def significance_image(infile,
outfile,
theta,
overwrite):
"""Make correlated significance image.
TODO: describe
"""
from astropy.io import fits
from gammapy.image import disk_correlate
from gammapy.stats import significance_on_off
log.info('Reading {0}'.format(infile))
hdus = fits.open(infile)
n_on = hdus['On'].data
n_off = hdus['Off'].data
a_on = hdus['OnExposure'].data
a_off = hdus['OffExposure'].data
log.info('Correlating n_on and a_on map')
theta = theta / hdus['On'].header['CDELT2']
n_on = disk_correlate(n_on, theta)
a_on = disk_correlate(a_on, theta)
log.info('Computing significance map')
alpha = a_on / a_off
significance = significance_on_off(n_on, n_off, alpha)
log.info('Writing {0}'.format(outfile))
fits.writeto(outfile, data=significance, header=hdus['On'].header, clobber=overwrite)
def significance_image(infile,
outfile,
theta,
overwrite):
"""Make correlated significance image.
TODO: describe
"""
import logging
logging.basicConfig(level=logging.DEBUG, format='%(levelname)s - %(message)s')
from astropy.io import fits
from gammapy.image import disk_correlate
from gammapy.stats import significance_on_off
logging.info('Reading {0}'.format(infile))
hdus = fits.open(infile)
n_on = hdus['On'].data
n_off = hdus['Off'].data
a_on = hdus['OnExposure'].data
a_off = hdus['OffExposure'].data
logging.info('Correlating n_on and a_on map')
theta = theta / hdus['On'].header['CDELT2']
n_on = disk_correlate(n_on, theta)
a_on = disk_correlate(a_on, theta)
logging.info('Computing significance map')
alpha = a_on / a_off
significance = significance_on_off(n_on, n_off, alpha)
logging.info('Writing {0}'.format(outfile))
fits.writeto(outfile, data=significance, header=hdus['On'].header, clobber=overwrite)
def make_significance_image():
counts_image = fits.open("counts_image.fits")[1]
bkg_image = fits.open("bkg_image.fits")[1]
counts = disk_correlate(counts_image.data, 10)
bkg = disk_correlate(bkg_image.data, 10)
s = significance(counts, bkg)
s_image = fits.ImageHDU(data=s, header=counts_image.header)
s_image.writeto("significance_image.fits", clobber=True)
def make_significance_image():
counts_image = fits.open("counts_image.fits")[1]
bkg_image = fits.open("bkg_image.fits")[1]
counts = disk_correlate(counts_image.data, 10)
bkg = disk_correlate(bkg_image.data, 10)
s = significance(counts, bkg)
s_image = fits.ImageHDU(data=s, header=counts_image.header)
s_image.writeto("significance_image.fits", clobber=True)
def residual_images(model_file, data_file, out_file, thetas, overwrite):
"""Compute source model residual images.
The input `data_file` must contain the following HDU extensions:
* 'On' -- Counts image
* 'Background' -- Background image
"""
import logging
logging.basicConfig(level=logging.DEBUG,
format='%(levelname)s - %(message)s')
import numpy as np
from astropy.io import fits
from gammapy.image import disk_correlate
from gammapy.stats import significance
logging.info('Reading {0}'.format(data_file))
hdu_list = fits.open(data_file)
header = hdu_list[0].header
counts = hdu_list['On'].data.astype(np.float64)
background = hdu_list['Background'].data.astype(np.float64)
diffuse = hdu_list['Diffuse'].data.astype(np.float64)
logging.info('Reading {0}'.format(model_file))
model = fits.getdata(model_file)
background_plus_model_diffuse = background + model + diffuse
out_hdu_list = fits.HDUList()
for theta in thetas:
logging.info('Processing theta = {0} deg'.format(theta))
theta_pix = theta / header['CDELT2']
counts_corr = disk_correlate(counts, theta_pix)
background_plus_model_corr = disk_correlate(
background_plus_model_diffuse, theta_pix)
# excess_corr = counts_corr - background_plus_model_corr
significance_corr = significance(counts_corr,
background_plus_model_corr)
name = 'RESIDUAL_SIGNIFICANCE_{0}'.format(theta)
logging.info('Appending HDU extension: {0}'.format(name))
hdu = fits.ImageHDU(significance_corr, header, name)
out_hdu_list.append(hdu)
logging.info('Writing {0}'.format(out_file))
out_hdu_list.writeto(out_file, clobber=overwrite)
def residual_images(model_file,
data_file,
out_file,
thetas,
overwrite):
"""Compute source model residual images.
The input `data_file` must contain the following HDU extensions:
* 'On' -- Counts image
* 'Background' -- Background image
"""
import numpy as np
from astropy.io import fits
from gammapy.image import disk_correlate
from gammapy.stats import significance
log.info('Reading {0}'.format(data_file))
hdu_list = fits.open(data_file)
header = hdu_list[0].header
counts = hdu_list['On'].data.astype(np.float64)
background = hdu_list['Background'].data.astype(np.float64)
diffuse = hdu_list['Diffuse'].data.astype(np.float64)
log.info('Reading {0}'.format(model_file))
model = fits.getdata(model_file)
background_plus_model_diffuse = background + model + diffuse
out_hdu_list = fits.HDUList()
for theta in thetas:
log.info('Processing theta = {0} deg'.format(theta))
theta_pix = theta / header['CDELT2']
counts_corr = disk_correlate(counts, theta_pix)
background_plus_model_corr = disk_correlate(background_plus_model_diffuse, theta_pix)
# excess_corr = counts_corr - background_plus_model_corr
significance_corr = significance(counts_corr, background_plus_model_corr)
name = 'RESIDUAL_SIGNIFICANCE_{0}'.format(theta)
log.info('Appending HDU extension: {0}'.format(name))
hdu = fits.ImageHDU(significance_corr, header, name)
out_hdu_list.append(hdu)
log.info('Writing {0}'.format(out_file))
out_hdu_list.writeto(out_file, clobber=overwrite)
"""Plot significance image with HESS and MILAGRO colormap.
"""
import numpy as np
import matplotlib.pyplot as plt
from gammapy.datasets import load_poisson_stats_image
from gammapy.image import disk_correlate
from gammapy.stats import significance
from gammapy.image import colormap_hess, colormap_milagro
from astropy.visualization.mpl_normalize import ImageNormalize
from astropy.visualization import LinearStretch
# Compute an example significance image
counts = load_poisson_stats_image()
counts = disk_correlate(counts, radius=5, mode='reflect')
background = np.median(counts) * np.ones_like(counts)
image = significance(counts, background)
# Plot with the HESS and Milagro colormap
vmin, vmax, vtransition = -5, 15, 5
plt.figure(figsize=(8, 4))
normalize = ImageNormalize(vmin=vmin, vmax=vmax, stretch=LinearStretch())
transition = normalize(vtransition)
plt.subplot(121)
cmap = colormap_hess(transition=transition)
plt.imshow(image, cmap=cmap, norm=normalize)
plt.axis('off')
plt.colorbar()
plt.title('HESS-style colormap')
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Plot significicance image with HESS- and MILAGRO-colormap.
"""
import numpy as np
import matplotlib.pyplot as plt
from gammapy.data import poisson_stats_image
from gammapy.image import disk_correlate
from gammapy.stats import significance
from gammapy.image import colormap_hess, colormap_milagro
# Compute an example significance image
counts = disk_correlate(poisson_stats_image(), radius=5, mode='reflect')
background = np.median(counts) * np.ones_like(counts)
image = significance(counts, background)
# Plot with the HESS and Milagro colormap
vmin, vmax, vtransition = -5, 15, 5
plt.figure(figsize=(8, 4))
plt.subplot(121)
cmap = colormap_hess(vtransition=vtransition, vmin=vmin, vmax=vmax)
plt.imshow(image, cmap=cmap, vmin=vmin, vmax=vmax)
plt.axis('off')
plt.colorbar()
plt.title('HESS-style colormap')
plt.subplot(122)
cmap = colormap_milagro(vtransition=vtransition, vmin=vmin, vmax=vmax)
plt.imshow(image, cmap=cmap, vmin=vmin, vmax=vmax)
plt.axis('off')
plt.colorbar()