def test_known_results(self):
known_results = [
((0,0,0), numpy.zeros([0,0])),
((0,0,100), numpy.zeros([0,0])),
((1,1,0), numpy.array([[True]])),
((1,1,1), numpy.array([[False]])),
((5,1,0), numpy.array([[True],[True],[True],[True],[True]])),
((5,1,2), numpy.array([[True],[False],[False],[False],[True]])),
((1,4,0), numpy.array([[True,True,True,True]])),
((1,4,1), numpy.array([[True,False,False,True]]))
]
for parameters, result in known_results:
assert_array_equal(circular_mask(*parameters), result)
def test_known_results(self):
known_results = [
((0, 0, 0), numpy.zeros([0, 0])), ((0, 0, 100), numpy.zeros([0,
0])),
((1, 1, 0), numpy.array([[True]])),
((1, 1, 1), numpy.array([[False]])),
((5, 1, 0), numpy.array([[True], [True], [True], [True], [True]])),
((5, 1, 2), numpy.array([[True], [False], [False], [False],
[True]])),
((1, 4, 0), numpy.array([[True, True, True, True]])),
((1, 4, 1), numpy.array([[True, False, False, True]]))
]
for parameters, result in known_results:
assert_array_equal(circular_mask(*parameters), result)
def _get_data(self):
"""Masked image data"""
# We will ignore all the data which is masked for the rest of the
# sourcefinding process. We build up the mask by stacking ("or-ing
# together") a number of different effects:
#
# * A margin from the edge of the image;
# * Any data outside a given radius from the centre of the image;
# * Data which is "obviously" bad (equal to 0 or NaN).
mask = numpy.zeros((self.xdim, self.ydim))
if self.margin:
margin_mask = numpy.ones((self.xdim, self.ydim))
margin_mask[self.margin:-self.margin, self.margin:-self.margin] = 0
mask = numpy.logical_or(mask, margin_mask)
if self.radius:
radius_mask = utils.circular_mask(self.xdim, self.ydim, self.radius)
mask = numpy.logical_or(mask, radius_mask)
mask = numpy.logical_or(mask, numpy.isnan(self.rawdata))
return numpy.ma.array(self.rawdata, mask=mask)
def _get_data(self):
"""Masked image data"""
# We will ignore all the data which is masked for the rest of the
# sourcefinding process. We build up the mask by stacking ("or-ing
# together") a number of different effects:
#
# * A margin from the edge of the image;
# * Any data outside a given radius from the centre of the image;
# * Data which is "obviously" bad (equal to 0 or NaN).
mask = numpy.zeros((self.xdim, self.ydim))
if self.margin:
margin_mask = numpy.ones((self.xdim, self.ydim))
margin_mask[self.margin:-self.margin, self.margin:-self.margin] = 0
mask = numpy.logical_or(mask, margin_mask)
if self.radius:
radius_mask = utils.circular_mask(self.xdim, self.ydim,
self.radius)
mask = numpy.logical_or(mask, radius_mask)
mask = numpy.logical_or(mask, numpy.isnan(self.rawdata))
return numpy.ma.array(self.rawdata, mask=mask)
