18732978364

Committed 22 Oct 2025 11:43PM UTC coverage: 80.372% (+1.1%) from 79.302%

Build # 18732978364

Build Type

Pull #14

github

Committed by

web-flow

Commit Message

Merge 50e45a420 into 3455fb19b

Pull Request Pull Request #14: add averaged data for multivar case

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54.01

/src/log_psplines/plotting/base.py

"""
Base plotting utilities for shared functionality across plotting modules.
"""

import os
from dataclasses import dataclass
from functools import wraps
from typing import Any, Callable, Dict, Optional, Tuple, Union

import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np

from ..logger import logger

# Color constants used across plotting modules
COLORS = {
    "data": "#d3d3d3",  # lightgray
    "model": "#ff7f0e",  # tab:orange
    "knots": "#d62728",  # tab:red
    "true": "#000000",  # black
    "empirical": "#404040",  # dark gray
    "ci_fill": "#1f77b4",  # tab:blue
    "coherence": "#1f77b4",  # tab:blue
    "real": "#2ca02c",  # tab:green
    "imag": "#ff7f0e",  # tab:orange
}


@dataclass
class PlotConfig:
    """Configuration for plotting parameters."""

    figsize: tuple = (12, 8)
    dpi: int = 150
    fontsize: int = 11
    labelsize: int = 12
    titlesize: int = 12
    linewidth: float = 1.2
    markersize: float = 4.5
    alpha: float = 0.7


def safe_plot(filename: str, dpi: int = 150):
    """Decorator for safe plotting with error handling."""

    def decorator(plot_func: Callable):
        @wraps(plot_func)
        def wrapper(*args, **kwargs):
            try:
                logger.debug(f"--- plotting: {os.path.basename(filename)}")
                result = plot_func(*args, **kwargs)
                plt.savefig(filename, dpi=dpi, bbox_inches="tight")
                plt.close()
                return True
            except Exception as e:
                logger.warning(
                    f"Failed to create {os.path.basename(filename)}: {e}"
                )
                plt.close("all")
                return False

        return wrapper

    return decorator


def extract_plotting_data(idata, weights_key: str = None) -> Dict[str, Any]:
    """
    Extract common plotting data from inference data object.

    Args:
        idata: ArviZ InferenceData object
        weights_key: Key for weights in posterior (optional)

    Returns:
        Dictionary containing extracted data
    """
    from ..arviz_utils import (
        get_periodogram,
        get_spline_model,
        get_weights,
    )

    data = {}

    # Extract core data - handle univariate, multivariate, and VI cases
    try:
        data["periodogram"] = get_periodogram(idata)
    except KeyError:
        # For multivariate or VI data, periodogram might not be available
        # or might be stored differently
        data["periodogram"] = None

    # Extract spline model - handle VI case where 'knots' might not exist
    try:
        data["spline_model"] = get_spline_model(idata)
    except KeyError:
        # For VI data, spline model might be stored differently
        data["spline_model"] = None

    # Extract weights - handle different data structures
    try:
        data["weights"] = get_weights(idata, weights_key)
    except (KeyError, AttributeError):
        # For VI data, weights might be stored differently
        data["weights"] = None

    # Extract posterior samples if available
    if hasattr(idata, "posterior_psd"):
        if "psd" in idata.posterior_psd:
            arr = idata.posterior_psd["psd"]
            data["posterior_psd_quantiles"] = {
                "percentile": np.asarray(arr.coords["percentile"].values),
                "values": np.asarray(arr.values),
            }
        if "psd_matrix_real" in idata.posterior_psd:
            data["posterior_psd_matrix_quantiles"] = {
                "percentile": np.asarray(
                    idata.posterior_psd["psd_matrix_real"]
                    .coords["percentile"]
                    .values
                ),
                "real": np.asarray(
                    idata.posterior_psd["psd_matrix_real"].values
                ),
                "imag": np.asarray(
                    idata.posterior_psd["psd_matrix_imag"].values
                ),
                "coherence": (
                    np.asarray(idata.posterior_psd["coherence"].values)
                    if "coherence" in idata.posterior_psd
                    else None
                ),
            }

    # Extract true PSD if available
    if hasattr(idata, "attrs") and "true_psd" in idata.attrs:
        data["true_psd"] = idata.attrs["true_psd"]

    # Extract frequencies if available
    if hasattr(idata, "attrs") and "frequencies" in idata.attrs:
        data["frequencies"] = idata.attrs["frequencies"]

    return data


def compute_confidence_intervals(
    samples: np.ndarray,
    quantiles: Tuple[float, float, float] = (16, 50, 84),
    method: str = "percentile",
    alpha: float = 0.1,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Compute confidence intervals from posterior samples.

    Args:
        samples: Array of posterior samples
        quantiles: Tuple of quantiles to compute (low, median, high)
        method: Method for CI computation ('percentile' or 'uniform')
        alpha: Significance level for uniform CI

    Returns:
        Tuple of (lower_bound, median, upper_bound)
    """
    if method == "percentile":
        return jnp.percentile(samples, q=jnp.array(quantiles), axis=0)
    elif method == "uniform":
        return _compute_uniform_ci(samples, alpha)
    else:
        raise ValueError(f"Unknown CI method: {method}")


def _compute_uniform_ci(samples: np.ndarray, alpha: float = 0.1):
    """
    Compute uniform (simultaneous) confidence intervals.

    Args:
        samples: Shape (num_samples, num_points) array of function samples
        alpha: Significance level

    Returns:
        Tuple of (lower_bound, median, upper_bound)
    """
    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, :]
    max_deviation = jnp.max(jnp.abs(deviations), axis=1)

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

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

    return lower_bound, median, upper_bound


def compute_coherence_ci(
    psd_samples: np.ndarray,
) -> Dict[Tuple[int, int], Tuple[np.ndarray, np.ndarray, np.ndarray]]:
    """
    Compute coherence confidence intervals from multivariate PSD samples.

    Args:
        psd_samples: Shape (n_samples, n_freq, n_channels, n_channels)

    Returns:
        Dictionary mapping (i,j) channel pairs to (q05, q50, q95) tuples
    """
    ci_dict = {}
    n_samples, n_freq, n_channels, _ = psd_samples.shape

    for i in range(n_channels):
        for j in range(n_channels):
            if i > j:  # Only compute for upper triangle
                coh = np.abs(psd_samples[:, :, i, j]) ** 2 / (
                    np.abs(psd_samples[:, :, i, i])
                    * np.abs(psd_samples[:, :, j, j])
                )
                q05 = np.percentile(coh, 5, axis=0)
                q50 = np.percentile(coh, 50, axis=0)
                q95 = np.percentile(coh, 95, axis=0)
                ci_dict[(i, j)] = (q05, q50, q95)

    return ci_dict


def compute_cross_spectra_ci(psd_samples: np.ndarray) -> Tuple[Dict, Dict]:
    """
    Compute real and imaginary parts of cross-spectra.

    Args:
        psd_samples: Shape (n_samples, n_freq, n_channels, n_channels)

    Returns:
        Tuple of (real_ci_dict, imag_ci_dict)
    """
    real_dict = {}
    imag_dict = {}
    n_samples, n_freq, n_channels, _ = psd_samples.shape

    for i in range(n_channels):
        for j in range(n_channels):
            if i != j:
                # Real part
                re_q05 = np.percentile(psd_samples[:, :, i, j].real, 5, axis=0)
                re_q50 = np.percentile(
                    psd_samples[:, :, i, j].real, 50, axis=0
                )
                re_q95 = np.percentile(
                    psd_samples[:, :, i, j].real, 95, axis=0
                )
                real_dict[(i, j)] = (re_q05, re_q50, re_q95)

                # Imaginary part
                im_q05 = np.percentile(psd_samples[:, :, i, j].imag, 5, axis=0)
                im_q50 = np.percentile(
                    psd_samples[:, :, i, j].imag, 50, axis=0
                )
                im_q95 = np.percentile(
                    psd_samples[:, :, i, j].imag, 95, axis=0
                )
                imag_dict[(i, j)] = (im_q05, im_q50, im_q95)

    return real_dict, imag_dict


def setup_plot_style(config: Optional[PlotConfig] = None) -> PlotConfig:
    """Setup consistent matplotlib styling for plots."""
    if config is None:
        config = PlotConfig()

    plt.rcParams.update(
        {
            "font.size": config.fontsize,
            "axes.labelsize": config.labelsize,
            "axes.titlesize": config.titlesize,
            "xtick.labelsize": config.fontsize - 1,
            "ytick.labelsize": config.fontsize - 1,
            "legend.fontsize": config.fontsize - 1,
            "axes.linewidth": config.linewidth,
            "xtick.major.width": config.linewidth - 0.1,
            "ytick.major.width": config.linewidth - 0.1,
            "figure.dpi": config.dpi,
            "savefig.dpi": config.dpi * 2,
        }
    )

    return config


def validate_plotting_data(data: Dict[str, Any], required_keys: list) -> bool:
    """Validate that required data is available for plotting."""
    missing_keys = [
        key for key in required_keys if key not in data or data[key] is None
    ]
    if missing_keys:
        logger.warning(f"Missing required data for plotting: {missing_keys}")
        return False
    return True


def subsample_weights(
    weights: np.ndarray, max_samples: int = 500
) -> np.ndarray:
    """Subsample weights array if it's too large for efficient computation."""
    if weights.shape[0] > max_samples:
        idx = np.random.choice(
            weights.shape[0], size=max_samples, replace=False
        )
        return weights[idx]
    return weights

1	"""
2	Base plotting utilities for shared functionality across plotting modules.
3	"""
4
5	import os	2✔
6	from dataclasses import dataclass	2✔
7	from functools import wraps	2✔
8	from typing import Any, Callable, Dict, Optional, Tuple, Union	2✔
9
10	import jax.numpy as jnp	2✔
11	import matplotlib.pyplot as plt	2✔
12	import numpy as np	2✔
13
14	from ..logger import logger	2✔
15
16	# Color constants used across plotting modules
17	COLORS = {	2✔
18	"data": "#d3d3d3", # lightgray
19	"model": "#ff7f0e", # tab:orange
20	"knots": "#d62728", # tab:red
21	"true": "#000000", # black
22	"empirical": "#404040", # dark gray
23	"ci_fill": "#1f77b4", # tab:blue
24	"coherence": "#1f77b4", # tab:blue
25	"real": "#2ca02c", # tab:green
26	"imag": "#ff7f0e", # tab:orange
27	}
28
29
30	@dataclass	2✔
31	class PlotConfig:	2✔
32	"""Configuration for plotting parameters."""
33
34	figsize: tuple = (12, 8)	2✔
35	dpi: int = 150	2✔
36	fontsize: int = 11	2✔
37	labelsize: int = 12	2✔
38	titlesize: int = 12	2✔
39	linewidth: float = 1.2	2✔
40	markersize: float = 4.5	2✔
41	alpha: float = 0.7	2✔
42
43
44	def safe_plot(filename: str, dpi: int = 150):	2✔
45	"""Decorator for safe plotting with error handling."""
46
47	def decorator(plot_func: Callable):	2✔
48	@wraps(plot_func)	2✔
49	def wrapper(args, *kwargs):	2✔
50	try:	2✔
51	logger.debug(f"--- plotting: {os.path.basename(filename)}")	2✔
52	result = plot_func(args, *kwargs)	2✔
53	plt.savefig(filename, dpi=dpi, bbox_inches="tight")	2✔
54	plt.close()	2✔
55	return True	2✔
56	except Exception as e:	×
57	logger.warning(	×
58	f"Failed to create {os.path.basename(filename)}: {e}"
59	)
60	plt.close("all")	×
61	return False	×
62
63	return wrapper	2✔
64
65	return decorator	2✔
66
67
68	def extract_plotting_data(idata, weights_key: str = None) -> Dict[str, Any]:	2✔
69	"""
70	Extract common plotting data from inference data object.
71
72	Args:
73	idata: ArviZ InferenceData object
74	weights_key: Key for weights in posterior (optional)
75
76	Returns:
77	Dictionary containing extracted data
78	"""
79	from ..arviz_utils import (	2✔
80	get_periodogram,
81	get_spline_model,
82	get_weights,
83	)
84
85	data = {}	2✔
86
87	# Extract core data - handle univariate, multivariate, and VI cases
88	try:	2✔
89	data["periodogram"] = get_periodogram(idata)	2✔
90	except KeyError:	2✔
91	# For multivariate or VI data, periodogram might not be available
92	# or might be stored differently
93	data["periodogram"] = None	2✔
94
95	# Extract spline model - handle VI case where 'knots' might not exist
96	try:	2✔
97	data["spline_model"] = get_spline_model(idata)	2✔
98	except KeyError:	2✔
99	# For VI data, spline model might be stored differently
100	data["spline_model"] = None	2✔
101
102	# Extract weights - handle different data structures
103	try:	2✔
104	data["weights"] = get_weights(idata, weights_key)	2✔
105	except (KeyError, AttributeError):	2✔
106	# For VI data, weights might be stored differently
107	data["weights"] = None	2✔
108
109	# Extract posterior samples if available
110	if hasattr(idata, "posterior_psd"):	2✔
111	if "psd" in idata.posterior_psd:	2✔
112	arr = idata.posterior_psd["psd"]	2✔
113	data["posterior_psd_quantiles"] = {	2✔
114	"percentile": np.asarray(arr.coords["percentile"].values),
115	"values": np.asarray(arr.values),
116	}
117	if "psd_matrix_real" in idata.posterior_psd:	2✔
118	data["posterior_psd_matrix_quantiles"] = {	2✔
119	"percentile": np.asarray(
120	idata.posterior_psd["psd_matrix_real"]
121	.coords["percentile"]
122	.values
123	),
124	"real": np.asarray(
125	idata.posterior_psd["psd_matrix_real"].values
126	),
127	"imag": np.asarray(
128	idata.posterior_psd["psd_matrix_imag"].values
129	),
130	"coherence": (
131	np.asarray(idata.posterior_psd["coherence"].values)
132	if "coherence" in idata.posterior_psd
133	else None
134	),
135	}
136
137	# Extract true PSD if available
138	if hasattr(idata, "attrs") and "true_psd" in idata.attrs:	2✔
139	data["true_psd"] = idata.attrs["true_psd"]	2✔
140
141	# Extract frequencies if available
142	if hasattr(idata, "attrs") and "frequencies" in idata.attrs:	2✔
143	data["frequencies"] = idata.attrs["frequencies"]	2✔
144
145	return data	2✔
146
147
148	def compute_confidence_intervals(	2✔
149	samples: np.ndarray,
150	quantiles: Tuple[float, float, float] = (16, 50, 84),
151	method: str = "percentile",
152	alpha: float = 0.1,
153	) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
154	"""
155	Compute confidence intervals from posterior samples.
156
157	Args:
158	samples: Array of posterior samples
159	quantiles: Tuple of quantiles to compute (low, median, high)
160	method: Method for CI computation ('percentile' or 'uniform')
161	alpha: Significance level for uniform CI
162
163	Returns:
164	Tuple of (lower_bound, median, upper_bound)
165	"""
166	if method == "percentile":	×
167	return jnp.percentile(samples, q=jnp.array(quantiles), axis=0)	×
168	elif method == "uniform":	×
169	return _compute_uniform_ci(samples, alpha)	×
170	else:
171	raise ValueError(f"Unknown CI method: {method}")	×
172
173
174	def _compute_uniform_ci(samples: np.ndarray, alpha: float = 0.1):	2✔
175	"""
176	Compute uniform (simultaneous) confidence intervals.
177
178	Args:
179	samples: Shape (num_samples, num_points) array of function samples
180	alpha: Significance level
181
182	Returns:
183	Tuple of (lower_bound, median, upper_bound)
184	"""
185	num_samples, num_points = samples.shape	×
186
187	# Compute pointwise median and standard deviation
188	median = jnp.median(samples, axis=0)	×
189	std = jnp.std(samples, axis=0)	×
190
191	# Compute the max deviation over all samples
192	deviations = (samples - median[None, :]) / std[None, :]	×
193	max_deviation = jnp.max(jnp.abs(deviations), axis=1)	×
194
195	# Compute the scaling factor using the distribution of max deviations
196	k_alpha = jnp.percentile(max_deviation, 100 * (1 - alpha))	×
197
198	# Compute uniform confidence bands
199	lower_bound = median - k_alpha * std	×
200	upper_bound = median + k_alpha * std	×
201
202	return lower_bound, median, upper_bound	×
203
204
205	def compute_coherence_ci(	2✔
206	psd_samples: np.ndarray,
207	) -> Dict[Tuple[int, int], Tuple[np.ndarray, np.ndarray, np.ndarray]]:
208	"""
209	Compute coherence confidence intervals from multivariate PSD samples.
210
211	Args:
212	psd_samples: Shape (n_samples, n_freq, n_channels, n_channels)
213
214	Returns:
215	Dictionary mapping (i,j) channel pairs to (q05, q50, q95) tuples
216	"""
UNCOV 217	ci_dict = {}	×
UNCOV 218	n_samples, n_freq, n_channels, _ = psd_samples.shape	×
219
UNCOV 220	for i in range(n_channels):	×
UNCOV 221	for j in range(n_channels):	×
UNCOV 222	if i > j: # Only compute for upper triangle	×
UNCOV 223	coh = np.abs(psd_samples[:, :, i, j]) ** 2 / (	×
224	np.abs(psd_samples[:, :, i, i])
225	* np.abs(psd_samples[:, :, j, j])
226	)
UNCOV 227	q05 = np.percentile(coh, 5, axis=0)	×
UNCOV 228	q50 = np.percentile(coh, 50, axis=0)	×
UNCOV 229	q95 = np.percentile(coh, 95, axis=0)	×
UNCOV 230	ci_dict[(i, j)] = (q05, q50, q95)	×
231
UNCOV 232	return ci_dict	×
233
234
235	def compute_cross_spectra_ci(psd_samples: np.ndarray) -> Tuple[Dict, Dict]:	2✔
236	"""
237	Compute real and imaginary parts of cross-spectra.
238
239	Args:
240	psd_samples: Shape (n_samples, n_freq, n_channels, n_channels)
241
242	Returns:
243	Tuple of (real_ci_dict, imag_ci_dict)
244	"""
245	real_dict = {}	×
246	imag_dict = {}	×
247	n_samples, n_freq, n_channels, _ = psd_samples.shape	×
248
249	for i in range(n_channels):	×
250	for j in range(n_channels):	×
251	if i != j:	×
252	# Real part
253	re_q05 = np.percentile(psd_samples[:, :, i, j].real, 5, axis=0)	×
254	re_q50 = np.percentile(	×
255	psd_samples[:, :, i, j].real, 50, axis=0
256	)
257	re_q95 = np.percentile(	×
258	psd_samples[:, :, i, j].real, 95, axis=0
259	)
260	real_dict[(i, j)] = (re_q05, re_q50, re_q95)	×
261
262	# Imaginary part
263	im_q05 = np.percentile(psd_samples[:, :, i, j].imag, 5, axis=0)	×
264	im_q50 = np.percentile(	×
265	psd_samples[:, :, i, j].imag, 50, axis=0
266	)
267	im_q95 = np.percentile(	×
268	psd_samples[:, :, i, j].imag, 95, axis=0
269	)
270	imag_dict[(i, j)] = (im_q05, im_q50, im_q95)	×
271
272	return real_dict, imag_dict	×
273
274
275	def setup_plot_style(config: Optional[PlotConfig] = None) -> PlotConfig:	2✔
276	"""Setup consistent matplotlib styling for plots."""
277	if config is None:	2✔
278	config = PlotConfig()	2✔
279
280	plt.rcParams.update(	2✔
281	{
282	"font.size": config.fontsize,
283	"axes.labelsize": config.labelsize,
284	"axes.titlesize": config.titlesize,
285	"xtick.labelsize": config.fontsize - 1,
286	"ytick.labelsize": config.fontsize - 1,
287	"legend.fontsize": config.fontsize - 1,
288	"axes.linewidth": config.linewidth,
289	"xtick.major.width": config.linewidth - 0.1,
290	"ytick.major.width": config.linewidth - 0.1,
291	"figure.dpi": config.dpi,
292	"savefig.dpi": config.dpi * 2,
293	}
294	)
295
296	return config	2✔
297
298
299	def validate_plotting_data(data: Dict[str, Any], required_keys: list) -> bool:	2✔
300	"""Validate that required data is available for plotting."""
301	missing_keys = [	×
302	key for key in required_keys if key not in data or data[key] is None
303	]
304	if missing_keys:	×
305	logger.warning(f"Missing required data for plotting: {missing_keys}")	×
306	return False	×
307	return True	×
308
309
310	def subsample_weights(	2✔
311	weights: np.ndarray, max_samples: int = 500
312	) -> np.ndarray:
313	"""Subsample weights array if it's too large for efficient computation."""
314	if weights.shape[0] > max_samples:	×
315	idx = np.random.choice(	×
316	weights.shape[0], size=max_samples, replace=False
317	)
318	return weights[idx]	×
319	return weights	×

nz-gravity / LogPSplinePSD / 18732978364

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