IDLE colors Failure

Rosa Maria via IDLE-dev <[email protected]> Mon, 7 Nov 2022 09:08:00 +0000 (UTC)
Newsgroups gmane.comp.python.idle
Message-ID <[email protected]>
------=_Part_855390_1434135015.1667812080927
Content-Type: multipart/alternative; 
	boundary="----=_Part_855389_1559854057.1667812080869"

------=_Part_855389_1559854057.1667812080869
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: quoted-printable

The Python IDLE change colors without warning, difficulting or cancelling t=
he capability of reading. See the attached image. I must change the Highlig=
ting or close-open the IDLE.
You can see that the result of the multiplication _*100 cannot be seen, thi=
s ocurrs very VERY often. In version 3.4.2 (used in my Windows-xp partition=
s I had no that problem).
Data:Python and IDLE 3.7.3TK 8.6.932 bit system with Linux Mint Debian Edit=
ion-4 with intel Celeron.
Thanks in advance and best regards.
Rosa Mar=C3=ADa.
p.s.Why Python?When my programs in GW-BASIC cannot run in the newer systems=
 (compiled in TurboBasic); I must choose a new language; Testing many inclu=
ding Ruby and Perl, but with Python was easier to translate my BASIC progra=
ms. I really miss the Matrix definition of BASIC (A#(N, M, 2)) for complex =
matrix (it allows up to 7 dimensions), in Python Matrix are really cumberso=
me. [[[0 for in range(n)] for in range(m)] for i in range(2)]. Most of my u=
se is 4 number crunching 4 engineering, but I made a scritp to read and tak=
e data from .xml invoices in Mexico, that are mandatory. I used regex inste=
ad of .xml tree because the specification is WRONG, if an error ocurrs it s=
otps, and if it is in the first invoice that mess. With regex only the row =
of the exported .csv is wrong.
I love Python, numpy and scipy. I use Octave 4 matrix calculations.
Thanks.
  =20
   - "Que tengamos Sabidur=C3=ADa para no temer a las Sombras de la Noche"
   - "Cu=C3=ADdate de la Ciencia que no Llora, de la Filosof=C3=ADa que no =
R=C3=ADe y de la Grandeza que no se inclina ante los ni=C3=B1os"
   - "Madre es el nombre de dios en los labios y en los corazones de todos =
los ni=C3=B1os"

------=_Part_855389_1559854057.1667812080869
Content-Type: text/html; charset=UTF-8
Content-Transfer-Encoding: quoted-printable

<html><head></head><body><div class=3D"ydpc60fabd5yahoo-style-wrap" style=
=3D"font-family:bookman old style, new york, times, serif;font-size:24px;">=
<div dir=3D"ltr" data-setdir=3D"false">The Python IDLE change colors withou=
t warning, difficulting or cancelling the capability of reading. See the at=
tached image. I must change the Highligting or close-open the IDLE.</div><d=
iv dir=3D"ltr" data-setdir=3D"false"><br></div><div dir=3D"ltr" data-setdir=
=3D"false">You can see that the result of the multiplication _*100 cannot b=
e seen, this ocurrs very VERY often. In version 3.4.2 (used in my Windows-x=
p partitions I had no that problem).</div><div dir=3D"ltr" data-setdir=3D"f=
alse"><br></div><div dir=3D"ltr" data-setdir=3D"false">Data:</div><div dir=
=3D"ltr" data-setdir=3D"false">Python and IDLE 3.7.3</div><div dir=3D"ltr" =
data-setdir=3D"false">TK 8.6.9</div><div dir=3D"ltr" data-setdir=3D"false">=
32 bit system with Linux Mint Debian Edition-4 with intel Celeron.</div><di=
v dir=3D"ltr" data-setdir=3D"false"><br></div><div dir=3D"ltr" data-setdir=
=3D"false">Thanks in advance and best regards.</div><div dir=3D"ltr" data-s=
etdir=3D"false"><br></div><div dir=3D"ltr" data-setdir=3D"false">Rosa Mar=
=C3=ADa.</div><div dir=3D"ltr" data-setdir=3D"false"><br></div><div dir=3D"=
ltr" data-setdir=3D"false">p.s.</div><div dir=3D"ltr" data-setdir=3D"false"=
>Why Python?</div><div dir=3D"ltr" data-setdir=3D"false">When my programs i=
n GW-BASIC cannot run in the newer systems (compiled in TurboBasic); I must=
 choose a new language; Testing many including Ruby and Perl, but <b><i>wit=
h Python was easier to translate my BASIC programs.</i></b> I really miss t=
he Matrix definition of BASIC (A#(N, M, 2)) for complex matrix (it allows u=
p to 7 dimensions), in Python Matrix are really cumbersome. [[[0 for in ran=
ge(n)] for in range(m)] for i in range(2)]. Most of my use is 4 number crun=
ching 4 engineering, but I made a scritp to read and take data from .xml in=
voices in Mexico, that are mandatory. I used regex instead of .xml tree bec=
ause the specification is WRONG, if an error ocurrs it sotps, and if it is =
in the first invoice that mess. With regex only the row of the exported .cs=
v is wrong.</div><div dir=3D"ltr" data-setdir=3D"false"><br></div><div dir=
=3D"ltr" data-setdir=3D"false">I love Python, numpy and scipy. I use Octave=
 4 matrix calculations.</div><div dir=3D"ltr" data-setdir=3D"false"><br></d=
iv><div dir=3D"ltr" data-setdir=3D"false">Thanks.</div><div><br></div><div =
class=3D"ydpc60fabd5signature"><ul><li><font color=3D"#5b8828"><b><span sty=
le=3D"background-color:rgb(255, 255, 255);"><font size=3D"2" style=3D"backg=
round-color: inherit;"><span style=3D"font-family:bookman old style, new yo=
rk, times, serif;">"Que tengamos Sabidur=C3=ADa para no temer <font size=3D=
"2" style=3D"background-color: inherit;">a las Sombras de la N<font size=3D=
"2">oche<font size=3D"2">"</font></font></font></span></font></span></b></f=
ont></li><li><font color=3D"#5b8828"><b><span style=3D"background-color:rgb=
(255, 255, 255);"><font size=3D"2" style=3D"background-color: inherit;"><sp=
an style=3D"font-family:bookman old style, new york, times, serif;">"Cu=C3=
=ADdate de la Ciencia que no Llora, de la Filosof=C3=ADa que no R=C3=ADe y =
de la Grandeza que no se inclina ante los ni=C3=B1os"</span></font></span><=
/b></font></li><li><font style=3D"color:rgb(64, 127, 0);font-weight:bold;" =
size=3D"2"><span style=3D"font-family:bookman old style, new york, times, s=
erif;">"Madre es el nombre de dios en los labios y en los corazones de todo=
s los ni=C3=B1os"</span></font></li></ul></div></div></body></html>
------=_Part_855389_1559854057.1667812080869--

------=_Part_855390_1434135015.1667812080927
Content-Type: application/octet-stream
Content-Transfer-Encoding: base64
Content-Disposition: attachment; filename="Falla-Idle-Python_3.7.3"
Content-ID: <[email protected]>
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------=_Part_855390_1434135015.1667812080927
Content-Type: text/plain; charset="us-ascii"
MIME-Version: 1.0
Content-Transfer-Encoding: 7bit
Content-Disposition: inline

_______________________________________________
IDLE-dev mailing list
[email protected]
https://mail.python.org/mailman/listinfo/idle-dev

------=_Part_855390_1434135015.1667812080927--