def kernels(self, image):
"""Ring kernels according to the specified method.
Parameters
----------
image : `~gammapy.image.SkyImage`
Sky image specifying the WCS information.
Returns
-------
kernels : list
List of `~astropy.convolution.Ring2DKernel`
"""
p = self.parameters
scale = image.wcs_pixel_scale()[0]
r_in = p['r_in'].to('deg') / scale
r_out_max = p['r_out_max'].to('deg') / scale
width = p['width'].to('deg') / scale
stepsize = p['stepsize'].to('deg') / scale
kernels = []
if p['method'] == 'fixed_width':
r_ins = np.arange(r_in.value, (r_out_max - width).value, stepsize.value)
widths = [width.value]
elif p['method'] == 'fixed_r_in':
widths = np.arange(width.value, (r_out_max - r_in).value, stepsize.value)
r_ins = [r_in.value]
for r_in, width in product(r_ins, widths):
kernel = Ring2DKernel(r_in, width)
kernel.normalize('peak')
kernels.append(kernel)
return kernels
def kernels(self, image):
"""Ring kernels according to the specified method.
Parameters
----------
image : `~gammapy.maps.WcsNDMap`
Map specifying the WCS information.
Returns
-------
kernels : list
List of `~astropy.convolution.Ring2DKernel`
"""
scale = image.geom.pixel_scales[0]
r_in = (self.r_in / scale).to_value("")
r_out_max = (self.r_out_max / scale).to_value("")
width = (self.width / scale).to_value("")
stepsize = (self.stepsize / scale).to_value("")
if self.method == "fixed_width":
r_ins = np.arange(r_in, (r_out_max - width), stepsize)
widths = [width]
elif self.method == "fixed_r_in":
widths = np.arange(width, (r_out_max - r_in), stepsize)
r_ins = [r_in]
else:
raise ValueError(f"Invalid method: {self.method!r}")
kernels = []
for r_in, width in itertools.product(r_ins, widths):
kernel = Ring2DKernel(r_in, width)
kernel.normalize("peak")
kernels.append(kernel)
return kernels
def background_skyimage_2fhl(counts):
log.info('Computing background map.')
images = GammaImages(counts.data, header=counts.wcs.to_header())
source_kernel = Tophat2DKernel(5)
source_kernel.normalize('peak')
background_kernel = Ring2DKernel(20, 20)
background_kernel.normalize('peak')
ikbe = IKBE(
images=images,
source_kernel=source_kernel.array,
background_kernel=background_kernel.array,
significance_threshold=5,
mask_dilation_radius=3,
)
mask_data, background_data = ikbe.run()
mask = SkyMap.empty_like(counts)
mask.data = mask_data
background = SkyMap.empty_like(counts)
background.data = background_data
return mask, background
def kernel(self, image):
"""Ring kernel.
Parameters
----------
image : `~gammapy.maps.WcsNDMap`
Input Map
Returns
-------
ring : `~astropy.convolution.Ring2DKernel`
Ring kernel.
"""
scale = image.geom.pixel_scales[0].to("deg")
r_in = self.r_in.to("deg") / scale
width = self.width.to("deg") / scale
ring = Ring2DKernel(r_in.value, width.value)
ring.normalize("peak")
return ring
def kernel(self, image):
"""Ring kernel.
Parameters
----------
image : `~gammapy.maps.WcsNDMap`
Input Map
Returns
-------
ring : `~astropy.convolution.Ring2DKernel`
Ring kernel.
"""
p = self.parameters
scale = image.geom.pixel_scales[0].to('deg')
r_in = p['r_in'].to('deg') / scale
width = p['width'].to('deg') / scale
ring = Ring2DKernel(r_in.value, width.value)
ring.normalize('peak')
return ring
def kernels(self, image):
"""Ring kernels according to the specified method.
Parameters
----------
image : `~gammapy.maps.WcsNDMap`
Map specifying the WCS information.
Returns
-------
kernels : list
List of `~astropy.convolution.Ring2DKernel`
"""
p = self.parameters
scale = image.geom.pixel_scales[0].to('deg')
r_in = p['r_in'].to('deg') / scale
r_out_max = p['r_out_max'].to('deg') / scale
width = p['width'].to('deg') / scale
stepsize = p['stepsize'].to('deg') / scale
if p['method'] == 'fixed_width':
r_ins = np.arange(r_in.value, (r_out_max - width).value,
stepsize.value)
widths = [width.value]
elif p['method'] == 'fixed_r_in':
widths = np.arange(width.value, (r_out_max - r_in).value,
stepsize.value)
r_ins = [r_in.value]
else:
raise ValueError('Invalid method: {}'.format(p['method']))
kernels = []
for r_in, width in product(r_ins, widths):
kernel = Ring2DKernel(r_in, width)
kernel.normalize('peak')
kernels.append(kernel)
return kernels
def kernel(self, image):
"""Ring kernel.
Parameters
----------
image : `gammapy.image.SkyImage`
Image
Returns
-------
ring : `~astropy.convolution.Ring2DKernel`
Ring kernel.
"""
p = self.parameters
scale = image.wcs_pixel_scale()[0]
r_in = p['r_in'].to('deg') / scale
width = p['width'].to('deg') / scale
ring = Ring2DKernel(r_in.value, width.value)
ring.normalize('peak')
return ring
import numpy as np
import pywt
import cv2
from astropy.stats import sigma_clip
from astropy.convolution import convolve, AiryDisk2DKernel,Box2DKernel,Gaussian2DKernel,MexicanHat2DKernel
from astropy.convolution import Ring2DKernel,Tophat2DKernel,TrapezoidDisk2DKernel
import myroutines as myr
from util import standard
kernels = [AiryDisk2DKernel(3),Box2DKernel(5),Gaussian2DKernel(2),Gaussian2DKernel(4),MexicanHat2DKernel(2)
,MexicanHat2DKernel(4),Tophat2DKernel(2),TrapezoidDisk2DKernel(2),Ring2DKernel(7,2)]
kernel_names = ['AiryDisk3','Box5','Gaussian2','Gaussian4',\
'MexicanHat2','MexicanHat4','Tophat2','TrapezoidDisk2','Ring']
#wts = ['db38','sym20','coif17','bior2.8','bior3.9',\
#'bior4.4','bior5.5','bior6.8','dmey','rbio1.5',\
#'rbio2.8','rbio6.8']
wts = ['db38','sym20','coif17','dmey']
def wavelet(data, wlf, threshold):
"""
wavelet: this function .
Arguments:
data (numpy array): input data.
wlf: wavelet fucntion.
threshold: threshold of high pass filter.
--------
Returns:
import numpy as np
from gammapy.maps import Map
from astropy.convolution import Ring2DKernel, Tophat2DKernel
from gammapy.detect import KernelBackgroundEstimator
counts = Map.create(npix=100, binsz=1)
counts.data += 42
counts.data[50][50] = 1000
source_kernel = Tophat2DKernel(3)
bkg_kernel = Ring2DKernel(radius_in=4, width=2)
kbe = KernelBackgroundEstimator(kernel_src=source_kernel.array,
kernel_bkg=bkg_kernel.array)
result = kbe.run({'counts': counts})
result['exclusion'].plot()
# Make a counts image for a single observation
events = datastore.obs(obs_id=obsid[0]).events
counts_image = SkyImage.empty_like(ref_image)
counts_image.fill_events(events)
norm = simple_norm(counts_image.data, stretch='sqrt', min_cut=0, max_cut=0.3)
counts_image.smooth(radius=0.1 * u.deg).plot(norm=norm, add_cbar=True)
#kernel background???
source_kernel = Tophat2DKernel(radius=5)
source_kernel.normalize(mode='peak')
source_kernel = source_kernel.array
background_kernel = Ring2DKernel(radius_in=20, width=10)
background_kernel.normalize(mode='peak')
background_kernel = background_kernel.array
plt.imshow(source_kernel, interpolation='nearest', cmap='gray')
plt.colorbar()
plt.grid('off')
plt.imshow(background_kernel, interpolation='nearest', cmap='gray')
plt.colorbar()
plt.grid('off')
obs_ids=obsid[0:3]
counts_image2 = SkyImage.empty_like(ref_image)
for obs_id in obs_ids:
events = datastore.obs(obs_id=obs_id).events
counts_image2.fill_events(events)
# To use the `KernelBackgroundEstimator` you first have to set
q2=counts_image2#.cutout(position=SkyCoord(0, 0, unit='deg', frame='galactic'),size=(10*u.deg, 10*u.deg)).plot(norm=norm, add_cbar=True)
if(show_it==False):
plt.show(q2)
print("2")
################QUI C'è IL CONTO SUL BKG
source_kernel = Tophat2DKernel(radius=5)#5
source_kernel.normalize(mode='peak')
source_kernel = source_kernel.array
background_kernel = Ring2DKernel(radius_in=5, width=20)#20-10
background_kernel.normalize(mode='peak')
background_kernel = background_kernel.array
a = plt.imshow(source_kernel, interpolation='nearest', cmap='gray')
plt.colorbar()
plt.grid('off')
if(show_it==False):
plt.show(a)
b = plt.imshow(background_kernel, interpolation='nearest', cmap='gray')
plt.colorbar()
plt.grid('off')
if(show_it==False):
plt.show(b)
from astropy.convolution import Ring2DKernel kernel = Ring2DKernel(1, 1) print(kernel.array)
