Interesting... ``` static void @NAME@_fill(@type@ *buffer, npy_intp length, void *NPY_UNUSED(ignored)) { npy_intp i; @type@ start = buffer[0]; @type@ delta = buffer[1]; delta -= start; for (i = 2; i < length; ++i) { buffer[i] = start + i*delta; } } ```

So, the second value is computed using the delta arange was given, but then tries to get the delta back, which incurs errors: ``` >>> a = np.float32(-115) >>> delta = np.float32(0.01) >>> b = a + delta >>> new_delta = b - a >>> "%.16f" % delta '0.0099999997764826' >>> "%.16f" % new_delta '0.0100021362304688' ``` Also, right there is a good example of where the use of fma() could be of value. Cheers! Ben Root On Fri, Feb 9, 2018 at 4:56 PM, Eric Wieser <wieser.eric+nu...@gmail.com> wrote: > Can’t arange and linspace operations with floats be done internally > > Yes, and they probably should be - they’re done this way as a hack because > the api exposed for custom dtypes is here > <https://github.com/numpy/numpy/blob/81e15e812574d956fcc304c3982e2b59aa18aafb/numpy/core/include/numpy/ndarraytypes.h#L507-L511>, > (example implementation here > <https://github.com/numpy/numpy/blob/81e15e812574d956fcc304c3982e2b59aa18aafb/numpy/core/src/multiarray/arraytypes.c.src#L3711-L3721>) > - essentially, you give it the first two elements of the array, and ask it > to fill in the rest. > > > On Fri, 9 Feb 2018 at 13:17 Matthew Harrigan <harrigan.matt...@gmail.com> > wrote: > >> I apologize if I'm missing something basic, but why are floats being >> accumulated in the first place? Can't arange and linspace operations with >> floats be done internally similar to `start + np.arange(num_steps) * >> step_size`? I.e. always accumulate (really increment) integers to limit >> errors. >> >> On Fri, Feb 9, 2018 at 3:43 PM, Benjamin Root <ben.v.r...@gmail.com> >> wrote: >> >>> >>> >>> On Fri, Feb 9, 2018 at 12:19 PM, Chris Barker <chris.bar...@noaa.gov> >>> wrote: >>> >>>> On Wed, Feb 7, 2018 at 12:09 AM, Ralf Gommers <ralf.gomm...@gmail.com> >>>> wrote: >>>>> >>>>> It is partly a plea for some development of numerically accurate >>>>>> functions for computing lat/lon grids from a combination of inputs: >>>>>> bounds, >>>>>> counts, and resolutions. >>>>>> >>>>> >>>> Can you be more specific about what problems you've run into -- I work >>>> with lat-lon grids all the time, and have never had a problem. >>>> >>>> float32 degrees gives you about 1 meter accuracy or better, so I can >>>> see how losing a few digits might be an issue, though I would argue that >>>> you maybe shouldn't use float32 if you are worried about anything close to >>>> 1m accuracy... -- or shift to a relative coordinate system of some sort. >>>> >>> >>> The issue isn't so much the accuracy of the coordinates themselves. I am >>> only worried about 1km resolution (which is approximately 0.01 degrees at >>> mid-latitudes). My concern is with consistent *construction* of a >>> coordinate grid with even spacing. As it stands right now. If I provide a >>> corner coordinate, a resolution, and the number of pixels, the result is >>> not terrible (indeed, this is the approach used by gdal/rasterio). If I >>> have start/end coordinates and the number of pixels, the result is not bad, >>> either (use linspace). But, if I have start/end coordinates and a >>> resolution, then determining the number of pixels from that is actually >>> tricky to get right in the general case, especially with float32 and large >>> grids, and especially if the bounding box specified isn't exactly divisible >>> by the resolution. >>> >>> >>>> >>>> I have been playing around with the decimal package a bit lately, >>>>>> >>>>> >>>> sigh. decimal is so often looked at a solution to a problem it isn't >>>> designed for. lat-lon is natively Sexagesimal -- maybe we need that dtype >>>> :-) >>>> >>>> what you get from decimal is variable precision -- maybe a binary >>>> variable precision lib is a better answer -- that would be a good thing to >>>> have easy access to in numpy, but in this case, if you want better accuracy >>>> in a computation that will end up in float32, just use float64. >>>> >>> >>> I am not concerned about computing distances or anything like that, I am >>> trying to properly construct my grid. I need consistent results regardless >>> of which way the grid is specified (start/end/count, start/res/count, >>> start/end/res). I have found that loading up the grid specs (using in a >>> config file or command-line) using the Decimal class allows me to exactly >>> and consistently represent the grid specification, and gets me most of the >>> way there. But the problems with arange() is frustrating, and I have to >>> have extra logic to go around that and over to linspace() instead. >>> >>> >>>> >>>> and I discovered the concept of "fused multiply-add" operations for >>>>>> improved accuracy. I have come to realize that fma operations could be >>>>>> used >>>>>> to greatly improve the accuracy of linspace() and arange(). >>>>>> >>>>> >>>> arange() is problematic for non-integer use anyway, by its very >>>> definition (getting the "end point" correct requires the right step, even >>>> without FP error). >>>> >>>> and would it really help with linspace? it's computing a delta with one >>>> division in fp, then multiplying it by an integer (represented in fp -- >>>> why? why not keep that an integer till the multiply?). >>>> >>> >>> Sorry, that was a left-over from a previous draft of my email after I >>> discovered that linspace's accuracy was on par with fma(). And while >>> arange() has inherent problems, it can still be made better than it is now. >>> In fact, I haven't investigated this, but I did recently discover some unit >>> tests of mine started to fail after a numpy upgrade, and traced it back to >>> a reduction in the accuracy of a usage of arange() with float32s. So, >>> something got worse at some point, which means we could still get accuracy >>> back if we can figure out what changed. >>> >>> >>>> >>>> In particular, I have been needing improved results for computing >>>>>> latitude/longitude grids, which tend to be done in float32's to save >>>>>> memory >>>>>> (at least, this is true in data I come across). >>>>>> >>>>> >>>>> If you care about saving memory *and* accuracy, wouldn't it make more >>>>> sense to do your computations in float64, and convert to float32 at the >>>>> end? >>>>> >>>> >>>> that does seem to be the easy option :-) >>>> >>> >>> Kinda missing the point, isn't it? Isn't that like saying "convert all >>> your data to float64s prior to calling np.mean()"? That's ridiculous. >>> Instead, we made np.mean() upcast the inner-loop operation, and even allow >>> an option to specify what the dtype that should be used for the aggregator. >>> >>> >>>> >>>> >>>>> Now, to the crux of my problem. It is next to impossible to generate a >>>>>> non-trivial numpy array of coordinates, even in double precision, without >>>>>> hitting significant numerical errors. >>>>>> >>>>> >>>> I'm confused, the example you posted doesn't have significant errors... >>>> >>> >>> Hmm, "errors" was the wrong word. "Differences between methods" might be >>> more along the lines of what I was thinking. Remember, I am looking for >>> consistency. >>> >>> >>>> >>>> >>>>> Which has lead me down the path of using the decimal package (which >>>>>> doesn't play very nicely with numpy because of the lack of casting rules >>>>>> for it). Consider the following: >>>>>> ``` >>>>>> $ cat test_fma.py >>>>>> from __future__ import print_function >>>>>> import numpy as np >>>>>> res = np.float32(0.01) >>>>>> cnt = 7001 >>>>>> x0 = np.float32(-115.0) >>>>>> x1 = res * cnt + x0 >>>>>> print("res * cnt + x0 = %.16f" % x1) >>>>>> x = np.arange(-115.0, -44.99 + (res / 2), 0.01, dtype='float32') >>>>>> print("len(arange()): %d arange()[-1]: %16f" % (len(x), x[-1])) >>>>>> x = np.linspace(-115.0, -44.99, cnt, dtype='float32') >>>>>> print("linspace()[-1]: %.16f" % x[-1]) >>>>>> >>>>>> $ python test_fma.py >>>>>> res * cnt + x0 = -44.9900015648454428 >>>>>> len(arange()): 7002 arange()[-1]: -44.975044 >>>>>> linspace()[-1]: -44.9900016784667969 >>>>>> ``` >>>>>> arange just produces silly results (puts out an extra element... >>>>>> adding half of the resolution is typically mentioned as a solution on >>>>>> mailing lists to get around arange()'s limitations -- I personally don't >>>>>> do >>>>>> this). >>>>>> >>>>> >>>> The real solution is "don't do that" arange is not the right tool for >>>> the job. >>>> >>> >>> Well, it isn't the right tool because as far as I am concerned, it is >>> useless for anything but integers. Why not fix it to be more suitable for >>> floating point? >>> >>> >>>> >>>> Then there is this: >>>> >>>> res * cnt + x0 = -44.9900015648454428 >>>> linspace()[-1]: -44.9900016784667969 >>>> >>>> that's as good as you are ever going to get with 32 bit floats... >>>> >>> >>> Consistency is the key thing. I am fine with one of those values, so >>> long as that value is what happens no matter which way I specify my grid. >>> >>> >>>> >>>> Though I just noticed something about your numbers -- there should be a >>>> nice even base ten delta if you have 7001 gaps -- but linspace produces N >>>> points, not N gaps -- so maybe you want: >>>> >>>> >>>> In [*17*]: l = np.linspace(-115.0, -44.99, 7002) >>>> >>>> >>>> In [*18*]: l[:5] >>>> >>>> Out[*18*]: array([-115. , -114.99, -114.98, -114.97, -114.96]) >>>> >>>> >>>> In [*19*]: l[-5:] >>>> >>>> Out[*19*]: array([-45.03, -45.02, -45.01, -45. , -44.99]) >>>> >>>> >>>> or, in float32 -- not as pretty: >>>> >>>> >>>> In [*20*]: l = np.linspace(-115.0, -44.99, 7002, dtype=np.float32) >>>> >>>> >>>> In [*21*]: l[:5] >>>> >>>> Out[*21*]: >>>> >>>> array([-115. , -114.98999786, -114.98000336, -114.97000122, >>>> >>>> -114.95999908], dtype=float32) >>>> >>>> >>>> In [*22*]: l[-5:] >>>> >>>> Out[*22*]: array([-45.02999878, -45.02000046, -45.00999832, -45. >>>> , -44.99000168], dtype=float32) >>>> >>>> >>>> but still as good as you get with float32, and exactly the same result >>>> as computing in float64 and converting: >>>> >>>> >>>> >>>> In [*25*]: l = np.linspace(-115.0, -44.99, 7002).astype(np.float32) >>>> >>>> >>>> In [*26*]: l[:5] >>>> >>>> Out[*26*]: >>>> >>>> array([-115. , -114.98999786, -114.98000336, -114.97000122, >>>> >>>> -114.95999908], dtype=float32) >>>> >>>> >>>> In [*27*]: l[-5:] >>>> >>>> Out[*27*]: array([-45.02999878, -45.02000046, -45.00999832, -45. >>>> , -44.99000168], dtype=float32) >>>> >>> >>> Argh! I got myself mixed up between specifying pixel corners versus >>> pixel centers. rasterio has been messing me up on this. >>> >>> >>>> >>>> >>>>>> So, does it make any sense to improve arange by utilizing fma() under >>>>>> the hood? >>>>>> >>>>> >>>> no -- this is simply not the right use-case for arange() anyway. >>>> >>> >>> arange() has accuracy problems, so why not fix it? >>> >>> >>> l4 = np.arange(-115, -44.99, 0.01, dtype=np.float32) >>> >>> np.median(np.diff(l4)) >>> 0.0099945068 >>> >>> np.float32(0.01) >>> 0.0099999998 >>> >>> There is something significantly wrong here if arange(), which takes a >>> resolution parameter, can't seem to produce a sequence with the proper >>> delta. >>> >>> >>> >>>> >>>> >>>>> Also, any plans for making fma() available as a ufunc? >>>>>> >>>>> >>>> could be nice -- especially if used internally. >>>> >>>> >>>>> Notice that most of my examples required knowing the number of grid >>>>>> points ahead of time. But what if I didn't know that? What if I just have >>>>>> the bounds and the resolution? Then arange() is the natural fit, but as I >>>>>> showed, its accuracy is lacking, and you have to do some sort of hack to >>>>>> do >>>>>> a closed interval. >>>>>> >>>>> >>>> no -- it's not -- if you have the bounds and the resolution, you have >>>> an over-specified problem. That is: >>>> >>>> x_min + (n * delta_x) == x_max >>>> >>>> If there is ANY error in either delta_x or x_max (or x_min), then >>>> you'll get a missmatch. which is why arange is not the answer (you can make >>>> the algorithm a bit more accurate, I suppose but there is still fp limited >>>> precision -- if you can't exactly represent either delta_x or x_max, then >>>> you CAN'T use the arange() definition and expect to work consistently. >>>> >>>> The "right" way to do it is to compute N with: round((x_max - x_min) / >>>> delta), and then use linspace: >>>> >>>> linspace(x_min, x_max, N+1) >>>> >>>> (note that it's too bad you need to do N+1 -- if I had to do it over >>>> again, I'd use N as the number of "gaps" rather than the number of points >>>> -- that's more commonly what people want, if they care at all) >>>> >>>> This way, you get a grid with the endpoints as exact as they can be, >>>> and the deltas as close to each-other as they can be as well. >>>> >>>> maybe you can do a better algorithm in linspace to save an ULP, but >>>> it's hard to imagine when that would matter. >>>> >>> >>> Yes, it is overspecified. My problem is that different tools require >>> different specs (ahem... rasterio/gdal), and I have gird specs coming from >>> other sources. And I need to produce data onto the same grid so that tools >>> like xarray won't yell at me when I am trying to do an operation between >>> gridded data that should have the same coordinates, but are off slightly >>> because they were computed differently for whatever reason. >>> >>> I guess I am crying out for some sort of tool that will help the >>> community stop making the same mistakes. A one-stop shop that'll allow us >>> to specify a grid in a few different ways and still produce the right >>> thing, and even do the inverse... provide a coordinate array and get grids >>> specs in whatever form we want. Maybe even have options for dealing with >>> pixel corner vs. pixel centers, too? There are additional fun problems such >>> as padding out coordinate arrays, which np.pad doesn't really do a great >>> job with. >>> >>> Cheers! >>> Ben Root >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@python.org >>> https://mail.python.org/mailman/listinfo/numpy-discussion >>> >>> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > >

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