Plotly.kql

Plotly.kql

#connect cluster('adxpm10774.eastus.kusto.windows.net').database('IoTAnalytics')

.set-or-replace PlotlyTemplate <| cluster('help.kusto.windows.net').database('Samples').PlotlyTemplate

PlotlyTemplate


.create-or-alter function with (folder = "Packages\\Plotly", docstring = "Render 3D scatter chart using plotly template")
plotly_scatter3d_fl(tbl:(*), x_col:string, y_col:string, z_col:string, aggr_col:string='', chart_title:string='3D Scatter chart')
{
    let scatter3d_chart = toscalar(PlotlyTemplate | where name == "scatter3d" | project plotly);
    let tbl_ex = tbl | extend _x = column_ifexists(x_col, 0.0), _y = column_ifexists(y_col, 0.0), _z = column_ifexists(z_col, 0.0), _aggr = column_ifexists(aggr_col, 'ALL');
    tbl_ex
    | serialize 
    | summarize _x=pack_array(make_list(_x)), _y=pack_array(make_list(_y)), _z=pack_array(make_list(_z)) by _aggr
    | summarize _aggr=make_list(_aggr), _x=make_list(_x), _y=make_list(_y), _z=make_list(_z)
    | extend plotly = scatter3d_chart
    | extend plotly=replace_string(plotly, '$CLASS1$', tostring(_aggr[0]))
    | extend plotly=replace_string(plotly, '$CLASS2$', tostring(_aggr[1]))
    | extend plotly=replace_string(plotly, '$CLASS3$', tostring(_aggr[2]))
    | extend plotly=replace_string(plotly, '$X_NAME$', x_col)
    | extend plotly=replace_string(plotly, '$Y_NAME$', y_col)
    | extend plotly=replace_string(plotly, '$Z_NAME$', z_col)
    | extend plotly=replace_string(plotly, '$CLASS1_X$', tostring(_x[0]))
    | extend plotly=replace_string(plotly, '$CLASS1_Y$', tostring(_y[0]))
    | extend plotly=replace_string(plotly, '$CLASS1_Z$', tostring(_z[0]))
    | extend plotly=replace_string(plotly, '$CLASS2_X$', tostring(_x[1]))
    | extend plotly=replace_string(plotly, '$CLASS2_Y$', tostring(_y[1]))
    | extend plotly=replace_string(plotly, '$CLASS2_Z$', tostring(_z[1]))
    | extend plotly=replace_string(plotly, '$CLASS3_X$', tostring(_x[2]))
    | extend plotly=replace_string(plotly, '$CLASS3_Y$', tostring(_y[2]))
    | extend plotly=replace_string(plotly, '$CLASS3_Z$', tostring(_z[2]))
    | extend plotly=replace_string(plotly, '$TITLE$', chart_title)
    | project plotly
}


.set-or-replace  Iris <| cluster('help.kusto.windows.net').database('Samples').Iris

Iris
| invoke plotly_scatter3d_fl(x_col='SepalLength', y_col='PetalLength', z_col='SepalWidth', aggr_col='Class', chart_title='3D scatter chart using plotly_scatter3d_fl()')


Iris
| evaluate python(typeof(plotly:string),
```if 1:
    import plotly.express as px
    fig = px.scatter_3d(df, x='SepalLength', y='SepalWidth', z='PetalLength', color='Class')
    fig.update_layout(title=dict(text='3D scatter chart using Plotly'))
    plotly_obj = fig.to_json()
    result = pd.DataFrame(data = [plotly_obj], columns = ['plotly'])
```)

.create-or-alter function with (folder = "Packages\\Plotly", docstring = "Render anomaly chart using plotly template")
plotly_anomaly_fl(tbl:(*), time_col:string, val_col:string, baseline_col:string, time_high_col:string , val_high_col:string, time_low_col:string , val_low_col:string,
                                chart_title:string='Anomaly chart', series_name:string='Metric', val_name:string='Value')
{
    let anomaly_chart = toscalar(PlotlyTemplate | where name == "anomaly" | project plotly);
    let tbl_ex = tbl | extend _timestamp = column_ifexists(time_col, datetime(null)), _values = column_ifexists(val_col, 0.0), _baseline = column_ifexists(val_col, 0.0),
                              _high_timestamp = column_ifexists(time_high_col, datetime(null)), _high_values = column_ifexists(val_high_col, 0.0),
                              _low_timestamp = column_ifexists(time_low_col, datetime(null)), _low_values = column_ifexists(val_low_col, 0.0);
    tbl_ex
    | extend plotly = anomaly_chart
    | extend plotly=replace_string(plotly, '$TIME_STAMPS$', tostring(_timestamp))
    | extend plotly=replace_string(plotly, '$SERIES_VALS$', tostring(_values))
    | extend plotly=replace_string(plotly, '$BASELINE_VALS$', tostring(_baseline))
    | extend plotly=replace_string(plotly, '$TIME_STAMPS_HIGH_ANOMALIES$', tostring(_high_timestamp))
    | extend plotly=replace_string(plotly, '$HIGH_ANOMALIES_VALS$', tostring(_high_values))
    | extend plotly=replace_string(plotly, '$TIME_STAMPS_LOW_ANOMALIES$', tostring(_low_timestamp))
    | extend plotly=replace_string(plotly, '$LOW_ANOMALIES_VALS$', tostring(_low_values))
    | extend plotly=replace_string(plotly, '$TITLE$', chart_title)
    | extend plotly=replace_string(plotly, '$SERIES_NAME$', series_name)
    | extend plotly=replace_string(plotly, '$Y_NAME$', val_name)
    | project plotly
}


.set demo_make_series2 <| cluster('help.kusto.windows.net').database('Samples').demo_make_series2


let min_t = datetime(2017-01-05);
let max_t = datetime(2017-02-03 22:00);
let dt = 2h;
let marker_scale = 8;
let s_name = 'TS1';
demo_make_series2
| make-series num=avg(num) on TimeStamp from min_t to max_t step dt by sid
| where sid == s_name
| extend (anomalies, score, baseline) = series_decompose_anomalies(num, 1.5, -1, 'linefit')
| mv-apply num1=num to typeof(double), anomalies1=anomalies to typeof(double), score1=score to typeof(double), TimeStamp1=TimeStamp to typeof(datetime)  on (
    summarize pAnomalies=make_list_if(num1, anomalies1 > 0), pTimeStamp=make_list_if(TimeStamp1, anomalies1 > 0), pSize=make_list_if(toint(score1*marker_scale), anomalies1 > 0),
              nAnomalies=make_list_if(num1, anomalies1 < 0), nTimeStamp=make_list_if(TimeStamp1, anomalies1 < 0), nSize=make_list_if(toint(-score1*marker_scale), anomalies1 < 0)
)
| invoke plotly_anomaly_fl('TimeStamp', 'num', 'baseline', 'pTimeStamp', 'pAnomalies', 'nTimeStamp', 'nAnomalies',
                           chart_title='Anomaly chart using plotly_anomaly_fl()', series_name=s_name, y_name='# of requests')