Data_preparation.ipynb

{
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   "cell_type": "markdown",
   "id": "0ff98397",
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    "## Loading the .mat files\n",
    "\n",
    "We load the data associated with subjects with **tricuspid aortic valve (TAV)**, **bicuspid aortic valve (BAV)**, and **CT to MRI** data into a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd1a50ed",
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "Found file: bav_data.mat\n",
      "Found file: ct2mri_data.mat\n",
      "Found file: tav_data.mat\n",
      "bav_data.mat was loaded successfuly!\n",
      "ct2mri_data.mat was loaded successfuly!\n",
      "tav_data.mat was loaded successfuly!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import h5py\n",
    "\n",
    "# specify the folder containing the .mat files\n",
    "fldr = r'C:\\Users\\mem1342\\OneDrive - Northwestern University\\gan ct to mri transformation\\MRI2MRIdataset'\n",
    "\n",
    "keywords = ['tav', 'bav', 'ct2mri']\n",
    "file_paths = [os.path.join(fldr, f) for f in os.listdir(fldr) if any(key in f for key in keywords)]\n",
    "if file_paths:\n",
    "    for file in file_paths:\n",
    "        print(f\"Found file: {os.path.basename(file)}\")\n",
    "else:\n",
    "    print(\"No files found.\")\n",
    "    \n",
    "# Initialize the data dictionary with empty lists for the 'anatomy' and 'flow' key \n",
    "# of each of the three keys 'tav', 'bav', and 'ct2mri'\n",
    "data = {'tav': {'anatomy': [], 'flow': []}, \n",
    "        'bav': {'anatomy': [], 'flow': []}, \n",
    "        'ct2mri': {'anatomy': [], 'flow': []}}\n",
    "\n",
    "# Iterate through the file paths\n",
    "for file in file_paths:\n",
    "    # Iterate through the keys in the data dictionary\n",
    "    for key in data.keys():\n",
    "        # Check if the key is present in the current file path\n",
    "        if key in file:\n",
    "            # Open the file using h5py\n",
    "            with h5py.File(file,'r') as f:\n",
    "                # Get the anatomy and flow datasets\n",
    "                anatomy_dataset = f['data']['input']\n",
    "                flow_dataset = f['data']['flow']\n",
    "                # Iterate through each element in the anatomy dataset\n",
    "                for ii in range(len(anatomy_dataset)):\n",
    "                    # Get the anatomy data and transpose it to the desired shape\n",
    "                    anatomy_data = f.get(anatomy_dataset[ii][0])[()]\n",
    "                    data[key]['anatomy'].append(np.transpose(anatomy_data, (2, 1, 0)))\n",
    "                    # Get the flow data and transpose it to the desired shape\n",
    "                    flow_data = f.get(flow_dataset[ii][0])[()]\n",
    "                    dims = flow_data.ndim\n",
    "                    if dims>3:\n",
    "                        # L2-norm of the flow data\n",
    "                        flow_data = np.linalg.norm(flow_data,ord=2,axis=0)\n",
    "                    data[key]['flow'].append(np.transpose(flow_data, (2, 1, 0)))\n",
    "            # Iterate through the data and check that the anatomy and flow data have the same shape\n",
    "            for ii,(anatomy,flow) in enumerate(zip(data[key]['anatomy'], data[key]['flow'])):\n",
    "                assert anatomy.shape == flow.shape, f\"Error: data at {ii} don't have the same shape\"\n",
    "            print(f\"{os.path.basename(file)} was loaded successfuly!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8859a452",
   "metadata": {},
   "source": [
    "## Post-processing\n",
    "\n",
    "The post processing has two steps: \n",
    "\n",
    "1. Removing the zero planes.\n",
    "2. Padding to a common size (192, 192, 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e32b90a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_dim = (192, 192, 64)\n",
    "\n",
    "def zero_pad(array):\n",
    "    \"\"\"\n",
    "    This function takes an input 3D array and pads it with zeros to match the size of max_dim.\n",
    "    The padding is added evenly on both sides in each dimension.\n",
    "    \"\"\"\n",
    "    pad_to = [x - y for x, y in zip(max_dim, array.shape)]\n",
    "    array = np.pad(array, [(x // 2, np.max(y - x // 2 - z, 0)) for x, y, z in zip(pad_to, max_dim, array.shape)], \n",
    "                   mode='constant', constant_values=0)\n",
    "    return array\n",
    "\n",
    "for key in data.keys():\n",
    "    \"\"\"\n",
    "    This loop iterates through the keys in the data dictionary.\n",
    "    For each key, it accesses the 'anatomy' and 'flow' data, removes any zero values,\n",
    "    and then pads the resulting data with zeros to match the size of max_dim.\n",
    "    \"\"\"\n",
    "    anatomy_data = data[key]['anatomy']\n",
    "    data[key]['anatomy'] = [zero_pad(anatomy[np.unique(np.where(anatomy != 0)[0]), :, :]\n",
    "                                     [:, np.unique(np.where(anatomy != 0)[1]), :]\n",
    "                                     [:, :, np.unique(np.where(anatomy != 0)[2])]) for anatomy in anatomy_data]\n",
    "    flow_data = data[key]['flow']\n",
    "    data[key]['flow'] = [zero_pad(flow[np.unique(np.where(flow != 0)[0]), :, :]\n",
    "                                  [:, np.unique(np.where(flow != 0)[1]), :]\n",
    "                                  [:, :, np.unique(np.where(flow != 0)[2])]) for flow in flow_data]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1066cba",
   "metadata": {},
   "source": [
    "## Visualize the anatomy and flow data for differernt subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1aac6e12",
   "metadata": {},
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       "};\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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.\n",
       "mpl.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",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\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",
       " */\n",
       "function 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",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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\n",
       "var _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\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var 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",
       "\n",
       "mpl.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(\"<div id='\" + id + \"'></div>\");\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",
       "\n",
       "mpl.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",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.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",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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 = '<i class=\"fa ' + image + ' fa-lg\"></i>';\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 = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.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.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib.animation import FuncAnimation\n",
    "\n",
    "# plot an anmiation that iterates over all subjects\n",
    "%matplotlib notebook\n",
    "\n",
    "key = list(data.keys())[0]\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10,5), facecolor='w')\n",
    "fig.suptitle(f\"{key.upper()} dataset\")\n",
    "im1 = axs[0].imshow(np.max(data[key]['anatomy'][0], axis=2), cmap='binary_r')\n",
    "axs[0].set_title('CTA')\n",
    "axs[0].set_xticks([])\n",
    "axs[0].set_yticks([])\n",
    "im2 = axs[1].imshow(np.max(data[key]['flow'][0], axis=2), cmap='jet', vmin=0, vmax=2)\n",
    "axs[1].set_title('PC-MRI')\n",
    "axs[1].set_xticks([])\n",
    "axs[1].set_yticks([])\n",
    "cb1 = fig.colorbar(im1, ax=axs[0])\n",
    "cb2 = fig.colorbar(im2, ax=axs[1])\n",
    "cb2.set_label('V$_{\\mathrm{max}}$ [m/s]')\n",
    "def update(ii):\n",
    "    im1.set_data(np.max(data[key]['anatomy'][ii], axis=2))\n",
    "    im2.set_data(np.max(data[key]['flow'][ii], axis=2))\n",
    "ani = FuncAnimation(fig, update, frames=range(1, len(data[key]['anatomy'])), repeat=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ebfcd89",
   "metadata": {},
   "source": [
    "## Create training and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a585c6dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " TAV dataset: traini size: 302, test size: 202\n",
      " BAV dataset: traini size: 756, test size: 504\n",
      " CT2MRI dataset: traini size: 37, test size: 25\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "\"\"\"\n",
    "This script creates a dictionary called \"indices\" that contains 3 keys: 'tav', 'bav', and 'ct2mri'.\n",
    "Each of these keys points to another dictionary that contains two keys: 'train' and 'test'.\n",
    "\n",
    "The script then iterates through the keys of the \"indices\" dictionary, and for each key:\n",
    "\n",
    "it gets the number of subjects in the 'flow' of the data dictionary.\n",
    "creates a list of integers from 0 to the number of subjects.\n",
    "shuffles the list using a seed of 1337\n",
    "splits the list into two parts, 60% of the list is for training and the rest for testing\n",
    "assigns the splits to the 'train' and 'test' keys of the inner dictionary corresponding to the current key of the outer dictionary\n",
    "prints the size of the training and test datasets for the current key\n",
    "\"\"\"\n",
    "\n",
    "indices= {'tav': {'train': [], 'test': []}, \n",
    "          'bav': {'train': [], 'test': []}, \n",
    "          'ct2mri': {'train': [], 'test': []}}\n",
    "\n",
    "for key in indices.keys():\n",
    "\n",
    "    number_subjects = len(data[key]['flow'])\n",
    "    lst = [x for x in range(number_subjects)]\n",
    "    random.Random(1337).shuffle(lst)\n",
    "    tr_size = number_subjects*60//100\n",
    "    te_size = number_subjects-tr_size\n",
    "    print(f\" {key.upper()} dataset: traini size: {tr_size}, test size: {te_size}\")\n",
    "    indices[key]['train'], indices[key]['test'] = lst[:tr_size], lst[tr_size:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad92bf5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "\"\"\"\n",
    "This code is using the TensorFlow library to convert data into Tensorflow's binary file format\n",
    "called TFRecord. It loops through indices_type and keys of indices, and for each key, \n",
    "it creates a filename for the TFRecord, creates a writer to write to the TFRecord, \n",
    "extracts anatomy and flow data from a data dictionary using the indices, converts the data to\n",
    "numpy arrays with dtype of float32, gets the shape of the anatomy data, converts the numpy arrays\n",
    "to bytes, creates a features dictionary, creates an example object, and writes the example to the\n",
    "TFRecord. It then closes the writer.\n",
    "\"\"\"\n",
    "\n",
    "# loop through the type of indices\n",
    "for indices_type in ['test','train']:\n",
    "    # loop through the keys of indices\n",
    "    for key in indices.keys():\n",
    "        # create the filename for the tfrecord\n",
    "        tfrecord_name = f\"{key}_{indices_type}.tfrecords\"\n",
    "        # create a writer object to write to the tfrecord\n",
    "        writer = tf.io.TFRecordWriter(tfrecord_name)\n",
    "        # extract anatomy data from the data dictionary using the indices\n",
    "        anatomy_data = [data[key]['anatomy'][ii] for ii in indices[key][indices_type]]\n",
    "        # extract flow data from the data dictionary using the indices\n",
    "        flow_data = [data[key]['flow'][ii] for ii in indices[key][indices_type]]\n",
    "        # loop through anatomy and flow data\n",
    "        for anatomy, flow in zip(anatomy_data, flow_data):\n",
    "            # convert the data to numpy arrays with dtype of float32\n",
    "            anatomy, flow = np.array(anatomy,dtype=np.float32), np.array(flow, dtype=np.float32)\n",
    "            # get the shape of the anatomy data\n",
    "            shape = np.shape(anatomy)\n",
    "            # convert the numpy arrays to bytes\n",
    "            anatomy = anatomy.tobytes()\n",
    "            flow = flow.tobytes()\n",
    "            # create the features dictionary\n",
    "            features = {'anatomy': tf.train.Feature(bytes_list=tf.train.BytesList(value=[anatomy])),\n",
    "                        'flow': tf.train.Feature(bytes_list=tf.train.BytesList(value=[flow])),\n",
    "                        'height':tf.train.Feature(int64_list=tf.train.Int64List(value=[shape[0]])),\n",
    "                        'width':tf.train.Feature(int64_list=tf.train.Int64List(value=[shape[1]])),\n",
    "                        'depth':tf.train.Feature(int64_list=tf.train.Int64List(value=[shape[2]]))}\n",
    "            # create the example object\n",
    "            example = tf.train.Example(features = tf.train.Features(feature = features))\n",
    "            # write the example to the tfrecord\n",
    "            writer.write(example.SerializeToString())\n",
    "        # close the writer\n",
    "        writer.close()\n"
   ]
  }
 ],
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