DensityHeatmapExplorer
Explorer that visualises the joint distribution of two columns as a 2-D heatmap.
The explorer partitions the value range of each selected column into a regular grid of rectangular bins and colours each cell according to the count of data points that fall inside it. Darker or warmer colours (depending on the colour scale) indicate regions of higher data density, making it easy to identify modes, concentration areas, and gaps in the joint distribution of the two variables.
This visualisation is especially useful when there are too many data points for a scatter plot to remain legible. It provides a non-parametric estimate of the joint density and reveals whether the relationship between the two columns is concentrated around a single peak, multimodal, or approximately uniform.
Exactly two columns must be selected: the first maps to the x-axis and the second to the y-axis.
Parameters
- nbinsx, default=
None - Number of bins along the x axis.
- nbinsy, default=
None - Number of bins along the y axis.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
DensityHeatmapExplorerLoad and return the saved density heatmap 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
- Dict[str, Any]
- Dictionary with keys
"data"(JSON-serialized Plotly figure),"type"("plotly_json"), and"config"(empty dict).
launch_exploration(self, dataset: 'DashAIDataset', explorer_info: DashAI.back.dependencies.database.models.Explorer)
DensityHeatmapExplorerGenerate a Plotly density heatmap for two selected columns.
Parameters
- dataset : DashAIDataset
- The prepared dataset with exactly two columns.
- explorer_info : Explorer
- Explorer record with column names and optional display name.
Returns
- plotly.graph_objects.Figure
- An interactive density heatmap figure.
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
DensityHeatmapExplorerSave the density heatmap figure to a JSON file on disk.
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 JSON file as a POSIX string.
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 dtypes, restricted 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_columns(cls, explorer_info: DashAI.back.dependencies.database.models.Explorer, column_spec: Dict[str, Dict[str, str]]) -> bool
BaseExplorerValidate that the selected columns satisfy the explorer's constraints.
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 a
"type"key describing the column's data type.
Returns
- bool
- True if all column constraints are satisfied, False otherwise.
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.