nutrimium.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from enum import Enum\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib notebook\n",
    "\n",
    "from scipy.ndimage.filters import uniform_filter1d\n",
    "\n",
    "import nutripy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CALORIES_STEP_SIZE=250\n",
    "\n",
    "class Goal(Enum):\n",
    "    LOSS = -CALORIES_STEP_SIZE\n",
    "    MAINTAIN = 0\n",
    "    GAIN = CALORIES_STEP_SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class UnhandledCaseException(Exception):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nut = nutripy.nutripy.Nutripy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_close(a, b, close=150):\n",
    "    if abs(a - b) < close:\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Phase(Enum):\n",
    "    LOSS = 0\n",
    "    STOP = 1\n",
    "    MAINTAINANCE = 2 \n",
    "    GAIN = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Model of weight variation over one week\n",
    "        - tdci Total Daily Calorie intake is the real number of calories eaten per day\n",
    "        - tdee Total Daily Energy Expenditure \n",
    "        - sigma represent the randomness, the higher the value, the higher the random \n",
    "          variations of the weight\n",
    "          \n",
    "          Returns the variation of weight aka weight derivative (NOT the weight) in kilograms\n",
    "\"\"\"\n",
    "\n",
    "def delta_weight(tdci, tdee, sigma=.6):\n",
    "    return np.random.normal(0, sigma) + ((tdci - tdee) * 7) / 7000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gain(x):\n",
    "    if x > 0.5: \n",
    "        return 0\n",
    "    if x <= 0.5:\n",
    "        return CALORIES_STEP_SIZE\n",
    "\n",
    "def maintain(x):\n",
    "    if x > -0.5 and x < 0.5: \n",
    "        return 0\n",
    "    if x > 0.5:\n",
    "        return -CALORIES_STEP_SIZE\n",
    "    if x < 0.5:\n",
    "        return CALORIES_STEP_SIZE\n",
    "    \n",
    "def loss(x):\n",
    "    if x < -0.5: \n",
    "        return 0\n",
    "    if x > -0.5:\n",
    "        return -CALORIES_STEP_SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_params = {\n",
    "    \"age\": 30, \n",
    "    \"weight\": 50, \n",
    "    \"height\": 1.8, \n",
    "    \"gender\": nutripy.nutripy.Gender.MALE, \n",
    "    \"activity\": nutripy.nutripy.Activity.SEDENTARY, \n",
    "    \"goal\": nutripy.nutripy.Goal.GAIN,\n",
    "}\n",
    "\n",
    "sim_params = {\n",
    "    \"profile\": profile_params,\n",
    "    \"n_weeks\": 100,\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate(sim_params):\n",
    "\n",
    "    weight_history = []\n",
    "    tdci_history = []\n",
    "    \n",
    "    tdee_hat = nut.get_daily_needs(**sim_params[\"profile\"]) # this is the daily expenditure in calories, it should have been estimated at the begining of the program, then, left untouched until it is computed again during Phase.STOP, see code below\n",
    "    tdci = tdee_hat # tdci is set to tdee_hat but in a real setting, the tdci must be estimated from what the user ate during the week or from its meal plan.\n",
    "    \n",
    "    weight_history.append(sim_params[\"profile\"][\"weight\"])\n",
    "    tdci_history.append(tdci)\n",
    "    \n",
    "    if sim_params[\"profile\"][\"goal\"] == nutripy.nutripy.Goal.GAIN:\n",
    "        phases_history = [Phase.GAIN]\n",
    "    elif sim_params[\"profile\"][\"goal\"] == nutripy.nutripy.Goal.LOSS:\n",
    "        phases_history = [Phase.LOSS]\n",
    "    else:\n",
    "        raise UnhandledCaseException\n",
    "        \n",
    "    for i in range(sim_params[\"n_weeks\"]):\n",
    "        delta_w = delta_weight(tdci, tdee_hat)\n",
    "        delta_cal = 0\n",
    "        \n",
    "        new_weight = weight_history[-1] + delta_w\n",
    "        weight_history.append(new_weight)\n",
    "\n",
    "        current_phase = phases_history[-1]\n",
    "        duration = 1 # counts the number of weeks within the phase\n",
    "        \n",
    "        for i in range(1, len(phases_history)):\n",
    "            if phases_history[-i-1] == current_phase:\n",
    "                duration += 1\n",
    "            else:\n",
    "                break\n",
    "\n",
    "        if sim_params[\"profile\"][\"goal\"] == nutripy.nutripy.Goal.GAIN:\n",
    "            if current_phase == Phase.GAIN and duration <= 16:\n",
    "                new_phase = Phase.GAIN\n",
    "\n",
    "            if current_phase == Phase.GAIN and duration > 16:\n",
    "                new_phase = Phase.STOP\n",
    "                duration = 1\n",
    "                \n",
    "        elif sim_params[\"profile\"][\"goal\"] == nutripy.nutripy.Goal.LOSS:\n",
    "            if current_phase == Phase.LOSS and duration <= 16:\n",
    "                new_phase = Phase.LOSS\n",
    "    \n",
    "            if current_phase == Phase.LOSS and duration > 16:\n",
    "                new_phase = Phase.STOP\n",
    "                duration = 1\n",
    "                \n",
    "        else:\n",
    "            raise UnhandledCaseException\n",
    "\n",
    "        if current_phase == Phase.STOP:\n",
    "            if is_close(tdee_hat, tdci, close=200):\n",
    "                new_phase = Phase.MAINTAINANCE\n",
    "                phases_history.append(new_phase)\n",
    "\n",
    "                \n",
    "                \n",
    "        if new_phase == Phase.MAINTAINANCE:\n",
    "            if len(weight_history) > 3:\n",
    "                weight_derivative = np.gradient(weight_history)\n",
    "                y = uniform_filter1d(weight_derivative, size=3)\n",
    "                delta_cal = maintain(y[-1])\n",
    "                \n",
    "        if sim_params[\"profile\"][\"goal\"] == nutripy.nutripy.Goal.GAIN:\n",
    "            if new_phase == Phase.GAIN:\n",
    "                if len(weight_history) > 3:\n",
    "                    weight_derivative = np.gradient(weight_history)\n",
    "                    y = uniform_filter1d(weight_derivative, size=3)\n",
    "                    delta_cal = gain(y[-1])\n",
    "            if new_phase == Phase.STOP:\n",
    "                if duration == 1:\n",
    "                    params = {k:v for k,v in profile_params.items()}\n",
    "                    params[\"weight\"] = weight_history[-1]\n",
    "                    tdee_hat = tdee_hat = nut.get_daily_needs(**params)\n",
    "\n",
    "                if not is_close(tdee_hat, tdci, close=200):\n",
    "                    delta_cal = -CALORIES_STEP_SIZE\n",
    "                \n",
    "        elif sim_params[\"profile\"][\"goal\"] == nutripy.nutripy.Goal.LOSS:\n",
    "            if new_phase == Phase.LOSS:\n",
    "                if len(weight_history) > 3:\n",
    "                    weight_derivative = np.gradient(weight_history)\n",
    "                    y = uniform_filter1d(weight_derivative, size=3)\n",
    "                    delta_cal = loss(y[-1])\n",
    "            if new_phase == Phase.STOP:\n",
    "                if duration == 1:\n",
    "                        params = {k:v for k,v in profile_params.items()}\n",
    "                        params[\"weight\"] = weight_history[-1]\n",
    "                        tdee_hat = tdee_hat = nut.get_daily_needs(**params)\n",
    "\n",
    "                if not is_close(tdee_hat, tdci, close=200):\n",
    "                    delta_cal = CALORIES_STEP_SIZE\n",
    "        else:\n",
    "            raise UnhandledCaseException\n",
    "\n",
    "\n",
    "        phases_history.append(new_phase)\n",
    "        tdci = tdci + delta_cal\n",
    "        tdci_history.append(tdci)\n",
    "        \n",
    "    return weight_history, tdci_history, phases_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_history, tdci_history, phases_history = simulate(sim_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "\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",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\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.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",
       "            /* 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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\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 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",
       "    }\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",
       " * 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",
       "        }\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.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"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.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 = 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",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\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",
       "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=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.set_ylabel('weight')\n",
    "ax1.set_xlabel('# of weeks')\n",
    "ax1.set_title('weight evolution across time')\n",
    "ax1.plot(weight_history)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.set_ylabel('TDCI')\n",
    "ax2.set_xlabel('# of weeks')\n",
    "ax2.set_title('TDCI evolution across time')\n",
    "ax2.plot(tdci_history)\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.set_ylabel('TDCI')\n",
    "ax3.set_xlabel('# of weeks')\n",
    "ax3.set_title('phase evolution across time')\n",
    "ax3.scatter(range(len(phases_history)), [x.value for x in phases_history]) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: given a user profile, the phases history, the weights history, the tdci history, return the next tdci recommended "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's focus on the simplest case, a user has just registered, all we have about the user is it's info given during the onboarding, that is: \n",
    "\n",
    "- age,\n",
    "- weight,\n",
    "- gender,\n",
    "- height, \n",
    "- activity level,\n",
    "- goal "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to return the estimated daily expenditure based on these values. How do we know that this is the user has just registered? We can simply use the history that should be empty. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "'return' outside function (<ipython-input-38-66ec5671b8c2>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-38-66ec5671b8c2>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    return nut.get_daily_needs(**profile)\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'return' outside function\n"
     ]
    }
   ],
   "source": [
    "if history.is_empty():\n",
    "    return nut.get_daily_needs(**profile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the other cases we need: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IN(user history including current_state) => OUT(new state)\n",
    "\n",
    "we need the following: \n",
    "- new weight \n",
    "- phases history\n",
    "- weight history\n",
    "- current goal\n",
    "- current tdci "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_new_state(age, height, gender, activity, goal, weight_history, phases_history, tdci, tdee):\n",
    "    \n",
    "    assert len(weight_history) == len(phases_history), \\\n",
    "    \"weight history should have the same length as phase history\"\n",
    "    \n",
    "    delta_cal = 0\n",
    "\n",
    "    new_weight = weight_history[-1]\n",
    "    current_phase = phases_history[-1]\n",
    "    \n",
    "    duration = 1 # counts the number of weeks within the phase\n",
    "\n",
    "    for i in range(1, len(phases_history)):\n",
    "        if phases_history[-i-1] == current_phase:\n",
    "            duration += 1\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    if goal == nutripy.nutripy.Goal.GAIN:\n",
    "        if current_phase == Phase.GAIN and duration <= 16:\n",
    "            new_phase = Phase.GAIN\n",
    "\n",
    "        if current_phase == Phase.GAIN and duration > 16:\n",
    "            new_phase = Phase.STOP\n",
    "            duration = 1\n",
    "\n",
    "    elif goal == nutripy.nutripy.Goal.LOSS:\n",
    "        if current_phase == Phase.LOSS and duration <= 16:\n",
    "            new_phase = Phase.LOSS\n",
    "\n",
    "        if current_phase == Phase.LOSS and duration > 16:\n",
    "            new_phase = Phase.STOP\n",
    "            duration = 1\n",
    "\n",
    "    else:\n",
    "        raise UnhandledCaseException\n",
    "\n",
    "    if current_phase == Phase.STOP:\n",
    "        if is_close(tdee, tdci, close=200):\n",
    "            new_phase = Phase.MAINTAINANCE\n",
    "        else:\n",
    "            new_phase = Phase.STOP\n",
    "            #phases_history.append(new_phase)\n",
    "            \n",
    "    if current_phase == Phase.MAINTAINANCE:\n",
    "            new_phase = Phase.MAINTAINANCE # todo: renew main Phase or do another goal\n",
    "      \n",
    "    print(current_phase)\n",
    "    if new_phase == Phase.MAINTAINANCE:\n",
    "        if len(weight_history) > 3:\n",
    "            weight_derivative = np.gradient(weight_history)\n",
    "            y = uniform_filter1d(weight_derivative, size=3)\n",
    "            delta_cal = maintain(y[-1])\n",
    "\n",
    "    if goal == nutripy.nutripy.Goal.GAIN:\n",
    "        if new_phase == Phase.GAIN:\n",
    "            if len(weight_history) > 3:\n",
    "                weight_derivative = np.gradient(weight_history)\n",
    "                y = uniform_filter1d(weight_derivative, size=3)\n",
    "                delta_cal = gain(y[-1])\n",
    "        if new_phase == Phase.STOP:\n",
    "            if duration == 1:\n",
    "                tdee = nut.get_daily_needs(age, new_weight, height, gender, activity, goal)\n",
    "                print(tdee)\n",
    "\n",
    "            if not is_close(tdee, tdci, close=200):\n",
    "                delta_cal = -CALORIES_STEP_SIZE\n",
    "\n",
    "    elif goal == nutripy.nutripy.Goal.LOSS:\n",
    "        if new_phase == Phase.LOSS:\n",
    "            if len(weight_history) > 3:\n",
    "                weight_derivative = np.gradient(weight_history)\n",
    "                y = uniform_filter1d(weight_derivative, size=3)\n",
    "                delta_cal = loss(y[-1])\n",
    "        if new_phase == Phase.STOP:\n",
    "            if duration == 1:\n",
    "                    tdee = nut.get_daily_needs(age, new_weight, height, gender, activity, goal)\n",
    "\n",
    "            if not is_close(tdee, tdci, close=200):\n",
    "                delta_cal = CALORIES_STEP_SIZE\n",
    "    else:\n",
    "        raise UnhandledCaseException\n",
    "\n",
    "\n",
    "    return {\n",
    "        \"phase\": new_phase,\n",
    "        \"tdci\": tdci + delta_cal,\n",
    "        \"tdee\": tdee\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "Phase.GAIN\n",
      "2901.8666665050264\n",
      "Phase.STOP\n",
      "2915.6042804039544\n",
      "Phase.STOP\n",
      "Phase.STOP\n",
      "Phase.STOP\n",
      "Phase.MAINTAINANCE\n",
      "Phase.MAINTAINANCE\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    \"age\": 30, \n",
    "    \"height\": 180, \n",
    "    \"gender\": nutripy.nutripy.Gender.MALE, \n",
    "    \"activity\": nutripy.nutripy.Activity.SEDENTARY, \n",
    "    \"goal\": nutripy.nutripy.Goal.GAIN,\n",
    "    \"weight_history\": [80, 80, 80.5, 80],\n",
    "    \"phases_history\":[Phase.GAIN, Phase.GAIN, Phase.GAIN, Phase.GAIN],\n",
    "    \"tdci\": nut.get_daily_needs(30, 80, 180, nutripy.nutripy.Gender.MALE, nutripy.nutripy.Activity.SEDENTARY, nutripy.nutripy.Goal.GAIN),\n",
    "    \"tdee\": nut.get_daily_needs(30, 80, 180, nutripy.nutripy.Gender.MALE, nutripy.nutripy.Activity.SEDENTARY, nutripy.nutripy.Goal.GAIN),\n",
    "}\n",
    "\n",
    "    \n",
    "for i in range(20):\n",
    "        state = get_new_state(**params)\n",
    "        \n",
    "        new_phase = state[\"phase\"]\n",
    "        new_tdci = state[\"tdci\"]\n",
    "        new_tdee = state[\"tdee\"]\n",
    "        \n",
    "        tdci = params[\"tdci\"]\n",
    "        tdee = params[\"tdee\"]\n",
    "        \n",
    "        delta_w = delta_weight(tdci, tdee)\n",
    "        new_weight = params[\"weight_history\"][-1] + delta_w\n",
    "        \n",
    "        params[\"phases_history\"].append(new_phase)\n",
    "        params[\"weight_history\"].append(new_weight)\n",
    "        \n",
    "        params[\"tdci\"] = new_tdci\n",
    "        params[\"tdee\"] = new_tdee\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 30,\n",
       " 'height': 180,\n",
       " 'gender': <Gender.MALE: 5>,\n",
       " 'activity': <Activity.SEDENTARY: 1.4>,\n",
       " 'goal': <Goal.GAIN: 250>,\n",
       " 'weight_history': [80,\n",
       "  80,\n",
       "  80.5,\n",
       "  80,\n",
       "  79.72629123615464,\n",
       "  80.9989393468775,\n",
       "  82.20927003925227,\n",
       "  83.2987932212371,\n",
       "  82.4299422886702,\n",
       "  82.96597476549172,\n",
       "  84.40194097944632,\n",
       "  83.99692945742567,\n",
       "  84.930921607453,\n",
       "  86.44500272969256,\n",
       "  88.2792340606551,\n",
       "  89.7817415104467,\n",
       "  91.4190476075019,\n",
       "  92.40030574313963,\n",
       "  92.83812187709121,\n",
       "  92.97319560696216,\n",
       "  93.83316031619442,\n",
       "  93.47798547909902,\n",
       "  92.39624019470435,\n",
       "  92.3360606651626],\n",
       " 'phases_history': [<Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.GAIN: 3>,\n",
       "  <Phase.STOP: 1>,\n",
       "  <Phase.STOP: 1>,\n",
       "  <Phase.STOP: 1>,\n",
       "  <Phase.STOP: 1>,\n",
       "  <Phase.MAINTAINANCE: 2>,\n",
       "  <Phase.MAINTAINANCE: 2>,\n",
       "  <Phase.MAINTAINANCE: 2>],\n",
       " 'tdci': 2992.0,\n",
       " 'tdee': 2915.6042804039544}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\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",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('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",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                '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",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\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",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\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",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\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.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",
       "            /* 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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\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 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",
       "    }\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",
       " * 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",
       "        }\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.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"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.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 = 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",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\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",
       "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": {
      "text/html": [
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.set_ylabel('weight')\n",
    "ax1.set_xlabel('# of weeks')\n",
    "ax1.set_title('weight evolution across time')\n",
    "ax1.plot(params[\"weight_history\"])\n",
    "\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(212)\n",
    "ax3.set_ylabel('Phase')\n",
    "ax3.set_xlabel('# of weeks')\n",
    "ax3.set_title('phase evolution across time')\n",
    "ax3.scatter(range(len(params[\"phases_history\"])), [x.value for x in params[\"phases_history\"]]) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}