def run_cwt(data):
cwt = CWT(nscales=2, min_scale=6.0, scale_step=1.3)
data['background'] = 1. * np.ones_like(data['background'], dtype=float)
cwt.set_data(data['image'], data['background'])
# TODO: run one step, try to understand what it does
# cwt.run_one_iteration(nsigma=100, nsigmap=4.0)
cwt.run_iteratively(nsigma=100, niter=5)
return cwt
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
# Let's start to analyse input data. Import Logging module to see how the algorithm works during data analysis.
# In[ ]:
from gammapy.detect import CWT
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
cwt = CWT(
kernels=cwt_kernels,
tol=TOL,
significance_threshold=SIGNIFICANCE_THRESHOLD,
significance_island_threshold=SIGNIFICANCE_ISLAND_THRESHOLD,
remove_isolated=REMOVE_ISOLATED,
keep_history=KEEP_HISTORY,
)
# In order to the algorithm was able to analyze source images, you need to convert them to a special format, i.e. create an CWTData object. Do this.
# In[ ]:
from gammapy.detect import CWTKernels, CWTData
cwt_data = CWTData(
counts=data["counts"], background=data["background"], n_scale=N_SCALE
)
