.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/Interactive/plot_threshold_explorer.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_Interactive_plot_threshold_explorer.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_Interactive_plot_threshold_explorer.py:


Live intensity thresholding on a multi-phase STEM image.
=========================================================

A side-by-side view: the left panel shows a synthetic 512×512 STEM
image with a red overlay marking pixels above the threshold; the right
panel shows a 32-bin intensity histogram with a yellow vertical line at
the current threshold value.

**Interaction**

* **Shift+Scroll** over the image — adjusts the threshold by ±2 per
  wheel tick (plain scroll pans/zooms the image as normal).
* **Click** a histogram bar — jumps the threshold to that bin's upper
  edge.
* **Dwell 400 ms** over the image — shows pixel coordinates and
  intensity in the bottom-left label.

.. GENERATED FROM PYTHON SOURCE LINES 19-139




.. raw:: html

    <div style="display:block;text-align:center;line-height:0;margin:12px 0;"><div id="sphx_glr_plot_threshold_explorer_001" class="awi-fig-wrap" data-awi-fig="sphx_glr_plot_threshold_explorer_001" style="display:inline-block;overflow:hidden;position:relative;width:684px;height:385px;"><iframe src="../../_static/viewer_widgets/sphx_glr_plot_threshold_explorer_001.html" data-awi-fig="sphx_glr_plot_threshold_explorer_001" frameborder="0" scrolling="no" style="width:916px;height:516px;border:none;overflow:hidden;display:block;transform-origin:top left;transform:scale(0.746725);position:absolute;top:0;left:0;"></iframe><div class="awi-badge" data-awi-badge="sphx_glr_plot_threshold_explorer_001"><button class="awi-badge-icon awi-activate-btn" data-awi-fig="sphx_glr_plot_threshold_explorer_001" title="Make interactive (boots Pyodide — may take ~10 s)">&#x26A1;</button></div></div><script>(function(){var wrap=document.getElementById('sphx_glr_plot_threshold_explorer_001'),ifr=wrap.querySelector('iframe'),nw=916,nh=516;function r(){var avail=wrap.parentElement?wrap.parentElement.offsetWidth:0;if(!avail)return;var s=Math.min(1,avail/nw);wrap.style.width=Math.round(nw*s)+'px';wrap.style.height=Math.round(nh*s)+'px';ifr.style.transform='scale('+s+')';}requestAnimationFrame(r);window.addEventListener('resize',r);})()</script></div>



.. raw:: html

    <script type="text/x-python" data-fig-id="sphx_glr_plot_threshold_explorer_001" data-fig-index="0" data-src-file="plot_threshold_explorer" data-src="&quot;\&quot;\&quot;\&quot;\nLive intensity thresholding on a multi-phase STEM image.\n=========================================================\n\nA side-by-side view: the left panel shows a synthetic 512\u00d7512 STEM\nimage with a red overlay marking pixels above the threshold; the right\npanel shows a 32-bin intensity histogram with a yellow vertical line at\nthe current threshold value.\n\n**Interaction**\n\n* **Shift+Scroll** over the image \u2014 adjusts the threshold by \u00b12 per\n  wheel tick (plain scroll pans/zooms the image as normal).\n* **Click** a histogram bar \u2014 jumps the threshold to that bin&#x27;s upper\n  edge.\n* **Dwell 400 ms** over the image \u2014 shows pixel coordinates and\n  intensity in the bottom-left label.\n\&quot;\&quot;\&quot;\nimport numpy as np\nimport anyplotlib as apl\n\n\n# \u2500\u2500 synthetic data \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n\ndef _make_multiphase_image(rng: np.random.Generator) -&gt; np.ndarray:\n    img = rng.normal(20, 5, (512, 512)).astype(np.float32)\n\n    # Grain A \u2014 6 large blobs\n    for _ in range(6):\n        cx, cy = rng.integers(60, 452, size=2)\n        r = rng.integers(40, 80)\n        ys, xs = np.ogrid[:512, :512]\n        mask = (xs - cx) ** 2 + (ys - cy) ** 2 &lt; r ** 2\n        img[mask] = rng.normal(80, 8, mask.sum())\n\n    # Grain B \u2014 8 smaller blobs\n    for _ in range(8):\n        cx, cy = rng.integers(40, 472, size=2)\n        r = rng.integers(15, 35)\n        ys, xs = np.ogrid[:512, :512]\n        mask = (xs - cx) ** 2 + (ys - cy) ** 2 &lt; r ** 2\n        img[mask] = rng.normal(130, 10, mask.sum())\n\n    # Voids \u2014 12 dark circular regions\n    for _ in range(12):\n        cx, cy = rng.integers(20, 492, size=2)\n        r = rng.integers(8, 20)\n        ys, xs = np.ogrid[:512, :512]\n        mask = (xs - cx) ** 2 + (ys - cy) ** 2 &lt; r ** 2\n        img[mask] = rng.normal(5, 2, mask.sum())\n\n    return np.clip(img, 0, 255).astype(np.float32)\n\n\nrng = np.random.default_rng(13)\nimage = _make_multiphase_image(rng)\n\nNBINS = 32\ncounts, bin_edges = np.histogram(image, bins=NBINS, range=(0, 255))\nbin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])\nx_labels = [f\&quot;{int(v)}\&quot; for v in bin_centers]\n\nthreshold = 100.0\n\n\n# \u2500\u2500 figure \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n\nfig, (ax_img, ax_hist) = apl.subplots(1, 2, figsize=(900, 500))\n\nimg_plot = ax_img.imshow(image, cmap=\&quot;gray\&quot;)\nhist_plot = ax_hist.bar(x_labels, counts.astype(float))\n\n# Track the threshold vline widget so we can remove/replace it\n_thresh_widget = None\n\n\ndef _pct_above(thresh: float) -&gt; float:\n    return 100.0 * float((image &gt;= thresh).sum()) / image.size\n\n\ndef _update_display(thresh: float) -&gt; None:\n    global threshold, _thresh_widget\n    threshold = float(np.clip(thresh, 0, 255))\n    mask = image &gt;= threshold\n    img_plot.set_overlay_mask(mask, color=\&quot;#ff0000\&quot;, alpha=0.35)\n    # Remove old threshold line widget and add a new one\n    if _thresh_widget is not None:\n        try:\n            hist_plot.remove_widget(_thresh_widget)\n        except KeyError:\n            pass\n    _thresh_widget = hist_plot.add_vline_widget(threshold, color=\&quot;#ffeb3b\&quot;)\n    pct = _pct_above(threshold)\n    print(f\&quot;Threshold: {threshold:.0f}  |  {pct:.1f}% above\&quot;)\n\n\n_update_display(threshold)\n\ninfo_label = img_plot.add_widget(\&quot;label\&quot;, x=10, y=490, text=\&quot;\&quot;, color=\&quot;#ffeb3b\&quot;, fontsize=11)\n\n\n# \u2500\u2500 event handlers \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n\ndef _on_wheel(event) -&gt; None:\n    if \&quot;shift\&quot; not in event.modifiers:\n        return\n    delta = -2.0 * np.sign(event.dy) if event.dy != 0 else 0.0\n    _update_display(threshold + delta)\n\n\ndef _on_bar_click(event) -&gt; None:\n    idx = event.bar_index\n    if idx is None:\n        return\n    new_thresh = float(bin_edges[idx + 1])\n    _update_display(new_thresh)\n\n\ndef _on_settled(event) -&gt; None:\n    if event.xdata is None or event.ydata is None:\n        return\n    x = int(np.clip(round(event.xdata), 0, 511))\n    y = int(np.clip(round(event.ydata), 0, 511))\n    intensity = float(image[y, x])\n    info_label.set(text=f\&quot;px ({x}, {y}): {intensity:.0f}\&quot;, x=10, y=490)\n\n\nimg_plot.add_event_handler(_on_wheel, \&quot;wheel\&quot;)\nimg_plot.add_event_handler(_on_settled, \&quot;pointer_settled\&quot;, ms=400, delta=4)\nhist_plot.add_event_handler(_on_bar_click, \&quot;pointer_down\&quot;)\n\nfig.set_help(\n    \&quot;Shift+Scroll over image: adjust threshold \u00b12\\n\&quot;\n    \&quot;Click histogram bar: jump to bin upper edge\\n\&quot;\n    \&quot;Dwell 400 ms over image: inspect pixel intensity\&quot;\n)\n\nfig  # Interactive\n&quot;"></script>



.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Threshold: 100  |  5.6% above






|

.. code-block:: Python

    import numpy as np
    import anyplotlib as apl


    # ── synthetic data ─────────────────────────────────────────────────────────────

    def _make_multiphase_image(rng: np.random.Generator) -> np.ndarray:
        img = rng.normal(20, 5, (512, 512)).astype(np.float32)

        # Grain A — 6 large blobs
        for _ in range(6):
            cx, cy = rng.integers(60, 452, size=2)
            r = rng.integers(40, 80)
            ys, xs = np.ogrid[:512, :512]
            mask = (xs - cx) ** 2 + (ys - cy) ** 2 < r ** 2
            img[mask] = rng.normal(80, 8, mask.sum())

        # Grain B — 8 smaller blobs
        for _ in range(8):
            cx, cy = rng.integers(40, 472, size=2)
            r = rng.integers(15, 35)
            ys, xs = np.ogrid[:512, :512]
            mask = (xs - cx) ** 2 + (ys - cy) ** 2 < r ** 2
            img[mask] = rng.normal(130, 10, mask.sum())

        # Voids — 12 dark circular regions
        for _ in range(12):
            cx, cy = rng.integers(20, 492, size=2)
            r = rng.integers(8, 20)
            ys, xs = np.ogrid[:512, :512]
            mask = (xs - cx) ** 2 + (ys - cy) ** 2 < r ** 2
            img[mask] = rng.normal(5, 2, mask.sum())

        return np.clip(img, 0, 255).astype(np.float32)


    rng = np.random.default_rng(13)
    image = _make_multiphase_image(rng)

    NBINS = 32
    counts, bin_edges = np.histogram(image, bins=NBINS, range=(0, 255))
    bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
    x_labels = [f"{int(v)}" for v in bin_centers]

    threshold = 100.0


    # ── figure ─────────────────────────────────────────────────────────────────────

    fig, (ax_img, ax_hist) = apl.subplots(1, 2, figsize=(900, 500))

    img_plot = ax_img.imshow(image, cmap="gray")
    hist_plot = ax_hist.bar(x_labels, counts.astype(float))

    # Track the threshold vline widget so we can remove/replace it
    _thresh_widget = None


    def _pct_above(thresh: float) -> float:
        return 100.0 * float((image >= thresh).sum()) / image.size


    def _update_display(thresh: float) -> None:
        global threshold, _thresh_widget
        threshold = float(np.clip(thresh, 0, 255))
        mask = image >= threshold
        img_plot.set_overlay_mask(mask, color="#ff0000", alpha=0.35)
        # Remove old threshold line widget and add a new one
        if _thresh_widget is not None:
            try:
                hist_plot.remove_widget(_thresh_widget)
            except KeyError:
                pass
        _thresh_widget = hist_plot.add_vline_widget(threshold, color="#ffeb3b")
        pct = _pct_above(threshold)
        print(f"Threshold: {threshold:.0f}  |  {pct:.1f}% above")


    _update_display(threshold)

    info_label = img_plot.add_widget("label", x=10, y=490, text="", color="#ffeb3b", fontsize=11)


    # ── event handlers ─────────────────────────────────────────────────────────────

    def _on_wheel(event) -> None:
        if "shift" not in event.modifiers:
            return
        delta = -2.0 * np.sign(event.dy) if event.dy != 0 else 0.0
        _update_display(threshold + delta)


    def _on_bar_click(event) -> None:
        idx = event.bar_index
        if idx is None:
            return
        new_thresh = float(bin_edges[idx + 1])
        _update_display(new_thresh)


    def _on_settled(event) -> None:
        if event.xdata is None or event.ydata is None:
            return
        x = int(np.clip(round(event.xdata), 0, 511))
        y = int(np.clip(round(event.ydata), 0, 511))
        intensity = float(image[y, x])
        info_label.set(text=f"px ({x}, {y}): {intensity:.0f}", x=10, y=490)


    img_plot.add_event_handler(_on_wheel, "wheel")
    img_plot.add_event_handler(_on_settled, "pointer_settled", ms=400, delta=4)
    hist_plot.add_event_handler(_on_bar_click, "pointer_down")

    fig.set_help(
        "Shift+Scroll over image: adjust threshold ±2\n"
        "Click histogram bar: jump to bin upper edge\n"
        "Dwell 400 ms over image: inspect pixel intensity"
    )

    fig  # Interactive


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.770 seconds)


.. _sphx_glr_download_auto_examples_Interactive_plot_threshold_explorer.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_threshold_explorer.ipynb <plot_threshold_explorer.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_threshold_explorer.py <plot_threshold_explorer.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_threshold_explorer.zip <plot_threshold_explorer.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_