Bug 649 – Too-picky typechecking on numeric types: use __float__ / __int__.

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Bug 649 - Too-picky typechecking on numeric types: use __float__ / __int__.

Summary:

Too-picky typechecking on numeric types: use __float__ / __int__.

Status:	CLOSED FIXED

Product:	PySide
Classification:	Unclassified
Component:	Shiboken
Version:	HEAD
Platform:	All All

Importance:	P3 normal
Assigned To:	Marcelo Lira

URL:

Depends on:
Blocks:
	Show dependency tree / graph

Reported:	2011-01-27 16:26 EET by Kenneth Arnold
Modified:	2011-02-17 19:18 EET (History)
CC List:	8 users (show)

See Also:

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Description Kenneth Arnold 2011-01-27 16:26:15 EET

Constructors for QPointF and QColor will not accept NumPy's C-like numeric
types (float32, int32, etc.):

QtCore.QPointF(np.float32(.5), np.float32(.5)) yields
TypeError: 'PySide.QtCore.QPointF' called with wrong argument types:
PySide.QtCore.QPointF(numpy.float32, numpy.float32)
Supported signatures:
PySide.QtCore.QPointF()
PySide.QtCore.QPointF(PySide.QtCore.QPoint)
PySide.QtCore.QPointF(PySide.QtCore.QPointF)
PySide.QtCore.QPointF(float, float)

and similarly for QColor:
TypeError: 'PySide.QtGui.QColor' called with wrong argument types:
PySide.QtGui.QColor(numpy.int32, numpy.int32, numpy.int32)
Supported signatures:
PySide.QtGui.QColor()
PySide.QtGui.QColor(QVariant)
PySide.QtGui.QColor(PySide.QtCore.Qt.GlobalColor)
PySide.QtGui.QColor(PySide.QtGui.QColor)
PySide.QtGui.QColor(QString)
PySide.QtGui.QColor(int, int, int, int = 255)
PySide.QtGui.QColor(unsigned int)

But they do support the standard __float__ and __int__ conversion methods. So
Shiboken wrappers should try to call those methods on numeric parameters.
(sip/PyQt4 does.)

Comment 1 Hugo Parente Lima 2011-01-27 16:57:05 EET

bug 535 is identical to this ans was marked as WONTFIX, but is was due to our
lack of information about __float__ and __int__ methods.

Thanks for doing a better bug report, it's valid. :-)

Comment 2 Kenneth Arnold 2011-01-27 18:03:21 EET

Did a bit of digging. One culprit is libshiboken/conversions.h line 41:
#define SbkNumber_Check(X) (PyInt_Check(X) || PyFloat_Check(X) ||
PyLong_Check(X))

It's not clear how to extend that to Numpy value types. See
https://github.com/numpy/numpy/blob/master/numpy/core/include/numpy/ndarrayobject.h
for a macro approach that would be awkward for Shiboken to take.

I'm unclear how PyQt4 accomplishes it (calls `builtin.float` and catches
exceptions perhaps?), but:

>>> from PyQt4 import QtCore
>>> import numpy as np
>>> QtCore.QPointF(np.float32(.5), np.float32(.5))
PyQt4.QtCore.QPointF(0.5, 0.5)

Comment 3 Matti Airas 2011-01-28 09:52:22 EET

Thanks for the bug report. Since Hugo already verified this is a valid issue,
I'm prioritizing this P3. :-)

Comment 4 Kenneth Arnold 2011-01-28 12:04:05 EET

Another note: the native Python int and float types are inherited by the
corresponding Numpy types, so test both int32 and int64 on 64-bit systems for
example:

>>> isinstance(np.float32(.5), float)
False
>>> isinstance(np.float64(.5), float)
True

Yet:
>>> from PyQt4 import QtCore
>>> import numpy as np
>>> QtCore.QPointF(np.float32(.5), np.float64(.5))
PyQt4.QtCore.QPointF(0.5, 0.5)

and even:
>>> QtCore.QPointF(np.uint8(6), np.int64(2))
PyQt4.QtCore.QPointF(6.0, 2.0)

Comment 5 Marcelo Lira 2011-02-16 13:25:21 EET

The problem is that for some historical reason we were using a custom macro
SbkNumber_Check, that checked if the object was of PyInt, PyLong or PyFloat
type, and Python's own PyNumber_Check looks for general number characteristics
(via tp_as_number PyObjectType attribute).
In shorter words, the object doesn't have to be of a numeric type, but to
follow the number protocol.

Comment 6 Marcelo Lira 2011-02-17 13:16:30 EET

Fixed in commit Shiboken/30d71761.

Comment 7 Hugo Parente Lima 2011-02-17 19:18:17 EET

Released in 1.0.0~rc1