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All Weather Portfolio
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all_weather_1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## All Weather Portfolio\n",
"This Notebook is designed to show a simple system which loops through daily data on three stocks and \n",
"rebalances the proportions held annually.\n",
"\n",
"Code assumes dates are MMDDYYYY. Or YYYYMMDD.\n",
"\n",
"Price data should be in the following format:\n",
"\n",
"**Date** | **[Name - EG 3M Tbill]**\n",
":------:|:------:\n",
"*1970-01-30* | *127.00*\n",
"\n",
"IE two columns headed \"Date\" as to the first column and with the name of the stock as to the second.\n",
"\n",
"If you want to test leverage on bonds, you should use futures data as I have done here. My data comes from Pinnacle Data \n",
"https://pinnacledata2.com/index.html\n",
"and I use the \"Ratio Adjusted Method\" for back adjusting. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## You will need to import the following Python packages into the Notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"import logging\n",
"import os\n",
"import sys\n",
"import ffn\n",
"from collections import OrderedDict\n",
"from typing import Dict, List\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## You may or may not find the following options useful."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"pd.set_option(\"max_colwidth\", 100)\n",
"pd.set_option(\"display.max_rows\", 100000)\n",
"pd.set_option(\"display.max_columns\", 1000)\n",
"pd.set_option('precision', 2)\n",
"pd.options.display.float_format = '{:20,.2f}'.format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up the built in Python logging facilty\n",
"Useful for de-bugging.\n",
"\n",
"Currently set to: logger.setLevel(logging.INFO)\n",
"\n",
"Set this to DEBUG if you want some verbose output: logger.setLevel(logging.DEBUG)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"logger = logging.getLogger(\"a_w\")\n",
"logger.setLevel(logging.INFO)\n",
"\n",
"# create a file handler\n",
"handler = logging.StreamHandler()\n",
"for handler in logger.handlers:\n",
" logger.removeHandler(handler)\n",
"handler.setLevel(logging.DEBUG)\n",
"\n",
"# create a logging format\n",
"formatter = logging.Formatter(\n",
" '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
"handler.setFormatter(formatter)\n",
"\n",
"# add the handlers to the logger\n",
"logger.addHandler(handler)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialise Variables\n",
"Make sure to set the directory to where your stock data is located. \n",
"\n",
"Change dates, allocations and stocks as desired"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"start: datetime.date = '1900-01-01'\n",
"end = datetime.date.today()\n",
"\n",
"starting_capital: float() =100000\n",
"\n",
"stock_directory: str = '..\\\\data\\\\Stocks\\\\'\n",
"\n",
"stock_1: str = 'WillshireTRFed'\n",
"allocation_1: float() = 0.50\n",
"\n",
"stock_2: str = '10YrTNote'\n",
"allocation_2: float() = 1.00\n",
"\n",
"stock_3: str = '3mTBill'\n",
"allocation_3: float() = 0.40\n",
"\n",
"stock_tickers_list: List[str] = [stock_1, stock_2, stock_3]\n",
"\n",
"\n",
"#list of files to be saved. Ensure you use your own directories and desired filenames\n",
"test: str = \"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\\\Articles//test.csv\"\n",
"drawdown: str = \"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\\\Articles//drawdown.csv\"\n",
"rebased: str = \"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\Articles//rebased.csv\"\n",
"stats: str = \"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\\\Articles//stats.csv\"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The following function checks to ensure the stock directory is correct and the data files exist:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def check_data_files(ticker_list: List, target_directory: str):\n",
" logger = logging.getLogger(\"a_w.check_data_files\")\n",
"\n",
" if not os.path.exists(stock_directory):\n",
" logger.info('stockdirectory does not exist')\n",
" for ticker in stock_tickers_list:\n",
" filename = os.path.join(stock_directory, ticker + '.csv')\n",
" if not os.path.exists(filename):\n",
" logger.info('file does not exist:' + \" \" + ticker)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The next function returns a dictionary of dataframes for the stock data:\n",
"Reads the desired files from the relevant directory"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def get_dataframe_dictionary(ticker_list: List,\n",
" target_directory: str) -> Dict[str, pd.DataFrame]:\n",
" \"\"\"\n",
" Get a list of files and put into a dictionary of dataframes\n",
" :param ticker_list:\n",
" :param target_directory:\n",
" :return:\n",
" \"\"\"\n",
" ret = OrderedDict()\n",
" logger = logging.getLogger(\"a_w.get_dataframe_dictionary\")\n",
" logger.debug(\"get_dataframe_dictionary: %s\", ticker_list)\n",
" for ticker in ticker_list:\n",
" filename = os.path.join(target_directory, ticker + '.csv')\n",
" if os.path.exists(filename):\n",
" try:\n",
" ret[ticker] = pd.read_csv(filename, parse_dates=[\"Date\"])\n",
" except:\n",
" continue\n",
" return ret"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge the three separate time series into a single dataframe for back testing:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def merge_data(stock_tickers_list: List[str], target_directory: str,\n",
" start: str, end: str) -> pd.DataFrame:\n",
" \"\"\"\n",
" take the dicts of stocks time series and create a dataframe to pass for backtesting\n",
" :param stock_tickers_list:\n",
" :param start: date like '2001-01-01' YYYY-MM-DD format\n",
" :param end: date like '2001-12-31' YYYY-MM-DD format\n",
" :return:\n",
" \"\"\"\n",
" logger = logging.getLogger('a_w.merge_data')\n",
" logger.debug(\"Running: stock_tickers_list: %d\", len(stock_tickers_list))\n",
" check_data_files(stock_tickers_list, target_directory)\n",
" stock_data = get_dataframe_dictionary(stock_tickers_list, target_directory)\n",
" logger.debug(\"Found Data: %d\", len(stock_tickers_list))\n",
" data = pd.DataFrame()\n",
"\n",
" for ticker in stock_tickers_list:\n",
" try:\n",
" if data.empty:\n",
" data = stock_data[ticker]\n",
" else:\n",
" a = stock_data[ticker]\n",
" data = data.merge(\n",
" a, how='left', left_on='Date',\n",
" right_on='Date').fillna(method='ffill')\n",
" data = data.dropna()\n",
" except:\n",
" continue\n",
" logger.debug(\"Cols:{}\".format(data.columns.values))\n",
" logger.debug(\"Now I have a datframe with shape: %s\", data.shape)\n",
"\n",
" if not data.empty:\n",
" new_index = pd.to_datetime(pd.Series(data['Date']))\n",
" data.set_index(new_index,drop=True,append=False,inplace=True,verify_integrity=False)\n",
" data.drop(['Date'], inplace=True, axis=1)\n",
" data = data.truncate(before=start, after=end, axis=0)\n",
" data['year'] = pd.Series(data.index.values).dt.year.values\n",
" data['rebalance'] = data.year.ne(data.year.shift(1))\n",
" data.drop('year', axis=1, inplace=True)\n",
" if len(data.columns) == 4:\n",
" data.columns = ['stock_a', 'stock_b', 'stock_c', 'rebalance']\n",
"\n",
" return data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Call the functions listed so far and inspect the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"data=merge_data(stock_tickers_list, stock_directory,start,end)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>stock_a</th>\n",
" <th>stock_b</th>\n",
" <th>stock_c</th>\n",
" <th>rebalance</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1983-01-03</th>\n",
" <td>2.86</td>\n",
" <td>30.70</td>\n",
" <td>283.26</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983-01-04</th>\n",
" <td>2.91</td>\n",
" <td>30.59</td>\n",
" <td>283.32</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983-01-05</th>\n",
" <td>2.92</td>\n",
" <td>30.60</td>\n",
" <td>283.39</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983-01-06</th>\n",
" <td>3.00</td>\n",
" <td>30.55</td>\n",
" <td>283.45</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983-01-07</th>\n",
" <td>3.00</td>\n",
" <td>30.58</td>\n",
" <td>283.51</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stock_a stock_b stock_c \\\n",
"Date \n",
"1983-01-03 2.86 30.70 283.26 \n",
"1983-01-04 2.91 30.59 283.32 \n",
"1983-01-05 2.92 30.60 283.39 \n",
"1983-01-06 3.00 30.55 283.45 \n",
"1983-01-07 3.00 30.58 283.51 \n",
"\n",
" rebalance \n",
"Date \n",
"1983-01-03 True \n",
"1983-01-04 False \n",
"1983-01-05 False \n",
"1983-01-06 False \n",
"1983-01-07 False "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inspect_data = pd.DataFrame()\n",
"if not data.empty:\n",
" inspect_data = data.head()\n",
" inspect_data.to_csv(\"C:\\\\Users\\\\agarn\\\\OneDrive\\\\Documents\\Articles//inspect_data.csv\") \n",
" inspect_data.head()\n",
"else:\n",
" print('data loading failed see logging')\n",
"inspect_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Here is the backtest function:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"#[0]row.Index #[1]shares_1 #[2]shares_2 #[3]shares_3 #[4]value #[5]equity #[6]\n",
"def backtest_data(stock_tickers_list: List[str], target_directory: str,\n",
" start: str, end: str) -> pd.DataFrame:\n",
" data = merge_data(stock_tickers_list, target_directory, start, end)\n",
" logger = logging.getLogger(\"a_w.backtest_data\")\n",
"\n",
" if not data.empty and len(data.columns) == 4:\n",
"\n",
" temp_data = {}\n",
"\n",
" for i, row in enumerate(data.itertuples(), 0):\n",
" if i == 0:\n",
" shares_1: int() = int(\n",
" (starting_capital * allocation_1) / row.stock_a)\n",
" shares_2: int() = int(\n",
" (starting_capital * allocation_2) / row.stock_b)\n",
" shares_3: int() = int(\n",
" (starting_capital * allocation_3) / row.stock_c)\n",
" value: float() = (shares_1 * row.stock_a) + (\n",
" shares_2 * row.stock_b) + (shares_3 * row.stock_c)\n",
" equity: float() = starting_capital\n",
" cash: float() = float(equity - value)\n",
" temp_data[\n",
" i] = row.Index, shares_1, shares_2, shares_3, value, equity, cash\n",
" if (i > 0) and row.rebalance == False:\n",
" value: float() = float((shares_1 * row.stock_a) + (\n",
" shares_2 * row.stock_b) + (shares_3 * row.stock_c))\n",
" equity: float() = float(value + cash)\n",
" temp_data[\n",
" i] = row.Index, shares_1, shares_2, shares_3, value, equity, cash\n",
"\n",
" if (i > 0) and row.rebalance == True:\n",
" lb: int() = int(i - 1)\n",
" shares_1: int() = int((equity * allocation_1) / row.stock_a)\n",
" shares_2: int() = int((equity * allocation_2) / row.stock_b)\n",
" shares_3: int() = int((equity * allocation_3) / row.stock_c)\n",
" cash: float() = float(equity - ((data.stock_a[lb] * shares_1) +\n",
" (data.stock_b[lb] * shares_2) +\n",
" (data.stock_c[lb] * shares_3)))\n",
" value: float() = float((shares_1 * row.stock_a) + (\n",
" shares_2 * row.stock_b) + (shares_3 * row.stock_c))\n",
" equity: float() = float(value + cash)\n",
" temp_data[\n",
" i] = row.Index, shares_1, shares_2, shares_3, value, equity, cash\n",
" performance_data = pd.DataFrame(temp_data).T\n",
" performance_data.columns = [\n",
" 'Date', 'shares_1', 'shares_2', 'shares_3', 'value', 'equity',\n",
" 'cash'\n",
" ]\n",
" performance_data.set_index('Date', inplace=True)\n",
" data.columns = [stock_1, stock_2, stock_3, 'rebalance']\n",
" test = pd.concat([data, performance_data], sort=False, axis=1)\n",
" return test\n",
" else:\n",
" logger.info('insufficient data - check directory, existence of files')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Call the backtest function and provide the required inputs."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"backtest=backtest_data(stock_tickers_list, stock_directory,start,end)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspect the data output."
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>WillshireTRFed</th>\n",
" <th>10YrTNote</th>\n",
" <th>3mTBill</th>\n",
" <th>rebalance</th>\n",
" <th>shares_1</th>\n",
" <th>shares_2</th>\n",
" <th>shares_3</th>\n",
" <th>value</th>\n",
" <th>equity</th>\n",
" <th>cash</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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-03-25</th>\n",
" <td>131.90</td>\n",
" <td>124.33</td>\n",
" <td>698.29</td>\n",
" <td>False</td>\n",
" <td>16946</td>\n",
" <td>32455</td>\n",
" <td>2286</td>\n",
" <td>7,866,529.26</td>\n",
" <td>4,296,456.13</td>\n",
" <td>-3,570,073.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-26</th>\n",
" <td>132.89</td>\n",
" <td>124.41</td>\n",
" <td>698.33</td>\n",
" <td>False</td>\n",
" <td>16946</td>\n",
" <td>32455</td>\n",
" <td>2286</td>\n",
" <td>7,885,946.74</td>\n",
" <td>4,315,873.62</td>\n",
" <td>-3,570,073.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-27</th>\n",
" <td>132.28</td>\n",
" <td>124.69</td>\n",
" <td>698.38</td>\n",
" <td>False</td>\n",
" <td>16946</td>\n",
" <td>32455</td>\n",
" <td>2286</td>\n",
" <td>7,884,842.18</td>\n",
" <td>4,314,769.06</td>\n",
" <td>-3,570,073.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-28</th>\n",
" <td>132.85</td>\n",
" <td>124.50</td>\n",
" <td>698.42</td>\n",
" <td>False</td>\n",
" <td>16946</td>\n",
" <td>32455</td>\n",
" <td>2286</td>\n",
" <td>7,888,520.19</td>\n",
" <td>4,318,447.06</td>\n",
" <td>-3,570,073.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-29</th>\n",
" <td>133.69</td>\n",
" <td>124.22</td>\n",
" <td>698.47</td>\n",
" <td>False</td>\n",
" <td>16946</td>\n",
" <td>32455</td>\n",
" <td>2286</td>\n",
" <td>7,893,729.66</td>\n",
" <td>4,323,656.53</td>\n",
" <td>-3,570,073.13</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WillshireTRFed 10YrTNote 3mTBill \\\n",
"Date \n",
"2019-03-25 131.90 124.33 698.29 \n",
"2019-03-26 132.89 124.41 698.33 \n",
"2019-03-27 132.28 124.69 698.38 \n",
"2019-03-28 132.85 124.50 698.42 \n",
"2019-03-29 133.69 124.22 698.47 \n",
"\n",
" rebalance shares_1 shares_2 shares_3 value \\\n",
"Date \n",
"2019-03-25 False 16946 32455 2286 7,866,529.26 \n",
"2019-03-26 False 16946 32455 2286 7,885,946.74 \n",
"2019-03-27 False 16946 32455 2286 7,884,842.18 \n",
"2019-03-28 False 16946 32455 2286 7,888,520.19 \n",
"2019-03-29 False 16946 32455 2286 7,893,729.66 \n",
"\n",
" equity cash \n",
"Date \n",
"2019-03-25 4,296,456.13 -3,570,073.13 \n",
"2019-03-26 4,315,873.62 -3,570,073.13 \n",
"2019-03-27 4,314,769.06 -3,570,073.13 \n",
"2019-03-28 4,318,447.06 -3,570,073.13 \n",
"2019-03-29 4,323,656.53 -3,570,073.13 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output = pd.DataFrame()\n",
"if backtest is not None:\n",
" output = backtest.tail()\n",
"else:\n",
" print('backtest failed see logging')\n",
"output.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save backtest data to file for further inspection. Use FFN package to create charts and statistics for the backtest."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
" def show_stats(backtest: pd.DataFrame):\n",
" if backtest is not None and not backtest.empty:\n",
" logger = logging.getLogger('a_w.charts')\n",
" res = backtest.equity.astype(np.float64)\n",
" benchmarked = pd.concat(\n",
" [res, backtest[stock_1], backtest[stock_2]], sort=False, axis=1) \n",
" log = True\n",
" benchmarked.rebase().plot(figsize=(7, 6), logy=log, title='All Weather Portfolio')\n",
" benchmarked.calc_stats().display()\n",
" benchmarked.to_drawdown_series().plot(figsize=(8, 6))\n",
"\n",
" def save_1():backtest.to_csv(test)\n",
" def save_2():benchmarked.rebase().to_csv(rebased)\n",
" def save_3():benchmarked.calc_stats().to_csv(sep=', ', path=stats)\n",
" def save_4():benchmarked.to_drawdown_series().to_csv(drawdown)\n",
" save_files=[save_1,save_2,save_3,save_4]\n",
" for s in save_files:\n",
" try:\n",
" s()\n",
" except IOError:\n",
" print('file open - close file ',s) \n",
" continue\n",
"\n",
" else:\n",
" print('backtest failed see logging')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Call the show-backtest function"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, 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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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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"text": [
"Stat equity WillshireTRFed 10YrTNote\n",
"------------------- ---------- ---------------- -----------\n",
"Start 1983-01-03 1983-01-03 1983-01-03\n",
"End 2019-03-29 2019-03-29 2019-03-29\n",
"Risk-free rate 0.00% 0.00% 0.00%\n",
"\n",
"Total Return 4223.66% 4574.48% 304.61%\n",
"Daily Sharpe 1.08 0.71 0.63\n",
"Daily Sortino 1.78 1.10 1.05\n",
"CAGR 10.96% 11.19% 3.93%\n",
"Max Drawdown -24.47% -55.39% -16.14%\n",
"Calmar Ratio 0.45 0.20 0.24\n",
"\n",
"MTD 2.56% 1.50% 1.82%\n",
"3m 9.55% 15.10% 1.86%\n",
"6m 3.51% -2.19% 4.43%\n",
"YTD 8.74% 14.11% 1.53%\n",
"1Y 8.07% 8.93% 2.78%\n",
"3Y (ann.) 6.74% 13.71% -0.47%\n",
"5Y (ann.) 7.10% 10.53% 1.55%\n",
"10Y (ann.) 10.80% 16.16% 2.51%\n",
"Since Incep. (ann.) 10.96% 11.19% 3.93%\n",
"\n",
"Daily Sharpe 1.08 0.71 0.63\n",
"Daily Sortino 1.78 1.10 1.05\n",
"Daily Mean (ann.) 10.90% 12.07% 4.07%\n",
"Daily Vol (ann.) 10.07% 17.04% 6.48%\n",
"Daily Skew -0.44 -0.65 0.05\n",
"Daily Kurt 11.85 16.28 3.42\n",
"Best Day 6.71% 11.36% 3.63%\n",
"Worst Day -10.21% -17.31% -2.40%\n",
"\n",
"Monthly Sharpe 1.11 0.78 0.63\n",
"Monthly Sortino 2.15 1.29 1.21\n",
"Monthly Mean (ann.) 10.93% 11.66% 4.15%\n",
"Monthly Vol (ann.) 9.84% 14.92% 6.57%\n",
"Monthly Skew -0.16 -0.88 0.19\n",
"Monthly Kurt 1.23 2.92 1.33\n",
"Best Month 10.40% 12.87% 8.72%\n",
"Worst Month -9.96% -22.81% -5.80%\n",
"\n",
"Yearly Sharpe 1.04 0.72 0.59\n",
"Yearly Sortino 7.89 1.62 1.83\n",
"Yearly Mean 11.17% 11.92% 4.30%\n",
"Yearly Vol 10.70% 16.61% 7.24%\n",
"Yearly Skew 0.82 -0.80 0.38\n",
"Yearly Kurt 0.91 0.87 -0.13\n",
"Best Year 40.42% 36.40% 21.74%\n",
"Worst Year -7.10% -37.23% -9.18%\n",
"\n",
"Avg. Drawdown -1.39% -1.94% -1.56%\n",
"Avg. Drawdown Days 24.52 27.37 56.44\n",
"Avg. Up Month 2.38% 3.37% 1.59%\n",
"Avg. Down Month -2.10% -3.48% -1.29%\n",
"Win Year % 83.33% 80.56% 72.22%\n",
"Win 12m % 85.38% 80.66% 70.28%\n"
]
},
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" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, 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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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=\"799.9999826604674\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_stats(backtest)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## This is my Python version\n",
"from platform import python_version\n",
"\n",
"python_version()\n",
"\n",
"'3.6.8'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Anaconda Version and System Info\n",
"\n",
"sys.version,sys.version_info\n",
"\n",
"('3.6.8 |Anaconda custom (64-bit)| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)]',\n",
" sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Version of the Imports\n",
"\n",
"import pip\n",
"\n",
"%pip list\n",
"\n",
"Package Version \n",
"---------------------------------- --------\n",
"ffn 0.3.4 \n",
"numpy 1.16.2 \n",
"pandas 0.24.2 \n",
" \n"
]
},
{
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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