plotter/docs/data-and-performance.md

# Data and performance

## The sample stores

All series derive from `plot::data::Series` (`include/plot/data/series.hpp`), which
describes the sample-indexing contract; a **uniform-grid** series additionally has
sample `i` at `x = x0() + i * dx()` (`ArraySeries`, `StreamSeries`), while
**`XYSeries`** carries its own sorted, ascending x array for data that is not on a
uniform grid.

```cpp
class Series {
 public:
  virtual u64 size() const;
  virtual f64 x0() const;                 // x of sample 0 (the oldest retained sample)
  virtual f64 dx() const;                 // 0 for XYSeries, which owns its x array
  virtual Range x_bounds() const;
  virtual Range y_bounds() const;
  virtual const MinMaxPyramid& pyramid() const;
  virtual bool streaming() const noexcept;                 // default false
  virtual f64 x_at(u64 i) const;                           // x0() + i*dx(), or x()[i]
  virtual bool neighbours(f64 x, u64& i0, u64& i1) const;  // bracketing samples
  virtual u64 snap_index(f64 x) const;                     // nearest sample
  virtual void gather(u32 level, u64 begin, u64 end, std::vector<f32>& out_xy,
                      GatherMode mode = GatherMode::Envelope) const;

  Range reduce(u64 begin, u64 end) const;                  // exact min/max over [begin, end)
  f64   sum(u64 begin, u64 end) const;                     // exact sum over [begin, end)
  f64   sumsq(u64 begin, u64 end) const;                   // exact sum of squares
};
```

Indices are the series' **own window**: index 0 is the oldest sample it currently holds, so
`size()`, `x0()`, `reduce()`, `gather()` and `pyramid()` are all in the same coordinates.

**`ArraySeries`** (`array_series.hpp`) owns a `std::vector<f32>` of y samples plus an
`x0`/`dx` grid. Construct with `ArraySeries(std::vector<f32> y, f64 x0, f64 dx)`. The vector
is taken by value and owned, so the samples cannot change behind the series' back; the store
changes only through `reset(y, x0, dx)` (a whole new vector, then a rebuild), `commit()`,
which rebuilds the pyramid and cached bounds from the current `y`, or `push(y)`, which
appends one sample incrementally — the pyramid folds it in amortised O(1) and the bounds
extend in place, NaN-aware, instead of rebuilding.

**`StreamSeries`** (`stream_series.hpp`) is the single-producer / single-consumer streaming
store: `StreamSeries(u64 capacity, f64 x0, f64 dx)`. `capacity` is rounded up to a power of
two by the ring, `size()` saturates at that rounded `capacity()` (a `StreamSeries(100)`
retains up to 128 samples), and the pyramid is reserved for the rounded value up front.
`freeze()` copies the window published by the last `acquire()` into a fresh `ArraySeries`
(consumer thread only): a stable snapshot that no later production can move.

## Non-finite samples

The library-wide NaN policy: **a non-finite sample is excluded from every aggregate** —
min, max, sum, sum of squares and the pyramid's per-bucket finite count — and from the
cached bounds. A window with no finite samples at all aggregates to a **zero-span
`Range` / zero sums**, never to NaN. The policy is applied everywhere an aggregate is
built: `MinMaxPyramid::append` (a NaN occupies its absolute position, so bucket
boundaries stay positional, but folds nothing), the edge folds in `Series::reduce`,
`sum` and `sumsq`, `ArraySeries::commit` and `push` bounds, and
`StreamSeries::acquire` bounds. A bucket whose samples were all non-finite is stored as
an *empty* aggregate (count 0, zero sums) that every reader skips — it is absent, not
zero-valued. `gather()` follows suit: Envelope and Mean emit nothing for such a bucket,
and Nearest draws its centre sample raw. Hosts that want gaps in the data should draw
them with NaN samples and will get exactly that: excluded from statistics, present in
raw geometry.

## Gather x is relative, and what each mode emits

**`Series::gather` emits x relative to `x0()`** — exactly `i * dx` for a uniform
series, `x_at(i) - x0()` for `XYSeries` — never the absolute coordinate. y is
unchanged. The renderer reconstructs absolute data x in f64 (`x0() + emitted`) before
transforming, so no precision is lost at the f32 narrowing: the staged value is
O(visible pixels), not O(epoch). (Staging an absolute epoch x as f32 would quantise it
to a grid coarser than the pixel — f32 carries 24 bits, about 1 s at 1.7e9 — while the
relative value is small and exact.) The emitted geometry always spans
`[begin, end)` exactly and never reaches outside it: at level ≥ 1 the bucket edges are
selected ceil/floor (`first` = first bucket starting at or after `begin`, `last` = one
past the last bucket ending at or before `end`) and the two partial edge spans are
drawn from raw samples, which is what keeps an unaligned window envelope-exact
(previously the buckets straddling the range leaked samples from outside it into the
geometry). `level` is clamped to `kMaxPyramidLevel` (57 — the deepest level whose
bucket span fits a `u64`), so any `u32` level is safe.

The `GatherMode` decides what each bucket contributes, at the same level the renderer
already picked:

- **`Envelope`** (the default): one (min, max) pair per bucket, min drawn at the
  bucket's first sample and max at its last. The dense, extreme-reading geometry a
  time-series trace wants.
- **`Nearest`**: one raw sample per bucket — the centre sample, the pick
  `pyramid().snap()` would make — drawn at its own x. Thinner geometry for traces
  whose *shape* matters more than their extremes.
- **`Mean`**: one point per bucket, y = the bucket's finite samples' mean (the exact
  `bucket_sum()` the pyramid stores divided by its finite count), x at the bucket
  centre. A smoothed trace for noisy data.

Level 0 is the raw samples for every mode.

## The SPSC contract

One producer thread, one consumer thread, and the division is strict
(`stream_series.hpp`):

- **The producer may only call** `push(f32 y)`, `push(const f32* ys, u64 n)` and
  `produced()`. Those touch the ring buffer and nothing else: **no lock, no pyramid, no GL**.
  The acquisition thread can therefore never stall the renderer.
- **The consumer calls `acquire()`**, which snapshots the produced count and brings the
  pyramid and bounds up to it, and then queries the published window. `size()`, `reduce()`,
  `sum()`, `sumsq()`, `gather()` and `pyramid()` all use the window published by the last
  `acquire()`.

`acquire()` costs O(samples produced since the last acquire), not O(capacity): the pyramid
is indexed in absolute sample space, so a sliding window only drops the leading buckets the
ring overwrote and appends what is new; aggregates still in the window are never recomputed.
The one exception is a producer that laps the consumer (more than `capacity()` samples
between two acquires), where the missing samples cannot be represented and the pyramid
restarts at the new base, re-reading the whole window.

The ring overwrites the oldest samples — that is what keeps a live stream showing the newest
data — so it is **not a frozen window**. Those slots are relaxed atomics, so this is not a
data race and no value tears; but if the producer pushes `k` samples while a read is in
flight, the first `k` positions of that read can hold one of those `k` newer samples rather
than the one the snapshot named. Ingest with no reuse underneath a read at all needs the
producer to stop before unread data (drop-on-full) or a claim protocol — a different
contract from this one.

`streaming()` returns `true` for `StreamSeries` and `false` for everything else. Only a
streaming series has a **live edge** (the newest x it holds, which moves on its own), and it
is that property `XFollow::Live` follows (see [interaction.md](interaction.md)).

## The min/max LOD pyramid

`data::MinMaxPyramid` (`minmax_pyramid.hpp`) is the index that makes per-frame cost depend on
the visible pixels rather than on the sample count.

- Samples are grouped into buckets of `kBucket = 64` (**`kBucket`** is a fixed constant, so
  all series share one LOD geometry). Level `l` bucket `k` covers the absolute samples
  `[k * (kBucket << l), (k + 1) * (kBucket << l))`, and bucket boundaries are fixed for the
  pyramid's life — appending never re-buckets existing aggregates, and a window can slide
  over the same buckets.
- Each level holds its complete buckets in a `std::deque` (O(1) `push_back` and
  `pop_front`) plus a `base`. `append()` folds a sample into the in-progress level-0 bucket
  and, when it completes, pushes it and walks up, stopping at the first level that gains no
  entry: an append costs **amortised O(1)**. `drop_before(u)` slides the window's leading
  edge, bounding the memory by the retained window rather than by everything ever appended.
- An entry is 32 bytes (`MinMax` plus the exact `sum`/`sumsq` the exact range queries fold
  over and the finite-sample `count` the mean folds by), so level 0 costs `0.5n` bytes and
  the geometric tail brings the whole pyramid to about **`1n` bytes — roughly 25% of the
  `4n` bytes of raw samples** (`f32`). A min/max-only index would be `0.25n` (~6%); the
  extra words are the price of answering mean/RMS/integral from whole buckets instead of
  raw samples.

Queries on the pyramid are **bucket-grained** and named `*_buckets` (`reduce_buckets`,
`sum_buckets`, `sumsq_buckets`): a pyramid stores aggregates, so it can only answer exactly
for whole buckets, and partial edge buckets are excluded rather than approximated. Exact
range queries live on `Series`, which owns the raw samples.

`level_for(u64 n, f64 samples_per_pixel)` returns the pyramid level whose buckets hold about
`samples_per_pixel` samples for a window of `n` samples (clamped to levels that exist, to
levels with more than one bucket, and to levels that give the window at least two buckets).

The renderer uses this: a trace's x range is mapped onto sample indices, and `Series::gather`
is asked for that window *at `pyramid().level_for(n, samples_per_pixel)`* whenever a pixel
covers `Style::dense_threshold_spp` samples or more (default 1.5). A 100-million-sample
series therefore stages O(visible pixels) vertices, not O(samples), and `FrameStats` records
which path each trace took. Below the threshold the raw samples are drawn instead; both
sides go through the same instanced anti-aliased quad pipeline.

## One sample or a statistic of many

Every number the library reports is one the samples actually hold. What differs between
these two is *how many* samples answer the question:

- **`Series::reduce` / `sum` / `sumsq` are exact over a sample range.** They answer "what
  are the samples' extremes / sum / sum of squares over `[begin, end)`". Each is
  implemented once over `copy_raw()`: the bucket-aligned interior comes from the pyramid
  (O(log n) bucket aggregates) and at most two partial edge buckets are read raw and
  scanned, so the cost is O(`kBucket`) worst case and **independent of the series length**,
  and no query allocates. `Measure::evaluate` and `Plot::region_stats` are built on these,
  never on the `*_buckets` forms, which would silently report wrong extremes for an
  unaligned interval.
- **The crosshair readout is a single sample — the nearest one — by default.**
  `crosshair_span(series, x)` returns the two samples `x` lies between (`begin`, `end`, `t`),
  and `CrosshairValue::NearestSample` takes whichever of the two is nearer: `t <= 0.5` the
  earlier, otherwise the later. It answers "which datum is under the pointer", not "what is
  the signal *at* this instant" — a linearly interpolated y is a number the series does not
  contain, and on a source whose samples are already averaged or decimated at ingest it is a
  claim about a signal nobody measured. That is why it is the default; a host whose x is a
  quantity it is willing to interpolate states `CrosshairValue::Interpolated` and gets
  `crosshair_interpolate(y_begin, y_end, t)` instead, at the crosshair's own x. The lookup is
  O(1) and allocation-free either way, and it reads level 0, so the value is a stored sample
  (or a blend of two) and never an LOD bucket's aggregate. `Plot::crosshair_point(trace, x,
  out_x, out_y)` exposes **both** coordinates to a host and `crosshair_value()` the y alone;
  `CrosshairRow::x`/`y` and its `marker` are placed from that same pair, so in
  `NearestSample` the mark snaps onto the quoted sample and the dot and the number cannot
  disagree. A row whose trace has no sample at the crosshair's x shows `kCrosshairNoValue`
  (an em dash, `"—"`), and an x outside the series window yields no value rather than
  clamping to the first or last sample.

`Measurement` (from `Plot::measurement()`) documents `integral` as a **left-Riemann sum**,
`sum(begin, end) * dx` — the samples taken as the value at the left edge of each step. That
is part of the result's contract, not an implementation detail.

## Measured frame times

These are the demos' own frame summaries, taken from a `linux-release` build, **all of them
llvmpipe (Mesa software rasteriser) under Xvfb** — no run touched a GPU. Treat them as
upper bounds and as a record of the software path, not as hardware numbers.

| demo | window | mean ms/frame | work |
|---|---|---|---|
| GLFW (`plot_glfw_demo`) | 1280×720 | 0.42 | one 1 MS/s `StreamSeries` |
| ImGui (`plot_imgui_demo`) | 1280×720 | 0.37 | the same stream, embedded |
| multiplot (`plot_multiplot_demo`) | 1280×720 | 0.66 | 3 rows, 6 traces, 500 kS/s each |
| Qt6 (`plot_qt_demo`) | 820×588 at exit | 3.23 | one stream, `QOpenGLWidget` FBO |
| GTK4 (`plot_gtk_demo`) | 810×564 at exit | 2.85 | one stream, `GtkGLArea` FBO |

The multiplot run stages 18 651 segments for ~4.5 million visible samples, which is the
pyramid doing its job: the frame is bounded by the pixels, not the samples. The Qt and GTK
figures are dominated by their toolkit's texture-backed FBO under llvmpipe, not by the
library.