[Numpy-discussion] find location of maximum values
Benjamin Root
ben.root@ou....
Mon Jan 9 17:22:39 CST 2012
On Monday, January 9, 2012, questions anon <questions.anon@gmail.com> wrote:
> thanks for the responses.
> Unfortunately they are not matching shapes
>>>> print TSFC.shape, TIME.shape, LAT.shape, LON.shape
> (721, 106, 193) (721,) (106,) (193,)
>
> So I still receive index out of bounds error:
>>>>tmax=TSFC.max(axis=0)
> numpy array of max values for the month
>>>>maxindex=tmax.argmax()
> 2928
>>>>maxtemp=tmax.ravel()[maxindex] #or maxtemp=TSFC.max()
> 35.5 (degrees celcius)
>
>>>>latloc=LAT[tmax.argmax()]
> IndexError: index out of bounds
>
> lonloc=LON[tmax.argmax()]
> timeloc=TIME[tmax.argmax()]
>
>
> Any other ideas for this type of situation?
> thanks
Right, we realize they are not the same shape. When you use argmax on the
temperature data, take that index number and use unravel_index(index,
TSFC.shape) to get a three-element tuple, each being the index in the TIME,
LAT, LON arrays, respectively.
Cheers,
Ben Root
>
> On Wed, Jan 4, 2012 at 10:29 PM, Derek Homeier <
derek@astro.physik.uni-goettingen.de> wrote:
>>
>> On 04.01.2012, at 5:10AM, questions anon wrote:
>>
>> > Thanks for your responses but I am still having difficuties with this
problem. Using argmax gives me one very large value and I am not sure what
it is.
>> > There shouldn't be any issues with the shape. The latitude and
longitude are the same shape always (covering a state) and the temperature
(TSFC) data are hourly for a whole month.
>>
>> There will be an issue if not TSFC.shape == TIME.shape == LAT.shape ==
LON.shape
>>
>> One needs more information on the structure of these data to say
anything definite,
>> but if e.g. your TSFC data have a time and a location dimension, argmax
will
>> per default return the index for the flattened array (see the argmax
documentation
>> for details, and how to use the axis keyword to get a different output).
>> This might be the very large value you mention, and if your location
data have fewer
>> dimensions, the index will easily be out of range. As Ben wrote, you'd
need extra work to
>> find the maximum location, depending on what maximum you are actually
looking for.
>>
>> As a speculative example, let's assume you have the temperature data in
an
>> array(ntime, nloc) and the position data in array(nloc). Then
>>
>> TSFC.argmax(axis=1)
>>
>> would give you the index for the hottest place for each hour of the month
>> (i.e. actually an array of ntime indices, and pointer to so many
different locations).
>>
>> To locate the maximum temperature for the entire month, your best way
would probably
>> be to first extract the array of (monthly) maximum temperatures in each
location as
>>
>> tmax = TSFC.max(axis=0)
>>
>> which would have (in this example) the shape (nloc,), so you could
directly use it to index
>>
>> LAT[tmax.argmax()] etc.
>>
>> Cheers,
>> Derek
>>
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>> NumPy-Discussion@scipy.org
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>
>
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