Filtering Masked Arrays
Mostly for rejection of outlying flux values during spectral fitting, the DAP
implements a boxcar filtering class that allows for masked pixels and a local
calculation of the rms. Although not as fast as other filtering algorithms
scipy.ndimage), these other routines (as far as I know) do not allow
for exclusion of masked pixels.
Here, we provide an example use of the function that demonstrates the accuracy of the spectral resolution matching. This is pulled from a unit test in test_filter.py.
First, we build a fake spectrum with noise and a set of outliers. The seed for the noise is fixed so that the test is deterministic.
import numpy from mangadap.util.filter import BoxcarFilter rng = numpy.random.default_rng(1001) wave = numpy.arange(3650.0, 10501.0, 0.5) x = (wave - numpy.mean(wave))/(wave[-1]-wave) sig = 1/(0.5 + 0.5*(x+0.5)**2) flux = 3 + 0.1*x + 0.5*x**2 + 3*(x+1)**4 dflux = rng.normal(scale=sig) indx = rng.integers(x.size, size=100) dflux[indx] += rng.normal(scale=10*sig)[indx]
Then I mask a few regions just for demonstration:
indx = ((wave > 4567) & (wave < 4587)) \ | ((wave > 5567) & (wave < 5587)) \ | ((wave > 9067) & (wave < 9087)) sim = numpy.ma.MaskedArray(flux + dflux, mask=indx)
Finally I perform the filtering; see
Note that the class currently expects to perform the filtering on multiple
vectors simultaneously such that its attributes are two dimensional, even in
this 1D example. The result of the filtering and the isolated outliers are
shown in the plot.
bf = BoxcarFilter(100, lo=3., hi=3., niter=None, y=sim, local_sigma=True) from matplotlib import pyplot pyplot.plot(wave, sim, zorder=1) pyplot.plot(wave, bf.smoothed_y, zorder=2) pyplot.scatter(wave[bf.output_mask], sim[bf.output_mask], marker='.', color='C3', s=100, lw=0, zorder=3) pyplot.xlabel('x') pyplot.ylabel('y') pyplot.show()