[Numpy-discussion] difficulty with numpy.where
Thu Oct 1 12:48:33 CDT 2009
On Thu, Oct 1, 2009 at 12:10 PM, Zachary Pincus <firstname.lastname@example.org>wrote:
> a < b < c (or any equivalent expression) is python syntactic sugar for
> (a < b) and (b < c).
> Now, for numpy arrays, a < b gives an array with boolean True or False
> where the elements of a are less than those of b. So this gives us two
> arrays that python now wants to "and" together. To do this, python
> tries to convert the array "a < b" to a single True or False value,
> and the array "b < c" to a single True or False value, which it then
> knows how to "and" together. Except that "a < b" could contain many
> True or False elements, so how to convert them to a single one?
> There's no obvious way to guess -- typically, one uses "any" or "all"
> to convert a boolean array to a single true or false value, depending,
> obviously, on what one needs.
> So this explains the error you see, but has nothing to do with the
> results you desire... you need to and-together two boolean arrays
> *element-wise* -- which is something Python doesn't know how to do
> with the builtin "and" operator (which cannot be overridden). To do
> this, you need to use the bitwise logic operators:
> (a < b) & (b < c).
> def sin_half_period(x): return where((0.0 <= x) & (x <= pi), sin(x),
Very well expressed Zach.
The reason that I wanted use this kind of conditional indexing is as
follows: I have a dataset with a main time-variable and various other
measurement results including some atmospheric data (cloud microphysics in
particular). In one instance of this dataset I have 8000 something rows for
each of the variables in the file. We wanted to segment cloud droplet
concentration data only for some certain time-window (only if a measurement
was done at cloud base conditions.) We have a-priori knowledge for this
time-window, the only other thing to do is conditionally indexing our cloud
drop concentration with this window. Putting in more technical terms:
time = 40000 to 48000 a numpy array
conc = 300 to 500 numpy array with 8000 elements.
say that cloud bases occur in 45000 and 45400, and I am only interested
analysing that portion of the data. Do a boxplot or even being fancier and
making violing plots out this section :) So I do:
conc[(time>45000) & (time<45400)]
> On Oct 1, 2009, at 12:55 PM, Dr. Phillip M. Feldman wrote:
> > I've defined the following one-line function that uses numpy.where:
> > def sin_half_period(x): return where(0.0 <= x <= pi, sin(x), 0.0)
> > When I try to use this function, I get an error message:
> > In : z=linspace(0,2*pi,9)
> > In : sin_half_period(z)
> > ValueError Traceback (most recent
> > call last)
> > The truth value of an array with more than one element is ambiguous.
> > Use
> > a.any
> > () or a.all()
> > Any suggestions will be appreciated.
> > --
> > View this message in context:
> > Sent from the Numpy-discussion mailing list archive at Nabble.com.
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