chaco.abstract_data_source module

Defines the AbstractDataSource class.

class chaco.abstract_data_source.AbstractDataSource

Bases: traits.has_traits.HasTraits

This abstract interface must be implemented by any class supplying data to Chaco.

Chaco does not have a notion of a “data format”. For the most part, a data source looks like an array of values with an optional mask and metadata. If you implement this interface, you are responsible for adapting your domain-specific or application-specific data to meet this interface.

Chaco provides some basic data source implementations. In most cases, the easiest strategy is to create one of these basic data source with the numeric data from a domain model. In cases when this strategy is not possible, domain classes (or an adapter) must implement AbstractDataSource.

bounds_changed = Event

Event that fires when just the bounds change.

data_changed = Event

Event that fires when the data values change.

get_bounds()

Returns a tuple (min, max) of the bounding values for the data source. In the case of 2-D data, min and max are 2-D points that represent the bounding corners of a rectangle enclosing the data set. Note that these values are not view-dependent, but represent intrinsic properties of the data source.

If data is the empty set, then the min and max vals are 0.0.

get_data() → data_array

Returns a data array of the dimensions of the data source. This data array must not be altered in-place, and the caller must assume it is read-only. This data is contiguous and not masked.

In the case of structured (gridded) 2-D data, this method may return two 1-D ArrayDataSources as an optimization.

get_data_mask()

Returns the full, raw, source data array and a corresponding binary mask array. Treat both arrays as read-only.

The mask is a superposition of the masks of all upstream data sources. The length of the returned array may be much larger than what get_size() returns; the unmasked portion, however, matches what get_size() returns.

get_size() → int

Returns an integer estimate or the exact size of the dataset that get_data() returns for this object. This method is useful for down-sampling.

index_dimension = DimensionTrait(transient=True)

The dimensionality of the indices into this data source. Subclasses re-declare this trait as a read-only trait with the right default value.

is_masked() → bool

Returns True if this data source’s data uses a mask. In this case, to retrieve the data, call get_data_mask() instead of get_data(). If you call get_data() for this data source, it returns data, but that data might not be the expected data.

metadata = Dict

A dictionary keyed on strings. In general, it maps to indices (or tuples of indices, depending on value_dimension), as in the case of selections and annotations. Applications and renderers can add their own custom metadata, but must avoid using keys that might result in name collision.

metadata_changed = Event

Event that fires when metadata structure is changed.

persist_data = Bool(True, transient=True)

Should the data that this datasource refers to be serialized when the datasource is serialized?

value_dimension = DimensionTrait(transient=True)

The dimensionality of the value at each index point. Subclasses re-declare this trait as a read-only trait with the right default value.