morphZ 0.1.0

KDE-based density estimation and approximation package.

morphZ

Morph-Z for high accuracy marginal likelihood estimation and morphological density approximation toolkit for scientific workflows, with utilities for dependency analysis.

• Flexible Morph backends: independent, pairwise, grouped, and tree-structured.
• Bandwidth selection: Scott, Silverman, Botev ISJ, and cross-validation variants.
• Evidence estimation via bridge sampling with robust diagnostics.
• Mutual information and Total correlation estimation.
• Mutual information and Chow–Liu dependency tree visualisation.

Installation

Python 3.8+ is recommended.

pip install morphz

From source (editable):

pip install -e .

Run The Examples

Interactive notebooks live in examples/:

• examples/eggbox.ipynb
• examples/gaussian shell.ipynb
• examples/peak_sampling_new.ipynb

Quick Starts

Minimal Morph fit and evaluate:

import numpy as np
from morphZ import Morph_Indep, select_bandwidth

rng = np.random.default_rng(0)
X = rng.normal(size=(500, 2))

bw = select_bandwidth(X, method="silverman")
morph_indep = Morph_Indep(X, kde_bw=bw)

pts = rng.normal(size=(5, 2))
print(morph_indep.logpdf(pts))

Compute MI heatmap and a Chow–Liu tree (artifacts saved to out_dir):

import numpy as np
from morphZ import dependency_tree

X = np.random.default_rng(0).normal(size=(1000, 4))
mi, tree, edges = dependency_tree.compute_and_plot_mi_tree(
    X, names=["x0", "x1", "x2", "x3"], out_path="out_dir", morph_type="tree"
)
print("Edges (parent -> child):", edges)

Compute n‑order Total Correlation (TC) and save results:

import numpy as np
from morphZ import Nth_TC

X = np.random.default_rng(0).normal(size=(1000, 5))
Nth_TC.compute_and_save_tc(
    X, names=[f"x{i}" for i in range(X.shape[1])], n_order=3, out_path="out_dir"
)

End‑to‑end morphological evidence with bridge sampling:

import numpy as np
from morphZ import evidence

rng = np.random.default_rng(0)
dim = 2

# Toy "posterior": standard Normal, known up to a constant
def log_target(theta: np.ndarray) -> float:
    return -0.5 * np.dot(theta, theta)

# Pretend these came from an MCMC chain
post_samples = rng.normal(size=(5000, dim))
log_post_vals = -0.5 * np.sum(post_samples**2, axis=1)

results = evidence(
    post_samples=post_samples,
    log_posterior_values=log_post_vals,
    log_posterior_function=log_target,
    n_resamples=2000,
    morph_type="tree",          # "indep" | "pair" | "tree" | "3_group" | ...
    kde_bw="isj",             # "scott" | "silverman" | "isj" | "cv_iso" | "cv_diag" | numeric
    param_names=[f"x{i}" for i in range(dim)],
    output_path="examples/morphZ_gaussian_demo",
    n_estimations=2,
    verbose=True,
)

print("log(z), err per run:\n", np.array(results))

Artifacts will be saved under examples/morphZ_gaussian_demo/ (bandwidths, MI/Tree files as needed, and logz_morph_z_<morph_type>_<bw_method>.txt).

API Highlights

• Morphs: Morph_Indep, Morph_Pairwise, Morph_Tree, Morph_Group.
• Bandwidths: select_bandwidth, compute_and_save_bandwidths.
• Evidence: evidence, bridge_sampling_ln (lower‑level), compute_bridge_rmse.
• Dependency analysis: dependency_tree.compute_and_plot_mi_tree.
• Total correlation: Nth_TC.compute_and_save_tc.

Notes:

• If you pass a numeric kde_bw (e.g., 0.9) the library skips bandwidth JSONs.
• Tree/group proposals will compute and cache tree.json/params_*_TC.json on first use.

Dependencies

• Core: numpy, scipy, matplotlib, networkx, emcee, statsmodels, scikit-learn
• Optional: pandas (CSV labels), pygraphviz (nicer tree layout), scikit-sparse (optional exception type)

Development

• Build wheels/sdist: python -m build
• Check metadata: twine check dist/*
• Tests live in tests/

Versioning & Release

Versioning is derived from git tags via setuptools_scm.

• Tag a release: git tag vX.Y.Z && git push --tags
• CI: publishes to TestPyPI on pushes to main/master; to PyPI on v* tags.
• Uses PyPI/TestPyPI Trusted Publishing (OIDC). You can also use API tokens if preferred.

License

BSD-3-Clause. See LICENSE for details.