Re: [Numpy-discussion] migration of all scipy.org mailing lists

nabble is the other site that probably should be notified of the mailing list move. On Wed, Mar 22, 2017 at 10:11 AM, Nathan Goldbaum wrote: > Are you sure about astropy? They recently moved to google groups. > > > On Wednesday, March 22, 2017, Ralf Gommers

Re: [Numpy-discussion] Move scipy.org docs to Github?

How is it that scipy.org's bandwidth usage is that much greater than matplotlib.org's? We are quite image-heavy, but we haven't hit any bandwidth limits that I am aware of. Ben Root On Wed, Mar 15, 2017 at 9:16 AM, Bryan Van de ven wrote: > NumPy is a NumFocus fiscally

Re: [Numpy-discussion] Numpy Overhead

You are going to need to provide much more context than that. Overhead compared to what? And where (io, cpu, etc.)? What are the size of your arrays, and what sort of operations are you doing? Finally, how much overhead are you seeing? There can be all sorts of reasons for overhead, and some can

Re: [Numpy-discussion] automatically avoiding temporary arrays

Root On Mon, Feb 27, 2017 at 2:31 PM, Matthew Brett <matthew.br...@gmail.com> wrote: > Hi, > > On Mon, Feb 27, 2017 at 11:27 AM, Charles R Harris > <charlesr.har...@gmail.com> wrote: > > > > > > On Mon, Feb 27, 2017 at 11:43 AM, Benjamin Root <ben.

Re: [Numpy-discussion] Deprecating matrices.

That's not a bad idea. Matplotlib is currently considering something similar for its mlab module. It has been there since the beginning, but it is very outdated and very out-of-scope for matplotlib. However, there are still lots of code out there that depends on it. So, we are looking to split it

Re: [Numpy-discussion] Casting to np.byte before clearing values

Might be os-specific, too. Some virtual memory management systems might special case the zeroing out of memory. Try doing the same thing with a different value than zero. On Dec 26, 2016 6:15 AM, "Nicolas P. Rougier" wrote: Thanks for the explanation Sebastian, makes

Re: [Numpy-discussion] How to use user input as equation directly

"only be used by engineers/scientists for research" Famous last words. I know plenty of scientists who would love to "do research" with an exposed eval(). Full disclosure, I personally added a security hole into matplotlib thinking I covered all my bases in protecting an eval() statement. Ben

Re: [Numpy-discussion] How to use user input as equation directly

Perhaps the numexpr package might be safer? Not exactly meant for this situation (meant for optimizations), but the evaluator is pretty darn safe. Ben Root On Thu, Oct 27, 2016 at 5:33 PM, John Ladasky wrote: > This isn't just a Numpy issue. You are interested in Python's

Re: [Numpy-discussion] assert_allclose equal_nan default value.

+1. I was almost always setting it to True anyway. On Thu, Oct 20, 2016 at 1:18 PM, Nathan Goldbaum wrote: > Agreed, especially given the prevalence of using this function in > downstream test suites: > > https://github.com/search?utf8=%E2%9C%93=numpy+assert_ >

Re: [Numpy-discussion] how to name "contagious" keyword in np.ma.convolve

Why not "propagated"? On Fri, Oct 14, 2016 at 1:08 PM, Sebastian Berg wrote: > On Fr, 2016-10-14 at 13:00 -0400, Allan Haldane wrote: > > Hi all, > > > > Eric Wieser has a PR which defines new functions np.ma.correlate and > > np.ma.convolve: > > > >

Re: [Numpy-discussion] automatically avoiding temporary arrays

With regards to arguments about holding onto large arrays, I would like to emphasize that my original suggestion mentioned weakref'ed numpy arrays. Essentially, the idea is to claw back only the raw memory blocks during that limbo period between discarding the numpy array python object and when

Re: [Numpy-discussion] Vendorize tempita

This is the first I am hearing of tempita (looks to be a templating language). How is it a dependency of numpy? Do I now need tempita in order to use numpy, or is it a build-time-only dependency? Ben On Fri, Sep 30, 2016 at 11:13 AM, Stephan Hoyer wrote: > One way to do this

Re: [Numpy-discussion] Which NumPy/Numpy/numpy spelling?

There was similar discussion almost two years ago with respect to capitalization of matplotlib in prose. Most of the time, it was lower-case in our documentation, but then the question was if it should be upper-case at the beginning of the sentence... or should it always be upper-cased like a

Re: [Numpy-discussion] NumPy in PyPy

Don't know if it is what you are looking for, but NumPy has a built-in suite of benchmarks: http://docs.scipy.org/doc/numpy/reference/generated/numpy.testing.Tester.bench.html Also, some projects have taken to utilizing the "airspeed velocity" utility to track benchmarking stats for their

Re: [Numpy-discussion] Is there any official position on PEP484/mypy?

One thing that I have always wished for from a project like mypy is the ability to annotate what the expected shape should be. Too often, I get a piece of code from a coworker and it comes with no docstring explaining the expected dimensions of the input arrays and what the output array is going

Re: [Numpy-discussion] Added atleast_nd, request for clarification/cleanup of atleast_3d

' or 'right' or maybe something akin to what at_least3d() implements. On Wed, Jul 6, 2016 at 3:20 PM, Joseph Fox-Rabinovitz < jfoxrabinov...@gmail.com> wrote: > On Wed, Jul 6, 2016 at 2:57 PM, Eric Firing <efir...@hawaii.edu> wrote: > > On 2016/07/06 8:25 AM, Benjamin Root wrote:

Re: [Numpy-discussion] Added atleast_nd, request for clarification/cleanup of atleast_3d

I wouldn't have the keyword be "where", as that collides with the notion of "where" elsewhere in numpy. On Wed, Jul 6, 2016 at 2:21 PM, Joseph Fox-Rabinovitz < jfoxrabinov...@gmail.com> wrote: > I still think this function is useful. I have made a change so that it > only accepts one array, as

Re: [Numpy-discussion] Added atleast_nd, request for clarification/cleanup of atleast_3d

While atleast_1d/2d/3d predates my involvement in numpy, I am probably partly to blame for popularizing them as I helped to fix them up a fair amount. I wouldn't call its use "guessing". Rather, I would treat them as useful input sanitizers. If your function is going to be doing 2d indexing on an

Re: [Numpy-discussion] Axis argument to np.unique

That seems like the only reasonable behavior, but I will admit that my initial desire is essentially a vectorized "unique" such that it returns the unique values of the stated axis. But that isn't possible because there can be different number of unique values in the given axis, resulting in a

Re: [Numpy-discussion] A numpy based Entity-Component-System

As a bit of a real-life example where things can go wrong with naming. The "pylab" name was accidentally hijacked a couple years ago on pypi, and caused several bug reports to be filed against matplotlib for failing scripts. Some people thought that one should do "pip install pylab" to do "from

Re: [Numpy-discussion] Numpy arrays shareable among related processes (PR #7533)

Oftentimes, if one needs to share numpy arrays for multiprocessing, I would imagine that it is because the array is huge, right? So, the pickling approach would copy that array for each process, which defeats the purpose, right? Ben Root On Wed, May 11, 2016 at 2:01 PM, Allan Haldane

Re: [Numpy-discussion] Help with bit arrays

ape (256, 256, 4). For > > grayscale images, the shape is (256, 256). So the image seems to have > > been loaded as a color image. > > > > 2016-04-29 13:38 GMT-03:00 Benjamin Root <ben.v.r...@gmail.com>: > >> What kind of array is "img"? What is its

Re: [Numpy-discussion] Help with bit arrays

What kind of array is "img"? What is its dtype and shape? plt.imshow() will use the default colormap for matplotlib if the given array is just 2D. But if it is 3D (a 2D array of RGB[A] channels), then it will forego the colormap and utilize that for the colors. It knows nothing of the colormap

[Numpy-discussion] Possible negative impact of future change to None comparison

Working on closing out some bug reports at work, and I ran into one about comparisons to 'None' will result in elementwise object comparison in the future. Now, I totally get the idea behind the change, and I am not here to argue that decision. However, I have come across a situation where the

Re: [Numpy-discussion] linux wheels coming soon

Yeah! That's the bug I encountered! So, that would explain why this seems to work fine now (I tried it out a bit on Friday on a CentOS6 system, but didn't run the test suite). Cheers! Ben Root On Sun, Apr 17, 2016 at 1:46 PM, Olivier Grisel wrote: > Thanks for the

Re: [Numpy-discussion] linux wheels coming soon

016 at 12:07 PM, Nathaniel Smith <n...@pobox.com> wrote: > >> On Apr 14, 2016 11:11 AM, "Benjamin Root" <ben.v.r...@gmail.com> wrote: >> > >> > Are we going to have to have documentation somewhere making it clear >> that the numpy wheel shouldn't

Re: [Numpy-discussion] linux wheels coming soon

Are we going to have to have documentation somewhere making it clear that the numpy wheel shouldn't be used in a conda environment? Not that I would expect this issue to come up all that often, but I could imagine a scenario where a non-scientist is simply using a base conda distribution because

Re: [Numpy-discussion] Multidimension array access in C via Python API

You might do better using scipy.spatial. It has very useful data structures for handling spatial coordinates. I am not exactly sure how to use them for this specific problem (not a domain expert), but I would imagine that the QHull wrappers there might give you some useful tools. Ben Root On

Re: [Numpy-discussion] Reflect array?

< jfoxrabinov...@gmail.com> wrote: > On Tue, Mar 29, 2016 at 1:46 PM, Benjamin Root <ben.v.r...@gmail.com> > wrote: > > Is there a quick-n-easy way to reflect a NxM array that represents a > > quadrant into a 2Nx2M array? Essentially, I am trying to reduce the size >

[Numpy-discussion] Reflect array?

Is there a quick-n-easy way to reflect a NxM array that represents a quadrant into a 2Nx2M array? Essentially, I am trying to reduce the size of an expensive calculation by taking advantage of the fact that the first part of the calculation is just computing gaussian weights, which is radially

Re: [Numpy-discussion] reshaping empty array bug?

On Tue, Feb 23, 2016 at 3:30 PM, Nathaniel Smith wrote: > What should this do? > > np.zeros((12, 0)).reshape((10, -1, 2)) > It should error out, I already covered that. 12 != 20. Ben Root ___ NumPy-Discussion mailing list

Re: [Numpy-discussion] reshaping empty array bug?

On Tue, Feb 23, 2016 at 3:14 PM, Nathaniel Smith wrote: > Sure, it's totally ambiguous. These are all legal: I would argue that except for the first reshape, all of those should be an error, and that the current algorithm is buggy. This isn't a heuristic. It isn't guessing.

Re: [Numpy-discussion] reshaping empty array bug?

bug to me. Ben Root On Tue, Feb 23, 2016 at 1:58 PM, Sebastian Berg <sebast...@sipsolutions.net> wrote: > On Di, 2016-02-23 at 11:45 -0500, Benjamin Root wrote: > > but, it isn't really ambiguous, is it? The -1 can only refer to a > > single dimension, and if you ignore th

Re: [Numpy-discussion] reshaping empty array bug?

On Tue, Feb 23, 2016 at 11:32 AM, Benjamin Root <ben.v.r...@gmail.com> > wrote: > >> Not exactly sure if this should be a bug or not. This came up in a fairly >> general function of mine to process satellite data. Unexpectedly, one of >> the satellite files had n

[Numpy-discussion] reshaping empty array bug?

Not exactly sure if this should be a bug or not. This came up in a fairly general function of mine to process satellite data. Unexpectedly, one of the satellite files had no scans in it, triggering an exception when I tried to reshape the data from it. >>> import numpy as np >>> a = np.zeros((0,

Re: [Numpy-discussion] [Suggestion] Labelled Array

tplotlib's mlab submodule. >> > >> > I've been in situations (::cough:: Esri production ::cough::) where I've >> > had one hand tied behind my back and unable to install pandas. mlab was >> > a big help there. >> > >> > https://goo.gl/M7M

Re: [Numpy-discussion] [Suggestion] Labelled Array

Seems like you are talking about xarray: https://github.com/pydata/xarray Cheers! Ben Root On Fri, Feb 12, 2016 at 9:40 AM, Sérgio wrote: > Hello, > > This is my first e-mail, I will try to make the idea simple. > > Similar to masked array it would be interesting to use a

Re: [Numpy-discussion] [Suggestion] Labelled Array

Re-reading your post, I see you are talking about something different. Not exactly sure what your use-case is. Ben Root On Fri, Feb 12, 2016 at 9:49 AM, Benjamin Root <ben.v.r...@gmail.com> wrote: > Seems like you are talking about xarray: https://github.com/pydata/xarray > > Che

Re: [Numpy-discussion] Deprecating `numpy.iterable`

Huh... matplotlib could use that! We have been using our own internal function left over from the numerix days, I think. Ben Root On Thu, Feb 11, 2016 at 2:12 PM, Nathaniel Smith wrote: > Oh wow, yeah, there are tons of uses: > >

Re: [Numpy-discussion] Bump warning stacklevel

I like the idea of bumping the stacklevel in principle, but I am not sure it is all that practical. For example, if a warning came up when doing "x / y", I am assuming that it is emitted from within the ufunc np.divide(). So, you would need two stacklevels based on whether the entry point was the

Re: [Numpy-discussion] Behavior of np.random.uniform

Are there other functions where this behavior may or may not be happening? If it isn't consistent across all np.random functions, it probably should be, one way or the other. Ben Root On Tue, Jan 19, 2016 at 5:10 AM, Jaime Fernández del Río < jaime.f...@gmail.com> wrote: > Hi all, > > There is

Re: [Numpy-discussion] Should I use pip install numpy in linux?

Travis - I will preface the following by pointing out how valuable miniconda and anaconda has been for our workplace because we were running into issues with ensuring that everyone in our mixed platform office had access to all the same tools, particularly GDAL, NetCDF4 and such. For the longest

Re: [Numpy-discussion] Fast Access to Container of Numpy Arrays on Disk?

A warning about HDF5. It is not a database format, so you have to be extremely careful if the data is getting updated while it is open for reading by anybody else. If it is strictly read-only, and no body else is updating it, then have at it! Cheers! Ben Root On Thu, Jan 14, 2016 at 9:16 AM,

Re: [Numpy-discussion] Should I use pip install numpy in linux?

The other half of the fun is how to deal with weird binary issues with libraries like libgeos_c, libhdf5 and such. You have to get all of the compile options right for your build of those libraries to get your build of GDAL and pyhdf working right. You also have packages like gdal and netcdf4

Re: [Numpy-discussion] what would you expect A[none] to do?

TBH, I wouldn't have expected it to work, but now that I see it, it does make some sense. I would have thought that it would error out as being ambiguous (prepend? append?). I have always used ellipses to make it explicit where the new axis should go. But, thinking in terms of how regular indexing

Re: [Numpy-discussion] How to find indices of values in an array (indirect in1d) ?

Maybe use searchsorted()? I will note that I have needed to do something like this once before, and I found that the list comprehension form of calling .index() for each item was faster than jumping through hoops to vectorize it using searchsorted (needing to sort and then map the sorted indices

Re: [Numpy-discussion] Proposal: stop providing official win32 downloads (for now)

I believe that a lot can be learned from matplotlib's recent foray into appveyor. Don't hesitate to ask questions on our dev mailing list (I wasn't personally involved, so I don't know what was learned). Cheers! Ben Root On Fri, Dec 18, 2015 at 5:27 PM, Nathaniel Smith wrote: >

Re: [Numpy-discussion] A minor clarification no why count_nonzero is faster for boolean arrays

Would it make sense to at all to bring that optimization to np.sum()? I know that I have np.sum() all over the place instead of count_nonzero, partly because it is a MatLab-ism and partly because it is easier to write. I had no clue that there was a performance difference. Cheers! Ben Root On

Re: [Numpy-discussion] FeatureRequest: support for array construction from iterators

Devil's advocate here: np.array() has become the de-facto "constructor" for numpy arrays. Right now, passing it a generator results in what, IMHO, is a useless result: >>> np.array((i for i in range(10))) array( at 0x7f28b2beca00>, dtype=object) Passing pretty much any dtype argument will cause

Re: [Numpy-discussion] FeatureRequest: support for array construction from iterators

Heh, never noticed that. Was it implemented more like a generator/iterator in older versions of Python? Thanks, Ben Root On Mon, Dec 14, 2015 at 12:38 PM, Robert Kern <robert.k...@gmail.com> wrote: > On Mon, Dec 14, 2015 at 3:56 PM, Benjamin Root <ben.v.r...@gmail.com> > wrote

Re: [Numpy-discussion] ENH: Add the function 'expand_view'

How is this different from using np.newaxis and broadcasting? Or am I misunderstanding this? Ben Root On Tue, Nov 24, 2015 at 9:13 PM, wrote: > > > On Tue, Nov 24, 2015 at 7:13 PM, Nathaniel Smith wrote: > >> On Nov 24, 2015 11:57 AM, "John Kirkham"

Re: [Numpy-discussion] New functions added in pull request

Oooh, this will be nice to have. This would be one of the few times I would love to see unicode versions of these function names supplied, too. On Wed, Nov 25, 2015 at 5:31 PM, Antonio Lara wrote: > Hello, I have added three new functions to the file function_base.py in >

Re: [Numpy-discussion] loadtxt and usecols

Just pointing out np.loadtxt(..., ndmin=2) will always return a 2D array. Notice that without that option, the result is effectively squeezed. So if you don't specify that option, and you load up a CSV file with only one row, you will get a very differently shaped array than if you load up a CSV

Re: [Numpy-discussion] loadtxt and usecols

My personal rule for flexible inputs like that is that it should be encouraged so long as it does not introduce ambiguity. Furthermore, Allowing a scalar as an input doesn't add a congitive disconnect on the user on how to specify multiple columns. Therefore, I'd give this a +1. On Mon, Nov 9,

Re: [Numpy-discussion] New behavior of allclose

allclose() needs to return a bool so that one can do "if np.allclose(foo, bar) is True" or some such. The "good behavior" is for np.isclose() to return a memmap, which still confuses the heck out of me, but I am not a memmap expert. On Thu, Nov 5, 2015 at 4:50 PM, Ralf Gommers

Re: [Numpy-discussion] New behavior of allclose

I am not sure I understand what you mean. Specifically that np.isclose will return a memmap if one of the inputs is a memmap. The result is a brand new array, right? So, what is that result memmapping from? Also, how does this impact np.allclose()? That function returns a scalar True/False, so

Re: [Numpy-discussion] deprecate fromstring() for text reading?

? Ben On Mon, Nov 2, 2015 at 6:44 PM, Chris Barker <chris.bar...@noaa.gov> wrote: > On Tue, Oct 27, 2015 at 7:30 AM, Benjamin Root <ben.v.r...@gmail.com> > wrote: > >> FWIW, when I needed a fast Fixed Width reader >> > > was there potentially no whitespac

Re: [Numpy-discussion] Proposal: stop supporting 'setup.py install'; start requiring 'pip install .' instead

Conda is for binary installs and largely targeted for end-users. This topic pertains to source installs, and is mostly relevant to developers, testers, and those who like to live on the bleeding edge of a particular project. On Tue, Oct 27, 2015 at 10:31 AM, Edison Gustavo Muenz <

Re: [Numpy-discussion] deprecate fromstring() for text reading?

FWIW, when I needed a fast Fixed Width reader for a very large dataset last year, I found that np.genfromtext() was faster than pandas' read_fwf(). IIRC, pandas' text reading code fell back to pure python for fixed width scenarios. On Fri, Oct 23, 2015 at 8:22 PM, Chris Barker - NOAA Federal <

Re: [Numpy-discussion] Nansum function behavior

The change to nansum() happened several years ago. The main thrust of it was to make the following consistent: np.sum([]) # zero np.nansum([np.nan]) # zero np.sum([1]) # one np.nansum([np.nan, 1]) # one If you want to propagate masks and such, use masked arrays. Ben Root On Fri, Oct 23,

Re: [Numpy-discussion] Behavior of numpy.copy with sub-classes

In many other parts of numpy, calling the numpy function that had an equivalent array method would result in the method being called. I would certainly be surprised if the copy() method behaved differently from the np.copy() function. Now it is time for me to do some grepping of my code-bases...

Re: [Numpy-discussion] Making datetime64 timezone naive

I'd be totally in support of switching to timezone naive form. While it would be ideal that everyone stores their dates in UTC, the real world is messy and most of the time, people are just loading dates as-is and don't even care about timezones. I work on machines with different TZs, and I hate

Re: [Numpy-discussion] Let's move forward with the current governance document.

There is the concept of consensus-driven development, which centers on veto rights. It does assume that all actors are driven by a common goal to improve the project. For example, the fact that we didn't have consensus back during the whole NA brouhaha was actually a good thing because IMHO

Re: [Numpy-discussion] Python needs goto

Ow! Ow! Ow! I am just a meteorologist that has an obsession with looking up unfamiliar technology terms. I need a Tylenol... Ben Root On Fri, Sep 25, 2015 at 12:27 PM, Anne Archibald wrote: > goto! and comefrom! Together with exceptions, threads, lambda, super, >

Re: [Numpy-discussion] Python needs goto

Most of the time when I wanted to use goto in my early days, I found that breaks and continues were better and easier to understand. I will admit that there are occasional nested if/elif/else code that get messy without a goto. But which smells worse? A "goto" package or a complex if/elif/else?

Re: [Numpy-discussion] Governance model request

To expand on Ryan's point a bit about recusal... this is why we have a general policy against self-merging and why peer review is so valuable. A ban on self-merging is much like recusal, and I think it is a fantastic policy. As for a BDFL, I used to like that idea having seen it work well for

Re: [Numpy-discussion] 1.10.0rc1

Ok, I tested matplotlib master against numpy master, and there were no errors. I did get a bunch of new deprecation warnings though such as: /nas/home/broot/centos6/lib/python2.7/site-packages/matplotlib-1.5.dev1-py2.7-linux-x86_64.egg/matplotlib/colorbar.py:539: VisibleDeprecationWarning:

Re: [Numpy-discussion] 1.10.0rc1

that at the REPL doesn't produce a warning, so i am guessing that it is valid. Ben Root On Thu, Aug 27, 2015 at 10:44 AM, Sebastian Berg sebast...@sipsolutions.net wrote: On Do, 2015-08-27 at 08:04 -0600, Charles R Harris wrote: On Thu, Aug 27, 2015 at 7:52 AM, Benjamin Root ben.v.r

Re: [Numpy-discussion] 1.10.0rc1

it is entrenched in our codebase. Ben Root On Thu, Aug 27, 2015 at 11:33 AM, Sebastian Berg sebast...@sipsolutions.net wrote: On Do, 2015-08-27 at 11:15 -0400, Benjamin Root wrote: Ok, I just wanted to make sure I understood the issue before going bug hunting. Chances are, it has been a bug on our end

Re: [Numpy-discussion] 1.10.0rc1

Just a data point, I just tested 1.9.0rc1 (built from source) with matplotlib master, and things appear to be fine there. In fact, matplotlib was built against 1.7.x (I was hunting down a regression), and worked against the 1.9.0 install, so the ABI appears intact. Cheers! Ben Root On Wed, Aug

Re: [Numpy-discussion] 1.10.0rc1

Aw, crap... I looked at the list of tags and saw the rc1... I'll test again in the morning Grumble, grumble... On Aug 26, 2015 10:53 PM, Nathaniel Smith n...@pobox.com wrote: On Aug 26, 2015 7:03 PM, Benjamin Root ben.v.r...@gmail.com wrote: Just a data point, I just tested 1.9.0rc1

Re: [Numpy-discussion] Development workflow (not git tutorial)

I used to be a huge advocate for the develop mode, but not anymore. I have run into way too many Heisenbugs that would clear up if I nuked my source tree and re-clone. I should also note that there is currently an open issue with pip install -e and namespace packages. This has been reported to

Re: [Numpy-discussion] Multiarray API size mismatch 301 302?

Did you do a git clean -fxd before re-installing? On Thu, Aug 13, 2015 at 2:34 PM, Sebastian Berg sebast...@sipsolutions.net wrote: Hey, just for hacking/testing, I tried to add to shape.c: /*NUMPY_API * * Checks if memory overlap exists */ NPY_NO_EXPORT int

Re: [Numpy-discussion] np.in1d() sets, bug?

...@sipsolutions.net wrote: On Mo, 2015-08-10 at 12:09 -0400, Benjamin Root wrote: Just came across this one today: np.in1d([1], set([0, 1, 2]), assume_unique=True) array([ False], dtype=bool) np.in1d([1], [0, 1, 2], assume_unique=True) array([ True], dtype=bool) I am assuming this has

[Numpy-discussion] np.in1d() sets, bug?

Just came across this one today: np.in1d([1], set([0, 1, 2]), assume_unique=True) array([ False], dtype=bool) np.in1d([1], [0, 1, 2], assume_unique=True) array([ True], dtype=bool) I am assuming this has something to do with the fact that order is not guaranteed with set() objects? I was kind

Re: [Numpy-discussion] numpy where and dtype in 1.9

What a coincidence! A very related bug just got re-opened today at my behest: https://github.com/numpy/numpy/issues/5095 Not the same, but I wouldn't be surprised if it stems from the same sources. The short of it... np.where(x, 0, x) where x is a masked array, will return a masked array in

Re: [Numpy-discussion] Numpy V1.4.1 software license

I think there is a misunderstanding. What you are seeing is documentation on how to use f90 for compiling, which then outputs some stuff to the terminal, which is being shown in the documentation. We don't actually include any compilers in numpy. Ben Root On Wed, Jul 8, 2015 at 11:53 AM, Nguyen,

Re: [Numpy-discussion] DEP: Deprecate boolean array indices with non-matching shape #4353

On Thu, Jun 4, 2015 at 10:41 PM, Nathaniel Smith n...@pobox.com wrote: My comment was about the second type. Are your comments about the second type? The second type definitely does not produce a flattened array: I was talking about the second type in that I never even knew it existed. My

Re: [Numpy-discussion] DEP: Deprecate boolean array indices with non-matching shape #4353

On Thu, Jun 4, 2015 at 9:04 PM, Nathaniel Smith n...@pobox.com wrote: On Thu, Jun 4, 2015 at 5:57 PM, Nathaniel Smith n...@pobox.com wrote: One place where the current behavior is particularly baffling and annoying is when you have multiple boolean masks in the same indexing operation. I

Re: [Numpy-discussion] Verify your sourceforge windows installer downloads

Speaking from the matplotlib project, our binaries are substantial due to our suite of test images. Pypi worked with us on relaxing size constraints. Also, I think the new cheese shop/warehouse server they are using scales better, so size is not nearly the same concern as before. Ben Root On May

Re: [Numpy-discussion] Two questions about PEP 465 dot product

, having a page I can point them to would be extremely valuable. Ben Root On Fri, May 22, 2015 at 4:05 PM, Nathaniel Smith n...@pobox.com wrote: On May 22, 2015 11:34 AM, Benjamin Root ben.r...@ou.edu wrote: At some point, someone is going to make a single documentation page describing all

Re: [Numpy-discussion] Two questions about PEP 465 dot product

Then add in broadcasting behavior... On Fri, May 22, 2015 at 4:58 PM, Nathaniel Smith n...@pobox.com wrote: On May 22, 2015 1:26 PM, Benjamin Root ben.r...@ou.edu wrote: That assumes that the said recently-confused ever get to the point of understanding it... Well, I don't think it's

Re: [Numpy-discussion] Two questions about PEP 465 dot product

At some point, someone is going to make a single documentation page describing all of this, right? Tables, mathtex, and such? I get woozy whenever I see this discussion go on. Ben Root On Fri, May 22, 2015 at 2:23 PM, Nathaniel Smith n...@pobox.com wrote: On May 22, 2015 11:00 AM, Alexander

Re: [Numpy-discussion] Bug in np.nonzero / Should index returning functions return ndarray subclasses?

On Sat, May 9, 2015 at 4:03 PM, Nathaniel Smith n...@pobox.com wrote: Not sure what this has to do with Jaime's post about nonzero? There is indeed a potential question about what 3-argument where() should do with subclasses, but that's effectively a different operation entirely and to

Re: [Numpy-discussion] Bug in np.nonzero / Should index returning functions return ndarray subclasses?

Absolutely, it should be writable. As for subclassing, that might be messy. Consider the following: inds = np.where(data 5) In that case, I'd expect a normal, bog-standard ndarray because that is what you use for indexing (although pandas might have a good argument for having it return one of

Re: [Numpy-discussion] read not byte aligned records

I have been very happy with the bitarray package. I don't know if it is faster than bitstring, but it is worth a mention. Just watch out for any hashing operations on its objects, it doesn't seem to do them right (set(), dict(), etc...), but comparison operations work just fine. Ben Root

[Numpy-discussion] how to set a fixed sized dtype suitable for bitwise operations

I have a need to have a numpy array of 17 byte (more specifically, at least 147 bits) values that I would be doing some bit twiddling on. I have found that doing a dtype of i17 yields a dtype of int32, which is completely not what I intended. Doing 'u17' gets an data type not understood. I have

Re: [Numpy-discussion] how to set a fixed sized dtype suitable for bitwise operations

at 10:19 AM, Jaime Fernández del Río jaime.f...@gmail.com wrote: On Tue, Apr 28, 2015 at 7:00 AM, Benjamin Root ben.r...@ou.edu wrote: I have a need to have a numpy array of 17 byte (more specifically, at least 147 bits) values that I would be doing some bit twiddling on. I have found

Re: [Numpy-discussion] Automatic number of bins for numpy histograms

Then you can set about convincing matplotlib and friends to use it by default Just to note, this proposal was originally made over in the matplotlib project. We sent it over here where its benefits would have wider reach. Matplotlib's plan is not to change the defaults, but to offload as much as

Re: [Numpy-discussion] Advanced indexing: fancy vs. orthogonal

The distinction that boolean indexing has over the other 2 methods of indexing is that it can guarantee that it references a position at most once. Slicing and scalar indexes are also this way, hence why these methods allow for in-place assignments. I don't see boolean indexing as an extension of

Re: [Numpy-discussion] IDE's for numpy development?

mixed C and python development? I would just wait for the Jupyter folks to create IC and maybe even IC++! On Wed, Apr 1, 2015 at 12:04 PM, Charles R Harris charlesr.har...@gmail.com wrote: Hi All, In a recent exchange Mark Wiebe suggested that the lack of support for numpy development in

Re: [Numpy-discussion] Adding 'where' to ufunc methods?

Another usecase would be for MaskedArrays. ma.masked_array.min() wouldn't have to make a copy anymore (there is a github issue about that). It could just pass its mask into the where= argument of min() and be done with it. Problem would be generalizing situations where where= effectively results

Re: [Numpy-discussion] Do you find this behavior surprising?

other magical object that doesn't work like anything else in python. Ben Root On Wed, Mar 25, 2015 at 5:11 PM, Jaime Fernández del Río jaime.f...@gmail.com wrote: On Wed, Mar 25, 2015 at 1:45 PM, Benjamin Root ben.r...@ou.edu wrote: I fail to see the wtf. flags = a.flags So, flags

Re: [Numpy-discussion] Do you find this behavior surprising?

I fail to see the wtf. flags = a.flags So, flags at this point is just an alias to a.flags, just like any other variable in python flags.writeable = False would then be equivalent to a.flags.writeable = False. There is nothing numpy-specific here. a.flags is mutable object. This is how Python

Re: [Numpy-discussion] Numpy 1.10

Release minion? Sounds a lot like an academic minion: https://twitter.com/academicminions On Fri, Mar 13, 2015 at 2:51 AM, Ralf Gommers ralf.gomm...@gmail.com wrote: On Fri, Mar 13, 2015 at 7:29 AM, Jaime Fernández del Río jaime.f...@gmail.com wrote: On Thu, Mar 12, 2015 at 10:16 PM,

Re: [Numpy-discussion] Numpy where

I think the question is if scalars should be acceptable for the first argument, not if it should be for the 2nd and 3rd argument. If scalar can be given for the first argument, the the first three makes sense. Although, I have no clue why we would allow that. Ben Root On Mar 12, 2015 9:25 PM,

Re: [Numpy-discussion] Adding keyword to asarray and asanyarray.

On Fri, Mar 6, 2015 at 7:59 AM, Charles R Harris charlesr.har...@gmail.com wrote: Datetime64 seems to use the highest precision In [12]: result_type(ones(1, dtype='datetime64[D]'), 'datetime64[us]') Out[12]: dtype('M8[us]') In [13]: result_type(ones(1, dtype='datetime64[D]'),

Re: [Numpy-discussion] numpy pickling problem - python 2 vs. python 3

A slightly different way to look at this is one of sharing data. If I am working on a system with 3.4 and I want to share data with others who may be using a mix of 2.7 and 3.3 systems, this problem makes npz format much less attractive. Ben Root On Fri, Mar 6, 2015 at 12:51 PM, Charles R Harris

Re: [Numpy-discussion] Adding keyword to asarray and asanyarray.

dare I say... datetime64/timedelta64 support? ::ducks:: Ben Root On Thu, Mar 5, 2015 at 11:40 AM, Charles R Harris charlesr.har...@gmail.com wrote: Hi All, This is apropos gh-5634 https://github.com/numpy/numpy/pull/5634, a PR adding a precision keyword to asarray and asanyarray. The PR

Re: [Numpy-discussion] Adding keyword to asarray and asanyarray.

On Thu, Mar 5, 2015 at 12:04 PM, Chris Barker chris.bar...@noaa.gov wrote: well, the precision of those is 64 bits, yes? so if you asked for less than that, you'd still get a dt64. If you asked for 64 bits, you'd get it, if you asked for datetime128 -- what would you get??? a 128 bit

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