def convolve(self, other):
result = NDistanceFunction(self.dim)
result.offsets = [
so + oo for so, oo in zip(self.offsets, other.offsets)
]
result.vector = Convolve.convolve(self.vector, other.vector)
result.resetMaxDistance()
return result
def main(*args):
resolution = 0.1
fwhm = 0.5
# get some (random) data, i.e. a stick-spectrum
x = num.arange(-50, 50+resolution, resolution)
y = num.zeros(x.shape, num.Float)
for i in range(10):
y[ran.randint(0, len(y)-1)] = ran.random()
# create lineshape objects
g = ls.GaussProfile(num.arange(-10*fwhm, 10*fwhm+resolution, resolution), 1.0)
l = ls.LorentzProfile(num.arange(-10*fwhm, 10*fwhm+resolution, resolution), 1.0)
v = ls.VoigtProfile(num.arange(-10*fwhm, 10*fwhm+resolution, resolution), (0.6, 0.6))
# convolute data with profile
res_g = Convolve.convolve(y, g(), Convolve.SAME)
res_l = Convolve.convolve(y, l(), Convolve.SAME)
res_v = Convolve.convolve(y, v(), Convolve.SAME)
for i in zip(x, y, res_g, res_l, res_v):
print "%12.6f %12.6f %12.6f %12.6f %12.6f" % i
print("Image is 160 Microns")
kern = '../../Convolve_Regrid/Kernels/Kernel_LoRes_PACS_70_to_PACS_160.fits'
im = '../../Sky_Remove/Median_Removed/160um_medianRemoved.fits'
conv_sname = '../../Convolve_Regrid/Convolutions/70Bkgd_ext_20_tstep_3000_to_160umRes.fits'
regrid_sname = '../../Convolve_Regrid/Convolutions/70Bkgd_ext_20_tstep_3000_to_160umRes_Resampled.fits'
sigma = np.loadtxt('../../Sky_Remove/Sigma.txt')[3]
sname = '160um/160um_70modeled'
# Arbitrary stuff for plotting
vmin=5;vmax=50 #bkgd
vmin_=10; vmax_=50 #img
vmin_sub = 0; vmax_sub= 10 # subtraction
xdim = [110,150]; ydim = [100,135]
if convolve_regrid: # Note that this gave me a lot of problems on windows but not on linux
Convolve.master_convolve(kern,bkgd,conv_sname)
Regrid.resample(conv_sname,im,regrid_sname)
bkgd = fits.open(regrid_sname)[0].data
bkgdhdr = fits.open(regrid_sname)[0].header
img = fits.open(im)[0].data
hdr = fits.open(im)[0].header
w = WCS(hdr)
# Region that is commonly over subtracted
# Used to just adjust median slope to a value
# That makes this region have a median of 0.
ra = 16.014763979322012
dec = -72.02709660389223
c = SkyCoord(ra=ra*u.degree,dec=dec*u.degree,frame='icrs')
print("Null Region Center Coordinate: "+str(c.ra.hms)+str(c.dec))
SubRadius = 3.; # ArcSeconds
# Kernels to Convolve with
k24 = '../Convolve_Regrid/Kernels/Kernel_LoRes_MIPS_24_to_PACS_160.fits'
k70 = '../Convolve_Regrid/Kernels/Kernel_LoRes_PACS_70_to_PACS_160.fits'
k100 = '../Convolve_Regrid/Kernels/Kernel_LoRes_PACS_100_to_PACS_160.fits'
Kernels = [k24, k70, k100]
# File Save Names
s24 = '../Final_Files/24/24_SNR_'
s70 = '../Final_Files/70/70_SNR_'
s100 = '../Final_Files/100/100_SNR_'
s160 = '../Final_Files/160/160_SNR_'
Save = [s24, s70, s100, s160]
for i in range(3):
#Convolve
Convolve.master_convolve(Kernels[i], Files[i], Save[i] + 'Convolve.fits')
#Regrid
Regrid.resample(Save[i] + 'Convolve.fits', f160,
Save[i] + 'Convolve_Regrid.fits')
#Prune
prune.SavePrune(Save[i] + 'Convolve_Regrid.fits',
Save[i] + 'Convolve_Regrid_Prune.fits')
prune.SavePrune(f160, Save[3] + 'Prune.fits')
if plot_ConvolveRegrid:
f, axes = plt.subplots(1, 4, sharey=False)
for i in range(4):
if i == 3:
file = f160
else:
def convolve(self, other):
result = NDistanceFunction(self.dim)
result.offsets = [so + oo for so, oo in zip(self.offsets, other.offsets)]
result.vector = Convolve.convolve(self.vector, other.vector)
result.resetMaxDistance()
return result
