16308981650

Committed 16 Jul 2025 02:36AM UTC coverage: 74.293% (-1.4%) from 75.644%

Build # 16308981650

Build Type

push

github

Committed by

avivajpeyi

Commit Message

refactoring to use a common parent class

Run Details

76 of 108 branches covered (70.37%)

Branch coverage included in aggregate %.

246 of 280 new or added lines in 7 files covered. (87.86%)

6 existing lines in 1 file now uncovered.

843 of 1129 relevant lines covered (74.67%)

1.49 hits per line

Source File
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82.93

/src/log_psplines/plotting/__init__.py

from typing import Tuple

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
from jax import numpy as jnp
from numpyro.infer import MCMC

from .pdgrm import plot_pdgrm

__all__ = ["plot_pdgrm", "plot_trace"]


def plot_trace(mcmc: MCMC, fname=None):
    inf_obj = az.from_numpyro(mcmc)
    ax = az.plot_trace(inf_obj)
    plt.tight_layout()
    if fname is not None:
        plt.savefig(fname)
        plt.close(ax.flatten()[0].figure)


def plot_basis(basis: np.ndarray, fname=None) -> Tuple[plt.Figure, plt.Axes]:
    """Plot the basis functions, and a histogram of the basis values"""
    fig, axes = plt.subplots(1, 2, figsize=(6, 4))

    ax = axes[0]
    for b in basis.T:
        ax.plot(b)
    print(basis.T.shape)
    ax.set_xlabel("Sample Index")
    ax.set_ylabel("Basis Value")

    # min non-zero value for histogram'
    basis_vals = basis.ravel()
    min_b = np.min(basis_vals[basis_vals > 0])
    max_b = np.max(basis_vals)
    min_b = np.max([min_b, 1e-1])  # avoid log(0)

    ax = axes[1]
    ax.hist(
        basis_vals,
        bins=np.geomspace(min_b, max_b, 50),
        density=True,
        alpha=0.7,
    )
    ax.set_xlabel("Basis Value")
    ax.set_xscale("log")
    # add a textbox of the sparsity of the basis
    sparsity = np.mean(basis == 0)
    ax.text(
        0.05,
        0.95,
        f"Sparsity: {sparsity:.2f}",
        transform=ax.transAxes,
        fontsize=12,
        verticalalignment="top",
    )

    plt.tight_layout()

    if fname is not None:
        plt.savefig(fname)
        plt.close(fig)

    return fig, ax

1	from typing import Tuple	2✔
2
3	import arviz as az	2✔
4	import matplotlib.pyplot as plt	2✔
5	import numpy as np	2✔
6	from jax import numpy as jnp	2✔
7	from numpyro.infer import MCMC	2✔
8
9	from .pdgrm import plot_pdgrm	2✔
10
11	__all__ = ["plot_pdgrm", "plot_trace"]	2✔
12
13
14	def plot_trace(mcmc: MCMC, fname=None):	2✔
UNCOV 15	inf_obj = az.from_numpyro(mcmc)	×
UNCOV 16	ax = az.plot_trace(inf_obj)	×
UNCOV 17	plt.tight_layout()	×
UNCOV 18	if fname is not None:	×
UNCOV 19	plt.savefig(fname)	×
UNCOV 20	plt.close(ax.flatten()[0].figure)	×
21
22
23	def plot_basis(basis: np.ndarray, fname=None) -> Tuple[plt.Figure, plt.Axes]:	2✔
24	"""Plot the basis functions, and a histogram of the basis values"""
25	fig, axes = plt.subplots(1, 2, figsize=(6, 4))	2✔
26
27	ax = axes[0]	2✔
28	for b in basis.T:	2✔
29	ax.plot(b)	2✔
30	print(basis.T.shape)	2✔
31	ax.set_xlabel("Sample Index")	2✔
32	ax.set_ylabel("Basis Value")	2✔
33
34	# min non-zero value for histogram'
35	basis_vals = basis.ravel()	2✔
36	min_b = np.min(basis_vals[basis_vals > 0])	2✔
37	max_b = np.max(basis_vals)	2✔
38	min_b = np.max([min_b, 1e-1]) # avoid log(0)	2✔
39
40	ax = axes[1]	2✔
41	ax.hist(	2✔
42	basis_vals,
43	bins=np.geomspace(min_b, max_b, 50),
44	density=True,
45	alpha=0.7,
46	)
47	ax.set_xlabel("Basis Value")	2✔
48	ax.set_xscale("log")	2✔
49	# add a textbox of the sparsity of the basis
50	sparsity = np.mean(basis == 0)	2✔
51	ax.text(	2✔
52	0.05,
53	0.95,
54	f"Sparsity: {sparsity:.2f}",
55	transform=ax.transAxes,
56	fontsize=12,
57	verticalalignment="top",
58	)
59
60	plt.tight_layout()	2✔
61
62	if fname is not None:	2✔
63	plt.savefig(fname)	2✔
64	plt.close(fig)	2✔
65
66	return fig, ax	2✔

nz-gravity / LogPSplinePSD / 16308981650

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