"""
Cadence Virtuoso / ADE Explorer style spectrum plot.
Dark canvas, red stem bars per FFT bin, fine dotted grid. Same set of
annotations as the analyzer-style plotter (fundamental marker, max-spur
diamond, harmonic squares, metrics text block, NSD line) but recolored
for the dark theme:
- stems : red ('1 stem = 1 bin' Virtuoso convention)
- fundamental: yellow dot + "Sig = X dB"
- max spur : yellow diamond + "MaxSpur"
- harmonics : cyan squares + cyan order numbers (2, 3, ...)
- metric box : white text
- NSD line : yellow dashed
- OSR line : dim white dashed
Mirrors `plot_spectrum.py`'s structure so the two stay in lock-step
when the underlying metrics / plot_data layout evolves.
"""
import numpy as np
import matplotlib.pyplot as plt
from adctoolbox.spectrum._bin_ranges import rfft_inband_bin_count
from adctoolbox.spectrum.plot_spectrum import (
_attach_max_spur_annotation,
_lobe_bounds,
_noise_floor_axis_min,
_should_label_harmonic,
)
# Color palette — single place to retune the dark theme
_C_STEM = '#ff3030' # red, slightly brighter than 'r' for visibility on black
_C_FUND = '#ffd000' # warm yellow — fundamental + spur (loud-and-clear)
_C_HARM = '#00e5ff' # cyan — harmonics (distinct from fundamental)
_C_METRIC = 'white' # metric text block
_C_NSD = '#ffd000' # NSD line — match fundamental color
_C_OSR = '#999999' # dim white — OSR cutoff line
_C_COH = '#ffe680' # light yellow — coherent gain note
_C_WARN = '#ffa500' # orange — collision warning
[文档]
def plot_spectrum_virtuoso(compute_results, show_title=True, show_label=True,
plot_harmonics_up_to=3, ax=None, baseline_db=None):
"""
Virtuoso/ADE-Explorer style spectrum plot with annotations.
Parameters
----------
compute_results : dict
Output of `compute_spectrum`.
show_title : bool
Show auto-generated plot title.
show_label : bool
Add fundamental marker, spur marker, harmonics markers, and the
metric text block. Set False for a bare-stem look.
plot_harmonics_up_to : int
Highlight harmonics up to this order (HD2..HDk).
ax : matplotlib.axes.Axes, optional
Target axes. If None, uses current axes. Axes + figure facecolor
will be set to black.
baseline_db : float, optional
Bottom of stem bars in dB. None → adaptive (min(spec) - 5).
"""
metrics = compute_results['metrics']
plot_data = compute_results['plot_data']
spec_db = plot_data['power_spectrum_db_plot']
freq = plot_data['freq']
fundamental_bin = plot_data['fundamental_bin']
side_bin = int(plot_data.get('side_bin', 0))
spur_bin_idx = plot_data['spur_bin_idx']
spur_db = spec_db[spur_bin_idx]
is_coherent = plot_data.get('is_coherent', False)
collided_harmonics = plot_data.get('collided_harmonics', [])
harmonic_bins = plot_data.get('harmonic_bins', [])
harmonics_dbc = metrics.get('harmonics_dbc', [])
N = compute_results['N']
M = compute_results['M']
fs = compute_results['fs']
osr = compute_results['osr']
n_inband = rfft_inband_bin_count(N, osr)
spur_bin_start, spur_bin_end = _lobe_bounds(spur_bin_idx, side_bin, n_inband)
nf_line_level = metrics['noise_floor_dbfs'] - 10 * np.log10(N / (2 * osr))
if ax is None:
ax = plt.gca()
fig = ax.figure
# ---- Dark theme: black canvas, white annotations -----------------
fig.set_facecolor('black')
ax.set_facecolor('black')
for spine in ax.spines.values():
spine.set_color('white')
ax.tick_params(axis='both', which='both', colors='white')
ax.xaxis.label.set_color('white')
ax.yaxis.label.set_color('white')
ax.title.set_color('white')
# ---- Axis floor + stem bars --------------------------------------
sndr_floor_level = metrics['sig_pwr_dbfs'] - metrics['sndr_dbc']
minx = _noise_floor_axis_min(nf_line_level, fallback_level=sndr_floor_level)
if baseline_db is None:
baseline_db = minx
else:
minx = min(minx, baseline_db)
ax.vlines(freq, baseline_db, spec_db, colors=_C_STEM, linewidth=0.8)
# ---- Grid --------------------------------------------------------
ax.minorticks_on()
ax.grid(True, which='major', linestyle=':', color='white', alpha=0.35, linewidth=0.6)
ax.grid(True, which='minor', linestyle=':', color='white', alpha=0.15, linewidth=0.4)
# ---- Y-axis follows the plotted NSD/bin line ---------------------
x_min = fs / N
x_max = fs / 2
ax.set_xlim(x_min, x_max)
ax.set_ylim(minx, 0)
# ---- Labels ------------------------------------------------------
ax.set_xlabel('freq (Hz)')
ax.set_ylabel('(dB)')
if show_title:
ax.set_title('Power Spectrum')
if not show_label:
return
# ---- Fundamental marker + "Sig" text -----------------------------
ax.plot(freq[fundamental_bin], spec_db[fundamental_bin],
'o', color=_C_FUND, markersize=8)
# ---- Harmonic markers --------------------------------------------
if plot_harmonics_up_to > 0 and len(harmonic_bins) and len(harmonics_dbc):
for idx in range(len(harmonics_dbc)):
order = idx + 2 # HD2, HD3, ...
if order > plot_harmonics_up_to or order in collided_harmonics:
continue
bin_center = harmonic_bins[idx]
if not _should_label_harmonic(spec_db[bin_center], nf_line_level):
continue
ax.plot(bin_center * fs / N, spec_db[bin_center],
's', color=_C_HARM, markersize=5)
ax.text(bin_center * fs / N, spec_db[bin_center] + 3, str(order),
color=_C_HARM, fontsize=11, ha='center', clip_on=True)
# ---- Max-spur diamond + "MaxSpur" text ---------------------------
ax.plot(
freq[spur_bin_start:spur_bin_end],
spec_db[spur_bin_start:spur_bin_end],
color=_C_FUND,
linestyle='--',
linewidth=1.0,
label='_max_spur_lobe',
)
max_spur_marker, = ax.plot(spur_bin_idx / N * fs, spur_db,
'd', color=_C_FUND, markersize=5)
max_spur_label = ax.text(spur_bin_idx / N * fs, spur_db + 10, 'MaxSpur',
color=_C_FUND, fontsize=10, ha='center', clip_on=True)
_attach_max_spur_annotation(ax, max_spur_marker, max_spur_label, spur_db)
# ---- Text-block positioning (mirrors plot_spectrum.py) -----------
# Axes-relative metric text stays fixed if callers change y-limits.
metric_x = 0.02 if osr > 1 or fundamental_bin / N >= 0.2 else 0.60
metric_y_start = 0.94
metric_y_step = 0.06
def _fmt_freq(f):
if f >= 1e9: return f'{f/1e9:.1f}G'
if f >= 1e6: return f'{f/1e6:.1f}M'
if f >= 1e3: return f'{f/1e3:.1f}K'
return f'{f:.1f}'
Fin = fundamental_bin / N * fs
ax.text(freq[fundamental_bin], 0.98,
f'Sig = {metrics["sig_pwr_dbfs"]:.2f} dB',
transform=ax.get_xaxis_transform(),
color=_C_FUND, fontsize=10, va='top')
snr_text = f'{metrics["snr_dbc"]:.2f} dB' if np.isfinite(metrics["snr_dbc"]) else 'N/A'
noise_floor_text = (
f'{metrics["noise_floor_dbfs"]:.2f} dB'
if np.isfinite(metrics["noise_floor_dbfs"])
else 'N/A'
)
nsd_text = (
f'{metrics["nsd_dbfs_hz"]:.2f} dBFS/Hz'
if np.isfinite(metrics["nsd_dbfs_hz"])
else 'N/A'
)
metric_lines = [
f'Fin/fs = {_fmt_freq(Fin)} / {_fmt_freq(fs)} Hz',
f'ENoB = {metrics["enob"]:.2f}',
f'SNDR = {metrics["sndr_dbc"]:.2f} dB',
f'SFDR = {metrics["sfdr_dbc"]:.2f} dB',
f'THD = {metrics["thd_dbc"]:.2f} dB',
f'SNR = {snr_text}',
f'Noise Floor = {noise_floor_text}',
f'NSD = {nsd_text}',
]
# ---- NSD baseline line -------------------------------------------
if not np.isfinite(nf_line_level):
pass
elif osr > 1:
ax.semilogx([fs / N, fs / 2 / osr],
[nf_line_level, nf_line_level],
'--', color=_C_NSD, linewidth=1)
metric_lines.append(f'OSR = {osr:.2f}')
ax.plot([fs / 2 / osr, fs / 2 / osr], [0, -1000],
'--', color=_C_OSR, linewidth=1)
else:
ax.plot([0, fs / 2], [nf_line_level, nf_line_level],
'--', color=_C_NSD, linewidth=1)
# ---- Optional gain / collision notes -----------------------------
warning_lines = []
if is_coherent and M > 1:
coh_gain_db = 10 * np.log10(M)
warning_lines.append((f'*Coherent Gain = {coh_gain_db:.2f} dB', _C_COH))
if collided_harmonics:
collision_str = ', '.join(f'HD{h}' for h in sorted(collided_harmonics))
warning_lines.append((f'*Collided with fundamental: {collision_str}', _C_WARN))
metric_rows = [(line, _C_METRIC) for line in metric_lines] + warning_lines
for row, (line, color) in enumerate(metric_rows):
ax.text(
metric_x,
metric_y_start - metric_y_step * row,
line,
transform=ax.transAxes,
color=color,
fontsize=10,
ha='left',
va='top',
)
