11.2. Quality-Control Confidence Scoring#

pycsamt.emtools.qc turns transfer-function quality control into tables and figures. It does two related jobs:

summarize station-level data quality, coverage, SNR, tipper presence, and phase-tensor skew;
compute confidence ratios from several finite, bounded scores so that stations and individual station-frequency samples can be ranked, plotted, masked, or down-weighted before inversion.

Full callable signatures live in the API reference. This page explains the confidence-ratio formulation, the table workflow, the plotting workflow, and how confidence values should be read in practice. Every example below uses pyCSAMT’s bundled AMT line, data/AMT/WILLY_DATA/L18PLT, since all 28 stations there carry real impedance-error tensors – the ingredient several of the component scores need.

11.2.1. Why QC Is More Than Coverage#

A station can be complete and still be questionable. frac_ok=1.0 only says that impedance rows are finite. It does not say that the off-diagonal modes agree, that diagonal leakage is small, that phase is smooth, that uncertainty is low, or that the station is coherent with its neighbours.

The QC module therefore separates three ideas:

coverage: Are the required transfer-function rows finite?
confidence: How trustworthy is a station or a station-frequency cell after combining coverage, uncertainty, tensor-shape, phase, and spatial criteria?
flags: Which simple thresholds does a station or frequency cell fail?

11.2.2. Load A Survey#

All QC functions accept the usual pyCSAMT inputs, but a reproducible script should normalize once with ensure_sites.

>>> from pathlib import Path
>>> from pycsamt.emtools import ensure_sites
>>> survey = ensure_sites(
...     Path("data/AMT/WILLY_DATA/L18PLT"),
...     recursive=True,
...     on_dup="replace",
...     strict=True,
...     verbose=1,
... )

Use strict=True for reports and automated processing. Use strict=False in exploratory notebooks if you want empty plots to render as “no data” messages.

11.2.3. The Confidence Ratio#

The composite confidence ratio is a weighted mean of available component scores:

(1)#\[\mathrm{CR}_{i,f} = \frac{\sum_{k\in\mathcal K} w_k s_{k,i,f} \mathbf{1}[s_{k,i,f}\ \mathrm{finite}]} {\sum_{k\in\mathcal K} w_k \mathbf{1}[s_{k,i,f}\ \mathrm{finite}]}, \qquad 0 \le s_k \le 1.\]

Missing scores are ignored. Finite scores are clipped to [0, 1]. The default weights are:

Score	Weight	Meaning
`coverage`	`0.35`	Finite impedance rows or components.
`uncertainty`	`0.20`	Median relative impedance error.
`offdiag`	`0.15`	Similarity of `Zxy` and `Zyx` amplitudes.
`diagonal`	`0.10`	Penalty for diagonal leakage into off-diagonal response.
`phase`	`0.10`	Penalty for abrupt off-diagonal phase jumps.
`spatial`	`0.10`	Coherence with neighbouring stations.

Except for coverage, each component reduces the tensor to a non-negative discrepancy \(m_k\), compares it with a positive tolerance \(\tau_k\), and maps the result to a bounded trust score:

(2)#\[s_k = \operatorname{clip}_{[0,1]} \left(1 - \frac{m_k}{\tau_k}\right).\]

A statistic of 0 scores a perfect 1; as \(m_k\) grows toward \(\tau_k\) the score decays linearly to 0 and stays there beyond it, so nothing is ever penalized twice or rewarded for being implausibly clean. The coverage score is computed directly. For station \(i\) with \(N_i\) frequencies and impedance tensor \(Z_{i,f,ab}\), it is

(3)#\[s_{\mathrm{cov},i} = \frac{1}{N_i} \sum_f \mathbf{1} [Z_{i,f,xx},Z_{i,f,xy},Z_{i,f,yx},Z_{i,f,yy}\ \mathrm{finite}],\]

whereas a frequency cell uses the fraction of its four finite tensor components. Thus station presence is deliberately strict: a row counts only when the complete impedance tensor is finite.

The remaining discrepancies used in (2) are

(4)#\[\begin{split}\begin{aligned} m_{\mathrm{unc}} &={\rm med}_{f,a,b} \frac{|Z_{{\rm err},i,f,ab}|}{|Z_{i,f,ab}|+\epsilon},\\ m_{\mathrm{off}} &={\rm med}_{f} \left|\log_{10}\frac{|Z_{i,f,xy}|+\epsilon} {|Z_{i,f,yx}|+\epsilon}\right|,\\ m_{\mathrm{diag}} &={\rm med}_{f} \frac{d_{i,f}}{d_{i,f}+o_{i,f}+\epsilon},\\ d_{i,f} &={\rm med}(|Z_{i,f,xx}|,|Z_{i,f,yy}|),\qquad o_{i,f}={\rm med}(|Z_{i,f,xy}|,|Z_{i,f,yx}|),\\ m_{\mathrm{phase}} &={\rm med}_{f,c\in\{xy,yx\}} |\Delta\,\operatorname{unwrap}(\arg Z_{i,f,c})|_{\rm deg}. \end{aligned}\end{split}\]

Their tolerances are respectively relerr_threshold=0.20, offdiag_tolerance_log10=0.35, diagonal_leakage_max=0.35, and phase_jump_tolerance_deg=90. At the frequency level, the first three statistics are evaluated on one row. The phase score uses the strongest adjacent jump touching that row for each off-diagonal mode, followed by the median across the available modes. The small \(\epsilon\) prevents division by zero and is numerical, not a tunable tolerance.

Spatial coherence first forms the log-response proxy

(5)#\[r_{i,f}=\frac{1}{2}\left[ \log_{10}\left(\frac{|Z_{i,f,xy}|^2}{f}+\epsilon\right)+ \log_{10}\left(\frac{|Z_{i,f,yx}|^2}{f}+\epsilon\right)\right].\]

At station level \(r_i={\rm med}_f(r_{i,f})\); at frequency level \(r_{i,f}\) is retained. The spatial discrepancy is

(6)#\[m_{\mathrm{sp},i,f}= \left|r_{i,f}-{\rm med}(r_{j,f}:j\in\mathcal N_i)\right|, \qquad \tau_{\mathrm{sp}}=0.60,\]

where \(\mathcal N_i\) contains the immediately adjacent available stations. The omitted physical constants cancel in this relative logarithmic comparison; \(r\) should therefore be read as a resistivity-like proxy, not a reported apparent resistivity.

At station level the discrepancies summarize the frequency axis, which is why station_confidence_table returns one score per station. At frequency level the analogous local quantities are evaluated row by row. Consequently, a coherent station aggregate can still contain weak individual frequencies.

The default confidence bands are:

CR >= 0.95: safe under the default policy;
0.85 <= CR < 0.95: marginal or recoverable;
CR < 0.85: high-priority review or down-weighting candidate.

Important

These are operational defaults, not universal geophysical laws. A low score can reflect genuine dimensionality or a sharp geological boundary. Inspect the component scores and response curves before deleting data.

11.2.4. Compute A Confidence Ratio Directly#

Use confidence_ratio when you already have scores and want to apply the same weighted formula used by the tables.

>>> from pycsamt.emtools.qc import confidence_ratio
>>> scores = {
...     "coverage": 1.00,
...     "uncertainty": 0.82,
...     "offdiag": 0.76,
...     "diagonal": 0.55,
...     "phase": 0.90,
...     "spatial": 0.88,
... }
>>> cr, cr_err = confidence_ratio(scores, n_freq=53, return_error=True)
>>> print(f"CR={cr:.3f} +/- {cr_err:.3f}")
CR=0.861 +/- 0.141

confidence_err is not a formal statistical uncertainty on \(\mathrm{CR}\) – it is a cheap, honest stand-in for one:

\[\begin{split}\sigma_{\mathrm{CR}} = \begin{cases} \mathrm{std}\bigl(\{s_k\}\bigr), & \text{two or more finite } s_k, \\[4pt] \sqrt{\mathrm{CR}\,(1 - \mathrm{CR}) / n_{\mathrm{freq}}}, & \text{otherwise.} \end{cases}\end{split}\]

With six components on hand, as in the example above, \(\sigma_{\mathrm{CR}}\) is simply the population standard deviation of {1.00, 0.82, 0.76, 0.55, 0.90, 0.88}, which is exactly the 0.141 printed above – the components disagree with each other, and that disagreement is the reported uncertainty. When only one component survives (most often coverage alone, when no error tensor or neighbouring station exists to score against), there is nothing to disagree with, so the formula falls back to the binomial-style standard error \(\sqrt{\mathrm{CR}(1-\mathrm{CR})/n_{\mathrm{freq}}}\) instead.

11.2.5. Station QC Summary#

build_qc_table is the first station-level table. It reports the number of frequencies, finite-row coverage, tipper availability, median row SNR when z_err exists, period range, and optional phase-tensor skew.

>>> from pycsamt.emtools import build_qc_table, ensure_sites
>>> survey = ensure_sites("data/AMT/WILLY_DATA/L18PLT", strict=True)
>>> qc = build_qc_table(survey, include_skew=True, api=False)
>>> qc[["station", "n_freq", "n_ok", "frac_ok", "n_tip", "snr_med", "pmin", "pmax", "skew_med"]].head()
   station  n_freq  n_ok  frac_ok  ...    snr_med      pmin      pmax   skew_med
0  18-001A      53    53      1.0  ...  17.658396  0.000096  0.992063   4.809977
1  18-002U      53    53      1.0  ...  16.687366  0.000096  0.992063   6.596544
2  18-003A      53    53      1.0  ...  12.031672  0.000096  0.992063  11.018588
3  18-004A      53    53      1.0  ...  10.430580  0.000096  0.992063  14.509282
4  18-005U      53    53      1.0  ...  14.360341  0.000096  0.992063   9.171151

[5 rows x 9 columns]

Read frac_ok as completeness, not as full confidence. Read snr_med as a row-level signal-to-error ratio when impedance error tensors are available. Read skew_med as structural/tensor complexity, not automatically as bad acquisition – it is the median absolute phase tensor asymmetry angle \(|\beta|\) across a station’s frequencies, the same quantity the skew glossary entry defines.

11.2.6. Station Flags#

qc_flags adds simple threshold labels to the station QC table.

>>> from pycsamt.emtools import qc_flags
>>> flags = qc_flags(
...     "data/AMT/WILLY_DATA/L18PLT",
...     min_frac_ok=0.60,
...     min_snr_med=2.0,
...     max_skew_med=6.0,
... )
>>> flagged = flags[flags["flags"] != ""]
>>> print(len(flagged), "of", len(flags), "stations flagged")
27 of 28 stations flagged
>>> flagged[["station", "frac_ok", "snr_med", "skew_med", "flags"]].head()
   station  frac_ok    snr_med   skew_med      flags
1  18-002U      1.0  16.687366   6.596544  high_skew
2  18-003A      1.0  12.031672  11.018588  high_skew
3  18-004A      1.0  10.430580  14.509282  high_skew
4  18-005U      1.0  14.360341   9.171151  high_skew
5  18-006A      1.0  13.272516  12.375357  high_skew

Every station is fully covered (frac_ok=1.0 throughout) and every station’s median row SNR clears min_snr_med=2.0 by a wide margin, so high_skew is the only flag this survey ever raises, and it raises it for all but one station: only 18-001A (skew_med=4.81) falls under the default max_skew_med=6.0 threshold, while the rest run from 6.60 up past 50 degrees. That is not a data defect – it is a genuinely 2-D/3-D structural line, expressed through a threshold tuned for near-1-D settings. Possible station flags include low_coverage, low_snr, and high_skew. A high-skew flag can be a real structural signal. It does not mean the station should automatically be deleted.

11.2.7. Presence Confidence Versus Composite Confidence#

station_confidence_table has two modes. method="presence" is coverage-only. method="composite" combines all available component scores.

>>> from pycsamt.emtools import station_confidence_table
>>> presence = station_confidence_table(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="presence",
...     api=False,
... )
>>> composite = station_confidence_table(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="composite",
...     api=False,
... )
>>> print("presence range", presence["confidence"].min(), presence["confidence"].max())
presence range 1.0 1.0
>>> print("composite range", composite["confidence"].min(), composite["confidence"].max())
composite range 0.5440199944767435 0.8119223880398303
>>> ranked = composite.sort_values("confidence")
>>> ranked[["station", "confidence", "coverage", "uncertainty", "offdiag", "diagonal", "phase", "spatial"]].head()
    station  confidence  coverage  ...  diagonal     phase   spatial
22  18-022U    0.544020       1.0  ...  0.000000  0.941638  0.000000
21  18-021B    0.574114       1.0  ...  0.000000  0.937891  0.232771
17  18-018A    0.578410       1.0  ...  0.086979  0.957546  0.000000
20  18-021U    0.594344       1.0  ...  0.000000  0.938165  0.233576
16  18-017U    0.595479       1.0  ...  0.261699  0.969783  0.038160

[5 rows x 8 columns]

Presence confidence is a flat 1.0 everywhere – every row is finite, so a coverage-only view has nothing left to say about this particular survey. Composite confidence spreads from \(\approx 0.54\) to \(\approx 0.81\) instead: the coverage-only view was hiding real, measurable quality variation. If presence confidence is high everywhere but composite confidence varies, the survey is complete but not equally trustworthy everywhere. That is common in real EM data.

11.2.8. Customize Confidence Weights#

Use custom weights when a project has a clear processing policy. For example, inversion preparation may emphasize uncertainty and coverage, while structural interpretation may care more about off-diagonal and spatial coherence.

>>> weights = {
...     "coverage": 0.40,
...     "uncertainty": 0.30,
...     "offdiag": 0.10,
...     "diagonal": 0.05,
...     "phase": 0.05,
...     "spatial": 0.10,
... }
>>> table = station_confidence_table(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="composite",
...     weights=weights,
...     relerr_threshold=0.25,
...     offdiag_tolerance_log10=0.40,
...     diagonal_leakage_max=0.40,
...     phase_jump_tolerance_deg=90.0,
...     spatial_tolerance_log10=0.60,
...     api=False,
... )
>>> table.sort_values("confidence")[["station", "distance_m", "confidence", "coverage", "uncertainty", "offdiag", "diagonal", "phase", "spatial"]].head()
    station  distance_m  confidence  ...  diagonal     phase   spatial
22  18-022U      4400.0    0.630495  ...  0.000000  0.941638  0.000000
21  18-021B      4200.0    0.658344  ...  0.000000  0.937891  0.232771
17  18-018A      3400.0    0.666682  ...  0.201107  0.957546  0.000000
16  18-017U      3200.0    0.672222  ...  0.353986  0.969783  0.038160
20  18-021U      4000.0    0.682870  ...  0.000000  0.938165  0.233576

[5 rows x 9 columns]

The worst station is still 18-022U under either weighting – its penalty is concentrated enough (zero diagonal and near-zero spatial) that it stays worst regardless of emphasis. Changing thresholds changes the meaning of the scores. Record custom weights and thresholds in reports so another user can reproduce your confidence classes.

11.2.9. Frequency-Level Confidence#

frequency_confidence_table returns one row per station-frequency sample, scored with the same six formulas and the same \(\mathrm{CR}_{i,f}\) weighting as the station table above – the only change is that each \(m_k\) is now evaluated at a single frequency rather than medianed across the whole station. This is the table to use for period-band decisions, masks, and inversion down-weighting.

>>> from pycsamt.emtools import frequency_confidence_table
>>> freq_qc = frequency_confidence_table(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="composite",
...     ci_hi=0.95,
...     ci_lo=0.85,
...     api=False,
... )
>>> freq_qc.columns.tolist()
['station', 'station_index', 'distance_m', 'frequency_hz', 'period_s', 'log10_period', 'confidence', 'confidence_err', 'method', 'n_components', 'coverage', 'uncertainty', 'offdiag', 'diagonal', 'phase', 'spatial', 'logrho_proxy', 'flags']
>>> freq_qc[["station", "frequency_hz", "period_s", "confidence", "flags"]].head()
   station  ...                                              flags
0  18-001A  ...  recoverable,high_error,offdiag_mismatch,diagon...
1  18-001A  ...  reject,high_error,offdiag_mismatch,diagonal_le...
2  18-001A  ...  reject,high_error,offdiag_mismatch,diagonal_le...
3  18-001A  ...  reject,high_error,offdiag_mismatch,diagonal_le...
4  18-001A  ...  reject,high_error,offdiag_mismatch,diagonal_le...

[5 rows x 5 columns]
>>> rejected = freq_qc[freq_qc["flags"].str.contains("reject", na=False)]
>>> print("rejected cells:", len(rejected), "of", len(freq_qc))
rejected cells: 1479 of 1484

Frequency flags can include reject, recoverable, missing, high_error, offdiag_mismatch, diagonal_leakage, phase_jump, and spatial_outlier. Nearly every one of this survey’s 1484 station-frequency cells reads reject at the frequency level. The station-level composite range of 0.54–0.81 is also below the default 0.85 boundary, but aggregation makes the station scores less extreme than many individual cells. Use the frequency table when the decision concerns a period band rather than a complete station.

11.2.10. Build A Mask From Confidence#

The QC module does not force one masking policy. You can build one from the confidence table.

>>> table = frequency_confidence_table(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="composite",
...     ci_lo=0.85,
...     api=False,
... )
>>> keep = table["confidence"] >= 0.85
>>> review = (table["confidence"] >= 0.70) & (table["confidence"] < 0.85)
>>> drop = table["confidence"] < 0.70
>>> print("keep:", int(keep.sum()))
keep: 5
>>> print("review:", int(review.sum()))
review: 533
>>> print("drop:", int(drop.sum()))
drop: 946

Use a review band instead of a hard delete when the flagged frequencies line up with known structural complexity. Low confidence is a prompt for inspection, not always proof of bad data.

11.2.11. Survey-Scale Confidence Views#

The L18 examples above explain one profile. Spatial confidence figures need more than one non-collinear line, so the examples in this section use all five WILLY_DATA profiles (128 stations). Line membership is derived from the station prefix only for display; it does not alter the confidence calculation.

>>> from pycsamt.emtools import ensure_sites, station_confidence_table
>>> all_lines = ensure_sites("data/AMT/WILLY_DATA", recursive=True)
>>> all_table = station_confidence_table(
...     all_lines, method="composite", api=False,
... )
>>> line_labels = {
...     str(station): f"L{str(station).split('-', 1)[0]}"
...     for station in all_table["station"]
... }
>>> len(all_table), sorted(set(line_labels.values()))
(128, ['L18', 'L22', 'L26', 'L30', 'L34'])

The complete, executable figure-generation script is available below. It writes every image in this section and exports the confidence surface as CSV and Surfer DSAA grid files.

Generate all survey-scale confidence figuresClick to inspect and copy the complete code

"""Generate confidence figures for the EMTools QC user guide.

Run from the repository root::

    python docs/scripts/generate_user_guide_emtools_qc_confidence_figures.py \
        --save-dir docs/source/images/user_guide/emtools/confidence

Use ``--save-dir DIR`` to save both figures instead of opening them.
The single-line example uses L18PLT. The contour example uses all five
WILLY_DATA profiles, because interpolation from one line is not a valid
two-dimensional confidence map.
"""

from __future__ import annotations

import argparse
from pathlib import Path

import matplotlib.pyplot as plt

from pycsamt.emtools import (
    ensure_sites,
    export_confidence_map,
    plot_confidence_before_after,
    plot_confidence_component_map,
    plot_confidence_coverage_curve,
    plot_confidence_distribution,
    plot_confidence_grid_map,
    plot_confidence_heatmap,
    plot_confidence_map,
    plot_confidence_method_comparison,
    plot_confidence_rank,
    plot_confidence_risk_map,
    station_confidence_table,
)


def _repository_root() -> Path:
    return Path(__file__).resolve().parents[2]


def _line_labels(sites) -> dict[str, str]:
    table = station_confidence_table(sites, method="composite", api=False)
    return {
        str(station): f"L{str(station).split('-', 1)[0]}"
        for station in table["station"]
    }


def make_figures():
    """Return route, filled-contour, and labelled-isoline map axes."""
    data_root = _repository_root() / "data" / "AMT" / "WILLY_DATA"
    line18 = data_root / "L18PLT"

    route_ax = plot_confidence_map(
        line18,
        method="composite",
        mode="route",
        station_labels=True,
        station_label_step=4,
        show_confidence_values=True,
        confidence_value_step=2,
        confidence_value_fmt="{:.2f}",
        confidence_value_fontsize=6.5,
        recursive=False,
    )
    route_ax.set_title("L18 confidence along the surveyed route")

    all_lines = ensure_sites(data_root, recursive=True)
    contour_ax = plot_confidence_map(
        all_lines,
        method="composite",
        mode="contour",
        line_labels=_line_labels(all_lines),
        boundary_levels=[0.50, 0.85, 0.90, 1.00],
        show_contour_lines=False,
        show_threshold_contours=True,
        threshold_linewidth=1.6,
    )
    contour_ax.set_title("WILLY_DATA confidence map — boundary contours only")

    line_ax = plot_confidence_map(
        all_lines,
        method="composite",
        mode="contour",
        line_labels=_line_labels(all_lines),
        show_contour_lines=True,
        contour_line_levels=[0.55, 0.60, 0.65, 0.70, 0.75, 0.80],
        contour_line_colors="#303030",
        contour_linewidths=0.8,
        contour_linestyles="solid",
        contour_labels=True,
        contour_label_fmt="%.2f",
        contour_label_fontsize=6.5,
        threshold_line_color="black",
        threshold_linewidth=1.6,
        threshold_linestyle="--",
    )
    line_ax.set_title("WILLY_DATA confidence contours with labelled isolines")
    return route_ax, contour_ax, line_ax


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--save-dir", type=Path)
    parser.add_argument(
        "--export-dir",
        type=Path,
        help="Write station CSV and Surfer DSAA confidence grid.",
    )
    args = parser.parse_args()
    axes = make_figures()
    data_root = _repository_root() / "data" / "AMT" / "WILLY_DATA"
    all_lines = ensure_sites(data_root, recursive=True)
    component_fig = plot_confidence_component_map(
        all_lines,
        method="composite",
        line_labels=_line_labels(all_lines),
        ncols=4,
        marker_size=32.0,
    )
    comparison_fig = plot_confidence_method_comparison(
        all_lines,
        line_labels=_line_labels(all_lines),
        marker_size=34.0,
        map_aspect="auto",
    )
    risk_ax = plot_confidence_risk_map(
        all_lines,
        method="composite",
        line_labels=_line_labels(all_lines),
        mode="contour",
        risk_levels=[0.0, 0.05, 0.10, 0.15, 0.25, 0.35, 0.50, 0.75, 1.0],
        station_labels=False,
        map_aspect="auto",
    )
    heatmap_ax = plot_confidence_heatmap(
        all_lines,
        method="composite",
        line_labels=_line_labels(all_lines),
        station_order="route",
        annotate=False,
        station_label_step=4,
    )
    distribution_fig = plot_confidence_distribution(
        all_lines,
        method="both",
        line_labels=_line_labels(all_lines),
        bins=18,
    )
    rank_fig = plot_confidence_rank(
        all_lines,
        method="composite",
        line_labels=_line_labels(all_lines),
        order="worst",
        annotate_stations=False,
        show_iqr=True,
    )
    grid_ax = plot_confidence_grid_map(
        all_lines,
        method="composite",
        line_labels=_line_labels(all_lines),
        grid_shape=(220, 180),
        interpolation="linear",
        show_grid_edges=False,
        map_aspect="auto",
    )
    coverage_fig = plot_confidence_coverage_curve(
        all_lines,
        method="composite",
        line_labels=_line_labels(all_lines),
    )
    before_after_fig = plot_confidence_before_after(
        all_lines,
        before_method="presence",
        after_method="composite",
        before_label="Presence",
        after_label="Composite",
        line_labels=_line_labels(all_lines),
        show_station_labels=False,
    )
    if args.save_dir is None:
        plt.show()
        return
    args.save_dir.mkdir(parents=True, exist_ok=True)
    names = (
        "confidence_route",
        "confidence_contour",
        "confidence_contour_lines",
    )
    for name, ax in zip(names, axes):
        ax.figure.savefig(
            args.save_dir / f"{name}.png",
            dpi=180,
            bbox_inches="tight",
        )
    component_fig.savefig(
        args.save_dir / "confidence_component_map.png",
        dpi=220,
        bbox_inches="tight",
    )
    comparison_fig.savefig(
        args.save_dir / "confidence_method_comparison.png",
        dpi=220,
        bbox_inches="tight",
    )
    risk_ax.figure.savefig(
        args.save_dir / "confidence_risk_map.png",
        dpi=220,
        bbox_inches="tight",
    )
    heatmap_ax.figure.savefig(
        args.save_dir / "confidence_heatmap.png",
        dpi=220,
        bbox_inches="tight",
    )
    distribution_fig.savefig(
        args.save_dir / "confidence_distribution.png",
        dpi=220,
        bbox_inches="tight",
    )
    rank_fig.savefig(
        args.save_dir / "confidence_rank.png",
        dpi=220,
        bbox_inches="tight",
    )
    grid_ax.figure.savefig(
        args.save_dir / "confidence_grid_map.png",
        dpi=220,
        bbox_inches="tight",
    )
    coverage_fig.savefig(
        args.save_dir / "confidence_coverage_curve.png",
        dpi=220,
        bbox_inches="tight",
    )
    before_after_fig.savefig(
        args.save_dir / "confidence_before_after.png",
        dpi=220,
        bbox_inches="tight",
    )
    if args.export_dir is not None:
        args.export_dir.mkdir(parents=True, exist_ok=True)
        export_confidence_map(
            all_lines,
            method="composite",
            line_labels=_line_labels(all_lines),
            csv_path=args.export_dir / "confidence_map.csv",
            surfer_path=args.export_dir / "confidence_map.grd",
            grid_shape=(200, 200),
        )


if __name__ == "__main__":
    main()

11.2.11.1. Route, contour, and regular-grid maps#

plot_confidence_map uses measured longitude/latitude when available and otherwise falls back to easting/northing. Route mode orders stations from coordinate-derived chainage; it does not trust lexical station names. Contour mode requires at least three non-collinear locations and is restricted to the triangulated survey footprint.

>>> from pycsamt.emtools import plot_confidence_map
>>> route_ax = plot_confidence_map(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="composite",
...     mode="route",
...     show_confidence_values=True,
...     confidence_value_step=2,
... )
>>> contour_ax = plot_confidence_map(
...     all_lines,
...     method="composite",
...     mode="contour",
...     line_labels=line_labels,
...     boundary_levels=[0.50, 0.85, 0.90, 1.00],
...     show_contour_lines=False,
...     show_threshold_contours=True,
... )

../../_images/confidence_route.png — Composite confidence along the measured L18 route.#

../../_images/confidence_contour.png — Five-line confidence surface with requested decision boundaries.#

The route view reveals station-scale variability without implying two-dimensional coverage. The multi-line contour is appropriate for a spatial overview, but no 0.85, 0.90, or 1.00 isoline appears in this dataset because the observed composite maximum is about 0.81. Omitting an absent boundary is the scientifically correct result.

Ordinary labelled isolines can be added independently of the decision boundaries. plot_confidence_grid_map instead evaluates the same triangulated field on a regular \(n_x\times n_y\) grid. Its masked cells outside the convex hull remain blank and its arrays are available as ax._pycsamt_grid_x, ax._pycsamt_grid_y, and ax._pycsamt_confidence_grid.

>>> from pycsamt.emtools import plot_confidence_grid_map
>>> isoline_ax = plot_confidence_map(
...     all_lines,
...     mode="contour",
...     line_labels=line_labels,
...     show_contour_lines=True,
...     contour_line_levels=[0.55, 0.60, 0.65, 0.70, 0.75, 0.80],
...     contour_line_colors="0.2",
...     contour_labels=True,
... )
>>> grid_ax = plot_confidence_grid_map(
...     all_lines,
...     line_labels=line_labels,
...     grid_shape=(220, 180),
...     interpolation="linear",
... )

../../_images/confidence_contour_lines.png — Labelled confidence isolines within the acquisition footprint.#

../../_images/confidence_grid_map.png — Regular 220 by 180 confidence grid; white cells are unsurveyed.#

The stepped edges in the regular-grid figure are raster-cell boundaries, not geological discontinuities. Use max_triangle_edge to suppress triangles spanning an unjustified gap between lines. interpolation may be "linear" or "cubic"; cubic interpolation is visually smoother but should not be interpreted as new measurements.

For external mapping, export_confidence_map writes station values and the regular field without coupling export to plotting:

>>> from pycsamt.emtools import export_confidence_map
>>> outputs = export_confidence_map(
...     all_lines,
...     method="composite",
...     line_labels=line_labels,
...     csv_path="confidence_map.csv",
...     surfer_path="confidence_map.grd",
...     grid_shape=(200, 200),
... )
>>> sorted(outputs)
['csv', 'surfer']

The Surfer file uses ASCII DSAA format. As with the plotted grid, cells outside the convex hull are blank. Export CSV instead of a grid for a single collinear profile.

11.2.11.2. Diagnosing which component controls confidence#

plot_confidence_component_map applies one fixed 0–1 scale to the overall score and all six terms in (1). This fixed normalization is essential: independently rescaling each panel would make a weak component look artificially strong.

>>> from pycsamt.emtools import plot_confidence_component_map
>>> component_fig = plot_confidence_component_map(
...     all_lines,
...     method="composite",
...     line_labels=line_labels,
...     ncols=4,
...     marker_size=32,
... )

../../_images/confidence_component_map.png — Overall confidence and the six component scores on shared coordinates.#

Coverage and phase smoothness are consistently strong in WILLY_DATA, whereas diagonal leakage, off-diagonal consistency, and spatial coherence contain repeated low-score stations. The overall composite therefore remains below the safe threshold even though the transfer functions are complete. This is precisely the distinction between presence and trustworthiness introduced above.

plot_confidence_heatmap presents the same decomposition as a compact station-by-component matrix. Gray cells mean that a component could not be evaluated; they do not mean zero confidence.

>>> from pycsamt.emtools import plot_confidence_heatmap
>>> heatmap_ax = plot_confidence_heatmap(
...     all_lines,
...     method="composite",
...     line_labels=line_labels,
...     station_order="route",
...     annotate=False,
...     station_label_step=4,
... )

../../_images/confidence_heatmap.png — Confidence diagnostic matrix with stations grouped by survey line.#

The dark-green coverage row confirms complete input, while coherent red blocks in the tensor-shape and spatial rows identify systematic rather than isolated penalties. Use cell annotations for small surveys; for 128 stations, thinning labels and suppressing values preserves the pattern.

11.2.11.3. Comparing methods and processing states#

plot_confidence_method_comparison matches station coordinates and shows presence, composite, and their difference \(\Delta\mathrm{CR}=\mathrm{CR}_{\rm composite}- \mathrm{CR}_{\rm presence}\). The first two panels share 0–1 limits; the difference panel is symmetric about zero.

>>> from pycsamt.emtools import plot_confidence_method_comparison
>>> comparison_fig = plot_confidence_method_comparison(
...     all_lines,
...     line_labels=line_labels,
...     marker_size=34,
... )

../../_images/confidence_method_comparison.png — Presence, composite, and paired method difference at 128 stations.#

Presence is 1.0 throughout because every impedance row is finite. Composite confidence is lower by about 0.19–0.50 because it includes the additional discrepancies in (4). This difference is not a processing loss; it is information that the presence method does not attempt to represent.

For genuine processing audits, pass separate datasets to plot_confidence_before_after. For matched station \(i\), the reported change is

(7)#\[\Delta\mathrm{CR}_i = \mathrm{CR}_{i,\mathrm{after}}-\mathrm{CR}_{i,\mathrm{before}}.\]

Values larger than change_tolerance are improvements, values below its negative are degradations, and smaller absolute changes are stable. The default tolerance is 0.01.

>>> from pycsamt.emtools import plot_confidence_before_after
>>> audit_fig = plot_confidence_before_after(
...     all_lines,
...     before_method="presence",
...     after_method="composite",
...     before_label="Presence",
...     after_label="Composite",
...     line_labels=line_labels,
...     show_station_labels=False,
... )

../../_images/confidence_before_after.png — Paired scoring audit, ordered station changes, and line-level median change.#

This reproducible example audits two scoring methods on one dataset, so all changes are negative by construction. In a processing workflow, replace the first argument with the unprocessed survey and pass the processed survey as after_sites while keeping both methods "composite". Unmatched station names are retained on the returned figure for auditability and excluded from paired statistics.

11.2.11.4. Risk, distributions, and priorities#

Confidence risk is the complement of confidence,

(8)#\[R_i = 1-\mathrm{CR}_i.\]

Consequently, the default CR limits 0.95 and 0.85 correspond to risk limits 0.05 and 0.15. plot_confidence_risk_map reports low, moderate, and high-risk station counts and does not invent a new quality metric.

>>> from pycsamt.emtools import plot_confidence_risk_map
>>> risk_ax = plot_confidence_risk_map(
...     all_lines,
...     line_labels=line_labels,
...     mode="contour",
... )

../../_images/confidence_risk_map.png — Complementary confidence risk across WILLY_DATA.#

All 128 composite scores are below 0.85, so every station belongs to the default high-risk review class. The orange-to-red variation still ranks relative risk within that class; it must not be read as evidence that the interpolated space between lines was directly measured.

plot_confidence_distribution combines density, empirical cumulative fraction, and line-wise violin summaries. The empirical curve is exact; the Gaussian density is a visualization whose bandwidth does not change the underlying station scores.

>>> from pycsamt.emtools import plot_confidence_distribution
>>> distribution_fig = plot_confidence_distribution(
...     all_lines,
...     method="both",
...     line_labels=line_labels,
...     bins=18,
... )

../../_images/confidence_distribution.png — Presence/composite density, cumulative fraction, and survey-line spread.#

The spike at presence CR = 1.0 and the broad composite mode near 0.6–0.75 summarize the method difference compactly. The line violins show that the composite spread is not controlled by a single profile.

plot_confidence_rank turns the same values into an operational priority list. Station rank is exact; the line panel uses the median and interquartile range by default so an isolated extreme station does not control an entire line’s rank.

>>> from pycsamt.emtools import plot_confidence_rank
>>> rank_fig = plot_confidence_rank(
...     all_lines,
...     method="composite",
...     line_labels=line_labels,
...     order="worst",
...     annotate_stations=False,
... )

../../_images/confidence_rank.png — Worst-first station priorities and robust survey-line ranking.#

The WILLY station curve rises from about 0.50 to 0.81. L34 has the lowest line median and L26 the highest in this run, but their overlapping IQRs caution against treating that ordering as a sharp statistical separation.

11.2.11.5. Coverage retained as a threshold rises#

plot_confidence_coverage_curve asks an operational question: how much survey remains when the minimum accepted CR is \(t\)? The station and valid-data retention functions are

(9)#\[C_{\rm st}(t)=\frac{1}{N}\sum_i\mathbf{1}[\mathrm{CR}_i\ge t], \qquad C_{\rm data}(t)= \frac{\sum_i n_{i,\rm ok}\mathbf{1}[\mathrm{CR}_i\ge t]} {\sum_i n_{i,\rm ok}}.\]

Connected-route retention sums only station-to-station segment lengths whose two endpoints meet the threshold, divided by total route length. It can therefore fall faster than station retention when accepted stations become spatially fragmented. The reported AUC is \(\int_0^1 C(t)\,dt\).

>>> from pycsamt.emtools import plot_confidence_coverage_curve
>>> coverage_fig = plot_confidence_coverage_curve(
...     all_lines,
...     method="composite",
...     line_labels=line_labels,
... )

../../_images/confidence_coverage_curve.png — Retained station, valid-data, and connected-route fractions versus CR.#

Station and valid-data AUC are about 0.68, while connected-route AUC is about 0.65. The earlier loss of route coverage means isolated weak stations fragment otherwise retained line segments. All curves reach zero before 0.85, consistent with the risk and ranking views.

11.2.12. Station Confidence Profile#

plot_confidence_profile plots station confidence along the line. It uses green, pink, and red markers for safe, recoverable, and rejected stations.

>>> import matplotlib.pyplot as plt
>>> from pycsamt.emtools import plot_confidence_profile
>>> fig, ax = plt.subplots(figsize=(9.5, 4.2))
>>> _ = plot_confidence_profile(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="composite",
...     ci_hi=0.95,
...     ci_lo=0.85,
...     shade_mode="score",
...     station_label_step=2,
...     show_errorbars=True,
...     ax=ax,
... )
>>> fig.tight_layout()
>>> fig.savefig("confidence_profile_l18plt.png", dpi=200)
>>> plt.close(fig)

Every station is red because its composite score lies below the default 0.85 boundary; none is marginal and none is safe. The error bars are component-score spread, as defined after (1), not a confidence interval from repeat measurements. L18 provides geographic coordinates, so pyCSAMT projects their along-line separation into metres; the corrected route is about 2.4 km long, rather than the former 5 km display. spacing_m is used only where coordinates are unavailable, or when force_spacing=True.

11.2.13. Frequency Confidence Pseudo-Section#

plot_frequency_confidence_psection shows confidence by station and period. It can plot any metric column from the frequency table, not only confidence.

>>> from pycsamt.emtools import plot_frequency_confidence_psection
>>> fig, ax = plt.subplots(figsize=(10.0, 4.8))
>>> _ = plot_frequency_confidence_psection(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="composite",
...     metric="confidence",
...     station_label_step=2,
...     ax=ax,
... )
>>> fig.savefig("frequency_confidence_psection.png", dpi=200)
>>> plt.close(fig)

../../_images/user-guide-emtools-qc-10.png

Change metric to "uncertainty", "offdiag", "diagonal", "phase", or "spatial" to see which component is driving low confidence at a given station and period, rather than only the combined score.

11.2.14. Single-Station Spectrum#

plot_station_confidence_spectrum overlays the overall confidence curve and component scores for one station.

>>> from pycsamt.emtools import plot_station_confidence_spectrum
>>> fig, ax = plt.subplots(figsize=(7.5, 4.2))
>>> _ = plot_station_confidence_spectrum(
...     "data/AMT/WILLY_DATA/L18PLT",
...     station="18-022U",
...     method="composite",
...     ax=ax,
... )
>>> fig.savefig("station_confidence_spectrum_18-022U.png", dpi=200)
>>> plt.close(fig)

../../_images/user-guide-emtools-qc-11.png

18-022U is this survey’s lowest-confidence station from the ranked table above. Use this plot when you know a station is weak and want to see whether the problem comes from uncertainty, diagonal leakage, off-diagonal mismatch, phase jumps, or spatial incoherence, before reaching for the dashboard’s split-panel view.

11.2.15. Single-Station Dashboard#

plot_station_confidence_dashboard breaks the same information into separate panels. It is easier to read than the overlaid spectrum when many score components overlap.

>>> from pycsamt.emtools import plot_station_confidence_dashboard
>>> fig = plot_station_confidence_dashboard(
...     "data/AMT/WILLY_DATA/L18PLT",
...     station="18-022U",
...     method="composite",
...     ci_hi=0.95,
...     ci_lo=0.85,
...     figsize=(11.0, 7.0),
... )
>>> fig.savefig("station_confidence_dashboard_18-022U.png", dpi=200)
>>> plt.close(fig)

../../_images/user-guide-emtools-qc-12.png

Split into six panels, the story reads clearly: “Data coverage” stays a flat, uninformative 1.0 throughout (top-middle), while “Offdiag consistency”, “Diagonal leakage”, and “Phase + spatial coherence” (bottom row) all repeatedly collapse toward zero – the composite penalty is concentrated in tensor-shape and spatial diagnostics, not missing data. Use dashboards for station-by-station review before deciding whether a low-confidence station should be edited, down-weighted, or retained.

11.2.16. Period-Band Summary#

plot_confidence_band_summary collapses the frequency table by period. It plots median and mean confidence and shades the fraction of stations in rejected or recoverable bands.

>>> from pycsamt.emtools import plot_confidence_band_summary
>>> fig, ax = plt.subplots(figsize=(8.5, 4.2))
>>> _ = plot_confidence_band_summary(
...     "data/AMT/WILLY_DATA/L18PLT",
...     method="composite",
...     ci_hi=0.95,
...     ci_lo=0.85,
...     ax=ax,
... )
>>> fig.savefig("confidence_band_summary.png", dpi=200)
>>> plt.close(fig)

../../_images/user-guide-emtools-qc-13.png

This view is useful when deciding whether a whole period band should be edited, down-weighted, or treated cautiously – it is also the figure that makes the “1479 of 1484 rejected cells” number from the frequency table above legible instead of abstract: the rejected-fraction shading runs high across nearly the entire period range shown here.

11.2.17. Coverage And SNR Quicklook#

plot_qc_quicklook combines three first-pass plots: a presence pseudo-section, an SNR pseudo-section, and an SNR histogram.

>>> from pycsamt.emtools.qc import plot_qc_quicklook
>>> fig = plot_qc_quicklook(
...     "data/AMT/WILLY_DATA/L18PLT",
...     figsize=(10.0, 8.0),
... )
>>> fig.savefig("qc_quicklook_l18plt.png", dpi=200)
>>> plt.close(fig)

../../_images/user-guide-emtools-qc-14.png

The top panel is solid green: 100% row presence everywhere, the same frac_ok=1.0 finding from the station table. The bottom-left SNR pseudo-section is where the real texture is – brighter (higher row SNR, \(|Z|/\sigma\)) around the shorter periods and near a few stations, fading elsewhere – and the histogram on the right shows the underlying distribution peaking in the low tens with a long tail. If the SNR histogram says error tensors are not available, the survey can still be inspected, but uncertainty-based scores will be missing from the composite confidence ratio.

11.2.18. Coverage Pseudo-Section And SNR Histogram#

For more control, call the helper plots separately.

>>> from pycsamt.emtools.qc import plot_coverage_psection, plot_snr_hist
>>> fig, axes = plt.subplots(1, 2, figsize=(12.0, 4.5))
>>> _ = plot_coverage_psection(
...     "data/AMT/WILLY_DATA/L18PLT",
...     metric="presence",
...     ax=axes[0],
... )
>>> _ = axes[0].set_title("Finite-row presence")
>>> _ = plot_snr_hist(
...     "data/AMT/WILLY_DATA/L18PLT",
...     bins=40,
...     ax=axes[1],
... )
>>> _ = axes[1].set_title("Row SNR distribution")
>>> fig.tight_layout()
>>> fig.savefig("coverage_and_snr.png", dpi=200)
>>> plt.close(fig)

../../_images/user-guide-emtools-qc-15.png

metric="presence" shows finite rows. metric="snr" colours by row SNR when z_err exists. metric="offdiag" shows an off-diagonal amplitude proxy.

11.2.19. Consistency Fan#

Apparent resistivity is a nonlinear function of impedance, so a symmetric error on \(Z\) does not become a symmetric error on \(\rho_a\) – the uncertainty score above summarizes that error as one number, but it cannot show its shape. plot_consistency_fan shows the shape directly by Monte Carlo sampling: it draws \(n_{\mathrm{draws}}\) complex Gaussian perturbations with standard deviation equal to the reported error,

\[Z^{(d)} = Z + E^{(d)}, \qquad E^{(d)} \sim \mathcal{CN}(0,\, |Z_{\mathrm{err}}|^2), \qquad d = 1, \dots, n_{\mathrm{draws}},\]

recomputes \(\rho_a^{(d)} = 0.2\,|Z^{(d)}|^2 / f\) for each draw, and plots the requested percentiles of the resulting distribution – the 10/50/90 default draws a band around the median rather than a single curve. It can compare xy and yx apparent resistivity bands for one station, which is exactly where the offdiag score above would flag a problem, but here you see the two bands and can judge for yourself whether they merely disagree or actually fail to overlap.

>>> import numpy as np
>>> from pycsamt.emtools.qc import overlay_noise_cone, plot_consistency_fan
>>> fig, ax = plt.subplots(figsize=(8.8, 4.5))
>>> _ = plot_consistency_fan(
...     "data/AMT/WILLY_DATA/L18PLT",
...     station="18-016A",
...     comps=("xy", "yx"),
...     pcts=(10.0, 50.0, 90.0),
...     n_draws=300,
...     ax=ax,
... )
>>> ax.set_yscale("log")
>>> period = np.logspace(-4, 0, 30)
>>> overlay_noise_cone(
...     ax,
...     period,
...     lo=np.full(period.size, 10.0),
...     hi=np.full(period.size, 100.0),
...     color="0.5",
...     alpha=0.20,
... )
>>> fig.savefig("consistency_fan_18-016A.png", dpi=200)
>>> plt.close(fig)

../../_images/user-guide-emtools-qc-16.png

18-016A is the same station flagged elsewhere in this survey for strong ratio anisotropy: on this log axis, \(\rho_{a,xy}\) climbs into the tens of thousands of \(\Omega\,\mathrm{m}\) while \(\rho_{a,yx}\) stays two to three decades lower throughout, and the shaded Monte Carlo bands around each curve are genuinely propagated from the EDI’s own error tensor rather than a linearized approximation. The grey noise cone overlay is a visual reference band, not estimated by the QC module – it happens to bracket most of this station’s real \(\rho_{a,yx}\) values here while sitting far below \(\rho_{a,xy}\). Supply project-specific lower and upper bounds when you use it in a report.

11.2.20. XY/YX Crossover Map#

Away from a 1-D earth, rho_xy and rho_yx need not agree, but which one is larger should not flip back and forth erratically with period. plot_xyyx_crossover_map tracks the sign of \(d(f) = \rho_{a,xy}(f) - \rho_{a,yx}(f)\) along each station’s sounding and marks every period where it changes,

\[\operatorname{sign}\bigl(d(f_j)\bigr) \ne \operatorname{sign}\bigl(d(f_{j+1})\bigr),\]

placing the marker at the log-period linearly interpolated between \(f_j\) and \(f_{j+1}\), weighted by how close each side came to zero. A station with one or two crossovers across a smooth sounding is unremarkable; a station peppered with them is a cheap, effective anisotropy and mode-consistency diagnostic worth following up with the consistency fan above.

>>> from pycsamt.emtools.qc import overlay_spectral_holes, plot_xyyx_crossover_map
>>> fig, ax = plt.subplots(figsize=(9.5, 4.8))
>>> _ = plot_xyyx_crossover_map(
...     "data/AMT/WILLY_DATA/L18PLT",
...     ax=ax,
... )
>>> overlay_spectral_holes(
...     ax,
...     "data/AMT/WILLY_DATA/L18PLT",
...     thresh_dec=0.30,
... )
>>> fig.savefig("xy_yx_crossover_map.png", dpi=200)
>>> plt.close(fig)

../../_images/user-guide-emtools-qc-17.png

overlay_spectral_holes shades large gaps in log-period sampling on top of a pseudo-section-style axis. L18PLT’s real frequency grid is dense enough that the default 0.30-decade threshold finds nothing to shade here – an honest negative result rather than a broken overlay. Lower thresh_dec only when you intentionally want to reveal small grid-spacing differences instead.

11.2.21. Propagation To Inversion#

For MARE2DEM exports created from EDI data, CR-derived uncertainty propagation can be enabled with:

>>> from pathlib import Path
>>> from pycsamt.models.mare2dem.edi import make_mt_data_from_edi
>>> out_path = Path("mare2dem_data_with_confidence.emdata")
>>> emd = make_mt_data_from_edi(
...     survey,
...     out_path,
...     confidence_weighting=True,
... )
>>> print(emd.n_mt_receivers, "receivers,", emd.n_mt_frequencies, "frequencies,", emd.n_data, "data")
28 receivers, 53 frequencies, 5936 data

The effective relative impedance error is inflated as confidence decreases:

\[\epsilon_{Z,\mathrm{eff}} = \epsilon_Z \left[{1 \over \max(\mathrm{CR}, \mathrm{CR}_{\min})}\right]^p .\]

The defaults are CR_min=0.05 and p=1. The usual propagation is then:

\[\sigma_{\rho_a,\mathrm{eff}} = 2\rho_a\,\epsilon_{Z,\mathrm{eff}}, \qquad \sigma_{\phi,\mathrm{eff}} = {180 \over \pi}\epsilon_{Z,\mathrm{eff}} .\]

Confidence weighting should increase uncertainty for low-confidence data. It should not make any datum artificially more precise.

11.2.22. Reading QC Results#

Use QC scores as evidence, not as an automatic delete button.

coverage is low: The station or frequency is genuinely incomplete. Investigate loading, editing, or acquisition gaps.
uncertainty is low: Error tensors are large relative to impedance magnitude. This is a strong reason to down-weight or review.
offdiag is low: Zxy and Zyx amplitudes disagree beyond the selected tolerance. Compare with anisotropy, impedance, and tensor tools.
diagonal is low: Diagonal leakage is high. Check dimensionality and coordinate orientation before calling it acquisition noise.
phase is low: Off-diagonal phase changes abruptly with frequency. Check frequency editing and processing history.
spatial is low: The station differs from neighbouring stations at the same frequency or in median response. Check station metadata and local geology.

11.2.23. Worked Example#

The gallery example applies the station, frequency, profile, dashboard, quicklook, fan, crossover, and hole-overlay workflows to the bundled L18PLT survey.

Open the rendered gallery page here: Quality-control confidence scoring (pycsamt.emtools.qc).