def select_rows(self, identifiers, using_ids=True, change_current=True, scroll=True): ''' Select rows identified by identifiers. identifiers can be a set of ids, row numbers or QModelIndexes. ''' rows = set([x.row() if hasattr(x, 'row') else x for x in identifiers]) if using_ids: rows = set([]) identifiers = set(identifiers) m = self.model() for row in xrange(m.rowCount(QModelIndex())): if m.id(row) in identifiers: rows.add(row) rows = list(sorted(rows)) if rows: row = rows[0] if change_current: self.set_current_row(row, select=False) if scroll: self.scroll_to_row(row) sm = self.selectionModel() sel = QItemSelection() m = self.model() max_col = m.columnCount(QModelIndex()) - 1 # Create a range based selector for each set of contiguous rows # as supplying selectors for each individual row causes very poor # performance if a large number of rows has to be selected. for k, g in itertools.groupby(enumerate(rows), lambda (i,x):i-x): group = list(map(operator.itemgetter(1), g)) sel.merge(QItemSelection(m.index(min(group), 0), m.index(max(group), max_col)), sm.Select)
def select_rows(self, rows): sel = QItemSelection() sm = self.selectionModel() m = self.model() # Create a range based selector for each set of contiguous rows # as supplying selectors for each individual row causes very poor # performance if a large number of rows has to be selected. for k, g in itertools.groupby(enumerate(rows), lambda (i,x):i-x): group = list(map(operator.itemgetter(1), g)) sel.merge(QItemSelection(m.index(min(group), 0), m.index(max(group), 0)), sm.Select)