:2.227220e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:3.171928e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.450465e+04 Δ⛰:1.347717e+03


SN: →:1.0 ↺:False #∇²:30 |↘|:2.572337e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+1.358024e+04 Δ⛰:9.244077e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:8.260485e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+4.253698e+03 Δ⛰:1.095468e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:4.205030e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.763599e+03 Δ⛰:1.490099e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:2.184212e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+2.391234e+03 Δ⛰:3.723646e+02


SN: →:1.0 ↺:False #∇²:24 |↘|:1.264314e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+2.194177e+03 Δ⛰:1.970576e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:8.831685e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+4.714809e+03 Δ⛰:2.180886e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:3.210306e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+3.205098e+03 Δ⛰:1.509712e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:1.707723e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+2.722876e+03 Δ⛰:4.822215e+02


M: →:1.0 ↺:False #∇²:06 |↘|:1.326437e+02 ➽:1.962800e+00
M: Iteration 1 ⛰:+1.966658e+04 Δ⛰:2.298444e+01


M: →:1.0 ↺:False #∇²:12 |↘|:4.500875e+01 ➽:1.962800e+00
M: Iteration 2 ⛰:+1.965917e+04 Δ⛰:7.410602e+00


M: →:1.0 ↺:False #∇²:18 |↘|:4.191493e+01 ➽:1.962800e+00
M: Iteration 3 ⛰:+1.965590e+04 Δ⛰:3.275729e+00


M: →:1.0 ↺:False #∇²:24 |↘|:2.203472e+01 ➽:1.962800e+00
M: Iteration 4 ⛰:+1.965382e+04 Δ⛰:2.081280e+00


M: →:1.0 ↺:False #∇²:30 |↘|:2.574028e+01 ➽:1.962800e+00
M: Iteration 5 ⛰:+1.965245e+04 Δ⛰:1.366654e+00


M: →:1.0 ↺:False #∇²:36 |↘|:1.379604e+01 ➽:1.962800e+00
M: Iteration 6 ⛰:+1.965146e+04 Δ⛰:9.933160e-01


M: →:1.0 ↺:False #∇²:42 |↘|:2.009895e+01 ➽:1.962800e+00
M: Iteration 7 ⛰:+1.965070e+04 Δ⛰:7.576196e-01


M: →:1.0 ↺:False #∇²:48 |↘|:9.901512e+00 ➽:1.962800e+00
M: Iteration 8 ⛰:+1.965008e+04 Δ⛰:6.133994e-01


M: →:1.0 ↺:False #∇²:54 |↘|:1.719607e+01 ➽:1.962800e+00
M: Iteration 9 ⛰:+1.964959e+04 Δ⛰:4.898318e-01


M: →:1.0 ↺:False #∇²:60 |↘|:7.199578e+00 ➽:1.962800e+00
M: Iteration 10 ⛰:+1.964918e+04 Δ⛰:4.124069e-01


M: →:1.0 ↺:False #∇²:66 |↘|:1.550052e+01 ➽:1.962800e+00
M: Iteration 11 ⛰:+1.964885e+04 Δ⛰:3.359728e-01


M: →:1.0 ↺:False #∇²:75 |↘|:1.442817e+02 ➽:1.962800e+00
M: Iteration 12 ⛰:+1.964783e+04 Δ⛰:1.019323e+00


M: →:1.0 ↺:False #∇²:81 |↘|:7.410333e+00 ➽:1.962800e+00
M: Iteration 13 ⛰:+1.964765e+04 Δ⛰:1.817394e-01


M: →:1.0 ↺:False #∇²:87 |↘|:5.866644e+00 ➽:1.962800e+00
M: Iteration 14 ⛰:+1.964752e+04 Δ⛰:1.259890e-01


M: →:1.0 ↺:False #∇²:93 |↘|:5.856961e+00 ➽:1.962800e+00
M: Iteration 15 ⛰:+1.964742e+04 Δ⛰:9.673144e-02


M: →:1.0 ↺:False #∇²:99 |↘|:4.335552e+00 ➽:1.962800e+00
M: Iteration 16 ⛰:+1.964735e+04 Δ⛰:7.745185e-02


M: →:1.0 ↺:False #∇²:105 |↘|:4.885251e+00 ➽:1.962800e+00
M: Iteration 17 ⛰:+1.964728e+04 Δ⛰:6.118570e-02


M: →:1.0 ↺:False #∇²:111 |↘|:3.277825e+00 ➽:1.962800e+00
M: Iteration 18 ⛰:+1.964723e+04 Δ⛰:5.039290e-02


M: →:1.0 ↺:False #∇²:117 |↘|:4.230206e+00 ➽:1.962800e+00
M: Iteration 19 ⛰:+1.964719e+04 Δ⛰:4.039063e-02


M: →:1.0 ↺:False #∇²:123 |↘|:2.524148e+00 ➽:1.962800e+00
M: Iteration 20 ⛰:+1.964716e+04 Δ⛰:3.403730e-02


M: →:1.0 ↺:False #∇²:129 |↘|:3.794250e+00 ➽:1.962800e+00
M: Iteration 21 ⛰:+1.964713e+04 Δ⛰:2.759298e-02


M: →:1.0 ↺:False #∇²:135 |↘|:1.877344e+00 ➽:1.962800e+00
M: Iteration 22 ⛰:+1.964711e+04 Δ⛰:2.336285e-02


OPTIMIZE_KL: Iteration 0004 ⛰:+1.9647e+04
OPTIMIZE_KL: #(Nonlinear sampling steps) (5, 5, 2, 3, 5, 5, 4, 3)
OPTIMIZE_KL: #(KL minimization steps) 22
OPTIMIZE_KL: Likelihood residual(s):
reduced χ²:     1.1±   0.046, avg: +9.1e-05±  0.0062, #dof:  16384
OPTIMIZE_KL: Prior residual(s):
cfax1asperity         :: reduced χ²:     1.7±     1.2, avg:   +0.062±     1.3, #dof:      1
cfax1flexibility      :: reduced χ²:     4.3±     1.3, avg:     -2.0±    0.31, #dof:      1
cfax1fluctuations     :: reduced χ²:     2.0±    0.55, avg:     +1.4±    0.19, #dof:      1
cfax1loglogavgslope   :: reduced χ²:     1.8±     1.2, avg:     +0.2±     1.3, #dof:      1
cfax1spectrum         :: reduced χ²:     1.0±   0.013, avg:  +0.0029±  0.0091, #dof:   3238
cfxi                  :: reduced χ²:     1.1±  0.0092, avg: +0.00021±  0.0017, #dof:  16384
cfzeromode            :: reduced χ²:     1.5±     1.6, avg:   -0.022±     1.2, #dof:      1
scaling               :: reduced χ²:    0.38±  0.0033, avg:    +0.62±  0.0027, #dof:      1




OPTIMIZE_KL: Starting 0005


SL: Iteration 0 ⛰:+1.9485e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.1593e+05 Δ⛰:7.8926e+04 ➽:1.9628e-01


SL: Iteration 2 ⛰:+1.0316e+05 Δ⛰:1.2767e+04 ➽:1.9628e-01


SL: Iteration 3 ⛰:+8.2250e+04 Δ⛰:2.0909e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+4.4232e+04 Δ⛰:3.8018e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+2.8555e+04 Δ⛰:1.5677e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.7202e+04 Δ⛰:1.3535e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.4466e+04 Δ⛰:1.2735e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+4.7141e+03 Δ⛰:9.7524e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-1.5251e+03 Δ⛰:6.2392e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-2.9629e+03 Δ⛰:1.4378e+03 ➽:1.9628e-01


SL: Iteration 11 ⛰:-4.9426e+03 Δ⛰:1.9797e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-6.5921e+03 Δ⛰:1.6495e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.1790e+03 Δ⛰:1.5869e+03 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.6232e+03 Δ⛰:4.4424e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.6249e+03 Δ⛰:1.7129e+00 ➽:1.9628e-01


SL: Iteration 16 ⛰:-8.9705e+03 Δ⛰:3.4554e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.3455e+03 Δ⛰:3.7505e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.5414e+03 Δ⛰:1.9586e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.5725e+03 Δ⛰:3.1128e+01 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.5725e+03 Δ⛰:4.1690e-02 ➽:1.9628e-01


SL: Iteration 0 ⛰:+1.4187e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.0934e+05 Δ⛰:3.2526e+04 ➽:1.9628e-01


SL: Iteration 2 ⛰:+1.0031e+05 Δ⛰:9.0315e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+8.6877e+04 Δ⛰:1.3436e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+3.9919e+04 Δ⛰:4.6958e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+2.6278e+04 Δ⛰:1.3641e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.4141e+04 Δ⛰:2.1366e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.1832e+04 Δ⛰:1.2309e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+2.5680e+03 Δ⛰:9.2645e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-3.1974e+03 Δ⛰:5.7653e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-5.4802e+03 Δ⛰:2.2829e+03 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.5126e+03 Δ⛰:3.2325e+01 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.3859e+03 Δ⛰:1.8734e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.2103e+03 Δ⛰:8.2439e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.7838e+03 Δ⛰:5.7342e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-9.1916e+03 Δ⛰:4.0790e+02 ➽:1.9628e-01


SL: Iteration 16 ⛰:-9.3216e+03 Δ⛰:1.2997e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.5182e+03 Δ⛰:1.9656e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.6555e+03 Δ⛰:1.3736e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.7836e+03 Δ⛰:1.2803e+02 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.7836e+03 Δ⛰:4.0074e-02 ➽:1.9628e-01


SL: Iteration 0 ⛰:+3.6235e+06 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.0476e+05 Δ⛰:3.5187e+06 ➽:1.9628e-01


SL: Iteration 2 ⛰:+9.7449e+04 Δ⛰:7.3152e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+7.5160e+04 Δ⛰:2.2289e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+3.9653e+04 Δ⛰:3.5507e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.2948e+04 Δ⛰:6.7051e+03 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.2430e+04 Δ⛰:1.0518e+04 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.2526e+04 Δ⛰:9.9036e+03 ➽:1.9628e-01


SL: Iteration 8 ⛰:+3.3154e+03 Δ⛰:9.2107e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-3.3836e+03 Δ⛰:6.6990e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-3.3888e+03 Δ⛰:5.1684e+00 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.3563e+03 Δ⛰:1.9676e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.4425e+03 Δ⛰:2.0862e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.2251e+03 Δ⛰:7.8258e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.7415e+03 Δ⛰:5.1647e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.7431e+03 Δ⛰:1.6021e+00 ➽:1.9628e-01


SL: Iteration 16 ⛰:-9.1306e+03 Δ⛰:3.8744e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.4982e+03 Δ⛰:3.6761e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.6078e+03 Δ⛰:1.0958e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.6394e+03 Δ⛰:3.1657e+01 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.7153e+03 Δ⛰:7.5846e+01 ➽:1.9628e-01


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SL: Iteration 22 ⛰:-9.8018e+03 Δ⛰:5.9625e+01 ➽:1.9628e-01


SL: Iteration 23 ⛰:-9.8411e+03 Δ⛰:3.9310e+01 ➽:1.9628e-01


SL: Iteration 24 ⛰:-9.8411e+03 Δ⛰:7.4036e-03 ➽:1.9628e-01


SL: Iteration 0 ⛰:+1.3433e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.0560e+05 Δ⛰:2.8733e+04 ➽:1.9628e-01


SL: Iteration 2 ⛰:+9.9411e+04 Δ⛰:6.1885e+03 ➽:1.9628e-01


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SL: Iteration 18 ⛰:-9.6487e+03 Δ⛰:2.3545e+02 ➽:1.9628e-01


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SL: Iteration 20 ⛰:-9.7495e+03 Δ⛰:5.1068e-02 ➽:1.9628e-01


SN: →:1.0 ↺:False #∇²:06 |↘|:9.914579e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+4.699657e+03 Δ⛰:3.562720e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:3.146406e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+3.465026e+03 Δ⛰:1.234631e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:1.976836e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+2.973416e+03 Δ⛰:4.916105e+02


SN: →:1.0 ↺:False #∇²:24 |↘|:1.502979e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+2.660345e+03 Δ⛰:3.130706e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:7.278681e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+5.112137e+03 Δ⛰:1.877076e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:3.472211e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+3.704845e+03 Δ⛰:1.407293e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:1.825027e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+3.194509e+03 Δ⛰:5.103361e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:7.346345e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+3.660931e+03 Δ⛰:3.844067e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:2.721742e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.699200e+03 Δ⛰:9.617306e+02


SN: →:1.0 ↺:False #∇²:18 |↘|:1.614283e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+2.338231e+03 Δ⛰:3.609688e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:7.012533e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+3.458256e+03 Δ⛰:1.901356e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:2.366246e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.668458e+03 Δ⛰:7.897986e+02


SN: →:1.0 ↺:False #∇²:18 |↘|:1.463971e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+2.341139e+03 Δ⛰:3.273189e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:5.173840e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+1.391330e+03 Δ⛰:2.855586e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.945843e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+9.963165e+02 Δ⛰:3.950139e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:4.570945e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+1.167807e+03 Δ⛰:2.490651e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.317353e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+9.179465e+02 Δ⛰:2.498605e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:8.497346e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+3.165729e+03 Δ⛰:2.624170e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:2.914771e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.287757e+03 Δ⛰:8.779717e+02


SN: →:1.0 ↺:False #∇²:18 |↘|:1.570548e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+2.005988e+03 Δ⛰:2.817689e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:7.685125e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+3.257014e+03 Δ⛰:1.720729e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:2.709493e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.461232e+03 Δ⛰:7.957822e+02


SN: →:1.0 ↺:False #∇²:18 |↘|:1.502320e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+2.163678e+03 Δ⛰:2.975539e+02


M: →:1.0 ↺:False #∇²:08 |↘|:1.929091e+02 ➽:1.962800e+00
M: Iteration 1 ⛰:+1.902160e+04 Δ⛰:2.809764e+01


M: →:1.0 ↺:False #∇²:14 |↘|:2.272618e+01 ➽:1.962800e+00
M: Iteration 2 ⛰:+1.901840e+04 Δ⛰:3.191259e+00


M: →:1.0 ↺:False #∇²:20 |↘|:2.080977e+01 ➽:1.962800e+00
M: Iteration 3 ⛰:+1.901714e+04 Δ⛰:1.266890e+00


M: →:1.0 ↺:False #∇²:26 |↘|:1.352495e+01 ➽:1.962800e+00
M: Iteration 4 ⛰:+1.901625e+04 Δ⛰:8.848059e-01


M: →:1.0 ↺:False #∇²:32 |↘|:1.561045e+01 ➽:1.962800e+00
M: Iteration 5 ⛰:+1.901555e+04 Δ⛰:6.996060e-01


M: →:1.0 ↺:False #∇²:38 |↘|:1.008018e+01 ➽:1.962800e+00
M: Iteration 6 ⛰:+1.901499e+04 Δ⛰:5.653024e-01


M: →:1.0 ↺:False #∇²:44 |↘|:1.259857e+01 ➽:1.962800e+00
M: Iteration 7 ⛰:+1.901453e+04 Δ⛰:4.622357e-01


M: →:1.0 ↺:False #∇²:50 |↘|:7.902452e+00 ➽:1.962800e+00
M: Iteration 8 ⛰:+1.901414e+04 Δ⛰:3.892576e-01


M: →:1.0 ↺:False #∇²:56 |↘|:1.057930e+01 ➽:1.962800e+00
M: Iteration 9 ⛰:+1.901381e+04 Δ⛰:3.269746e-01


M: →:1.0 ↺:False #∇²:62 |↘|:6.370607e+00 ➽:1.962800e+00
M: Iteration 10 ⛰:+1.901353e+04 Δ⛰:2.823269e-01


M: →:1.0 ↺:False #∇²:68 |↘|:9.003610e+00 ➽:1.962800e+00
M: Iteration 11 ⛰:+1.901329e+04 Δ⛰:2.395209e-01


M: →:1.0 ↺:False #∇²:74 |↘|:5.254227e+00 ➽:1.962800e+00
M: Iteration 12 ⛰:+1.901308e+04 Δ⛰:2.103028e-01


M: →:1.0 ↺:False #∇²:83 |↘|:1.385442e+02 ➽:1.962800e+00
M: Iteration 13 ⛰:+1.901214e+04 Δ⛰:9.388061e-01


M: →:1.0 ↺:False #∇²:89 |↘|:1.776181e+01 ➽:1.962800e+00
M: Iteration 14 ⛰:+1.901190e+04 Δ⛰:2.349533e-01


M: →:1.0 ↺:False #∇²:95 |↘|:4.254235e+00 ➽:1.962800e+00
M: Iteration 15 ⛰:+1.901173e+04 Δ⛰:1.764225e-01


M: →:1.0 ↺:False #∇²:101 |↘|:9.778098e+00 ➽:1.962800e+00
M: Iteration 16 ⛰:+1.901164e+04 Δ⛰:9.057464e-02


M: →:1.0 ↺:False #∇²:107 |↘|:2.501076e+00 ➽:1.962800e+00
M: Iteration 17 ⛰:+1.901156e+04 Δ⛰:7.597383e-02


M: →:1.0 ↺:False #∇²:113 |↘|:8.344651e+00 ➽:1.962800e+00
M: Iteration 18 ⛰:+1.901151e+04 Δ⛰:5.319863e-02


M: →:1.0 ↺:False #∇²:119 |↘|:1.762625e+00 ➽:1.962800e+00
M: Iteration 19 ⛰:+1.901146e+04 Δ⛰:4.734444e-02


OPTIMIZE_KL: Iteration 0005 ⛰:+1.9011e+04
OPTIMIZE_KL: #(Nonlinear sampling steps) (4, 3, 3, 3, 2, 2, 3, 3)
OPTIMIZE_KL: #(KL minimization steps) 19
OPTIMIZE_KL: Likelihood residual(s):
reduced χ²:     1.1±   0.011, avg: -8.8e-07±  0.0077, #dof:  16384
OPTIMIZE_KL: Prior residual(s):
cfax1asperity         :: reduced χ²:     1.8±    0.39, avg:   -0.023±     1.3, #dof:      1
cfax1flexibility      :: reduced χ²:     3.6±     1.7, avg:     -1.8±    0.48, #dof:      1
cfax1fluctuations     :: reduced χ²:     3.2±    0.43, avg:     +1.8±    0.12, #dof:      1
cfax1loglogavgslope   :: reduced χ²:     3.2±     3.9, avg:   +0.085±     1.8, #dof:      1
cfax1spectrum         :: reduced χ²:     1.0±   0.019, avg:  +0.0027±    0.02, #dof:   3238
cfxi                  :: reduced χ²:     1.1±   0.011, avg: +0.00027±  0.0019, #dof:  16384
cfzeromode            :: reduced χ²:    0.17±    0.22, avg:    +0.15±    0.38, #dof:      1
scaling               :: reduced χ²:    0.38±  0.0036, avg:    +0.61±   0.003, #dof:      1




OPTIMIZE_KL: Starting 0006


SL: Iteration 0 ⛰:+2.2452e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.1204e+05 Δ⛰:1.1247e+05 ➽:1.9628e-01


SL: Iteration 2 ⛰:+1.0106e+05 Δ⛰:1.0979e+04 ➽:1.9628e-01


SL: Iteration 3 ⛰:+8.6969e+04 Δ⛰:1.4094e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+4.9159e+04 Δ⛰:3.7810e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.4842e+04 Δ⛰:1.4316e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+3.2661e+04 Δ⛰:2.1813e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.6857e+04 Δ⛰:1.5804e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+7.8176e+03 Δ⛰:9.0390e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:+5.4200e+02 Δ⛰:7.2756e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:+2.9053e+02 Δ⛰:2.5147e+02 ➽:1.9628e-01


SL: Iteration 11 ⛰:-4.3637e+03 Δ⛰:4.6543e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-6.7684e+03 Δ⛰:2.4046e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-7.6518e+03 Δ⛰:8.8341e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.3326e+03 Δ⛰:6.8082e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.3328e+03 Δ⛰:1.6988e-01 ➽:1.9628e-01


SL: Iteration 0 ⛰:+2.1235e+06 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.1008e+05 Δ⛰:2.0135e+06 ➽:1.9628e-01


SL: Iteration 2 ⛰:+1.0246e+05 Δ⛰:7.6200e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+8.8191e+04 Δ⛰:1.4270e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+4.6851e+04 Δ⛰:4.1339e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.3605e+04 Δ⛰:1.3247e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.9269e+04 Δ⛰:4.3361e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.6270e+04 Δ⛰:1.2999e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+6.2375e+03 Δ⛰:1.0032e+04 ➽:1.9628e-01


SL: Iteration 9 ⛰:-1.8856e+03 Δ⛰:8.1231e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-2.0778e+03 Δ⛰:1.9217e+02 ➽:1.9628e-01


SL: Iteration 11 ⛰:-5.1011e+03 Δ⛰:3.0233e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.2159e+03 Δ⛰:2.1148e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-8.0656e+03 Δ⛰:8.4972e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.7522e+03 Δ⛰:6.8652e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.7523e+03 Δ⛰:1.5930e-01 ➽:1.9628e-01


SL: Iteration 0 ⛰:+1.8578e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.1092e+05 Δ⛰:7.4866e+04 ➽:1.9628e-01


SL: Iteration 2 ⛰:+9.5228e+04 Δ⛰:1.5691e+04 ➽:1.9628e-01


SL: Iteration 3 ⛰:+8.5114e+04 Δ⛰:1.0114e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+5.0710e+04 Δ⛰:3.4405e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.0394e+04 Δ⛰:2.0316e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+2.7460e+04 Δ⛰:2.9340e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.5823e+04 Δ⛰:1.1637e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+6.7413e+03 Δ⛰:9.0814e+03 ➽:1.9628e-01


SL: Iteration 9 ⛰:-2.5187e+03 Δ⛰:9.2599e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-2.8615e+03 Δ⛰:3.4282e+02 ➽:1.9628e-01


SL: Iteration 11 ⛰:-4.7264e+03 Δ⛰:1.8649e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-7.0389e+03 Δ⛰:2.3125e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-7.9490e+03 Δ⛰:9.1009e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.5589e+03 Δ⛰:6.0987e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.5591e+03 Δ⛰:2.3430e-01 ➽:1.9628e-01


SL: Iteration 16 ⛰:-9.1112e+03 Δ⛰:5.5207e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.3700e+03 Δ⛰:2.5882e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.5646e+03 Δ⛰:1.9459e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.6906e+03 Δ⛰:1.2601e+02 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.6908e+03 Δ⛰:1.7345e-01 ➽:1.9628e-01


SL: Iteration 0 ⛰:+1.3090e+05 Δ⛰:inf ➽:1.9628e-01


SL: Iteration 1 ⛰:+1.1236e+05 Δ⛰:1.8543e+04 ➽:1.9628e-01


SL: Iteration 2 ⛰:+1.0513e+05 Δ⛰:7.2303e+03 ➽:1.9628e-01


SL: Iteration 3 ⛰:+8.0896e+04 Δ⛰:2.4231e+04 ➽:1.9628e-01


SL: Iteration 4 ⛰:+5.1038e+04 Δ⛰:2.9858e+04 ➽:1.9628e-01


SL: Iteration 5 ⛰:+3.2062e+04 Δ⛰:1.8977e+04 ➽:1.9628e-01


SL: Iteration 6 ⛰:+3.1055e+04 Δ⛰:1.0066e+03 ➽:1.9628e-01


SL: Iteration 7 ⛰:+1.8068e+04 Δ⛰:1.2987e+04 ➽:1.9628e-01


SL: Iteration 8 ⛰:+7.1224e+03 Δ⛰:1.0945e+04 ➽:1.9628e-01


SL: Iteration 9 ⛰:-8.3348e+02 Δ⛰:7.9559e+03 ➽:1.9628e-01


SL: Iteration 10 ⛰:-2.8727e+03 Δ⛰:2.0392e+03 ➽:1.9628e-01


SL: Iteration 11 ⛰:-4.4067e+03 Δ⛰:1.5340e+03 ➽:1.9628e-01


SL: Iteration 12 ⛰:-6.6881e+03 Δ⛰:2.2813e+03 ➽:1.9628e-01


SL: Iteration 13 ⛰:-7.6742e+03 Δ⛰:9.8615e+02 ➽:1.9628e-01


SL: Iteration 14 ⛰:-8.3367e+03 Δ⛰:6.6248e+02 ➽:1.9628e-01


SL: Iteration 15 ⛰:-8.3369e+03 Δ⛰:2.1437e-01 ➽:1.9628e-01


SL: Iteration 16 ⛰:-8.8795e+03 Δ⛰:5.4257e+02 ➽:1.9628e-01


SL: Iteration 17 ⛰:-9.1696e+03 Δ⛰:2.9013e+02 ➽:1.9628e-01


SL: Iteration 18 ⛰:-9.4055e+03 Δ⛰:2.3591e+02 ➽:1.9628e-01


SL: Iteration 19 ⛰:-9.4860e+03 Δ⛰:8.0500e+01 ➽:1.9628e-01


SL: Iteration 20 ⛰:-9.4899e+03 Δ⛰:3.8837e+00 ➽:1.9628e-01


SL: Iteration 21 ⛰:-9.5317e+03 Δ⛰:4.1756e+01 ➽:1.9628e-01


SL: Iteration 22 ⛰:-9.6241e+03 Δ⛰:9.2455e+01 ➽:1.9628e-01


SL: Iteration 23 ⛰:-9.6725e+03 Δ⛰:4.8366e+01 ➽:1.9628e-01


SL: Iteration 24 ⛰:-9.6730e+03 Δ⛰:5.5192e-01 ➽:1.9628e-01


SL: Iteration 25 ⛰:-9.7206e+03 Δ⛰:4.7551e+01 ➽:1.9628e-01


SL: Iteration 26 ⛰:-9.7396e+03 Δ⛰:1.9040e+01 ➽:1.9628e-01


SL: Iteration 27 ⛰:-9.7500e+03 Δ⛰:1.0345e+01 ➽:1.9628e-01


SL: Iteration 28 ⛰:-9.7538e+03 Δ⛰:3.8413e+00 ➽:1.9628e-01


SL: Iteration 29 ⛰:-9.7578e+03 Δ⛰:3.9891e+00 ➽:1.9628e-01


SL: Iteration 30 ⛰:-9.7623e+03 Δ⛰:4.4917e+00 ➽:1.9628e-01


SL: Iteration 31 ⛰:-9.7639e+03 Δ⛰:1.5726e+00 ➽:1.9628e-01


SL: Iteration 32 ⛰:-9.7652e+03 Δ⛰:1.3611e+00 ➽:1.9628e-01


SL: Iteration 33 ⛰:-9.7653e+03 Δ⛰:2.8674e-02 ➽:1.9628e-01


SN: →:1.0 ↺:False #∇²:06 |↘|:1.565863e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+5.831403e+04 Δ⛰:2.511122e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:1.112826e+01 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.251460e+04 Δ⛰:3.579943e+04


SN: →:1.0 ↺:False #∇²:18 |↘|:4.696903e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.889028e+04 Δ⛰:3.624322e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:3.328807e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.737779e+04 Δ⛰:1.512489e+03


SN: →:1.0 ↺:False #∇²:30 |↘|:2.585447e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+1.629830e+04 Δ⛰:1.079496e+03


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.866237e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.919831e+04 Δ⛰:2.600490e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:6.912662e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+2.155166e+04 Δ⛰:7.646645e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:4.191915e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.884427e+04 Δ⛰:2.707390e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:3.455819e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.726579e+04 Δ⛰:1.578486e+03


SN: →:1.0 ↺:False #∇²:30 |↘|:2.652437e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+1.617263e+04 Δ⛰:1.093153e+03


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.531310e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.312430e+04 Δ⛰:5.407852e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:6.724357e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.704247e+04 Δ⛰:6.081835e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.332864e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.505645e+04 Δ⛰:1.986018e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:3.026159e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.378894e+04 Δ⛰:1.267509e+03


SN: →:1.0 ↺:False #∇²:30 |↘|:2.226915e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+1.289291e+04 Δ⛰:8.960320e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:1.631277e+01 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.249705e+04 Δ⛰:2.521517e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:6.959988e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.620632e+04 Δ⛰:6.290727e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:3.528709e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+1.423205e+04 Δ⛰:1.974273e+03


SN: →:1.0 ↺:False #∇²:24 |↘|:2.911090e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+1.305075e+04 Δ⛰:1.181295e+03


SN: →:1.0 ↺:False #∇²:30 |↘|:2.252680e+00 ➽:1.962800e+00
SN: Iteration 5 ⛰:+1.221474e+04 Δ⛰:8.360140e+02


SN: Iteration Limit Reached


SN: →:1.0 ↺:False #∇²:06 |↘|:7.888250e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+6.432985e+03 Δ⛰:3.920094e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:3.950178e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+4.176143e+03 Δ⛰:2.256842e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:1.806913e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+3.522804e+03 Δ⛰:6.533389e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:8.373188e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+5.304496e+03 Δ⛰:3.574734e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:3.256932e+00 ➽:1.962800e+00
SN: Iteration 2 ⛰:+3.764972e+03 Δ⛰:1.539524e+03


SN: →:1.0 ↺:False #∇²:18 |↘|:1.975306e+00 ➽:1.962800e+00
SN: Iteration 3 ⛰:+3.232855e+03 Δ⛰:5.321169e+02


SN: →:1.0 ↺:False #∇²:24 |↘|:1.478001e+00 ➽:1.962800e+00
SN: Iteration 4 ⛰:+2.904045e+03 Δ⛰:3.288094e+02


SN: →:1.0 ↺:False #∇²:06 |↘|:3.767126e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.292275e+02 Δ⛰:2.526394e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:6.786235e-01 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.716797e+02 Δ⛰:5.754777e+01


SN: →:1.0 ↺:False #∇²:06 |↘|:3.824937e+00 ➽:1.962800e+00
SN: Iteration 1 ⛰:+2.000053e+02 Δ⛰:2.691513e+06


SN: →:1.0 ↺:False #∇²:12 |↘|:7.621528e-01 ➽:1.962800e+00
SN: Iteration 2 ⛰:+1.445835e+02 Δ⛰:5.542187e+01


M: →:1.0 ↺:False #∇²:07 |↘|:1.543891e+02 ➽:1.962800e+00
M: Iteration 1 ⛰:+1.912178e+04 Δ⛰:2.190281e+01


M: →:1.0 ↺:False #∇²:13 |↘|:3.146562e+01 ➽:1.962800e+00
M: Iteration 2 ⛰:+1.911791e+04 Δ⛰:3.870992e+00


M: →:1.0 ↺:False #∇²:19 |↘|:2.557113e+01 ➽:1.962800e+00
M: Iteration 3 ⛰:+1.911669e+04 Δ⛰:1.221444e+00


M: →:1.0 ↺:False #∇²:25 |↘|:1.100514e+01 ➽:1.962800e+00
M: Iteration 4 ⛰:+1.911604e+04 Δ⛰:6.437498e-01


M: →:1.0 ↺:False #∇²:31 |↘|:1.181459e+01 ➽:1.962800e+00
M: Iteration 5 ⛰:+1.911565e+04 Δ⛰:3.886671e-01


M: →:1.0 ↺:False #∇²:37 |↘|:6.362436e+00 ➽:1.962800e+00
M: Iteration 6 ⛰:+1.911536e+04 Δ⛰:2.904379e-01


M: →:1.0 ↺:False #∇²:43 |↘|:7.960683e+00 ➽:1.962800e+00
M: Iteration 7 ⛰:+1.911513e+04 Δ⛰:2.281717e-01


M: →:1.0 ↺:False #∇²:49 |↘|:4.528201e+00 ➽:1.962800e+00
M: Iteration 8 ⛰:+1.911494e+04 Δ⛰:1.909379e-01


M: →:1.0 ↺:False #∇²:55 |↘|:5.945447e+00 ➽:1.962800e+00
M: Iteration 9 ⛰:+1.911478e+04 Δ⛰:1.598203e-01


M: →:1.0 ↺:False #∇²:61 |↘|:3.538082e+00 ➽:1.962800e+00
M: Iteration 10 ⛰:+1.911464e+04 Δ⛰:1.406450e-01


M: →:1.0 ↺:False #∇²:67 |↘|:4.806002e+00 ➽:1.962800e+00
M: Iteration 11 ⛰:+1.911452e+04 Δ⛰:1.230560e-01


M: →:1.0 ↺:False #∇²:73 |↘|:2.909426e+00 ➽:1.962800e+00
M: Iteration 12 ⛰:+1.911441e+04 Δ⛰:1.107209e-01


M: →:1.0 ↺:False #∇²:79 |↘|:4.053708e+00 ➽:1.962800e+00
M: Iteration 13 ⛰:+1.911431e+04 Δ⛰:9.873168e-02


M: →:1.0 ↺:False #∇²:85 |↘|:2.487302e+00 ➽:1.962800e+00
M: Iteration 14 ⛰:+1.911422e+04 Δ⛰:9.001319e-02


M: →:1.0 ↺:False #∇²:91 |↘|:3.818582e+00 ➽:1.962800e+00
M: Iteration 15 ⛰:+1.911414e+04 Δ⛰:8.558009e-02


M: →:1.0 ↺:False #∇²:97 |↘|:2.163779e+00 ➽:1.962800e+00
M: Iteration 16 ⛰:+1.911406e+04 Δ⛰:7.825489e-02


M: →:1.0 ↺:False #∇²:103 |↘|:3.120745e+00 ➽:1.962800e+00
M: Iteration 17 ⛰:+1.911399e+04 Δ⛰:6.740385e-02


M: →:1.0 ↺:False #∇²:109 |↘|:1.921231e+00 ➽:1.962800e+00
M: Iteration 18 ⛰:+1.911393e+04 Δ⛰:6.220224e-02


OPTIMIZE_KL: Iteration 0006 ⛰:+1.9114e+04
OPTIMIZE_KL: #(Nonlinear sampling steps) (5, 5, 5, 5, 3, 4, 2, 2)
OPTIMIZE_KL: #(KL minimization steps) 18
OPTIMIZE_KL: Likelihood residual(s):
reduced χ²:     1.1±   0.056, avg: -6.2e-05±   0.005, #dof:  16384
OPTIMIZE_KL: Prior residual(s):
cfax1asperity         :: reduced χ²:     1.7±     1.7, avg:    +0.28±     1.3, #dof:      1
cfax1flexibility      :: reduced χ²:     3.4±     1.7, avg:     -1.8±    0.46, #dof:      1
cfax1fluctuations     :: reduced χ²:     2.8±    0.39, avg:     +1.7±    0.12, #dof:      1
cfax1loglogavgslope   :: reduced χ²:     1.2±     1.1, avg:   -0.029±     1.1, #dof:      1
cfax1spectrum         :: reduced χ²:    0.99±   0.021, avg:   +0.002±  0.0047, #dof:   3238
cfxi                  :: reduced χ²:     1.1±   0.011, avg: +0.00018±  0.0022, #dof:  16384
cfzeromode            :: reduced χ²:    0.15±    0.15, avg:   +0.055±    0.38, #dof:      1
scaling               :: reduced χ²:    0.38±  0.0037, avg:    +0.61±   0.003, #dof:      1

namps = cfm.get_normalized_amplitudes()
post_sr_mean = jft.mean(tuple(signal(s) for s in samples))
post_a_mean = jft.mean(tuple(cfm.amplitude(s)[1:] for s in samples))
to_plot = [
    ("Signal", signal(pos_truth), "im"),
    ("Noise", noise_truth, "im"),
    ("Data", data, "im"),
    ("Reconstruction", post_sr_mean, "im"),
    ("Ax1", (cfm.amplitude(pos_truth)[1:], post_a_mean), "loglog"),
]
fig, axs = plt.subplots(2, 3, figsize=(16, 9))
for ax, (title, field, tp) in zip(axs.flat, to_plot):
    ax.set_title(title)
    if tp == "im":
        im = ax.imshow(field, cmap="inferno")
        plt.colorbar(im, ax=ax, orientation="horizontal")
    else:
        ax_plot = ax.loglog if tp == "loglog" else ax.plot
        field = field if isinstance(field, (tuple, list)) else (field,)
        for f in field:
            ax_plot(f, alpha=0.7)
fig.tight_layout()
fig.savefig("results_intro_full_reconstruction.png", dpi=400)
plt.show()
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