From f6de0165d69029cb5a82675a5582130d73d6c2f0 Mon Sep 17 00:00:00 2001 From: Chris Nicholas <4948774+cnicholas@users.noreply.github.com> Date: Mon, 2 Nov 2026 21:34:52 -0500 Subject: [PATCH 1/2] Refresh the docs notebooks and fail CI when they drift from the library ## Summary - **What:** a script, `scripts/refresh_notebooks.py`, that re-executes the docs notebooks; CI runs it with `--check` in place of nbmake; the notebooks are refreshed. - **Why:** the docs site shows each notebook's saved outputs without running them, and nbmake only checked that they execute. Eight tutorials had drifted (limits from before 0.4.0 in the Design-State-1 analysis; flat Xbar limits where they now vary with n in the reference validation; older process means, lane charts and signal counts), and Derived Variables had never been executed, so the site showed its code with no output. - **Scope:** the script and its tests; ci.yml (one step); the `[test]` extra; nine refreshed notebooks with three prose fixes; CONTRIBUTING.md; CHANGELOG. Stacked on #168, whose signal-rule fixes the refreshed outputs show. ## Contract / Invariants (must remain true) - [x] **Default behavior unchanged:** nothing under `processbehavior/` changes; Bishop gate 1547/0/4 - [x] **Residuals unaffected:** no library change - [x] **Row/index alignment preserved:** no library change - [x] **Lane boundaries preserved:** no library change - [x] **Output schema compatible:** no library change; notebook code cells and metadata unchanged ## Behavior Changes (explicit) - **New API:** `python scripts/refresh_notebooks.py [PATH ...] [--check]` - **New semantics:** - CI executes every docs notebook (globbed, no hand-kept list) and fails if what a reader sees differs from what is saved: printed text, errors, tables, and each figure's data, title, axis titles, annotations and shape positions - Ignored as noise: the temporary path in a warning's location, a derived variable's random id, how the kernel chunks printed text, plotly templates, typed-array encoding, NaN vs null, and float digits past 10 significant figures - A refresh keeps the saved bytes of every cell whose visible content is unchanged, so float noise does not churn the diff ## Key Design Decisions - **Compare what a reader sees, not bytes:** CI resolves plotly 7.x while local venvs may have 6.x; figures are compared on decoded data and labels - **Refresh the whole set from one command;** CONTRIBUTING says when to run it and to reread the prose around changed outputs - **Prose:** only text a refreshed output contradicted was changed - signal-detection: Rules 4 and 5 read "Suggests a sustained shift" and "Suggests drift" - sds-validation: the Slide 19 and 32 reference values now quote the VAS rerun of 13 October 2026 used by the gate (PP 1.58, PPL 1.84 on Slide 32); the current process mean is explained as the mean of the cell means (237.83) beside the plain average (237.81) - stratified-analysis: Lane D's one signal is a chance six-point trend in a lane with no special cause ## Risks & Mitigations - **P0 risks:** a false drift report in CI from an environment difference - **Mitigations:** the check passed three runs in a row locally; normalisation covers the differences found (stream chunking, NaN, random ids, temp paths); if CI finds another, normalise that field rather than fall back to text only ## Tests ### Added / Updated - [x] `tests/test_refresh_notebooks.py` (8 tests): docs notebooks found; warning paths, random ids, stream chunking, plotly template and bdata-vs-list ignored; a changed number in text, a changed figure value and a changed figure label all count as drift ### Regression Focus (must fail if broken) - [x] a stale saved output passes CI (negative check: one saved digit changed in a scratch copy fails with the cell named and the difference shown) - [x] noise fails CI (three consecutive clean runs) ## Manual Verification - [x] `python scripts/refresh_notebooks.py --check`: all 14 current - [x] `pytest tests/`: 2704 passed, 7 skipped - [x] `python validation/e2e_bishop_report.py`: 1547 passed, 0 failed - [x] `ruff check .` clean; mypy shows the same 15 advisory errors - [x] `mystmd@1.10.1 build --html` succeeds; only the three existing changelog citation warnings ## Notes - **Docs:** CONTRIBUTING.md "Docs notebooks"; CHANGELOG [Unreleased] Fixed and CI / Infrastructure - **sds-validation diff** is large (about 100k lines) because two R2/X charts now plot 1599 points where 1565 were saved, which shifts whole arrays; the encoding is unchanged and the file grew by 12 KB - **Follow-ups (not in this PR):** none --- .github/workflows/ci.yml | 24 +- CHANGELOG.md | 11 + CONTRIBUTING.md | 14 + docs/getting-started/quickstart.ipynb | 741 +- docs/tutorials/coffee-shop.ipynb | 790 +- docs/tutorials/derived-variables.ipynb | 1292 +- docs/tutorials/design-state-tour.ipynb | 383 +- docs/tutorials/first-xmr-chart.ipynb | 30 +- docs/tutorials/sds-validation.ipynb | 100122 +++++++++-------- docs/tutorials/sds1-complete-analysis.ipynb | 3144 +- docs/tutorials/signal-detection.ipynb | 161 +- docs/tutorials/stratified-analysis.ipynb | 388 +- pyproject.toml | 8 +- scripts/refresh_notebooks.py | 161 + tests/test_refresh_notebooks.py | 84 + 15 files changed, 55352 insertions(+), 52001 deletions(-) create mode 100644 scripts/refresh_notebooks.py create mode 100644 tests/test_refresh_notebooks.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index cfbe6a2..b26a880 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -52,26 +52,12 @@ jobs: run: mypy processbehavior - name: Tests run: pytest tests/ -m "not slow" --cov=processbehavior --cov-report=xml --cov-report=term-missing - - name: Execute docs notebooks (ubuntu / 3.13 only) - # Allowlist of notebooks known to execute against the current API. + - name: Docs notebooks run and match their saved outputs (ubuntu / 3.13 only) + # The docs site shows each notebook's saved outputs without running it. This executes + # every docs notebook and fails if what a reader sees no longer matches; the fix is + # `python scripts/refresh_notebooks.py`. if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.13' - run: | - pytest --nbmake \ - docs/getting-started/quickstart.ipynb \ - docs/tutorials/first-xmr-chart.ipynb \ - docs/tutorials/coffee-shop.ipynb \ - docs/tutorials/process-capability.ipynb \ - docs/tutorials/process-capability-before-after.ipynb \ - docs/tutorials/derived-variables.ipynb \ - docs/tutorials/loss-function.ipynb \ - docs/tutorials/xbar-s-analysis.ipynb \ - docs/tutorials/staged-charts.ipynb \ - docs/tutorials/signal-detection.ipynb \ - docs/tutorials/stratified-analysis.ipynb \ - docs/tutorials/design-state-tour.ipynb \ - docs/tutorials/sds-validation.ipynb \ - docs/tutorials/sds1-complete-analysis.ipynb \ - --tb=short + run: python scripts/refresh_notebooks.py --check - name: Validate against Bishop's reference results (ubuntu / 3.13 only) # The ground-truth check: PB against every VAS run of Dr. Bishop's on hand # (tests/fixtures/bishop_analyses/). It lives outside tests/ because it diff --git a/CHANGELOG.md b/CHANGELOG.md index 03005ac..b9d8e40 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -47,6 +47,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 report. The `detect_signals()` docstring no longer says Rules 1-4 are the default. ### Fixed +- **Tutorial outputs match the library again.** The docs site shows each notebook's saved outputs, + and eight tutorials had drifted: limits from before 0.4.0 in the Design-State-1 analysis, flat + limits where they now vary by subgroup size in the reference validation, older process means and + signal counts. All were re-executed. The Derived Variables tutorial, saved without outputs, now + shows them. Prose that the new outputs contradicted was corrected; the Bishop reference values in + the reference validation now quote his VAS rerun of 13 October 2026. - **Three signal rules now match their definitions.** Rule 5 (trend) fired only on 7 rising or falling points, not 6; Rule 6 (oscillation) only on 15 alternating points, not 14; and Rule 2 counted only points in Zone A, leaving out points beyond 3σ, which Western Electric and Wheeler @@ -81,6 +87,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 variable), and that the study has none. ### CI / Infrastructure +- **CI checks that docs notebooks match their saved outputs.** `scripts/refresh_notebooks.py` + re-executes the docs notebooks; with `--check` (in CI, replacing the nbmake run) it fails when a + notebook no longer runs or what a reader would see has changed, comparing printed text, tables + and each figure's data and labels. The `[test]` extra now needs nbclient, nbformat and ipykernel + instead of nbmake. - **Python 3.14 is tested** on Ubuntu and Windows, in CI and in the release gate, and listed in the package classifiers. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index d5815bd..3398e3d 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -97,6 +97,20 @@ summary. If a Bishop check fails after your change, the change is wrong — not the reference. +## Docs notebooks + +The docs site shows each notebook's saved outputs; it does not run them. After a change that +alters what a notebook prints or plots, refresh the saved outputs: + +```bash +python scripts/refresh_notebooks.py # every docs notebook +python scripts/refresh_notebooks.py docs/tutorials/signal-detection.ipynb +``` + +CI runs `python scripts/refresh_notebooks.py --check` and fails when a notebook no longer runs or +no longer matches its saved outputs. Then read the text around any changed output: the check +compares outputs, not prose. + ## Citing the methodology Code and docs cite Bishop's VAS documentation manual by edition and equation number: diff --git a/docs/getting-started/quickstart.ipynb b/docs/getting-started/quickstart.ipynb index c3c9fe8..7730289 100644 --- a/docs/getting-started/quickstart.ipynb +++ b/docs/getting-started/quickstart.ipynb @@ -2921,7 +2921,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2026-05-19T19:32:03.629357Z", @@ -2943,152 +2943,152 @@ [ "A", 1, - 0 + 1 ], [ "A", 1, - 1 + 2 ], [ "A", 1, - 2 + 3 ], [ "A", 2, - 3 + 13 ], [ "A", 2, - 4 + 14 ], [ "A", 2, - 5 + 15 ], [ "A", 3, - 6 + 25 ], [ "A", 3, - 7 + 26 ], [ "A", 3, - 8 + 27 ], [ "A", 4, - 9 + 37 ], [ "A", 4, - 10 + 38 ], [ "A", 4, - 11 + 39 ], [ "A", 5, - 12 + 49 ], [ "A", 5, - 13 + 50 ], [ "A", 5, - 14 + 51 ], [ "A", 6, - 15 + 61 ], [ "A", 6, - 16 + 62 ], [ "A", 6, - 17 + 63 ], [ "A", 7, - 18 + 73 ], [ "A", 7, - 19 + 74 ], [ "A", 7, - 20 + 75 ], [ "A", 8, - 21 + 85 ], [ "A", 8, - 22 + 86 ], [ "A", 8, - 23 + 87 ], [ "A", 9, - 24 + 97 ], [ "A", 9, - 25 + 98 ], [ "A", 9, - 26 + 99 ], [ "A", 10, - 27 + 109 ], [ "A", 10, - 28 + 110 ], [ "A", 10, - 29 + 111 ] ], "hovertemplate": "X_A = %{y:.3f}
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CL = 2.125 | LPL = 0.000", "x": 1.0, "xanchor": "right", "xref": "x8 domain", @@ -4665,6 +4665,9 @@ }, "height": 1600, "hovermode": "closest", + "margin": { + "b": 100 + }, "paper_bgcolor": "#2D2D2D", "plot_bgcolor": "#1E1E1E", "shapes": [ @@ -4679,8 +4682,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 97.139, - "y1": 101.183, + "y0": 97.13933333333334, + "y1": 101.18266666666666, "yref": "y" }, { @@ -4694,8 +4697,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 101.183, - "y1": 103.205, + "y0": 101.18266666666666, + "y1": 103.20433333333334, "yref": "y" }, { @@ -4709,8 +4712,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 95.117, - "y1": 97.139, + "y0": 95.11766666666666, + "y1": 97.13933333333334, "yref": "y" }, { @@ -4724,8 +4727,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 103.205, - "y1": 105.227, + "y0": 103.20433333333334, + "y1": 105.226, "yref": "y" }, { @@ -4739,8 +4742,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 93.095, - "y1": 95.117, + "y0": 93.096, + 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fermentation_tempcell_countattenuation
count144.00144.00144.00
mean21.405520825.990.81
std3.432090917.130.12
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25%18.654059205.500.74
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75%24.426632784.250.90
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" + ], + "text/plain": [ + " fermentation_temp cell_count attenuation\n", + "count 144.00 144.00 144.00\n", + "mean 21.40 5520825.99 0.81\n", + "std 3.43 2090917.13 0.12\n", + "min 15.50 1468019.00 0.40\n", + "25% 18.65 4059205.50 0.74\n", + "50% 21.30 5267361.00 0.83\n", + "75% 24.42 6632784.25 0.90\n", + "max 26.90 12116326.00 0.98" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df[['fermentation_temp', 'cell_count', 'attenuation']].describe().round(2)" ] @@ -97,10 +280,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "dv-code-06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "raw cell_count : mean= 5,520,826 median= 5,267,361 skew=0.75\n", + "log(cell_count): mean= 15.45 median= 15.48 skew=-0.25\n" + ] + } + ], "source": [ "raw = df['cell_count']\n", "logged = evaluate(Derivation.transform('cell_count', 'log'), raw).values\n", @@ -125,10 +317,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "dv-code-08", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "attenuation in [0.405, 0.982] domain violations: 0\n", + "arcsin(sqrt(x)) head: [0.945, 1.147, 0.9]\n" + ] + } + ], "source": [ "att = df['attenuation']\n", "res = evaluate(Derivation.transform('attenuation', 'arcsin'), att)\n", @@ -150,10 +351,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "dv-code-10", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fitted (frozen at formulate): {'mu': 5520826.0, 'sigma': 2090917.1}\n", + "standardized -> mean 0.000, sd 1.000\n" + ] + } + ], "source": [ "z = evaluate(Derivation.transform('cell_count', 'zscore'), df['cell_count'])\n", "print('fitted (frozen at formulate):', {k: round(v, 1) for k, v in z.fitted.items()})\n", @@ -175,10 +385,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "dv-code-12", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fitted edges : [15.5, 18.6, 21.3, 24.4, 26.9]\n", + "range labels : ['[15.5, 18.65)', '[18.65, 21.3)', '[21.3, 24.425)', '[24.425, 26.9]']\n" + ] + } + ], "source": [ "temp = df['fermentation_temp']\n", "binned = evaluate(Derivation.bin('fermentation_temp', method='equal_freq', n=4), temp)\n", @@ -205,10 +424,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "dv-code-14", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " range -> ['[15.5, 18.65)', '[18.65, 21.3)', '[21.3, 24.425)', '[24.425, 26.9]']\n", + " ordinal -> ['Low', 'Medium-Low', 'Medium-High', 'High']\n", + " number -> ['Bin 1', 'Bin 2', 'Bin 3', 'Bin 4']\n", + "explicit list -> ['Cool', 'Optimal', 'Warm', 'Hot']\n" + ] + } + ], "source": [ "styles = ['range', 'ordinal', 'number', ['Cool', 'Optimal', 'Warm', 'Hot']]\n", "for style in styles:\n", @@ -247,10 +477,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "dv-code-17", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "equal_width edges: [15.5, 18.4, 21.2, 24.0, 26.9]\n", + "breaks [18, 22] : ['< 18', '[18, 22)', '>= 22']\n", + "sd zones : 5 bins (mu=21.4, sigma=3.4)\n" + ] + } + ], "source": [ "ew = evaluate(Derivation.bin('fermentation_temp', method='equal_width', n=4), temp)\n", "print('equal_width edges:', [round(e, 1) for e in ew.fitted['edges']])\n", @@ -277,10 +517,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "dv-code-19", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Study(response='cell_count_log', factors=[fermentation_temp_bin], time='week', ads=1)\n", + " Valid: Histogram, Xbar, S, X, mR | Recommended: Xbar\n", + " Residuals: R1, R2, R3, R4, R5, R6\n", + " → study.execute() or study.support for details\n", + "zone order on the chart: ['Cool', 'Optimal', 'Warm', 'Hot']\n" + ] + } + ], "source": [ "study = (\n", " ProcessBehavior(df)\n", @@ -303,10 +555,994 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "dv-code-21", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "%{x}
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"category" + }, + "yaxis": { + "gridcolor": "#E5E5E5", + "gridwidth": 1.0, + "linecolor": "#999999", + "linewidth": 1.0, + "showgrid": true, + "showline": true, + "title": { + "text": "Sample Average" + }, + "zeroline": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "study.execute(chart='Xbar').plot()" ] @@ -326,10 +1562,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "dv-code-23", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fermentation_temp_bin family=bin id=dbd714149292\n", + "cell_count_log family=transform id=9bed45d1ffb5\n", + "\n", + "frozen bin edges: [15.5, 18.6, 21.3, 24.4, 26.9]\n", + "valid? False -> 5 labels supplied but 4 bins fitted.\n" + ] + } + ], "source": [ "for d in study.derivations:\n", " print(f'{d.output_name:22} family={d.family:10} id={d.id}')\n", diff --git a/docs/tutorials/design-state-tour.ipynb b/docs/tutorials/design-state-tour.ipynb index 98f4911..6f77185 100644 --- a/docs/tutorials/design-state-tour.ipynb +++ b/docs/tutorials/design-state-tour.ipynb @@ -280,101 +280,101 @@ "F1_3_F2_2_8" ], "y": [ - 52.137, - 52.137, - 51.707, - 51.707, - 51.891, - 51.891, - 51.707, - 51.707, - 52.137, - 52.137, - 51.891, - 51.891, - 51.707, - 51.707, - 51.891, - 51.891, - 52.137, - 52.137, - 51.777, - 51.777, - 51.777, - 51.777, - 51.891, - 51.891, - 52.137, - 52.137, - 52.137, - 52.137, - 51.891, - 51.891, - 51.777, - 51.777, - 51.707, - 51.707, - 51.777, - 51.777, - 52.137, - 52.137, - 52.137, - 52.137, - 51.707, - 51.707, - 52.137, - 52.137, - 51.777, - 51.777, - 51.707, - 51.707, - 51.777, - 51.777, - 51.777, - 51.777, - 51.777, - 51.777, - 51.777, - 51.777, - 51.707, - 51.707, - 51.707, - 51.707, - 51.777, - 51.777, - 51.777, - 51.777, - 51.891, - 51.891, - 51.891, - 51.891, - 52.137, - 52.137, - 51.891, - 51.891, - 51.891, - 51.891, - 51.777, - 51.777, - 51.891, - 51.891, - 52.137, - 52.137, - 51.707, - 51.707, - 51.777, - 51.777, - 51.707, - 51.707, - 52.137, - 52.137, - 51.707, - 51.707, - 51.777, - 51.777, - 51.777, - 51.777, - 51.891 + 52.048, + 52.048, + 51.74, + 51.74, + 51.895, + 51.895, + 51.74, + 51.74, + 52.048, + 52.048, + 51.895, + 51.895, + 51.74, + 51.74, + 51.895, + 51.895, + 52.048, + 52.048, + 51.803, + 51.803, + 51.803, + 51.803, + 51.895, + 51.895, + 52.048, + 52.048, + 52.048, + 52.048, + 51.895, + 51.895, + 51.803, + 51.803, + 51.74, + 51.74, + 51.803, + 51.803, + 52.048, + 52.048, + 52.048, + 52.048, + 51.74, + 51.74, + 52.048, + 52.048, + 51.803, + 51.803, + 51.74, + 51.74, + 51.803, + 51.803, + 51.803, + 51.803, + 51.803, + 51.803, + 51.803, + 51.803, + 51.74, + 51.74, + 51.74, + 51.74, + 51.803, + 51.803, + 51.803, + 51.803, + 51.895, + 51.895, + 51.895, + 51.895, + 52.048, + 52.048, + 51.895, + 51.895, + 51.895, + 51.895, + 51.803, + 51.803, + 51.895, + 51.895, + 52.048, + 52.048, + 51.74, + 51.74, + 51.803, + 51.803, + 51.74, + 51.74, + 52.048, + 52.048, + 51.74, + 51.74, + 51.803, + 51.803, + 51.803, + 51.803, + 51.895 ] }, { @@ -486,101 +486,101 @@ "F1_3_F2_2_8" ], "y": [ - 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"F1_1_F2_1_4", "F1_1_F2_1_5", "F1_1_F2_1_7", "F1_1_F2_1_8", @@ -637,7 +636,7 @@ "F1_3_F2_2_8" ], "y": { - "bdata": "AiuHFtlOSEB56SYxCNxIQNejcD0K10hAfT81XrpZSUD+1HjpJpFHQIGVQ4tst0hAjZduEoPASEB1kxgEVl5IQPCnxks3KUlA/Knx0k2iSEC0yHa+n6pIQD81XrpJrEdAzczMzMxsSEDNzMzMzNxIQM/3U+Ola0pA9P3UeOlGSkDwp8ZLNylJQLpJDAIrx0pAAAAAAACASkDVeOkmMXhKQLx0kxgEZkpAppvEILCSSkCe76fGS0dJQAwCK4cWKUtA+n5qvHQzSkDXo3A9CndKQBSuR+F61EhAWmQ730/9SkCq8dJNYhBKQBkEVg4tYkpARIts5/tJSkAnMQisHFpKQNejcD0KJ0lACtejcD1aSkBt5/up8SJKQKwcWmQ7X0pA", + "bdata": "AiuHFtlOSEB56SYxCNxIQNejcD0K10hA/tR46SaRR0CBlUOLbLdIQI2XbhKDwEhAdZMYBFZeSEDwp8ZLNylJQPyp8dJNokhAtMh2vp+qSEA/NV66SaxHQM3MzMzMbEhAzczMzMzcSEDP91PjpWtKQPT91HjpRkpA8KfGSzcpSUC6SQwCK8dKQAAAAAAAgEpA1XjpJjF4SkC8dJMYBGZKQKabxCCwkkpAnu+nxktHSUAMAiuHFilLQPp+arx0M0pA16NwPQp3SkAUrkfhetRIQFpkO99P/UpAqvHSTWIQSkAZBFYOLWJKQESLbOf7SUpAJzEIrBxaSkDXo3A9CidJQArXo3A9WkpAbef7qfEiSkCsHFpkO19KQA==", "dtype": "f8" } } diff --git a/docs/tutorials/first-xmr-chart.ipynb b/docs/tutorials/first-xmr-chart.ipynb index afb6a0b..e0318ab 100644 --- a/docs/tutorials/first-xmr-chart.ipynb +++ b/docs/tutorials/first-xmr-chart.ipynb @@ -183,8 +183,8 @@ "output_type": "stream", "text": [ "Charts: ['X', 'mR']\n", - "X: {'N': 1, 'center': np.float64(72.379), 'lpl': np.float64(68.366), 'upl': np.float64(76.392)}\n", - "mR: {'N': 2, 'center': np.float64(1.509), 'lpl': np.float64(0.0), 'upl': np.float64(4.93)}\n" + "X: {'N': 1, 'center': np.float64(72.379), 'lpl': np.float64(68.367), 'upl': np.float64(76.391)}\n", + "mR: {'N': 2, 'center': np.float64(1.509), 'lpl': np.float64(0.0), 'upl': np.float64(4.929)}\n" ] } ], @@ -231,8 +231,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Signals found: 9\n", - "Flagged days: [np.int64(20), np.int64(22), np.int64(24), np.int64(25), np.int64(28), np.int64(29)]\n" + "Signals found: 14\n", + "Flagged days: [np.int64(20), np.int64(22), np.int64(24), np.int64(25), np.int64(26), np.int64(27), np.int64(28), np.int64(29)]\n" ] } ], @@ -396,7 +396,7 @@ "size": 10 }, "showarrow": false, - "text": "UPL = 4.930 | CL = 1.509 | LPL = 0.000", + "text": "UPL = 4.929 | CL = 1.509 | LPL = 0.000", "x": 1.0, "xanchor": "right", "xref": "x2 domain", @@ -428,8 +428,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - 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%{y:.3f}", "marker": { @@ -65139,13 +65311,27 @@ "2" ], "y": { - "bdata": "pHA9CterbUBmZmZmZsZtQA==", + "bdata": "FK5H4XqsbUD2KFyPwsVtQA==", "dtype": "f8" } } ], "layout": { "annotations": [ + { + "bgcolor": "rgba(255,255,255,0.8)", + "borderpad": 3, + "font": { + "color": "gray", + "size": 10 + }, + "showarrow": false, + "text": "Process limits vary by subgroup size (n)", + "x": 0.02, + "xref": "paper", + "y": 0.98, + "yref": "paper" + }, { "bgcolor": "rgba(255,255,255,0.7)", "font": { @@ -65153,7 +65339,7 @@ "size": 10 }, "showarrow": false, - "text": "UPL = 237.9 | CL = 237.8 | LPL = 237.7", + "text": "CL = 237.8", "x": 1.0, "xanchor": "right", "xref": "paper", @@ -65175,32 +65361,6 @@ "paper_bgcolor": "white", "plot_bgcolor": "#EBEBEB", "shapes": [ - { - "line": { - "color": "#E31A1C", - "dash": "dash", - "width": 1.2 - }, - "type": "line", - "x0": 0, - "x1": 1, - "xref": "x domain", - "y0": 237.93, - "y1": 237.93 - }, - { - "line": { - "color": "#E31A1C", - "dash": "dash", - "width": 1.2 - }, - "type": "line", - "x0": 0, - "x1": 1, - "xref": "x domain", - "y0": 237.65, - "y1": 237.65 - }, { "line": { "color": "#33A02C", @@ -65211,8 +65371,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 237.79, - "y1": 237.79 + "y0": 237.78, + "y1": 237.78 } ], "showlegend": false, @@ -73138,6 +73298,8 @@ "1_2_27", "1_2_28", "1_2_29", + "1_2_30", + "1_2_31", "1_2_32", "1_2_33", "1_2_34", @@ -73188,6 +73350,7 @@ "1_2_79", "1_2_80", "1_2_81", + "1_2_82", "1_2_83", "1_2_84", "1_2_85", @@ -73329,6 +73492,7 @@ "2_2_21", "2_2_22", "2_2_23", + "2_2_24", "2_2_25", "2_2_26", "2_2_27", @@ -73378,9 +73542,11 @@ "2_2_71", "2_2_72", "2_2_73", + "2_2_74", "2_2_75", "2_2_76", "2_2_77", + "2_2_78", "2_2_79", "2_2_80", "2_2_81", @@ -73456,6 +73622,7 @@ "3_1_51", "3_1_52", "3_1_53", + "3_1_54", "3_1_55", "3_1_56", "3_1_57", @@ -73491,6 +73658,7 @@ "3_1_87", "3_1_88", "3_1_89", + "3_1_90", "3_1_91", "3_1_92", "3_1_93", @@ -73498,6 +73666,7 @@ "3_1_95", "3_1_96", "3_1_97", + "3_1_98", "3_1_99", "3_1_100", "3_2_1", @@ -73531,6 +73700,7 @@ "3_2_29", "3_2_30", "3_2_31", + "3_2_32", "3_2_33", "3_2_34", "3_2_35", @@ -73611,6 +73781,7 @@ "4_1_10", "4_1_11", "4_1_12", + "4_1_13", "4_1_14", "4_1_15", "4_1_16", @@ -73629,6 +73800,7 @@ "4_1_29", "4_1_30", "4_1_31", + "4_1_32", "4_1_33", "4_1_34", "4_1_35", @@ -73636,6 +73808,7 @@ "4_1_37", "4_1_38", "4_1_39", + "4_1_40", "4_1_41", "4_1_42", "4_1_43", @@ -73686,6 +73859,7 @@ "4_1_88", "4_1_89", "4_1_90", + "4_1_91", "4_1_92", "4_1_93", "4_1_94", @@ -73706,6 +73880,7 @@ "4_2_9", "4_2_10", "4_2_11", + "4_2_12", "4_2_13", "4_2_14", "4_2_15", @@ -73722,6 +73897,7 @@ "4_2_26", "4_2_27", "4_2_28", + "4_2_29", "4_2_30", "4_2_31", "4_2_32", @@ -73767,6 +73943,7 @@ "4_2_72", "4_2_73", "4_2_74", + "4_2_75", "4_2_76", "4_2_77", "4_2_78", @@ -73795,7 +73972,7 @@ ], "xaxis": "x", "y": { - "bdata": 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", "dtype": "f8" }, "yaxis": "y" @@ -74069,6 +74246,10 @@ "1_2_28", "1_2_29", "1_2_29", + "1_2_30", + "1_2_30", + "1_2_31", + "1_2_31", "1_2_32", "1_2_32", "1_2_33", @@ -74169,6 +74350,8 @@ "1_2_80", "1_2_81", "1_2_81", + "1_2_82", + "1_2_82", "1_2_83", "1_2_83", "1_2_84", @@ -74451,6 +74634,8 @@ "2_2_22", "2_2_23", "2_2_23", + "2_2_24", + "2_2_24", "2_2_25", "2_2_25", "2_2_26", @@ -74549,12 +74734,16 @@ "2_2_72", "2_2_73", "2_2_73", + "2_2_74", + "2_2_74", "2_2_75", "2_2_75", "2_2_76", "2_2_76", "2_2_77", "2_2_77", + "2_2_78", + "2_2_78", "2_2_79", "2_2_79", "2_2_80", @@ -74705,6 +74894,8 @@ "3_1_52", "3_1_53", "3_1_53", + "3_1_54", + "3_1_54", "3_1_55", "3_1_55", "3_1_56", @@ -74775,6 +74966,8 @@ "3_1_88", "3_1_89", "3_1_89", + "3_1_90", + "3_1_90", "3_1_91", "3_1_91", "3_1_92", @@ -74789,6 +74982,8 @@ "3_1_96", "3_1_97", "3_1_97", + "3_1_98", + "3_1_98", "3_1_99", "3_1_99", "3_1_100", @@ -74855,6 +75050,8 @@ "3_2_30", "3_2_31", "3_2_31", + "3_2_32", + "3_2_32", "3_2_33", "3_2_33", "3_2_34", @@ -75015,6 +75212,8 @@ "4_1_11", "4_1_12", "4_1_12", + "4_1_13", + "4_1_13", "4_1_14", "4_1_14", "4_1_15", @@ -75051,6 +75250,8 @@ "4_1_30", "4_1_31", "4_1_31", + "4_1_32", + "4_1_32", "4_1_33", "4_1_33", "4_1_34", @@ -75065,6 +75266,8 @@ "4_1_38", "4_1_39", "4_1_39", + "4_1_40", + "4_1_40", "4_1_41", "4_1_41", "4_1_42", @@ -75165,6 +75368,8 @@ "4_1_89", "4_1_90", "4_1_90", + "4_1_91", + "4_1_91", "4_1_92", "4_1_92", "4_1_93", @@ -75205,6 +75410,8 @@ "4_2_10", "4_2_11", "4_2_11", + "4_2_12", + "4_2_12", "4_2_13", "4_2_13", "4_2_14", @@ -75237,6 +75444,8 @@ "4_2_27", "4_2_28", "4_2_28", + "4_2_29", + "4_2_29", "4_2_30", "4_2_30", "4_2_31", @@ -75327,6 +75536,8 @@ "4_2_73", "4_2_74", "4_2_74", + "4_2_75", + "4_2_75", "4_2_76", "4_2_76", "4_2_77", @@ -75380,49705 +75591,50116 @@ ], "xaxis": "x", "y": [ - 238.97, - 238.97, - 239.13, - 239.13, - 239.95, - 239.95, - 238.97, - 238.97, - 238.97, - 238.97, - 239.39, - 239.39, - 239.39, - 239.39, - 239.39, - 239.39, - 238.97, - 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" companion=True\n", - ").plot(theme='ggplot').show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Current Process Capability (Slide 19)\n", - "\n", - "Bishop's reference values:\n", - "- **Process Mean = 237.81**, **Process StdEv = 1.74**\n", - "- **PPL = 1.12**, **PP = 0.96**, **PPU = 0.8**\n", - "- **Pct below LSL = 0.53%**, **Pct above USL = 0.46%**" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "execution": { - "iopub.execute_input": "2026-10-05T00:32:57.891331Z", - "iopub.status.busy": "2026-10-05T00:32:57.891247Z", - "iopub.status.idle": "2026-10-05T00:32:57.909582Z", - "shell.execute_reply": "2026-10-05T00:32:57.909267Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CapabilityResult:\n", - " Specs: LSL=232, USL=242, Target=237\n", - " N=3042, Ybar=237.81, S=1.74, sigma_hat=1.74\n", - "\n", - " Current Capability (overall sigma):\n", - " Pp=0.96 Ppk=0.8 (lower=1.12, upper=0.8)\n", - "\n", - " Potential Capability (R2 sigma):\n", - " centre=237.81, sigma_hat_r2=1.06\n", - " Cp=1.58 Cpk=1.32 (lower=1.83, upper=1.32)\n", - "\n", - " Z-scores:\n", - " Z_lower=3.35, Z_upper=2.41\n", - "\n", - " Empirical (current):\n", - " Below LSL: 16 (0.53%) Above USL: 14 (0.46%) Total outside: 30 (0.99%)\n", - " Empirical (potential):\n", - " Below LSL: 0 (0.0%) Above USL: 4 (0.13%)\n", - "\n", - " Note: Stability not assessed; run study.execute() and review signals before interpreting indices.\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "histnorm": "percent", - "hovertemplate": "Range: %{x}
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- "dtype": "f8" - } - }, - { - "line": { - "color": "#DC143C", - "dash": "solid", - "width": 2 - }, - "mode": "lines", - "name": "Spec Limits (LSL=232, USL=242)", - "type": "scatter", - "x": [ - null - ], - "y": [ - null - ] - }, - { - "line": { - "color": "#2E8B57", - "dash": "dashdot", - "width": 1.5 - }, - "mode": "lines", - "name": "Target (237)", - "type": "scatter", - "x": [ - null - ], - "y": [ - null - ] - }, - { - "line": { - "color": "#2E8B57", - "dash": "solid", - "width": 2 - }, - "mode": "lines", - "name": "Y-bar (237.81)", - "type": "scatter", - "x": [ - null - ], - "y": [ - null - ] - }, - { - "line": { - "color": "#2E8B57", - "dash": "dash", - "width": 1.5 - }, - "mode": "lines", - "name": "NPL (232.6, 243.02)", - "type": "scatter", - "x": [ - null - ], - "y": [ - null - ] - } - ], - "layout": { - "annotations": [ - { - "align": "left", - "bgcolor": "rgba(255, 255, 255, 0.9)", - "bordercolor": "#CCCCCC", - "borderwidth": 1, - "font": { - "color": "#333333", - "family": "monospace", - "size": 10 - }, - "showarrow": false, - "text": "n = 3042
Ȳ = 237.81
σ̂ = 1.74

PP Index = 0.96
PPL Index = 1.12
PPU Index = 0.80

Pct Below LSL = 0.53%
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", 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of the 8 factor combinations (PDC RSG: 1-1, 1-2, 2-1, 2-2, 3-1, 3-2, 4-1, 4-2). 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The process standard deviation is 1.74." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-05T00:32:57.891331Z", + "iopub.status.busy": "2026-10-05T00:32:57.891247Z", + "iopub.status.idle": "2026-10-05T00:32:57.909582Z", + "shell.execute_reply": "2026-10-05T00:32:57.909267Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CapabilityResult:\n", + " Specs: LSL=232, USL=242, Target=237\n", + " N=3042, Ybar=237.83, S=1.74, sigma_hat=1.74\n", + "\n", + " Current Capability (overall sigma):\n", + " Pp=0.96 Ppk=0.8 (lower=1.12, upper=0.8)\n", + "\n", + " Potential Capability (R2 sigma):\n", + " centre=237.83, sigma_hat_r2=1.06\n", + " Cp=1.58 Cpk=1.31 (lower=1.84, upper=1.31)\n", + "\n", + " Z-scores:\n", + " Z_lower=3.36, Z_upper=2.4\n", + "\n", + " Empirical (current):\n", + " Below LSL: 16 (0.53%) Above USL: 14 (0.46%) Total outside: 30 (0.99%)\n", + " Empirical (potential):\n", + " Below LSL: 0 (0.0%) Above USL: 4 (0.13%)\n", + "\n", + " Note: Stability not assessed; run study.execute() and review signals before interpreting indices.\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "histnorm": "percent", + "hovertemplate": "Range: %{x}
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"monospace", + "size": 10 + }, + "showarrow": false, + "text": "n = 3042
Ȳ = 237.83
σ̂ = 1.74

PP Index = 0.96
PPL Index = 1.12
PPU Index = 0.80

Pct Below LSL = 0.53%
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Standard Deviations (Slides 28-29)\n", + "\n", + "Bishop's reference: **Xbar CL = 237.83**, **S CL = 1.04**. Limits vary with subgroup size (some subgroups have one observation), so the slide prints the centre line only." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-05T00:32:58.006782Z", + "iopub.status.busy": "2026-10-05T00:32:58.006722Z", + "iopub.status.idle": "2026-10-05T00:32:58.047274Z", + "shell.execute_reply": "2026-10-05T00:32:58.046942Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "%{x}
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"dtype": "f8" }, "yaxis": "y" @@ -127651,8 +128273,8 @@ ], "xaxis": "x2", "y": [ - 2.18, - 2.18, + 2.36, + 2.36, 2.36, 2.36, 3.41, @@ -135210,9 +135832,9 @@ "source": [ "### Potential Process Capability (Slide 32)\n", "\n", - "Bishop's reference values:\n", + "Bishop's reference values (VAS rerun of 13 October 2026):\n", "- **Process Mean = 237.83**\n", - "- **PPL = 1.83**, **PP = 1.57**, **PPU = 1.31**\n", + "- **PPL = 1.84**, **PP = 1.58**, **PPU = 1.31**\n", "- **Pct below LSL = 0%**, **Pct above USL = 0.13%**" ] }, @@ -135246,7 +135868,7 @@ "showlegend": true, "type": "histogram", "x": { - "bdata": 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", 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", "dtype": "f8" } }, @@ -135289,7 +135911,7 @@ "width": 2 }, "mode": "lines", - "name": "Y-bar (237.81)", + "name": "Y-bar (237.83)", "type": "scatter", "x": [ null @@ -135312,7 +135934,7 @@ "size": 10 }, "showarrow": false, - "text": "n = 3042
Ȳ = 237.81
σ̂(R2) = 1.06

CP Index = 1.58
CPL Index = 1.83
CPU Index = 1.32

Pct Below LSL = 0.00%
Pct Above USL = 0.13%", + "text": "n = 3042
Ȳ = 237.83
σ̂(R2) = 1.06

CP Index = 1.58
CPL Index = 1.84
CPU Index = 1.31

Pct Below LSL = 0.00%
Pct Above USL = 0.13%", "x": 0.98, "xanchor": "right", "xref": "paper", @@ -135421,8 +136043,8 @@ "width": 2 }, "type": "line", - "x0": 237.8108518134907, - "x1": 237.8108518134907, + "x0": 237.82682425313126, + "x1": 237.82682425313126, "xref": "x", "y0": 0, "y1": 1, diff --git a/docs/tutorials/sds1-complete-analysis.ipynb b/docs/tutorials/sds1-complete-analysis.ipynb index 3120655..fc7cd91 100644 --- a/docs/tutorials/sds1-complete-analysis.ipynb +++ b/docs/tutorials/sds1-complete-analysis.ipynb @@ -2200,6 +2200,7 @@ " \n", " \n", " group\n", + " n\n", " xbar\n", " center\n", " lpl\n", @@ -2211,433 +2212,481 @@ " \n", " 0\n", " F1_1_F2_1_1\n", + " 2\n", " 51.432\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " 1\n", " \n", " \n", " 1\n", " F1_1_F2_1_2\n", + " 5\n", " 52.506\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " 1\n", " \n", " \n", " 2\n", " F1_1_F2_1_3\n", + " 3\n", " 50.831\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " 1\n", " \n", " \n", " 3\n", " F1_1_F2_1_4\n", + " 3\n", " 52.365\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " 1\n", " \n", " \n", " 4\n", " F1_1_F2_1_5\n", + " 2\n", " 50.845\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " 1\n", " \n", " \n", " 5\n", " F1_1_F2_1_6\n", + " 4\n", " 53.251\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " 1\n", " \n", " \n", " 6\n", " F1_1_F2_1_7\n", + " 2\n", " 52.631\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " 1\n", " \n", " \n", " 7\n", " F1_1_F2_1_8\n", + " 2\n", " 51.902\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " 1\n", " \n", " \n", " 8\n", " F1_1_F2_2_1\n", + " 5\n", " 46.081\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " -1\n", " \n", " \n", " 9\n", " F1_1_F2_2_2\n", + " 2\n", " 47.795\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " -1\n", " \n", " \n", " 10\n", " F1_1_F2_2_3\n", + " 3\n", " 47.089\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " -1\n", " \n", " \n", " 11\n", " F1_1_F2_2_4\n", + " 2\n", " 47.636\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " -1\n", " \n", " \n", " 12\n", " F1_1_F2_2_5\n", + " 3\n", " 46.600\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " -1\n", " \n", " \n", " 13\n", " F1_1_F2_2_6\n", + " 5\n", " 48.689\n", " 49.167\n", - " 48.698\n", - " 49.637\n", - " -1\n", + " 48.665\n", + " 49.670\n", + " 0\n", " \n", " \n", " 14\n", " F1_1_F2_2_7\n", + " 5\n", " 48.462\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " -1\n", " \n", " \n", " 15\n", " F1_1_F2_2_8\n", + " 4\n", " 47.991\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " -1\n", " \n", " \n", " 16\n", " F1_2_F2_1_1\n", + " 5\n", " 48.583\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " -1\n", " \n", " \n", " 17\n", " F1_2_F2_1_2\n", + " 3\n", " 50.162\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " 1\n", " \n", " \n", " 18\n", " F1_2_F2_1_3\n", + " 5\n", " 48.535\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " -1\n", " \n", " \n", " 19\n", " F1_2_F2_1_4\n", + " 4\n", " 49.664\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " 0\n", " \n", " \n", " 20\n", " F1_2_F2_1_5\n", + " 3\n", " 47.690\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " -1\n", " \n", " \n", " 21\n", " F1_2_F2_1_6\n", + " 5\n", " 50.445\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " 1\n", " \n", " \n", " 22\n", " F1_2_F2_1_7\n", + " 2\n", " 50.434\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " 1\n", " \n", " \n", " 23\n", " F1_2_F2_1_8\n", + " 5\n", " 49.315\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " 0\n", " \n", " \n", " 24\n", " F1_2_F2_2_1\n", + " 3\n", " 42.918\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " -1\n", " \n", " \n", " 25\n", " F1_2_F2_2_2\n", + " 2\n", " 44.990\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " -1\n", " \n", " \n", " 26\n", " F1_2_F2_2_3\n", + " 3\n", " 45.118\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " -1\n", " \n", " \n", " 27\n", " F1_2_F2_2_4\n", + " 5\n", " 45.347\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " -1\n", " \n", " \n", " 28\n", " F1_2_F2_2_5\n", + " 4\n", " 44.337\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " -1\n", " \n", " \n", " 29\n", " F1_2_F2_2_6\n", + " 2\n", " 45.548\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " -1\n", " \n", " \n", " 30\n", " F1_2_F2_2_7\n", + " 2\n", " 46.192\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " -1\n", " \n", " \n", " 31\n", " F1_2_F2_2_8\n", + " 4\n", " 45.384\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " -1\n", " \n", " \n", " 32\n", " F1_3_F2_1_1\n", + " 3\n", " 51.593\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " 1\n", " \n", " \n", " 33\n", " F1_3_F2_1_2\n", + " 3\n", " 53.630\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " 1\n", " \n", " \n", " 34\n", " F1_3_F2_1_3\n", + " 3\n", " 52.526\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " 1\n", " \n", " \n", " 35\n", " F1_3_F2_1_4\n", + " 4\n", " 53.212\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " 1\n", " \n", " \n", " 36\n", " F1_3_F2_1_5\n", + " 2\n", " 52.239\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " 1\n", " \n", " \n", " 37\n", " F1_3_F2_1_6\n", + " 2\n", " 53.516\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " 1\n", " \n", " \n", " 38\n", " F1_3_F2_1_7\n", + " 5\n", " 53.778\n", " 49.167\n", - " 48.698\n", - " 49.637\n", + " 48.665\n", + " 49.670\n", " 1\n", " \n", " \n", " 39\n", " F1_3_F2_1_8\n", + " 4\n", " 52.271\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " 1\n", " \n", " \n", " 40\n", " F1_3_F2_2_1\n", + " 4\n", " 47.139\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " -1\n", " \n", " \n", " 41\n", " F1_3_F2_2_2\n", + " 4\n", " 48.410\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " -1\n", " \n", " \n", " 42\n", " F1_3_F2_2_3\n", + " 4\n", " 48.264\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " -1\n", " \n", " \n", " 43\n", " F1_3_F2_2_4\n", + " 3\n", " 48.188\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " -1\n", " \n", " \n", " 44\n", " F1_3_F2_2_5\n", + " 4\n", " 48.099\n", " 49.167\n", - " 48.631\n", - " 49.703\n", + " 48.606\n", + " 49.729\n", " -1\n", " \n", " \n", " 45\n", " F1_3_F2_2_6\n", + " 2\n", " 48.702\n", " 49.167\n", - " 48.292\n", - " 50.043\n", + " 48.373\n", + " 49.962\n", " 0\n", " \n", " \n", " 46\n", " F1_3_F2_2_7\n", + " 2\n", " 50.042\n", " 49.167\n", - " 48.292\n", - " 50.043\n", - " 0\n", + " 48.373\n", + " 49.962\n", + " 1\n", " \n", " \n", " 47\n", " F1_3_F2_2_8\n", + " 3\n", " 47.653\n", " 49.167\n", - " 48.524\n", - " 49.811\n", + " 48.519\n", + " 49.816\n", " -1\n", " \n", " \n", @@ -2645,55 +2694,55 @@ "" ], "text/plain": [ - " group xbar center lpl upl beyond_limits\n", - "0 F1_1_F2_1_1 51.432 49.167 48.292 50.043 1\n", - "1 F1_1_F2_1_2 52.506 49.167 48.698 49.637 1\n", - "2 F1_1_F2_1_3 50.831 49.167 48.524 49.811 1\n", - "3 F1_1_F2_1_4 52.365 49.167 48.524 49.811 1\n", - "4 F1_1_F2_1_5 50.845 49.167 48.292 50.043 1\n", - "5 F1_1_F2_1_6 53.251 49.167 48.631 49.703 1\n", - "6 F1_1_F2_1_7 52.631 49.167 48.292 50.043 1\n", - "7 F1_1_F2_1_8 51.902 49.167 48.292 50.043 1\n", - "8 F1_1_F2_2_1 46.081 49.167 48.698 49.637 -1\n", - "9 F1_1_F2_2_2 47.795 49.167 48.292 50.043 -1\n", - "10 F1_1_F2_2_3 47.089 49.167 48.524 49.811 -1\n", - "11 F1_1_F2_2_4 47.636 49.167 48.292 50.043 -1\n", - "12 F1_1_F2_2_5 46.600 49.167 48.524 49.811 -1\n", - "13 F1_1_F2_2_6 48.689 49.167 48.698 49.637 -1\n", - "14 F1_1_F2_2_7 48.462 49.167 48.698 49.637 -1\n", - "15 F1_1_F2_2_8 47.991 49.167 48.631 49.703 -1\n", - "16 F1_2_F2_1_1 48.583 49.167 48.698 49.637 -1\n", - "17 F1_2_F2_1_2 50.162 49.167 48.524 49.811 1\n", - "18 F1_2_F2_1_3 48.535 49.167 48.698 49.637 -1\n", - "19 F1_2_F2_1_4 49.664 49.167 48.631 49.703 0\n", - "20 F1_2_F2_1_5 47.690 49.167 48.524 49.811 -1\n", - "21 F1_2_F2_1_6 50.445 49.167 48.698 49.637 1\n", - "22 F1_2_F2_1_7 50.434 49.167 48.292 50.043 1\n", - "23 F1_2_F2_1_8 49.315 49.167 48.698 49.637 0\n", - "24 F1_2_F2_2_1 42.918 49.167 48.524 49.811 -1\n", - "25 F1_2_F2_2_2 44.990 49.167 48.292 50.043 -1\n", - "26 F1_2_F2_2_3 45.118 49.167 48.524 49.811 -1\n", - "27 F1_2_F2_2_4 45.347 49.167 48.698 49.637 -1\n", - "28 F1_2_F2_2_5 44.337 49.167 48.631 49.703 -1\n", - "29 F1_2_F2_2_6 45.548 49.167 48.292 50.043 -1\n", - "30 F1_2_F2_2_7 46.192 49.167 48.292 50.043 -1\n", - "31 F1_2_F2_2_8 45.384 49.167 48.631 49.703 -1\n", - "32 F1_3_F2_1_1 51.593 49.167 48.524 49.811 1\n", - "33 F1_3_F2_1_2 53.630 49.167 48.524 49.811 1\n", - "34 F1_3_F2_1_3 52.526 49.167 48.524 49.811 1\n", - "35 F1_3_F2_1_4 53.212 49.167 48.631 49.703 1\n", - "36 F1_3_F2_1_5 52.239 49.167 48.292 50.043 1\n", - "37 F1_3_F2_1_6 53.516 49.167 48.292 50.043 1\n", - "38 F1_3_F2_1_7 53.778 49.167 48.698 49.637 1\n", - "39 F1_3_F2_1_8 52.271 49.167 48.631 49.703 1\n", - "40 F1_3_F2_2_1 47.139 49.167 48.631 49.703 -1\n", - "41 F1_3_F2_2_2 48.410 49.167 48.631 49.703 -1\n", - "42 F1_3_F2_2_3 48.264 49.167 48.631 49.703 -1\n", - "43 F1_3_F2_2_4 48.188 49.167 48.524 49.811 -1\n", - "44 F1_3_F2_2_5 48.099 49.167 48.631 49.703 -1\n", - "45 F1_3_F2_2_6 48.702 49.167 48.292 50.043 0\n", - "46 F1_3_F2_2_7 50.042 49.167 48.292 50.043 0\n", - "47 F1_3_F2_2_8 47.653 49.167 48.524 49.811 -1" + " group n xbar center lpl upl beyond_limits\n", + "0 F1_1_F2_1_1 2 51.432 49.167 48.373 49.962 1\n", + "1 F1_1_F2_1_2 5 52.506 49.167 48.665 49.670 1\n", + "2 F1_1_F2_1_3 3 50.831 49.167 48.519 49.816 1\n", + "3 F1_1_F2_1_4 3 52.365 49.167 48.519 49.816 1\n", + "4 F1_1_F2_1_5 2 50.845 49.167 48.373 49.962 1\n", + "5 F1_1_F2_1_6 4 53.251 49.167 48.606 49.729 1\n", + "6 F1_1_F2_1_7 2 52.631 49.167 48.373 49.962 1\n", + "7 F1_1_F2_1_8 2 51.902 49.167 48.373 49.962 1\n", + "8 F1_1_F2_2_1 5 46.081 49.167 48.665 49.670 -1\n", + "9 F1_1_F2_2_2 2 47.795 49.167 48.373 49.962 -1\n", + "10 F1_1_F2_2_3 3 47.089 49.167 48.519 49.816 -1\n", + "11 F1_1_F2_2_4 2 47.636 49.167 48.373 49.962 -1\n", + "12 F1_1_F2_2_5 3 46.600 49.167 48.519 49.816 -1\n", 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F1_2_F2_2_8 4 45.384 49.167 48.606 49.729 -1\n", + "32 F1_3_F2_1_1 3 51.593 49.167 48.519 49.816 1\n", + "33 F1_3_F2_1_2 3 53.630 49.167 48.519 49.816 1\n", + "34 F1_3_F2_1_3 3 52.526 49.167 48.519 49.816 1\n", + "35 F1_3_F2_1_4 4 53.212 49.167 48.606 49.729 1\n", + "36 F1_3_F2_1_5 2 52.239 49.167 48.373 49.962 1\n", + "37 F1_3_F2_1_6 2 53.516 49.167 48.373 49.962 1\n", + "38 F1_3_F2_1_7 5 53.778 49.167 48.665 49.670 1\n", + "39 F1_3_F2_1_8 4 52.271 49.167 48.606 49.729 1\n", + "40 F1_3_F2_2_1 4 47.139 49.167 48.606 49.729 -1\n", + "41 F1_3_F2_2_2 4 48.410 49.167 48.606 49.729 -1\n", + "42 F1_3_F2_2_3 4 48.264 49.167 48.606 49.729 -1\n", + "43 F1_3_F2_2_4 3 48.188 49.167 48.519 49.816 -1\n", + "44 F1_3_F2_2_5 4 48.099 49.167 48.606 49.729 -1\n", + "45 F1_3_F2_2_6 2 48.702 49.167 48.373 49.962 0\n", + "46 F1_3_F2_2_7 2 50.042 49.167 48.373 49.962 1\n", + "47 F1_3_F2_2_8 3 47.653 49.167 48.519 49.816 -1" ] }, "execution_count": 6, @@ -2907,101 +2956,101 @@ "F1_3_F2_2_8" ], "y": [ - 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beyond_limits\n", - "0 F1_1 49.592 49.167 48.141 50.194 0\n", - "1 F1_2 47.380 49.167 48.188 50.147 -1\n", - "2 F1_3 50.543 49.167 48.141 50.194 1" + " factor 1 n xbar center lpl upl beyond_limits\n", + "0 F1_1 52 49.592 49.167 48.141 50.194 0\n", + "1 F1_2 57 47.380 49.167 48.187 50.147 -1\n", + "2 F1_3 52 50.543 49.167 48.141 50.194 1" ] }, "execution_count": 8, @@ -4373,8 +4426,8 @@ "y": [ 48.141, 48.141, - 48.188, - 48.188, + 48.187, + 48.187, 48.141 ] }, @@ -5349,6 +5402,7 @@ " \n", " \n", " factor 2\n", + " n\n", " xbar\n", " center\n", " lpl\n", @@ -5360,6 +5414,7 @@ " \n", " 0\n", " F2_1\n", + " 81\n", " 51.257\n", " 49.167\n", " 48.579\n", @@ -5369,6 +5424,7 @@ " \n", " 1\n", " F2_2\n", + " 80\n", " 46.949\n", " 49.167\n", " 48.576\n", @@ -5380,9 +5436,9 @@ "" ], "text/plain": [ - " factor 2 xbar center lpl upl beyond_limits\n", - "0 F2_1 51.257 49.167 48.579 49.755 1\n", - "1 F2_2 46.949 49.167 48.576 49.759 -1" + " factor 2 n xbar center lpl upl beyond_limits\n", + "0 F2_1 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" \n", " 4\n", " F1_1_F2_1_5\n", + " 2\n", " 0.132\n", " 0.329\n", " 0.0\n", @@ -7978,6 +8040,7 @@ " \n", " 5\n", " F1_1_F2_1_6\n", + " 4\n", " 0.191\n", " 0.329\n", " 0.0\n", @@ -7987,6 +8050,7 @@ " \n", " 6\n", " F1_1_F2_1_7\n", + " 2\n", " 0.476\n", " 0.329\n", " 0.0\n", @@ -7996,6 +8060,7 @@ " \n", " 7\n", " F1_1_F2_1_8\n", + " 2\n", " 0.136\n", " 0.329\n", " 0.0\n", @@ -8005,6 +8070,7 @@ " \n", " 8\n", " F1_1_F2_2_1\n", + " 5\n", " 0.177\n", " 0.329\n", " 0.0\n", @@ -8014,6 +8080,7 @@ " \n", " 9\n", " F1_1_F2_2_2\n", + " 2\n", " 0.068\n", " 0.329\n", " 0.0\n", @@ -8023,6 +8090,7 @@ " \n", " 10\n", " F1_1_F2_2_3\n", + " 3\n", " 0.313\n", " 0.329\n", " 0.0\n", @@ -8032,6 +8100,7 @@ " \n", " 11\n", " F1_1_F2_2_4\n", + " 2\n", " 0.474\n", " 0.329\n", " 0.0\n", @@ -8041,6 +8110,7 @@ " \n", " 12\n", " F1_1_F2_2_5\n", + " 3\n", " 0.335\n", " 0.329\n", " 0.0\n", @@ -8050,6 +8120,7 @@ " \n", " 13\n", " F1_1_F2_2_6\n", + " 5\n", " 0.414\n", " 0.329\n", " 0.0\n", @@ -8059,6 +8130,7 @@ " \n", " 14\n", " F1_1_F2_2_7\n", + " 5\n", " 0.265\n", " 0.329\n", " 0.0\n", @@ -8068,6 +8140,7 @@ " \n", " 15\n", " F1_1_F2_2_8\n", + " 4\n", " 0.441\n", " 0.329\n", " 0.0\n", @@ -8077,6 +8150,7 @@ " \n", " 16\n", " F1_2_F2_1_1\n", + " 5\n", " 0.226\n", " 0.329\n", " 0.0\n", @@ -8086,6 +8160,7 @@ " \n", " 17\n", " F1_2_F2_1_2\n", + " 3\n", " 0.110\n", " 0.329\n", " 0.0\n", @@ -8095,6 +8170,7 @@ " \n", " 18\n", " F1_2_F2_1_3\n", + " 5\n", " 0.356\n", " 0.329\n", " 0.0\n", @@ -8104,6 +8180,7 @@ " \n", " 19\n", " F1_2_F2_1_4\n", + " 4\n", " 0.467\n", " 0.329\n", " 0.0\n", @@ -8113,6 +8190,7 @@ " \n", " 20\n", " F1_2_F2_1_5\n", + " 3\n", " 0.480\n", " 0.329\n", " 0.0\n", @@ -8122,6 +8200,7 @@ " \n", " 21\n", " F1_2_F2_1_6\n", + " 5\n", " 0.296\n", " 0.329\n", " 0.0\n", @@ -8131,6 +8210,7 @@ " \n", " 22\n", " F1_2_F2_1_7\n", + " 2\n", " 0.248\n", " 0.329\n", " 0.0\n", @@ -8140,6 +8220,7 @@ " \n", " 23\n", " F1_2_F2_1_8\n", + " 5\n", " 0.497\n", " 0.329\n", " 0.0\n", @@ -8149,6 +8230,7 @@ " \n", " 24\n", " F1_2_F2_2_1\n", + " 3\n", " 0.140\n", " 0.329\n", " 0.0\n", @@ -8158,6 +8240,7 @@ " \n", " 25\n", " F1_2_F2_2_2\n", + " 2\n", " 0.807\n", " 0.329\n", " 0.0\n", @@ -8167,6 +8250,7 @@ " \n", " 26\n", " F1_2_F2_2_3\n", + " 3\n", " 0.413\n", " 0.329\n", " 0.0\n", @@ -8176,6 +8260,7 @@ " \n", " 27\n", " F1_2_F2_2_4\n", + " 5\n", " 0.179\n", " 0.329\n", " 0.0\n", @@ -8185,6 +8270,7 @@ " \n", " 28\n", " F1_2_F2_2_5\n", + " 4\n", " 0.542\n", " 0.329\n", " 0.0\n", @@ -8194,6 +8280,7 @@ " \n", " 29\n", " F1_2_F2_2_6\n", + " 2\n", " 0.079\n", " 0.329\n", " 0.0\n", @@ -8203,6 +8290,7 @@ " \n", " 30\n", " F1_2_F2_2_7\n", + " 2\n", " 0.307\n", " 0.329\n", " 0.0\n", @@ -8212,6 +8300,7 @@ " \n", " 31\n", " F1_2_F2_2_8\n", + " 4\n", " 0.340\n", " 0.329\n", " 0.0\n", @@ -8221,6 +8310,7 @@ " \n", " 32\n", " F1_3_F2_1_1\n", + " 3\n", " 0.681\n", " 0.329\n", " 0.0\n", @@ -8230,6 +8320,7 @@ " \n", " 33\n", " F1_3_F2_1_2\n", + " 3\n", " 0.465\n", " 0.329\n", " 0.0\n", @@ -8239,6 +8330,7 @@ " \n", " 34\n", " F1_3_F2_1_3\n", + " 3\n", " 0.382\n", " 0.329\n", " 0.0\n", @@ -8248,6 +8340,7 @@ " \n", " 35\n", " F1_3_F2_1_4\n", + " 4\n", " 0.123\n", " 0.329\n", " 0.0\n", @@ -8257,6 +8350,7 @@ " \n", " 36\n", " F1_3_F2_1_5\n", + " 2\n", " 0.157\n", " 0.329\n", " 0.0\n", @@ -8266,6 +8360,7 @@ " \n", " 37\n", " F1_3_F2_1_6\n", + " 2\n", " 0.566\n", " 0.329\n", " 0.0\n", @@ -8275,6 +8370,7 @@ " \n", " 38\n", " F1_3_F2_1_7\n", + " 5\n", " 0.366\n", " 0.329\n", " 0.0\n", @@ -8284,6 +8380,7 @@ " \n", " 39\n", " F1_3_F2_1_8\n", + " 4\n", " 0.267\n", " 0.329\n", " 0.0\n", @@ -8293,6 +8390,7 @@ " \n", " 40\n", " F1_3_F2_2_1\n", + " 4\n", " 0.352\n", " 0.329\n", " 0.0\n", @@ -8302,6 +8400,7 @@ " \n", " 41\n", " F1_3_F2_2_2\n", + " 4\n", " 0.252\n", " 0.329\n", " 0.0\n", @@ -8311,6 +8410,7 @@ " \n", " 42\n", " F1_3_F2_2_3\n", + " 4\n", " 0.654\n", " 0.329\n", " 0.0\n", @@ -8320,6 +8420,7 @@ " \n", " 43\n", " F1_3_F2_2_4\n", + " 3\n", " 0.356\n", " 0.329\n", " 0.0\n", @@ -8329,6 +8430,7 @@ " \n", " 44\n", " F1_3_F2_2_5\n", + " 4\n", " 0.342\n", " 0.329\n", " 0.0\n", @@ -8338,6 +8440,7 @@ " \n", " 45\n", " F1_3_F2_2_6\n", + " 2\n", " 0.151\n", " 0.329\n", " 0.0\n", @@ -8347,6 +8450,7 @@ " \n", " 46\n", " F1_3_F2_2_7\n", + " 2\n", " 0.345\n", " 0.329\n", " 0.0\n", @@ -8356,6 +8460,7 @@ " \n", " 47\n", " F1_3_F2_2_8\n", + " 3\n", " 0.866\n", " 0.329\n", " 0.0\n", @@ -8367,55 +8472,55 @@ "" ], "text/plain": [ - " group s center lpl upl beyond_limits\n", - "0 F1_1_F2_1_1 0.120 0.329 0.0 1.075 0\n", - "1 F1_1_F2_1_2 0.263 0.329 0.0 0.688 0\n", - "2 F1_1_F2_1_3 0.072 0.329 0.0 0.845 0\n", - "3 F1_1_F2_1_4 0.111 0.329 0.0 0.845 0\n", - "4 F1_1_F2_1_5 0.132 0.329 0.0 1.075 0\n", - "5 F1_1_F2_1_6 0.191 0.329 0.0 0.746 0\n", - "6 F1_1_F2_1_7 0.476 0.329 0.0 1.075 0\n", - "7 F1_1_F2_1_8 0.136 0.329 0.0 1.075 0\n", - "8 F1_1_F2_2_1 0.177 0.329 0.0 0.688 0\n", - "9 F1_1_F2_2_2 0.068 0.329 0.0 1.075 0\n", - "10 F1_1_F2_2_3 0.313 0.329 0.0 0.845 0\n", - "11 F1_1_F2_2_4 0.474 0.329 0.0 1.075 0\n", - "12 F1_1_F2_2_5 0.335 0.329 0.0 0.845 0\n", - "13 F1_1_F2_2_6 0.414 0.329 0.0 0.688 0\n", - "14 F1_1_F2_2_7 0.265 0.329 0.0 0.688 0\n", - "15 F1_1_F2_2_8 0.441 0.329 0.0 0.746 0\n", - "16 F1_2_F2_1_1 0.226 0.329 0.0 0.688 0\n", - "17 F1_2_F2_1_2 0.110 0.329 0.0 0.845 0\n", - "18 F1_2_F2_1_3 0.356 0.329 0.0 0.688 0\n", - "19 F1_2_F2_1_4 0.467 0.329 0.0 0.746 0\n", - "20 F1_2_F2_1_5 0.480 0.329 0.0 0.845 0\n", - "21 F1_2_F2_1_6 0.296 0.329 0.0 0.688 0\n", - "22 F1_2_F2_1_7 0.248 0.329 0.0 1.075 0\n", - "23 F1_2_F2_1_8 0.497 0.329 0.0 0.688 0\n", - "24 F1_2_F2_2_1 0.140 0.329 0.0 0.845 0\n", - "25 F1_2_F2_2_2 0.807 0.329 0.0 1.075 0\n", - "26 F1_2_F2_2_3 0.413 0.329 0.0 0.845 0\n", - "27 F1_2_F2_2_4 0.179 0.329 0.0 0.688 0\n", - "28 F1_2_F2_2_5 0.542 0.329 0.0 0.746 0\n", - "29 F1_2_F2_2_6 0.079 0.329 0.0 1.075 0\n", - "30 F1_2_F2_2_7 0.307 0.329 0.0 1.075 0\n", - "31 F1_2_F2_2_8 0.340 0.329 0.0 0.746 0\n", - "32 F1_3_F2_1_1 0.681 0.329 0.0 0.845 0\n", - "33 F1_3_F2_1_2 0.465 0.329 0.0 0.845 0\n", - "34 F1_3_F2_1_3 0.382 0.329 0.0 0.845 0\n", - "35 F1_3_F2_1_4 0.123 0.329 0.0 0.746 0\n", - "36 F1_3_F2_1_5 0.157 0.329 0.0 1.075 0\n", - "37 F1_3_F2_1_6 0.566 0.329 0.0 1.075 0\n", - "38 F1_3_F2_1_7 0.366 0.329 0.0 0.688 0\n", - "39 F1_3_F2_1_8 0.267 0.329 0.0 0.746 0\n", - "40 F1_3_F2_2_1 0.352 0.329 0.0 0.746 0\n", - "41 F1_3_F2_2_2 0.252 0.329 0.0 0.746 0\n", - "42 F1_3_F2_2_3 0.654 0.329 0.0 0.746 0\n", - "43 F1_3_F2_2_4 0.356 0.329 0.0 0.845 0\n", - "44 F1_3_F2_2_5 0.342 0.329 0.0 0.746 0\n", - "45 F1_3_F2_2_6 0.151 0.329 0.0 1.075 0\n", - "46 F1_3_F2_2_7 0.345 0.329 0.0 1.075 0\n", - "47 F1_3_F2_2_8 0.866 0.329 0.0 0.845 1" + " group n s center lpl upl beyond_limits\n", + "0 F1_1_F2_1_1 2 0.120 0.329 0.0 1.075 0\n", + "1 F1_1_F2_1_2 5 0.263 0.329 0.0 0.688 0\n", + "2 F1_1_F2_1_3 3 0.072 0.329 0.0 0.845 0\n", + "3 F1_1_F2_1_4 3 0.111 0.329 0.0 0.845 0\n", + "4 F1_1_F2_1_5 2 0.132 0.329 0.0 1.075 0\n", + "5 F1_1_F2_1_6 4 0.191 0.329 0.0 0.746 0\n", + "6 F1_1_F2_1_7 2 0.476 0.329 0.0 1.075 0\n", + "7 F1_1_F2_1_8 2 0.136 0.329 0.0 1.075 0\n", + "8 F1_1_F2_2_1 5 0.177 0.329 0.0 0.688 0\n", + "9 F1_1_F2_2_2 2 0.068 0.329 0.0 1.075 0\n", + "10 F1_1_F2_2_3 3 0.313 0.329 0.0 0.845 0\n", + "11 F1_1_F2_2_4 2 0.474 0.329 0.0 1.075 0\n", + "12 F1_1_F2_2_5 3 0.335 0.329 0.0 0.845 0\n", + "13 F1_1_F2_2_6 5 0.414 0.329 0.0 0.688 0\n", + "14 F1_1_F2_2_7 5 0.265 0.329 0.0 0.688 0\n", + "15 F1_1_F2_2_8 4 0.441 0.329 0.0 0.746 0\n", + "16 F1_2_F2_1_1 5 0.226 0.329 0.0 0.688 0\n", + "17 F1_2_F2_1_2 3 0.110 0.329 0.0 0.845 0\n", + "18 F1_2_F2_1_3 5 0.356 0.329 0.0 0.688 0\n", + "19 F1_2_F2_1_4 4 0.467 0.329 0.0 0.746 0\n", + "20 F1_2_F2_1_5 3 0.480 0.329 0.0 0.845 0\n", + "21 F1_2_F2_1_6 5 0.296 0.329 0.0 0.688 0\n", + "22 F1_2_F2_1_7 2 0.248 0.329 0.0 1.075 0\n", + "23 F1_2_F2_1_8 5 0.497 0.329 0.0 0.688 0\n", + "24 F1_2_F2_2_1 3 0.140 0.329 0.0 0.845 0\n", + "25 F1_2_F2_2_2 2 0.807 0.329 0.0 1.075 0\n", + "26 F1_2_F2_2_3 3 0.413 0.329 0.0 0.845 0\n", + "27 F1_2_F2_2_4 5 0.179 0.329 0.0 0.688 0\n", + "28 F1_2_F2_2_5 4 0.542 0.329 0.0 0.746 0\n", + "29 F1_2_F2_2_6 2 0.079 0.329 0.0 1.075 0\n", + "30 F1_2_F2_2_7 2 0.307 0.329 0.0 1.075 0\n", + "31 F1_2_F2_2_8 4 0.340 0.329 0.0 0.746 0\n", + "32 F1_3_F2_1_1 3 0.681 0.329 0.0 0.845 0\n", + "33 F1_3_F2_1_2 3 0.465 0.329 0.0 0.845 0\n", + "34 F1_3_F2_1_3 3 0.382 0.329 0.0 0.845 0\n", + "35 F1_3_F2_1_4 4 0.123 0.329 0.0 0.746 0\n", + "36 F1_3_F2_1_5 2 0.157 0.329 0.0 1.075 0\n", + "37 F1_3_F2_1_6 2 0.566 0.329 0.0 1.075 0\n", + "38 F1_3_F2_1_7 5 0.366 0.329 0.0 0.688 0\n", + "39 F1_3_F2_1_8 4 0.267 0.329 0.0 0.746 0\n", + "40 F1_3_F2_2_1 4 0.352 0.329 0.0 0.746 0\n", + "41 F1_3_F2_2_2 4 0.252 0.329 0.0 0.746 0\n", + "42 F1_3_F2_2_3 4 0.654 0.329 0.0 0.746 0\n", + "43 F1_3_F2_2_4 3 0.356 0.329 0.0 0.845 0\n", + "44 F1_3_F2_2_5 4 0.342 0.329 0.0 0.746 0\n", + "45 F1_3_F2_2_6 2 0.151 0.329 0.0 1.075 0\n", + "46 F1_3_F2_2_7 2 0.345 0.329 0.0 1.075 0\n", + "47 F1_3_F2_2_8 3 0.866 0.329 0.0 0.845 1" ] }, "execution_count": 13, @@ -9894,6 +9999,7 @@ " \n", " \n", " factor 1\n", + " n\n", " s\n", " center\n", " lpl\n", @@ -9905,6 +10011,7 @@ " \n", " 0\n", " F1_1\n", + " 52\n", " 2.457\n", " 2.455\n", " 1.724\n", @@ -9914,6 +10021,7 @@ " \n", " 1\n", " F1_2\n", + " 57\n", " 2.383\n", " 2.455\n", " 1.757\n", @@ -9923,6 +10031,7 @@ " \n", " 2\n", " F1_3\n", + " 52\n", " 2.524\n", " 2.455\n", " 1.724\n", @@ -9934,10 +10043,10 @@ "" ], "text/plain": [ - " factor 1 s center lpl upl beyond_limits\n", - "0 F1_1 2.457 2.455 1.724 3.186 0\n", - "1 F1_2 2.383 2.455 1.757 3.152 0\n", - "2 F1_3 2.524 2.455 1.724 3.186 0" + " factor 1 n s center lpl upl beyond_limits\n", + "0 F1_1 52 2.457 2.455 1.724 3.186 0\n", + "1 F1_2 57 2.383 2.455 1.757 3.152 0\n", + "2 F1_3 52 2.524 2.455 1.724 3.186 0" ] }, "execution_count": 15, @@ -9995,6 +10104,7 @@ " \n", " \n", " factor 2\n", + " n\n", " s\n", " center\n", " lpl\n", @@ -10006,6 +10116,7 @@ " \n", " 0\n", " F2_1\n", + " 81\n", " 1.848\n", " 1.759\n", " 1.341\n", @@ -10015,6 +10126,7 @@ " \n", " 1\n", " F2_2\n", + " 80\n", " 1.669\n", " 1.759\n", " 1.338\n", @@ -10026,9 +10138,9 @@ "" ], "text/plain": [ - " factor 2 s center lpl upl beyond_limits\n", - "0 F2_1 1.848 1.759 1.341 2.177 0\n", - "1 F2_2 1.669 1.759 1.338 2.179 0" + " factor 2 n s center lpl upl beyond_limits\n", + "0 F2_1 81 1.848 1.759 1.341 2.177 0\n", + "1 F2_2 80 1.669 1.759 1.338 2.179 0" ] }, "execution_count": 16, @@ -21534,6 +21646,7 @@ " \n", " \n", " group\n", + " n\n", " xbar\n", " center\n", " lpl\n", @@ -21545,433 +21658,481 @@ " \n", " 0\n", " F1_1_F2_1_1\n", + " 2\n", " 2.803\n", " -0.0\n", - " -0.875\n", - " 0.875\n", + " -0.794\n", + " 0.794\n", " 1\n", " \n", " \n", " 1\n", " F1_1_F2_1_2\n", + " 5\n", " 2.803\n", " -0.0\n", - " -0.470\n", - " 0.470\n", + " -0.502\n", + " 0.502\n", " 1\n", " \n", " \n", " 2\n", " F1_1_F2_1_3\n", + " 3\n", " 2.803\n", " -0.0\n", - " -0.643\n", - " 0.643\n", + " -0.648\n", + " 0.648\n", " 1\n", " \n", " \n", " 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F1_2_F2_2_2 44.979 49.167 48.292 50.043 -1\n", - "26 F1_2_F2_2_3 44.979 49.167 48.524 49.811 -1\n", - "27 F1_2_F2_2_4 44.979 49.167 48.698 49.637 -1\n", - "28 F1_2_F2_2_5 44.979 49.167 48.631 49.703 -1\n", - "29 F1_2_F2_2_6 44.979 49.167 48.292 50.043 -1\n", - "30 F1_2_F2_2_7 44.979 49.167 48.292 50.043 -1\n", - "31 F1_2_F2_2_8 44.979 49.167 48.631 49.703 -1\n", - "32 F1_3_F2_1_1 52.846 49.167 48.524 49.811 1\n", - "33 F1_3_F2_1_2 52.846 49.167 48.524 49.811 1\n", - "34 F1_3_F2_1_3 52.846 49.167 48.524 49.811 1\n", - "35 F1_3_F2_1_4 52.846 49.167 48.631 49.703 1\n", - "36 F1_3_F2_1_5 52.846 49.167 48.292 50.043 1\n", - "37 F1_3_F2_1_6 52.846 49.167 48.292 50.043 1\n", - "38 F1_3_F2_1_7 52.846 49.167 48.698 49.637 1\n", - "39 F1_3_F2_1_8 52.846 49.167 48.631 49.703 1\n", - "40 F1_3_F2_2_1 48.312 49.167 48.631 49.703 -1\n", - "41 F1_3_F2_2_2 48.312 49.167 48.631 49.703 -1\n", - "42 F1_3_F2_2_3 48.312 49.167 48.631 49.703 -1\n", - "43 F1_3_F2_2_4 48.312 49.167 48.524 49.811 -1\n", - "44 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"F1_3_F2_2_8" ], "y": { - "bdata": "nu+nxku3SUBU46WbxEBKQO58PzVeaklAH4XrUbguSkBcj8L1KGxJQOOlm8QgoEpAVOOlm8RQSkD6fmq8dPNJQO58PzVeCkdA9ihcj8LlR0AIrBxaZItHQMUgsHJo0UdAzczMzMxMR0DVeOkmMVhIQEJg5dAiO0hAAiuHFtn+R0C0yHa+n0pIQNv5fmq8FElAFK5H4XpESEC4HoXrUdhHQClcj8L1OElAZDvfT403SUAv3SQGgXVFQB+F61G4fkZAyXa+nxqPRkAj2/l+aqxGQEJg5dAiK0ZAoBov3STGRkB/arx0kxhHQP7UeOkmsUZAlkOLbOfLSUBxPQrXo9BKQBfZzvdTQ0pAQmDl0CKbSkA730+Nlx5KQDVeukkMwkpA3SQGgZXjSkCmm8QgsCJKQG8Sg8DKkUdAFK5H4Xo0SEBvEoPAyiFIQPLSTWIQGEhA6SYxCKwMSEDdJAaBldNHQA==", + "bdata": "nu+nxku3SUBU46WbxEBKQO58PzVeaklAH4XrUbguSkBcj8L1KGxJQOOlm8QgoEpAVOOlm8RQSkD6fmq8dPNJQO58PzVeCkdA9ihcj8LlR0AIrBxaZItHQMUgsHJo0UdAzczMzMxMR0BCYOXQIjtIQAIrhxbZ/kdAtMh2vp9KSEDb+X5qvBRJQBSuR+F6REhAuB6F61HYR0ApXI/C9ThJQGQ730+NN0lAL90kBoF1RUAfhetRuH5GQMl2vp8aj0ZAI9v5fmqsRkBCYOXQIitGQKAaL90kxkZAf2q8dJMYR0D+1HjpJrFGQJZDi2zny0lAcT0K16PQSkAX2c73U0NKQEJg5dAim0pAO99PjZceSkA1XrpJDMJKQN0kBoGV40pAppvEILAiSkBvEoPAypFHQBSuR+F6NEhAbxKDwMohSEDy0k1iEBhIQOkmMQisDEhATDeJQWAFSUDdJAaBldNHQA==", "dtype": "f8" } } diff --git a/docs/tutorials/signal-detection.ipynb b/docs/tutorials/signal-detection.ipynb index 469f047..c865999 100644 --- a/docs/tutorials/signal-detection.ipynb +++ b/docs/tutorials/signal-detection.ipynb @@ -259,8 +259,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Signals found: 11\n", - "standard (1-4): 8 | extended (1-8): 11\n" + "Signals found: 12\n", + "standard (1-4): 8 | extended (1-8): 12\n" ] } ], @@ -301,9 +301,9 @@ "output_type": "stream", "text": [ "Has signals: True\n", - "Total count: 11\n", + "Total count: 12\n", "\n", - "Flagged observations: {np.int64(10), np.int64(23), np.int64(24), np.int64(25), np.int64(26), np.int64(27), np.int64(28)}\n" + "Flagged observations: {np.int64(40), np.int64(10), np.int64(23), np.int64(24), np.int64(25), np.int64(26), np.int64(27), np.int64(28)}\n" ] } ], @@ -375,8 +375,8 @@ " Point beyond control limits\n", " 112.00\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 1\n", @@ -386,8 +386,8 @@ " 4 of 5 consecutive points in Zone B or beyond\n", " 103.31\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 2\n", @@ -397,8 +397,8 @@ " 4 of 5 consecutive points in Zone B or beyond\n", " 103.52\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 3\n", @@ -408,8 +408,8 @@ " 4 of 5 consecutive points in Zone B or beyond\n", " 103.47\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 4\n", @@ -419,8 +419,8 @@ " 8+ consecutive points avoiding Zone C\n", " 103.47\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 5\n", @@ -430,8 +430,8 @@ " 4 of 5 consecutive points in Zone B or beyond\n", " 102.58\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 6\n", @@ -441,8 +441,8 @@ " 8+ consecutive points avoiding Zone C\n", " 102.58\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 7\n", @@ -452,8 +452,8 @@ " 4 of 5 consecutive points in Zone B or beyond\n", " 102.85\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 8\n", @@ -463,8 +463,8 @@ " 8+ consecutive points on same side of center\n", " 102.85\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 9\n", @@ -474,8 +474,8 @@ " 8+ consecutive points avoiding Zone C\n", " 102.85\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", " \n", " \n", " 10\n", @@ -485,8 +485,19 @@ " 4 of 5 consecutive points in Zone B or beyond\n", " 98.80\n", " 100.542\n", - " 106.558\n", - " 94.526\n", + " 106.557\n", + " 94.527\n", + " \n", + " \n", + " 11\n", + " 40\n", + " rule_5\n", + " 5\n", + " 6+ consecutive points trending\n", + " 103.00\n", + " 100.542\n", + " 106.557\n", + " 94.527\n", " \n", " \n", "\n", @@ -505,32 +516,35 @@ "8 27 rule_4 4 \n", "9 27 rule_8 8 \n", "10 28 rule_3 3 \n", + "11 40 rule_5 5 \n", "\n", " description value center upl \\\n", - "0 Point beyond control limits 112.00 100.542 106.558 \n", - "1 4 of 5 consecutive points in Zone B or beyond 103.31 100.542 106.558 \n", - "2 4 of 5 consecutive points in Zone B or beyond 103.52 100.542 106.558 \n", - "3 4 of 5 consecutive points in Zone B or beyond 103.47 100.542 106.558 \n", - "4 8+ consecutive points avoiding Zone C 103.47 100.542 106.558 \n", - "5 4 of 5 consecutive points in Zone B or beyond 102.58 100.542 106.558 \n", - "6 8+ consecutive points avoiding Zone C 102.58 100.542 106.558 \n", - "7 4 of 5 consecutive points in Zone B or beyond 102.85 100.542 106.558 \n", - "8 8+ consecutive points on same side of center 102.85 100.542 106.558 \n", - "9 8+ consecutive points avoiding Zone C 102.85 100.542 106.558 \n", - "10 4 of 5 consecutive points in Zone B or beyond 98.80 100.542 106.558 \n", + "0 Point beyond control limits 112.00 100.542 106.557 \n", + "1 4 of 5 consecutive points in Zone B or beyond 103.31 100.542 106.557 \n", + "2 4 of 5 consecutive points in Zone B or beyond 103.52 100.542 106.557 \n", + "3 4 of 5 consecutive points in Zone B or beyond 103.47 100.542 106.557 \n", + "4 8+ consecutive points avoiding Zone C 103.47 100.542 106.557 \n", + "5 4 of 5 consecutive points in Zone B or beyond 102.58 100.542 106.557 \n", + "6 8+ consecutive points avoiding Zone C 102.58 100.542 106.557 \n", + "7 4 of 5 consecutive points in Zone B or beyond 102.85 100.542 106.557 \n", + "8 8+ consecutive points on same side of center 102.85 100.542 106.557 \n", + "9 8+ consecutive points avoiding Zone C 102.85 100.542 106.557 \n", + "10 4 of 5 consecutive points in Zone B or beyond 98.80 100.542 106.557 \n", + "11 6+ consecutive points trending 103.00 100.542 106.557 \n", "\n", " lpl \n", - "0 94.526 \n", - "1 94.526 \n", - "2 94.526 \n", - "3 94.526 \n", - "4 94.526 \n", - "5 94.526 \n", - "6 94.526 \n", - "7 94.526 \n", - "8 94.526 \n", - "9 94.526 \n", - "10 94.526 " + "0 94.527 \n", + "1 94.527 \n", + "2 94.527 \n", + "3 94.527 \n", + "4 94.527 \n", + "5 94.527 \n", + "6 94.527 \n", + "7 94.527 \n", + "8 94.527 \n", + "9 94.527 \n", + "10 94.527 \n", + "11 94.527 " ] }, "execution_count": 6, @@ -565,14 +579,15 @@ "======================================================================\n", "Signal Detection Summary: X\n", "======================================================================\n", - "Total violations: 11\n", - "Flagged observations: 7\n", + "Total violations: 12\n", + "Flagged observations: 8\n", "\n", "Violations by rule:\n", " rule_3: 6\n", " rule_8: 3\n", " rule_1: 1\n", " rule_4: 1\n", + " rule_5: 1\n", "\n", "First violations:\n", " • Obs 10: Point beyond control limits (value=112.000)\n", @@ -580,7 +595,7 @@ " • Obs 24: 4 of 5 consecutive points in Zone B or beyond (value=103.520)\n", " • Obs 25: 4 of 5 consecutive points in Zone B or beyond (value=103.470)\n", " • Obs 25: 8+ consecutive points avoiding Zone C (value=103.470)\n", - " ... and 6 more\n", + " ... and 7 more\n", "\n", "======================================================================\n", "\n" @@ -614,6 +629,7 @@ " rule_1: 1 violation(s)\n", " rule_3: 6 violation(s)\n", " rule_4: 1 violation(s)\n", + " rule_5: 1 violation(s)\n", " rule_8: 3 violation(s)\n" ] } @@ -708,7 +724,8 @@ "Rule violations:
4 of 5 in Zone B+
Avoiding center
Value: 103.470", "Rule violations:
4 of 5 in Zone B+
Avoiding center
Value: 102.580", "Rule violations:
4 of 5 in Zone B+
8+ same side
Avoiding center
Value: 102.850", - "Rule violations:
4 of 5 in Zone B+
Value: 98.800" + "Rule violations:
4 of 5 in Zone B+
Value: 98.800", + "Rule violations:
Trend
Value: 103.000" ], "marker": { "color": "#FF8C00", @@ -729,7 +746,8 @@ 26.0, 27.0, 28.0, - 29.0 + 29.0, + 41.0 ], "y": [ 103.31, @@ -737,7 +755,8 @@ 103.47, 102.58, 102.85, - 98.8 + 98.8, + 103.0 ] } ], @@ -783,8 +802,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 98.53666666666666, - "y1": 102.54733333333334, + "y0": 98.537, + "y1": 102.547, "yref": "y" }, { @@ -798,8 +817,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 102.54733333333334, - "y1": 104.55266666666667, + "y0": 102.547, + "y1": 104.552, "yref": "y" }, { @@ -813,8 +832,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 96.53133333333334, - "y1": 98.53666666666666, + "y0": 96.532, + "y1": 98.537, "yref": "y" }, { @@ -828,8 +847,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 104.55266666666667, - "y1": 106.558, + "y0": 104.552, + "y1": 106.557, "yref": "y" }, { @@ -843,8 +862,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 94.526, - "y1": 96.53133333333334, + "y0": 94.527, + "y1": 96.532, "yref": "y" }, { @@ -857,8 +876,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 106.558, - "y1": 106.558 + "y0": 106.557, + "y1": 106.557 }, { "line": { @@ -870,8 +889,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 94.526, - "y1": 94.526 + "y0": 94.527, + "y1": 94.527 }, { "line": { @@ -1854,7 +1873,7 @@ "output_type": "stream", "text": [ "Rules applied: ['rule_1', 'rule_4', 'rule_5']\n", - "Signals found: 2\n" + "Signals found: 3\n" ] } ], @@ -1949,7 +1968,7 @@ "\n", "**Pattern**: 8+ consecutive points on same side of centerline\n", "\n", - "**Interpretation**: The process has shifted. Even small shifts (< 1σ) will eventually produce runs.\n", + "**Interpretation**: Suggests a sustained shift. Even small shifts (< 1σ) will eventually produce runs.\n", "\n", "**Action**: Look for what caused the sustained change." ] @@ -2002,7 +2021,7 @@ "\n", "**Pattern**: 6+ consecutive points increasing or decreasing\n", "\n", - "**Interpretation**: Process is drifting. Common causes: tool wear, temperature changes, material degradation.\n", + "**Interpretation**: Suggests drift. Common causes: tool wear, temperature changes, material degradation.\n", "\n", "**Action**: Identify and address the source of drift." ] diff --git a/docs/tutorials/stratified-analysis.ipynb b/docs/tutorials/stratified-analysis.ipynb index 46aa48c..eb7914b 100644 --- a/docs/tutorials/stratified-analysis.ipynb +++ b/docs/tutorials/stratified-analysis.ipynb @@ -391,8 +391,8 @@ " Lane_A\n", " 100.50\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 1\n", " \n", @@ -403,8 +403,8 @@ " Lane_A\n", " 99.77\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 5\n", " \n", @@ -415,8 +415,8 @@ " Lane_A\n", " 99.53\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 9\n", " \n", @@ -427,8 +427,8 @@ " Lane_A\n", " 100.24\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 13\n", " \n", @@ -439,8 +439,8 @@ " Lane_A\n", " 98.99\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 17\n", " \n", @@ -451,8 +451,8 @@ " Lane_A\n", " 101.47\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 21\n", " \n", @@ -463,8 +463,8 @@ " Lane_A\n", " 99.46\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 25\n", " \n", @@ -475,8 +475,8 @@ " Lane_A\n", " 99.40\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 29\n", " \n", @@ -487,8 +487,8 @@ " Lane_A\n", " 99.99\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 33\n", " \n", @@ -499,8 +499,8 @@ " Lane_A\n", " 100.21\n", " 99.919\n", - " 97.585\n", - " 102.253\n", + " 97.586\n", + " 102.252\n", " 0\n", " 37\n", " \n", @@ -510,16 +510,16 @@ ], "text/plain": [ " batch rsg lane fillweight center lpl upl beyond_limits \\\n", - "0 1 Lane_A Lane_A 100.50 99.919 97.585 102.253 0 \n", - "1 2 Lane_A Lane_A 99.77 99.919 97.585 102.253 0 \n", - "2 3 Lane_A Lane_A 99.53 99.919 97.585 102.253 0 \n", - "3 4 Lane_A Lane_A 100.24 99.919 97.585 102.253 0 \n", - "4 5 Lane_A Lane_A 98.99 99.919 97.585 102.253 0 \n", - "5 6 Lane_A Lane_A 101.47 99.919 97.585 102.253 0 \n", - "6 7 Lane_A Lane_A 99.46 99.919 97.585 102.253 0 \n", - "7 8 Lane_A Lane_A 99.40 99.919 97.585 102.253 0 \n", - "8 9 Lane_A Lane_A 99.99 99.919 97.585 102.253 0 \n", - "9 10 Lane_A Lane_A 100.21 99.919 97.585 102.253 0 \n", + "0 1 Lane_A Lane_A 100.50 99.919 97.586 102.252 0 \n", + "1 2 Lane_A Lane_A 99.77 99.919 97.586 102.252 0 \n", + "2 3 Lane_A Lane_A 99.53 99.919 97.586 102.252 0 \n", + "3 4 Lane_A Lane_A 100.24 99.919 97.586 102.252 0 \n", + "4 5 Lane_A Lane_A 98.99 99.919 97.586 102.252 0 \n", + "5 6 Lane_A Lane_A 101.47 99.919 97.586 102.252 0 \n", + "6 7 Lane_A Lane_A 99.46 99.919 97.586 102.252 0 \n", + "7 8 Lane_A Lane_A 99.40 99.919 97.586 102.252 0 \n", + "8 9 Lane_A Lane_A 99.99 99.919 97.586 102.252 0 \n", + "9 10 Lane_A Lane_A 100.21 99.919 97.586 102.252 0 \n", "\n", " obs_id \n", "0 1 \n", @@ -569,7 +569,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Lane_A: CL=99.92, UCL=102.25, LCL=97.58\n", + "Lane_A: CL=99.92, UCL=102.25, LCL=97.59\n", "Lane_B: CL=100.62, UCL=105.58, LCL=95.66\n", "Lane_C: CL=98.98, UCL=104.41, LCL=93.56\n", "Lane_D: CL=100.58, UCL=103.25, LCL=97.91\n" @@ -803,7 +803,7 @@ "size": 10 }, "showarrow": false, - "text": "UPL = 102.3 | CL = 99.92 | LPL = 97.58", + "text": "UPL = 102.3 | CL = 99.92 | LPL = 97.59", "x": 1.0, "xanchor": "right", "xref": "x domain", @@ -881,8 +881,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 99.14099999999999, - "y1": 100.697, + "y0": 99.14133333333334, + "y1": 100.69666666666666, "yref": "y" }, { @@ -896,8 +896,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 100.697, - "y1": 101.475, + "y0": 100.69666666666666, + "y1": 101.47433333333333, "yref": "y" }, { @@ -911,8 +911,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 98.363, - "y1": 99.14099999999999, + "y0": 98.36366666666666, + "y1": 99.14133333333334, "yref": "y" }, { @@ -926,8 +926,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 101.475, - "y1": 102.253, + "y0": 101.47433333333333, + "y1": 102.252, "yref": "y" }, { @@ -941,8 +941,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 97.585, - "y1": 98.363, + "y0": 97.586, + "y1": 98.36366666666666, "yref": "y" }, { @@ -955,8 +955,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 102.253, - "y1": 102.253, + "y0": 102.252, + "y1": 102.252, "yref": "y" }, { @@ -969,8 +969,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 97.585, - "y1": 97.585, + "y0": 97.586, + "y1": 97.586, "yref": "y" }, { @@ -998,8 +998,8 @@ "x0": 0, "x1": 1, "xref": "x2 domain", - "y0": 98.968, - "y1": 102.272, + "y0": 98.96833333333333, + "y1": 102.27166666666668, "yref": "y2" }, { @@ -1013,8 +1013,8 @@ "x0": 0, "x1": 1, "xref": "x2 domain", - "y0": 102.272, - "y1": 103.92399999999999, + "y0": 102.27166666666668, + "y1": 103.92333333333333, "yref": "y2" }, { @@ -1028,8 +1028,8 @@ "x0": 0, "x1": 1, "xref": "x2 domain", - "y0": 97.31600000000002, - "y1": 98.968, + "y0": 97.31666666666668, + "y1": 98.96833333333333, "yref": "y2" }, { @@ -1043,8 +1043,8 @@ "x0": 0, "x1": 1, "xref": "x2 domain", - "y0": 103.92399999999999, - "y1": 105.576, + "y0": 103.92333333333333, + "y1": 105.575, "yref": "y2" }, { @@ -1059,7 +1059,7 @@ "x1": 1, "xref": "x2 domain", "y0": 95.664, - "y1": 97.31600000000002, + "y1": 97.31666666666668, "yref": "y2" }, { @@ -1072,8 +1072,8 @@ "x0": 0, "x1": 1, "xref": "x2 domain", - "y0": 105.576, - "y1": 105.576, + "y0": 105.575, + "y1": 105.575, "yref": "y2" }, { @@ -1115,8 +1115,8 @@ "x0": 0, "x1": 1, "xref": "x3 domain", - "y0": 97.17366666666668, - "y1": 100.79233333333333, + "y0": 97.174, + "y1": 100.792, "yref": "y3" }, { @@ -1130,8 +1130,8 @@ "x0": 0, "x1": 1, "xref": "x3 domain", - "y0": 100.79233333333333, - "y1": 102.60166666666667, + "y0": 100.792, + "y1": 102.601, "yref": "y3" }, { @@ -1145,8 +1145,8 @@ "x0": 0, "x1": 1, "xref": "x3 domain", - "y0": 95.36433333333333, - "y1": 97.17366666666668, + "y0": 95.36500000000001, + "y1": 97.174, "yref": "y3" }, { @@ -1160,8 +1160,8 @@ "x0": 0, "x1": 1, "xref": "x3 domain", - "y0": 102.60166666666667, - "y1": 104.411, + "y0": 102.601, + "y1": 104.41, "yref": "y3" }, { @@ -1175,8 +1175,8 @@ "x0": 0, "x1": 1, "xref": "x3 domain", - "y0": 93.555, - "y1": 95.36433333333333, + "y0": 93.556, + "y1": 95.36500000000001, "yref": "y3" }, { @@ -1189,8 +1189,8 @@ "x0": 0, "x1": 1, "xref": "x3 domain", - "y0": 104.411, - "y1": 104.411, + "y0": 104.41, + "y1": 104.41, "yref": "y3" }, { @@ -1203,8 +1203,8 @@ "x0": 0, "x1": 1, "xref": "x3 domain", - "y0": 93.555, - "y1": 93.555, + "y0": 93.556, + "y1": 93.556, "yref": "y3" }, { @@ -2208,8 +2208,8 @@ "linecolor": "#999999", "linewidth": 1.0, "range": [ - 92.95395, - 106.17705 + 92.95505, + 106.17595 ], "showgrid": true, "showline": true, @@ -2230,8 +2230,8 @@ "linecolor": "#999999", "linewidth": 1.0, "range": [ - 92.95395, - 106.17705 + 92.95505, + 106.17595 ], "showgrid": true, "showline": true @@ -2248,8 +2248,8 @@ "linecolor": "#999999", "linewidth": 1.0, "range": [ - 92.95395, - 106.17705 + 92.95505, + 106.17595 ], "showgrid": true, "showline": true, @@ -2269,8 +2269,8 @@ "linecolor": "#999999", "linewidth": 1.0, "range": [ - 92.95395, - 106.17705 + 92.95505, + 106.17595 ], "showgrid": true, "showline": true @@ -2320,7 +2320,7 @@ "output_type": "stream", "text": [ "Focused on: Lane_C\n", - "Stats: {'N': 1, 'center': np.float64(98.983), 'lpl': np.float64(93.555), 'upl': np.float64(104.411)}\n" + "Stats: {'N': 1, 'center': np.float64(98.983), 'lpl': np.float64(93.556), 'upl': np.float64(104.41)}\n" ] }, { @@ -2420,8 +2420,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 97.17366666666668, - "y1": 100.79233333333333, + "y0": 97.174, + "y1": 100.792, "yref": "y" }, { @@ -2435,8 +2435,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 100.79233333333333, - "y1": 102.60166666666667, + "y0": 100.792, + "y1": 102.601, "yref": "y" }, { @@ -2450,8 +2450,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 95.36433333333333, - "y1": 97.17366666666668, + "y0": 95.36500000000001, + "y1": 97.174, "yref": "y" }, { @@ -2465,8 +2465,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 102.60166666666667, - "y1": 104.411, + "y0": 102.601, + "y1": 104.41, "yref": "y" }, { @@ -2480,8 +2480,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 93.555, - "y1": 95.36433333333333, + "y0": 93.556, + "y1": 95.36500000000001, "yref": "y" }, { @@ -2494,8 +2494,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 104.411, - "y1": 104.411 + "y0": 104.41, + "y1": 104.41 }, { "line": { @@ -2507,8 +2507,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 93.555, - "y1": 93.555 + "y0": 93.556, + "y1": 93.556 }, { "line": { @@ -3378,7 +3378,7 @@ "Lane_A: 0 signal(s)\n", "Lane_B: 0 signal(s)\n", "Lane_C: 1 signal(s)\n", - "Lane_D: 0 signal(s)\n", + "Lane_D: 1 signal(s)\n", "\n", "Lane_C flagged observations: [np.int64(14)]\n" ] @@ -3423,7 +3423,7 @@ "output_type": "stream", "text": [ "chart X: 80 points\n", - " Lane_A: center=99.92, upl=102.25, lpl=97.58\n", + " Lane_A: center=99.92, upl=102.25, lpl=97.59\n", " Lane_B: center=100.62, upl=105.58, lpl=95.66\n", " Lane_C: center=98.98, upl=104.41, lpl=93.56\n", " Lane_D: center=100.58, upl=103.25, lpl=97.91\n" @@ -3465,7 +3465,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Combined-chart signals: 9\n" + "Combined-chart signals: 10\n" ] }, { @@ -3475,6 +3475,188 @@ "plotlyServerURL": "https://plot.ly" }, "data": [ + { + "hoverinfo": "skip", + "line": { + "color": "steelblue", + "width": 1.0 + }, + "mode": "lines", + "name": "X (lanes)", + "opacity": 0.8, + "showlegend": false, + "type": "scatter", + "x": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79 + ], + "y": [ + 100.5, + 99.77, + 99.53, + 100.24, + 98.99, + 101.47, + 99.46, + 99.4, + 99.99, + 100.21, + 100.74, + 98.52, + 100.34, + 99.32, + 99.16, + 99.52, + 100.81, + 100.36, + 99.96, + 100.09, + null, + 100.79, + 100.65, + 101.81, + 98.13, + 101.47, + 100.66, + 101.17, + 100.56, + 99.41, + 98.06, + 101.26, + 99.92, + 98.36, + 101.92, + 100.54, + 100.72, + 103.03, + 100.03, + 103.35, + 100.55, + null, + 99.78, + 100.9, + 98.44, + 96.93, + 97.91, + 99.08, + 97.62, + 98.28, + 99.99, + 97.41, + 98.86, + 98.45, + 99.39, + 100.24, + 105.4, + 97.67, + 98.91, + 99.43, + 95.86, + 99.11, + null, + 101.72, + 101.11, + 100.13, + 100.05, + 99.37, + 99.36, + 100.8, + 101.98, + 99.52, + 100.66, + 100.26, + 101.35, + 100.19, + 101.25, + 101.28, + 99.54, + 101.3, + 101.73, + 101.16, + 98.91 + ] + }, { "customdata": [ [ @@ -3807,7 +3989,7 @@ "color": "steelblue", "size": 5 }, - "mode": "lines+markers", + "mode": "markers", "name": "X", "opacity": 0.8, "type": "scatter", @@ -3999,7 +4181,7 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 96.188, + "y0": 96.189, "y1": 97.46733333333333, "yref": "y" }, @@ -4026,8 +4208,8 @@ "x0": 0, "x1": 1, "xref": "x domain", - "y0": 96.188, - "y1": 96.188 + "y0": 96.189, + "y1": 96.189 }, { "line": { @@ -4950,7 +5132,9 @@ "\n", "The combined chart fires far *more* signals than the stratified charts — and that's the problem. Interleaving four lanes with different baselines makes every lane-to-lane jump look like a time signal: the between-lane structure aliases as within-stream behavior, and the one real special cause (Lane C at batch 15) drowns in the noise.\n", "\n", - "The **stratified approach** gives each lane its own center and limits, so the Lane C shift stands out alone." + "The **stratified approach** gives each lane its own center and limits, so the Lane C shift stands out\n", + "as a point beyond its lane's limits. Lane D's one signal is a six-point trend in a lane with no special\n", + "cause: run rules fire by chance on a stable stream (see [Signal Detection](signal-detection.ipynb))." ] }, { diff --git a/pyproject.toml b/pyproject.toml index 1dd507c..b642f79 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -67,9 +67,11 @@ test = [ "pytest-timeout>=2.4", "pyarrow>=14.0", "openpyxl>=3.1", - # nbmake executes notebooks as pytest items; the CI job uses it to - # catch broken examples in docs/tutorials and docs/getting-started. - "nbmake>=1.5", + # scripts/refresh_notebooks.py executes the docs notebooks; CI runs it with + # --check to catch notebooks that break or drift from the library. + "nbclient>=0.10", + "nbformat>=5.10", + "ipykernel>=6", ] lint = [ "ruff>=0.5", diff --git a/scripts/refresh_notebooks.py b/scripts/refresh_notebooks.py new file mode 100644 index 0000000..4843117 --- /dev/null +++ b/scripts/refresh_notebooks.py @@ -0,0 +1,161 @@ +"""Re-execute the docs notebooks so their saved outputs match the library. + +The docs site shows each notebook's saved outputs; it does not run them. After a change to the +library, run this to bring them up to date. CI runs it with --check, which executes every docs +notebook and fails if what a reader would see differs from what is saved. + + python scripts/refresh_notebooks.py # refresh every docs notebook in place + python scripts/refresh_notebooks.py PATH ... # refresh some + python scripts/refresh_notebooks.py --check # fail if a fresh run differs from the saved outputs + +What is compared is what a reader sees: printed text, errors, tables, and each figure's data and +labels. Plotly templates and styling are left out, so a plotly upgrade alone is not drift. Two +things that differ on every run are ignored: the temporary path in a warning's location, and the +random id of a derived variable. +""" + +from __future__ import annotations + +import argparse +import base64 +import difflib +import pprint +import re +import sys +from pathlib import Path + +import nbformat +import numpy as np +from nbclient import NotebookClient + +ROOT = Path(__file__).resolve().parent.parent +NOTEBOOKS = sorted([*ROOT.glob('docs/getting-started/*.ipynb'), *ROOT.glob('docs/tutorials/*.ipynb')]) +PLOTLY = 'application/vnd.plotly.v1+json' +TEMP_PATH = re.compile(r'\S*ipykernel_\d+[/\\]\d+\.py') # where a warning was raised; differs every run +DERIVATION_ID = re.compile(r'\bid=[0-9a-f]{12}\b') # Derivation.id is a random uuid by design +AXES = ('xaxis', 'yaxis') + + +def execute(path: Path) -> nbformat.NotebookNode: + """Run a notebook top to bottom in its own folder, as a reader would.""" + nb = nbformat.read(path, as_version=4) + NotebookClient( + nb, timeout=900, kernel_name='python3', record_timing=False, resources={'metadata': {'path': str(path.parent)}} + ).execute() + return nb + + +def _values(v): + """A trace's x or y as plain values: decode plotly's base64 typed arrays and round floats. + + A missing value becomes None, whether it was saved as null or as NaN, because NaN never + equals itself and would make identical figures compare as different. + """ + if isinstance(v, dict) and 'bdata' in v: + arr = np.frombuffer(base64.b64decode(v['bdata']), dtype=np.dtype(v['dtype'])) + if v.get('shape'): + arr = arr.reshape([int(n) for n in str(v['shape']).split(',')]) + v = arr.tolist() + if isinstance(v, (list, tuple)): + return [_values(x) for x in v] + if isinstance(v, float): + return None if v != v else float(f'{v:.10g}') + return v + + +def _label(title): + return title.get('text') if isinstance(title, dict) else title + + +def _figure(fig: dict) -> dict: + layout = fig.get('layout', {}) + return { + 'traces': [ + {'type': t.get('type'), 'name': t.get('name'), 'x': _values(t.get('x')), 'y': _values(t.get('y'))} + for t in fig.get('data', []) + ], + 'title': _label(layout.get('title')), + 'axes': {k: _label(v.get('title')) for k, v in sorted(layout.items()) if k[:5] in AXES and isinstance(v, dict)}, + 'annotations': [a.get('text') for a in layout.get('annotations', [])], + 'shapes': [_values([s.get(k) for k in ('x0', 'x1', 'y0', 'y1')]) for s in layout.get('shapes', [])], + } + + +def seen(nb: nbformat.NotebookNode) -> list[list]: + """What a reader sees in each code cell: printed text, errors, tables and figures.""" + cells = [] + for cell in nb.cells: + if cell.cell_type != 'code': + continue + out: list = [] + for o in cell.get('outputs', []): + kind = o.get('output_type') + if kind == 'stream': + text = DERIVATION_ID.sub('id=', TEMP_PATH.sub('', o.get('text', ''))) + if out and out[-1][:2] == ('stream', o.get('name')): + # The kernel splits printed text into chunks by timing; a reader sees one block. + out[-1] = ('stream', o.get('name'), out[-1][2] + text) + else: + out.append(('stream', o.get('name'), text)) + elif kind == 'error': + out.append(('error', o.get('ename'), o.get('evalue'))) + elif 'data' in o: + if PLOTLY in o['data']: + out.append(('figure', _figure(o['data'][PLOTLY]))) + elif 'text/plain' in o['data']: + out.append(('text', o['data']['text/plain'])) + cells.append(out) + return cells + + +def _first_difference(saved: list, fresh: list, limit: int = 20) -> str: + before, after = (pprint.pformat(x, width=110).splitlines() for x in (saved, fresh)) + lines = difflib.unified_diff(before, after, 'saved', 'fresh', n=1, lineterm='') + return '\n'.join(list(lines)[:limit]) + + +def _keep_unchanged(saved, fresh, before: list, after: list) -> None: + """Keep the saved bytes of every cell a reader would see unchanged, so float noise is not churn.""" + code = [(s, f) for s, f in zip(saved.cells, fresh.cells, strict=True) if s.cell_type == 'code'] + for (s_cell, f_cell), a, b in zip(code, before, after, strict=True): + if a == b: + f_cell.outputs, f_cell.execution_count = s_cell.outputs, s_cell.execution_count + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument('paths', nargs='*', type=Path, help='notebooks to refresh (default: every docs notebook)') + parser.add_argument('--check', action='store_true', help='fail if a fresh run differs from the saved outputs') + args = parser.parse_args(argv) + paths = [p.resolve() for p in args.paths] or NOTEBOOKS + + drifted = [] + for path in paths: + name = path.relative_to(ROOT) if path.is_relative_to(ROOT) else path + saved = nbformat.read(path, as_version=4) + fresh = execute(path) + before, after = seen(saved), seen(fresh) + if before == after: + print(f'{name}: current') + continue + changed = [i + 1 for i, (a, b) in enumerate(zip(before, after, strict=True)) if a != b] + if args.check: + drifted.append(name) + first = changed[0] - 1 + print(f'{name}: code cells {changed} differ from a fresh run') + print(_first_difference(before[first], after[first])) + else: + fresh.metadata = saved.metadata # keep the kernel and Python version the notebook was saved with + _keep_unchanged(saved, fresh, before, after) + nbformat.write(fresh, path) + print(f'{name}: refreshed (code cells {changed})') + + if drifted: + print(f'\n{len(drifted)} notebook(s) no longer match the library. Refresh them with:') + print(' python scripts/refresh_notebooks.py ' + ' '.join(str(n) for n in drifted)) + return 1 + return 0 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/tests/test_refresh_notebooks.py b/tests/test_refresh_notebooks.py new file mode 100644 index 0000000..de9349c --- /dev/null +++ b/tests/test_refresh_notebooks.py @@ -0,0 +1,84 @@ +""" +scripts/refresh_notebooks.py: what counts as a docs notebook drifting from the library. + +CI runs the script with --check, so seen() must ignore what differs on every run (warning paths, +random derivation ids, how the kernel chunks printed text, plotly styling and number encoding) and +must still catch a changed number in text or in a figure. +""" + +import base64 +import importlib.util +from pathlib import Path + +import nbformat +import numpy as np +from nbformat.v4 import new_code_cell, new_notebook, new_output + +SCRIPT = Path(__file__).resolve().parent.parent / 'scripts' / 'refresh_notebooks.py' +_spec = importlib.util.spec_from_file_location('refresh_notebooks', SCRIPT) +refresh = importlib.util.module_from_spec(_spec) +_spec.loader.exec_module(refresh) + +PLOTLY = 'application/vnd.plotly.v1+json' + + +def _nb(*outputs) -> nbformat.NotebookNode: + return new_notebook(cells=[new_code_cell('x', outputs=list(outputs))]) + + +def _stream(text, name='stdout'): + return new_output('stream', name=name, text=text) + + +def _figure(y, template='plotly', title='X chart'): + fig = {'data': [{'type': 'scatter', 'name': 'X', 'x': [1, 2, 3], 'y': y}], + 'layout': {'template': {'layout': {'font': template}}, 'title': {'text': title}}} + return new_output('display_data', data={PLOTLY: fig, 'text/plain': 'Figure()'}) + + +def _bdata(values): + return {'dtype': 'f8', 'bdata': base64.b64encode(np.array(values, dtype='f8').tobytes()).decode()} + + +def test_docs_notebooks_are_found(): + names = {p.name for p in refresh.NOTEBOOKS} + assert {'quickstart.ipynb', 'signal-detection.ipynb', 'sds-validation.ipynb'} <= names + + +def test_warning_location_is_ignored(): + a = _stream('/var/folders/x/T/ipykernel_123/456.py:4: ProcessBehaviorWarning: blank values', 'stderr') + b = _stream('/tmp/ipykernel_9/789.py:4: ProcessBehaviorWarning: blank values', 'stderr') + assert refresh.seen(_nb(a)) == refresh.seen(_nb(b)) + + +def test_random_derivation_id_is_ignored(): + assert refresh.seen(_nb(_stream('temp_bin family=bin id=565862afc3e4\n'))) == refresh.seen( + _nb(_stream('temp_bin family=bin id=5251aeb49f5f\n')) + ) + + +def test_chunked_printing_reads_as_one_block(): + assert refresh.seen(_nb(_stream('Observed: 3'), _stream('\nValid: X\n'))) == refresh.seen( + _nb(_stream('Observed: 3\nValid: X\n')) + ) + + +def test_plotly_styling_and_encoding_are_ignored(): + saved = _nb(_figure([1.0, 2.5, float('nan')], template='plotly')) + fresh = _nb(_figure(_bdata([1.0, 2.5, np.nan]), template='plotly_white')) + assert refresh.seen(saved) == refresh.seen(fresh) + + +def test_a_changed_number_in_text_is_drift(): + a = new_output('execute_result', data={'text/plain': "{'center': 72.379, 'upl': 76.392}"}, execution_count=1) + b = new_output('execute_result', data={'text/plain': "{'center': 72.379, 'upl': 76.391}"}, execution_count=1) + assert refresh.seen(_nb(a)) != refresh.seen(_nb(b)) + + +def test_a_changed_figure_value_is_drift(): + assert refresh.seen(_nb(_figure([1.0, 2.5, 3.0]))) != refresh.seen(_nb(_figure([1.0, 2.5, 3.1]))) + + +def test_a_changed_figure_label_is_drift(): + saved, fresh = _nb(_figure([1.0], title='UPL = 4.930')), _nb(_figure([1.0], title='UPL = 4.929')) + assert refresh.seen(saved) != refresh.seen(fresh) From 3ff5bf9c35fd1575b23dff9c36a1892c21085301 Mon Sep 17 00:00:00 2001 From: Chris Nicholas <4948774+cnicholas@users.noreply.github.com> Date: Tue, 3 Nov 2026 09:45:32 -0500 Subject: [PATCH 2/2] Draw interaction-chart lines in factor order; compare figure numbers to a tolerance CI's first run of the notebook check found two differences between Linux CI and macOS, neither a stale saved output: - **Factor x time interaction chart:** it sorted its points by time with pandas' default (unstable) sort, so the order of factor combinations within a time, and with it the legend order and each line's colour, depended on the numpy version (CI 2.4.6/2.5.4, local 2.4.0). It now sorts stably, keeping the groupby's factor order. Test: `test_lines_are_in_factor_order`. The one notebook cell showing this chart is refreshed. - **Float noise:** one value differed in its 11th significant figure, on the boundary of the 10-figure rounding the check used. Figure numbers are now compared to a relative 1e-9 (`same()`), with tests for noise ignored and for real changes, including a reordered legend, still caught. Checks: `refresh_notebooks.py --check` passes twice in a row; pytest 2707 passed, 7 skipped; Bishop gate 1547/0; ruff clean. --- CHANGELOG.md | 3 ++ docs/tutorials/sds1-complete-analysis.ipynb | 30 ++++++------ processbehavior/plotting/effects_charts.py | 5 +- scripts/refresh_notebooks.py | 31 ++++++++---- tests/test_effects_plotting.py | 12 +++++ tests/test_refresh_notebooks.py | 54 ++++++++++++++------- 6 files changed, 92 insertions(+), 43 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index b9d8e40..9e925f6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -53,6 +53,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 signal counts. All were re-executed. The Derived Variables tutorial, saved without outputs, now shows them. Prose that the new outputs contradicted was corrected; the Bishop reference values in the reference validation now quote his VAS rerun of 13 October 2026. +- **The factor x time interaction chart draws its lines in factor order on every machine.** It + sorted by time with an unstable sort, so the legend order, and the colour each line got, depended + on the numpy version. - **Three signal rules now match their definitions.** Rule 5 (trend) fired only on 7 rising or falling points, not 6; Rule 6 (oscillation) only on 15 alternating points, not 14; and Rule 2 counted only points in Zone A, leaving out points beyond 3σ, which Western Electric and Wheeler diff --git a/docs/tutorials/sds1-complete-analysis.ipynb b/docs/tutorials/sds1-complete-analysis.ipynb index fc7cd91..c7d37e3 100644 --- a/docs/tutorials/sds1-complete-analysis.ipynb +++ b/docs/tutorials/sds1-complete-analysis.ipynb @@ -39475,7 +39475,7 @@ } }, { - "hovertemplate": "F1_3_F2_1
Time: %{x}
Effect: %{y:.4f}", + "hovertemplate": "F1_1_F2_2
Time: %{x}
Effect: %{y:.4f}", "line": { "color": "#2E8B57", "width": 2 @@ -39485,19 +39485,19 @@ "size": 8 }, "mode": "lines+markers", - "name": "F1_3_F2_1", + "name": "F1_1_F2_2", "type": "scatter", "x": { "bdata": "AQIDBAUGBwg=", "dtype": "i1" }, "y": { - "bdata": "AGS/oVrQpb8AvG89lafXPwDcTkst3b4/ABqWaNjSwD/AjGIRdZbQP4A6C4INCsi/zfXsZ8ggxL/gBr2MS4/fvw==", + "bdata": "AEspGl4j0L8AqVCbGdLEvwDgiCVGeoy/AMnGVlgewr9VHUB568azv4BxRxsvcNI/ZoLVJU7Ixb+gUSoNjvLgPw==", "dtype": "f8" } }, { - "hovertemplate": "F1_2_F2_2
Time: %{x}
Effect: %{y:.4f}", + "hovertemplate": "F1_2_F2_1
Time: %{x}
Effect: %{y:.4f}", "line": { "color": "#FF6B6B", "width": 2 @@ -39507,19 +39507,19 @@ "size": 8 }, "mode": "lines+markers", - "name": "F1_2_F2_2", + "name": "F1_2_F2_1", "type": "scatter", "x": { "bdata": "AQIDBAUGBwg=", "dtype": "i1" }, "y": { - "bdata": "ANfbO0w/679AXZbXYN/Zv0DikmWGh+I/mn3mkaD8wD+Azo4BIo/MP0AAbDFCfNK/AGS0Myyzvz/ghuxeghjfPw==", + "bdata": "gDBAAg0Z3D9VZX29ZjTZPxootkVtMti/AFXceoFtsz9V3WxydYbpvwBdrBQ14c0/ACjair7Lgb8A1N6wO6KlPw==", "dtype": "f8" } }, { - "hovertemplate": "F1_3_F2_2
Time: %{x}
Effect: %{y:.4f}", + "hovertemplate": "F1_2_F2_2
Time: %{x}
Effect: %{y:.4f}", "line": { "color": "#4ECDC4", "width": 2 @@ -39529,19 +39529,19 @@ "size": 8 }, "mode": "lines+markers", - "name": "F1_3_F2_2", + "name": "F1_2_F2_2", "type": "scatter", "x": { "bdata": "AQIDBAUGBwg=", "dtype": "i1" }, "y": { - "bdata": "ACKeNXKRoj9AkYaGKVDUvwBxB3aTFtk/1XX49z/01r/gC8787OPkP0DP+rqt7t2/4JqzmWOA5D9rViLYJX/ivw==", + "bdata": "ANfbO0w/679AXZbXYN/Zv0DikmWGh+I/mn3mkaD8wD+Azo4BIo/MP0AAbDFCfNK/AGS0Myyzvz/ghuxeghjfPw==", "dtype": "f8" } }, { - "hovertemplate": "F1_2_F2_1
Time: %{x}
Effect: %{y:.4f}", + "hovertemplate": "F1_3_F2_1
Time: %{x}
Effect: %{y:.4f}", "line": { "color": "#45B7D1", "width": 2 @@ -39551,19 +39551,19 @@ "size": 8 }, "mode": "lines+markers", - "name": "F1_2_F2_1", + "name": "F1_3_F2_1", "type": "scatter", "x": { "bdata": "AQIDBAUGBwg=", "dtype": "i1" }, "y": { - "bdata": "gDBAAg0Z3D9VZX29ZjTZPxootkVtMti/AFXceoFtsz9V3WxydYbpvwBdrBQ14c0/ACjair7Lgb8A1N6wO6KlPw==", + "bdata": "AGS/oVrQpb8AvG89lafXPwDcTkst3b4/ABqWaNjSwD/AjGIRdZbQP4A6C4INCsi/zfXsZ8ggxL/gBr2MS4/fvw==", "dtype": "f8" } }, { - "hovertemplate": "F1_1_F2_2
Time: %{x}
Effect: %{y:.4f}", + "hovertemplate": "F1_3_F2_2
Time: %{x}
Effect: %{y:.4f}", "line": { "color": "#96CEB4", "width": 2 @@ -39573,14 +39573,14 @@ "size": 8 }, "mode": "lines+markers", - "name": "F1_1_F2_2", + "name": "F1_3_F2_2", "type": "scatter", "x": { "bdata": "AQIDBAUGBwg=", "dtype": "i1" }, "y": { - "bdata": "AEspGl4j0L8AqVCbGdLEvwDgiCVGeoy/AMnGVlgewr9VHUB568azv4BxRxsvcNI/ZoLVJU7Ixb+gUSoNjvLgPw==", + "bdata": "ACKeNXKRoj9AkYaGKVDUvwBxB3aTFtk/1XX49z/01r/gC8787OPkP0DP+rqt7t2/4JqzmWOA5D9rViLYJX/ivw==", "dtype": "f8" } } diff --git a/processbehavior/plotting/effects_charts.py b/processbehavior/plotting/effects_charts.py index 8c271e2..2fe7e3b 100644 --- a/processbehavior/plotting/effects_charts.py +++ b/processbehavior/plotting/effects_charts.py @@ -485,8 +485,9 @@ def create_time_interaction_chart( else: agg_data['_factor_key'] = agg_data[list(factors)].apply(lambda x: '_'.join(str(v) for v in x), axis=1) - # Sort by time - agg_data = agg_data.sort_values(time_var) + # Sort by time, keeping the groupby's factor order within each time: an unstable sort leaves + # it to the platform, so the legend order and each line's colour would change between machines. + agg_data = agg_data.sort_values(time_var, kind='stable') # Create figure fig = go.Figure() diff --git a/scripts/refresh_notebooks.py b/scripts/refresh_notebooks.py index 4843117..e4916d1 100644 --- a/scripts/refresh_notebooks.py +++ b/scripts/refresh_notebooks.py @@ -9,9 +9,10 @@ python scripts/refresh_notebooks.py --check # fail if a fresh run differs from the saved outputs What is compared is what a reader sees: printed text, errors, tables, and each figure's data and -labels. Plotly templates and styling are left out, so a plotly upgrade alone is not drift. Two -things that differ on every run are ignored: the temporary path in a warning's location, and the -random id of a derived variable. +labels. Plotly templates and styling are left out, so a plotly upgrade alone is not drift. Numbers +in figures are compared to a relative 1e-9, since the order of floating-point sums differs between +machines. Two things that differ on every run are ignored: the temporary path in a warning's +location, and the random id of a derived variable. """ from __future__ import annotations @@ -19,6 +20,7 @@ import argparse import base64 import difflib +import math import pprint import re import sys @@ -46,7 +48,7 @@ def execute(path: Path) -> nbformat.NotebookNode: def _values(v): - """A trace's x or y as plain values: decode plotly's base64 typed arrays and round floats. + """A trace's x or y as plain values, decoding plotly's base64 typed arrays. A missing value becomes None, whether it was saved as null or as NaN, because NaN never equals itself and would make identical figures compare as different. @@ -58,8 +60,8 @@ def _values(v): v = arr.tolist() if isinstance(v, (list, tuple)): return [_values(x) for x in v] - if isinstance(v, float): - return None if v != v else float(f'{v:.10g}') + if isinstance(v, float) and v != v: + return None return v @@ -108,6 +110,17 @@ def seen(nb: nbformat.NotebookNode) -> list[list]: return cells +def same(a, b) -> bool: + """Equal as a reader would judge: floats within a relative 1e-9, everything else exactly.""" + if isinstance(a, float) and isinstance(b, float): + return math.isclose(a, b, rel_tol=1e-9, abs_tol=1e-12) + if isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)): + return len(a) == len(b) and all(same(x, y) for x, y in zip(a, b, strict=True)) + if isinstance(a, dict) and isinstance(b, dict): + return a.keys() == b.keys() and all(same(a[k], b[k]) for k in a) + return a == b + + def _first_difference(saved: list, fresh: list, limit: int = 20) -> str: before, after = (pprint.pformat(x, width=110).splitlines() for x in (saved, fresh)) lines = difflib.unified_diff(before, after, 'saved', 'fresh', n=1, lineterm='') @@ -118,7 +131,7 @@ def _keep_unchanged(saved, fresh, before: list, after: list) -> None: """Keep the saved bytes of every cell a reader would see unchanged, so float noise is not churn.""" code = [(s, f) for s, f in zip(saved.cells, fresh.cells, strict=True) if s.cell_type == 'code'] for (s_cell, f_cell), a, b in zip(code, before, after, strict=True): - if a == b: + if same(a, b): f_cell.outputs, f_cell.execution_count = s_cell.outputs, s_cell.execution_count @@ -135,10 +148,10 @@ def main(argv: list[str] | None = None) -> int: saved = nbformat.read(path, as_version=4) fresh = execute(path) before, after = seen(saved), seen(fresh) - if before == after: + if same(before, after): print(f'{name}: current') continue - changed = [i + 1 for i, (a, b) in enumerate(zip(before, after, strict=True)) if a != b] + changed = [i + 1 for i, (a, b) in enumerate(zip(before, after, strict=True)) if not same(a, b)] if args.check: drifted.append(name) first = changed[0] - 1 diff --git a/tests/test_effects_plotting.py b/tests/test_effects_plotting.py index 4cf9fec..fedacdd 100644 --- a/tests/test_effects_plotting.py +++ b/tests/test_effects_plotting.py @@ -335,6 +335,18 @@ def test_line_per_factor_level(self, result_single_factor, theme): # Should have 3 traces (A, B, C) assert len(fig.data) == 3 + def test_lines_are_in_factor_order(self, result_with_effects, theme): + """Legend order (and so each line's colour) follows the factor levels, the same on every machine.""" + fig = create_time_interaction_chart( + interactions=result_with_effects.interactions, + effects=result_with_effects.effects, + factors=['factor1', 'factor2'], + time_var='time', + dataset=result_with_effects.dataset, + theme=theme, + ) + assert [t.name for t in fig.data] == ['A_1', 'A_2', 'B_1', 'B_2'] + def test_raises_if_no_factor_time(self, theme, result_no_time): """Should raise error if factor_time not available.""" with pytest.raises(ChartNotAvailableError, match='Factor × time interaction not available'): diff --git a/tests/test_refresh_notebooks.py b/tests/test_refresh_notebooks.py index de9349c..6d00628 100644 --- a/tests/test_refresh_notebooks.py +++ b/tests/test_refresh_notebooks.py @@ -1,9 +1,10 @@ """ scripts/refresh_notebooks.py: what counts as a docs notebook drifting from the library. -CI runs the script with --check, so seen() must ignore what differs on every run (warning paths, -random derivation ids, how the kernel chunks printed text, plotly styling and number encoding) and -must still catch a changed number in text or in a figure. +CI runs the script with --check, so seen() and same() must ignore what differs between runs and +machines (warning paths, random derivation ids, how the kernel chunks printed text, plotly styling +and number encoding, float noise in the last digits) and must still catch a changed number in text +or in a figure. """ import base64 @@ -31,8 +32,10 @@ def _stream(text, name='stdout'): def _figure(y, template='plotly', title='X chart'): - fig = {'data': [{'type': 'scatter', 'name': 'X', 'x': [1, 2, 3], 'y': y}], - 'layout': {'template': {'layout': {'font': template}}, 'title': {'text': title}}} + fig = { + 'data': [{'type': 'scatter', 'name': 'X', 'x': list(range(1, 4)), 'y': y}], + 'layout': {'template': {'layout': {'font': template}}, 'title': {'text': title}}, + } return new_output('display_data', data={PLOTLY: fig, 'text/plain': 'Figure()'}) @@ -40,6 +43,11 @@ def _bdata(values): return {'dtype': 'f8', 'bdata': base64.b64encode(np.array(values, dtype='f8').tobytes()).decode()} +def _alike(saved, fresh) -> bool: + """Whether the check would call these two single-cell notebooks the same.""" + return refresh.same(refresh.seen(saved), refresh.seen(fresh)) + + def test_docs_notebooks_are_found(): names = {p.name for p in refresh.NOTEBOOKS} assert {'quickstart.ipynb', 'signal-detection.ipynb', 'sds-validation.ipynb'} <= names @@ -48,37 +56,49 @@ def test_docs_notebooks_are_found(): def test_warning_location_is_ignored(): a = _stream('/var/folders/x/T/ipykernel_123/456.py:4: ProcessBehaviorWarning: blank values', 'stderr') b = _stream('/tmp/ipykernel_9/789.py:4: ProcessBehaviorWarning: blank values', 'stderr') - assert refresh.seen(_nb(a)) == refresh.seen(_nb(b)) + assert _alike(_nb(a), _nb(b)) def test_random_derivation_id_is_ignored(): - assert refresh.seen(_nb(_stream('temp_bin family=bin id=565862afc3e4\n'))) == refresh.seen( - _nb(_stream('temp_bin family=bin id=5251aeb49f5f\n')) - ) + a = _stream('temp_bin family=bin id=565862afc3e4\n') + b = _stream('temp_bin family=bin id=5251aeb49f5f\n') + assert _alike(_nb(a), _nb(b)) def test_chunked_printing_reads_as_one_block(): - assert refresh.seen(_nb(_stream('Observed: 3'), _stream('\nValid: X\n'))) == refresh.seen( - _nb(_stream('Observed: 3\nValid: X\n')) - ) + assert _alike(_nb(_stream('Observed: 3'), _stream('\nValid: X\n')), _nb(_stream('Observed: 3\nValid: X\n'))) def test_plotly_styling_and_encoding_are_ignored(): saved = _nb(_figure([1.0, 2.5, float('nan')], template='plotly')) fresh = _nb(_figure(_bdata([1.0, 2.5, np.nan]), template='plotly_white')) - assert refresh.seen(saved) == refresh.seen(fresh) + assert _alike(saved, fresh) + + +def test_float_noise_in_a_figure_is_ignored(): + """Sums run in a different order on another machine; the last digits are not drift.""" + assert _alike(_nb(_figure([0.006711006926])), _nb(_figure([0.006711006927]))) def test_a_changed_number_in_text_is_drift(): a = new_output('execute_result', data={'text/plain': "{'center': 72.379, 'upl': 76.392}"}, execution_count=1) b = new_output('execute_result', data={'text/plain': "{'center': 72.379, 'upl': 76.391}"}, execution_count=1) - assert refresh.seen(_nb(a)) != refresh.seen(_nb(b)) + assert not _alike(_nb(a), _nb(b)) def test_a_changed_figure_value_is_drift(): - assert refresh.seen(_nb(_figure([1.0, 2.5, 3.0]))) != refresh.seen(_nb(_figure([1.0, 2.5, 3.1]))) + assert not _alike(_nb(_figure([1.0, 2.5, 3.0])), _nb(_figure([1.0, 2.5, 3.1]))) def test_a_changed_figure_label_is_drift(): - saved, fresh = _nb(_figure([1.0], title='UPL = 4.930')), _nb(_figure([1.0], title='UPL = 4.929')) - assert refresh.seen(saved) != refresh.seen(fresh) + assert not _alike(_nb(_figure([1.0], title='UPL = 4.930')), _nb(_figure([1.0], title='UPL = 4.929'))) + + +def test_reordered_traces_are_drift(): + """Trace order sets the legend and each line's colour, so a reorder is something a reader sees.""" + + def two(first, second): + traces = [{'type': 'scatter', 'name': first, 'y': [1.0]}, {'type': 'scatter', 'name': second, 'y': [2.0]}] + return _nb(new_output('display_data', data={PLOTLY: {'data': traces}})) + + assert not _alike(two('A_1', 'A_2'), two('A_2', 'A_1'))