plots_by_state_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instructions\n",
    "1. Click \"Restart and run all\" from the kernel menu to run the notebook.\n",
    "\n",
    "2. Then change the state as needed by making your selections in the menu below. You can use \"Shift\" or \"Ctrl\" to select multiple countries.\n",
    "\n",
    "3. Then select the cell that starts with \"% matplotlib notebook\" and press \"Shift + Enter\" to update the plot.\n",
    "\n",
    "4. The curves are exponential functions fit to a range of data for each state.  You can adjust the range of data points used to fit these curves using the textboxes.  The estimated numbers of cases on the indicated day based on those exponential models will be printed below.\n",
    "\n",
    "Note: You can display nationwide totals by selecting \"All\" from the state menu. Keep in mind that the models are fit independently so the predictions for a single country may even exceed the predictions for all if the current growth rate is high. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "781d530a8347441f95d125896bf856a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Label(value='Select a state (or multiple states):'), SelectMultiple(index=(1,), options=('All',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from covid_plotting_tools_state import *\n",
    "select,states=create_selection_box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After making your selection above, select the cell below and hit \"Run\" or press \"Shift\"+ \"Enter\" to update the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "        alert('Your browser does not have WebSocket support. ' +\n",
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       "              'Firefox 4 and 5 are also supported but you ' +\n",
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       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
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       "    this.context = canvas[0].getContext(\"2d\");\n",
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       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
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       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
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       "\n",
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       "    nav_element.attr('style', 'width: 100%');\n",
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       "\n",
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       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
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       "\n",
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       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
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       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ca4e971db14408cb0a2963b9ffac5e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "GridBox(children=(FloatRangeSlider(value=(-0.1, 1.1), description='x range', max=1.5, min=-0.2, readout=False,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a2fe8deee174e379fe0af7c3222f500",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "last_month,last_day,maxval,state,df_dict=compute_data_props(states,select)\n",
    "#print(last_month,last_day,maxval)\n",
    "out,ui,lines,lstart,lend=make_interactive_plot(end_month_plot=last_month,end_day_plot=last_day,\n",
    "                                               def_end_month_fit=last_month,def_end_day_fit=last_day,\n",
    "                                               def_month_pred=last_month,def_day_pred=last_day,\n",
    "                                              maxval=maxval,country=state,df_dict=df_dict)\n",
    "display(ui,out)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: An exponential model may not be appropriate for describing the spread of COVID-19 in every state. The data points will follow a linear trend when the y-axis is logarithmic if an exponential model is appropriate.  If the datapoints fall below the fit line on the right side of the plot, then that suggests that the growth is slower than exponential."
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}