python - Smooth Data and Find Maximum -

i have dataset (see below) of 2 variables, x , y. want find value of x, maximum in y occur. current approach x gives me maximum y. not ideal data quite noisy, perform sort of smoothing first, , find max.

so far, have tried use r smooth data npreg (kernel regression) np package obtain curve:

but i'm not sure how find max.

i solution following in python:

1) smooth data (doesn't kernel regression)

2) find value of x max in y occurs using smoothed data

x   y -20 0.006561733 -19 -4.48e-08 -18 -4.48e-08 -17 -4.48e-08 -16 0.003281305 -15 0.00164063 -14 0.003280565 -13 0.003282537 -12 -4.48e-08 -11 0.003281286 -10 0.004921239 -9  0.00491897 -8  -1.52e-06 -7  0.004925867 -6  -1.27e-06 -5  0.009839438 -4  0.001643726 -3  -4.48e-08 -2  2.09e-06 -1  -0.001640027 0   0.006559627 1   0.001636958 2   2.36e-06 3   0.003281469 4   0.011481469 5   0.004922279 6   0.018044207 7   0.011483134 8   0.014765087 9   0.008201379 10  0.00492497 11  0.006560482 12  0.009844796 13  0.011483199 14  0.008202129 15  0.001641621 16  0.004921645 17  0.006563377 18  0.006561068 19  0.008201004 

i'd run gaussian filter on data smooth:

# first, make function linearly interpolate data f = scipy.interpolate.interp1d(x,y)  # resample 1000 samples xx = np.linspace(-20,19, 1000)  # compute function on finer interval yy = f(xx)  # make gaussian window window = scipy.signal.gaussian(200, 60)  # convolve arrays smoothed = scipy.signal.convolve(yy, window/window.sum(), mode='same')  # maximum xx[np.argmax(smoothed)] 

here's smoothed result:

the max occurs @ 6.93.

there whole bunch of other window functions , filtering options in scipy.signal. see documentation more.