コード例 #1
0
def check_imgdata_convolved():
    """What is the behavior when we add the PSF to plot_pvalue?"""

    r1 = ui.get_psf()
    ui.plot_pvalue(c1, c1 + g1, conv_model=r1, num=40, bins=5)

    tmp = ui.get_pvalue_results()

    # these values are different to test_plot_pvalue_imgpsf_unconvolved
    #
    assert tmp.null == pytest.approx(2391.26963100235)
    assert tmp.alt == pytest.approx(563.3992697080881)
    assert tmp.lr == pytest.approx(1827.8703612942618)

    assert tmp.samples.shape == (40, 1)
    assert tmp.stats.shape == (40, 2)
    assert tmp.ratios.shape == (40, )

    tmp = ui.get_pvalue_plot()

    assert tmp.lr == pytest.approx(1827.8703612942618)

    assert tmp.xlabel == 'Likelihood Ratio'
    assert tmp.ylabel == 'Frequency'
    assert tmp.title == 'Likelihood Ratio Distribution'

    assert tmp.ratios.shape == (40, )
    assert tmp.xlo.shape == (6, )
    assert tmp.xhi.shape == (6, )
    assert tmp.y.shape == (6, )
コード例 #2
0
ファイル: psf.py プロジェクト: mwcraig/gammapy
 def center_psf(self):
     """Set ``xpos`` and ``ypos`` of the PSF to the dataspace center."""
     import sherpa.astro.ui as sau
     try:
         ny, nx = sau.get_data().shape
         for par in sau.get_psf().kernel.pars:
             if par.name is 'xpos':
                 par.val = (nx + 1) / 2.
             elif par.name is 'ypos':
                 par.val = (ny + 1) / 2.
     except:
         raise Exception('PSF is not centered.')
コード例 #3
0
 def center_psf(self):
     """Set ``xpos`` and ``ypos`` of the PSF to the dataspace center."""
     import sherpa.astro.ui as sau
     try:
         ny, nx = sau.get_data().shape
         for par in sau.get_psf().kernel.pars:
             if par.name is 'xpos':
                 par.val = (nx + 1) / 2.
             elif par.name is 'ypos':
                 par.val = (ny + 1) / 2.
     except:
         raise Exception('PSF is not centered.')
コード例 #4
0
ファイル: psf.py プロジェクト: ignasi-reichardt/gammapy
 def center_psf(self):
     """Set xpos and ypos of the PSF to the dataspace center"""
     import sherpa.astro.ui as sau
     try:
         ny, nx = sau.get_data().shape
         for par in sau.get_psf().kernel.pars:
             if par.name is 'xpos':
                 par.val = (nx + 1) / 2.
             elif par.name is 'ypos':
                 par.val = (ny + 1) / 2.
     except:
         logging.warning('PSF is not centered.')
コード例 #5
0
ファイル: psf_core.py プロジェクト: vorugantia/gammapy
 def containment_fraction(self, theta, npix=1000):
     """Compute fraction of PSF contained inside theta."""
     import sherpa.astro.ui as sau
     sau.dataspace2d((npix, npix))
     self.set()
     # x_center = get_psf().kernel.pars.xpos
     # y_center = get_psf().kernel.pars.ypos
     x_center, y_center = sau.get_psf().model.center
     x_center, y_center = x_center + 0.5, y_center + 0.5  # shift seen on image.
     x, y = sau.get_data().x0, sau.get_data().x1
     # Note: Here we have to use the source image, before I used
     # get_model_image(), which returns the PSF-convolved PSF image,
     # which is a factor of sqrt(2) ~ 1.4 too wide!!!
     p = sau.get_source_image().y.flatten()
     p /= np.nansum(p)
     mask = (x - x_center)**2 + (y - y_center)**2 < theta**2
     fraction = np.nansum(p[mask])
     if 0:  # debug
         sau.get_data().y = p
         sau.save_data('psf_sherpa.fits', clobber=True)
         sau.get_data().y = mask.astype('int')
         sau.save_data('mask_sherpa.fits', clobber=True)
     return fraction
コード例 #6
0
ファイル: psf.py プロジェクト: mwcraig/gammapy
 def containment_fraction(self, theta, npix=1000):
     """Compute fraction of PSF contained inside theta."""
     import sherpa.astro.ui as sau
     sau.dataspace2d((npix, npix))
     self.set()
     # x_center = get_psf().kernel.pars.xpos
     # y_center = get_psf().kernel.pars.ypos
     x_center, y_center = sau.get_psf().model.center
     x_center, y_center = x_center + 0.5, y_center + 0.5  # shift seen on image.
     x, y = sau.get_data().x0, sau.get_data().x1
     # @note Here we have to use the source image, before I used
     # get_model_image(), which returns the PSF-convolved PSF image,
     # which is a factor of sqrt(2) ~ 1.4 too wide!!!
     p = sau.get_source_image().y.flatten()
     p /= np.nansum(p)
     mask = (x - x_center) ** 2 + (y - y_center) ** 2 < theta ** 2
     fraction = np.nansum(p[mask])
     if 0:  # debug
         sau.get_data().y = p
         sau.save_data('psf_sherpa.fits', clobber=True)
         sau.get_data().y = mask.astype('int')
         sau.save_data('mask_sherpa.fits', clobber=True)
     return fraction