def setup(self):
self.kernels_new = CWTKernels(n_scale=2,
min_scale=3.0,
step_scale=2.6,
old=False)
self.kernels_old = CWTKernels(n_scale=2,
min_scale=3.0,
step_scale=2.6,
old=True)
def setup(self):
filename = "$GAMMAPY_DATA/tests/unbundled/poisson_stats_image/counts.fits.gz"
image = Map.read(filename)
background = image.copy(data=np.ones(image.data.shape, dtype=float))
self.kernels = CWTKernels(n_scale=2,
min_scale=3.0,
step_scale=2.6,
old=False)
self.data = dict(image=image, background=background)
self.cwt = CWT(kernels=self.kernels,
significance_threshold=2.0,
keep_history=True)
def run_cwt():
data = make_poisson_data()
cwt_kernels = CWTKernels(n_scale=2,
min_scale=3.0,
step_scale=2.6,
old=False)
cwt = CWT(kernels=cwt_kernels,
significance_threshold=2.,
keep_history=True)
cwt_data = CWTData(counts=data['image'],
background=data['background'],
n_scale=cwt_kernels.n_scale)
cwt.analyze(data=cwt_data)
return cwt_data
class TestCWTKernels:
"""Test CWTKernels"""
def setup(self):
self.kernels_new = CWTKernels(n_scale=2,
min_scale=3.0,
step_scale=2.6,
old=False)
self.kernels_old = CWTKernels(n_scale=2,
min_scale=3.0,
step_scale=2.6,
old=True)
def test_info(self):
info_dict = self.kernels_new._info()
assert_allclose(info_dict["Minimal scale"], self.kernels_new.min_scale)
assert_allclose(info_dict["Kernels approx sum"], 0.99988318386)
assert_allclose(info_dict["Kernels approx max"], 0.000386976177431)
def test_cwt_kernels_new(self):
assert_allclose(self.kernels_new.scales, [3.0, 7.8])
assert_allclose(self.kernels_new.kern_approx.sum(), 0.99988318386)
assert_allclose(self.kernels_new.kern_approx.max(), 0.000386976177431)
assert_allclose(self.kernels_new.kern_base[0].sum(), 3.01663209714e-05)
assert_allclose(self.kernels_new.kern_base[0].max(), 8.59947060958e-05)
assert_allclose(self.kernels_new.kern_base[1].sum(), 4.84548284275e-06)
assert_allclose(self.kernels_new.kern_base[1].max(), 1.88182106053e-06)
def test_cwt_kernels_old(self):
assert_allclose(self.kernels_old.scales, [3.0, 7.8])
assert_allclose(self.kernels_old.kern_approx.sum(), 0.99988318386)
assert_allclose(self.kernels_old.kern_approx.max(), 0.000386976177431)
assert_allclose(self.kernels_old.kern_base[0].sum(), 0.000207093150419)
assert_allclose(self.kernels_old.kern_base[0].max(), 0.0150679236063)
assert_allclose(self.kernels_old.kern_base[1].sum(), 0.000146764160432)
assert_allclose(self.kernels_old.kern_base[1].max(), 0.002228982782)
def test_info_table(self):
t = self.kernels_new.info_table
assert_equal(t.colnames, ["Name", "Source"])
assert_equal(len(t), 13)
# In[ ]:
# Input parameters for CWTKernels
N_SCALE = 2 # Number of scales considered.
MIN_SCALE = 6. # First scale used.
STEP_SCALE = 1.3 # Base scaling factor.
# In[ ]:
from gammapy.detect import CWTKernels
cwt_kernels = CWTKernels(
n_scale=N_SCALE, min_scale=MIN_SCALE, step_scale=STEP_SCALE
)
print(cwt_kernels.info_table)
# Other parameters are optional, in this demonstration define them all.
# In[ ]:
MAX_ITER = 10 # The maximum number of iterations of the CWT algorithm.
TOL = 1e-5 # Tolerance for stopping criterion.
SIGNIFICANCE_THRESHOLD = 2. # Measure of statistical significance.
SIGNIFICANCE_ISLAND_THRESHOLD = (
None
) # Measure is used for cleaning of isolated pixel islands.