.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/Interactive/plot_particle_picker.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_Interactive_plot_particle_picker.py: HAADF STEM nanoparticle picker. ================================= Synthetic HAADF-STEM image with 18 Gaussian nanoparticles on a Poisson noise background. Candidate peaks are detected automatically using a 7×7 local-maximum filter and marked with small grey circles. **Interaction** * **Dwell 300 ms** over a candidate — shows the sub-pixel centroid, peak intensity, and estimated FWHM in a floating label. * **Double-click** — confirms the pick (green ring). * **Shift+double-click** — marks the pick as uncertain (orange ring). * **Delete / Backspace** — removes the confirmed pick nearest the cursor. * **c** — clears all picks. .. GENERATED FROM PYTHON SOURCE LINES 19-210 .. raw:: html

.. raw:: html .. code-block:: Python import numpy as np import anyplotlib as apl # ── synthetic data ───────────────────────────────────────────────────────────── def _make_stem_image(rng: np.random.Generator) -> np.ndarray: img = rng.poisson(lam=5, size=(512, 512)).astype(np.float32) for _ in range(18): cx, cy = rng.integers(30, 482, size=2) sigma = rng.uniform(4, 9) peak = rng.uniform(80, 200) r = int(np.ceil(3 * sigma)) y0, y1 = max(0, cy - r), min(512, cy + r + 1) x0, x1 = max(0, cx - r), min(512, cx + r + 1) ys = np.arange(y0, y1)[:, None] xs = np.arange(x0, x1)[None, :] img[y0:y1, x0:x1] += peak * np.exp( -((xs - cx) ** 2 + (ys - cy) ** 2) / (2 * sigma ** 2) ) return np.clip(img, 0, 255).astype(np.float32) def _find_candidates(img: np.ndarray) -> list[tuple[int, int]]: """Local maxima via 7x7 sliding-window max filter (pure NumPy).""" from numpy.lib.stride_tricks import sliding_window_view pad = 3 padded = np.pad(img, pad, mode="edge") windows = sliding_window_view(padded, (7, 7)) local_max = windows.max(axis=(-2, -1)) mask = (img == local_max) & (img > 20) ys, xs = np.where(mask) return list(zip(xs.tolist(), ys.tolist())) def _parabolic_centroid(img: np.ndarray, r: int, c: int) -> tuple[float, float]: def _delta(left, center, right): denom = 2 * (2 * center - left - right) return 0.0 if abs(denom) < 1e-6 else (right - left) / denom dc = _delta(float(img[r, c - 1]), float(img[r, c]), float(img[r, c + 1])) dr = _delta(float(img[r - 1, c]), float(img[r, c]), float(img[r + 1, c])) return c + dc, r + dr def _gaussian_fwhm(profile: np.ndarray) -> float: p = np.clip(profile.astype(float), 1e-6, None) peak_idx = int(np.argmax(p)) if peak_idx == 0 or peak_idx >= len(p) - 1: return 2.0 try: a, b, c_ = np.log(p[peak_idx - 1]), np.log(p[peak_idx]), np.log(p[peak_idx + 1]) sigma = np.sqrt(-1.0 / (2 * (a + c_ - 2 * b))) except Exception: return 2.0 return 2.355 * abs(sigma) def _safe_remove(plot, marker_type: str, name: str) -> None: try: plot.remove_marker(marker_type, name) except KeyError: pass # ── build data ───────────────────────────────────────────────────────────────── rng = np.random.default_rng(42) image = _make_stem_image(rng) candidates = _find_candidates(image) # ── figure ───────────────────────────────────────────────────────────────────── fig, ax = apl.subplots(1, 1, figsize=(640, 640)) plot = ax.imshow(image, cmap="gray") if candidates: cand_arr = np.array(candidates, dtype=float) plot.add_circles(cand_arr, name="candidates", radius=6, facecolors="none", edgecolors="#555555") info_label = plot.add_widget("label", x=10, y=10, text="", color="#00e5ff", fontsize=11) picks: list[dict] = [] # ── helpers ──────────────────────────────────────────────────────────────────── def _redraw_picks() -> None: _safe_remove(plot, "circles", "picks_certain") _safe_remove(plot, "circles", "picks_uncertain") certain = [p for p in picks if not p["uncertain"]] uncertain = [p for p in picks if p["uncertain"]] if certain: arr = np.array([[p["cx"], p["cy"]] for p in certain]) plot.add_circles(arr, name="picks_certain", radius=10, facecolors="none", edgecolors="#00ff88") if uncertain: arr = np.array([[p["cx"], p["cy"]] for p in uncertain]) plot.add_circles(arr, name="picks_uncertain", radius=10, facecolors="none", edgecolors="#ff9100") def _nearest_candidate(x: float, y: float, max_dist: float = 12.0): best, best_d = None, max_dist for cx, cy in candidates: d = float(np.hypot(cx - x, cy - y)) if d < best_d: best, best_d = (cx, cy), d return best def _nearest_pick_idx(x: float, y: float) -> int | None: if not picks: return None dists = [float(np.hypot(p["cx"] - x, p["cy"] - y)) for p in picks] return int(np.argmin(dists)) def _inspect(cx_f: float, cy_f: float) -> tuple[float, float, float, float]: """Return (sub_cx, sub_cy, intensity, fwhm) for the pixel at (cx_f, cy_f).""" r = int(np.clip(round(cy_f), 4, 507)) c = int(np.clip(round(cx_f), 4, 507)) sub_cx, sub_cy = _parabolic_centroid(image, r, c) intensity = float(image[r, c]) row_profile = image[r, max(0, c - 4):min(512, c + 5)] col_profile = image[max(0, r - 4):min(512, r + 5), c] fwhm = (_gaussian_fwhm(row_profile) + _gaussian_fwhm(col_profile)) / 2 return sub_cx, sub_cy, intensity, fwhm # ── event handlers ───────────────────────────────────────────────────────────── def _on_settled(event) -> None: if event.xdata is None or event.ydata is None: return hit = _nearest_candidate(event.xdata, event.ydata) if hit is None: info_label.set(text="") return hx, hy = hit sub_cx, sub_cy, intensity, fwhm = _inspect(hx, hy) info_label.set( text=f"centroid ({sub_cx:.1f}, {sub_cy:.1f})\npeak {intensity:.0f}\nFWHM {fwhm:.2f} px", x=hx + 12, y=hy - 30, ) def _on_double_click(event) -> None: if event.xdata is None or event.ydata is None: return hit = _nearest_candidate(event.xdata, event.ydata) if hit is None: return sub_cx, sub_cy, intensity, fwhm = _inspect(*hit) uncertain = "shift" in event.modifiers picks.append({"cx": sub_cx, "cy": sub_cy, "intensity": intensity, "fwhm": fwhm, "uncertain": uncertain}) _redraw_picks() tag = "uncertain" if uncertain else "certain" print(f"Pick #{len(picks)} [{tag}]: ({sub_cx:.1f}, {sub_cy:.1f}) " f"peak={intensity:.0f} FWHM={fwhm:.2f} px") def _on_key(event) -> None: if event.key in ("Delete", "Backspace"): x = event.xdata if event.xdata is not None else 256.0 y = event.ydata if event.ydata is not None else 256.0 idx = _nearest_pick_idx(x, y) if idx is not None: picks.pop(idx) _redraw_picks() elif event.key == "c": picks.clear() _redraw_picks() plot.add_event_handler(_on_settled, "pointer_settled", ms=300, delta=6) plot.add_event_handler(_on_double_click, "double_click") plot.add_event_handler(_on_key, "key_down") fig.set_help( "Dwell 300 ms: inspect peak\n" "Double-click: confirm pick (green)\n" "Shift+double-click: uncertain pick (orange)\n" "Delete / Backspace: remove nearest pick\n" "c: clear all picks" ) fig # interactive .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.864 seconds) .. _sphx_glr_download_auto_examples_Interactive_plot_particle_picker.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_particle_picker.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_particle_picker.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_particle_picker.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_