
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples\models\plot_thompson2003.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_models_plot_thompson2003.py>`
        to download the full example code.

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

.. _sphx_glr_examples_models_plot_thompson2003.py:


===============================================================================
Thompson et al. (2003): Circular phosphenes
===============================================================================

This example shows how to use the
:py:class:`~pulse2percept.models.Thompson2003Model`.

The model introduced in [Thompson2003]_ assumes that electrical stimulation
leads to circular percepts with discrete gray levels.
The model also allows for a fraction of phosphenes to be omitted at random
(dropout rate).

The model can be loaded as follows (using 10% dropout rate):

.. GENERATED FROM PYTHON SOURCE LINES 17-24

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    import pulse2percept as p2p
    model = p2p.models.Thompson2003Model(step=0.2, dropout=0.1)
    model.build()





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

 .. code-block:: none


    Thompson2003Model(dropout=0.1, grid_type='rectangular', 
                      min_current_spread=1e-08, n_gray=None, 
                      n_jobs=14, n_threads=14, ndim=[2], 
                      noise=None, radius=None, 
                      spatial=Thompson2003Spatial, step=0.2, 
                      temporal=None, thresh_percept=0, 
                      verbose=True, 
                      vfmap=Curcio1990Map(ndim=2), 
                      xrange=(-15, 15), yrange=(-15, 15))



.. GENERATED FROM PYTHON SOURCE LINES 26-34

After building the model, we are ready to predict percepts.
Here we will use an :py:class:`~pulse2percept.implants.ArgusII` implant.

One way to assign a stimulus is to pass a NumPy array with the same number of
elements as there are electrodes in the array (i.e., 60).
Choosing values from ``np.arange(60)`` will assign a different number to
every electrode. We should thus expect to see 60 circular phosphenes that get
gradually brighter from one electrode to the next:

.. GENERATED FROM PYTHON SOURCE LINES 34-39

.. code-block:: Python


    implant = p2p.implants.ArgusII(stim=np.arange(60))
    percept = model.predict_percept(implant)
    percept.plot()




.. image-sg:: /examples/models/images/sphx_glr_plot_thompson2003_001.png
   :alt: plot thompson2003
   :srcset: /examples/models/images/sphx_glr_plot_thompson2003_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <Axes: xlabel='x (degrees of visual angle)', ylabel='y (degrees of visual angle)'>



.. GENERATED FROM PYTHON SOURCE LINES 40-42

Setting a nonzero dropout rate will randomly choose a fraction of phosphenes
to disappear:

.. GENERATED FROM PYTHON SOURCE LINES 42-51

.. code-block:: Python


    fig, axes = plt.subplots(ncols=4, figsize=(15, 6))
    for ax, drop in zip(axes, [0, 0.25, 0.5, 0.75]):
        model.build(dropout=drop)
        model.predict_percept(implant).plot(ax=ax)
        ax.set_title(f"{100*drop}% dropout")
    fig.tight_layout()





.. image-sg:: /examples/models/images/sphx_glr_plot_thompson2003_002.png
   :alt: 0% dropout, 25.0% dropout, 50.0% dropout, 75.0% dropout
   :srcset: /examples/models/images/sphx_glr_plot_thompson2003_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 52-66

Finally, the model can also be applied to
:py:class:`~pulse2percept.stimuli.VideoStimulus` objects, where every frame
of the video will be encoded with circular phosphenes and a given dropout
rate.

A video is a sequence of gray levels, and a model reads current, so the
video has to be encoded first: that is the step that says how much current
a gray level stands for (here 0-50 uA, sampled at the implant's electrodes).
See :py:class:`~pulse2percept.stimuli.AmplitudeEncoder`.

The pulse rate has to keep up with the frame rate, or some frames go by
without a pulse and are never seen; this video runs at 29.97 fps, so 30 Hz
it is. Asking for a percept at the video's own frame times then gives one
percept frame per video frame:

.. GENERATED FROM PYTHON SOURCE LINES 66-71

.. code-block:: Python


    video = p2p.stimuli.BostonTrain()
    implant.stim = video.encode(implant=implant, freq=30)
    model.build(dropout=0.2)
    model.predict_percept(implant, t_percept=video.time).play()





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" alt="">
        <canvas class="p2p-canvas" width="800" height="500"></canvas>
      </div>
      <div class="p2p-controls">
        <button class="p2p-btn" data-p2p-go="first"
                title="First frame">&#9198;</button>
        <button class="p2p-btn" data-p2p-go="prev"
                title="Previous frame">&#9194;</button>
        <button class="p2p-btn p2p-toggle" title="Play/Pause">&#9654;</button>
        <button class="p2p-btn" data-p2p-go="next"
                title="Next frame">&#9193;</button>
        <button class="p2p-btn" data-p2p-go="last"
                title="Last frame">&#9197;</button>
        <input class="p2p-slider" type="range" min="0" max="93" value="0"
               step="1">
        <span class="p2p-count">1/94</span>
        <select class="p2p-mode" title="Loop mode">
          <option value="once">Once</option>
          <option value="loop">Loop</option>
          <option value="reflect">Reflect</option>
        </select>
      </div>
    </div>
    <style>
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 { display: inline-block; max-width: 100%; width: 800px;
            font-family: sans-serif; font-size: 13px; }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-stage { position: relative; line-height: 0; }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-bg { width: 100%; height: auto; display: block; }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-canvas { position: absolute; left: 0; top: 0;
                        width: 100%; height: 100%; }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-controls { display: flex; align-items: center; gap: 4px;
                          padding-top: 4px; }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-btn { cursor: pointer; border: 1px solid rgba(128,128,128,0.4);
                     border-radius: 3px; background: transparent; color: inherit;
                     padding: 1px 6px; font-size: 13px; line-height: 1.4; }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-btn:hover { background: rgba(128,128,128,0.2); }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-slider { flex: 1 1 auto; min-width: 40px; margin: 0 4px; }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-count { font-variant-numeric: tabular-nums; opacity: 0.7;
                       white-space: nowrap; }
    #p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7 .p2p-mode { background: transparent; color: inherit; font-size: 12px;
                      border-radius: 3px;
                      border: 1px solid rgba(128,128,128,0.4); }
    </style>
    <script>
    (function () {
      var cfg = {"n": 94, "ncols": 10, "fw": 151, "fh": 151, "sw": 151, "sh": 151, "rect": [211, 60, 386, 385], "smooth": true, "interval": 33.366700033366705, "mode": "loop", "title": {"rect": [0, 37, 800, 20], "x": 404, "y": 47, "align": "center", "font": "normal 16.7px \"DejaVu Sans\", Verdana, sans-serif", "color": "#000000"}, "labels": ["t = 0.00 ms", "t = 33.37 ms", "t = 66.73 ms", "t = 100.10 ms", "t = 133.47 ms", "t = 166.83 ms", "t = 200.20 ms", "t = 233.57 ms", "t = 266.93 ms", "t = 300.30 ms", "t = 333.67 ms", "t = 367.03 ms", "t = 400.40 ms", "t = 433.77 ms", "t = 467.13 ms", "t = 500.50 ms", "t = 533.87 ms", "t = 567.23 ms", "t = 600.60 ms", "t = 633.97 ms", "t = 667.33 ms", "t = 700.70 ms", "t = 734.07 ms", "t = 767.43 ms", "t = 800.80 ms", "t = 834.17 ms", "t = 867.53 ms", "t = 900.90 ms", "t = 934.27 ms", "t = 967.63 ms", "t = 1001.00 ms", "t = 1034.37 ms", "t = 1067.73 ms", "t = 1101.10 ms", "t = 1134.47 ms", "t = 1167.83 ms", "t = 1201.20 ms", "t = 1234.57 ms", "t = 1267.93 ms", "t = 1301.30 ms", "t = 1334.67 ms", "t = 1368.03 ms", "t = 1401.40 ms", "t = 1434.77 ms", "t = 1468.13 ms", "t = 1501.50 ms", "t = 1534.87 ms", "t = 1568.23 ms", "t = 1601.60 ms", "t = 1634.97 ms", "t = 1668.34 ms", "t = 1701.70 ms", "t = 1735.07 ms", "t = 1768.44 ms", "t = 1801.80 ms", "t = 1835.17 ms", "t = 1868.54 ms", "t = 1901.90 ms", "t = 1935.27 ms", "t = 1968.64 ms", "t = 2002.00 ms", "t = 2035.37 ms", "t = 2068.74 ms", "t = 2102.10 ms", "t = 2135.47 ms", "t = 2168.84 ms", "t = 2202.20 ms", "t = 2235.57 ms", "t = 2268.94 ms", "t = 2302.30 ms", "t = 2335.67 ms", "t = 2369.04 ms", "t = 2402.40 ms", "t = 2435.77 ms", "t = 2469.14 ms", "t = 2502.50 ms", "t = 2535.87 ms", "t = 2569.24 ms", "t = 2602.60 ms", "t = 2635.97 ms", "t = 2669.34 ms", "t = 2702.70 ms", "t = 2736.07 ms", "t = 2769.44 ms", "t = 2802.80 ms", "t = 2836.17 ms", "t = 2869.54 ms", "t = 2902.90 ms", "t = 2936.27 ms", "t = 2969.64 ms", "t = 3003.00 ms", "t = 3036.37 ms", "t = 3069.74 ms", "t = 3103.10 ms"]};
      var root = document.getElementById("p2p-anim-dd8a03e4ba3c4c1487e57e99516acbb7");
      if (!root) { return; }
      var ctx = root.querySelector(".p2p-canvas").getContext("2d");
      var slider = root.querySelector(".p2p-slider");
      var counter = root.querySelector(".p2p-count");
      var mode = root.querySelector(".p2p-mode");
      var toggle = root.querySelector(".p2p-toggle");
      var frame = 0, dir = 1, timer = null, sheet = new Image();

      function draw() {
        if (!sheet.complete || !sheet.naturalWidth) { return; }
        var col = frame % cfg.ncols, row = (frame - col) / cfg.ncols;
        ctx.imageSmoothingEnabled = cfg.smooth;
        // Frames with an alpha channel are composited onto the static background,
        // so the previous frame has to go first or they stack up:
        ctx.clearRect(cfg.rect[0], cfg.rect[1], cfg.rect[2], cfg.rect[3]);
        ctx.drawImage(sheet, col * cfg.sw, row * cfg.sh, cfg.fw, cfg.fh,
                      cfg.rect[0], cfg.rect[1], cfg.rect[2], cfg.rect[3]);
        if (cfg.title) {
          ctx.clearRect(cfg.title.rect[0], cfg.title.rect[1],
                        cfg.title.rect[2], cfg.title.rect[3]);
          ctx.font = cfg.title.font;
          ctx.fillStyle = cfg.title.color;
          ctx.textAlign = cfg.title.align;
          ctx.textBaseline = "middle";
          ctx.fillText(cfg.labels[frame], cfg.title.x, cfg.title.y);
        }
      }

      function show(i) {
        frame = Math.max(0, Math.min(cfg.n - 1, i));
        slider.value = frame;
        counter.textContent = (frame + 1) + "/" + cfg.n;
        draw();
      }

      function pause() {
        if (timer !== null) { clearInterval(timer); timer = null; }
        toggle.innerHTML = "&#9654;";
      }

      function play() {
        if (timer !== null) { return; }
        if (mode.value === "once" && frame === cfg.n - 1) { show(0); }
        toggle.innerHTML = "&#10074;&#10074;";
        timer = setInterval(advance, Math.max(1, cfg.interval));
      }

      function advance() {
        var next = frame + dir;
        if (next > cfg.n - 1 || next < 0) {
          if (mode.value === "loop") {
            next = dir > 0 ? 0 : cfg.n - 1;
          } else if (mode.value === "reflect" && cfg.n > 1) {
            dir = -dir;
            next = frame + dir;
          } else {
            pause();
            return;
          }
        }
        show(next);
      }

      root.querySelectorAll("[data-p2p-go]").forEach(function (btn) {
        btn.addEventListener("click", function () {
          pause();
          dir = 1;
          var go = btn.dataset.p2pGo;
          show(go === "first" ? 0 : go === "last" ? cfg.n - 1 :
               go === "next" ? frame + 1 : frame - 1);
        });
      });
      toggle.addEventListener("click", function () {
        if (timer === null) { play(); } else { pause(); }
      });
      slider.addEventListener("input", function () {
        pause();
        dir = 1;
        show(parseInt(slider.value, 10));
      });
      mode.value = cfg.mode;
      sheet.onload = draw;
      sheet.src = 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      show(0);
    })();
    </script>

    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 1.475 seconds)


.. _sphx_glr_download_examples_models_plot_thompson2003.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_thompson2003.ipynb <plot_thompson2003.ipynb>`

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

      :download:`Download Python source code: plot_thompson2003.py <plot_thompson2003.py>`

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

      :download:`Download zipped: plot_thompson2003.zip <plot_thompson2003.zip>`


.. only:: html

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

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