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 )