difficulty with numpy.where
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 [4]: z=linspace(0,2*pi,9) In [5]: 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: http://www.nabble.com/difficulty-with-numpy.where-tp25702676p25702676.html Sent from the Numpy-discussion mailing list archive at Nabble.com.
On Thu, Oct 1, 2009 at 9:55 AM, Dr. Phillip M. Feldman
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 [4]: z=linspace(0,2*pi,9) In [5]: 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.
Take a look at this thread: http://www.nabble.com/Compound-conditional-indexing-td25686443.html
Hello, 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). So: def sin_half_period(x): return where((0.0 <= x) & (x <= pi), sin(x), 0.0) Zach 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 [4]: z=linspace(0,2*pi,9) In [5]: 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: http://www.nabble.com/difficulty-with-numpy.where-tp25702676p25702676.html Sent from the Numpy-discussion mailing list archive at Nabble.com.
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On Thu, Oct 1, 2009 at 12:10 PM, Zachary Pincus
Hello,
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).
So:
def sin_half_period(x): return where((0.0 <= x) & (x <= pi), sin(x), 0.0)
Zach
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)] Voila!
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 [4]: z=linspace(0,2*pi,9) In [5]: 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: http://www.nabble.com/difficulty-with-numpy.where-tp25702676p25702676.html Sent from the Numpy-discussion mailing list archive at Nabble.com.
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
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-- Gökhan
participants (4)
-
Dr. Phillip M. Feldman -
Gökhan Sever -
Keith Goodman -
Zachary Pincus