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wrighter/show_bars.ipynb

Created February 9, 2020 23:54
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show_bars.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('./data/STK/1_day/AAPL.csv', parse_dates=[0], index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>barCount</th>\n",
" <th>average</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980-12-12</th>\n",
" <td>4.105</td>\n",
" <td>4.125</td>\n",
" <td>4.105</td>\n",
" <td>4.105</td>\n",
" <td>146573</td>\n",
" <td>1</td>\n",
" <td>4.107143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-12-15</th>\n",
" <td>3.895</td>\n",
" <td>3.910</td>\n",
" <td>3.895</td>\n",
" <td>3.895</td>\n",
" <td>54964</td>\n",
" <td>1</td>\n",
" <td>3.892857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-12-16</th>\n",
" <td>3.605</td>\n",
" <td>3.625</td>\n",
" <td>3.605</td>\n",
" <td>3.605</td>\n",
" <td>33040</td>\n",
" <td>1</td>\n",
" <td>3.607143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-12-17</th>\n",
" <td>3.695</td>\n",
" <td>3.715</td>\n",
" <td>3.695</td>\n",
" <td>3.695</td>\n",
" <td>27013</td>\n",
" <td>1</td>\n",
" <td>3.696571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-12-18</th>\n",
" <td>3.805</td>\n",
" <td>3.820</td>\n",
" <td>3.805</td>\n",
" <td>3.805</td>\n",
" <td>22953</td>\n",
" <td>1</td>\n",
" <td>3.803714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-03</th>\n",
" <td>304.290</td>\n",
" <td>313.490</td>\n",
" <td>302.220</td>\n",
" <td>308.660</td>\n",
" <td>335525</td>\n",
" <td>163166</td>\n",
" <td>309.182500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-04</th>\n",
" <td>315.450</td>\n",
" <td>319.640</td>\n",
" <td>313.630</td>\n",
" <td>318.850</td>\n",
" <td>246776</td>\n",
" <td>115649</td>\n",
" <td>317.324000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-05</th>\n",
" <td>323.510</td>\n",
" <td>324.760</td>\n",
" <td>318.950</td>\n",
" <td>321.450</td>\n",
" <td>224189</td>\n",
" <td>112571</td>\n",
" <td>321.468000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-06</th>\n",
" <td>322.450</td>\n",
" <td>325.220</td>\n",
" <td>320.260</td>\n",
" <td>325.210</td>\n",
" <td>180908</td>\n",
" <td>90119</td>\n",
" <td>323.106500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-07</th>\n",
" <td>322.370</td>\n",
" <td>323.400</td>\n",
" <td>318.000</td>\n",
" <td>320.030</td>\n",
" <td>223554</td>\n",
" <td>108226</td>\n",
" <td>321.051500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9872 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" open high low close volume barCount average\n",
"date \n",
"1980-12-12 4.105 4.125 4.105 4.105 146573 1 4.107143\n",
"1980-12-15 3.895 3.910 3.895 3.895 54964 1 3.892857\n",
"1980-12-16 3.605 3.625 3.605 3.605 33040 1 3.607143\n",
"1980-12-17 3.695 3.715 3.695 3.695 27013 1 3.696571\n",
"1980-12-18 3.805 3.820 3.805 3.805 22953 1 3.803714\n",
"... ... ... ... ... ... ... ...\n",
"2020-02-03 304.290 313.490 302.220 308.660 335525 163166 309.182500\n",
"2020-02-04 315.450 319.640 313.630 318.850 246776 115649 317.324000\n",
"2020-02-05 323.510 324.760 318.950 321.450 224189 112571 321.468000\n",
"2020-02-06 322.450 325.220 320.260 325.210 180908 90119 323.106500\n",
"2020-02-07 322.370 323.400 318.000 320.030 223554 108226 321.051500\n",
"\n",
"[9872 rows x 7 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
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" 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",
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" this.format_dropdown = undefined;\n",
"\n",
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"\n",
" this.root = $('<div/>');\n",
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"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
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"\n",
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"\n",
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"\n",
"\n",
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"\n",
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"\n",
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"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
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" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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"\n",
" canvas_div.resizable({\n",
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" pass_mouse_events = false;\n",
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" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
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"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
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"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
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" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
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" // upon first draw.\n",
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"\n",
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" });\n",
"\n",
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" }\n",
"\n",
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"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
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" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
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"\n",
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"}\n",
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"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
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" fig._resize_canvas(size[0], size[1]);\n",
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"}\n",
"\n",
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" 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",
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" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
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" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
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" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
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" {\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",
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" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1168f89d0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib notebook\n",
"\n",
"df['close'].plot()"
]
},
{
"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": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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