def __init__(self,roi,**kwargs):
super(ResidualImage,self).__init__(roi,**kwargs)
self.model=ModelImage(self.roi,**keyword_options.defaults_to_kwargs(self,ModelImage))
self.counts=CountsImage(self.roi,**keyword_options.defaults_to_kwargs(self,CountsImage))
self.image = self.counts.image - self.model.image
SmoothedImage.add_to_skyimage(self.skyimage,self.image)
def __init__(self, *args, **kwargs):
super(SmoothedImage, self).__init__(*args, **kwargs)
self.kernel = self.get_kernel(self.kerneltype, self.kernel_rad,
self.pixelsize)
self.kernelsize = self.kernel.shape[0]
# Make an image, bigger then the desired one, by twice
# the smooth radius in each direction.
self.pass_dict = keyword_options.defaults_to_kwargs(self, self.object)
self.pass_dict['size'] = self.size + self.kernelsize * self.pixelsize
self.smoothed = self.object(self.roi, **self.pass_dict)
self.smoothed.skyimage = SmoothedImage.convolve_skyimage(
self.smoothed.skyimage, self.kernel)
self.smoothed.image = ROIImage.skyimage2numpy(self.smoothed.skyimage)
# now, shrink down smoothed image and replace the current skyimage with it
self.image = self.smoothed.image[self.kernelsize / 2:-self.kernelsize /
2, self.kernelsize /
2:-self.kernelsize / 2]
if self.per_solid_angle:
# convert from counts to counts per square degree
self.image = self.image / self.pixelsize**2
SmoothedImage.add_to_skyimage(self.skyimage, self.image)
def __init__(self, like, name, *args, **kwargs):
keyword_options.process(self, kwargs)
self.like = like
self.name = name
if self.ul_algorithm not in BaseGtlikeSED.ul_choices:
raise Exception("Upper Limit Algorithm %s not in %s" % (self.ul_algorithm,str(BaseGtlikeSED.ul_choices)))
if self.bin_edges is not None:
if not BaseGtlikeSED.good_binning(like, self.bin_edges):
raise Exception("bin_edges is not commensurate with the underlying energy binning of pyLikelihood.")
self.bin_edges = np.asarray(self.bin_edges)
energy = np.sqrt(self.bin_edges[1:]*self.bin_edges[:-1])
else:
# These energies are always in MeV
self.bin_edges = like.energies
energy = like.e_vals
self.lower=self.bin_edges[:-1]
self.upper=self.bin_edges[1:]
source=self.like.logLike.getSource(name)
self.init_spectrum=source.spectrum()
self.init_model=build_pointlike_model(self.init_spectrum)
self.results = dict(
Name=name,
spectrum=name_to_spectral_dict(like,name, errors=True, covariance_matrix=True),
)
self._calculate(like)
super(GtlikeSED,self).__init__(self.results, **keyword_options.defaults_to_kwargs(self, SED))
def __init__(self, roi):
self.roi = roi
self.point_sources = self._copy_ps(roi.psm.point_sources)
self.bgmodels = self._copy_bg(roi.dsm.bgmodels)
from uw.like.roi_analysis import ROIAnalysis
self.roi_kwargs = defaults_to_kwargs(roi, ROIAnalysis)
def __init__(self, roi, **kwargs):
if kwargs.has_key('show_colorbar') or \
kwargs.has_key('overlay_psf'):
raise Exception("This feature doesn't work, for now...")
keyword_options.process(self, kwargs)
self.roi = roi
sources_kwargs = keyword_options.defaults_to_kwargs(
self, ROISmoothedSources)
sources_kwargs['show_colorbar'] = False
self.smoothed_sources = ROISmoothedSources(roi, **sources_kwargs)
source_kwargs = keyword_options.defaults_to_kwargs(
self, ROISmoothedSources)
source_kwargs['overlay_psf'] = False
self.smoothed_source = ROISmoothedSource(roi, **source_kwargs)
def change_binning(self, fit_emin, fit_emax):
""" This function recreates the ROI using a new energy binning.
Kind of inefficient, but easy. """
kwargs = keyword_options.defaults_to_kwargs(self, ROIAnalysis)
kwargs['fit_emin'] = fit_emin
kwargs['fit_emax'] = fit_emax
self.__init__(roi_dir=self.roi_dir,
ps_manager=self.psm,
ds_manager=self.dsm,
spectral_analysis=self.sa,
**kwargs)
def __init__(self, roi, name, **kwargs):
keyword_options.process(self, kwargs)
self.roi = roi
self.name = name
bf = BandFlux(self.roi, which=self.name, merge=self.merge, scale_factor=1)
results = PointlikeSED.pointlike_sed_to_dict(bf, flux_units=self.flux_units, energy_units=self.energy_units)
results['spectrum'] = name_to_spectral_dict(roi, name, errors=True, covariance_matrix=True)
super(PointlikeSED,self).__init__(results, **keyword_options.defaults_to_kwargs(self, SED))
def __init__(self, roi, **kwargs):
keyword_options.process(self, kwargs)
self.roi = roi
self.cmap = colormaps.b
# Fit many pixels inside of the summing radius
self.pixelsize = self.kernel_rad / 5.0
# Background subtracted counts
counts_kwargs = keyword_options.defaults_to_kwargs(
self, ROISmoothedSources)
counts_kwargs['show_colorbar'] = False
counts_kwargs['overlay_psf'] = False
self.counts = ROISmoothedSources(roi, **counts_kwargs)
# Model counts for non-background sources.
model_kwargs = keyword_options.defaults_to_kwargs(
self, ROISmoothedSources)
model_kwargs['overlay_psf'] = False
self.model = ROISmoothedModel(roi, **model_kwargs)
def __init__(self, like, name, *args, **kwargs):
keyword_options.process(self, kwargs)
self.like = like
self.name = name
if self.ul_algorithm not in BaseGtlikeSED.ul_choices:
raise Exception("Upper Limit Algorithm %s not in %s" %
(self.ul_algorithm, str(BaseGtlikeSED.ul_choices)))
if self.bin_edges is not None:
if not BaseGtlikeSED.good_binning(like, self.bin_edges):
raise Exception(
"bin_edges is not commensurate with the underlying energy binning of pyLikelihood."
)
self.bin_edges = np.asarray(self.bin_edges)
energy = np.sqrt(self.bin_edges[1:] * self.bin_edges[:-1])
else:
# These energies are always in MeV
self.bin_edges = like.energies
energy = like.e_vals
self.lower = self.bin_edges[:-1]
self.upper = self.bin_edges[1:]
source = self.like.logLike.getSource(name)
self.init_spectrum = source.spectrum()
self.init_model = build_pointlike_model(self.init_spectrum)
self.results = dict(
Name=name,
spectrum=name_to_spectral_dict(like,
name,
errors=True,
covariance_matrix=True),
)
self._calculate(like)
super(GtlikeSED,
self).__init__(self.results,
**keyword_options.defaults_to_kwargs(self, SED))
def __init__(self, roi, name, **kwargs):
keyword_options.process(self, kwargs)
self.roi = roi
self.name = name
bf = BandFlux(self.roi,
which=self.name,
merge=self.merge,
scale_factor=1)
results = PointlikeSED.pointlike_sed_to_dict(
bf, flux_units=self.flux_units, energy_units=self.energy_units)
results['spectrum'] = name_to_spectral_dict(roi,
name,
errors=True,
covariance_matrix=True)
super(PointlikeSED,
self).__init__(results,
**keyword_options.defaults_to_kwargs(self, SED))
def fill(self):
tscalc = TSCalc(self.roi,
**keyword_options.defaults_to_kwargs(self, TSCalc))
temp = TSCalcPySkyFunction(tscalc)
self.skyimage.fill(temp.get_pyskyfun())
