# Visualization¶

Module of visualization.

adtk.visualization.plot(ts=None, anomaly=None, curve_group='each', ts_linewidth=0.5, ts_color=None, ts_alpha=1.0, ts_marker='.', ts_markersize=2, match_curve_name=True, anomaly_tag='span', anomaly_color=None, anomaly_alpha=0.3, anomaly_marker='o', anomaly_markersize=4, freq_as_period=True, axes=None, figsize=None, legend=True)[source]

Plot time series and/or anomalies.

Parameters
• ts (pandas Series or DataFrame, optional) – Time series to plot.

• anomaly (list, pandas Series, DataFrame, or (nested) dict of them, optional) –

Anomalies to plot.

• If list, a list of anomalous events (pandas Timestamp for an instantaneous event or 2-tuple of pandas Timestamps for an interval);

• If pandas Series, a binary series indicating normal/anomalous;

• If pandas DataFrame, each column is treated independently as a binary Series.

• If (nested) dict, every leaf node (list, Series, or DataFrame) is treated independently as above.

• curve_group (str or list, optional) –

Groups of curves to be drawn at same plots.

• If str, ‘each’ means every dimension is drawn in a separated plot, ‘all’ means all dimensions are drawn in the same plot.

• If list, each element corresponds to a subplot, which is the name of time series to plot in this subplot, or a list of names. For example, [“A”, (“B”, “C”)] means two subplots, where the first one contain series A, while the second one contains series B and C.

Default: ‘each’.

• ts_linewidth (float or dict, optional) –

Line width of each time series curve.

• If float, all curves have the same line width.

• If dict, the key is series name, the value is line width of that series.

Default: 0.5.

• ts_color (str or dict, optional) –

Color of each time series curve.

• If str, all curves have the same color.

• If dict, the key is series name, the value is color of that series.

• If None, color will be assigned automatically.

Default: None.

• ts_alpha (float or dict, optional) –

Opacity of each time series curve.

• If float, all curves have the same opacity.

• If dict, the key is series name, the value is opacity of that series.

Default: 1.0.

• ts_marker (str or dict, optional) –

Marker type of each time series curve.

• If str, all curves have the same marker type.

• If dict, the key is series name, the value is marker type of that series.

Default: “.”.

• ts_markersize (int or dict, optional) –

Marker size of each time series curve.

• If int, all curves have the same marker size.

• If dict, the key is series name, the value is marker size of that series.

Default: 2.

• match_curve_name (bool, optional) – Whether to plot anomaly with corresponding curve by matching series names. If False, plot anomaly with all curves. Default: True.

• anomaly_tag (str, or (nested) dict, optional) –

Plot anomaly as horizontal spans or markers on curves.

• If str, either ‘span’ or ‘marker’, all anomalies are marked with the same type of tag.

• If (nested) dict, it must have a tree structure identical to or smaller than that of (nested) dict argument anomaly, which can define tags for all leaf nodes in anomaly.

Default: “span”.

• anomaly_color (str, or (nested) dict, optional) –

Color of each anomaly tag.

• If str, all anomalies are marked with the same color.

• If (nested) dict, it must have a tree structure identical to or smaller than that of (nested) dict argument anomaly, which can define colors for all leaf nodes in anomaly.

• If None, color will be assigned automatically.

Default: None.

• anomaly_alpha (float, or (nested) dict, optional) –

Opacity of each anomaly tag. Only used for anomaly drawn as horizontal spans.

• If float, all anomalies are marked with the same opacity.

• If (nested) dict, it must have a tree structure identical to or smaller than that of (nested) dict argument anomaly, which can define opacity for all leaf nodes in anomaly.

Default: 0.3.

• anomaly_marker (str, or (nested) dict, optional) –

Marker type of each anomaly marker. Only used for anomaly drawn as markers on curves.

• If str, all anomalies are marked with the same type of marker.

• If (nested) dict, it must have a tree structure identical to or smaller than that of (nested) dict argument anomaly, which can define marker types for all leaf nodes in anomaly.

Default: “o”.

• anomaly_markersize (int, or (nested) dict, optional) –

Marker size of each anomaly marker. Only used for anomaly drawn as markers on curves.

• If int, all anomalies are marked with the same size of marker.

• If (nested) dict, it must have a tree structure identical to or smaller than that of (nested) dict argument anomaly, which can define marker sizes for all leaf nodes in anomaly.

Default: 4.

• freq_as_period (bool, optional) –

Whether to regard time index with regular frequency (i.e. attribute freq of time index is not None) as time intervals. Only used when anomaly is given as binary series.

For example, DatetimeIndex([‘2017-01-01’, ‘2017-01-02’, ‘2017-01-03’, ‘2017-01-04’, ‘2017-01-05’], dtype=’datetime64[ns]’, freq=’D’) has daily frequency. If freq_as_period=True, each time point in the index represents that day (24 hours). Otherwsie, each time point represents the instantaneous time instance of 00:00:00 on that day.

Default: True.

• axes (matplotlib Axes object, or array of Axes objects, optional) – Axes to plot at. The number of Axes objects should be equal to the number of plots. If not specified, figure axes will be automatically generated. Default: None.

• figsize (tuple, optional) – Size of the figure. If not specified, the size of each subplot is 16 x 4. Default: None.

• legend (bool, optional) – Whether to show legend in the plot. Default: True.

Returns

Axes where the plot(s) is drawn.

Return type

matplotlib Axes object or array of Axes objects