tutorials-and-examples/feature-tutorials/TimeSeriesAnomaliesVisualization.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#msticpy---Time-Series-Analysis-and-anomalies-Visualization\" data-toc-modified-id=\"msticpy---Time-Series-Analysis-and-anomalies-Visualization-1\">msticpy - Time Series Analysis and anomalies Visualization</a></span></li><li><span><a href=\"#Generating-Time-Series-Data\" data-toc-modified-id=\"Generating-Time-Series-Data-2\">Generating Time Series Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Using-LogAnalytics-Query-Provider\" data-toc-modified-id=\"Using-LogAnalytics-Query-Provider-2.1\">Using LogAnalytics Query Provider</a></span><ul class=\"toc-item\"><li><span><a href=\"#Displaying-available-timeseries-queries\" data-toc-modified-id=\"Displaying-available-timeseries-queries-2.1.1\">Displaying available timeseries queries</a></span></li><li><span><a href=\"#Get-TimeSeries-Data-from-LogAnalytics-Table\" data-toc-modified-id=\"Get-TimeSeries-Data-from-LogAnalytics-Table-2.1.2\">Get TimeSeries Data from LogAnalytics Table</a></span></li></ul></li></ul></li><li><span><a href=\"#Time-Series-Analysis-and-discovering-Anomalies\" data-toc-modified-id=\"Time-Series-Analysis-and-discovering-Anomalies-3\">Time Series Analysis and discovering Anomalies</a></span><ul class=\"toc-item\"><li><span><a href=\"#Using-Built-in-KQL-to-generate-TimeSeries-decomposition\" data-toc-modified-id=\"Using-Built-in-KQL-to-generate-TimeSeries-decomposition-3.1\">Using Built-in KQL to generate TimeSeries decomposition</a></span></li><li><span><a href=\"#Using-MSTICPY---Seasonal-Trend-decomposition-using-LOESS-(STL)\" data-toc-modified-id=\"Using-MSTICPY---Seasonal-Trend-decomposition-using-LOESS-(STL)-3.2\">Using MSTICPY - Seasonal-Trend decomposition using LOESS (STL)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Documentation-of-timeseries_anomalies_stl-function\" data-toc-modified-id=\"Documentation-of-timeseries_anomalies_stl-function-3.2.1\">Documentation of timeseries_anomalies_stl function</a></span></li><li><span><a href=\"#Discover-anomalies-using-timeseries_anomalies_stl-function\" data-toc-modified-id=\"Discover-anomalies-using-timeseries_anomalies_stl-function-3.2.2\">Discover anomalies using timeseries_anomalies_stl function</a></span></li><li><span><a href=\"#Displaying-Anomalies-using-STL\" data-toc-modified-id=\"Displaying-Anomalies-using-STL-3.2.3\">Displaying Anomalies using STL</a></span></li></ul></li><li><span><a href=\"#Read-From-External-Sources\" data-toc-modified-id=\"Read-From-External-Sources-3.3\">Read From External Sources</a></span><ul class=\"toc-item\"><li><span><a href=\"#Displaying-Anomalies-Separately\" data-toc-modified-id=\"Displaying-Anomalies-Separately-3.3.1\">Displaying Anomalies Separately</a></span></li></ul></li><li><span><a href=\"#Displaying-Time-Series-anomaly-alerts\" data-toc-modified-id=\"Displaying-Time-Series-anomaly-alerts-3.4\">Displaying Time Series anomaly alerts</a></span></li></ul></li><li><span><a href=\"#Time-Series-Anomalies-Visualization\" data-toc-modified-id=\"Time-Series-Anomalies-Visualization-4\">Time Series Anomalies Visualization</a></span><ul class=\"toc-item\"><li><span><a href=\"#Using-Bokeh-Visualization-Library\" data-toc-modified-id=\"Using-Bokeh-Visualization-Library-4.1\">Using Bokeh Visualization Library</a></span></li><li><span><a href=\"#Documentation-for-display_timeseries_anomalies\" data-toc-modified-id=\"Documentation-for-display_timeseries_anomalies-4.2\">Documentation for display_timeseries_anomalies</a></span></li><li><span><a href=\"#Exporting-Plots-as-PNGs\" data-toc-modified-id=\"Exporting-Plots-as-PNGs-4.3\">Exporting Plots as PNGs</a></span></li><li><span><a href=\"#Using-Built-in-KQL-render-operator\" data-toc-modified-id=\"Using-Built-in-KQL-render-operator-4.4\">Using Built-in KQL render operator</a></span></li></ul></li></ul></div>"
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      "# msticpy - Time Series Analysis and anomalies Visualization\n",
      "\n",
      "This notebook demonstrates the time series analysis and anomalies visualization built using the [Bokeh library](https://bokeh.pydata.org) as well as using built-in native KQL operators.\n",
      "\n",
      "You must have msticpy installed along with the timeseries dependencies to run this notebook:\n",
      "```\n",
      "%pip install --upgrade msticpy[timeseries]\n",
      "```\n",
      "To run the Microsoft Sentinel timeseries queries you will also need the \"azsentinel\" dependencies\n",
      "```\n",
      "%pip install --upgrade msticpy[timeseries, azsentinel]\n",
      "```\n",
      "\n",
      "Time Series analysis generally involves below steps\n",
      "- Generating TimeSeries Data\n",
      "- Use Time Series Analysis functions to discover anomalies\n",
      "- Visualize Time Series anomalies\n",
      "\n",
      "Read more about time series analysis in detail from reference microsoft TechCommunity blog posts\n",
      "\n",
      "<br>***Reference Blog Posts:***\n",
      "- [Looking for unknown anomalies - what is normal? Time Series analysis & its applications in Security](https://techcommunity.microsoft.com/t5/azure-sentinel/looking-for-unknown-anomalies-what-is-normal-time-series/ba-p/555052)\n",
      "- [Time Series visualization of Palo Alto logs to detect data exfiltration](https://techcommunity.microsoft.com/t5/azure-sentinel/time-series-visualization-of-palo-alto-logs-to-detect-data/ba-p/666344)"
    ]
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       "<style>.container { width:90% !important; }</style>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "# Imports\n",
    "import sys\n",
    "import warnings\n",
    "\n",
    "from msticpy.nbtools.utility import check_py_version\n",
    "\n",
    "MIN_REQ_PYTHON = (3, 6)\n",
    "check_py_version(MIN_REQ_PYTHON)\n",
    "\n",
    "from IPython import get_ipython\n",
    "from IPython.display import display, HTML, Markdown\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# setting pandas display options for dataframe\n",
    "pd.set_option(\"display.max_rows\", 100)\n",
    "pd.set_option(\"display.max_columns\", 50)\n",
    "pd.set_option(\"display.max_colwidth\", 100)\n",
    "\n",
    "# msticpy imports\n",
    "from msticpy.data import QueryProvider\n",
    "from msticpy.nbtools import *\n",
    "from msticpy.sectools import *\n",
    "from msticpy.nbtools.wsconfig import WorkspaceConfig\n",
    "from msticpy.nbtools.timeseries import display_timeseries_anomolies \n",
    "from msticpy.analysis.timeseries import timeseries_anomalies_stl\n",
    "\n",
    "WIDGET_DEFAULTS = {\n",
    "    \"layout\": widgets.Layout(width=\"95%\"),\n",
    "    \"style\": {\"description_width\": \"initial\"},\n",
    "}\n",
    "\n",
    "# Adjusting width of the screen\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Time Series Data\n",
    "Time Series is a series of data points indexed (or listed or graphed) in time order. \n",
    "<br>The data points are often discrete numeric points such as frequency of counts or occurrences against a timestamp column of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using LogAnalytics Query Provider\n",
    "msticpy has QueryProvider through which you can connect to LogAnalytics Data environment. via `QueryProvider(data_environment=\"LogAnalytics\")`\n",
    "<br>Once you connect to data environment (`qry_prov.connect()`), you can list the available queries (`qry_prov.list_queries()`) for the data environment which in this case is LogAnalytics.\n",
    "\n",
    "```python\n",
    "# Read workspace configuration from msticpyconfig.yaml\n",
    "ws_config = WorkspaceConfig()\n",
    "# Authentication\n",
    "qry_prov = QueryProvider(data_environment=\"LogAnalytics\")\n",
    "qry_prov.connect(connection_str=ws_config.code_connect_str)\n",
    "```"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying available timeseries queries\n",
    "For this notebook, we are interested in time series queries only, so we will filter and display only those.\n",
    "\n",
    "```python\n",
    "queries = qry_prov.list_queries()\n",
    "for query in queries:\n",
    "    if \"timeseries\" in query:\n",
    "        print(query)\n",
    "```\n",
    "\n",
    "```\n",
    "MultiDataSource.get_timeseries_anomalies\n",
    "MultiDataSource.get_timeseries_data\n",
    "MultiDataSource.get_timeseries_decompose\n",
    "MultiDataSource.plot_timeseries_datawithbaseline\n",
    "MultiDataSource.plot_timeseries_scoreanomolies\n",
    "```"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get TimeSeries Data from LogAnalytics Table\n",
    "\n",
    "You can get more details about the individual query by executing  `qry_prov.MultiDataSource.get_timeseries_data('?')` which will display Query, data source, parameters and parameterized raw KQL query \n",
    "```\n",
    "Query:  get_timeseries_data\n",
    "Data source:  LogAnalytics\n",
    "Retrieves TimeSeriesData prepared to use with built-in KQL time series functions\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "add_query_items: str (optional)\n",
    "    Additional query clauses\n",
    "aggregatecolumn: str (optional)\n",
    "    field to agregate from source dataset\n",
    "    (default value is: Total)\n",
    "aggregatefunction: str (optional)\n",
    "    Aggregation functions to use - count(), sum(), avg() etc\n",
    "    (default value is: count())\n",
    "end: datetime\n",
    "    Query end time\n",
    "groupbycolumn: str (optional)\n",
    "    Group by field to aggregate results\n",
    "    (default value is: Type)\n",
    "scorethreshold: str (optional)\n",
    "    Score threshold for alerting\n",
    "    (default value is: 3)\n",
    "start: datetime\n",
    "    Query start time\n",
    "table: str\n",
    "    Table name\n",
    "timeframe: str (optional)\n",
    "    Aggregation TimeFrame\n",
    "    (default value is: 1h)\n",
    "timestampcolumn: str (optional)\n",
    "    Timestamp field to use from source dataset\n",
    "    (default value is: TimeGenerated)\n",
    "where_clause: str (optional)\n",
    "    Optional additional filter clauses\n",
    "Query:\n",
    " {table} {where_clause} | project {timestampcolumn},{aggregatecolumn},{groupbycolumn} | where {timestampcolumn} >= datetime({start}) | where {timestampcolumn} <= datetime({end}) | make-series {aggregatecolumn}={aggregatefunction} on {timestampcolumn} from datetime({start}) to datetime({end}) step {timeframe} by {groupbycolumn} {add_query_items}\n",
    "```\n",
    "\n",
    "***Sample python code leveraging KQL query will look like this***\n",
    "\n",
    "```python\n",
    "\n",
    "# Specify start and end timestamps\n",
    "start = \"2020-02-09 00:00:00.000000\"\n",
    "end = \"2020-03-10 00:00:00.000000\"\n",
    "\n",
    "# Execute the query by passing required and optional parameters\n",
    "time_series_data = qry_prov.MultiDataSource.get_timeseries_data(\n",
    "    start=start,\n",
    "    end=end,\n",
    "    table=\"CommonSecurityLog\",\n",
    "    timestampcolumn=\"TimeGenerated\",\n",
    "    aggregatecolumn=\"SentBytes\",\n",
    "    groupbycolumn=\"DeviceVendor\",\n",
    "    aggregatefunction=\"sum(SentBytes)\",\n",
    "    where_clause='|where DeviceVendor==\"Palo Alto Networks\"',\n",
    "    add_query_items='|mv-expand TimeGenerated to typeof(datetime), SentBytes to typeof(long)',\n",
    "    )\n",
    "\n",
    " #display output\n",
    "time_series_data\n",
    "```"
   ]
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   "source": [
    "# Time Series Analysis and discovering Anomalies\n",
    "By analyzing time series data over an extended period, we can identify time-based patterns (e.g. seasonality, trend etc.) in the data and extract meaningful statistics which can help in flagging outliers.  A particular example in a security context is user logon patterns over a period of time exhibiting different behavior after hours and on weekends: computing deviations from these changing patterns is rather difficult in traditional atomic detections with static thresholds. KQL built-in functions can automatically identify such seasonality and trend from the input data and take it into consideration when flagging anomalies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Built-in KQL to generate TimeSeries decomposition\n",
    "In this case, we will use built-in KQL function `series_decompose()` to decompose time series to generate additional data points such as baseline, seasonal , trend etc.\n",
    "\n",
    "***KQL Reference Documentation:***\n",
    "- [series_decompose](https://docs.microsoft.com/azure/kusto/query/series-decomposefunction)\n",
    "\n",
    "You can use available query `qry_prov.MultiDataSource.get_timeseries_decompose()` to get the similar details\n",
    "```\n",
    "Query:  get_timeseries_decompose\n",
    "Data source:  LogAnalytics\n",
    "Time Series decomposition and anomalies generated using built-in KQL time series function- series_decompose\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "add_query_items: str (optional)\n",
    "    Additional query clauses\n",
    "aggregatecolumn: str (optional)\n",
    "    field to agregate from source dataset\n",
    "    (default value is: Total)\n",
    "aggregatefunction: str (optional)\n",
    "    Aggregation functions to use - count(), sum(), avg() etc\n",
    "    (default value is: count())\n",
    "end: datetime\n",
    "    Query end time\n",
    "groupbycolumn: str (optional)\n",
    "    Group by field to aggregate results\n",
    "    (default value is: Type)\n",
    "scorethreshold: str (optional)\n",
    "    Score threshold for alerting\n",
    "    (default value is: 3)\n",
    "start: datetime\n",
    "    Query start time\n",
    "table: str\n",
    "    Table name\n",
    "timeframe: str (optional)\n",
    "    Aggregation TimeFrame\n",
    "    (default value is: 1h)\n",
    "timestampcolumn: str (optional)\n",
    "    Timestamp field to use from source dataset\n",
    "    (default value is: TimeGenerated)\n",
    "where_clause: str (optional)\n",
    "    Optional additional filter clauses\n",
    "Query:\n",
    " {table} {where_clause} | project {timestampcolumn},{aggregatecolumn},{groupbycolumn} | where {timestampcolumn} >= datetime({start}) | where {timestampcolumn} <= datetime({end}) | make-series {aggregatecolumn}={aggregatefunction} on {timestampcolumn} from datetime({start}) to datetime({end}) step {timeframe} by {groupbycolumn} | extend (baseline,seasonal,trend,residual) = series_decompose({aggregatecolumn}) | mv-expand {aggregatecolumn} to typeof(double), {timestampcolumn} to typeof(datetime), baseline to typeof(double), seasonal to typeof(double), trend to typeof(long), residual to typeof(long) {add_query_items}\n",
    "```\n",
    "\n",
    "***Sample python code leveraging KQL query will look like this***\n",
    "\n",
    "```python\n",
    "time_series_baseline = qry_prov.MultiDataSource.get_timeseries_decompose(\n",
    "    start=start,\n",
    "    end=end,\n",
    "    table=\"CommonSecurityLog\",\n",
    "    timestampcolumn=\"TimeGenerated\",\n",
    "    aggregatecolumn=\"SentBytes\",\n",
    "    groupbycolumn=\"DeviceVendor\",\n",
    "    aggregatefunction=\"sum(SentBytes)\",\n",
    "    where_clause='|where DeviceVendor==\"Palo Alto Networks\"',\n",
    ")\n",
    "\n",
    "#Show sample records\n",
    "time_series_baseline.head()\n",
    "```"
   ]
  },
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   "source": [
    "## Using MSTICPY - Seasonal-Trend decomposition using LOESS (STL)\n",
    "\n",
    "In this case, we will use function msticpy function `timeseries_anomalies_stl` which leverages `STL` method from `statsmodels` API to decompose a time series into three components: trend, seasonal and residual. STL uses LOESS (locally estimated scatterplot smoothing) to extract smooths estimates of the three components. The key inputs into STL are:\n",
    "\n",
    "- season - The length of the seasonal smoother. Must be odd.\n",
    "- trend - The length of the trend smoother, usually around 150% of season. Must be odd and larger than season.\n",
    "- low_pass - The length of the low-pass estimation window, usually the smallest odd number larger than the periodicity of the data.\n",
    "\n",
    "More info : https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Documentation of timeseries_anomalies_stl function\n",
    "\n",
    "timeseries_anomalies_stl(data: pandas.core.frame.DataFrame, **kwargs) -> pandas.core.frame.DataFrame\n",
    "    Discover anomalies in Timeseries data using\n",
    "    STL (Seasonal-Trend Decomposition using LOESS) method using statsmodels package.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data : pd.DataFrame\n",
    "        DataFrame as a time series data set retrived from data connector or external data source.\n",
    "        Dataframe must have 2 columns with time column set as index and other numeric value.\n",
    "    \n",
    "    Other Parameters\n",
    "    ----------------\n",
    "    seasonal : int, optional\n",
    "        Seasonality period of the input data required for STL. \n",
    "        Must be an odd integer, and should normally be >= 7 (default).\n",
    "    period: int, optional\n",
    "        Periodicity of the the input data. by default 24 (Hourly).\n",
    "    score_threshold : float, optional\n",
    "        standard deviation threshold value calculated using Z-score used to flag anomalies,\n",
    "        by default 3\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    pd.DataFrame\n",
    "        Returns a dataframe with additional columns by decomposing time series data\n",
    "        into residual, trend, seasonal, weights, baseline, score and anomalies.\n",
    "        The anomalies column will have 0, 1,-1 values based on score_threshold set.\n"
   ]
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       "      <th></th>\n",
       "      <th>TotalBytesSent</th>\n",
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       "    <tr>\n",
       "      <th>TimeGenerated</th>\n",
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       "                      TotalBytesSent\n",
       "TimeGenerated                       \n",
       "2019-05-01T06:00:00Z       873713587\n",
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       "2019-05-01T08:00:00Z       852506841\n",
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       "2019-05-01T10:00:00Z       891598085"
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   "source": [
    "# Read Time series data with date as index and other column\n",
    "stldemo = pd.read_csv(\n",
    "    \"data/TimeSeriesDemo.csv\", index_col=[\"TimeGenerated\"], usecols=[\"TimeGenerated\",\"TotalBytesSent\"])\n",
    "stldemo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discover anomalies using timeseries_anomalies_stl function\n",
    "We will run msticpy function `timeseries_anomalies_stl` on the input data to discover anomalies."
   ]
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       "      <th></th>\n",
       "      <th>TimeGenerated</th>\n",
       "      <th>TotalBytesSent</th>\n",
       "      <th>residual</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>weights</th>\n",
       "      <th>baseline</th>\n",
       "      <th>score</th>\n",
       "      <th>anomalies</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>2019-05-01T06:00:00Z</td>\n",
       "      <td>873713587</td>\n",
       "      <td>-7258970</td>\n",
       "      <td>786685528</td>\n",
       "      <td>94287029</td>\n",
       "      <td>1</td>\n",
       "      <td>880972557</td>\n",
       "      <td>-0.097114</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-01T07:00:00Z</td>\n",
       "      <td>882187669</td>\n",
       "      <td>2291183</td>\n",
       "      <td>789268398</td>\n",
       "      <td>90628087</td>\n",
       "      <td>1</td>\n",
       "      <td>879896485</td>\n",
       "      <td>0.029661</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-01T08:00:00Z</td>\n",
       "      <td>852506841</td>\n",
       "      <td>-2875384</td>\n",
       "      <td>791851068</td>\n",
       "      <td>63531157</td>\n",
       "      <td>1</td>\n",
       "      <td>855382225</td>\n",
       "      <td>-0.038923</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-01T09:00:00Z</td>\n",
       "      <td>898793650</td>\n",
       "      <td>17934415</td>\n",
       "      <td>794432848</td>\n",
       "      <td>86426386</td>\n",
       "      <td>1</td>\n",
       "      <td>880859234</td>\n",
       "      <td>0.237320</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-01T10:00:00Z</td>\n",
       "      <td>891598085</td>\n",
       "      <td>8677706</td>\n",
       "      <td>797012590</td>\n",
       "      <td>85907788</td>\n",
       "      <td>1</td>\n",
       "      <td>882920378</td>\n",
       "      <td>0.114440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          TimeGenerated  TotalBytesSent  residual      trend  seasonal  \\\n",
       "0  2019-05-01T06:00:00Z       873713587  -7258970  786685528  94287029   \n",
       "1  2019-05-01T07:00:00Z       882187669   2291183  789268398  90628087   \n",
       "2  2019-05-01T08:00:00Z       852506841  -2875384  791851068  63531157   \n",
       "3  2019-05-01T09:00:00Z       898793650  17934415  794432848  86426386   \n",
       "4  2019-05-01T10:00:00Z       891598085   8677706  797012590  85907788   \n",
       "\n",
       "   weights   baseline     score  anomalies  \n",
       "0        1  880972557 -0.097114          0  \n",
       "1        1  879896485  0.029661          0  \n",
       "2        1  855382225 -0.038923          0  \n",
       "3        1  880859234  0.237320          0  \n",
       "4        1  882920378  0.114440          0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = timeseries_anomalies_stl(stldemo)\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying Anomalies using STL\n",
    "We will filter only the anomalies (with value 1 from anomalies column) of the output dataframe retrieved after running the msticpy function `timeseries_anomalies_stl`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T16:46:31.310294Z",
     "start_time": "2020-06-09T16:46:31.276153Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeGenerated</th>\n",
       "      <th>TotalBytesSent</th>\n",
       "      <th>residual</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>weights</th>\n",
       "      <th>baseline</th>\n",
       "      <th>score</th>\n",
       "      <th>anomalies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>2019-05-13T17:00:00Z</td>\n",
       "      <td>916767394</td>\n",
       "      <td>288355070</td>\n",
       "      <td>523626111</td>\n",
       "      <td>104786212</td>\n",
       "      <td>1</td>\n",
       "      <td>628412323</td>\n",
       "      <td>3.827062</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>2019-05-17T21:00:00Z</td>\n",
       "      <td>1555286702</td>\n",
       "      <td>296390627</td>\n",
       "      <td>1132354860</td>\n",
       "      <td>126541214</td>\n",
       "      <td>1</td>\n",
       "      <td>1258896074</td>\n",
       "      <td>3.933731</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2019-05-26T05:00:00Z</td>\n",
       "      <td>1768911488</td>\n",
       "      <td>347810809</td>\n",
       "      <td>1300005332</td>\n",
       "      <td>121095345</td>\n",
       "      <td>1</td>\n",
       "      <td>1421100678</td>\n",
       "      <td>4.616317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TimeGenerated  TotalBytesSent   residual       trend   seasonal  \\\n",
       "299  2019-05-13T17:00:00Z       916767394  288355070   523626111  104786212   \n",
       "399  2019-05-17T21:00:00Z      1555286702  296390627  1132354860  126541214   \n",
       "599  2019-05-26T05:00:00Z      1768911488  347810809  1300005332  121095345   \n",
       "\n",
       "     weights    baseline     score  anomalies  \n",
       "299        1   628412323  3.827062          1  \n",
       "399        1  1258896074  3.933731          1  \n",
       "599        1  1421100678  4.616317          1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output[output['anomalies']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read From External Sources\n",
    "If you have time series data in other locations, you can read it via pandas or respective data store API where data is stored. The pandas I/O API is a set of top level reader functions accessed like pandas.read_csv() that generally return a pandas object.\n",
    "\n",
    "Read More at Pandas Documentation:\n",
    "- [I/O Tools (Text ,CSV,HDF5..)](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)\n",
    "\n",
    "Example of using Pandas `read_csv` to read local csv file containing TimeSeries demo dataset. Additional columns in the csv such as `baseline`, `score` and `anoamlies` are generated using built-in KQL Time series functions such as `series_decompose_anomalies()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T16:46:33.369019Z",
     "start_time": "2020-06-09T16:46:33.326428Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeGenerated</th>\n",
       "      <th>TotalBytesSent</th>\n",
       "      <th>baseline</th>\n",
       "      <th>score</th>\n",
       "      <th>anomalies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-01 06:00:00</td>\n",
       "      <td>873713587</td>\n",
       "      <td>782728212</td>\n",
       "      <td>0.224776</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-01 07:00:00</td>\n",
       "      <td>882187669</td>\n",
       "      <td>838492449</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-01 08:00:00</td>\n",
       "      <td>852506841</td>\n",
       "      <td>816772273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-01 09:00:00</td>\n",
       "      <td>898793650</td>\n",
       "      <td>878871426</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-01 10:00:00</td>\n",
       "      <td>891598085</td>\n",
       "      <td>862639955</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "        TimeGenerated  TotalBytesSent   baseline     score  anomalies\n",
       "0 2019-05-01 06:00:00       873713587  782728212  0.224776          0\n",
       "1 2019-05-01 07:00:00       882187669  838492449  0.000000          0\n",
       "2 2019-05-01 08:00:00       852506841  816772273  0.000000          0\n",
       "3 2019-05-01 09:00:00       898793650  878871426  0.000000          0\n",
       "4 2019-05-01 10:00:00       891598085  862639955  0.000000          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseriesdemo = pd.read_csv(\n",
    "    \"data/TimeSeriesDemo.csv\", parse_dates=[\"TimeGenerated\"], infer_datetime_format=True\n",
    ")\n",
    "\n",
    "#show sample records\n",
    "timeseriesdemo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying Anomalies Separately\n",
    "We will filter only the anomalies shown in the above plot and display below along with associated aggreageted hourly timewindow. You can later query for the time windows scope for additional alerts triggered or any other suspicious activity from other datasources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T16:46:35.343635Z",
     "start_time": "2020-06-09T16:46:35.309202Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>index</th>\n",
       "      <th>TimeGenerated</th>\n",
       "      <th>TotalBytesSent</th>\n",
       "      <th>baseline</th>\n",
       "      <th>score</th>\n",
       "      <th>anomalies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>299</td>\n",
       "      <td>2019-05-13 17:00:00</td>\n",
       "      <td>916767394</td>\n",
       "      <td>662107538</td>\n",
       "      <td>3.247957</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>399</td>\n",
       "      <td>2019-05-17 21:00:00</td>\n",
       "      <td>1555286702</td>\n",
       "      <td>1212399509</td>\n",
       "      <td>4.877577</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>599</td>\n",
       "      <td>2019-05-26 05:00:00</td>\n",
       "      <td>1768911488</td>\n",
       "      <td>1391114419</td>\n",
       "      <td>5.522387</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index       TimeGenerated  TotalBytesSent    baseline     score  anomalies\n",
       "0    299 2019-05-13 17:00:00       916767394   662107538  3.247957          1\n",
       "1    399 2019-05-17 21:00:00      1555286702  1212399509  4.877577          1\n",
       "2    599 2019-05-26 05:00:00      1768911488  1391114419  5.522387          1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering anomales column for 1 and displaying records\n",
    "timeseriesdemo[timeseriesdemo['anomalies'] == 1].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying Time Series anomaly alerts\n",
    "\n",
    "You can also use `series_decompose_anomalies()` which will run Anomaly Detection based on series decomposition. This takes an expression containing a series (dynamic numerical array) as input and extract anomalous points with scores.\n",
    "\n",
    "***KQL Reference Documentation:***\n",
    "- [series_decompose_anomalies](https://docs.microsoft.com/azure/kusto/query/series-decompose-anomaliesfunction)\n",
    "\n",
    "You can use available query `qry_prov.MultiDataSource.get_timeseries_alerts()` to get the similar details\n",
    "```\n",
    "Query:  get_timeseries_anomalies\n",
    "Data source:  LogAnalytics\n",
    "Time Series anomalies detected using built-in KQL time series function-series_decompose_anomalies\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "add_query_items: str (optional)\n",
    "    Additional query clauses\n",
    "aggregatecolumn: str (optional)\n",
    "    field to agregate from source dataset\n",
    "    (default value is: Total)\n",
    "aggregatefunction: str (optional)\n",
    "    Aggregation functions to use - count(), sum(), avg() etc\n",
    "    (default value is: count())\n",
    "end: datetime\n",
    "    Query end time\n",
    "groupbycolumn: str (optional)\n",
    "    Group by field to aggregate results\n",
    "    (default value is: Type)\n",
    "scorethreshold: str (optional)\n",
    "    Score threshold for alerting\n",
    "    (default value is: 3)\n",
    "start: datetime\n",
    "    Query start time\n",
    "table: str\n",
    "    Table name\n",
    "timeframe: str (optional)\n",
    "    Aggregation TimeFrame\n",
    "    (default value is: 1h)\n",
    "timestampcolumn: str (optional)\n",
    "    Timestamp field to use from source dataset\n",
    "    (default value is: TimeGenerated)\n",
    "where_clause: str (optional)\n",
    "    Optional additional filter clauses\n",
    "Query:\n",
    " {table} {where_clause} | project {timestampcolumn},{aggregatecolumn},{groupbycolumn} | where {timestampcolumn} >= datetime({start}) | where {timestampcolumn} <= datetime({end}) | make-series {aggregatecolumn}={aggregatefunction} on {timestampcolumn} from datetime({start}) to datetime({end}) step {timeframe} by {groupbycolumn} | extend (anomalies, score, baseline) = series_decompose_anomalies({aggregatecolumn}, {scorethreshold},-1,\"linefit\") | mv-expand {aggregatecolumn} to typeof(double), {timestampcolumn} to typeof(datetime), anomalies to typeof(double), score to typeof(double), baseline to typeof(long) | extend score = round(score,2) {add_query_items}\n",
    "```\n",
    "\n",
    "***Sample python code leveraging KQL query will look like this***\n",
    "\n",
    "```python\n",
    "\n",
    "# Specify start and end timestamps\n",
    "start = \"2020-02-09 00:00:00.000000\"\n",
    "end = \"2020-03-10 00:00:00.000000\"\n",
    "\n",
    "# Execute the query by passing required and optional parameters. you can also add add_query_items='|where anomalies>0' to filter and show only anomalies, else it will show all records.\n",
    "time_series_anomalies = qry_prov.MultiDataSource.get_timeseries_anomalies(\n",
    "    start=start,\n",
    "    end=end,\n",
    "    table=\"CommonSecurityLog\",\n",
    "    timestampcolumn=\"TimeGenerated\",\n",
    "    aggregatecolumn=\"SentBytes\",\n",
    "    groupbycolumn=\"DeviceVendor\",\n",
    "    aggregatefunction=\"sum(SentBytes)\",\n",
    "    where_clause='|where DeviceVendor==\"Palo Alto Networks\"',\n",
    "    scorethreshold='1.5',\n",
    "    )\n",
    "\n",
    "#display output\n",
    "time_series_anomalies\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Anomalies Visualization\n",
    "Time series anomalies once discovered, you can visualize with line chart type to display outliers.\n",
    "Below we will see 2 types to visualize, using msticpy function `display_timeseries_anomalies()` via Bokeh library as well as using built-in KQL `render` operator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Bokeh Visualization Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-12T07:44:03.559172Z",
     "start_time": "2020-03-12T07:44:03.545051Z"
    }
   },
   "source": [
    "## Documentation for display_timeseries_anomalies\n",
    "```\n",
    "display_timeseries_anomolies(\n",
    "    data: pandas.core.frame.DataFrame,\n",
    "    y: str = 'Total',\n",
    "    time_column: str = 'TimeGenerated',\n",
    "    anomalies_column: str = 'anomalies',\n",
    "    source_columns: list = None,\n",
    "    period: int = 30,\n",
    "    **kwargs,\n",
    ") -> <function figure at 0x7f0de9ae2598>\n",
    "Docstring:\n",
    "Display time series anomalies visualization\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "data : pd.DataFrame\n",
    "    DataFrame as a time series data set retreived from KQL time series functions\n",
    "    dataframe will have columns as TimeGenerated, y, baseline, score, anomalies\n",
    "\n",
    "Other Parameters\n",
    "----------------\n",
    "y : str, optional\n",
    "    Name of column holding numeric values to plot against time series to determine anomolies\n",
    "    (the default is 'Total')\n",
    "time_column : str, optional\n",
    "    Name of the timestamp column\n",
    "    (the default is 'TimeGenerated')\n",
    "anomolies_column : str, optional\n",
    "    Name of the column holding binary status(1/0) for anomaly/benign\n",
    "    (the default is 'anomolies')\n",
    "ref_time : datetime, optional\n",
    "    Input reference line to display (the default is None)\n",
    "title : str, optional\n",
    "    Title to display (the default is None)\n",
    "legend: str, optional\n",
    "    Where to position the legend\n",
    "    None, left, right or inline (default is None)\n",
    "yaxis : bool, optional\n",
    "    Whether to show the yaxis and labels\n",
    "range_tool : bool, optional\n",
    "    Show the the range slider tool (default is True)\n",
    "source_columns : list, optional\n",
    "    List of default source columns to use in tooltips\n",
    "    (the default is None)\n",
    "height : int, optional\n",
    "    The height of the plot figure\n",
    "    (the default is auto-calculated height)\n",
    "width : int, optional\n",
    "    The width of the plot figure (the default is 900)\n",
    "xgrid : bool, optional\n",
    "        Whether to show the xaxis grid (default is True)\n",
    "ygrid : bool, optional\n",
    "        Whether to show the yaxis grid (default is False)\n",
    "color : list, optional\n",
    "        List of colors to use in 3 plots as specified in order \n",
    "        3 plots- line(observed), circle(baseline), circle_x/user specified(anomalies).\n",
    "        (the default is [\"navy\", \"green\", \"firebrick\"])\n",
    "\n",
    "Returns\n",
    "-------\n",
    "figure\n",
    "    The bokeh plot figure.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T16:46:40.785086Z",
     "start_time": "2020-06-09T16:46:40.628060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\": \"JpP8FXbgAZLkfur7LiK3j9AGBhHNIvF742meBJrjO2ShJDhCG2I1uVvW+0DUtrmc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\": \"xZlADit0Q04ISQEdKg2k3L4W9AwQBAuDs9nJL9fM/WwzL1tEU9VPNezOFX0nLEAz\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\": \"4BuPRZkdMKSnj3zoxiNrQ86XgNw0rYmBOxe7nshquXwwcauupgBF2DHLVG1WuZlV\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\": \"Dv1SQ87hmDqK6S5OhBf0bCuwAEvL5QYL0PuR/F1SPVhCS/r/abjkbpKDYL2zeM19\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\": \"JpP8FXbgAZLkfur7LiK3j9AGBhHNIvF742meBJrjO2ShJDhCG2I1uVvW+0DUtrmc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\": \"xZlADit0Q04ISQEdKg2k3L4W9AwQBAuDs9nJL9fM/WwzL1tEU9VPNezOFX0nLEAz\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\": \"4BuPRZkdMKSnj3zoxiNrQ86XgNw0rYmBOxe7nshquXwwcauupgBF2DHLVG1WuZlV\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\": \"Dv1SQ87hmDqK6S5OhBf0bCuwAEvL5QYL0PuR/F1SPVhCS/r/abjkbpKDYL2zeM19\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"cbb2b4b8-7f1b-40c4-b7a4-2312b538e373\" data-root-id=\"1142\"></div>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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the middle or edges of the selection box to change the range in the main chart\",\"text_font_size\":{\"value\":\"10px\"}},\"id\":\"1128\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1119\",\"type\":\"LinearScale\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"1056\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"1121\",\"type\":\"LinearScale\"},{\"attributes\":{\"label\":{\"value\":\"anomalies\"},\"renderers\":[{\"id\":\"1091\"}]},\"id\":\"1111\",\"type\":\"LegendItem\"},{\"attributes\":{\"source\":{\"id\":\"1002\"}},\"id\":\"1044\",\"type\":\"CDSView\"}],\"root_ids\":[\"1142\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.1\"}};\n",
       "  var render_items = [{\"docid\":\"510ea36a-1825-4b95-885b-3ea6e271064e\",\"root_ids\":[\"1142\"],\"roots\":{\"1142\":\"cbb2b4b8-7f1b-40c4-b7a4-2312b538e373\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1142"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeseries_anomalies_plot = display_timeseries_anomolies(data=timeseriesdemo, y=\"TotalBytesSent\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting Plots as PNGs\n",
    "To use bokeh.io image export functions you need selenium, phantomjs and pillow installed:\n",
    "\n",
    "`conda install -c bokeh selenium phantomjs pillow`\n",
    "\n",
    "or\n",
    "\n",
    "`pip install selenium pillow`\n",
    "`npm install -g phantomjs-prebuilt`\n",
    "\n",
    "For phantomjs see https://phantomjs.org/download.html.\n",
    "\n",
    "Once the prerequisites are installed you can create a plot and save the return value to a variable. \n",
    "Then export the plot using `export_png` function.\n",
    "\n",
    "***Sample code to export png***\n",
    "\n",
    "```python\n",
    "from bokeh.io import export_png\n",
    "from IPython.display import Image\n",
    "\n",
    "# Create a plot\n",
    "timeseries_anomaly_plot = display_timeseries_anomolies(\n",
    "    data=timeseriesdemo, y=\"TotalBytesSent\"\n",
    ")\n",
    "\n",
    "# Export\n",
    "file_name = \"plot.png\"\n",
    "export_png(timeseries_anomaly_plot, filename=file_name)\n",
    "\n",
    "# Read it and show it\n",
    "display(Markdown(f\"## Here is our saved plot: {file_name}\"))\n",
    "Image(filename=file_name)\n",
    "```"
   ]
  },
  {
   "attachments": {
    "TimeSeriesKQLPlotly.PNG": {
     "image/png": 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    "## Using Built-in KQL render operator\n",
    "Render operator instructs the user agent to render the results of the query in a particular way. In this case, we are using timechart which will display linegraph.\n",
    "\n",
    "***KQL Reference Documentation:***\n",
    "- [render](https://docs.microsoft.com/azure/kusto/query/renderoperator?pivots=azuremonitor)\n",
    "\n",
    "***sample python code with KQL query leveraging render operator on time series data will look like below***\n",
    "```python\n",
    "timechartquery = \"\"\"\n",
    "let TimeSeriesData = PaloAltoTimeSeriesDemo_CL\n",
    "| extend TimeGenerated = todatetime(EventTime_s), TotalBytesSent = todouble(TotalBytesSent_s) \n",
    "| summarize TimeGenerated=make_list(TimeGenerated, 10000),TotalBytesSent=make_list(TotalBytesSent, 10000) by deviceVendor_s\n",
    "| project TimeGenerated, TotalBytesSent;\n",
    "TimeSeriesData\n",
    "| extend (baseline,seasonal,trend,residual) = series_decompose(TotalBytesSent)\n",
    "| mv-expand TotalBytesSent to typeof(double), TimeGenerated to typeof(datetime), baseline to typeof(long), seasonal to typeof(long), trend to typeof(long), residual to typeof(long)\n",
    "| project TimeGenerated, TotalBytesSent, baseline\n",
    "| render timechart with (title=\"Palo Alto Outbound Data Transfer Time Series decomposition\")\n",
    "\"\"\"\n",
    "%kql -query timechartquery\n",
    "```\n",
    "\n",
    "***Rendered output for the above code look like below image***\n",
    "\n",
    "![TimeSeriesKQLPlotly.PNG](attachment:TimeSeriesKQLPlotly.PNG)"
   ]
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