def generate_result_maps(data, sourcelist):
"""Return a source and residual image
Given a data array (image) and list of sources, return two images, one
showing the sources themselves and the other the residual after the
sources have been removed from the input data.
"""
residual_map = numpy.array(data) # array constructor copies by default
gaussian_map = numpy.zeros(residual_map.shape)
for src in sourcelist:
# Include everything with 6 times the std deviation along the major
# axis. Should be very very close to 100% of the flux.
box_size = 6 * src.smaj.value / math.sqrt(2 * math.log(2))
lower_bound_x = max(0, int(src.x.value - 1 - box_size))
upper_bound_x = min(residual_map.shape[0], int(src.x.value - 1 + box_size))
lower_bound_y = max(0, int(src.y.value - 1 - box_size))
upper_bound_y = min(residual_map.shape[1], int(src.y.value - 1 + box_size))
local_gaussian = gaussian(
src.peak.value,
src.x.value,
src.y.value,
src.smaj.value,
src.smin.value,
src.theta.value
)(
numpy.indices(residual_map.shape)[0,lower_bound_x:upper_bound_x,lower_bound_y:upper_bound_y],
numpy.indices(residual_map.shape)[1,lower_bound_x:upper_bound_x,lower_bound_y:upper_bound_y]
)
gaussian_map[lower_bound_x:upper_bound_x, lower_bound_y:upper_bound_y] += local_gaussian
residual_map[lower_bound_x:upper_bound_x, lower_bound_y:upper_bound_y] -= local_gaussian
return gaussian_map, residual_map
def setUp(self):
Xin, Yin = numpy.indices((500, 500))
self.height = 10
self.x = 250
self.y = 250
self.maj = 40
self.min = 40
self.theta = 0
self.mygauss = numpy.ma.array(gaussian(self.height, self.x, self.y, self.maj, self.min, self.theta)(Xin, Yin))
self.moments = moments(self.mygauss, beam, 0)
self.fit = fitgaussian(self.mygauss, self.moments)
def setUp(self):
Xin, Yin = numpy.indices((500, 500))
self.peak = 10
self.xbar = 250
self.ybar = 250
self.semimajor = 40
self.semiminor = 20
self.theta = 0
self.mygauss = numpy.ma.array(
gaussian(self.peak, self.xbar, self.ybar, self.semimajor,
self.semiminor, self.theta)(Xin, Yin))
self.moments = moments(self.mygauss, beam, 0)
def setUp(self):
Xin, Yin = numpy.indices((500, 500))
self.peak = 10
self.xbar = 250
self.ybar = 250
self.semimajor = 40
self.semiminor = 20
self.theta = 0
self.mygauss = numpy.ma.array(
gaussian(self.peak, self.xbar, self.ybar,
self.semimajor, self.semiminor, self.theta)(Xin, Yin))
self.moments = moments(self.mygauss, beam, 0)
def setUp(self):
Xin, Yin = numpy.indices((500, 500))
self.height = 10
self.x = 250
self.y = 250
self.maj = 40
self.min = 40
self.theta = 0
self.mygauss = numpy.ma.array(
gaussian(self.height, self.x, self.y, self.maj, self.min,
self.theta)(Xin, Yin))
self.moments = moments(self.mygauss, beam, 0)
self.fit = fitgaussian(self.mygauss, self.moments)
def generate_result_maps(data, sourcelist):
"""Return a source and residual image
Given a data array (image) and list of sources, return two images, one
showing the sources themselves and the other the residual after the
sources have been removed from the input data.
"""
residual_map = numpy.array(data) # array constructor copies by default
gaussian_map = numpy.zeros(residual_map.shape)
for src in sourcelist:
# Include everything with 6 times the std deviation along the major
# axis. Should be very very close to 100% of the flux.
box_size = 6 * src.smaj.value / math.sqrt(2 * math.log(2))
lower_bound_x = max(0, int(src.x.value - 1 - box_size))
upper_bound_x = min(residual_map.shape[0],
int(src.x.value - 1 + box_size))
lower_bound_y = max(0, int(src.y.value - 1 - box_size))
upper_bound_y = min(residual_map.shape[1],
int(src.y.value - 1 + box_size))
local_gaussian = gaussian(
src.peak.value, src.x.value, src.y.value, src.smaj.value,
src.smin.value, src.theta.value)(
numpy.indices(residual_map.shape)[0,
lower_bound_x:upper_bound_x,
lower_bound_y:upper_bound_y],
numpy.indices(residual_map.shape)[1,
lower_bound_x:upper_bound_x,
lower_bound_y:upper_bound_y])
gaussian_map[lower_bound_x:upper_bound_x,
lower_bound_y:upper_bound_y] += local_gaussian
residual_map[lower_bound_x:upper_bound_x,
lower_bound_y:upper_bound_y] -= local_gaussian
return gaussian_map, residual_map
