"""Generate AOUT analysis dashboard with 8 analysis plots in a 2x4 panel."""
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from adctoolbox.spectrum import analyze_spectrum
from adctoolbox.spectrum import analyze_spectrum_polar
from adctoolbox.aout import analyze_error_by_value
from adctoolbox.aout import analyze_error_by_phase
from adctoolbox.aout import analyze_decomposition_time
from adctoolbox.aout import analyze_decomposition_polar
from adctoolbox.aout import analyze_error_pdf
from adctoolbox.aout import analyze_error_autocorr
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def generate_aout_dashboard(signal, fs=1.0, freq=None, output_path=None, resolution=12):
"""
Generate comprehensive analysis dashboard with 8 subplots in a 2x4 panel.
Parameters
----------
signal : array_like
Input signal (ADC output or analog signal)
fs : float, optional
Sampling frequency (default: 1.0 for normalized frequency)
freq : float, optional
Signal frequency in Hz (default: None, auto-estimate)
Will be converted to normalized frequency where needed
output_path : str or Path, optional
Path to save figure (default: None, don't save)
resolution : int, optional
ADC resolution in bits (default: 12)
Returns
-------
fig : matplotlib.figure.Figure
Figure object containing the dashboard
axes : ndarray
Array of axes objects (2x4 grid, flattened)
"""
signal = np.asarray(signal).flatten()
# Calculate normalized frequency if freq is provided
norm_freq = freq / fs if freq is not None else None
# Create 2x4 panel
fig, axes = plt.subplots(2, 4, figsize=(26, 12))
axes = axes.flatten()
# Recreate polar axes for plots that need them (plot 2 and plot 6)
fig.delaxes(axes[1])
axes[1] = fig.add_subplot(2, 4, 2, projection='polar')
fig.delaxes(axes[5])
axes[5] = fig.add_subplot(2, 4, 6, projection='polar')
# Plot 1: analyze_spectrum
plt.sca(axes[0])
analyze_spectrum(signal, fs=fs)
axes[0].set_title('(1) Spectrum', fontsize=12)
# Plot 2: analyze_spectrum_polar
plt.sca(axes[1])
analyze_spectrum_polar(signal, fs=fs)
axes[1].set_title('(2) Spectrum Polar', fontsize=12, pad=20)
# Plot 3: analyze_error_by_value
analyze_error_by_value(signal, ax=axes[2], title='(3) Error by Value')
# Plot 4: analyze_error_by_phase
analyze_error_by_phase(signal, ax=axes[3], title='(4) Error by Phase')
# Plot 5: analyze_decomposition_time
analyze_decomposition_time(signal, ax=axes[4], title='(5) Time Domain Error Decomposition')
# Plot 6: analyze_decomposition_polar
plt.sca(axes[5])
analyze_decomposition_polar(signal)
axes[5].set_title('(6) Polar Plot of Error Decomposition', fontsize=12, pad=20)
# Plot 7: analyze_error_pdf
analyze_error_pdf(signal, resolution=resolution, ax=axes[6], title='(7) Error PDF')
# Plot 8: analyze_error_autocorr
analyze_error_autocorr(signal, frequency=norm_freq, ax=axes[7], title='(8) Error Autocorrelation')
plt.tight_layout()
if output_path is not None:
output_path = Path(output_path)
output_path.parent.mkdir(parents=True, exist_ok=True)
fig.savefig(output_path, dpi=150, bbox_inches='tight')
print(f"[Dashboard saved] -> {output_path}")
plt.close(fig)
return fig, axes