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feat: initial release of human_mouse v0.1.0

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A small Python library for replaying real human mouse trajectories from
the SapiMouse dataset onto a Playwright page. Designed for ML-based
bot-detection research, behavioral biometrics prototyping, and
replay-based test fixtures.

Public API: load_all_segments, pick_segments, affine_warp, upsample,
replay, replay_random, download_sapimouse.

- src/ layout with hatchling build backend
- 23 pytest tests (10 transform unit + 13 integration)
- MIT license, PEP 561 py.typed marker
- python -m human_mouse download for one-shot dataset fetch
- examples/cloakbrowser_demo.py demonstrates end-to-end use with CloakBrowser

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Huang Qi
2026-05-12 00:30:18 +08:00
commit 65ef838bd7
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# Python-generated files
__pycache__/
*.py[oc]
build/
dist/
wheels/
*.egg-info
# Virtual environments
.venv
# Test &amp; tooling caches
.pytest_cache/
.mypy_cache/
.ruff_cache/
# Runtime artifacts (not part of the package)
outputs/
sapimouse_data/

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3.12

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MIT License
Copyright (c) 2026 human_mouse authors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# human_mouse
Replay real human mouse trajectories from the [SapiMouse](https://www.ms.sapientia.ro/~manyi/sapimouse/sapimouse.html)
dataset onto a Playwright page.
Useful when you need mouse movement that is statistically indistinguishable
from a real user — for ML-based bot-detection research, behavioral biometrics
prototyping, or replay-based test fixtures.
## Install
```bash
uv add human_mouse # or: pip install human_mouse
uv add "human_mouse[demo]" # also installs cloakbrowser for the example
```
## One-time dataset download
```python
import human_mouse as hm
hm.download_sapimouse("./data") # ~8 MB, no registration
```
This unzips SapiMouse to `./data/sapimouse/userN/...`.
## Quick start
```python
import human_mouse as hm
from playwright.sync_api import sync_playwright
with sync_playwright() as pw:
browser = pw.chromium.launch(headless=False)
page = browser.new_page()
page.goto("https://example.com")
# one-shot: pick a real human trajectory matching the distance and replay it
seg = hm.replay_random(
page,
start=(100, 100),
end=(900, 500),
data_root="./data/sapimouse",
density=4, # 4x spatial upsample for smoother visuals
seed=42,
)
print(f"replayed {seg.user}/{seg.session}, {len(seg.points)} source points")
```
## Step-by-step API
For full control over which segment is picked and how it's processed:
```python
import human_mouse as hm
segs = hm.load_all_segments("./data/sapimouse")
seg = hm.pick_segments(
segs,
n=1,
target_distance=1000, # only match segments ~1000 px long
distance_tolerance=0.3, # ±30 %
max_path_ratio=2.0, # skip erratic meanderers
seed=42,
)[0]
points = hm.affine_warp(seg, (100, 100), (900, 500))
points = hm.upsample(points, factor=4)
hm.replay(page, points, speed=1.0)
```
## Public API
| Symbol | Purpose |
|---|---|
| `Segment` | dataclass with `.points`, `.start`, `.end`, `.straight_distance`, `.path_length`, `.path_ratio`, `.duration_ms` |
| `load_all_segments(data_root)` | scan SapiMouse, return every continuous Move-only segment ≥ 50 pts |
| `pick_segments(segments, n, ...)` | filter by distance / path-ratio / distinct-session, randomly choose `n` |
| `affine_warp(seg, start, end)` | translate + rotate + uniform-scale onto new endpoints (shape preserved) |
| `upsample(points, factor)` | linearly interpolate `factor-1` sub-points between every adjacent pair |
| `replay(page, points, speed=1.0)` | drive `page.mouse.move(...)` honoring the recorded `dt` |
| `replay_random(page, start, end, data_root, ...)` | one-shot: pick + warp + upsample + replay |
| `download_sapimouse(dest)` | download and unzip the dataset |
## Running the bundled example
The `examples/` folder ships a Playwright canvas page (`demo.html`) plus a
runner script that uses [CloakBrowser](https://github.com/CloakHQ/CloakBrowser):
```bash
uv add "human_mouse[demo]"
python -m human_mouse download ./sapimouse_data # one-time, ~8 MB
uv run python examples/cloakbrowser_demo.py sapi # one trajectory
uv run python examples/cloakbrowser_demo.py multi --n 10 # 10 overlaid
```
Outputs land in `./outputs/`:
- `sapi.png` + `sapi.json` — single trajectory screenshot and per-event coords
- `multi/overlay.png` + per-run JSONs + `summary.json`
## What `replay` is and isn't
**Is**: a deterministic replay of one real human's mouse path, warped to your
endpoints. The shape (curvature, hesitation, end-point fumbling) and the
timing (`dt` distribution) come straight from a real recording.
**Isn't**: a generator. It samples from a fixed dataset of ~4 000 segments.
For per-call novelty, randomize the seed; for true synthesis, look at
trajectory generative models (e.g. SapiAgent, DMTG).
## Dataset attribution
If you use SapiMouse data in published work, cite:
> Antal, M. et al. *SapiMouse: Mouse Dynamics-based User Authentication Using
> Deep Feature Learning*. IEEE SACI 2021.
## License
MIT.

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"""End-to-end example of human_mouse driving CloakBrowser.
Subcommands:
sapi one real human trajectory, replayed
multi N trajectories overlaid on a single canvas in different hues
Usage::
uv run python examples/cloakbrowser_demo.py sapi
uv run python examples/cloakbrowser_demo.py multi --n 10
The dataset path defaults to ./sapimouse_data/sapimouse. Override with
--data-root or call human_mouse.download_sapimouse() first.
"""
from __future__ import annotations
import argparse
import json
import math
import statistics
import time
from pathlib import Path
from cloakbrowser import launch
import human_mouse as hm
HERE = Path(__file__).parent
DEMO_HTML = HERE / "demo.html"
DEFAULT_DATA_ROOT = HERE.parent / "sapimouse_data" / "sapimouse"
DEFAULT_OUT = HERE.parent / "outputs"
VIEWPORT = {"width": 1280, "height": 720}
START = (150, 150)
TARGET = (1060, 550)
TARGET_DIST = math.hypot(TARGET[0] - START[0], TARGET[1] - START[1])
def open_page(browser, mode: str):
context = browser.new_context(viewport=VIEWPORT)
page = context.new_page()
page.goto(DEMO_HTML.as_uri() + f"?mode={mode}")
page.wait_for_selector("#target")
return page
def save_artifacts(page, name: str, out: Path) -> tuple[Path, Path]:
out.mkdir(parents=True, exist_ok=True)
png, js = out / f"{name}.png", out / f"{name}.json"
js.write_text(json.dumps(page.evaluate("window.__points"), indent=2))
page.screenshot(path=str(png), full_page=False)
return png, js
def cmd_sapi(args: argparse.Namespace) -> None:
print("loading SapiMouse segments...")
segs = hm.load_all_segments(args.data_root)
print(f" -> {len(segs)} segments available")
seg = hm.pick_segments(segs, n=1,
target_distance=TARGET_DIST,
max_path_ratio=args.max_ratio,
seed=args.seed)[0]
print(f" -> {seg.user}/{seg.session} "
f"src={len(seg.points)} dist={seg.straight_distance:.0f}px "
f"dur={seg.duration_ms:.0f}ms ratio={seg.path_ratio:.2f}")
dense = hm.upsample(hm.affine_warp(seg, START, TARGET), args.density)
print("launching CloakBrowser (humanize=False)...")
browser = launch(humanize=False, headless=False)
page = open_page(browser, "sapi")
time.sleep(0.3)
t0 = time.perf_counter()
hm.replay(page, dense)
elapsed = (time.perf_counter() - t0) * 1000
page.locator("#target").click()
png, js = save_artifacts(page, "sapi", args.out)
captured = json.loads(js.read_text())
browser.close()
print(f"\n sent={len(dense)} captured={len(captured)} "
f"replay={elapsed:.0f}ms")
print(f" -> {png}")
def cmd_multi(args: argparse.Namespace) -> None:
multi_dir = args.out / "multi"
print("loading SapiMouse segments...")
segs = hm.load_all_segments(args.data_root)
print(f" -> {len(segs)} segments total")
picks = hm.pick_segments(segs, n=args.n,
target_distance=TARGET_DIST,
max_path_ratio=args.max_ratio,
seed=args.seed)
print("launching CloakBrowser (humanize=False)...")
browser = launch(humanize=False, headless=False)
page = open_page(browser, "multi")
runs = []
for i, seg in enumerate(picks, 1):
hue = round(360 * (i - 1) / len(picks))
page.evaluate(f"window.__setHue({hue}); window.__resetTrace();")
dense = hm.upsample(hm.affine_warp(seg, START, TARGET), args.density)
t0 = time.perf_counter()
hm.replay(page, dense)
elapsed = (time.perf_counter() - t0) * 1000
points = page.evaluate("window.__points")
multi_dir.mkdir(parents=True, exist_ok=True)
(multi_dir / f"run_{i:02d}.json").write_text(json.dumps(points, indent=2))
dts = [b["t"] - a["t"] for a, b in zip(points, points[1:])]
runs.append({
"i": i, "hue": hue, "user": seg.user, "session": seg.session,
"src_points": len(seg.points), "sent_points": len(dense),
"captured_points": len(points),
"captured_median_dt_ms": round(statistics.median(dts), 2) if dts else 0.0,
"replay_ms": round(elapsed, 1),
})
print(f" run {i:>2}/{len(picks)} hue={hue:>3} "
f"{seg.user}/{seg.session} captured={len(points)} pts")
overlay = multi_dir / "overlay.png"
page.screenshot(path=str(overlay), full_page=False)
(multi_dir / "summary.json").write_text(json.dumps({
"n": len(picks), "runs": runs,
}, indent=2))
browser.close()
print(f"\n overlay -> {overlay}")
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="human_mouse demo driven by CloakBrowser",
)
parser.add_argument("--data-root", type=Path, default=DEFAULT_DATA_ROOT,
help="path to unzipped SapiMouse folder")
parser.add_argument("--out", type=Path, default=DEFAULT_OUT,
help="output directory for screenshots and JSON")
sub = parser.add_subparsers(dest="cmd", required=True)
p_sapi = sub.add_parser("sapi", help="one real SapiMouse trajectory replayed")
p_sapi.add_argument("--density", type=int, default=4)
p_sapi.add_argument("--max-ratio", type=float, default=2.0)
p_sapi.add_argument("--seed", type=int, default=42)
p_sapi.set_defaults(func=cmd_sapi)
p_multi = sub.add_parser("multi", help="N trajectories overlaid in different hues")
p_multi.add_argument("--n", type=int, default=10)
p_multi.add_argument("--density", type=int, default=4)
p_multi.add_argument("--max-ratio", type=float, default=2.0)
p_multi.add_argument("--seed", type=int, default=None)
p_multi.set_defaults(func=cmd_multi)
return parser
def main() -> None:
args = build_parser().parse_args()
args.func(args)
if __name__ == "__main__":
main()

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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<title>human_mouse — Trajectory Demo</title>
<style>
html, body { margin: 0; padding: 0; background: #fafafa; font-family: ui-sans-serif, system-ui, sans-serif; overflow: hidden; }
#label { position: fixed; top: 8px; left: 8px; padding: 6px 12px; background: #111; color: #fff; border-radius: 6px; font-size: 14px; z-index: 10; }
#count { position: fixed; top: 8px; right: 8px; padding: 6px 12px; background: #fff; border: 1px solid #ddd; border-radius: 6px; font-size: 14px; z-index: 10; }
#start { position: fixed; left: 130px; top: 130px; width: 40px; height: 40px; border-radius: 50%; background: #16a34a; color: #fff; display: flex; align-items: center; justify-content: center; font-size: 12px; z-index: 10; }
#target { position: fixed; left: 1030px; top: 530px; width: 60px; height: 40px; background: #2563eb; color: #fff; border: none; border-radius: 6px; font-size: 14px; cursor: pointer; z-index: 10; }
canvas { position: fixed; inset: 0; }
</style>
</head>
<body>
<div id="label">mode: …</div>
<div id="count">points: 0</div>
<div id="start">start</div>
<button id="target">target</button>
<canvas id="trail"></canvas>
<script>
const params = new URLSearchParams(location.search);
const mode = params.get("mode") || "unknown";
document.getElementById("label").textContent = "mode: " + mode;
const canvas = document.getElementById("trail");
const ctx = canvas.getContext("2d");
function resize() {
canvas.width = window.innerWidth;
canvas.height = window.innerHeight;
}
resize();
window.addEventListener("resize", resize);
window.__points = [];
let last = null;
let hue = 0; // degrees on the HSL wheel; 0 = red (default)
window.__setHue = (h) => { hue = h; last = null; };
window.__resetTrace = () => { window.__points = []; last = null; };
function recordPoint(x, y, t) {
const p = { x, y, t };
window.__points.push(p);
ctx.fillStyle = `hsla(${hue}, 75%, 45%, 0.85)`;
ctx.beginPath();
ctx.arc(p.x, p.y, 2, 0, Math.PI * 2);
ctx.fill();
if (last) {
ctx.strokeStyle = `hsla(${hue}, 75%, 45%, 0.45)`;
ctx.lineWidth = 1;
ctx.beginPath();
ctx.moveTo(last.x, last.y);
ctx.lineTo(p.x, p.y);
ctx.stroke();
}
last = p;
}
// pointermove fires once per render frame, but getCoalescedEvents() returns
// every sub-event the browser folded into this frame — so we recover the
// full CDP event stream the driver sent us.
window.addEventListener("pointermove", (e) => {
const subs = e.getCoalescedEvents ? e.getCoalescedEvents() : [e];
const events = subs.length ? subs : [e];
for (const s of events) {
recordPoint(s.clientX, s.clientY, s.timeStamp);
}
document.getElementById("count").textContent = "points: " + window.__points.length;
}, { passive: true });
window.__clickedTarget = false;
document.getElementById("target").addEventListener("click", () => {
window.__clickedTarget = true;
});
</script>
</body>
</html>

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[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "human_mouse"
version = "0.1.0"
description = "Replay real human mouse trajectories from the SapiMouse dataset onto a Playwright page."
readme = "README.md"
requires-python = ">=3.10"
license = "MIT"
keywords = ["mouse", "trajectory", "sapimouse", "playwright", "bot", "automation"]
classifiers = [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Software Development :: Libraries :: Python Modules",
]
dependencies = [
"numpy>=1.24",
"playwright>=1.40",
]
[project.optional-dependencies]
demo = ["cloakbrowser>=0.3.27"]
dev = ["pytest>=7.0"]
[project.urls]
Homepage = "https://github.com/your-org/human_mouse"
"SapiMouse dataset" = "https://www.ms.sapientia.ro/~manyi/sapimouse/sapimouse.html"
[tool.hatch.build.targets.wheel]
packages = ["src/human_mouse"]
[tool.hatch.build.targets.sdist]
include = [
"src/human_mouse",
"examples",
"tests",
"README.md",
"pyproject.toml",
]

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"""human_mouse: replay real human mouse trajectories from SapiMouse.
Quick start::
import human_mouse as hm
hm.download_sapimouse("./data") # one time
# one-shot
hm.replay_random(page, start=(100, 100), end=(900, 500),
data_root="./data/sapimouse")
# or step-by-step
segs = hm.load_all_segments("./data/sapimouse")
seg = hm.pick_segments(segs, target_distance=1000, n=1)[0]
pts = hm.upsample(hm.affine_warp(seg, (100, 100), (900, 500)), factor=4)
hm.replay(page, pts)
"""
from __future__ import annotations
from ._replay import replay, replay_random
from .dataset import SAPIMOUSE_URL, download_sapimouse
from .segments import Segment, load_all_segments, pick_segments
from .transform import affine_warp, upsample
__version__ = "0.1.0"
__all__ = [
"Segment",
"affine_warp",
"download_sapimouse",
"load_all_segments",
"pick_segments",
"replay",
"replay_random",
"upsample",
"SAPIMOUSE_URL",
"__version__",
]

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"""CLI entry: ``python -m human_mouse <command>``."""
from __future__ import annotations
import argparse
import sys
from .dataset import download_sapimouse
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(prog="python -m human_mouse")
sub = parser.add_subparsers(dest="cmd", required=True)
p_dl = sub.add_parser("download", help="download the SapiMouse dataset")
p_dl.add_argument("dest", help="target directory (will be created)")
p_dl.add_argument("--overwrite", action="store_true",
help="re-download even if data appears to be present")
args = parser.parse_args(argv)
if args.cmd == "download":
out = download_sapimouse(args.dest, overwrite=args.overwrite)
print(f"SapiMouse ready at: {out}")
return 0
return 1
if __name__ == "__main__":
sys.exit(main())

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"""Drive a Playwright Page with a real human trajectory.
The `Page` argument is duck-typed: anything with `mouse.move(x, y)` will work,
so this also accepts CloakBrowser pages, patchright pages, etc.
"""
from __future__ import annotations
__all__ = ["replay", "replay_random"]
import math
import time
from pathlib import Path
from typing import Protocol
from .segments import Segment, load_all_segments, pick_segments
from .transform import affine_warp, upsample
class _MouseLike(Protocol):
def move(self, x: float, y: float) -> None: ...
class _PageLike(Protocol):
@property
def mouse(self) -> _MouseLike: ...
def replay(
page: _PageLike,
points: list[tuple[float, float, float]],
*,
speed: float = 1.0,
) -> None:
"""Drive page.mouse.move along (x, y, t_ms), honoring the recorded dt.
Args:
page: a Playwright-compatible Page (must expose page.mouse.move).
points: list of (x, y, t_ms) tuples, monotonically non-decreasing in t.
speed: multiplier on playback speed. speed=1.0 reproduces the recorded
timing; speed=2.0 plays back twice as fast.
"""
if not points:
return
page.mouse.move(round(points[0][0]), round(points[0][1]))
prev_t = points[0][2]
for x, y, t in points[1:]:
dt_ms = (t - prev_t) / speed
if dt_ms > 0:
time.sleep(dt_ms / 1000.0)
page.mouse.move(round(x), round(y))
prev_t = t
# Module-level segment cache so repeated replay_random() calls don't re-scan
# the dataset on every invocation.
_SEGMENT_CACHE: dict[Path, list[Segment]] = {}
def _cached_segments(data_root: str | Path) -> list[Segment]:
root = Path(data_root).resolve()
if root not in _SEGMENT_CACHE:
_SEGMENT_CACHE[root] = load_all_segments(root)
return _SEGMENT_CACHE[root]
def replay_random(
page: _PageLike,
start: tuple[float, float],
end: tuple[float, float],
*,
data_root: str | Path,
density: int = 4,
max_path_ratio: float = 2.0,
speed: float = 1.0,
seed: int | None = None,
) -> Segment:
"""One-shot: pick a SapiMouse segment matching `start`->`end` and replay it.
The first call for a given `data_root` scans the dataset (slow); subsequent
calls reuse the cached segment list.
Args:
page: a Playwright-compatible Page.
start: desired starting (x, y) — the cursor will be parked here first.
end: desired ending (x, y).
data_root: path to the unzipped SapiMouse folder.
density: spatial upsample factor on the picked segment.
max_path_ratio: filter to skip erratic segments. See pick_segments.
speed: playback speed multiplier.
seed: RNG seed for picking. None = nondeterministic.
Returns:
The Segment that was picked, for downstream inspection.
"""
target_dist = math.hypot(end[0] - start[0], end[1] - start[1])
segs = _cached_segments(data_root)
seg = pick_segments(
segs, n=1,
target_distance=target_dist,
max_path_ratio=max_path_ratio,
seed=seed,
)[0]
points = upsample(affine_warp(seg, start, end), density)
replay(page, points, speed=speed)
return seg

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"""Helpers for obtaining the SapiMouse dataset.
The dataset isn't bundled with the package (8 MB CSV files); call
download_sapimouse() once or follow the link in the README to get it.
"""
from __future__ import annotations
__all__ = ["SAPIMOUSE_URL", "download_sapimouse"]
import urllib.request
import zipfile
from pathlib import Path
SAPIMOUSE_URL = "https://www.ms.sapientia.ro/~manyi/sapimouse/sapimouse.zip"
def download_sapimouse(dest: str | Path, *, overwrite: bool = False) -> Path:
"""Download and unzip SapiMouse into `dest`.
Args:
dest: target directory. Will be created if missing. After this call,
`dest/sapimouse/user1/...` will exist.
overwrite: if False (default) and the data appears to already exist,
skip the download.
Returns:
Path to the unzipped `sapimouse` folder (suitable for
load_all_segments).
"""
dest_dir = Path(dest)
dest_dir.mkdir(parents=True, exist_ok=True)
extracted = dest_dir / "sapimouse"
if extracted.exists() and not overwrite:
if any(extracted.iterdir()):
return extracted
zip_path = dest_dir / "sapimouse.zip"
print(f"downloading {SAPIMOUSE_URL} -> {zip_path} ...")
urllib.request.urlretrieve(SAPIMOUSE_URL, zip_path)
print(f"extracting -> {dest_dir} ...")
with zipfile.ZipFile(zip_path) as zf:
zf.extractall(dest_dir)
zip_path.unlink()
return extracted

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"""Loading and filtering SapiMouse mouse-move segments."""
from __future__ import annotations
__all__ = ["Segment", "load_all_segments", "pick_segments"]
import csv
import math
import random
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable
@dataclass
class Segment:
"""A continuous Move-only trajectory from one SapiMouse session."""
user: str
session: str
points: list[tuple[float, float, float]] # (x, y, t_ms)
@property
def start(self) -> tuple[float, float]:
return self.points[0][0], self.points[0][1]
@property
def end(self) -> tuple[float, float]:
return self.points[-1][0], self.points[-1][1]
@property
def straight_distance(self) -> float:
sx, sy = self.start
ex, ey = self.end
return math.hypot(ex - sx, ey - sy)
@property
def path_length(self) -> float:
return sum(math.hypot(b[0] - a[0], b[1] - a[1])
for a, b in zip(self.points, self.points[1:]))
@property
def path_ratio(self) -> float:
"""Path length / straight distance. ~1 = direct, large = erratic."""
d = self.straight_distance
return self.path_length / d if d > 0 else float("inf")
@property
def duration_ms(self) -> float:
return self.points[-1][2] - self.points[0][2]
def _iter_segments_from_csv(path: Path, min_len: int) -> Iterable[Segment]:
"""Yield each maximal run of consecutive Move rows from one CSV."""
buf: list[tuple[float, float, float]] = []
user = path.parent.name
session = path.stem
def flush() -> Iterable[Segment]:
if len(buf) >= min_len:
yield Segment(user=user, session=session, points=buf.copy())
buf.clear()
with path.open("r", newline="") as fh:
for row in csv.DictReader(fh):
if row["state"] == "Move":
buf.append((
float(row["x"]),
float(row["y"]),
float(row["client timestamp"]),
))
else:
yield from flush()
yield from flush()
def load_all_segments(
data_root: str | Path,
min_len: int = 50,
) -> list[Segment]:
"""Scan a SapiMouse directory tree and return every Move segment.
Args:
data_root: path to the unzipped SapiMouse folder (the one containing
user1/, user2/, ...).
min_len: minimum number of consecutive Move events to keep a segment.
Raises:
FileNotFoundError: if data_root does not exist.
"""
root = Path(data_root)
if not root.exists():
raise FileNotFoundError(
f"SapiMouse dataset not found at {root}. "
f"Use human_mouse.download_sapimouse() or download manually from "
f"https://www.ms.sapientia.ro/~manyi/sapimouse/sapimouse.zip"
)
return [
seg
for csv_path in root.rglob("*.csv")
for seg in _iter_segments_from_csv(csv_path, min_len)
]
def pick_segments(
segments: list[Segment],
n: int = 1,
*,
target_distance: float | None = None,
distance_tolerance: float = 0.5,
max_path_ratio: float = 2.0,
distinct_sessions: bool = True,
seed: int | None = None,
) -> list[Segment]:
"""Pick `n` segments matching the filters.
Args:
segments: candidate pool, typically from load_all_segments().
n: how many to return.
target_distance: if set, keep only segments whose start->end straight
distance lies in [target_distance * (1 - tolerance),
target_distance * (1 + tolerance)].
distance_tolerance: fractional half-width around target_distance.
max_path_ratio: reject segments whose path_length / straight_distance
exceeds this. 2.0 filters out erratic real-human meanderers.
distinct_sessions: require each pick from a different
(user, session) pair.
seed: seed for the random shuffle. None = nondeterministic.
Raises:
ValueError: if filters leave fewer than `n` candidates.
"""
rng = random.Random(seed)
candidates = [s for s in segments if s.path_ratio <= max_path_ratio]
if target_distance is not None:
lo = target_distance * (1 - distance_tolerance)
hi = target_distance * (1 + distance_tolerance)
candidates = [s for s in candidates if lo <= s.straight_distance <= hi]
if not candidates:
raise ValueError(
f"No segments matched filters (target={target_distance}, "
f"tol={distance_tolerance}, max_ratio={max_path_ratio})"
)
rng.shuffle(candidates)
if not distinct_sessions:
if len(candidates) < n:
raise ValueError(f"Wanted {n} segments, only {len(candidates)} candidates")
return candidates[:n]
picked, seen = [], set()
for s in candidates:
key = (s.user, s.session)
if key in seen:
continue
seen.add(key)
picked.append(s)
if len(picked) >= n:
break
if len(picked) < n:
raise ValueError(
f"Wanted {n} distinct (user, session) but only found {len(picked)}"
)
return picked

89
src/human_mouse/transform.py Normal file
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"""Spatial transforms on trajectory point sequences.
These functions are pure — no IO, no playwright — so they're trivially
unit-testable and reusable outside this package.
"""
from __future__ import annotations
__all__ = ["affine_warp", "upsample"]
import math
import numpy as np
from .segments import Segment
def affine_warp(
seg: Segment,
start: tuple[float, float],
end: tuple[float, float],
) -> list[tuple[float, float, float]]:
"""Map a segment onto (start, end) via similarity transform.
Combines translation, uniform scale, and rotation. The shape (curvature,
relative positions of all intermediate points) is preserved exactly; only
the endpoints are pinned to the user-supplied positions. Time values are
rebased to start at 0 but otherwise untouched.
Args:
seg: source segment.
start: desired (x, y) for the warped first point.
end: desired (x, y) for the warped last point.
Returns:
List of (x, y, t_ms) tuples, len == len(seg.points).
"""
pts = np.asarray([(p[0], p[1]) for p in seg.points], dtype=float)
ts = [p[2] - seg.points[0][2] for p in seg.points]
sx, sy = seg.start
tsx, tsy = start
tex, tey = end
src_v = np.array([seg.end[0] - sx, seg.end[1] - sy])
tgt_v = np.array([tex - tsx, tey - tsy])
src_len = float(np.linalg.norm(src_v)) or 1.0
scale = float(np.linalg.norm(tgt_v)) / src_len
dtheta = math.atan2(tgt_v[1], tgt_v[0]) - math.atan2(src_v[1], src_v[0])
c, s = math.cos(dtheta), math.sin(dtheta)
R = np.array([[c, -s], [s, c]])
warped = (R @ (pts - np.array([sx, sy])).T).T * scale + np.array([tsx, tsy])
# Cancel float drift at the endpoints
warped[0] = [tsx, tsy]
warped[-1] = [tex, tey]
return [(float(x), float(y), float(t)) for (x, y), t in zip(warped, ts)]
def upsample(
points: list[tuple[float, float, float]],
factor: int,
) -> list[tuple[float, float, float]]:
"""Linearly interpolate `factor - 1` sub-points between every adjacent pair.
Time is interpolated proportionally so the total duration is preserved.
factor=1 returns the input unchanged.
Args:
points: list of (x, y, t_ms) tuples.
factor: how many sub-points per original interval. Must be >= 1.
Returns:
Densified list. For N input points and factor k, returns k*(N-1)+1.
"""
if factor <= 1 or len(points) < 2:
return list(points)
out: list[tuple[float, float, float]] = []
for (x0, y0, t0), (x1, y1, t1) in zip(points, points[1:]):
for k in range(factor):
f = k / factor
out.append((
x0 + (x1 - x0) * f,
y0 + (y1 - y0) * f,
t0 + (t1 - t0) * f,
))
out.append(points[-1])
return out

154
tests/test_integration.py Normal file
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"""Integration tests: real SapiMouse loading, filtering, caching, error paths.
These tests assume the SapiMouse dataset is present at the standard project
location (sapimouse_data/sapimouse/). They will skip otherwise — so the
core unit tests still pass on a fresh clone.
"""
from __future__ import annotations
import math
from pathlib import Path
import pytest
import human_mouse as hm
from human_mouse import _replay as replay_mod
REPO_ROOT = Path(__file__).resolve().parents[1]
DATA_ROOT = REPO_ROOT / "sapimouse_data" / "sapimouse"
pytestmark = pytest.mark.skipif(
not DATA_ROOT.exists(),
reason="SapiMouse dataset not available at the default location",
)
# --------------------------------------------------------- happy-path loading
def test_load_all_segments_finds_segments():
segs = hm.load_all_segments(DATA_ROOT)
assert len(segs) > 1000, f"expected thousands, got {len(segs)}"
assert all(len(s.points) >= 50 for s in segs)
def test_segment_properties_consistent():
segs = hm.load_all_segments(DATA_ROOT)
s = segs[0]
sx, sy = s.start
ex, ey = s.end
assert s.straight_distance == pytest.approx(math.hypot(ex - sx, ey - sy))
assert s.path_length >= s.straight_distance - 1e-6
assert s.path_ratio >= 1.0 - 1e-6
assert s.duration_ms >= 0
# -------------------------------------------------------------- pick_segments
def test_pick_segments_basic():
segs = hm.load_all_segments(DATA_ROOT)
picked = hm.pick_segments(segs, n=3, target_distance=500, seed=42)
assert len(picked) == 3
assert all(0.25 * 500 < p.straight_distance < 1.75 * 500 for p in picked)
def test_pick_segments_distinct_sessions():
segs = hm.load_all_segments(DATA_ROOT)
picked = hm.pick_segments(segs, n=5, target_distance=800,
distinct_sessions=True, seed=1)
keys = {(p.user, p.session) for p in picked}
assert len(keys) == 5
def test_pick_segments_seed_is_deterministic():
segs = hm.load_all_segments(DATA_ROOT)
a = hm.pick_segments(segs, n=3, target_distance=800, seed=7)
b = hm.pick_segments(segs, n=3, target_distance=800, seed=7)
assert [(p.user, p.session) for p in a] == [(p.user, p.session) for p in b]
def test_pick_segments_path_ratio_filter():
segs = hm.load_all_segments(DATA_ROOT)
picked = hm.pick_segments(segs, n=10, target_distance=800,
max_path_ratio=1.3, seed=1)
assert all(p.path_ratio <= 1.3 for p in picked)
# ------------------------------------------------------------------ errors
def test_load_missing_directory_raises():
with pytest.raises(FileNotFoundError, match="SapiMouse"):
hm.load_all_segments("/nonexistent/path")
def test_pick_segments_no_match_raises():
segs = hm.load_all_segments(DATA_ROOT)
with pytest.raises(ValueError, match="No segments matched"):
hm.pick_segments(segs, n=1, target_distance=999_999,
distance_tolerance=0.01)
def test_pick_segments_not_enough_distinct_raises():
segs = hm.load_all_segments(DATA_ROOT)
with pytest.raises(ValueError, match="distinct"):
hm.pick_segments(segs, n=10_000, target_distance=800)
# ------------------------------------------------------------------- cache
def test_replay_cache_reuses_same_data_root(monkeypatch):
replay_mod._SEGMENT_CACHE.clear()
calls = {"n": 0}
original = hm.load_all_segments
def counting(*a, **kw):
calls["n"] += 1
return original(*a, **kw)
monkeypatch.setattr(replay_mod, "load_all_segments", counting)
_ = replay_mod._cached_segments(DATA_ROOT)
_ = replay_mod._cached_segments(DATA_ROOT)
_ = replay_mod._cached_segments(DATA_ROOT)
assert calls["n"] == 1
# ------------------------------------------------------ end-to-end replay_random
class _FakeMouse:
def __init__(self) -> None:
self.moves: list[tuple[int, int]] = []
def move(self, x: int, y: int) -> None:
self.moves.append((x, y))
class _FakePage:
"""A Page-like duck that satisfies the _PageLike Protocol."""
def __init__(self) -> None:
self.mouse = _FakeMouse()
def test_replay_random_drives_page_endpoints():
page = _FakePage()
seg = hm.replay_random(
page, start=(100, 100), end=(900, 500),
data_root=DATA_ROOT, density=2, speed=10_000.0, seed=1,
)
assert isinstance(seg, hm.Segment)
assert len(page.mouse.moves) >= 50
assert page.mouse.moves[0] == (100, 100)
assert page.mouse.moves[-1] == (900, 500)
def test_replay_random_returns_filtered_segment():
page = _FakePage()
seg = hm.replay_random(
page, start=(0, 0), end=(800, 0),
data_root=DATA_ROOT, density=1, speed=10_000.0, seed=2,
max_path_ratio=1.5,
)
assert seg.path_ratio <= 1.5

93
tests/test_transform.py Normal file
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"""Pure-algorithm tests for human_mouse.transform — no IO, no playwright."""
from __future__ import annotations
import math
import pytest
from human_mouse import Segment, affine_warp, upsample
def _seg(points: list[tuple[float, float, float]]) -> Segment:
return Segment(user="t", session="s", points=points)
# ---------------------------------------------------------------- affine_warp
def test_affine_warp_pins_endpoints():
seg = _seg([(0, 0, 0), (50, 50, 10), (100, 100, 20)])
out = affine_warp(seg, start=(500, 300), end=(700, 400))
assert math.isclose(out[0][0], 500) and math.isclose(out[0][1], 300)
assert math.isclose(out[-1][0], 700) and math.isclose(out[-1][1], 400)
def test_affine_warp_uniform_along_axis():
"""A straight segment along +x maps to a straight segment between targets."""
seg = _seg([(0, 0, 0), (50, 0, 10), (100, 0, 20)])
out = affine_warp(seg, start=(0, 0), end=(200, 0))
assert math.isclose(out[1][0], 100, abs_tol=1e-6)
assert math.isclose(out[1][1], 0, abs_tol=1e-6)
def test_affine_warp_preserves_time():
seg = _seg([(0, 0, 100), (10, 5, 130), (20, 0, 160)])
out = affine_warp(seg, start=(0, 0), end=(40, 0))
assert out[0][2] == 0 # rebased
assert out[1][2] == 30
assert out[2][2] == 60
def test_affine_warp_rotates_perpendicular_offset():
"""A 90-degree rotation: source goes east, target goes north."""
seg = _seg([(0, 0, 0), (50, 10, 10), (100, 0, 20)])
out = affine_warp(seg, start=(0, 0), end=(0, 100))
# The middle point sat 10 px to the left of the source axis (east-pointing
# axis -> left is +y); after rotating the axis to north, "left of axis"
# means -x. So midpoint x should be approximately -10.
assert math.isclose(out[1][0], -10, abs_tol=1e-6)
assert math.isclose(out[1][1], 50, abs_tol=1e-6)
# ------------------------------------------------------------------- upsample
def test_upsample_factor_1_is_identity():
pts = [(0.0, 0.0, 0.0), (1.0, 1.0, 1.0), (2.0, 2.0, 2.0)]
assert upsample(pts, 1) == pts
def test_upsample_factor_4_count_formula():
# For N input points and factor k, expect k*(N-1) + 1 output points.
pts = [(0.0, 0.0, 0.0), (4.0, 0.0, 4.0), (8.0, 0.0, 8.0)]
out = upsample(pts, 4)
assert len(out) == 4 * (3 - 1) + 1
def test_upsample_endpoints_unchanged():
pts = [(0.0, 0.0, 0.0), (10.0, 10.0, 5.0)]
out = upsample(pts, 5)
assert out[0] == pts[0]
assert out[-1] == pts[-1]
def test_upsample_midpoint_correct_for_factor_2():
pts = [(0.0, 0.0, 0.0), (10.0, 20.0, 100.0)]
out = upsample(pts, 2)
# Should be: P0, P0+0.5*delta, P1
assert out[1] == (5.0, 10.0, 50.0)
def test_upsample_time_monotonic_with_monotonic_input():
pts = [(0.0, 0.0, 0.0), (1.0, 0.0, 5.0), (2.0, 0.0, 12.0), (3.0, 0.0, 20.0)]
out = upsample(pts, 4)
times = [p[2] for p in out]
assert all(t1 >= t0 for t0, t1 in zip(times, times[1:]))
@pytest.mark.parametrize("factor", [0, -1])
def test_upsample_non_positive_factor_returns_copy(factor: int):
pts = [(0.0, 0.0, 0.0), (1.0, 1.0, 1.0)]
out = upsample(pts, factor)
assert out == pts
assert out is not pts # returns a copy, not the same list

498
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