TimeSeriesPlotExplorer
Plot one or more numeric columns against a date column, over time.
Select the date column plus the series to look at. The dates are read with the format the column already carries, sorted, and handed to the plot as real datetimes, so the horizontal axis is a genuine time axis: gaps show up as gaps and irregular spacing is visible rather than flattened into evenly spaced categories.
This is the plot to look at before forecasting anything, since trend, seasonality, level shifts, missing stretches and outliers are all obvious here and nearly invisible in a summary table.
Several numeric columns can be selected at once and are drawn as separate lines sharing the time axis.
Parameters
- markers : boolean, default=
False - Draw a point at each observation as well as the line. Useful for short or irregular series, where the line alone hides how many readings there actually are.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> List[DashAI.back.core.artifacts.Artifact]
TimeSeriesPlotExplorerLoad and return the saved figure for the frontend.
Parameters
- exploration_path : str
- Path to the JSON file saved by
save_notebook. - options : Dict[str, Any]
- Rendering options from the frontend (unused).
Returns
- List[Artifact]
- A single-element list with the plotly artifact of the saved figure.
launch_exploration(self, dataset: 'DashAIDataset', explorer_info: DashAI.back.dependencies.database.models.Explorer)
TimeSeriesPlotExplorerDraw the selected series against the selected date column.
Parameters
- dataset : DashAIDataset
- The prepared dataset holding the selected columns.
- explorer_info : Explorer
- Explorer record with the column names and optional display name.
Returns
- plotly.graph_objects.Figure
- An interactive line plot with a time axis.
save_notebook(self, notebook_info: DashAI.back.dependencies.database.models.Notebook, explorer_info: DashAI.back.dependencies.database.models.Explorer, save_path: 'Path', result: Any) -> str
TimeSeriesPlotExplorerSave the figure to disk (JSON content, .pickle extension).
Parameters
- notebook_info : Notebook
- The notebook database record (unused).
- explorer_info : Explorer
- The explorer record used for filename generation.
- save_path : Path
- Directory where the file will be saved.
- result : Any
- The Plotly figure returned by
launch_exploration.
Returns
- str
- The path of the saved file as a POSIX string.
validate_columns(cls, explorer_info: DashAI.back.dependencies.database.models.Explorer, column_spec: Dict[str, Dict[str, str]]) -> bool
TimeSeriesPlotExplorerCheck the selection is one date column plus at least one series.
Parameters
- explorer_info : Explorer
- The database record for the explorer instance, including the selected columns.
- column_spec : Dict[str, Dict[str, str]]
- A mapping from column name to a dict with at least
"type"and"dtype".
Returns
- bool
- True if the selection holds exactly one Date column and at least one numeric column, and the inherited checks also pass.
get_credential(self, name: str)
ConfigObjectResolve a registered credential component by name.
Parameters
- name : str
- Credential component class name (e.g. "HuggingFaceCredential").
Returns
- BaseCredential
- An instance of the requested credential component.
get_metadata(cls) -> Dict[str, Any]
BaseExplorerGet metadata for the explorer, used by the DashAI frontend.
Returns
- Dict[str, Any]
- Dictionary containing display name, description, image preview path, category, icon, color, allowed semantic types, allowed dtypes, and input cardinality constraints.
get_schema(cls) -> dict
ConfigObjectGenerates the component related Json Schema.
Returns
- dict
- Dictionary representing the Json Schema of the component.
prepare_dataset(self, loaded_dataset: 'DashAIDataset', columns: List[Dict[str, Any]]) -> 'DashAIDataset'
BaseExplorerPrepare the dataset by selecting only the columns needed for this exploration.
Parameters
- loaded_dataset : DashAIDataset
- The full dataset loaded from storage.
- columns : List[Dict[str, Any]]
- List of column descriptor dicts, each containing at least
"columnName". Optional keys:"id","valueType","dataType".
Returns
- DashAIDataset
- Dataset restricted to the requested columns.
validate_and_transform(self, raw_data: dict) -> dict
ConfigObjectIt takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.
Parameters
- raw_data : dict
- A dictionary with the data provided by the user to initialize the model.
Returns
- dict
- A validated dictionary with the necessary objects.
validate_parameters(cls, params: Dict[str, Any]) -> bool
BaseExplorerValidate explorer parameters against the explorer's schema.
Parameters
- params : Dict[str, Any]
- The configuration parameters to validate.
Returns
- BaseExplorerSchema
- The validated and parsed schema instance. Subclasses that override this method may return a bool to indicate pass/fail without returning the model instance.