# Compute azimuthal statistics¶

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A function to reduce an image to a radial cross-section.

INPUT: data - whatever data you are radially averaging. Data is binned into a series of annuli of width ‘annulus_width’ pixels. annulus_width - width of each annulus. Default is 1. working_mask - array of same size as ‘data’, with zeros at whichever ‘data’ points you don’t want included in the radial data computations. x,y - coordinate system in which the data exists (used to set the center of the data). By default, these are set to integer meshgrids rmax – maximum radial value over which to compute statistics r - a data structure containing the following statistics, computed across each annulus: .r - the radial coordinate used (outer edge of annulus) .mean - mean of the data in the annulus .sum - the sum of all enclosed values at the given radius .std - standard deviation of the data in the annulus .median - median value in the annulus .max - maximum value in the annulus .min - minimum value in the annulus .numel - number of elements in the annulus ```import numpy as np import pylab as py import radial_data as rad # Create coordinate grid npix = 50. x = np.arange(npix) - npix/2. xx, yy = np.meshgrid(x, x) r = np.sqrt(xx**2 + yy**2) fake_psf = np.exp(-(r/5.)**2) noise = 0.1 * np.random.normal(0, 1, r.size).reshape(r.shape) simulation = fake_psf + noise rad_stats = rad.radial_data(simulation, x=xx, y=yy) py.figure() py.plot(rad_stats.r, rad_stats.mean / rad_stats.std) py.xlabel('Radial coordinate') py.ylabel('Signal to Noise') ```

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