Mailman 3 June 2014 - scikit-image

SLIC for Grayscale
by Shadi AlBarqouni June 4, 2014

June 4, 2014

Hi Guys, Actually i would like to apply SLIC on grayscale images, i have tried the available one in MATLAB, but still needs the image to be color images, so is there any proposed solution? Regards, Shadi

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Re: ANN: scikit-image 0.10.0
by Matthew Brett June 4, 2014

June 4, 2014

On Monday, June 2, 2014 4:05:03 PM UTC-7, Stéfan van der Walt wrote: > > On Tue, Jun 3, 2014 at 1:01 AM, Michael Aye <kmicha...(a)gmail.com > <javascript:>> wrote: > > I just went through a quite painful learning process with all this, > because > > the docs are quite scattered, with lots of old stuff mixed in. So I'd > like > > to comment, in case you don't insist on the Travis build machinery to be > > MacPython (is that even possible, as Travis runs on Linux?), to speed > things > > up IMMENSELY over using pip for requirements install, one can use conda > for > > the env setup in Travis. It speeds things up, because pip doesn't store > > binaries and instead spends a lot of time compiling things (or so it > says > > here: http://sburns.org/2014/03/28/faster-travis-builds.html). Find the > > instructions on how to setup Travis with conda in that link, I tried it > and > > it works fine here. > > I did one thing different compared to that blog entry though: In the > travis > > yaml I prefer to "script: python setup.py develop && python setup.py > test", > > because that way I don't get a useless test run in case the develop > install > > fails due to a bug. > > This works great--I've used that approach for our Windows buildbot too. > > On Linux, since we have apt-get, it's less of an issue, even though we > currently build Matplotlib from source to get the latest version. > Some of us are building wheels for the travis linux machines too - see for example: https://github.com/matthew-brett/nipy/blob/12f96d576be0b7c841a65b095ae0b70a… That gives me the latest released version of matplotlib. The wheels are pretty easy to build too, just create a vagrant virtual machine, and you're ready to go: https://gist.github.com/matthew-brett/714b50bd3159d416981a Cheers, Matthew

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Re: ANN: scikit-image 0.10.0
by Matthew Brett June 4, 2014

June 4, 2014

Hi, On Monday, June 2, 2014 1:35:11 PM UTC-7, Stéfan van der Walt wrote: > > Hi Matthew > > On Mon, Jun 2, 2014 at 7:29 PM, Matthew Brett <matthe...(a)gmail.com > <javascript:>> wrote: > > I guess it would be good to build them automatically next time ... so > maybe > > yes to include the scripts - should be only a few lines of shell I > think, > > once you've got the MacPythons installed. > > I admit that I am out of my depth here. Would you be willing to help > us on this one? Is it, e.g., possible to add a target to setup.py so > that anyone on OSX can build these? > Er - I am not sure how that could sensibly be done in the setup.py, because you'll need to follow something like the recipe on the Spinning Wheels page, installing MacPythons, pip and packages, then running ``$PYTHON setup.py bdist_wheel`` for each Python. You're welcome to a login to one of our Berkeley machines where the basic setup is already done. In the Travis-CI example, how does Travis get triggered? (I presume > you don't commit to that repository each time you want to test MPL?) > Actually, yes, that is how I / we trigger builds at the moment, because we just got going with these megatesting things. There seem to be various options for triggering builds, including (I suppose) making fake commits, but I haven't investigated very far yet. See you, Matthew

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Re: Numpy Array printing algorithms
by Stéfan van der Walt June 3, 2014

June 3, 2014

On Tue, Jun 3, 2014 at 4:13 PM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> wrote: > @Stéfan, yes, but why the two spaces after each comma? I think that's weird. I don't know, but my guess would be readability: In [15]: np.linspace(999, 1024, 13) Out[15]: array([ 999. , 1001.08333333, 1003.16666667, 1005.25 , 1007.33333333, 1009.41666667, 1011.5 , 1013.58333333, 1015.66666667, 1017.75 , 1019.83333333, 1021.91666667, 1024. ]) In [15]: np.linspace(999, 1024, 13) Out[15]: array([ 999. , 1001.08333333, 1003.16666667, 1005.25 , 1007.33333333, 1009.41666667, 1011.5 , 1013.58333333, 1015.66666667, 1017.75 , 1019.83333333, 1021.91666667, 1024. ])

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Re: Numpy Array printing algorithms
by Juan Nunez-Iglesias June 3, 2014

June 3, 2014

@Stéfan, yes, but why the two spaces after each comma? I think that's weird. On Tue, Jun 3, 2014 at 5:03 PM, Stéfan van der Walt <stefan(a)mentat.za.net> wrote: > On Tue, Jun 3, 2014 at 7:00 AM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> > wrote: > > In [15]: lifio.parse_series_name(name2)[1][:5] > > Out[15]: array([ 63. , 63.5, 64. , 64.5, 65. ]) > > > > In [16]: lifio.parse_series_name(name2, 1)[1][:5] > > Out[16]: array([ 63., 64., 65., 66., 67.]) > > Numpy is lining up the decimals for you: > > In [15]: np.linspace(999, 1024, 13) > Out[15]: > array([ 999. , 1001.08333333, 1003.16666667, 1005.25 , > 1007.33333333, 1009.41666667, 1011.5 , 1013.58333333, > 1015.66666667, 1017.75 , 1019.83333333, 1021.91666667, > 1024. ]) > > Stéfan > > -- > You received this message because you are subscribed to the Google Groups > "scikit-image" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to scikit-image+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/d/optout. >

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Re: Numpy Array printing algorithms
by Stéfan van der Walt June 3, 2014

June 3, 2014

On Tue, Jun 3, 2014 at 7:00 AM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> wrote: > In [15]: lifio.parse_series_name(name2)[1][:5] > Out[15]: array([ 63. , 63.5, 64. , 64.5, 65. ]) > > In [16]: lifio.parse_series_name(name2, 1)[1][:5] > Out[16]: array([ 63., 64., 65., 66., 67.]) Numpy is lining up the decimals for you: In [15]: np.linspace(999, 1024, 13) Out[15]: array([ 999. , 1001.08333333, 1003.16666667, 1005.25 , 1007.33333333, 1009.41666667, 1011.5 , 1013.58333333, 1015.66666667, 1017.75 , 1019.83333333, 1021.91666667, 1024. ]) Stéfan

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Re: Numpy Array printing algorithms
by Juan Nunez-Iglesias June 3, 2014

June 3, 2014

Thanks Tony, that was very informative! Particularly the bit about look-ahead for alignment. But, since I presumably don't want to litter all my docstrings with `np.set_printoptions` statements, it's not useful to my use-case, since I need to be able to predict the "standard" output from numpy. In my case, there are definitely no values > 100 in that array, so the two spaces are superfluous. Ech. On Tue, Jun 3, 2014 at 3:58 PM, Tony Yu <tsyu80(a)gmail.com> wrote: > > > > On Tue, Jun 3, 2014 at 12:00 AM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> > wrote: > >> Hi All, >> >> This is more suited for a numpy list but thought I'd try the home crowd >> first. Does anyone know how numpy decides to print arrays? It is a pain >> getting doctests to pass without knowing the system. Example: >> >> In [15]: lifio.parse_series_name(name2)[1][:5] >> Out[15]: array([ 63. , 63.5, 64. , 64.5, 65. ]) >> >> In [16]: lifio.parse_series_name(name2, 1)[1][:5] >> Out[16]: array([ 63., 64., 65., 66., 67.]) >> >> Note: >> >> It goes 63.<space>,<space-space>63.5,<space-space>64.<space> etc. Note >> also the space after the opening brace but not the closing brace. >> Basically, you can't go by PEP8, so it's a pain predicting what the actual >> printout is going to be. I get the space after the . so that the .5's will >> align, but what about the leading spaces? >> >> Juan. >> > > Part of what you're looking for is set by `np.set_printoptions`. For > example, you can write something like > > In [31]: np.set_printoptions(formatter={'all': lambda x: '|{}'.format(x)}, > precision=3) > > In [32]: a > Out[32]: > array([|10.0, |10.5, |11.0, |11.5, |12.0, |12.5, |13.0, |13.5, |14.0, > |14.5, |15.0, |15.5, |16.0, |16.5, |17.0, |17.5, |18.0, |18.5, > |19.0, |19.5, |20.0]) > > In [33]: print a > [|10.0 |10.5 |11.0 |11.5 |12.0 |12.5 |13.0 |13.5 |14.0 |14.5 |15.0 |15.5 > |16.0 |16.5 |17.0 |17.5 |18.0 |18.5 |19.0 |19.5 |20.0] > > Note that `repr` adds commas after values (and the `array(` prefix), while > `str` doesn't. There's some magic in the default formatter that I haven't > dug into. Somehow, it's able to read all the values beforehand so that it > can determine the maximum width needed to align values. So for example, you > might write this > > In [38]: a = np.array([1, 10, 100, 1000]) > > and get this as output > > In [39]: print a > [|1 |10 |100 |1000] > > But what you really wanted was this > > In [40]: np.set_printoptions(formatter={'all': lambda x: > '|{:4}'.format(x)}, precision=3) > > In [41]: print a > [| 1 | 10 | 100 |1000] > > Ideally that `{:4}` could be determined after looking at the entire array. > That part is a bit of a mystery to me. > > -- > You received this message because you are subscribed to the Google Groups > "scikit-image" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to scikit-image+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/d/optout. >

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Re: Numpy Array printing algorithms
by Tony Yu June 3, 2014

June 3, 2014

On Tue, Jun 3, 2014 at 12:00 AM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> wrote: > Hi All, > > This is more suited for a numpy list but thought I'd try the home crowd > first. Does anyone know how numpy decides to print arrays? It is a pain > getting doctests to pass without knowing the system. Example: > > In [15]: lifio.parse_series_name(name2)[1][:5] > Out[15]: array([ 63. , 63.5, 64. , 64.5, 65. ]) > > In [16]: lifio.parse_series_name(name2, 1)[1][:5] > Out[16]: array([ 63., 64., 65., 66., 67.]) > > Note: > > It goes 63.<space>,<space-space>63.5,<space-space>64.<space> etc. Note > also the space after the opening brace but not the closing brace. > Basically, you can't go by PEP8, so it's a pain predicting what the actual > printout is going to be. I get the space after the . so that the .5's will > align, but what about the leading spaces? > > Juan. > Part of what you're looking for is set by `np.set_printoptions`. For example, you can write something like In [31]: np.set_printoptions(formatter={'all': lambda x: '|{}'.format(x)}, precision=3) In [32]: a Out[32]: array([|10.0, |10.5, |11.0, |11.5, |12.0, |12.5, |13.0, |13.5, |14.0, |14.5, |15.0, |15.5, |16.0, |16.5, |17.0, |17.5, |18.0, |18.5, |19.0, |19.5, |20.0]) In [33]: print a [|10.0 |10.5 |11.0 |11.5 |12.0 |12.5 |13.0 |13.5 |14.0 |14.5 |15.0 |15.5 |16.0 |16.5 |17.0 |17.5 |18.0 |18.5 |19.0 |19.5 |20.0] Note that `repr` adds commas after values (and the `array(` prefix), while `str` doesn't. There's some magic in the default formatter that I haven't dug into. Somehow, it's able to read all the values beforehand so that it can determine the maximum width needed to align values. So for example, you might write this In [38]: a = np.array([1, 10, 100, 1000]) and get this as output In [39]: print a [|1 |10 |100 |1000] But what you really wanted was this In [40]: np.set_printoptions(formatter={'all': lambda x: '|{:4}'.format(x)}, precision=3) In [41]: print a [| 1 | 10 | 100 |1000] Ideally that `{:4}` could be determined after looking at the entire array. That part is a bit of a mystery to me.

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Numpy Array printing algorithms
by Juan Nunez-Iglesias June 3, 2014

June 3, 2014

Hi All, This is more suited for a numpy list but thought I'd try the home crowd first. Does anyone know how numpy decides to print arrays? It is a pain getting doctests to pass without knowing the system. Example: In [15]: lifio.parse_series_name(name2)[1][:5] Out[15]: array([ 63. , 63.5, 64. , 64.5, 65. ]) In [16]: lifio.parse_series_name(name2, 1)[1][:5] Out[16]: array([ 63., 64., 65., 66., 67.]) Note: It goes 63.<space>,<space-space>63.5,<space-space>64.<space> etc. Note also the space after the opening brace but not the closing brace. Basically, you can't go by PEP8, so it's a pain predicting what the actual printout is going to be. I get the space after the . so that the .5's will align, but what about the leading spaces? Juan.

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Re: ANN: scikit-image 0.10.0
by Matthew Brett June 2, 2014

June 2, 2014

Hi, On Sunday, June 1, 2014 3:35:24 AM UTC-7, Stefan van der Walt wrote: > > On Fri, May 30, 2014 at 2:26 AM, Matthew Brett <matthe...(a)gmail.com > <javascript:>> wrote: > > Congratulations? Any chance of putting up some OSX wheels on pypi? > > Sorry, folks, I've been living in a terminal for the past few days. > > This is quite a landmark: our first release that comes with a paper! > Johannes, thanks very much for handling the logistics. > > Matthew, thanks for those Wheels--can I simply upload them by hand? > Also, you pointed us to the recipes, so is the idea that we include > scripts in the project to build them automatically next time? You can just simply upload them by hand, please do if that is easiest. I guess it would be good to build them automatically next time ... so maybe yes to include the scripts - should be only a few lines of shell I think, once you've got the MacPythons installed. Alternatively, you could do something super-fancy like using travis-ci to build and test them for you - template of something related here: https://github.com/matthew-brett/mpl_mac_testing luv, Matthew

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