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Analysis: summarise a stack of categorical rasters over time: mode, frequency, changes, longest run and more - #798
Yearly land-cover maps (MapBiomas, ESA WorldCover, CORINE) are a data cube of classes, and the questions asked of them are about classes:
which class a pixel was most of the time, and for how many years;
how often each class occurred;
how many classes it went through;
how many times it changed;
which class it kept longest without a break, and for how long;
in how many years it was forest.
cube.reduce (#774) summarises any cube over time, but only with statistics of quantities (mean, min, max, sum, std, count, countAbove). A mean of class codes means nothing.
This PR adds eight categorical statistics to cube.reduce. They work over every cube it already reads (layers, a raster's bands, a Zarr variable, a STAC collection) and with its groups (by: all, year, month), periods, seasons and minValid:
stat
Each cell's value
mode (or majority)
the most frequent class, the least of those tied
modeCount
at how many dates it had that class
frequency
a layer for each class met (… frequency 1985–2023 class 3): how many dates it had that class
variety
how many classes it had
changes
how many times its class changed from one date with a class to the next, in date order (A, no data, B is one change)
longestRun
the class it kept for the most dates in a row, in date order, the least of those tied (A, no data, A is a run of two)
longestRunLength
how many dates that run lasted
countIn
at how many dates it was one of "classes", whole numbers 0–255, such as the years a cell was forest
rustgis-cli run --data cobertura_1985.tif --data cobertura_1986.tif --data cobertura_1987.tif --cmd 'cube.reduce={"layers":["cobertura_1985","cobertura_1986","cobertura_1987"],"stat":"changes"}'
Choices in this PR
In cube.reduce, not a new command. The cube sources, groups, pieces and outputs are all there, and these are more ways to summarise a cell's series. They go in the same Stat and Totals, with a few more fields:
last, each cell's last class, for changes and the longest run;
run and longest, each cell's current run and longest run (class and length), for longestRun and longestRunLength;
each class's count in each cell, as u16, for mode, modeCount, variety and frequency (a cube has at most 2,000 dates).
frequency gives a layer for each class. The classes are only known once read, and a STAC cube read in pieces may meet a class only in a later piece. So ReduceRun::run keeps each group's outputs as columns keyed by class (Columns), and stack_columns puts each piece's columns under the earlier ones:
a class met first in a later piece gets the earlier pieces' value, which is 0, or nothing where a cell had too few dates for minValid;
a class a piece didn't meet gets that piece's value.
The other statistics give one column keyed None, as before. The outputs' cap (MAX_REDUCED) counts every class's layer and is checked after each piece. A run where no date had a class is refused, rather than giving no layer.
Values are classes. A value that isn't a whole number is refused, with the value (0.25 isn't a class: mode takes whole numbers…). This holds for countIn too: an NDVI given by mistake gets an error, not a zero that looks right. No-data and NaN are skipped, as for the other statistics.
Bounded: at most 1,000 classes in a run of cells, and at most MAX_VALUES cells × classes across the groups being read at once (64 million, so 128 MB of counts, on the desktop; 16 million in the browser). By month all twelve groups are open at once and share that cap (classes_over, checked as each slice is placed). For frequency, whose classes each become an output column, the cap is the outputs' own (MAX_REDUCED, 32 million), so it is refused before its counts and columns are made. A run past a cap is refused with the cap named, rather than growing.
changes, longestRun and longestRunLength read in date order. They compare each date with the one before, and the order matters:
With several readers (a STAC cube's), slices are placed as they are read, and one slow read lets the others run ahead with no limit. So read_slices takes in_order, and these three read with one reader. That makes them slower over a STAC collection, as the guide says.
The steps are sorted by date. A Zarr axis out of order ([31, 0, 59]) would otherwise count along the axis rather than through time.
Ties go to the least class in mode and longestRun. The result is then deterministic and doesn't depend on the order of arrival.
Counts with no value:count, countAbove, countIn, modeCount, variety, changes, longestRunLength and frequency give 0 where a cell never had a value (with the default minValid). mode and longestRun give nothing, as mean does.
By group: changes and runs are counted within each group, so by year they are within the year.
zarr.reduce refuses them and points to cube.reduce, which takes a Zarr layer or store too. zarr.reduce shares Stat::parse, and its pane's list of statistics, its docs and its reading order stay as they are.
No UI in this PR, as for cube.reduce itself. The statistics are reached from the Script pane, the assistant, the CLI and Python.
Testing
a_categorical_stack_summarised_over_time runs over four cells and six years, with the layers added out of date order. In one cell the mode (5, the lesser of two tied) and the longest run's class (7) differ.
Values: mode, majority, modeCount, variety, changes, longestRun, longestRunLength, countIn and frequency give the expected values. Frequency gives seven layers, named by class, with each cell's counts.
minValid: a cell with too few dates is left empty, in every class's layer for frequency.
Groups: by year, each year gets its own layer.
Order: a raster whose bands are the years out of order, with their dates given, gives the same changes and longest runs. A Zarr axis out of date order counts changes and runs in date order: one change and a run of two dates in class 2, where the axis gives two changes and runs of one date each.
Keys: none is said to be ignored.
Refusals: bad classes values are refused (missing, empty, 3.5, 256, a string, more than 256 entries). An NDVI stack is refused by the statistics, with the value named. 1,001 classes are refused. zarr.reduce with mode is refused and names cube.reduce.
slices_asked_in_order_come_in_order (in cube.rs) uses a cube of eight readers whose early dates are the slowest to read. With in_order the slices come in order.
a_class_met_in_a_later_piece_is_counted_0_before (in cube.rs) checks stack_columns: a class met first in a later piece gets 0, or nothing, for the earlier rows; a single output is extended piece after piece; and a column is made at its full length when the cube knows it.
groups_being_read_share_the_class_cap (in cube.rs): two groups each within the cap are over it together.
The tests fail without their fixes:
without in_order, the slices came as [7, 6, 5, 4, 3, 10, 11, 8, 9, 2, 1, 0];
without the sort, the shuffled Zarr axis gave 2 changes where the dates give 1, and a longest run in class 1 where the dates give 2;
without the earlier pieces' value, a class met in the second piece was one row long.
hostile_params_never_panic has four more entries:
junk classes (1e308, -1, text, null, 3.5, an object);
classes given as a string;
a mode by month with minValid: 2;
frequency by year with minValid: -1 and an array as the name.
The full cargo test -p rustgis-engine passes (322, 3 ignored). cargo fmt --all --check and cargo clippy -p rustgis-engine -p rustgis-cli --all-targets -- -D warnings are clean. The wasm build wasn't checked here, since there's no clang for zstd-sys on this machine. The new code has no cfg of its own, and in the browser read_slices already reads with one reader.
Added categorical statistics for time-series data cubes, including mode, mode count, variety, changes, class counts, longest runs, and per-class frequency.
Added countIn to count values from selected classes. Sequence statistics process observations in date order, with defined tie-breaking and handling for missing values.
Limitations
Categorical statistics are available through cube.reduce; zarr.reduce returns an error for these statistics.
Class counts and frequency outputs are subject to documented limits.
Documentation
Added guidance and an example for categorical summaries, including class requirements and missing-value behavior.
…de, modeCount, frequency, variety, changes, longestRun, longestRunLength, countIn)
Yearly land-cover maps (MapBiomas, ESA WorldCover) are a data cube of classes.
cube.reduce takes seven statistics for them, over any cube it already reads
(layers, a raster's bands, a Zarr variable, a STAC collection) and by the same
groups: mode (the most frequent class, the least of those tied), modeCount
(its count of dates), frequency (a layer for each class met: its count of
dates), variety (how many classes), changes (how many times the class changed
from one valued date to the next, in date order), longestRun and longestRunLength (the class and length of the
longest run of dates in one class) and countIn (at how many dates a cell was
one of "classes", 0-255). Values that aren't whole numbers are refused with
the value, as are more than 1,000 classes or MAX_VALUES cells x classes.
changes and longestRun read their dates in order: one reader (read_slices'
in_order) and the steps sorted by date (a Zarr axis out of order). Frequency's
classes, known as they are met, are stacked piece by piece by class
(stack_columns). zarr.reduce refuses them and points to cube.reduce.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Walkthrough
cube.reduce now supports categorical summaries, including class counts and date-ordered sequence statistics. It assembles frequency outputs across pieces and enforces class and output limits. zarr.reduce rejects categorical statistics and directs callers to cube.reduce.
Changes: Categorical cube summaries
Layer / File(s)
Summary
Parse options and compute categorical statistics crates/engine/src/cmd/cube.rs
The parser accepts mode, variety, changes, countIn, mode count, longest-run, and frequency options. Per-cell totals compute these results and enforce class-count limits.
Order dates and assemble outputs crates/engine/src/cmd/cube.rs, crates/engine/src/tests.rs
Date-sequence statistics sort selected steps by date and request ordered slice reads. The reducer merges class columns across pieces, applies validity masks, and checks output limits. Tests cover ordering and categorical results.
Reject unsupported Zarr statistics and document options crates/engine/src/cmd/zarr.rs, crates/engine/src/tests.rs, docs/user-guide/analysis.md
zarr.reduce rejects categorical statistics. Tests cover invalid parameters and this rejection. The guide describes categorical options, limits, missing-value behavior, and a CLI example.
sequenceDiagram
participant cube.reduce
participant read_slices
participant ReduceRun
participant Totals
cube.reduce->>read_slices: Read selected slices in date order when required
read_slices-->>ReduceRun: Provide slice data
ReduceRun->>Totals: Add valued cells
Totals-->>ReduceRun: Return categorical columns and masks
ReduceRun-->>cube.reduce: Return summary rasters
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Possibly related issues
opengeos/RustGIS#579: This PR adds categorical temporal summaries to cube.reduce, but does not add Sen's slope or Mann–Kendall tests.
opengeos/RustGIS#748: The PR adds temporal summaries that fall within the issue's testing scope for time-dependent analysis features.
Possibly related PRs
opengeos/RustGIS#774: PR #774 introduced the cube.reduce infrastructure that this PR extends with categorical statistics and outputs.
Suggested reviewers:giswqs
Merge Risk: Minimal · up to 6aa38
No identified issue currently blocks merging the categorical cube summaries after normal checks.
Pre-merge checks: 5
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The title clearly summarizes the primary change: adding categorical time-series statistics to cube.reduce.
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The description provides a detailed summary, implementation choices, testing coverage, and reported verification results. It omits the Related issue section, but the description is otherwise complet…
Docstring Coverage
Docstring coverage is 82.50% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 40 functions across 2 files.
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A rabbit counts each class in rows, And sorts the dates before it goes. The longest run receives its say, While absent classes fill the way. New raster layers join the stack, Then hop through tests and bound right back.
Comment @coderabbitai help to get the list of available commands.
I read the first 600 lines of the diff, which is the cube.rs changes. I did not read the diff for tests.rs, zarr.rs or analysis.md, and I could not build or run anything. The code reads as sound: it keeps to the no-panic rules (get(), saturating arithmetic, no unwrap). The tie-breaking, the run tracking and the piece stitching all look correct. I found no bugs.
Bugs
None found. I traced these by hand:
run tracking and the tie-break to the least class;
the stack_columns mask logic when a class first appears in a later piece;
the zero-versus-NaN rule for empty cells.
Security
None found.
Performance
Memory cap is per group, not total (medium): count_class checks MAX_VALUES for one Totals. With by: "month" up to 12 groups are held at once, so peak memory can far exceed the "128 MB of counts" the doc comment states.
Late reserve_exact (medium): it now runs after the column is extended, so it reallocates and copies the column instead of avoiding the doubling.
Quality
Undocumented alias (low): the "majority" alias is not in the error text or the params doc. classes also silently ignores entries past the 256th.
Comment formatting (low): the Totals doc comment has ragged line lengths.
AGENTS.md
No violations seen in the lines I read. They add no unwrap, panic! or unsafe, no UI strings and no new dependencies.
I did not check that the new tests.rs cases include hostile-parameter coverage for the new stats (classes junk, countIn without classes).
Change size
Compared: the whole PR against main (merge base 015a046 to PR head 0caa1b6)
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Inline comments:
Review comments at @crates/engine/src/cmd/cube.rs:
- Line 1072: In the classes-list parsing path, reject lists longer than 256
entries with NEED (or a specific error) before processing them. Remove the
take(256) limit so every accepted entry is validated and added to the set.
- Around line 1216-1218: Update the class-limit check in the `count_class` flow
to use `MAX_REDUCED` as the per-group cells-by-classes cap for
`Stat::Frequency`, while retaining `MAX_VALUES` for other statistics and the
existing `MAX_CLASSES` limit. Use the selected cap in the refusal message so it
reports the limit that triggered the check.
Review comments at @crates/engine/src/tests.rs:
- Around line 9341-9342: Add a non-wasm target cfg gate to the
a_categorical_stack_summarised_over_time test, matching the existing gate on the
sibling a_data_cube_summarised_over_time test.
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…s at the outputs' cap); columns made at their full length; more than 256 classes refused; majority said
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
I read the diff, which adds categorical statistics to cube.reduce. I could not run the build or tests. I found no high-confidence defects and posted no inline comments. The points below are lower-confidence notes.
Bugs
Classes above 2^24 (low confidence).Totals::add accepts any whole number as a class, then compares and counts it as f32 (v as f32). Two distinct large integer classes, such as 16777217 and 16777216, collapse into one class. Land-cover codes are small, so this only matters for unusual data. Restricting categorical stats to 0–65535 or so would remove the corner case.
Run counters are u16 (low confidence). The per-class counts and the run length use saturating_add, so they cap silently at 65535 dates. That is safe from panics, but a very long daily series would report wrong counts without any message.
Reader order for ordered stats (low confidence).in_order forces a single reader for changes and longestRun. This is correct. It also makes these stats slower on large cubes, because reads that were parallel become sequential. The docs could mention that.
Security
None found. classes is bounded to at most 256 entries, each a whole number from 0 to 255. Class growth is capped by MAX_CLASSES and by the cells × classes caps, which are checked as each slice is placed.
Performance
count_class insertion (low confidence). Each new class does a sorted Vec::insert into classes and counts, which is O(classes). It is bounded by MAX_CLASSES = 1000, so it is fine.
Totals::mode and Variety loops. Both scan every class for every cell in finish. That is O(cells × classes), so up to about 16M iterations at the MAX_REDUCED cap, which is acceptable.
Frequency output (low confidence).stack_columns and masks keep a mask vector per group for the whole run, which adds memory. The memory is bounded by the MAX_REDUCED check after each piece. That check runs after the piece's columns are built, so one piece can briefly exceed the cap.
Quality
Doc comment on Totals (nit). It is long and its line wrapping is uneven. The 4-to-20-bytes-per-cell memory note and the MAX_REDUCED doc comment are dense and could be split into per-stat bullets.
classes() check order (nit). The list.len() > 256 check comes after .ok_or(NEED)?, so an oversized list is still rejected correctly. Moving the check ahead of the allocation would read more naturally.
Class-count message (nit). The class_cap message says "more than {} classes", but the number shown is the count when the cap was hit, which is not always the limit. The count can come from the cells × classes cap rather than MAX_CLASSES, so the message can be misleading.
majority alias. It maps to Mode, and the stat's code() reports mode in error messages. That is fine.
AGENTS.md
No unwrap, expect, panic or unsafe, and indexing uses get/get_mut. Hostile input (non-integer values, a bad classes list) returns an Err.
New stats should be covered by hostile_params_never_panic and by regression tests. I could not verify this. tests.rs grew by about 10 KB, which suggests tests were added.
No new UI strings. The command's parameter doc and docs/user-guide/analysis.md were updated.
No dependencies or assets changed. There are no wasm concerns, since read_slices still uses a single reader on wasm32.
Change size
Compared: the whole PR against main (merge base 015a046 to PR head 8f62f5c).
On the three low-confidence notes, no change is needed:
Classes above 2^24: the slices are f32 already (bands[b].data, cube.rs:1418), so a class reaches Totals::add as an f32. v as f32 gives back the same value, and two classes can only be one if the raster itself stores them as one.
u16 counts: a cube is read at MAX_DATES (2,000) dates at most (cube.rs:50), so a class count or a run can't reach 65,535.
Ordered stats are slower: the guide says so. changes, longestRun and longestRunLength read a STAC collection one date at a time, in order, so they are slower there (docs/user-guide/analysis.md, in the Classes item).
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @crates/engine/src/cmd/cube.rs:
- Around line 1438-1441: Update the shared-cap check using classes_over to
include the values held by columns already stored in finished, and extend
classes_over to accept that starting amount when calculating the total. Also run
the check after a group finishes in the place flow so newly retained columns are
counted immediately.
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- Include completed groups' retained columns in the categorical reduction memory cap.
- Recheck the cap after finishing a group and cover completed groups in the regression.
This PR adds categorical statistics to cube.reduce: mode/majority, modeCount, variety, changes, longestRun, longestRunLength, frequency and countIn. It also makes zarr.reduce reject them. I read the diff and the surrounding source. I could not build or run tests in this environment. The logic looks sound, and I found no crash paths or correctness bugs.
Bugs
None found. I checked the following:
The stack_columns mask logic across pieces.
Totals::add and finish for NaN and empty cells.
The tie-breaking rules.
The u16 saturation.
The refused propagation.
The cell and class caps.
Security
No issues. Class counts, class caps and the classes list length are all bounded, which suits the hostile-input rule.
Performance
read_slices forces a single reader for changes, longestRun and longestRunLength (cube.rs:741). This is correct, but remote cubes (STAC, remote Zarr) read serially. A reorder buffer would keep the ordering and still allow parallel reads. The PR documents the tradeoff. Confidence: low.
classes_over runs for every stat, not only the class-counting ones (cube.rs:1539). For mean, min and similar it does extra summing on every slice and its message would be misleading, but it can never fire. Confidence: medium-low, minor.
Quality
The finite-date check runs after the new in-order sort_by (cube.rs:1603). This is harmless, but the error would be clearer if the check ran first. Confidence: low, nit.
Several doc comments on Totals and MAX_REDUCED are very long run-on paragraphs, and a few lines are unwrapped. This is cosmetic.
The new tests cover reader ordering, piece stacking and the shared class cap. I did not see a regression case showing that hostile_params_never_panic includes the new stat names. The tests.rs hunk was not in the part of the diff I read, so please check that it covers them.
AGENTS.md
The code has no unwrap or expect and no unsafe in non-test code. It uses get/get_mut throughout.
It builds for wasm: read_slices uses a single reader when cfg!(target_arch = "wasm32").
The new zarr.reduce error and STATS message strings are command output, not interface text, so no tl! is needed.
The docs were updated (docs/user-guide/analysis.md).
Change size
The whole PR against main (merge base 015a046 to PR head 6aa3869).
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automergeApprove and squash-merge once the required checks pass
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Summary
Yearly land-cover maps (MapBiomas, ESA WorldCover, CORINE) are a data cube of classes, and the questions asked of them are about classes:
cube.reduce(#774) summarises any cube over time, but only with statistics of quantities (mean, min, max, sum, std, count, countAbove). A mean of class codes means nothing.This PR adds eight categorical statistics to
cube.reduce. They work over every cube it already reads (layers, a raster's bands, a Zarr variable, a STAC collection) and with its groups (by: all, year, month), periods, seasons andminValid:statmode(ormajority)modeCountfrequency… frequency 1985–2023 class 3): how many dates it had that classvarietychangeslongestRunlongestRunLengthcountIn"classes", whole numbers 0–255, such as the years a cell was forestrustgis-cli run --data cobertura_1985.tif --data cobertura_1986.tif --data cobertura_1987.tif --cmd 'cube.reduce={"layers":["cobertura_1985","cobertura_1986","cobertura_1987"],"stat":"changes"}'Choices in this PR
In
cube.reduce, not a new command. The cube sources, groups, pieces and outputs are all there, and these are more ways to summarise a cell's series. They go in the sameStatandTotals, with a few more fields:last, each cell's last class, forchangesand the longest run;runandlongest, each cell's current run and longest run (class and length), forlongestRunandlongestRunLength;u16, formode,modeCount,varietyandfrequency(a cube has at most 2,000 dates).frequencygives a layer for each class. The classes are only known once read, and a STAC cube read in pieces may meet a class only in a later piece. SoReduceRun::runkeeps each group's outputs as columns keyed by class (Columns), andstack_columnsputs each piece's columns under the earlier ones:minValid;The other statistics give one column keyed
None, as before. The outputs' cap (MAX_REDUCED) counts every class's layer and is checked after each piece. A run where no date had a class is refused, rather than giving no layer.Values are classes. A value that isn't a whole number is refused, with the value (
0.25 isn't a class: mode takes whole numbers…). This holds forcountIntoo: an NDVI given by mistake gets an error, not a zero that looks right. No-data and NaN are skipped, as for the other statistics.Bounded: at most 1,000 classes in a run of cells, and at most
MAX_VALUEScells × classes across the groups being read at once (64 million, so 128 MB of counts, on the desktop; 16 million in the browser). By month all twelve groups are open at once and share that cap (classes_over, checked as each slice is placed). Forfrequency, whose classes each become an output column, the cap is the outputs' own (MAX_REDUCED, 32 million), so it is refused before its counts and columns are made. A run past a cap is refused with the cap named, rather than growing.changes,longestRunandlongestRunLengthread in date order. They compare each date with the one before, and the order matters:read_slicestakesin_order, and these three read with one reader. That makes them slower over a STAC collection, as the guide says.[31, 0, 59]) would otherwise count along the axis rather than through time.Ties go to the least class in
modeandlongestRun. The result is then deterministic and doesn't depend on the order of arrival.Counts with no value:
count,countAbove,countIn,modeCount,variety,changes,longestRunLengthandfrequencygive 0 where a cell never had a value (with the defaultminValid).modeandlongestRungive nothing, asmeandoes.By group: changes and runs are counted within each group, so by year they are within the year.
zarr.reducerefuses them and points tocube.reduce, which takes a Zarr layer or store too.zarr.reducesharesStat::parse, and its pane's list of statistics, its docs and its reading order stay as they are.No UI in this PR, as for
cube.reduceitself. The statistics are reached from the Script pane, the assistant, the CLI and Python.Testing
a_categorical_stack_summarised_over_timeruns over four cells and six years, with the layers added out of date order. In one cell the mode (5, the lesser of two tied) and the longest run's class (7) differ.minValid: a cell with too few dates is left empty, in every class's layer for frequency.classesvalues are refused (missing, empty, 3.5, 256, a string, more than 256 entries). An NDVI stack is refused by the statistics, with the value named. 1,001 classes are refused.zarr.reducewithmodeis refused and namescube.reduce.slices_asked_in_order_come_in_order(incube.rs) uses a cube of eight readers whose early dates are the slowest to read. Within_orderthe slices come in order.a_class_met_in_a_later_piece_is_counted_0_before(incube.rs) checksstack_columns: a class met first in a later piece gets 0, or nothing, for the earlier rows; a single output is extended piece after piece; and a column is made at its full length when the cube knows it.groups_being_read_share_the_class_cap(incube.rs): two groups each within the cap are over it together.in_order, the slices came as[7, 6, 5, 4, 3, 10, 11, 8, 9, 2, 1, 0];hostile_params_never_panichas four more entries:classes(1e308,-1, text, null,3.5, an object);classesgiven as a string;minValid: 2;minValid: -1and an array as the name.cargo test -p rustgis-enginepasses (322, 3 ignored).cargo fmt --all --checkandcargo clippy -p rustgis-engine -p rustgis-cli --all-targets -- -D warningsare clean. The wasm build wasn't checked here, since there's no clang for zstd-sys on this machine. The new code has nocfgof its own, and in the browserread_slicesalready reads with one reader.rustgis-cli run, with progress for trends and summaries (#579) #792, which also touchescube.rs.🤖 Generated with Claude Code
Summary by CodeRabbit
countInto count values from selected classes. Sequence statistics process observations in date order, with defined tie-breaking and handling for missing values.cube.reduce;zarr.reducereturns an error for these statistics.