Inherits from
- AbstractDataSource: enthought.chaco.abstract_data_source.AbstractDataSource
Attributes
- index_dimension
The dimensionality of the indices into this data source (overrides AbstractDataSource).
index_dimension = Int(0)
- sort_order
The sort order of the data. This is a specialized optimization for 1-D arrays, but it's an important one that's used everywhere.
sort_order = SortOrderTrait
- value_dimension
The dimensionality of the value at each index point (overrides AbstractDataSource).
value_dimension = Int(1)
Inherited from base classes
Method summary
- __init__(self, data = array([ ]), sort_order = 'ascending', **traits)
- get_bounds(self, value = None, index = None)
- get_data(self, axes = None, remove_nans = False)
- get_data_mask(self)
- get_shape(self)
- get_size(self)
- get_value_size(self)
- is_masked(self)
- set_data(self, value)
Inherited from base classes
- __deepcopy__(self, memo)
- __prefix_trait__(self, name, is_set)
- __reduce_ex__(self, protocol)
- __setstate__(self, state, trait_change_notify = True)
- add_class_trait(cls, name, *trait)
- add_trait(self, name, *trait)
- add_trait_category(cls, category)
- add_trait_listener(self, object, prefix = '')
- all_trait_names(self)
- base_trait(self, name)
- class_default_traits_view(cls)
- class_editable_traits(cls)
- class_trait_names(cls, **metadata)
- class_trait_view(cls, name = None, view_element = None)
- class_trait_view_elements(cls)
- class_traits(cls, **metadata)
- clone_traits(self, traits = None, memo = None, copy = None, **metadata)
- configure_traits(self, filename = None, view = None, kind = None, edit = True, context = None, handler = None, id = '', scrollable = None, **args)
- copy_traits(self, other, traits = None, memo = None, copy = None, **metadata)
- copyable_trait_names(self, **metadata)
- default_traits_view(self)
- edit_traits(self, view = None, parent = None, kind = None, context = None, handler = None, id = '', scrollable = None, **args)
- editable_traits(self)
- has_traits_interface(self, *interfaces)
- on_trait_change(self, handler, name = None, remove = False, dispatch = 'same', priority = False, deferred = False)
- print_traits(self, show_help = False, **metadata)
- remove_trait(self, name)
- remove_trait_listener(self, object, prefix = '')
- reset_traits(self, traits = None, **metadata)
- set_trait_dispatch_handler(cls, name, klass, override = False)
- sync_trait(self, trait_name, object, alias = None, mutual = True, remove = False)
- trait(self, name, force = False, copy = False)
- trait_context(self)
- trait_get(self, *names, **metadata)
- trait_monitor(cls, handler, remove = False)
- trait_names(self, **metadata)
- trait_set(self, trait_change_notify = True, **traits)
- trait_setq(self, **traits)
- trait_subclasses(cls, all = False)
- trait_view(self, name = None, view_element = None)
- trait_view_elements(self)
- trait_views(self, klass = None)
- traits(self, **metadata)
- validate_trait(self, name, value)
Methods
- __init__(self, data = array([ ]), sort_order = 'ascending', **traits)
- get_bounds(self, value = None, index = None)
get_bounds() -> tuple(min, max)
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.
If value and index are both None, then the method returns the global minimum and maximum for the entire data set. If value is an integer, then the method returns the minimum and maximum along the value slice in the value_dimension. If index is an integer, then the method returns the minimum and maximum along the index slice in the index_direction.
- get_data(self, axes = None, remove_nans = False)
get_data() -> data_array
If called with no arguments, this method returns a data array. Treat this data array as read-only, and do not alter it in-place. This data is contiguous and not masked.
If axes is an integer or tuple, this method returns the data array, sliced along the index_dimension.
- get_data_mask(self)
get_data_mask() -> (data_array, mask_array)
Implements AbstractDataSource.
- get_shape(self)
Returns the shape of the multi-dimensional data source.
- get_size(self)
get_size() -> int
Implements AbstractDataSource. Returns an integer estimate, or the exact size, of the dataset that get_data() returns. This method is useful for downsampling.
- get_value_size(self)
get_value_size() -> size
Returns the size along the value dimension.
- is_masked(self)
is_masked() -> bool
Returns True if this data source's data uses a mask. In this case, retrieve the data using get_data_mask() instead of get_data(). If you call get_data() for this data source, it returns data, but that data may not be the expected data.)
- set_data(self, value)
Sets the data for this data source.
Parameters
- value : array
- The data to use.