17394129984

Committed 02 Sep 2025 05:25AM UTC coverage: 90.419% (+9.4%) from 81.047%

Build # 17394129984

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avivajpeyi

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78.46

/src/log_psplines/plotting/utils.py

import dataclasses

import arviz as az
import jax.numpy as jnp
import numpy as np

from ..datatypes import Periodogram
from ..psplines import LogPSplines

__all__ = ["unpack_data"]


@dataclasses.dataclass
class PlottingData:
    freqs: np.ndarray = None
    pdgrm: np.ndarray = None
    model: np.ndarray = None
    ci: np.ndarray = None

    @property
    def n(self):
        if self.freqs is not None:
            return len(self.freqs)
        elif self.pdgrm is not None:
            return len(self.pdgrm)
        elif self.model is not None:
            return len(self.model)
        else:
            raise ValueError("No data to get length from.")


def unpack_data(
    pdgrm: Periodogram = None,
    spline_model: LogPSplines = None,
    weights=None,
    yscalar=1.0,
    use_uniform_ci=True,
    use_parametric_model=True,
    freqs=None,
):
    plt_dat = PlottingData()
    if pdgrm is not None:
        plt_dat.pdgrm = np.array(pdgrm.power, dtype=np.float64) * yscalar
        plt_dat.freqs = np.array(pdgrm.freqs)

    if spline_model is not None:

        if weights is None:
            # just use the initial weights/0 weights
            ln_spline = spline_model(use_parametric_model=use_parametric_model)

        elif weights.ndim == 1:
            # only one set of weights -- no CI possible
            ln_spline = spline_model(weights, use_parametric_model)

        else:  # weights.ndim == 2
            # multiple sets of weights -- CI possible
            ln_splines = jnp.array(
                [spline_model(w, use_parametric_model) for w in weights]
            )

            if use_uniform_ci:
                ln_ci = _get_uni_ci(ln_splines)
            else:  # percentile
                ln_ci = jnp.percentile(
                    ln_splines, q=jnp.array([16, 50, 84]), axis=0
                )
            ln_ci = jnp.array(ln_ci)
            plt_dat.ci = np.exp(ln_ci, dtype=np.float64) * yscalar
            ln_spline = ln_ci[1]
        plt_dat.model = np.exp(ln_spline, dtype=np.float64) * yscalar

    if plt_dat.freqs is None and freqs is None:
        plt_dat.freqs = np.linspace(0, 1, plt_dat.n)
    elif freqs is not None:
        plt_dat.freqs = freqs

    return plt_dat


def _get_uni_ci(samples, alpha=0.1):
    """
    Compute a uniform (simultaneous) confidence band for a set of function samples.

    Args:
        samples (jnp.ndarray): Shape (num_samples, num_points) array of function samples.
        alpha (float): Significance level (default 0.1 for 90% CI).

    Returns:
        tuple: (lower_bound, median, upper_bound) arrays.
    """
    num_samples, num_points = samples.shape

    # Compute pointwise median and standard deviation
    median = jnp.median(samples, axis=0)
    std = jnp.std(samples, axis=0)

    # Compute the max deviation over all samples
    deviations = (samples - median[None, :]) / std[
        None, :
    ]  # Normalize deviations
    max_deviation = jnp.max(
        jnp.abs(deviations), axis=1
    )  # Max deviation per sample

    # Compute the scaling factor using the distribution of max deviations
    k_alpha = jnp.percentile(
        max_deviation, 100 * (1 - alpha)
    )  # Critical value

    # Compute uniform confidence bands
    lower_bound = median - k_alpha * std
    upper_bound = median + k_alpha * std

    return lower_bound, median, upper_bound

1	import dataclasses	2✔
2
3	import arviz as az	2✔
4	import jax.numpy as jnp	2✔
5	import numpy as np	2✔
6
7	from ..datatypes import Periodogram	2✔
8	from ..psplines import LogPSplines	2✔
9
10	__all__ = ["unpack_data"]	2✔
11
12
13	@dataclasses.dataclass	2✔
14	class PlottingData:	2✔
15	freqs: np.ndarray = None	2✔
16	pdgrm: np.ndarray = None	2✔
17	model: np.ndarray = None	2✔
18	ci: np.ndarray = None	2✔
19
20	@property	2✔
21	def n(self):	2✔
22	if self.freqs is not None:	×
23	return len(self.freqs)	×
24	elif self.pdgrm is not None:	×
25	return len(self.pdgrm)	×
26	elif self.model is not None:	×
27	return len(self.model)	×
28	else:
29	raise ValueError("No data to get length from.")	×
30
31
32	def unpack_data(	2✔
33	pdgrm: Periodogram = None,
34	spline_model: LogPSplines = None,
35	weights=None,
36	yscalar=1.0,
37	use_uniform_ci=True,
38	use_parametric_model=True,
39	freqs=None,
40	):
41	plt_dat = PlottingData()	2✔
42	if pdgrm is not None:	2✔
43	plt_dat.pdgrm = np.array(pdgrm.power, dtype=np.float64) * yscalar	2✔
44	plt_dat.freqs = np.array(pdgrm.freqs)	2✔
45
46	if spline_model is not None:	2✔
47
48	if weights is None:	2✔
49	# just use the initial weights/0 weights
50	ln_spline = spline_model(use_parametric_model=use_parametric_model)	2✔
51
52	elif weights.ndim == 1:	2✔
53	# only one set of weights -- no CI possible
54	ln_spline = spline_model(weights, use_parametric_model)	×
55
56	else: # weights.ndim == 2
57	# multiple sets of weights -- CI possible
58	ln_splines = jnp.array(	2✔
59	[spline_model(w, use_parametric_model) for w in weights]
60	)
61
62	if use_uniform_ci:	2✔
63	ln_ci = _get_uni_ci(ln_splines)	2✔
64	else: # percentile
UNCOV 65	ln_ci = jnp.percentile(	×
66	ln_splines, q=jnp.array([16, 50, 84]), axis=0
67	)
68	ln_ci = jnp.array(ln_ci)	2✔
69	plt_dat.ci = np.exp(ln_ci, dtype=np.float64) * yscalar	2✔
70	ln_spline = ln_ci[1]	2✔
71	plt_dat.model = np.exp(ln_spline, dtype=np.float64) * yscalar	2✔
72
73	if plt_dat.freqs is None and freqs is None:	2✔
UNCOV 74	plt_dat.freqs = np.linspace(0, 1, plt_dat.n)	×
75	elif freqs is not None:	2✔
76	plt_dat.freqs = freqs	×
77
78	return plt_dat	2✔
79
80
81	def _get_uni_ci(samples, alpha=0.1):	2✔
82	"""
83	Compute a uniform (simultaneous) confidence band for a set of function samples.
84
85	Args:
86	samples (jnp.ndarray): Shape (num_samples, num_points) array of function samples.
87	alpha (float): Significance level (default 0.1 for 90% CI).
88
89	Returns:
90	tuple: (lower_bound, median, upper_bound) arrays.
91	"""
92	num_samples, num_points = samples.shape	2✔
93
94	# Compute pointwise median and standard deviation
95	median = jnp.median(samples, axis=0)	2✔
96	std = jnp.std(samples, axis=0)	2✔
97
98	# Compute the max deviation over all samples
99	deviations = (samples - median[None, :]) / std[	2✔
100	None, :
101	] # Normalize deviations
102	max_deviation = jnp.max(	2✔
103	jnp.abs(deviations), axis=1
104	) # Max deviation per sample
105
106	# Compute the scaling factor using the distribution of max deviations
107	k_alpha = jnp.percentile(	2✔
108	max_deviation, 100 * (1 - alpha)
109	) # Critical value
110
111	# Compute uniform confidence bands
112	lower_bound = median - k_alpha * std	2✔
113	upper_bound = median + k_alpha * std	2✔
114
115	return lower_bound, median, upper_bound	2✔

nz-gravity / LogPSplinePSD / 17394129984

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