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Fix the audit's five P0s: facet by= length, object-numeric columns, colormap stop order, category-axis type_, tick labels past 1e6 - #507
Fixes the five P0 (silent wrong output) findings from the 0.0.7 framework audit. Each was reproduced on origin/main before the change and has a regression test after it.
#
Was
Now
1
facet_chart(by=['a','b'], data=df) with 3 rows: every panel drew all 3 rows
ValueError: facet_chart by= has 2 values but data has 3 rows … (chart- and mark-level data=)
mantissa digits follow the step: 1.00e6, 1.05e6, 1.10e6 …, 1.25e6; Python _exp_digits and TS expDigits are the same function, asserted equal by a node parity test
Notes
Mapping data= keeps its documented pass-through for scalars and short config lists (existing test_subset_data_masks_only_row_aligned_columns); the DataFrame case is the one that silently duplicated data.
Missing values in object columns become NaN through a shared columns.object_missing_to_nan, which also handles pd.NA (NumPy's astype(float) refuses it).
The CSS gradient-string colormap form is unchanged: CSS clamping is that grammar's rule. Only the (position, color) sequence form now rejects decreasing positions.
spec/design/chart-grammar.md G3 and the facet section, spec/api/styling.md colormap stops, spec/design/renderer-architecture.md §6.2.
Tests
New: test_object_numeric_columns.py, test_axis_type_conflicts.py, test_tick_label_precision.py (Python + node parity + exported SVG); additions to test_facets.py and test_custom_ramps_and_palette.py. Full suite, ruff check/format, ty check (no new diagnostics vs main) run locally.
Summary by CodeRabbit
Bug Fixes
Exponential tick labels retain enough precision to remain distinct on very large or small axes.
Numeric object columns with missing values are handled as continuous data with NaN gaps.
Invalid facet arrays now raise clear errors when lengths do not match the data.
Reversed colormap stops and incompatible scale types on categorical axes now raise clear errors.
Browser and static chart exports format tick labels consistently.
Documentation
Clarified colormap stop ordering, scale compatibility, numeric value detection, and facet array length requirements.
Before / after (live client, headless Chrome, same chart and view)
Fix 5, a 50,000-step axis from 1,000,000 to 1,300,000. Left: v0.0.7 labels. Right: this branch.
The other four fixes replace silent wrong output with a ValueError (1, 3, 4) or change axis classification (2), so their evidence is the regression tests rather than a picture.
…ap stop order, category-axis type_, tick labels past 1e6
Five silent-wrong-output defects found by the 0.0.7 audit, each reproduced
before the fix and covered by a test after it.
- facet_chart: a by= array whose length differs from the data's row count
passed the DataFrame through unsplit, so every panel drew the whole dataset
under its own label. It is now a ValueError naming both counts, for
chart-level and mark-level data= tables alike. Mapping data keeps its
documented pass-through for scalar and short config values.
- Category detection: an object-dtype column whose non-missing values are all
real numbers (a list holding a None, an object ndarray, Decimals, a pandas
object Series with NaN) became a categorical axis with labels '1',
'(missing)', '3' at positions 0, 1, 2, while the color channel classified
the same input as continuous. `Figure._is_category_like` now applies the
color channel's rule (`channels._object_array_is_real_numeric`); missing
entries ingest as NaN through a shared `columns.object_missing_to_nan`
(pandas NA included, which NumPy alone refuses). Strings, bytes, bools and
mixed values stay categorical.
- Colormap stops: out-of-order positioned stops were clamped CSS-style, so
[(1, "red"), (0, "blue")] resolved to 255 red texels and one blue. A
decreasing position is now a ValueError that points at the offending stop.
- Axes: `x_axis(type_="time"|"log"|"symlog")` on an axis the marks made
categorical was accepted; time turned the labels into 1970 epoch ticks and
log put category 0 off the axis. `_axis_kind` raises a build-time error
(G3: scale conflicts are errors, not coercions).
- Tick labels: `_fmt_linear` / `fmtLinear` formatted every |v| >= 1e6 (or
< 1e-4) tick with one mantissa decimal regardless of step, so a 50,000-step
axis read "1.0e6, 1.1e6, 1.1e6, 1.2e6, 1.2e6, 1.3e6, 1.3e6" and 1,250,000
was labelled "1.2e6" — in the browser, SVG, PNG and PDF alike. The mantissa
now carries the digits between the value's magnitude and the step's last
significant digit, implemented identically in Python and TypeScript with a
node-backed parity test.
Spec: chart-grammar.md G3 (category/type conflict, value-based category
detection, by= row coverage), styling.md (non-decreasing stops),
renderer-architecture.md §6.2 (exponential label precision).
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The change updates chart input handling and validation, and changes exponential tick-label precision. Regression tests and documentation cover the updated behavior.
Object columns with real values and missing entries become numeric float columns with NaN holes. Strings, bytes, booleans, and mixed values remain categorical.
Axis type conflict validation python/xy/_figure.py, tests/test_axis_type_conflicts.py, spec/design/chart-grammar.md
Forcing a time, log, or symlog scale on a categorical axis now raises ValueError, including when the axis has no category labels.
Python and JavaScript formatters derive mantissa precision from the value and tick step. Tests cover distinct labels, SVG output, subnormal steps, half-up rounding, and Python/JavaScript parity.
SVG export can fail when an application sets low decimal precision. The issue is narrow, but the formatter should use its own context.
Pre-merge checks: 4 · 1
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Title check
The title names five specific fixes: facet by= length validation, object-numeric column handling, colormap stop ordering, category-axis type_ errors, and tick label precision past 1e6. Each one matche…
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Explanation
The title names five specific fixes: facet by= length validation, object-numeric column handling, colormap stop ordering, category-axis type_ errors, and tick label precision past 1e6. Each one matches a file group in the raw summary: facets.py, columns.py/_figure.py, _validate.py, the axis type tests, and the tick formatting in 30_ticks.ts and _svg.py. The title is specific and accurate. It does not describe the full set of spec and test changes, which is acceptable. The term P0 refers to audit priority and may need context for some readers, but it does not make the title misleading or vague.
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Write docstrings for the functions missing them to satisfy the coverage threshold.
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System calls cannot be simulated consistently and are left out of the measure. The reported value understates the real cost. Switch to the Walltime instrument to measure them.
The flamegraph wasn't generated: the symbols of the profiling trace could not be resolved, because no information about the language frames was collected.
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Inline comments:
In `@python/xy/_figure.py`:
- Line 1663: Update the forced-scale validation around the categories check to
use categorical-axis registry membership rather than the truthiness of the
categories list, so empty categorical axes still reject forced “time”, “log”,
and “symlog” scales. Add regression coverage for empty categorical x data with
each forced scale.
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📒 Files selected for processing (15)
js/src/30_ticks.ts
news/507.bugfix.md
python/xy/_figure.py
python/xy/_svg.py
python/xy/_validate.py
python/xy/columns.py
python/xy/facets.py
spec/api/styling.md
spec/design/chart-grammar.md
spec/design/renderer-architecture.md
tests/test_axis_type_conflicts.py
tests/test_custom_ramps_and_palette.py
tests/test_facets.py
tests/test_object_numeric_columns.py
tests/test_tick_label_precision.py
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…g, exponential label edge cases
- facet_chart: a mapping-backed column that a mark channel names as row data
must have the by= length too (checked in _facet_check_mark_channels, where
the channel->column linkage is known); short config values in the mapping
keep passing through.
- columns: the object->NaN hole-filling pass is counted as an ingest copy
(§29 honest accounting).
- Exponential tick labels: clamp the step exponent so 10**e cannot underflow
to zero on subnormal steps; raise the mantissa cap from 8 to 15 so a 1e-3
step at 1e6 magnitude still labels distinctly; format the mantissa with
JavaScript's half-up tie rounding on the exact binary value (Python's :e is
half-even, so 1.25e6 at one digit read 1.3e6 live and 1.2e6 exported).
Parity test cases added for all three.
Guard the parity test against unsupported Node.js versions.
CI uses Node.js 22, which supports direct .ts imports. Local Node.js versions below 22.6 can fail at the import before the assertions run because the test checks only whether node exists. Check the Node.js capability or use a TypeScript loader.
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In `@tests/test_tick_label_precision.py` around lines 70 - 91, Update
test_python_and_client_formatters_agree to verify that the detected Node.js
runtime supports direct TypeScript imports before executing the parity script,
and skip the test when it does not; preserve the existing assertions for
supported runtimes.
This mapping case always raises on x. It does not prove that matching mapping columns are masked per panel or that short configuration values remain pass-through. Add a non-raising mapping case with x and y lengths equal to len(by), a shorter config value, and assertions for panel point counts and sliced values.
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In `@tests/test_facets.py` around lines 394 - 401, Add a successful mapping-data
facet test alongside the existing ValueError case, using x and y columns whose
lengths match len(by) and a shorter config value that remains pass-through. Call
facet_chart with xy.scatter and assert each panel’s point count and sliced x/y
values, confirming matching mapping columns are masked per panel without
raising.
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Inline comments:
In `@python/xy/_svg.py`:
- Around line 679-701: Increase the exponential-label digit cap from 15 to 16 in
_EXP_DIGITS_MAX within _exp_digits in python/xy/_svg.py (lines 679-701) and in
EXP_DIGITS_MAX within the JavaScript formatter at js/src/30_ticks.ts (lines
185-196). Add a regression case using adjacent f64 values with a one-ULP step,
while preserving f64 tick and hover calculations and the existing f32
offset-encoding behavior.
In `@python/xy/components.py`:
- Line 5335: Update the row-validation branch in the relevant mark/channel
handling logic to resolve channel semantics by mark kind before treating a
string as a mapping-backed column. Skip literal color channels, including direct
CSS color strings used by line and scatter marks, and only perform the
mapping-key row-length check for channels that are data-backed.
- Line 5332: Update the facet row-check logic around _FACET_CHANNEL_PROPS to
include all registered MarkPlugin.columns in items before panels are built,
ensuring plugin-backed columns such as value participate in row-count validation
and are sliced consistently by _subset_data().
---
Outside diff comments:
In `@tests/test_tick_label_precision.py`:
- Around line 70-91: Update test_python_and_client_formatters_agree to verify
that the detected Node.js runtime supports direct TypeScript imports before
executing the parity script, and skip the test when it does not; preserve the
existing assertions for supported runtimes.
---
Nitpick comments:
In `@tests/test_facets.py`:
- Around line 394-401: Add a successful mapping-data facet test alongside the
existing ValueError case, using x and y columns whose lengths match len(by) and
a shorter config value that remains pass-through. Call facet_chart with
xy.scatter and assert each panel’s point count and sliced x/y values, confirming
matching mapping columns are masked per panel without raising.
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js/src/30_ticks.ts
python/xy/_svg.py
python/xy/columns.py
python/xy/components.py
spec/design/renderer-architecture.md
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… 16-digit label cap, node guard
- _facet_check_mark_channels also checks a plugin mark's declared columns, and
skips a color channel whose value is a CSS color literal even when the
mapping holds a key of that name (paint, not a row column).
- Exponential tick labels may carry 16 fractional digits (17 significant), so
two adjacent f64 values at a one-ulp step still print distinctly; parity
case added.
- The TS/Python parity test skips on node < 22.6 and passes
--experimental-strip-types on 22.x instead of failing at the import.
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In `@tests/test_tick_label_precision.py`:
- Line 85: Update _node_type_stripping() so Node.js 23.0–23.5 returns
["--experimental-strip-types"], while Node.js 23.6+ and other currently
supported versions retain their existing behavior.
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No blocking issues found. I checked out the PR head and built the core and the JS bundles. Then I ran each of the five fixes through the public API, with static export and with the live client in headless Chromium.
Facet by=: a length mismatch raises for a chart-level DataFrame, a chart-level mapping, a mark-level DataFrame and a mark-level mapping. A matching by=, by="col" and color="red" with a "red" key in the mapping still build.
Object-numeric columns:[1, None, 3], Decimals, pd.array(..., dtype="Int64") with pd.NA, and an object Series with NaN now give linear axes. Strings, mixed values and all-None columns stay categorical. Object date and datetime columns stay time axes.
Colormap: reversed positioned stops raise. The CSS-string form still clamps, and unpositioned middle stops still build.
Category axis type_:time, log and symlog raise; linear builds. A numeric axis with log is unaffected.
Tick labels: the live client shows 1.00e6 … 1.30e6 and the SVG export has the same labels. The 1e15 and float32 inputs also give distinct labels.
Ruff passes on the PR files, and all 30 CI checks are green on 5cfcc48. The full local suite gave 4082 passed and 1 failed. The failure is test_shared_glhost.py::test_mixed_size_presentation_at_device_pixel_ratio_two, a browser DPR test that this PR does not touch, and it passes in CI. I have three optional notes inline.
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Optional, not blocking. With this cap a y label can now be 20 characters, and the left gutter does not fit it. Repro: xy.line_chart(xy.line([0, 1, 2], [1e15, 1e15 + 3, 1e15 + 6]), width=500, height=400).to_html(...). In headless Chromium every y label (1.000000000000000e15 … 1.000000000000006e15) starts at x = −9 to −12 px, so the leading 1. is clipped and the axis reads 000000000000006e15. The gutter estimate drift already exists on main (a 99990.00000001 label is also at −8 px), so this PR does not cause it. But the PR makes much longer labels possible. A follow-up could fix _estimateTickLabel for long labels, or use the SI/offset labels that the PR description mentions.
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Nit: this sentence says the check is in facets._subset_data and covers any data= table. That is true only for a DataFrame. For a mapping, components._facet_check_mark_channels does the check, and only on the columns that a mark channel names. A mismatched column that no channel names still passes through. For example, data={"x": [1, 2], "y": [4, 5], "extra": [1, 2, 3]}, by=["a", "b"] builds without an error. That behavior is intended and the PR notes describe it. Please name both places in the spec and state the mapping rule, so that the spec agrees with the code.
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Nit: the pyplot shim also reaches this error. fig, ax = xy.pyplot.subplots(); ax.bar(["a", "b"], [1, 2]); ax.set_xscale("log"); fig.savefig(...) now raises ... cannot be a log axis; drop type_= or pass numeric positions. A pyplot user does not set type_=, so the hint does not help them. Matplotlib accepts this call. Consider a shim-specific message, or a note in spec/matplotlib/compat.md.
Facet panels split data only when its rows match by=.
Object columns with real numbers stay numeric when values are missing.
Categorical axes reject forced time, log, and symlog types.
Positioned colormap stops must stay in non-decreasing order.
Large and tiny tick labels keep enough digits to stay distinct.
Two fixes need follow-up: exported labels depend on the caller's Decimal precision, and missing-value cleanup can write to a temporary copy for transposed matrices.
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Decimal precision breaks exports
If the caller sets decimal.localcontext() to prec=6, the new _fmt_exponential raises decimal.InvalidOperation when formatting 1000000.002 at a 0.001 step. scaleb first rounds the value, and quantize then needs more digits than the context allows. Chart exports inside that context fail, while browser labels still work.
Use a private Decimal context with enough precision and add a regression test.
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Matrix missing values survive
For a Fortran-order matrix, astype preserves the layout and out.reshape(-1) can create a copy. This assignment changes that temporary copy rather than out. A transposed object matrix containing pd.NA therefore reaches this helper through heatmap, keeps its missing value, and fails the later float conversion.
Assign through out.flat, or make out C-contiguous before reshaping, and add a transposed-matrix test.
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Review comments at @python/xy/_svg.py:
- Line 718: Update _fmt_linear to perform decimal scaling and quantization
within a local decimal context whose precision is sufficient for the value and
requested fractional digits, independent of the caller’s decimal context;
preserve the existing rounding and tick-formatting behavior.
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Minor · Stability & Availability · Quick win
Use a local decimal context for tick formatting.
If a caller sets decimal precision to 6, _fmt_linear(1_000_001, 1) requests six fractional digits. quantize() then needs a seven-digit coefficient and raises InvalidOperation, so SVG rendering fails. Run the scaling and quantization in a local context with sufficient precision, independent of the caller's context. Python documents this precision limit for quantize(). (docs.python.org)
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Review comment at @python/xy/_svg.py at line 718:
Update _fmt_linear to perform decimal scaling and quantization within a local
decimal context whose precision is sufficient for the value and requested
fractional digits, independent of the caller’s decimal context; preserve the
existing rounding and tick-formatting behavior.
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Fixes the five P0 (silent wrong output) findings from the 0.0.7 framework audit. Each was reproduced on
origin/mainbefore the change and has a regression test after it.facet_chart(by=['a','b'], data=df)with 3 rows: every panel drew all 3 rowsValueError: facet_chart by= has 2 values but data has 3 rows …(chart- and mark-leveldata=)scatter(x=[1, None, 3]),np.array([1,2,3], dtype=object), Decimals → categorical axis'1', '(missing)', '3'at 0,1,2_object_array_is_real_numeric); strings/bytes/bools/mixed stay categoricalcolormap=[(1,'red'),(0,'blue')]→ 255 red texels + 1 blue (CSS-style clamp)ValueError: colormap stop positions must be non-decreasing …bar(x=['a','b']) + x_axis(type_='time')→kind: time, 1970 epoch ticks;type_='log'→ category 0 off-axisValueError: x axis is categorical (2 categories …) and cannot be a time axis(G3)1.0e6, 1.1e6, 1.1e6, 1.2e6, 1.2e6, 1.3e6, 1.3e6; 1,250,000 →1.2e6— browser, SVG, PNG, PDF alike1.00e6, 1.05e6, 1.10e6 …,1.25e6; Python_exp_digitsand TSexpDigitsare the same function, asserted equal by a node parity testNotes
data=keeps its documented pass-through for scalars and short config lists (existingtest_subset_data_masks_only_row_aligned_columns); the DataFrame case is the one that silently duplicated data.columns.object_missing_to_nan, which also handlespd.NA(NumPy'sastype(float)refuses it).(position, color)sequence form now rejects decreasing positions.1.25M) is the better long-term fix for Harden GL context-loss recovery and gate dashboard reliability in CI #5 and is listed as an improvement in the audit; this change removes the duplicate-label defect without changing the label style.Spec
spec/design/chart-grammar.mdG3 and the facet section,spec/api/styling.mdcolormap stops,spec/design/renderer-architecture.md§6.2.Tests
New:
test_object_numeric_columns.py,test_axis_type_conflicts.py,test_tick_label_precision.py(Python + node parity + exported SVG); additions totest_facets.pyandtest_custom_ramps_and_palette.py. Full suite,ruff check/format,ty check(no new diagnostics vs main) run locally.Summary by CodeRabbit
Bug Fixes
Documentation
Before / after (live client, headless Chrome, same chart and view)
Fix 5, a 50,000-step axis from 1,000,000 to 1,300,000. Left: v0.0.7 labels. Right: this branch.
The other four fixes replace silent wrong output with a ValueError (1, 3, 4) or change axis classification (2), so their evidence is the regression tests rather than a picture.