def test_save_model_pha_ascii(clean_astro_ui, tmp_path):
"""Can we write out data for save_model? DataPHA and ASCII"""
ui.load_arrays(1, [1, 2], [5, 10], ui.DataPHA)
# we need a response
egrid = np.asarray([0.1, 0.2, 0.4])
elo = egrid[:-1]
ehi = egrid[1:]
rmf = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
ui.set_rmf(rmf)
yarf = np.asarray([10, 20])
arf = create_arf(elo, ehi, yarf)
ui.set_arf(arf)
ui.set_source(ui.const1d.cmdl)
cmdl.c0 = 2
out = tmp_path / 'model.dat'
ui.save_model(str(out), ascii=True)
cts = out.read_text()
check_output(cts, ['XLO', 'XHI', 'MODEL'],
[[0.1, 0.2, 20], [0.2, 0.4, 40]])
def test_save_model_pha_fits(clean_astro_ui, tmp_path):
"""Can we write out data for save_model? DataPHA and FITS
"""
from sherpa.astro.io import read_table_blocks
ui.load_arrays(1, [1, 2], [5, 10], ui.DataPHA)
# we need a response
egrid = np.asarray([0.1, 0.2, 0.4])
elo = egrid[:-1]
ehi = egrid[1:]
rmf = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
ui.set_rmf(rmf)
yarf = np.asarray([10, 20])
arf = create_arf(elo, ehi, yarf)
ui.set_arf(arf)
ui.set_source(ui.const1d.cmdl)
cmdl.c0 = 2
out = tmp_path / 'model.dat'
outfile = str(out)
ui.save_model(outfile)
ans = read_table_blocks(outfile)
blocks = ans[1]
assert len(blocks) == 2
check_table(blocks[2], {
'XLO': [0.1, 0.2],
'XHI': [0.2, 0.4],
'MODEL': [20, 40]
})
def image_model_sherpa(exposure,
psf,
sources,
model_image,
overwrite):
"""Compute source model image with Sherpa.
Inputs:
* Source list (JSON file)
* PSF (JSON file)
* Exposure image (FITS file)
Outputs:
* Source model flux image (FITS file)
* Source model excess image (FITS file)
"""
import sherpa.astro.ui as sau
from ..image.models.psf import Sherpa
from ..image.models.utils import read_json
log.info('Reading exposure: {0}'.format(exposure))
# Note: We don't really need the exposure as data,
# but this is a simple way to init the dataspace to the correct shape
sau.load_data(exposure)
sau.load_table_model('exposure', exposure)
log.info('Reading PSF: {0}'.format(psf))
Sherpa(psf).set()
log.info('Reading sources: {0}'.format(sources))
read_json(sources, sau.set_source)
name = sau.get_source().name
full_model = 'exposure * psf({})'.format(name)
sau.set_full_model(full_model)
log.info('Computing and writing model_image: {0}'.format(model_image))
sau.save_model(model_image, clobber=overwrite)
sau.clean()
sau.delete_psf()
def sherpa_model_image(exposure, psf, sources, model_image, overwrite):
"""Compute source model image with Sherpa.
Inputs:
* Source list (JSON file)
* PSF (JSON file)
* Exposure image (FITS file)
Outputs:
* Source model flux image (FITS file)
* Source model excess image (FITS file)
"""
import logging
logging.basicConfig(level=logging.DEBUG,
format='%(levelname)s - %(message)s')
import sherpa.astro.ui as sau # @UnresolvedImport
from ..morphology.psf import Sherpa
from ..morphology.utils import read_json
logging.info('Reading exposure: {0}'.format(exposure))
# Note: We don't really need the exposure as data,
# but this is a simple way to init the dataspace to the correct shape
sau.load_data(exposure)
sau.load_table_model('exposure', exposure)
logging.info('Reading PSF: {0}'.format(psf))
Sherpa(psf).set()
logging.info('Reading sources: {0}'.format(sources))
read_json(sources, sau.set_source)
name = sau.get_source().name
full_model = 'exposure * psf({})'.format(name)
sau.set_full_model(full_model)
logging.info('Computing and writing model_image: {0}'.format(model_image))
sau.save_model(model_image, clobber=overwrite)
# Results are automatically printed to the screen
sau.fit()
sau.covar()
# Sherpa uses fwhm instead of sigma as extension parameter ... need to convert
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
fwhm_to_sigma = 1. / np.sqrt(8 * np.log(2))
cov = sau.get_covar_results()
sigma = fwhm_to_sigma * cov.parvals[0]
sigma_err = fwhm_to_sigma * cov.parmaxes[0]
print('sigma: {0} +- {1}'.format(sigma, sigma_err))
# Compute correlation coefficient for sigma and norm
c = cov.extra_output
c_norm = c[3, 3]
c_sigma = fwhm_to_sigma**2 * c[0, 0]
c_norm_sigma = fwhm_to_sigma * c[0, 3]
corr_norm_sigma = c_norm_sigma / np.sqrt(c_norm * c_sigma)
print('corr_norm_sigma: {0}'.format(corr_norm_sigma))
# Save model excess image
sau.save_model('model_sherpa.fits.gz', clobber=True)
# Compute TS
L1 = sau.calc_stat()
sau.set_source('const2d.background')
sau.fit()
L0 = sau.calc_stat()
TS = 2 * (L0 - L1)
print('TS: {:.5f}'.format(TS))
import sherpa.astro.ui as sau
# Define width of the source and the PSF
sigma_psf, sigma_source = 3, 4
# for relation of sigma and fwhm see
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
sigma_to_fwhm = np.sqrt(8 * np.log(2)) # ~ 2.35
sigma = np.sqrt(sigma_psf ** 2 + sigma_source ** 2)
fwhm = sigma_to_fwhm * sigma
# Seed the random number generator to make the output reproducible
np.random.seed(0)
sau.dataspace2d((200, 200))
sau.set_source('normgauss2d.source + const2d.background')
sau.set_par('source.xpos', 100)
sau.set_par('source.ypos', 100)
sau.set_par('source.ampl', 1e3)
sau.set_par('source.fwhm', fwhm)
sau.set_par('background.c0', 1)
sau.fake()
sau.save_model('model.fits.gz', clobber=True)
sau.save_data('counts.fits.gz', clobber=True)
sau.set_source('source')
sau.save_model('source.fits.gz', clobber=True)
sau.set_source('background')
sau.save_model('background.fits.gz', clobber=True)
sau.fit()
sau.covar()
# Sherpa uses fwhm instead of sigma as extension parameter ... need to convert
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
fwhm_to_sigma = 1. / np.sqrt(8 * np.log(2))
cov = sau.get_covar_results()
sigma = fwhm_to_sigma * cov.parvals[0]
sigma_err = fwhm_to_sigma * cov.parmaxes[0]
print('sigma: {0} +- {1}'.format(sigma, sigma_err))
# Compute correlation coefficient for sigma and norm
c = cov.extra_output
c_norm = c[3, 3]
c_sigma = fwhm_to_sigma ** 2 * c[0, 0]
c_norm_sigma = fwhm_to_sigma * c[0, 3]
corr_norm_sigma = c_norm_sigma / np.sqrt(c_norm * c_sigma)
print('corr_norm_sigma: {0}'.format(corr_norm_sigma))
# Save model excess image
sau.save_model('model_sherpa.fits.gz', clobber=True)
# Compute TS
L1 = sau.calc_stat()
sau.set_source('const2d.background')
sau.fit()
L0 = sau.calc_stat()
TS = 2 * (L0 - L1)
print('TS: {:.5f}'.format(TS))
# Results are automatically printed to the screen
sau.fit()
sau.covar()
# Sherpa uses fwhm instead of sigma as extension parameter ... need to convert
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
fwhm_to_sigma = 1.0 / np.sqrt(8 * np.log(2))
cov = sau.get_covar_results()
sigma = fwhm_to_sigma * cov.parvals[0]
sigma_err = fwhm_to_sigma * cov.parmaxes[0]
print("sigma: {0} +- {1}".format(sigma, sigma_err))
# Compute correlation coefficient for sigma and norm
c = cov.extra_output
c_norm = c[3, 3]
c_sigma = fwhm_to_sigma ** 2 * c[0, 0]
c_norm_sigma = fwhm_to_sigma * c[0, 3]
corr_norm_sigma = c_norm_sigma / np.sqrt(c_norm * c_sigma)
print("corr_norm_sigma: {0}".format(corr_norm_sigma))
# Save model excess image
sau.save_model("model_sherpa.fits.gz", clobber=True)
# Compute TS
L1 = sau.calc_stat()
sau.set_source("const2d.background")
sau.fit()
L0 = sau.calc_stat()
TS = 2 * (L0 - L1)
print("TS: {:.5f}".format(TS))
import sherpa.astro.ui as sau
# Define width of the source and the PSF
sigma_psf, sigma_source = 3, 4
# for relation of sigma and fwhm see
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
sigma_to_fwhm = np.sqrt(8 * np.log(2)) # ~ 2.35
sigma = np.sqrt(sigma_psf**2 + sigma_source**2)
fwhm = sigma_to_fwhm * sigma
# Seed the random number generator to make the output reproducible
np.random.seed(0)
sau.dataspace2d((200, 200))
sau.set_source('normgauss2d.source + const2d.background')
sau.set_par('source.xpos', 100)
sau.set_par('source.ypos', 100)
sau.set_par('source.ampl', 1e3)
sau.set_par('source.fwhm', fwhm)
sau.set_par('background.c0', 1)
sau.fake()
sau.save_model('model.fits.gz', clobber=True)
sau.save_data('counts.fits.gz', clobber=True)
sau.set_source('source')
sau.save_model('source.fits.gz', clobber=True)
sau.set_source('background')
sau.save_model('background.fits.gz', clobber=True)
