commit 65ef838bd7eb18494d44931ac2eade8271292ca4
Author: Huang Qi <yellowdog016@gmail.com>
Date:   Tue May 12 00:30:18 2026 +0800

    feat: initial release of human_mouse v0.1.0
    
    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>

diff --git a/.gitignore b/.gitignore
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+++ b/.gitignore
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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/
diff --git a/.python-version b/.python-version
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+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/LICENSE b/LICENSE
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--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+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.
diff --git a/README.md b/README.md
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+++ b/README.md
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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.
diff --git a/examples/cloakbrowser_demo.py b/examples/cloakbrowser_demo.py
new file mode 100644
index 0000000..d2ac3a8
--- /dev/null
+++ b/examples/cloakbrowser_demo.py
@@ -0,0 +1,172 @@
+"""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()
diff --git a/examples/demo.html b/examples/demo.html
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--- /dev/null
+++ b/examples/demo.html
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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>
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..bcd0aa1
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,47 @@
+[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",
+]
diff --git a/src/human_mouse/__init__.py b/src/human_mouse/__init__.py
new file mode 100644
index 0000000..2714f23
--- /dev/null
+++ b/src/human_mouse/__init__.py
@@ -0,0 +1,39 @@
+"""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__",
+]
diff --git a/src/human_mouse/__main__.py b/src/human_mouse/__main__.py
new file mode 100644
index 0000000..d9c85dc
--- /dev/null
+++ b/src/human_mouse/__main__.py
@@ -0,0 +1,30 @@
+"""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())
diff --git a/src/human_mouse/_replay.py b/src/human_mouse/_replay.py
new file mode 100644
index 0000000..b413641
--- /dev/null
+++ b/src/human_mouse/_replay.py
@@ -0,0 +1,105 @@
+"""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
diff --git a/src/human_mouse/dataset.py b/src/human_mouse/dataset.py
new file mode 100644
index 0000000..7cc59b9
--- /dev/null
+++ b/src/human_mouse/dataset.py
@@ -0,0 +1,47 @@
+"""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
diff --git a/src/human_mouse/py.typed b/src/human_mouse/py.typed
new file mode 100644
index 0000000..e69de29
diff --git a/src/human_mouse/segments.py b/src/human_mouse/segments.py
new file mode 100644
index 0000000..685cee4
--- /dev/null
+++ b/src/human_mouse/segments.py
@@ -0,0 +1,163 @@
+"""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
diff --git a/src/human_mouse/transform.py b/src/human_mouse/transform.py
new file mode 100644
index 0000000..8dfa870
--- /dev/null
+++ b/src/human_mouse/transform.py
@@ -0,0 +1,89 @@
+"""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
diff --git a/tests/test_integration.py b/tests/test_integration.py
new file mode 100644
index 0000000..4dc63e2
--- /dev/null
+++ b/tests/test_integration.py
@@ -0,0 +1,154 @@
+"""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
diff --git a/tests/test_transform.py b/tests/test_transform.py
new file mode 100644
index 0000000..92ae749
--- /dev/null
+++ b/tests/test_transform.py
@@ -0,0 +1,93 @@
+"""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
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..f242ea6
--- /dev/null
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