tslumen.report.html package

tslumen.report.html.report module

Module with the main class HtmlReport.

class tslumen.report.html.report.HtmlReport(df: pandas.core.frame.DataFrame, meta: Optional[dict] = None, result: Optional[tslumen.profile.base.BundledResult] = None, profiler: Optional[tslumen.profile.base.BundledProfiler] = None, profiler_config: Optional[dict] = None, scheduler: Optional[tslumen.scheduling.Scheduler] = None, scheduler_config: Optional[dict] = None)

Bases: tslumen.report.base.Report, tslumen.report.html.base.HtmlBlock

Renders the profiling results as an interactive, fully self-contained HTML report that can be downloaded and shared without the need for a running server or Python kernel.

Parameters

• df (pd.DataFrame) – Timeseries data.

• meta (Optional[dict]) – Timeseries metadata, a 2-level dictionary, first level indexed by {'frame': {<key>: <value>}, {'series': {<series name>: <desc>}}.

• result (Optional[BundledResult]) – For instantiating the report with pre-computed results from a profiler.

• profiler (Optional[BundledProfiler]) – The BundledProfiler to run the profiling, defaults to DefaultProfiler.

• profiler_config (Optional[dict]) – Profiler’s configurations.

• scheduler (Optional[Scheduler]) – A Scheduler, default’s to Scheduler.

• scheduler_config (Optional[dict]) – Scheduler’s configurations.

SECTIONS = [<class 'tslumen.report.html.sections.SectionSummary'>, <class 'tslumen.report.html.sections.SectionTimeSeries'>, <class 'tslumen.report.html.sections.SectionTSFeatures'>, <class 'tslumen.report.html.sections.SectionRelations'>]

duration: Optional[datetime.timedelta]

property html

Lazy loading property with the HTML representation of the report.

save(path_or_buffer: Optional[Union[str, io.TextIOBase]] = None, mode: str = 'w', encoding: Optional[str] = None) → Optional[str]

Save rendered html to disk

sections: List[tslumen.report.html.base.HtmlBlock]

tslumen.report.html.sections module

Module with the sections that go into HtmlReport.

class tslumen.report.html.sections.SectionRelations(result: tslumen.profile.base.BundledResult, meta: dict, df: pandas.core.frame.DataFrame, scheduler: Optional[tslumen.scheduling.Scheduler] = None)

Bases: tslumen.report.html.base.HtmlBlock

Class holding the contents of the “Correlations” section

class tslumen.report.html.sections.SectionSummary(result: tslumen.profile.base.BundledResult, meta: dict, df: pandas.core.frame.DataFrame, scheduler: Optional[tslumen.scheduling.Scheduler] = None)

Bases: tslumen.report.html.base.HtmlBlock

Class holding the contents of the “Summary” section

class tslumen.report.html.sections.SectionTSFeatures(result: tslumen.profile.base.BundledResult, meta: dict, df: pandas.core.frame.DataFrame, scheduler: Optional[tslumen.scheduling.Scheduler] = None)

Bases: tslumen.report.html.base.HtmlBlock

Class holding the contents of the “Features” section

class tslumen.report.html.sections.SectionTimeSeries(result: tslumen.profile.base.BundledResult, meta: dict, df: pandas.core.frame.DataFrame, scheduler: Optional[tslumen.scheduling.Scheduler] = None)

Bases: tslumen.report.html.base.HtmlBlock

Class representing the Time Series section

property html

Returns: str: Class representation as a HTML block, as rendered by Jinja.

class tslumen.report.html.sections.SubTimeSeries(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Class holding the content for each time series in the “Time Series” section

class tslumen.report.html.sections.TabTSAutoCorrelation(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Time Series tab: Auto Correlation

class tslumen.report.html.sections.TabTSComponents(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Time Series tab: Components

class tslumen.report.html.sections.TabTSDistribution(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Time Series tab: Distribution

class tslumen.report.html.sections.TabTSFeatures(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Time Series tab: Features

class tslumen.report.html.sections.TabTSLagPlots(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Time Series tab: Lag Plots

class tslumen.report.html.sections.TabTSSeasonality(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Time Series tab: Seasonality

class tslumen.report.html.sections.TabTSSmoothing(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Time Series tab: Smoothing

class tslumen.report.html.sections.TabTSStatistics(name: str, result: Dict[str, Any], ser: pandas.core.series.Series)

Bases: tslumen.report.html.base.HtmlBlock

Time Series tab: Statistics

tslumen.report.html.base module

Base classes for building the HTML report.

class tslumen.report.html.base.HtmlBlock

Bases: object

Basic HTML building block.

property html

Returns

Class representation as a HTML block, as rendered by Jinja.

Return type

str

property html_page

Returns

Class representation as a full HTML page.

Return type

str