"""Runs commands to produce convolved predicted counts map in current directory.""" import numpy as np import matplotlib.pyplot as plt from astropy.io import fits from gammapy.stats import significance from gammapy.image.utils import disk_correlate from aplpy import FITSFigure from npred_general import prepare_images model, gtmodel, ratio, counts, header = prepare_images() # Top hat correlation correlation_radius = 3 correlated_gtmodel = disk_correlate(gtmodel, correlation_radius) correlated_counts = disk_correlate(counts, correlation_radius) correlated_model = disk_correlate(model, correlation_radius) # Fermi significance fermi_significance = np.nan_to_num(significance(correlated_counts, gtmodel, method='lima')) # Gammapy significance significance = np.nan_to_num(significance(correlated_counts, correlated_model, method='lima')) titles = ['Gammapy Significance', 'Fermi Tools Significance'] # Plot fig = plt.figure(figsize=(10, 5)) hdu1 = fits.ImageHDU(significance, header)
"""Runs commands to produce convolved predicted counts map in current directory.""" import numpy as np import matplotlib.pyplot as plt from astropy.io import fits from gammapy.stats import significance from gammapy.image.utils import disk_correlate from npred_general import prepare_images from aplpy import FITSFigure model, gtmodel, ratio, counts, header = prepare_images() # Top hat correlation correlation_radius = 3 correlated_gtmodel = disk_correlate(gtmodel, correlation_radius) correlated_counts = disk_correlate(counts, correlation_radius) correlated_model = disk_correlate(model, correlation_radius) # Fermi significance fermi_significance = np.nan_to_num(significance(correlated_counts, gtmodel, method='lima')) # Gammapy significance significance = np.nan_to_num(significance(correlated_counts, correlated_model, method='lima')) titles = ['Gammapy Significance', 'Fermi Tools Significance'] # Plot fig = plt.figure(figsize=(10, 5)) hdu1 = fits.ImageHDU(significance, header)