chaco.abstract_data_range module

Defines the base class for data ranges.

class chaco.abstract_data_range.AbstractDataRange(*sources, **kwargs)

Bases: traits.has_traits.HasTraits

Abstract class for ranges that represent sub-regions of data space.

They support “autoscaling” by querying their associated data sources.

bound_data(data)

Returns a tuple of indices for the start and end of the first run of data that falls within the range.

Given an array of data values of the same dimensionality as the range, returns a tuple of indices (start, end) corresponding to the first and last elements of the first run of data that falls within the range. For monotonic data, this basically returns the first and last elements that fall within the range. Using this method is not advised for non-monotonic data; in that case, it returns the first and last elements of the first “chunk” of data that falls within the range.

clip_data(data)

Returns a list of data values that are within the range.

Given an array of data values of the same dimensionality as the range, returns a list of data values that are inside the range.

high = Float(1.0)

The actual value of the upper bound of this range. To set it, use high_setting. (Setting this attribute directly just calls the setter for high_setting.) Although the default value is specified as 1.0, subclasses can redefine the default. Also, subclasses can redefined the type to correspond to their dimensionality.

high_setting = Union(Constant("auto"), Float)

Setting for the upper bound of this range.

low = Float(0.0)

The actual value of the lower bound of this range. To set it, use low_setting. (Setting this attribute directly just calls the setter for low_setting.) Although the default value is specified as 0.0, subclasses can redefine the default. Also, subclasses can redefined the type to correspond to their dimensionality.

low_setting = Union(Constant("auto"), Float)

Setting for the lower bound of this range.

mask_data(data)

Returns a mask array, indicating whether values in the given array are inside the range.

Given an array of data values of the same dimensionality as the range, this method returns a mask array of the same length as data, filled with 1s and 0s corresponding to whether the data value at that index is inside or outside the range.

set_bounds(*new_bounds)

Sets all the bounds of the range simultaneously.

Because each bounds change probably fires an event, this method allows tools to set all range elements in a single, atomic step.

Parameters

• new_bounds (a tuple of (low, high)) – The new bounds for the range; the dimensionality and cardinality depend on the specific subclass.

• method not only reduces the number of spurious events (the (This) –

• that result from having to set both high and low) (ones) –

• also (but) –

• listeners to differentiate between translation and resize (allows) –

• operations. –

sources = List(Instance(AbstractDataSource))

The list of data sources to which this range responds.

updated = Event

Event that is fired when the actual bounds values change; the value of the event is a tuple (low_bound, high_bound)