I just wasted quite a bit of time trying to debug my code. It took a long
time because I was using imshow to debug, and didn't know that imshow
displays a matrix not using cartesian coordinates. The axes are transposed
and one axis is reversed. The documentation doesn't mention this.
Thanks,
Neal
This is discussed in the description of the "origin" keyword argument:
*origin* : {'upper', 'lower'}, optional
Place the [0,0] index of the array in the upper left or lower left corner
of the axes. The convention 'upper' is typically used for matrices and
images. If not given, rcParams["image.origin"]
<https://matplotlib.org/tutorials/introductory/customizing.html#matplotlib-rcparams>
is used, defaulting to 'upper'.
Note that the vertical axes points upward for 'lower' but downward for
'upper'.
On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
I just wasted quite a bit of time trying to debug my code. It took a long
time because I was using imshow to debug, and didn't know that imshow
displays a matrix not using cartesian coordinates. The axes are
transposed
and one axis is reversed. The documentation doesn't mention this.Thanks,
Neal_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page
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The origin only discusses the inversion of the y axis - in addition
the x-y are transposed.
On Thu, Jun 6, 2019 at 9:30 AM Nathan Goldbaum <nathan12343 at gmail.com> wrote:
This is discussed in the description of the "origin" keyword argument:
origin : {'upper', 'lower'}, optional
Place the [0,0] index of the array in the upper left or lower left corner of the axes. The convention 'upper' is typically used for matrices and images. If not given, rcParams["image.origin"] is used, defaulting to 'upper'.
Note that the vertical axes points upward for 'lower' but downward for 'upper'.
On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
I just wasted quite a bit of time trying to debug my code. It took a long
time because I was using imshow to debug, and didn't know that imshow
displays a matrix not using cartesian coordinates. The axes are transposed
and one axis is reversed. The documentation doesn't mention this.Thanks,
Neal_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page
--
Those who don't understand recursion are doomed to repeat it
There is also a tutorial addressing the ways that `origin` and `extent`
interact with each other:
https://matplotlib.org/tutorials/intermediate/imshow_extent.html
As for the transpose, that is due to one branch of math teaching use to
think (x, y) and different branch of math teaching us to think (row,
column). For `imshow` we use the second one which is consistent with how
numpy's repr of on array. It would be confusing if the data for
`my_array[0, :]` was displayed as the first _column_ of the image.
I do not disagree that this is frustrating/confusing, there is a comment in
some of my grad school code which is basically "getting the (x,y) vs (r,c)
conversions right here took you an afternoon, don't touch this again!" 
Tom
On Thu, Jun 6, 2019 at 9:31 AM Nathan Goldbaum <nathan12343 at gmail.com> wrote:
This is discussed in the description of the "origin" keyword argument:
*origin* : {'upper', 'lower'}, optional
Place the [0,0] index of the array in the upper left or lower left corner
of the axes. The convention 'upper' is typically used for matrices and
images. If not given, rcParams["image.origin"]
<https://matplotlib.org/tutorials/introductory/customizing.html#matplotlib-rcparams>
is used, defaulting to 'upper'.Note that the vertical axes points upward for 'lower' but downward for
'upper'.On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
I just wasted quite a bit of time trying to debug my code. It took a
long
time because I was using imshow to debug, and didn't know that imshow
displays a matrix not using cartesian coordinates. The axes are
transposed
and one axis is reversed. The documentation doesn't mention this.Thanks,
Neal_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page
--
Thomas Caswell
tcaswell at gmail.com
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My suggestion is to enhance the documentation. If there is a nice
tutorial, adding a link to it would be great.
Thanks,
Neal
On Thu, Jun 6, 2019 at 9:39 AM Thomas Caswell <tcaswell at gmail.com> wrote:
There is also a tutorial addressing the ways that `origin` and `extent` interact with each other: https://matplotlib.org/tutorials/intermediate/imshow_extent.html
As for the transpose, that is due to one branch of math teaching use to think (x, y) and different branch of math teaching us to think (row, column). For `imshow` we use the second one which is consistent with how numpy's repr of on array. It would be confusing if the data for `my_array[0, :]` was displayed as the first _column_ of the image.
I do not disagree that this is frustrating/confusing, there is a comment in some of my grad school code which is basically "getting the (x,y) vs (r,c) conversions right here took you an afternoon, don't touch this again!"
Tom
On Thu, Jun 6, 2019 at 9:31 AM Nathan Goldbaum <nathan12343 at gmail.com> wrote:
This is discussed in the description of the "origin" keyword argument:
origin : {'upper', 'lower'}, optional
Place the [0,0] index of the array in the upper left or lower left corner of the axes. The convention 'upper' is typically used for matrices and images. If not given, rcParams["image.origin"] is used, defaulting to 'upper'.
Note that the vertical axes points upward for 'lower' but downward for 'upper'.
On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
I just wasted quite a bit of time trying to debug my code. It took a long
time because I was using imshow to debug, and didn't know that imshow
displays a matrix not using cartesian coordinates. The axes are transposed
and one axis is reversed. The documentation doesn't mention this.Thanks,
Neal_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page--
Thomas Caswell
tcaswell at gmail.com
--
Those who don't understand recursion are doomed to repeat it
I went through the exact same issue some years ago and wrote a note about this confusing definition of the directions in 2D arrays.
It?s written in Japanese but I think it's still understandable for most of you who can read matplotlib and numpy codes.
I hope this would help for improving a tutorial or the documentation.
Best regards,
Kotaro
//================//================//
Paul Scherrer Institut
Kotaro SAITO ???
Laboratory for Neutron Scattering and Imaging
WHGA/348
5232 Villigen PSI, Schweiz
+41 56 310 3179
kotaro.saito at psi.ch
//================//================//
On 2019/6/ 6, at 15:42, Neal Becker <ndbecker2 at gmail.com> wrote:
My suggestion is to enhance the documentation. If there is a nice
tutorial, adding a link to it would be great.
Thanks,
NealOn Thu, Jun 6, 2019 at 9:39 AM Thomas Caswell <tcaswell at gmail.com> wrote:
There is also a tutorial addressing the ways that `origin` and `extent` interact with each other: https://matplotlib.org/tutorials/intermediate/imshow_extent.html
As for the transpose, that is due to one branch of math teaching use to think (x, y) and different branch of math teaching us to think (row, column). For `imshow` we use the second one which is consistent with how numpy's repr of on array. It would be confusing if the data for `my_array[0, :]` was displayed as the first _column_ of the image.
I do not disagree that this is frustrating/confusing, there is a comment in some of my grad school code which is basically "getting the (x,y) vs (r,c) conversions right here took you an afternoon, don't touch this again!"
Tom
On Thu, Jun 6, 2019 at 9:31 AM Nathan Goldbaum <nathan12343 at gmail.com> wrote:
This is discussed in the description of the "origin" keyword argument:
origin : {'upper', 'lower'}, optional
Place the [0,0] index of the array in the upper left or lower left corner of the axes. The convention 'upper' is typically used for matrices and images. If not given, rcParams["image.origin"] is used, defaulting to 'upper'.
Note that the vertical axes points upward for 'lower' but downward for 'upper'.
On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
I just wasted quite a bit of time trying to debug my code. It took a long
time because I was using imshow to debug, and didn't know that imshow
displays a matrix not using cartesian coordinates. The axes are transposed
and one axis is reversed. The documentation doesn't mention this.Thanks,
Neal_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page--
Thomas Caswell
tcaswell at gmail.com--
Those who don't understand recursion are doomed to repeat it
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page
btw, you can do stuff like this yourself 
On Thu, Jun 6, 2019 at 9:43 AM Neal Becker <ndbecker2 at gmail.com> wrote:
My suggestion is to enhance the documentation. If there is a nice
tutorial, adding a link to it would be great.
Thanks,
NealOn Thu, Jun 6, 2019 at 9:39 AM Thomas Caswell <tcaswell at gmail.com> wrote:
>
> There is also a tutorial addressing the ways that `origin` and `extent`
interact with each other:
https://matplotlib.org/tutorials/intermediate/imshow_extent.html
>
> As for the transpose, that is due to one branch of math teaching use to
think (x, y) and different branch of math teaching us to think (row,
column). For `imshow` we use the second one which is consistent with how
numpy's repr of on array. It would be confusing if the data for
`my_array[0, :]` was displayed as the first _column_ of the image.
>
> I do not disagree that this is frustrating/confusing, there is a comment
in some of my grad school code which is basically "getting the (x,y) vs
(r,c) conversions right here took you an afternoon, don't touch this
again!"
>
> Tom
>
> On Thu, Jun 6, 2019 at 9:31 AM Nathan Goldbaum <nathan12343 at gmail.com> > wrote:
>>
>> This is discussed in the description of the "origin" keyword argument:
>>
>> origin : {'upper', 'lower'}, optional
>>
>> Place the [0,0] index of the array in the upper left or lower left
corner of the axes. The convention 'upper' is typically used for matrices
and images. If not given, rcParams["image.origin"] is used, defaulting to
'upper'.
>>
>> Note that the vertical axes points upward for 'lower' but downward for
'upper'.
>>
>>
>> On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
>>>
>>> I just wasted quite a bit of time trying to debug my code. It took a
long
>>> time because I was using imshow to debug, and didn't know that imshow
>>> displays a matrix not using cartesian coordinates. The axes are
transposed
>>> and one axis is reversed. The documentation doesn't mention this.
>>>
>>> Thanks,
>>> Neal
>>>
>>> _______________________________________________
>>> Matplotlib-users mailing list
>>> Matplotlib-users at python.org
>>> Matplotlib-users Info Page
>>
>> _______________________________________________
>> Matplotlib-users mailing list
>> Matplotlib-users at python.org
>> Matplotlib-users Info Page
>
>
>
> --
> Thomas Caswell
> tcaswell at gmail.com--
Those who don't understand recursion are doomed to repeat it
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Actually I found this pdf
https://www.mubdirahman.com/assets/lecture-6---advanced-plotting.pdf
starting at page 6 explained things quite well
On Thu, Jun 6, 2019 at 10:02 AM Saito Kotaro (PSI) <kotaro.saito at psi.ch> wrote:
I went through the exact same issue some years ago and wrote a note about this confusing definition of the directions in 2D arrays.
It?s written in Japanese but I think it's still understandable for most of you who can read matplotlib and numpy codes.
N行N列のnumpy arrayを扱う際に気をつけるべき方向と座標の定義 #Python - QiitaI hope this would help for improving a tutorial or the documentation.
Best regards,
Kotaro
//================//================//
Paul Scherrer Institut
Kotaro SAITO ???
Laboratory for Neutron Scattering and Imaging
WHGA/348
5232 Villigen PSI, Schweiz
+41 56 310 3179
kotaro.saito at psi.ch
Kotaro Saito - 斉藤耕太郎
//================//================//> On 2019/6/ 6, at 15:42, Neal Becker <ndbecker2 at gmail.com> wrote:
>
> My suggestion is to enhance the documentation. If there is a nice
> tutorial, adding a link to it would be great.
> Thanks,
> Neal
>
> On Thu, Jun 6, 2019 at 9:39 AM Thomas Caswell <tcaswell at gmail.com> wrote:
>>
>> There is also a tutorial addressing the ways that `origin` and `extent` interact with each other: https://matplotlib.org/tutorials/intermediate/imshow_extent.html
>>
>> As for the transpose, that is due to one branch of math teaching use to think (x, y) and different branch of math teaching us to think (row, column). For `imshow` we use the second one which is consistent with how numpy's repr of on array. It would be confusing if the data for `my_array[0, :]` was displayed as the first _column_ of the image.
>>
>> I do not disagree that this is frustrating/confusing, there is a comment in some of my grad school code which is basically "getting the (x,y) vs (r,c) conversions right here took you an afternoon, don't touch this again!"
>>
>> Tom
>>
>> On Thu, Jun 6, 2019 at 9:31 AM Nathan Goldbaum <nathan12343 at gmail.com> wrote:
>>>
>>> This is discussed in the description of the "origin" keyword argument:
>>>
>>> origin : {'upper', 'lower'}, optional
>>>
>>> Place the [0,0] index of the array in the upper left or lower left corner of the axes. The convention 'upper' is typically used for matrices and images. If not given, rcParams["image.origin"] is used, defaulting to 'upper'.
>>>
>>> Note that the vertical axes points upward for 'lower' but downward for 'upper'.
>>>
>>>
>>> On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
>>>>
>>>> I just wasted quite a bit of time trying to debug my code. It took a long
>>>> time because I was using imshow to debug, and didn't know that imshow
>>>> displays a matrix not using cartesian coordinates. The axes are transposed
>>>> and one axis is reversed. The documentation doesn't mention this.
>>>>
>>>> Thanks,
>>>> Neal
>>>>
>>>> _______________________________________________
>>>> Matplotlib-users mailing list
>>>> Matplotlib-users at python.org
>>>> Matplotlib-users Info Page
>>>
>>> _______________________________________________
>>> Matplotlib-users mailing list
>>> Matplotlib-users at python.org
>>> Matplotlib-users Info Page
>>
>>
>>
>> --
>> Thomas Caswell
>> tcaswell at gmail.com
>
>
>
> --
> Those who don't understand recursion are doomed to repeat it
> _______________________________________________
> Matplotlib-users mailing list
> Matplotlib-users at python.org
> Matplotlib-users Info Page
--
Those who don't understand recursion are doomed to repeat it
I think a useful way to think about it is to ask how you?d present an x-y array of numbers if you wrote them in a table. If you saw a table:
1 3 4 7
2 7 8 1
5 9 6 5
and you said this was an array in x and y, I think most people would naturally assume x increases column-wise (i.e. the x-axis of the table) and y increases row-wise (i.e. the y-axis of the table). Of course the opposite is another sometimes used convention, so its not like there is any fast rule, but I find that a useful justification for the convention numpy/matplotlib use.
C uses the column-major order, but FORTRAN, (and hence Matlab, Numpy, and most other scientific software packages) use row-major:
ie in numpy:
In [4]: a = np.array([[1, 2, 3],[4, 5, 6]])
In [6]: print(a)
[[1 2 3]
[4 5 6]]
Cheers, Jody
On 6 Jun 2019, at 07:11, Neal Becker <ndbecker2 at gmail.com> wrote:
Actually I found this pdf
https://www.mubdirahman.com/assets/lecture-6---advanced-plotting.pdf
starting at page 6 explained things quite wellOn Thu, Jun 6, 2019 at 10:02 AM Saito Kotaro (PSI) <kotaro.saito at psi.ch <mailto:kotaro.saito at psi.ch>> wrote:
I went through the exact same issue some years ago and wrote a note about this confusing definition of the directions in 2D arrays.
It?s written in Japanese but I think it's still understandable for most of you who can read matplotlib and numpy codes.
N行N列のnumpy arrayを扱う際に気をつけるべき方向と座標の定義 #Python - QiitaI hope this would help for improving a tutorial or the documentation.
Best regards,
Kotaro
//================//================//
Paul Scherrer Institut
Kotaro SAITO ???
Laboratory for Neutron Scattering and Imaging
WHGA/348
5232 Villigen PSI, Schweiz
+41 56 310 3179
kotaro.saito at psi.ch
Kotaro Saito - 斉藤耕太郎
//================//================//On 2019/6/ 6, at 15:42, Neal Becker <ndbecker2 at gmail.com> wrote:
My suggestion is to enhance the documentation. If there is a nice
tutorial, adding a link to it would be great.
Thanks,
NealOn Thu, Jun 6, 2019 at 9:39 AM Thomas Caswell <tcaswell at gmail.com> wrote:
There is also a tutorial addressing the ways that `origin` and `extent` interact with each other: https://matplotlib.org/tutorials/intermediate/imshow_extent.html
As for the transpose, that is due to one branch of math teaching use to think (x, y) and different branch of math teaching us to think (row, column). For `imshow` we use the second one which is consistent with how numpy's repr of on array. It would be confusing if the data for `my_array[0, :]` was displayed as the first _column_ of the image.
I do not disagree that this is frustrating/confusing, there is a comment in some of my grad school code which is basically "getting the (x,y) vs (r,c) conversions right here took you an afternoon, don't touch this again!"
Tom
On Thu, Jun 6, 2019 at 9:31 AM Nathan Goldbaum <nathan12343 at gmail.com> wrote:
This is discussed in the description of the "origin" keyword argument:
origin : {'upper', 'lower'}, optional
Place the [0,0] index of the array in the upper left or lower left corner of the axes. The convention 'upper' is typically used for matrices and images. If not given, rcParams["image.origin"] is used, defaulting to 'upper'.
Note that the vertical axes points upward for 'lower' but downward for 'upper'.
On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
I just wasted quite a bit of time trying to debug my code. It took a long
time because I was using imshow to debug, and didn't know that imshow
displays a matrix not using cartesian coordinates. The axes are transposed
and one axis is reversed. The documentation doesn't mention this.Thanks,
Neal_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page--
Thomas Caswell
tcaswell at gmail.com--
Those who don't understand recursion are doomed to repeat it
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page--
Those who don't understand recursion are doomed to repeat it
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org <mailto:Matplotlib-users at python.org>
Matplotlib-users Info Page
--
Jody Klymak
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MATLAB and FORTRAN are column-major, C/c++ and numpy are row-major.
Tom
On Thu, Jun 6, 2019 at 12:49 PM Jody Klymak <jklymak at uvic.ca> wrote:
I think a useful way to think about it is to ask how you?d present an x-y
array of numbers if you wrote them in a table. If you saw a table:1 3 4 7
2 7 8 1
5 9 6 5and you said this was an array in x and y, I think most people would
naturally assume x increases column-wise (i.e. the x-axis of the table) and
y increases row-wise (i.e. the y-axis of the table). Of course the
opposite is another sometimes used convention, so its not like there is any
fast rule, but I find that a useful justification for the convention
numpy/matplotlib use.C uses the column-major order, but FORTRAN, (and hence Matlab, Numpy, and
most other scientific software packages) use row-major:Row- and column-major order - Wikipedia
ie in numpy:
In [4]: a = np.array([[1, 2, 3],[4, 5, 6]]) In [6]: print(a) [[1 2 3] [4 5 6]]Cheers, Jody
On 6 Jun 2019, at 07:11, Neal Becker <ndbecker2 at gmail.com> wrote:
Actually I found this pdf
https://www.mubdirahman.com/assets/lecture-6---advanced-plotting.pdf
starting at page 6 explained things quite wellOn Thu, Jun 6, 2019 at 10:02 AM Saito Kotaro (PSI) <kotaro.saito at psi.ch> > wrote:
I went through the exact same issue some years ago and wrote a note about
this confusing definition of the directions in 2D arrays.
It?s written in Japanese but I think it's still understandable for most of
you who can read matplotlib and numpy codes.
N行N列のnumpy arrayを扱う際に気をつけるべき方向と座標の定義 #Python - QiitaI hope this would help for improving a tutorial or the documentation.
Best regards,
Kotaro
//================//================//
Paul Scherrer Institut
Kotaro SAITO ???
Laboratory for Neutron Scattering and Imaging
WHGA/348
5232 Villigen PSI, Schweiz
+41 56 310 3179
kotaro.saito at psi.ch
Kotaro Saito - 斉藤耕太郎
//================//================//On 2019/6/ 6, at 15:42, Neal Becker <ndbecker2 at gmail.com> wrote:
My suggestion is to enhance the documentation. If there is a nice
tutorial, adding a link to it would be great.
Thanks,
NealOn Thu, Jun 6, 2019 at 9:39 AM Thomas Caswell <tcaswell at gmail.com> wrote:
There is also a tutorial addressing the ways that `origin` and `extent`
interact with each other:
https://matplotlib.org/tutorials/intermediate/imshow_extent.htmlAs for the transpose, that is due to one branch of math teaching use to
think (x, y) and different branch of math teaching us to think (row,
column). For `imshow` we use the second one which is consistent with how
numpy's repr of on array. It would be confusing if the data for
`my_array[0, :]` was displayed as the first _column_ of the image.I do not disagree that this is frustrating/confusing, there is a comment
in some of my grad school code which is basically "getting the (x,y) vs
(r,c) conversions right here took you an afternoon, don't touch this
again!"Tom
On Thu, Jun 6, 2019 at 9:31 AM Nathan Goldbaum <nathan12343 at gmail.com> > wrote:
This is discussed in the description of the "origin" keyword argument:
origin : {'upper', 'lower'}, optional
Place the [0,0] index of the array in the upper left or lower left corner
of the axes. The convention 'upper' is typically used for matrices and
images. If not given, rcParams["image.origin"] is used, defaulting to
'upper'.Note that the vertical axes points upward for 'lower' but downward for
'upper'.On Thu, Jun 6, 2019 at 9:25 AM Neal Becker <ndbecker2 at gmail.com> wrote:
I just wasted quite a bit of time trying to debug my code. It took a long
time because I was using imshow to debug, and didn't know that imshow
displays a matrix not using cartesian coordinates. The axes are transposed
and one axis is reversed. The documentation doesn't mention this.Thanks,
Neal_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page--
Thomas Caswell
tcaswell at gmail.com--
Those who don't understand recursion are doomed to repeat it
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page--
Those who don't understand recursion are doomed to repeat it
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page--
Jody Klymak
Jody M. Klymak - UVic Ocean Physics_______________________________________________
Matplotlib-users mailing list
Matplotlib-users at python.org
Matplotlib-users Info Page
--
Thomas Caswell
tcaswell at gmail.com
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Unfortunately, that "-major" terminology is hopelessly confusing--as we
have just seen.
On 2019/06/06 7:12 AM, Thomas Caswell wrote:
MATLAB and FORTRAN are column-major, C/c++ and numpy are row-major.