1 Mar 08:31 2011

### Re: faster interpolations (interp1d)

eat <e.antero.tammi <at> gmail.com>

2011-03-01 07:31:13 GMT

2011-03-01 07:31:13 GMT

Hi James,

On Mon, Feb 28, 2011 at 5:25 PM, James McCormac <jmccormac01 <at> qub.ac.uk> wrote:

Hi eat,

you sent me a suggestion for faster 1d interpolations using matrices a few

weeks back but I cannot find the email anywhere when I looked for it

today.

Here is a better explanation of what I am trying to do. For example I have

a 1d array of 500 elements. I want to interpolate them quadratically so

each array becomes 10 values, 50,000 in total.

I have 500x500 pixels and I want to get 0.01 pixel resolution.

code snipet:

# collapse an image in the x direction

ref_xproj=np.sum(refarray,axis=0)

# make an array for the 1d spectra

x = np.linspace(0, (x_2-x_1), (x_2-x_1))

# interpolation

f2_xr = interp1d(x, ref_xproj, kind='quadratic')

# new x array for interpolated data

xnew = np.linspace(0, (x_2-x_1), (x_2-x_1)*100)

# FFT of interpolated spectra

F_ref_xproj = fftpack.fft(f2_xr(xnew))

Can I do this type of interpolation faster using the method you described

before?

I'll misinterpreted your original question and the method I suggested there is not applicable.

To better understand your situation, few questions:

- what you described above; it does work for you in technical sense?

- if so, then the problem is with the execution performance?

- what are your current timings?

- how much you'll need to enhance them?

Regards,

eat

Cheers

James

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