"""Wrapper for value-based error analysis."""
from typing import Any
import numpy as np
from adctoolbox.aout.rearrange_error_by_value import rearrange_error_by_value
from adctoolbox.aout.plot_rearranged_error_by_value import plot_rearranged_error_by_value
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def analyze_error_by_value(
signal: np.ndarray,
norm_freq: float = None,
n_bins: int = 100,
clip_percent: float = 0.01,
value_range: tuple[float, float | None] = None,
create_plot: bool = True,
axes=None, ax=None,
title: str = None
) -> dict[str, Any]:
"""
Analyze error binned by value (INL/DNL/Noise).
Combines core computation and optional plotting.
Parameters
----------
signal : np.ndarray
Input signal (1D array).
norm_freq : float, optional
Normalized frequency (f/fs). If None, auto-detected.
n_bins : int, default=100
Number of bins for analysis (x-axis resolution).
clip_percent : float, default=0.01
Ratio of values to clip from edges.
value_range : tuple(min, max), optional
Physical range mapping to bin 0 and bin (N-1).
create_plot : bool, default=True
Whether to display result plot.
axes : tuple or array, optional
Tuple of (ax1, ax2) to plot on.
ax : matplotlib.axes.Axes, optional
Single axis to plot on (will be split).
title : str, optional
Test setup description for title.
Returns
-------
results : dict
Dictionary containing 'error_mean', 'error_rms', 'bin_centers', etc.
"""
# 1. Compute
results = rearrange_error_by_value(
signal=signal,
norm_freq=norm_freq,
n_bins=n_bins,
clip_percent=clip_percent,
value_range=value_range
)
# 2. Plot
if create_plot:
plot_rearranged_error_by_value(results, axes=axes, ax=ax, title=title)
return results