From 5d17dca1446041d512ef189c0c43cf8f5380bfad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?= Date: Thu, 11 Jul 2024 21:51:16 -0400 Subject: [PATCH] ENH: Experiment using a single tensor in GP notebook Experiment using a single tensor in the GP notebook. --- docs/notebooks/dwi_simulated_gp.ipynb | 642 +++++++++++++++++++++----- 1 file changed, 528 insertions(+), 114 deletions(-) diff --git a/docs/notebooks/dwi_simulated_gp.ipynb b/docs/notebooks/dwi_simulated_gp.ipynb index 3cb6e5c2..de8d6da8 100644 --- a/docs/notebooks/dwi_simulated_gp.ipynb +++ b/docs/notebooks/dwi_simulated_gp.ipynb @@ -3,46 +3,28 @@ { "metadata": {}, "cell_type": "markdown", - "source": "We define a method below to create a noise-free DWI signal using a multi-tensor model.", - "id": "6d3d512da282ff52" + "source": "Define a method to create a DWI signal using a single tensor", + "id": "28be0e6f9dbd2c9d" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-07-10T23:49:14.750996Z", - "start_time": "2024-07-10T23:49:14.735540Z" + "end_time": "2024-08-11T22:31:08.305250Z", + "start_time": "2024-08-11T22:31:08.300753Z" } }, "cell_type": "code", "source": [ - "import numpy as np\n", - "\n", "from dipy.core.gradients import gradient_table\n", "from dipy.core.sphere import disperse_charges, HemiSphere, Sphere\n", - "from dipy.sims.voxel import multi_tensor\n", - "from sklearn.gaussian_process import GaussianProcessRegressor\n", - "\n", - "def create_multitensor_dmri_signal(hsph_dirs):\n", - " \"\"\"Create a multi-tensor, noise-free dMRI signal for simulation purposes. It\n", - " simulates two tensors crossing at 90 degrees with equal signal fraction, and\n", - " ``hsph_dirs`` diffusion-encoding gradients at b=1000 s/mm^2, plus a b0\n", - " volume.\"\"\"\n", - "\n", - " # Eigenvalues of tensors\n", - " eval1 = [0.0015, 0.0003, 0.0003]\n", - " eval2 = [0.0015, 0.0003, 0.0003]\n", - " mevals = np.array([eval1, eval2])\n", - "\n", - " # Polar coordinates (theta, phi) of the principal axis of each tensor\n", - " angles = [(0, 0), (90, 0)]\n", "\n", - " # Percentage of the contribution of each tensor\n", - " fractions = [50, 50]\n", + "def create_single_tensor_signal(_hsph_dirs, _evals, _evecs, _bval_shell, _S0, _snr):\n", + " \"\"\"Create a multi-tensor dMRI signal for simulation purposes. It adds a b0 volume.\"\"\"\n", "\n", " # Create the gradient table placing random points on a hemisphere\n", " rng = np.random.default_rng(1234)\n", - " theta = np.pi * rng.random(hsph_dirs)\n", - " phi = 2 * np.pi * rng.random(hsph_dirs)\n", + " theta = np.pi * rng.random(_hsph_dirs)\n", + " phi = 2 * np.pi * rng.random(_hsph_dirs)\n", " hsph_initial = HemiSphere(theta=theta, phi=phi)\n", "\n", " # Move the points so that the electrostatic potential energy is minimized\n", @@ -55,123 +37,249 @@ " vertices = sph.vertices\n", " values = np.ones(vertices.shape[0])\n", " bvecs = vertices\n", - " bval_shell1 = 1000\n", - " bvals = bval_shell1 * values\n", + " bvals = _bval_shell * values\n", "\n", " # Add a b0 value to the gradient table\n", " bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)\n", " bvals = np.insert(bvals, 0, 0)\n", - " gtab = gradient_table(bvals, bvecs)\n", + " _gtab = gradient_table(bvals, bvecs)\n", "\n", - " # Create a noise-free signal\n", - " snr = None\n", - " S0 = 100\n", - " signal, sticks = multi_tensor(\n", - " gtab, mevals, S0=S0, angles=angles, fractions=fractions, snr=snr\n", - " )\n", - "\n", - " grad = np.vstack([gtab.bvecs.T, gtab.bvals])\n", + " # Create the signal\n", + " _signal = single_tensor(_gtab, S0=_S0, evals=_evals, evecs=_evecs, snr=_snr, rng=None)\n", "\n", - " return signal, sticks, grad, mevals, angles, fractions" + " return _signal, _gtab" ], - "id": "a0f5bab019855954", + "id": "962dd0e463ccf0e", "outputs": [], - "execution_count": 16 + "execution_count": 21 }, { "metadata": {}, "cell_type": "markdown", - "source": "We now create the DWI signal using 30 directions defined on the half sphere.", + "source": "Create the DWI signal using a single tensor.", "id": "7d5b5cbebaa82e19" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-07-10T23:49:20.804385Z", - "start_time": "2024-07-10T23:49:16.980337Z" + "end_time": "2024-08-11T22:31:12.269308Z", + "start_time": "2024-08-11T22:31:08.364614Z" } }, "cell_type": "code", "source": [ + "import numpy as np\n", + "from dipy.core.geometry import sphere2cart\n", + "from dipy.sims.voxel import single_tensor, all_tensor_evecs\n", + "\n", + "# Polar coordinates (theta, phi) of the principal axis of the tensor\n", + "angles = np.array([0, 0])\n", + "sticks = np.array(sphere2cart(1, np.deg2rad(angles[0]), np.deg2rad(angles[1])))\n", + "evecs = all_tensor_evecs(sticks)\n", + "\n", + "# Eigenvalues of the tensor\n", + "evals1 = [0.0015, 0.0003, 0.0003]\n", + "\n", + "# Half the number of the gradient vectors\n", "hsph_dirs = 90\n", - "signal, sticks, grad, mevals, angles, fractions = create_multitensor_dmri_signal(hsph_dirs)" + "\n", + "# Single shell: b = 1000 mm/s^2\n", + "bval_shell = 1000\n", + "\n", + "S0 = 100\n", + "# Noise-free signal\n", + "snr = None\n", + "\n", + "signal, gtab = create_single_tensor_signal(hsph_dirs, evals1, evecs, bval_shell, S0, snr)" ], "id": "e9545781fe5cf3b8", "outputs": [], - "execution_count": 17 + "execution_count": 22 }, { "metadata": {}, "cell_type": "markdown", - "source": "Since there is only a single voxel in the simulated DWI signal, we add 3 axes before the diffusion-encoding gradient axis so that the plotting method can appropriately represent it. ", - "id": "a31eef208433f772" + "source": "Define a method to plot the fODFs of the signal", + "id": "61c7bd2fa7406c07" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-07-10T23:49:20.807577Z", - "start_time": "2024-07-10T23:49:20.805325Z" + "end_time": "2024-08-11T22:31:12.272924Z", + "start_time": "2024-08-11T22:31:12.270341Z" } }, "cell_type": "code", "source": [ - "voxel_idx = [0, 0, 0]\n", - "dwi_data = signal[np.newaxis, np.newaxis, np.newaxis, :]" + "from dipy.viz import window, actor\n", + "\n", + "def plot_dwi_fodf(_odf, sphere):\n", + "\n", + " scene = window.Scene()\n", + " \n", + " odf_actor = actor.odf_slicer(_odf[None, None, None, :], sphere=sphere, colormap='plasma')\n", + " odf_actor.RotateX(90)\n", + " \n", + " scene.add(odf_actor)\n", + " _scene_array = window.snapshot(scene, offscreen=True)\n", + "\n", + " return _scene_array" ], - "id": "c07f103d9bd347cc", + "id": "ce7bcfbd73447d81", "outputs": [], - "execution_count": 18 + "execution_count": 23 }, { "metadata": {}, "cell_type": "markdown", - "source": "Plot the data", - "id": "cf8e5fb998123a47" + "source": "Plot the fODFs of the signal", + "id": "1e429c27665565a4" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-07-10T23:49:23.598578Z", - "start_time": "2024-07-10T23:49:22.999134Z" + "end_time": "2024-08-11T22:31:12.635187Z", + "start_time": "2024-08-11T22:31:12.273627Z" } }, "cell_type": "code", "source": [ - "from dipy.sims.voxel import multi_tensor, multi_tensor_odf\n", "from dipy.data import get_sphere\n", + "from dipy.sims.voxel import single_tensor_odf\n", "\n", - "from matplotlib import pyplot as plt \n", + "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "sphere = get_sphere('symmetric724')\n", "sphere = sphere.subdivide(2)\n", "\n", - "odf = multi_tensor_odf(sphere.vertices, mevals, angles, fractions)\n", - "\n", - "from dipy.viz import window, actor\n", - "scene = window.Scene()\n", - "\n", - "odf_actor = actor.odf_slicer(odf[None, None, None, :], sphere=sphere, colormap='plasma')\n", - "odf_actor.RotateX(90)\n", + "odf = single_tensor_odf(sphere.vertices, evals1, evecs)\n", "\n", - "scene.add(odf_actor)\n", - "scene_array = window.snapshot(scene, offscreen=True)\n", + "scene_array = plot_dwi_fodf(odf, sphere)\n", "plt.imshow(scene_array)\n", + "plt.show()" + ], + "id": "f91383788a7e821c", + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute('tabindex', '0');\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;' +\n 'z-index: 2;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'pointer-events: none;' +\n 'position: relative;' +\n 'z-index: 0;'\n );\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'left: 0;' +\n 'pointer-events: none;' +\n 'position: absolute;' +\n 'top: 0;' +\n 'z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n /* This rescales the canvas back to display pixels, so that it\n * appears correct on HiDPI screens. */\n canvas.style.width = width + 'px';\n canvas.style.height = height + 'px';\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n /* User Agent sniffing is bad, but WebKit is busted:\n * https://bugs.webkit.org/show_bug.cgi?id=144526\n * https://bugs.webkit.org/show_bug.cgi?id=181818\n * The worst that happens here is that they get an extra browser\n * selection when dragging, if this check fails to catch them.\n */\n var UA = navigator.userAgent;\n var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n if(isWebKit) {\n return function (event) {\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We\n * want to control all of the cursor setting manually through\n * the 'cursor' event from matplotlib */\n event.preventDefault()\n return fig.mouse_event(event, name);\n };\n } else {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n }\n\n canvas_div.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n canvas_div.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n canvas_div.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n canvas_div.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n canvas_div.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n canvas_div.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n canvas_div.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\nfunction getModifiers(event) {\n var mods = [];\n if (event.ctrlKey) {\n mods.push('ctrl');\n }\n if (event.altKey) {\n mods.push('alt');\n }\n if (event.shiftKey) {\n mods.push('shift');\n }\n if (event.metaKey) {\n mods.push('meta');\n }\n return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n // from https://stackoverflow.com/q/1114465\n var boundingRect = this.canvas.getBoundingClientRect();\n var x = (event.clientX - boundingRect.left) * this.ratio;\n var y = (event.clientY - boundingRect.top) * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n modifiers: getModifiers(event),\n guiEvent: simpleKeys(event),\n });\n\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 24 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Now plot the DWI data itself. Since there is only a single voxel in the simulated DWI signal, we add 3 axes before the diffusion-encoding gradient axis so that the plotting method can appropriately represent it. ", + "id": "a31eef208433f772" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.638565Z", + "start_time": "2024-08-11T22:31:12.636491Z" + } + }, + "cell_type": "code", + "source": [ + "voxel_idx = [0, 0, 0]\n", + "dwi_data = signal[np.newaxis, np.newaxis, np.newaxis, :]" + ], + "id": "c07f103d9bd347cc", + "outputs": [], + "execution_count": 25 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Define a method to get the appropriate signal representation", + "id": "25fc548550feb308" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.644665Z", + "start_time": "2024-08-11T22:31:12.639430Z" + } + }, + "cell_type": "code", + "source": [ + "def compute_raw_signal_representation(_dwi_data, _bvecs, _b0_mask, _voxel_idx):\n", "\n", - "def plot_dwi(dwi_data, bvecs, b0_mask, voxel_idx, b0_idx):\n", - "\n", - " s0_voxel = dwi_data[voxel_idx[0], voxel_idx[1], voxel_idx[2], b0_idx]\n", - " s_voxel = dwi_data[voxel_idx[0], voxel_idx[1], voxel_idx[2], ~b0_mask]\n", + " # There is only one b0 volume in the simulated signal\n", + " s0_voxel = _dwi_data[_voxel_idx[0], _voxel_idx[1], _voxel_idx[2], _b0_mask]\n", + " s_voxel = _dwi_data[_voxel_idx[0], _voxel_idx[1], _voxel_idx[2], ~_b0_mask]\n", "\n", " # Scale gradient vector values with DWI signal\n", " s_normal = s_voxel/s0_voxel\n", - " scaled_vectors = bvecs[:, ~b0_mask] / s_normal[np.newaxis, :]\n", + " s_dir_values = _bvecs[~_b0_mask, :] / s_normal[:, np.newaxis]\n", + "\n", + " return s_dir_values" + ], + "id": "3abdf5122238f9c1", + "outputs": [], + "execution_count": 26 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Obtain the appropriate representation for the DWI data", + "id": "9edff59aa8dadbcb" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.647956Z", + "start_time": "2024-08-11T22:31:12.645672Z" + } + }, + "cell_type": "code", + "source": "signal_repr = compute_raw_signal_representation(dwi_data, gtab.bvecs, gtab.b0s_mask, voxel_idx)", + "id": "70d1630d58576a72", + "outputs": [], + "execution_count": 27 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Define a method to plot the DWI data", + "id": "cf8e5fb998123a47" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.651852Z", + "start_time": "2024-08-11T22:31:12.648998Z" + } + }, + "cell_type": "code", + "source": [ + "def plot_dwi_scatter(_dwi_data):\n", "\n", " # Plot the DWI signal as a 3D point cloud\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111, projection=\"3d\")\n", "\n", - " _ = ax.scatter(scaled_vectors[0, :], scaled_vectors[1, :], scaled_vectors[2, :], c=\"blue\", marker=\"*\")\n", + " _ = ax.scatter(_dwi_data[:, 0], _dwi_data[:, 1], _dwi_data[:, 2], c=\"blue\", marker=\"*\")\n", "\n", " # Set labels\n", " ax.set_xlabel(\"X\")\n", @@ -179,24 +287,41 @@ " ax.set_zlabel(\"Z\")\n", " ax.set_title(\"Normalized dMRI signal\")\n", "\n", - " return fig\n", + " return fig" + ], + "id": "764dff2e2c5a9b8d", + "outputs": [], + "execution_count": 28 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Plot the DWI data as a scatter plot", + "id": "95cfad7bd0b98fd6" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.672024Z", + "start_time": "2024-08-11T22:31:12.652827Z" + } + }, + "cell_type": "code", + "source": [ + "# Create an interactive plot\n", + "%matplotlib notebook\n", "\n", - "voxel_idx = [0, 0, 0]\n", - "dwi_data = signal[np.newaxis, np.newaxis, np.newaxis, :]\n", - "b0_mask = grad[3, :] <= 50\n", - "# There is only one b0 volume in the simulated signal\n", - "b0_idx = 0\n", - "_ = plot_dwi(dwi_data, grad[:3, :], b0_mask, voxel_idx, b0_idx)\n", + "fig = plot_dwi_scatter(signal_repr)\n", "plt.show()" ], - "id": "70d1630d58576a72", + "id": "d1f011c94a0d32ea", "outputs": [ { "data": { "text/plain": [ - "
" + "" ], - "image/png": "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" + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute('tabindex', '0');\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;' +\n 'z-index: 2;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'pointer-events: none;' +\n 'position: relative;' +\n 'z-index: 0;'\n );\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'left: 0;' +\n 'pointer-events: none;' +\n 'position: absolute;' +\n 'top: 0;' +\n 'z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n /* This rescales the canvas back to display pixels, so that it\n * appears correct on HiDPI screens. */\n canvas.style.width = width + 'px';\n canvas.style.height = height + 'px';\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n /* User Agent sniffing is bad, but WebKit is busted:\n * https://bugs.webkit.org/show_bug.cgi?id=144526\n * https://bugs.webkit.org/show_bug.cgi?id=181818\n * The worst that happens here is that they get an extra browser\n * selection when dragging, if this check fails to catch them.\n */\n var UA = navigator.userAgent;\n var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n if(isWebKit) {\n return function (event) {\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We\n * want to control all of the cursor setting manually through\n * the 'cursor' event from matplotlib */\n event.preventDefault()\n return fig.mouse_event(event, name);\n };\n } else {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n }\n\n canvas_div.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n canvas_div.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n canvas_div.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n canvas_div.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n canvas_div.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n canvas_div.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n canvas_div.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\nfunction getModifiers(event) {\n var mods = [];\n if (event.ctrlKey) {\n mods.push('ctrl');\n }\n if (event.altKey) {\n mods.push('alt');\n }\n if (event.shiftKey) {\n mods.push('shift');\n }\n if (event.metaKey) {\n mods.push('meta');\n }\n return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n // from https://stackoverflow.com/q/1114465\n var boundingRect = this.canvas.getBoundingClientRect();\n var x = (event.clientX - boundingRect.left) * this.ratio;\n var y = (event.clientY - boundingRect.top) * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n modifiers: getModifiers(event),\n guiEvent: simpleKeys(event),\n });\n\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" }, "metadata": {}, "output_type": "display_data" @@ -204,71 +329,140 @@ { "data": { "text/plain": [ - "
" + "" ], - "image/png": "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" + "text/html": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 19 + "execution_count": 29 + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# ToDo\n", + "# Plot as surface" + ], + "id": "fa5462318503e68d", + "outputs": [], + "execution_count": 30 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.679857Z", + "start_time": "2024-08-11T22:31:12.677408Z" + } + }, + "cell_type": "code", + "source": [ + "# ToDo\n", + "# Define and call Ariel's method to plot according the diffusivity and plot again" + ], + "id": "fe3d1698a5fb5128", + "outputs": [], + "execution_count": 31 }, { "metadata": {}, "cell_type": "markdown", - "source": "We now define the kernel that we will be using for the Gaussian process", + "source": "We now define the Gaussian process model instance with a spherical kernel.", "id": "37e6f400ed44f2d9" }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.684328Z", + "start_time": "2024-08-11T22:31:12.681127Z" + } + }, "cell_type": "code", "source": [ - "from src.eddymotion.model.dipy import PairwiseOrientationKernel\n", + "from eddymotion.model.dipy import GaussianProcessModel\n", "\n", + "kernel_model = \"spherical\"\n", "lambda_s = 2.0\n", "a = 1.0\n", "sigma_sq = 0.5\n", - "kernel = PairwiseOrientationKernel(weighting=\"spherical\", lambda_s=lambda_s, a=a, sigma_sq=sigma_sq)" + "\n", + "gp_model = GaussianProcessModel(kernel_model=kernel_model, lambda_s=lambda_s, a=a, sigma_sq=sigma_sq)" ], "id": "a66400cc9ee4c084", "outputs": [], - "execution_count": null + "execution_count": 32 }, { "metadata": {}, "cell_type": "markdown", - "source": "The ``grad`` gradient table instance is in RAS+b format, so we choose the diffusion-encoding gradient vectors (leaving out the first index, which corresponds to the b0 volume) to fit the Gaussian process.", + "source": "We fit the Gaussian process with the diffusion-encoding gradient vectors: leave out the first index, which corresponds to the b0 volume, and an additional random index.", "id": "12b7491f09d7ea74" }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.849776Z", + "start_time": "2024-08-11T22:31:12.685127Z" + } + }, "cell_type": "code", "source": [ - "_grad = grad[:3, 1:]\n", - "gpr = GaussianProcessRegressor(kernel=kernel, random_state=0)\n", - "gpr.fit(_grad.T, signal[1:])" + "rng = np.random.default_rng(1234)\n", + "idx = rng.integers(low=1, high=len(gtab.bvals), size=1).item()\n", + "lo = [0, idx]\n", + "train_mask = np.ones(len(gtab.bvals), dtype=bool)\n", + "train_mask[lo] = False\n", + "\n", + "gpfit = gp_model.fit(signal[train_mask], gtab[train_mask])" ], "id": "b64f511b41456359", - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n", + "ABNORMAL_TERMINATION_IN_LNSRCH.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " _check_optimize_result(\"lbfgs\", opt_res)\n", + "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n", + " warnings.warn(\n", + "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n", + " warnings.warn(\n", + "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n", + " warnings.warn(\n" + ] + } + ], + "execution_count": 33 }, { "metadata": {}, "cell_type": "markdown", - "source": "Now predict the signal on the last diffusion-encoding gradient vector. ", + "source": "Now predict the signal on a set of diffusion-encoding gradient vectors: use all vectors except the b0", "id": "351b0db081b0e890" }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.888183Z", + "start_time": "2024-08-11T22:31:12.851394Z" + } + }, "cell_type": "code", "source": [ - "_grad_pred = grad[:3, -1]\n", - "y_mean, y_std = gpr.predict(_grad_pred, return_std=True)" + "X_qry = gtab.bvecs[~gtab.b0s_mask]\n", + "prediction = gpfit.predict(X_qry)" ], "id": "ecfc20d16f567c91", "outputs": [], - "execution_count": null + "execution_count": 34 }, { "metadata": {}, @@ -277,30 +471,42 @@ "id": "c025c7d8a8c47b4c" }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:12.892029Z", + "start_time": "2024-08-11T22:31:12.889483Z" + } + }, "cell_type": "code", - "source": "gpr.kernel_", + "source": "# gp_model.kernel_", "id": "1ee8a8d2584ea19", "outputs": [], - "execution_count": null + "execution_count": 35 }, { "metadata": {}, "cell_type": "markdown", - "source": "Plot the training data and the predictions from the Gaussian process", + "source": "Plot the training data and the predictions from the Gaussian process. Mark with a different color the prediction corresponding to the index that was left out from the training set.", "id": "8913c3c3ef7d50f7" }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:13.117670Z", + "start_time": "2024-08-11T22:31:12.893199Z" + } + }, "cell_type": "code", "source": [ - "from matplotlib import pyplot as plt \n", + "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", - "s = dwi_data[voxel_idx[0], voxel_idx[1], voxel_idx[2], :]\n", - "s_hat_mean = y_mean[voxel_idx[0], voxel_idx[1], voxel_idx[2], :]\n", - "s_hat_stddev = y_std[voxel_idx[0], voxel_idx[1], voxel_idx[2], :]\n", - "x = np.asarray(range(len(grad.bvals)))\n", + "# Exclude the b0s\n", + "s = dwi_data[voxel_idx[0], voxel_idx[1], voxel_idx[2], :][1:]\n", + "s_hat_mean = prediction\n", + "# s_hat_stddev = y_std[voxel_idx[0], voxel_idx[1], voxel_idx[2], :]\n", + "s_hat_stddev = np.ones_like(s_hat_mean)\n", + "x = np.asarray(range(len(gtab.bvals)))[1:]\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x, s_hat_mean, c=\"orange\", label=\"predicted\")\n", @@ -313,6 +519,7 @@ " label=r\"95% confidence interval\",\n", ")\n", "plt.scatter(x, s, c=\"b\", label=\"ground truth\")\n", + "plt.scatter(x[idx], s[idx], c=\"r\", label=\"predicted lo\")\n", "ax.set_xlabel(\"bvec indices\")\n", "ax.set_ylabel(\"signal\")\n", "ax.legend()\n", @@ -321,8 +528,215 @@ "plt.show()" ], "id": "58f01aeb70aff1c1", + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute('tabindex', '0');\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;' +\n 'z-index: 2;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'pointer-events: none;' +\n 'position: relative;' +\n 'z-index: 0;'\n );\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'left: 0;' +\n 'pointer-events: none;' +\n 'position: absolute;' +\n 'top: 0;' +\n 'z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n /* This rescales the canvas back to display pixels, so that it\n * appears correct on HiDPI screens. */\n canvas.style.width = width + 'px';\n canvas.style.height = height + 'px';\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n /* User Agent sniffing is bad, but WebKit is busted:\n * https://bugs.webkit.org/show_bug.cgi?id=144526\n * https://bugs.webkit.org/show_bug.cgi?id=181818\n * The worst that happens here is that they get an extra browser\n * selection when dragging, if this check fails to catch them.\n */\n var UA = navigator.userAgent;\n var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n if(isWebKit) {\n return function (event) {\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We\n * want to control all of the cursor setting manually through\n * the 'cursor' event from matplotlib */\n event.preventDefault()\n return fig.mouse_event(event, name);\n };\n } else {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n }\n\n canvas_div.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n canvas_div.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n canvas_div.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n canvas_div.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n canvas_div.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n canvas_div.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n canvas_div.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\nfunction getModifiers(event) {\n var mods = [];\n if (event.ctrlKey) {\n mods.push('ctrl');\n }\n if (event.altKey) {\n mods.push('alt');\n }\n if (event.shiftKey) {\n mods.push('shift');\n }\n if (event.metaKey) {\n mods.push('meta');\n }\n return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n // from https://stackoverflow.com/q/1114465\n var boundingRect = this.canvas.getBoundingClientRect();\n var x = (event.clientX - boundingRect.left) * this.ratio;\n var y = (event.clientY - boundingRect.top) * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n modifiers: getModifiers(event),\n guiEvent: simpleKeys(event),\n });\n\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 36 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Now do the same with a pair of crossing fibers. Define a method below to create a DWI signal using a multi-tensor model.", + "id": "aef844ebb465f1e8" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:13.122366Z", + "start_time": "2024-08-11T22:31:13.118435Z" + } + }, + "cell_type": "code", + "source": [ + "from dipy.sims.voxel import multi_tensor\n", + "\n", + "def create_multi_tensor_dmri_signal(_hsph_dirs, _angles, _mevals, _fractions, _bval_shell, _S0, _snr):\n", + " \"\"\"Create a multi-tensor dMRI signal for simulation purposes. It adds a b0 volume.\"\"\"\n", + "\n", + " # Create the gradient table placing random points on a hemisphere\n", + " rng = np.random.default_rng(1234)\n", + " theta = np.pi * rng.random(_hsph_dirs)\n", + " phi = 2 * np.pi * rng.random(_hsph_dirs)\n", + " hsph_initial = HemiSphere(theta=theta, phi=phi)\n", + "\n", + " # Move the points so that the electrostatic potential energy is minimized\n", + " iterations = 5000\n", + " hsph_updated, potential = disperse_charges(hsph_initial, iterations)\n", + " # Create a sphere\n", + " sph = Sphere(xyz=np.vstack((hsph_updated.vertices, -hsph_updated.vertices)))\n", + "\n", + " # Create the gradients\n", + " vertices = sph.vertices\n", + " values = np.ones(vertices.shape[0])\n", + " bvecs = vertices\n", + " bvals = _bval_shell * values\n", + "\n", + " # Add a b0 value to the gradient table\n", + " bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)\n", + " bvals = np.insert(bvals, 0, 0)\n", + " _gtab = gradient_table(bvals, bvecs)\n", + "\n", + " # Create a noise-free signal\n", + " _signal, _sticks = multi_tensor(\n", + " _gtab, _mevals, S0=_S0, angles=_angles, fractions=_fractions, snr=_snr\n", + " )\n", + "\n", + " return _signal, _gtab," + ], + "id": "33119302042f8918", + "outputs": [], + "execution_count": 37 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We now create the DWI signal using again 90 directions defined on the half sphere.", + "id": "6aa6f248f3922562" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:16.921320Z", + "start_time": "2024-08-11T22:31:13.123183Z" + } + }, + "cell_type": "code", + "source": [ + "# The crossing fibers will form an angle\n", + "angle = 90\n", + "\n", + "# Polar coordinates (theta, phi) of the principal axis of each tensor\n", + "angles = [(0, 0), (angle, 0)]\n", + "\n", + "# Eigenvalues of tensors\n", + "eval1 = [0.0015, 0.0003, 0.0003]\n", + "eval2 = [0.0015, 0.0003, 0.0003]\n", + "mevals = np.array([eval1, eval2])\n", + "\n", + "# Percentage of the contribution of each tensor\n", + "fractions = [50, 50]\n", + "\n", + "bval_shell = 1000\n", + "S0 = 100\n", + "snr = None\n", + "\n", + "signal, gtab = create_multi_tensor_dmri_signal(hsph_dirs, angles, mevals, fractions, bval_shell, S0, snr)" + ], + "id": "a9f35a054ebc913f", + "outputs": [], + "execution_count": 38 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Define a method to plot the fODFs of the signal", + "id": "31f040a251fbbc47" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:17.373066Z", + "start_time": "2024-08-11T22:31:16.922263Z" + } + }, + "cell_type": "code", + "source": [ + "from dipy.sims.voxel import multi_tensor_odf\n", + "\n", + "sphere = get_sphere('symmetric724')\n", + "sphere = sphere.subdivide(2)\n", + "\n", + "odf = multi_tensor_odf(sphere.vertices, mevals, angles, fractions)\n", + "\n", + "scene_array = plot_dwi_fodf(odf, sphere)\n", + "plt.figure()\n", + "plt.imshow(scene_array)" + ], + "id": "a9d6e0d431ba3df6", + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 39 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-11T22:31:17.391982Z", + "start_time": "2024-08-11T22:31:17.373991Z" + } + }, + "cell_type": "code", + "source": [ + "voxel_idx = [0, 0, 0]\n", + "dwi_data = signal[np.newaxis, np.newaxis, np.newaxis, :]\n", + "\n", + "signal_repr = compute_raw_signal_representation(dwi_data, gtab.bvecs, gtab.b0s_mask, voxel_idx)\n", + "\n", + "# Create an interactive plot\n", + "%matplotlib notebook\n", + "\n", + "fig = plot_dwi_scatter(signal_repr)\n", + "plt.show()" + ], + "id": "c2fcc8bd38d67e2e", "outputs": [], "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Now perform the Gaussian process prediction", + "id": "7327ee91ecde4095" } ], "metadata": {