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# (C) Copyright 2005-2023 Enthought, Inc., Austin, TX
# All rights reserved.
#
# This software is provided without warranty under the terms of the BSD
# license included in LICENSE.txt and may be redistributed only under
# the conditions described in the aforementioned license. The license
# is also available online at http://www.enthought.com/licenses/BSD.txt
#
# Thanks for using Enthought open source!
""" Provides an N-dimensional array data model implementation.
This module provides a concrete implementation of a data model for an
n-dim numpy array.
"""
from traits.api import Array, HasRequiredTraits, Instance, observe
from pyface.data_view.abstract_data_model import AbstractDataModel
from pyface.data_view.data_view_errors import DataViewSetError
from pyface.data_view.abstract_value_type import AbstractValueType
from pyface.data_view.value_types.api import (
ConstantValue, IntValue, no_value
)
from pyface.data_view.index_manager import TupleIndexManager
class _AtLeastTwoDArray(Array):
""" Trait type that holds an array that at least two dimensional.
"""
def validate(self, object, name, value):
value = super().validate(object, name, value)
if value.ndim == 0:
value = value.reshape((0, 0))
elif value.ndim == 1:
value = value.reshape((-1, 1))
return value
class ArrayDataModel(AbstractDataModel, HasRequiredTraits):
""" A data model for an n-dim array.
This data model presents the data from a multidimensional array
hierarchically by dimension. The underlying array must be at least 2
dimensional.
Values are adapted by the ``value_type`` trait. This provides sensible
default values for integer, float and text dtypes, but other dtypes may
need the user of the class to supply an appropriate value type class to
adapt values.
There are additional value types which provide data sources for row
headers, column headers, and the label of the row header column. The
defaults are likely suitable for most cases, but can be overriden if
required.
"""
#: The array being displayed. This must have dimension at least 2.
data = _AtLeastTwoDArray()
#: The index manager that helps convert toolkit indices to data view
#: indices.
index_manager = Instance(TupleIndexManager, args=())
#: The value type of the row index column header.
label_header_type = Instance(
AbstractValueType,
factory=ConstantValue,
kw={'text': "Index"},
allow_none=False,
)
#: The value type of the column titles.
column_header_type = Instance(
AbstractValueType,
factory=IntValue,
kw={'is_editable': False},
allow_none=False,
)
#: The value type of the row titles.
row_header_type = Instance(
AbstractValueType,
factory=IntValue,
kw={'is_editable': False},
allow_none=False,
)
#: The type of value being displayed in the data model.
value_type = Instance(AbstractValueType, allow_none=False, required=True)
# Data structure methods
def get_column_count(self):
""" How many columns in the data view model.
The number of columns is the size of the last dimension of the array.
Returns
-------
column_count : non-negative int
The number of columns in the data view model, which is the size of
the last dimension of the array.
"""
return self.data.shape[-1]
def can_have_children(self, row):
""" Whether or not a row can have child rows.
A row is a leaf row if the length of the index is one less than
the dimension of the array: the final coordinate for the value will
be supplied by the column index.
Parameters
----------
row : sequence of int
The indices of the row as a sequence from root to leaf.
Returns
-------
can_have_children : bool
Whether or not the row can ever have child rows.
"""
if len(row) < self.data.ndim - 1:
return True
return False
def get_row_count(self, row):
""" Whether or not the row currently has any child rows.
The number of rows in a non-leaf row is equal to the size of the
next dimension.
Parameters
----------
row : sequence of int
The indices of the row as a sequence from root to leaf.
Returns
-------
has_children : bool
Whether or not the row currently has child rows.
"""
if len(row) < self.data.ndim - 1:
return self.data.shape[len(row)]
return 0
# Data value methods
def get_value(self, row, column):
""" Return the Python value for the row and column.
Parameters
----------
row : sequence of int
The indices of the row as a sequence from root to leaf.
Returns
-------
row_count : non-negative int
The number of child rows that the row has.
"""
if len(row) == 0:
if len(column) == 0:
return None
return column[0]
elif len(column) == 0:
return row[-1]
else:
index = tuple(row + column)
if len(index) != self.data.ndim:
return None
return self.data[index]
def can_set_value(self, row, column):
""" Whether the value in the indicated row and column can be set.
This returns False for row and column headers, but True for all
array values.
Parameters
----------
row : sequence of int
The indices of the row as a sequence from root to leaf.
column : sequence of int
The indices of the column as a sequence of length 0 or 1.
Returns
-------
can_set_value : bool
Whether or not the value can be set.
"""
# can only set values when we have the full index
index = tuple(row + column)
return len(index) == self.data.ndim
def set_value(self, row, column, value):
""" Return the Python value for the row and column.
Parameters
----------
row : sequence of int
The indices of the row as a sequence from root to leaf.
column : sequence of int
The indices of the column as a sequence of length 1.
Returns
-------
value : Any
The value represented by the given row and column.
"""
if self.can_set_value(row, column):
index = tuple(row + column)
self.data[index] = value
self.values_changed = (row, column, row, column)
else:
raise DataViewSetError()
def get_value_type(self, row, column):
""" Return the value type of the given row and column.
This method returns the value of ``column_header_type`` for column
headers, the value of ``row_header_type`` for row headers, the value
of ``label_header_type`` for the top-left corner value, the value of
``value_type`` for all array values, and ``no_value`` for everything
else.
Parameters
----------
row : sequence of int
The indices of the row as a sequence from root to leaf.
column : sequence of int
The indices of the column as a sequence of length 0 or 1.
Returns
-------
value_type : AbstractValueType
The value type of the given row and column.
"""
if len(row) == 0:
if len(column) == 0:
return self.label_header_type
return self.column_header_type
elif len(column) == 0:
return self.row_header_type
elif len(row) < self.data.ndim - 1:
return no_value
else:
return self.value_type
# data update methods
@observe('data')
def data_updated(self, event):
""" Handle the array being replaced with a new array. """
if event.new.shape == event.old.shape:
if self.data.size > 0:
self.values_changed = (
(0,), (0,),
(event.old.shape[0] - 1,), (event.old.shape[-1] - 1,)
)
else:
self.structure_changed = True
@observe('value_type.updated')
def value_type_updated(self, event):
""" Handle the value type being updated. """
if self.data.size > 0:
self.values_changed = (
(0,), (0,), (self.data.shape[0] - 1,), (self.data.shape[-1] - 1,)
)
@observe('column_header_type.updated')
def column_header_type_updated(self, event):
""" Handle the column header type being updated. """
if self.data.shape[-1] > 0:
self.values_changed = ((), (0,), (), (self.data.shape[-1] - 1,))
@observe('row_header_type.updated')
def value_header_type_updated(self, event):
""" Handle the value header type being updated. """
if self.data.shape[0] > 0:
self.values_changed = ((0,), (), (self.data.shape[0] - 1,), ())
@observe('label_header_type.updated')
def label_header_type_updated(self, event):
""" Handle the label header type being updated. """
self.values_changed = ((), (), (), ())
# default array value
def _data_default(self):
from numpy import zeros
return zeros(shape=(0, 0))
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