signal_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install imageio-ffmpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import img_as_float\n",
    "from imageio import get_reader, get_writer\n",
    "import skimage.transform as sktransform\n",
    "from numpy import asarray, array\n",
    "from numpy.linalg import inv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageFilter\n",
    "import cv2\n",
    "\n",
    "\n",
    "from mpl_point_clicker import clicker\n",
    "\n",
    "from IPython.display import display, Image\n",
    "import ipywidgets as widgets\n",
    "import threading\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert RBG to YIQ\n",
    "def rgb2ntsc(src):\n",
    "    [rows,cols]=src.shape[:2]\n",
    "    dst=np.zeros((rows,cols,3),dtype=np.float64)\n",
    "    T = np.array([[0.114, 0.587, 0.298], [-0.321, -0.275, 0.596], [0.311, -0.528, 0.212]])\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            dst[i, j]=np.dot(T,src[i,j])\n",
    "    return dst\n",
    "\n",
    "#convert YIQ to RBG\n",
    "def ntsc2rbg(src):\n",
    "    [rows, cols] = src.shape[:2]\n",
    "    dst=np.zeros((rows,cols,3),dtype=np.float64)\n",
    "    T = np.array([[1, -1.108, 1.705], [1, -0.272, -0.647], [1, 0.956, 0.620]])\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            dst[i, j]=np.dot(T,src[i,j])\n",
    "    return dst\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def load_video(filename):\n",
    "    reader = get_reader(filename)\n",
    "    orig_vid = []\n",
    "    for i, im in enumerate(reader):\n",
    "        orig_vid.append(im)\n",
    "    return asarray(orig_vid)\n",
    "\n",
    "def write_video(video, fps, name):\n",
    "    writer = get_writer(name, fps=fps)\n",
    "    for i in range(video.shape[0]):\n",
    "        writer.append_data(video[i])\n",
    "    writer.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build Gaussian Pyramid\n",
    "def build_gaussian_pyramid(src,level=3):\n",
    "    s=src.copy()\n",
    "    pyramid=[s]\n",
    "    for i in range(level):\n",
    "        s=cv2.pyrDown(s)\n",
    "        pyramid.append(s)\n",
    "    return pyramid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build Laplacian Pyramid\n",
    "def build_laplacian_pyramid(src,levels=3):\n",
    "    \n",
    "    pyramid=[]\n",
    "    gaussianPyramid = build_gaussian_pyramid(src, levels)\n",
    "        \n",
    "    for i in range(levels,0,-1):\n",
    "        GE=cv2.pyrUp(gaussianPyramid[i])\n",
    "        L=cv2.subtract(gaussianPyramid[i-1],GE)\n",
    "        pyramid.append(L)\n",
    "    return pyramid\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show users the frame and ask them to choose ROI\n",
    "def ROI_choice(video):\n",
    "    # display a frame of the video, ask user to input ROI by mouse click\n",
    "    # return the yiq version of the ROI\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_file = \"ex_after.mp4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "video = load_video(video_file)\n",
    "\n",
    "# Resize video\n",
    "#video2 = np.zeros((len(video), 200, 200, 3))\n",
    "#for i in range(len(video)):\n",
    "#    video2[i] = sktransform.resize(video[i], (200,200))\n",
    "    \n",
    "#video = video2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_frames, h, w, num_channels = video.shape\n",
    "#num_frames = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "546"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\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",
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       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
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       "\n",
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       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
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       "        '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('style', 'box-sizing: content-box;');\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",
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       "        'canvas'\n",
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       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
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       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\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",
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       "                fig.request_resize(width, height);\n",
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       "        }\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",
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       "\n",
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       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
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       "};\n",
       "\n",
       "mpl.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",
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       "            return fig.toolbar_button_onclick(name);\n",
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       "\n",
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       "\n",
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       "    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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'event': array([], dtype=float64)}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plt.imshow(video[0])\n",
    "%matplotlib notebook\n",
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "ax.imshow(video[0], cmap=\"gray\")\n",
    "\n",
    "klicker = clicker(ax, [\"event\"], markers=[\"x\"])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "klicker.get_positions()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[301.7268395 , 309.51061199],\n",
       "       [375.07128977, 305.51000552],\n",
       "       [304.39391012, 345.51606948],\n",
       "       [367.07007698, 344.18253401]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klicker.get_positions()[\"event\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ystart = int(klicker.get_positions()[\"event\"][0][1])\n",
    "yend = int(klicker.get_positions()[\"event\"][2][1])\n",
    "xstart = int(klicker.get_positions()[\"event\"][0][0])\n",
    "xend=int(klicker.get_positions()[\"event\"][1][0])\n",
    "\n",
    "h_crop = yend-ystart\n",
    "w_crop = xend-xstart\n",
    "\n",
    "#convert video to yiq\n",
    "video_yiq = np.zeros((num_frames,h_crop,w_crop,num_channels))\n",
    "\n",
    "for frame in range(num_frames):\n",
    "    video_yiq[frame] = rgb2ntsc(video[frame][ystart:yend,xstart:xend])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Construct gaussian levels\n",
    "pyramid=[]\n",
    "levels = 3\n",
    "l0 = []\n",
    "l1 =[]\n",
    "l2=[]\n",
    "l3=[]\n",
    "for frames in range(num_frames):\n",
    "    \n",
    "    gaussianPyramid = build_gaussian_pyramid(video_yiq[frames], levels)\n",
    "    \n",
    "    l0.append(video[frames][ystart:yend,xstart:xend])\n",
    "    l1.append(gaussianPyramid[0])\n",
    "    l2.append(gaussianPyramid[1])\n",
    "    l3.append(gaussianPyramid[2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 74, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# l1 is the original in ntsc color scheme\n",
    "# l0 is the original in rgb color scheme\n",
    "l1[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "px_0 = np.zeros(num_frames)\n",
    "px_1 = np.zeros(num_frames)\n",
    "px_2 =np.zeros(num_frames)\n",
    "px_3 =np.zeros(num_frames)\n",
    "\n",
    "\n",
    "for frame in range(num_frames):\n",
    "    \n",
    "    px_0[frame] = np.average(l0[frame])\n",
    "    px_1[frame] = np.average(l1[frame])\n",
    "    px_2[frame] = np.average(l2[frame])\n",
    "    px_3[frame] = np.average(l3[frame])\n",
    "    \n",
    "    \n",
    "    #px_R[frame] = video[frame][600:601,450:451][0][0][0]\n",
    "   # px_G[frame] = video[frame][600:601,450:451][0][0][1]\n",
    "    #px_B[frame] = video[frame][600:601,450:451][0][0][2]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f25da9e688>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.plot(px_1,'g')\n",
    "plt.plot(px_2,'k')\n",
    "plt.plot(px_3,'b')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detrend and normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "def advance_detrend(signal, lam = 10):\n",
    "    T = len(signal)\n",
    "    I  = np.identity(T)\n",
    "\n",
    "    filt = [1,-2,1]* np.ones((1,T-2),dtype=np.int).T\n",
    "    D2 = sparse.spdiags(filt.T, (range(0,3)),T-2,T)\n",
    "\n",
    "    # detrend_sig = (I - np.linalg.inv(I + lam**2*D2.T*D2))@signal\n",
    "    trend = (np.linalg.inv(I + lam**2*D2.T*D2))@signal\n",
    "    detrend_sig = np.squeeze(np.array(signal - trend))\n",
    "    trend = np.squeeze(np.array(trend))\n",
    "    return detrend_sig, trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalization section\n",
    "def normalizeToUnitVariance(sig):\n",
    "    normalized = (sig - np.mean(sig))/np.std(sig)\n",
    "    return normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Smart\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "sig,trend = advance_detrend(px_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f25fd93308>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sig[:400])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = 3\n",
    "f,t, Sxx = signal.spectrogram(px_0,fs,nperseg = 128, noverlap = 10, nfft = 128,mode='magnitude')\n",
    "plt.figure(figsize=(8,10))\n",
    "plt.pcolormesh(t, f, Sxx, shading='auto')\n",
    "plt.ylabel('Frequency [Hz]')\n",
    "plt.xlabel('Time [sec]')\n",
    "plt.ylim(0,1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert pixel data to pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df1 = pd.DataFrame(px_0,columns=['orig'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['l1'] = sig\n",
    "df1['l2'] = px_2\n",
    "df1['l3'] = px_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig</th>\n",
       "      <th>l1</th>\n",
       "      <th>l2</th>\n",
       "      <th>l3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127.570120</td>\n",
       "      <td>-0.087980</td>\n",
       "      <td>83.833074</td>\n",
       "      <td>83.762904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>127.780671</td>\n",
       "      <td>-0.020050</td>\n",
       "      <td>83.946834</td>\n",
       "      <td>83.878990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>127.790365</td>\n",
       "      <td>-0.051195</td>\n",
       "      <td>83.959135</td>\n",
       "      <td>83.890345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>128.143711</td>\n",
       "      <td>0.073521</td>\n",
       "      <td>84.129686</td>\n",
       "      <td>84.069551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>128.335600</td>\n",
       "      <td>0.129994</td>\n",
       "      <td>84.225556</td>\n",
       "      <td>84.167657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>130.700907</td>\n",
       "      <td>55.013966</td>\n",
       "      <td>84.383497</td>\n",
       "      <td>84.376783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>130.682677</td>\n",
       "      <td>67.101705</td>\n",
       "      <td>84.377620</td>\n",
       "      <td>84.371102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>130.269001</td>\n",
       "      <td>77.569063</td>\n",
       "      <td>84.304820</td>\n",
       "      <td>84.297207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>130.413918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.352808</td>\n",
       "      <td>84.345275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>130.351032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.366989</td>\n",
       "      <td>84.360122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>546 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           orig         l1         l2         l3\n",
       "0    127.570120  -0.087980  83.833074  83.762904\n",
       "1    127.780671  -0.020050  83.946834  83.878990\n",
       "2    127.790365  -0.051195  83.959135  83.890345\n",
       "3    128.143711   0.073521  84.129686  84.069551\n",
       "4    128.335600   0.129994  84.225556  84.167657\n",
       "..          ...        ...        ...        ...\n",
       "541  130.700907  55.013966  84.383497  84.376783\n",
       "542  130.682677  67.101705  84.377620  84.371102\n",
       "543  130.269001  77.569063  84.304820  84.297207\n",
       "544  130.413918   0.000000  84.352808  84.345275\n",
       "545  130.351032   0.000000  84.366989  84.360122\n",
       "\n",
       "[546 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_pickle(\"ex_after.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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