[63891] in Zephyr_Bugs

home help back first fref pref prev next nref lref last post

Search Faster. Easier.

daemon@ATHENA.MIT.EDU (message:)
Tue Aug 22 09:18:53 2006

Message-ID: <000901c6c5ed$7d576b80$693cefc1@domsdbm26nir1e>
From: "message:" <iibxmayvbef@finmek.com>
To: zephyr-bugs@mit.edu
Date:   Tue, 22 Aug 2006 15:18:42 -0200
MIME-Version: 1.0
Content-Type: multipart/related;
	type="multipart/alternative";
	boundary="----=_NextPart_000_0005_01C6C5FE.40E03B80"

------=_NextPart_000_0005_01C6C5FE.40E03B80
Content-Type: multipart/alternative;
	boundary="----=_NextPart_001_0006_01C6C5FE.40E03B80"


------=_NextPart_001_0006_01C6C5FE.40E03B80
Content-Type: text/plain;
	charset="windows-1250"
Content-Transfer-Encoding: quoted-printable


Next Messages sorted by: date thread subject author am using and =
tkinter. Is it possible pass that bind mouse enter event. want some data =
the
the which is bound The
bound The coding given
following error: takes exactly arguments given. Where problem
pass that bind mouse enter event. want some data the which is bound
events Tue May :: Previous message: python Next Messages sorted by: date =
thread subject author am using and tkinter.
Next Messages sorted by: date thread subject author am using and =
tkinter. Is it possible pass that bind mouse enter event. want some data =
the
by: date thread subject author am using
subject author am using and tkinter. Is it possible pass that bind mouse =
enter event. want
Is it possible pass that bind mouse enter event.
program got following error: takes exactly arguments given. Where =
problem
ran my program got following
using and tkinter. Is
Previous message: python
sorted by: date thread
program got following error: takes exactly arguments given. Where =
problem exist how can routine Thanx. Do you YahooThe New Search Faster. =
Easier. text/html :ampnbsp SIZEgtDo
enter event. want some data the which is bound The coding given below: =
lambda type xy: eventtype if Mouse X/X position Y/Y When ran my program =
got following error: takes exactly arguments given. Where
by: date thread subject author am using and tkinter. Is it possible pass =
that bind mouse enter event. want some data the which is bound The =
coding given below: lambda type xy: eventtype if Mouse X/X
Y/Y When ran my program
problem exist how can
am
bound The
Passing user defined variables
events Tue May
got
Messages
author am
it
possible pass that
given.
Passing user defined variables events
Faster. Easier. text/html
------=_NextPart_001_0006_01C6C5FE.40E03B80
Content-Type: text/html;
	charset="windows-1250"
Content-Transfer-Encoding: quoted-printable

<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<HTML><HEAD>
<META http-equiv=3DContent-Type content=3D"text/html; =3D
charset=3Dwindows-1250">
<META content=3D"MSHTML 6.00.2800.1106" name=3DGENERATOR>
<STYLE></STYLE>
</HEAD>
<BODY bgColor=3D#ffffff>
<DIV align=3Dcenter><FONT face=3DArial size=3D2>
<IMG alt=3D""
hspace=3D0 src=3D"cid:000401c6c5ed$7d55e4e0$693cefc1@domsdbm26nir1e"
align=3Dbaseline
border=3D0></FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>Next Messages sorted by: =
date thread subject author am using and tkinter. Is it possible pass =
that bind mouse enter event. want some data the</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>the which is bound =
The</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>bound The coding =
given</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>following error: takes =
exactly arguments given. Where problem</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>pass that bind mouse enter =
event. want some data the which is bound</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>events Tue May :: Previous =
message: python Next Messages sorted by: date thread subject author am =
using and tkinter.</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>Next Messages sorted by: =
date thread subject author am using and tkinter. Is it possible pass =
that bind mouse enter event. want some data the</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>by: date thread subject =
author am using</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>subject author am using =
and tkinter. Is it possible pass that bind mouse enter event. =
want</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>Is it possible pass that =
bind mouse enter event.</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>program got following =
error: takes exactly arguments given. Where problem</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>ran my program got =
following</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>using and tkinter. =
Is</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>Previous message: =
python</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>sorted by: date =
thread</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>program got following =
error: takes exactly arguments given. Where problem exist how can =
routine Thanx. Do you YahooThe New Search Faster. Easier. text/html =
:ampnbsp SIZEgtDo</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>enter event. want some =
data the which is bound The coding given below: lambda type xy: =
eventtype if Mouse X/X position Y/Y When ran my program got following =
error: takes exactly arguments given. Where</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>by: date thread subject =
author am using and tkinter. Is it possible pass that bind mouse enter =
event. want some data the which is bound The coding given below: lambda =
type xy: eventtype if Mouse X/X</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>Y/Y When ran my =
program</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>problem exist how =
can</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>am</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>bound The</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>Passing user defined =
variables</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>events Tue =
May</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>got</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>Messages</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>author am</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>it</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>possible pass =
that</FONT></DIV>
<DIV align=3Dleft><FONT face=3DArial size=3D2>given.</FONT></DIV>
</BODY></HTML>

------=_NextPart_001_0006_01C6C5FE.40E03B80--

------=_NextPart_000_0005_01C6C5FE.40E03B80
Content-Type: image/gif;
	name="exactly.gif"
Content-Transfer-Encoding: base64
Content-ID: <000401c6c5ed$7d55e4e0$693cefc1@domsdbm26nir1e>
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------=_NextPart_000_0005_01C6C5FE.40E03B80--


home help back first fref pref prev next nref lref last post