[Numpy-discussion] Finding many ways to incorrectly create a numpy array. Please advice
Jeremy Conlin
jlconlin@gmail....
Tue Aug 2 15:40:01 CDT 2011
On Tue, Aug 2, 2011 at 11:15 AM, Christopher Barker
<Chris.Barker@noaa.gov> wrote:
> On 8/2/11 8:38 AM, Jeremy Conlin wrote:
>> Thanks, Brett. Using StringIO and numpy.loadtxt worked great. I'm
>> still curious why what I was doing didn't work. Everything I can see
>> indicates it should work.
>
> In [11]: tfc_dtype
> Out[11]: dtype([('nps', '>u8'), ('t', '>f8'), ('e', '>f8'), ('fom', '>f8')])
>
> In [15]: n = numpy.fromstring(l, dtype=tfc_dtype, sep=' ')
> ---------------------------------------------------------------------------
> ValueError Traceback (most recent call last)
>
> /Users/cbarker/<ipython console> in <module>()
>
> ValueError: don't know how to read character strings with that array type
>
> means just what it says. In theory, numpy.fromstring() (and fromfile() )
> provides a way to quickly and efficiently generate arrays from text, but
> it practice, the code is quite limited (and has a bug or two). I don't
> think anyone has gotten around to writing the code to use structured
> dtypes with it -- so it can't do what you want (rational though that
> expectation is)
>
> In [21]: words
> Out[21]: ['32000', '7.89131E-01', '8.05999E-03', '3.88222E+03']
>
> In [22]: p =
> Display all 249 possibilities? (y or n)
>
> In [22]: p = numpy.array(words, dtype=tfc_dtype)
>
> In [23]: p
> Out[23]:
> array([(3689064028291727360L, 0.0, 0.0, 0.0),
> (3976177339304456517L, 4.967820413490985e-91, 0.0, 0.0),
> (4048226120204106053L, 4.970217431784588e-91, 0.0, 0.0),
> (3687946958874489413L, 1.1572189237420885e-100, 0.0, 0.0)],
> dtype=[('nps', '>u8'), ('t', '>f8'), ('e', '>f8'), ('fom', '>f8')])
>
> similarly here -- converting from text to structured dtypes is not fully
> supported
>
> In [29]: a
> Out[29]: [32000, 0.789131, 0.00805999, 3882.22]
>
> In [30]: r = numpy.array(a)
>
> In [31]: r
> Out[31]:
> array([ 3.20000000e+04, 7.89131000e-01, 8.05999000e-03,
> 3.88222000e+03])
>
> sure -- numpy's default behavior is to find a dtype that will hold all
> the input array -- this pre-dates structured dtypes, and probably what
> you would want b default anyway.
>
> In [32]: s = numpy.array(a, dtype=tfc_dtype)
> ---------------------------------------------------------------------------
> TypeError Traceback (most recent call last)
>
> /Users/cbarker/<ipython console> in <module>()
>
> TypeError: expected a readable buffer object
>
> OK -- I can see why you'd expect that to work. However, the trick with
> structured dtypes is that the dimensionality of the inputs can be less
> than obvious -- you are passing in a 1-d list of 4 numbers -- do you
> want a 1-d array? or ? -- in this case, it's pretty obvious (as a human)
> what you would want -- you have a dtype with four fields, and you're
> passing in four numbers, but there are so many possible combinations
> that numpy doesn't try to be "smart" about it. So as a rule, you need to
> be quite specific when working with structured dtypes.
>
> However, the default is for numpy to map tuples to dtypes, so if you
> pass in a tuple instead, it works:
>
> In [34]: t = tuple(a)
>
> In [35]: s = numpy.array(t, dtype=tfc_dtype)
>
> In [36]: s
> Out[36]:
> array((32000L, 0.789131, 0.00805999, 3882.22),
> dtype=[('nps', '>u8'), ('t', '>f8'), ('e', '>f8'), ('fom', '>f8')])
>
> you were THIS close!
>
> -Chris
>
>
>
>
>
>
> --
> Christopher Barker, Ph.D.
> Oceanographer
Chris,
Thanks for that information. It helps greatly in understanding what is
happening. Next time I'll put my data into tuples.
Jeremy
More information about the NumPy-Discussion
mailing list