y-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
--
Regards
Yogesh Karpate
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I am facing the error mentioned in subject line.
error is encountered at
data_temp=filtered[a:b]
TypeError: only integer arrays with one element can be converted to an index
what can be error
The code snippet is as follows
'''left=some array
right =some array, both are having same length.
filtere
Does Numpy Support CGI scripting? DO scipy and matplotlib also support?
Regards
~ymk
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@ Pauli and @ Colin:
Sorry for the late reply. I was busy in
some other assignments.
# As far as normalization by(n) is concerned then its common assumption
that the population is normally distributed and population size is fairly
large enough to fit the normal di
The thing is that the normalization by (n-1) is done for the no. of samples
>20 or23(Not sure about this no. but sure about the thing that this no isnt
greater than 25) and below that we use normalization by n.
Regards
~ymk
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Dear All,
I'm facing a bog problem in following . the code snippet is
as follows
% Compute the area
indicator###
for kT in range(leftbound,rightbound):
# Here the left bound and rightbound both are indexing array is
Dear Fabrice
Finally your suggestions worked :).Thanks a lot...
soon the code I'm working will be available as a part of Free Software
Foundation.
Regards
Yogesh
On Tue, Sep 22, 2009 at 11:23 PM, Fabrice Silva wrote:
> Le mardi 22 septembre 2009 à 23:00 +0530
On Tue, Sep 22, 2009 at 7:01 PM, Fabrice Silva wrote:
> Le mardi 22 septembre 2009 à 17:42 +0530, yogesh karpate a écrit :
> > I just tried your idea but the result is same. it didnt help .
> >
> > 2009/9/22 Nadav Horesh
> > A quick answer with going in
רית-
> מאת: numpy-discussion-boun...@scipy.org בשם yogesh karpate
> נשלח: ג 22-ספטמבר-09 14:11
> אל: numpy-discussion@scipy.org
> נושא: [Numpy-discussion] The problem with arrays
>
> Please kindly go through following code snippet
> for i in range(a1):
>data_temp=(bpf[l
Please kindly go through following code snippet
for i in range(a1):
data_temp=(bpf[left[0][i]:right[0][i]])# left is an array and right
is also an array
maxloc=data_temp.argmax() #taking indices of max. value of
data segment
maxval=data_temp[maxloc]
minloc=da
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