MLPythonItemModelWrapper Class Reference

#include <mlPythonItemModelWrapper.h>

Inherits MLAbstractItemModelWrapper.

Public Slots
bool	isCaching () const
void	setDataSource (PyObject *object, bool caching=false)
Public Slots inherited from MLAbstractItemModelWrapper
QObject *	qmlModel () const
bool	isFlat () const
bool	hasChildren (const ModelIndexWrapper *parent)
int	getChildCount (const ModelIndexWrapper *parent)
ModelIndexWrapper *	getChild (const ModelIndexWrapper *parent, unsigned int index)
ModelIndexWrapper *	getParent (const ModelIndexWrapper *child)
ModelIndexWrapper *	findFirst (const QVariant &attribute, const QVariant &value)
ModelIndexWrapper *	searchItem (const QVariant &attribute, const QVariantList &values)
int	getAttributeCount () const
QStringList	getAttributeNames () const
QString	getAttributeName (int index) const
QVariant	getAttributeDefault (int index) const
int	getAttributeIndex (const QString &name)
QVariant	getData (const ModelIndexWrapper *item, int attributeIndex)
bool	setData (const ModelIndexWrapper *item, int attributeIndex, const QVariant &data)
bool	bulkSetData (const QList< ModelIndexWrapper * > &items, int attributeIndex, const QVariant &data)

Static Public Member Functions
static ml::RefCountedBase *	createObject (const QVariantList &arguments)

Additional Inherited Members
Signals inherited from MLAbstractItemModelWrapper
void	itemChanged (ModelIndexWrapper *item, bool after)
void	itemsInserted (ModelIndexWrapper *parent, unsigned int childIndex, unsigned int itemsInserted, bool after)
void	itemsRemoved (ModelIndexWrapper *parent, unsigned int childIndex, unsigned int itemsRemoved, bool after)
void	dataChanged (const QList< ModelIndexWrapper * > &items, const QList< int > &attributeIndices)

Detailed Description

This class wraps the class ml::PythonItemModel and thus provides an item model that takes its data from a Python object.

You should not use this wrapper directly.

Instead you should derive from class ItemModelDataSource contained in the ItemModelSupport Python module (source code at Packages/MeVisLab/Standard/Modules/Scripts/python/ItemModelSupport.py).

You can also have a look at the TestItemModelView module for an example. This module implements both the StandardItemModel and PythonItemModel and you can switch between these implementations.

Member Function Documentation

createObject()

static ml::RefCountedBase * MLPythonItemModelWrapper::createObject ( const QVariantList & arguments )

static

Create new Python item model; this is used by MLAB.createMLBaseObject to create a PythonItemModel. This takes a list with 1 or 2 arguments.

The first, required element is the list of attribute names.

The second, optional element is a bool. If set this is a hint that the model will have no sub-items. This can be used by views to avoid reserving space for sub-item indicators.

Example:

myModel = MLAB.createMLBaseObject("PythonItemModel",
 [["name", "size", "directory", "writable"], False])

isCaching

bool MLPythonItemModelWrapper::isCaching ( ) const

inlineslot

Return if the model is caching the values.

setDataSource

void MLPythonItemModelWrapper::setDataSource ( PyObject * object, bool caching = false )

slot

Changing the data source object for the model.

If caching is set to true, most results obtained from the data source are cached.

The object must have certain methods, as e.g. found in the ItemModelDataSource class mentioned above.