def ConfidenceArea(self, adLevels):
# create a normalized cumulative distribution
self.log_skymap_sorted = np.sort(self.log_skymap.flatten())[::-1]
self.log_skymap_cum = cumulative.fast_log_cumulative(self.log_skymap_sorted)
# find the indeces corresponding to the given CLs
adLevels = np.ravel([adLevels])
args = [(self.log_skymap_sorted,self.log_skymap_cum,level) for level in adLevels]
adHeights = self.pool.map(FindHeights,args)
dA = hp.nside2pixarea(self.nside, degrees=True)
areas = []
for height in adHeights:
(index_hp,) = np.where(self.log_skymap>height)
areas.append(len(index_hp)*dA)
self.area_confidence = np.array(areas)
if self.injection!=None:
ra,dec = self.injection.get_ra_dec()
theta,phi = eq2ang(ra,dec)
ipix = hp.pixelfunc.ang2pix(self.nside,theta,phi, nest=True)
logPval = self.log_skymap[ipix]
confidence_level = np.exp(self.log_skymap_cum[np.abs(self.log_skymap_sorted-logPval).argmin()])
height = FindHeights((self.log_skymap_sorted,self.log_skymap_cum,confidence_level))
(index_hp,) = np.where(self.log_skymap >= height)
searched_area = len(index_hp)*dA
return self.area_confidence,(confidence_level,searched_area)
del self.log_skymap_sorted
del self.log_skymap_cum
return self.area_confidence,None
def ConfidenceVolume(self, adLevels):
# create a normalized cumulative distribution
self.log_volume_map_sorted = np.sort(self.log_volume_map.flatten())[::-1]
self.log_volume_map_cum = cumulative.fast_log_cumulative(self.log_volume_map_sorted)
# find the indeces corresponding to the given CLs
adLevels = np.ravel([adLevels])
args = [(self.log_volume_map_sorted,self.log_volume_map_cum,level) for level in adLevels]
adHeights = self.pool.map(FindHeights,args)
self.heights = {str(lev):hei for lev,hei in zip(adLevels,adHeights)}
dd = np.diff(self.d_grid)[0]
dA = hp.nside2pixarea(self.nside)
volumes = []
for height in adHeights:
(index_d, index_hp) = np.where(self.log_volume_map>height)
volumes.append(np.sum([self.d_grid[i_d]**2. * dd * dA for i_d in index_d]))
self.volume_confidence = np.array(volumes)
if self.injection!=None:
ra,dec = self.injection.get_ra_dec()
distance = self.injection.distance
logPval = self.logPosterior(distance,ra,dec)
confidence_level = np.exp(self.log_volume_map_cum[np.abs(self.log_volume_map_sorted-logPval).argmin()])
height = FindHeights((self.log_volume_map_sorted,self.log_volume_map_cum,confidence_level))
(index_d, index_hp) = np.where(self.log_volume_map >= height)
searched_volume = np.sum([self.d_grid[i_d]**2. * dd * dA for i_d in index_d])
self.injection_volume_confidence = confidence_level
self.injection_volume_height = height
return self.volume_confidence,(confidence_level,searched_volume)
del self.log_volume_map_sorted
del self.log_volume_map_cum
return self.volume_confidence,None
def ConfidenceVolume(self, adLevels):
# create a normalized cumulative distribution
self.log_volume_map_sorted = np.sort(
self.log_volume_map.flatten())[::-1]
self.log_volume_map_cum = cumulative.fast_log_cumulative(
self.log_volume_map_sorted)
# find the indeces corresponding to the given CLs
adLevels = np.ravel([adLevels])
args = [(self.log_volume_map_sorted, self.log_volume_map_cum, level)
for level in adLevels]
adHeights = self.pool.map(FindHeights, args)
self.heights = {str(lev): hei for lev, hei in zip(adLevels, adHeights)}
dd = np.diff(self.d_grid)[0]
dA = hp.nside2pixarea(self.nside)
volumes = []
for height in adHeights:
(index_d, index_hp) = np.where(self.log_volume_map > height)
volumes.append(
np.sum([self.d_grid[i_d]**2. * dd * dA for i_d in index_d]))
self.volume_confidence = np.array(volumes)
if self.injection != None:
ra, dec = self.injection.get_ra_dec()
distance = self.injection.distance
logPval = self.logPosterior(distance, ra, dec)
confidence_level = np.exp(
self.log_volume_map_cum[np.abs(self.log_volume_map_sorted -
logPval).argmin()])
height = FindHeights((self.log_volume_map_sorted,
self.log_volume_map_cum, confidence_level))
(index_d, index_hp) = np.where(self.log_volume_map >= height)
searched_volume = np.sum(
[self.d_grid[i_d]**2. * dd * dA for i_d in index_d])
self.injection_volume_confidence = confidence_level
self.injection_volume_height = height
return self.volume_confidence, (confidence_level, searched_volume)
del self.log_volume_map_sorted
del self.log_volume_map_cum
return self.volume_confidence, None
def ConfidenceArea(self, adLevels):
# create a normalized cumulative distribution
self.log_skymap_sorted = np.sort(self.log_skymap.flatten())[::-1]
self.log_skymap_cum = cumulative.fast_log_cumulative(
self.log_skymap_sorted)
# find the indeces corresponding to the given CLs
adLevels = np.ravel([adLevels])
args = [(self.log_skymap_sorted, self.log_skymap_cum, level)
for level in adLevels]
adHeights = self.pool.map(FindHeights, args)
dA = hp.nside2pixarea(self.nside, degrees=True)
areas = []
for height in adHeights:
(index_hp, ) = np.where(self.log_skymap > height)
areas.append(len(index_hp) * dA)
self.area_confidence = np.array(areas)
if self.injection != None:
ra, dec = self.injection.get_ra_dec()
theta, phi = eq2ang(ra, dec)
ipix = hp.pixelfunc.ang2pix(self.nside, theta, phi, nest=True)
logPval = self.log_skymap[ipix]
confidence_level = np.exp(
self.log_skymap_cum[np.abs(self.log_skymap_sorted -
logPval).argmin()])
height = FindHeights((self.log_skymap_sorted, self.log_skymap_cum,
confidence_level))
(index_hp, ) = np.where(self.log_skymap >= height)
searched_area = len(index_hp) * dA
return self.area_confidence, (confidence_level, searched_area)
del self.log_skymap_sorted
del self.log_skymap_cum
return self.area_confidence, None
