5-6-data-analysis-round-2.ipynb 207 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.003175\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9bf7983e50>]"
      ]
     },
     "execution_count": 1,
     "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": [
    "import numpy as np\n",
    "import time\n",
    "import shelve\n",
    "import logging\n",
    "import queue\n",
    "from collect_data import DataPoint, RawDataPacket\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.signal import medfilt\n",
    "\n",
    "\n",
    "TEST_NAMES = ['6']\n",
    "results = list()\n",
    "with shelve.open('results') as db:\n",
    "    for test_name in TEST_NAMES:\n",
    "        results += db[test_name]\n",
    "\n",
    "data_points = list(map(lambda x: x.decompose_packet(), results))\n",
    "\n",
    "\n",
    "print(data_points[0].W)\n",
    "\n",
    "x = list(map(lambda x: x.f_r, data_points))\n",
    "y = list(map(lambda x: x.T, data_points))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = 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.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",
       "            /* 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",
       "            evt.data.type = \"image/png\";\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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       " * http://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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             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",
       "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";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.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",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\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",
       "\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('matplotlib', mpl.mpl_figure_comm);\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"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "new_data = list(map(lambda x: RawDataPacket(x.D, x.W, x.R, x.N, x.f_r, x.w, x.measurements), results))\n",
    "data_points = list(map(lambda x: x.decompose_packet_sorta(), new_data))\n",
    "plt.figure()\n",
    "last_time = 0\n",
    "for tTs, tFys, Ts, Fys in data_points:\n",
    "    times = [t + last_time for t in tTs]\n",
    "    stuff = Ts\n",
    "    filter_size = 3\n",
    "    fc = int((filter_size + 1) / 2)\n",
    "    plt.plot(times[fc: -fc], medfilt(stuff, filter_size)[fc: -fc])\n",
    "    last_time = times[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plan\n",
    "We have three datasets. Two variables are tweaked across dataset. We want to perform some kind of regression to figure out our cutting force coefficients.\n",
    "\n",
    "Regression is linear if we only vary feed, but becomes nonlinear once we incorporate WOC.\n",
    "\n",
    "First, maybe just try using a linear regressor for both?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import least_squares\n",
    "def single_residual(d, K_tc, K_te, p):\n",
    "    return T_exp_lin(d.D, d.N, d.R, d.W, d.f_r, d.w, K_tc, K_te, p) - d.T\n",
    "\n",
    "def gen_train_pairs():\n",
    "    X = [[d.D, d.N, d.R, d.W, d.f_r, d.w] for d in data_points]\n",
    "    y = [d.T for d in data_points]\n",
    "    return (X, y)\n",
    "\n",
    "residual_array = np.vectorize(single_residual)\n",
    "\n",
    "def objective(x):\n",
    "    K_tc = x[0]\n",
    "    K_te = x[1]\n",
    "    p = x[2]\n",
    "    return residual_array(data_points, K_tc, K_te, p)\n",
    "\n",
    "class NonLinRegressor:\n",
    "    \n",
    "    \n",
    "    def fit(self, X, Y):\n",
    "        self.coef = least_squares(lambda coef: [self.evaluate(x, coef) - y for x, y in zip(X, Y)], self.coef).x\n",
    "        return self\n",
    "    \n",
    "    def predict(self, X):\n",
    "        return [self.evaluate(x, self.coef) for x in X]\n",
    "    \n",
    "    def get_params(self, deep=True):\n",
    "        return {}\n",
    "    \n",
    "    def set_params(self, **params):\n",
    "        ...\n",
    "    \n",
    "    def score(self, X, Y):\n",
    "        u = sum([(y_true - y_pred) ** 2 for y_true, y_pred in zip(Y, self.predict(X))])\n",
    "        y_true_mean  = np.mean(Y)\n",
    "        v = sum([(y_true - y_true_mean) ** 2 for y_true in Y])\n",
    "        return (1 - u/v)\n",
    "    \n",
    "class TRegressor(NonLinRegressor):\n",
    "    def __init__(self):\n",
    "        self.coef = [0,0,1]\n",
    "        \n",
    "    def evaluate(self, x, coef):\n",
    "        return T_exp_lin(*x, coef[0], coef[1], coef[2])    \n",
    "        \n",
    "\n",
    "class FRegressor(NonLinRegressor):\n",
    "    def __init__(self):\n",
    "        self.coef = [0,0,0,0, 0, 0]\n",
    "        \n",
    "    def evaluate(self, x, coef):\n",
    "        return F_exp_lin(*x, *coef)[0][1]\n",
    "    \n",
    "        \n",
    "        \n",
    "estimator = TRegressor()\n",
    "regressor = RANSACRegressor(base_estimator = estimator, min_samples = 5)\n",
    "\n",
    "X, y = gen_train_pairs()\n",
    "\n",
    "# y = [d.Fy * 9.81 ** 2 for d in data_points]\n",
    "\n",
    "regressor.fit(X, y)\n",
    "# K_tc, K_te, K_rc, K_re, p, q = regressor.estimator_.coef\n",
    "K_tc, K_te, p = regressor.estimator_.coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc984255c10>]"
      ]
     },
     "execution_count": 13,
     "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": [
    "# print(K_tc, K_te, K_rc, K_re, p, q)\n",
    "y_est = list(map(lambda d : T_exp_lin(d.D, d.N, d.R, d.W, d.f_r, d.w, K_tc, K_te, p), data_points))\n",
    "# y_est = list(map(lambda d : F_exp_lin(d.D, d.N, d.R, d.W, d.f_r, d.w, K_tc, K_te, K_rc, K_re, p, q), data_points))\n",
    "# y_est = np.array(y_est)\n",
    "# y_est = np.squeeze(y_est)[:, 1]\n",
    "# print(y_est)\n",
    "plt.figure()\n",
    "plt.plot(x, y)\n",
    "plt.plot(x, y_est)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K_tc =  858494934.9591976 , K_te =  -696.3150933941946\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RANSACRegressor, LinearRegression\n",
    "from models import T_x_vector, T_lin, T_exp_lin, calc_h_a, calc_f_t, F_exp_lin\n",
    "\n",
    "def dataPoint_to_X(d):\n",
    "    return T_x_vector(d.D, d.N, d.R, d.W, d.f_r, d.w)\n",
    "\n",
    "def single_residual(d, K_tc, K_te, p):\n",
    "    return T_exp_lin(d.D, d.N, d.R, d.W, d.f_r, d.w, K_tc, K_te, p) - d.T\n",
    "\n",
    "residual_array = np.vectorize(single_residual)\n",
    "\n",
    "def objective(x):\n",
    "    K_tc = x[0]\n",
    "    K_te = x[1]\n",
    "    p = x[2]\n",
    "    return residual_array(data_points, K_tc, K_te, p)\n",
    "\n",
    "\n",
    "X = list(map(dataPoint_to_X, data_points))\n",
    "\n",
    "estimator = LinearRegression(fit_intercept=False)\n",
    "regressor = RANSACRegressor(base_estimator = estimator)\n",
    "regressor.fit(X, y)\n",
    "K_tc, K_te = regressor.estimator_.coef_\n",
    "print(\"K_tc = \", K_tc, \", K_te = \", K_te)\n",
    "\n",
    "x_h_a = np.linspace(0.1e-3, 4e-3, 100)\n",
    "y_h_a = [calc_h_a(6.35e-3 / 2 , x, calc_f_t(3, 0.0016, 500)) for x in x_h_a]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we check if we actually fit anything useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f22f8fe0640>]"
      ]
     },
     "execution_count": 138,
     "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"
    },
    {
     "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": [
    "y_est = list(map(lambda d : T_lin(d.D, d.N, d.R, d.W, d.f_r, d.w, K_tc, K_te), data_points))\n",
    "plt.figure()\n",
    "plt.plot(x, y)\n",
    "plt.plot(x, y_est)\n",
    "plt.figure()\n",
    "plt.plot(x_h_a, y_h_a)\n",
    "plt.plot([0.00079375, 0.0015875, 0.003175], [0,0,0], 'd')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}