def unWISE_BOSS_cutouts(tilename, channelNumber, BOSSra, BOSSdec, pixSize,TMASS_RA, TMASS_DEC, TMASS_K, TMASS_X, TMASS_Y, TMASS_Z,saveCubes=True, maskSave=True): RA = BOSSra DEC =BOSSdec tileName = tilename channel = channelNumber # x, y coordinates. x, y = BOSS_unWISE_conversion.unWISE2BOSS(tileName, channel, RA, DEC, plot=False) if x.size == 0: return # Only selecting galaxies that are away from the boundary # by at least pixSize/2 amount. tol = pixSize/2 # This automatically rounds "down". So if I give 51, tol is 25. iBool = (x>(tol)) & (x<(2047-tol)) & (y>(tol)) & (y<(2047-tol)) # Here the indexing might be wrong. cutoutX = x[iBool] cutoutY = y[iBool] # I think the indices are correct here. intX = np.around(cutoutX) intY = np.around(cutoutY) diffX = cutoutX - intX diffY = cutoutY - intY #### Cutting out square pixels "centered" at the object, stacking them, # and returning them as an image cube. # Getting the image file address and importing the image. fileaddress = BOSS_unWISE_conversion.get_unwise_filename(tileName, channel) objs1 = fitsio.FITS(fileaddress) blockImage =objs1[0][:,:] mask = unWISE_mask_map(tileName,channel, TMASS_RA, TMASS_DEC, TMASS_K, TMASS_X, TMASS_Y, TMASS_Z,plot=False, plotsave=maskSave) # The mask will be saved separately. if type(mask) == int: # If the number of objects in 2MASS is too large then. return imageCube=np.zeros((2*tol+1, 2*tol+1),dtype=float) maskCube = np.zeros((2*tol+1, 2*tol+1),dtype=float) # Cutting out and stacking are done here. if cutoutX.size > 0: for X, Y in zip(intX, intY): addBlock = blockImage[Y-tol:Y+tol+1, X-tol:X+tol+1] addBlock2 = mask[Y-tol:Y+tol+1, X-tol:X+tol+1] # print addBlock.shape #For Debugging purpose. imageCube = np.dstack((imageCube, addBlock)) maskCube = np.dstack((maskCube, addBlock2)) imageCube = imageCube[:,:,1:] #I am not sure what is the proper way to think about this. I think this is OK because it won't contribute to the sum but there might be a problem with normalization. maskCube = maskCube[:,:,1:] if saveCubes: fits = fitsio.FITS(get_imageCube_filename(tileName,channel),'rw', clobber=True) # clobber=True is there to overwrrite the existing file. fits.write(imageCube) fits.write(maskCube) fits.close() objs1.close() return blockImage, mask, imageCube, maskCube, cutoutX, cutoutY, diffX, diffY, pixSize
def view_tile_mask_compare(tName, channel, vminPercentile=0, vmaxPercentile=95): objs1 = fitsio.FITS(BOSS_unWISE_conversion.get_unwise_filename(tName, channel)) blockImage =objs1[0][:,:] vmin = np.percentile(blockImage,vminPercentile) vmax = np.percentile(blockImage,vmaxPercentile) plt.subplot(1,2, 1) plt.imshow(blockImage, cmap='gray', vmin=vmin, vmax=vmax, origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix. objs2 = fitsio.FITS(get_TMASS_mask_filename(tName)) array = objs2[0][:,:] plt.subplot(1,2,2) filtered_image = np.copy(blockImage) filtered_image[array==0] = np.median(filtered_image) plt.imshow(filtered_image, cmap='gray', vmin=vmin, vmax=vmax, origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix. plt.savefig(tName+'_'+channel+'_masked_compare.eps', bbox_inches='tight',interpolation='nearest') plt.show() # Turning up the contrast plt.imshow(filtered_image, cmap='gray', vmin=filtered_image.min(), vmax=np.percentile(filtered_image,99), origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix. plt.savefig(tName+'_'+channel+'_masked.eps', bbox_inches='tight',interpolation='nearest') plt.show() objs1.close() objs2.close()
def view_tile(tName, channel): objs1 = fitsio.FITS(BOSS_unWISE_conversion.get_unwise_filename(tName, channel)) blockImage =objs1[0][:,:] plt.imshow(blockImage, cmap='gray', vmin=-50, vmax=300, origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix. plt.show() objs1.close()
def unWISE_mask_map(tileName,channel, TMASS_RA, TMASS_DEC, TMASS_K, TMASS_X, TMASS_Y, TMASS_Z,plot=True, plotsave=True, vmin=-50,vmax=300):
# Input: tileName, channel, and ra,dec,k of tmass objects that are near the tile.
# Output: mask_map is saved in my directory. Also, shows the masked map, 2 2plots.
fileaddress = BOSS_unWISE_conversion.get_unwise_filename(tileName,channel)
tol = 2.00 # This is pretty large.
if 'm' in tileName:
ra = float(tileName.split('m')[0])/10.0
dec = float(tileName.split('m')[1])/10.0
else:
ra = float(tileName.split('p')[0])/10.0
dec = float(tileName.split('p')[1])/10.0
iBool = iBoolNearby(ra, dec, TMASS_X, TMASS_Y, TMASS_Z, tol=tol)
tmass_ra = TMASS_RA[iBool]
tmass_dec = TMASS_DEC[iBool]
tmass_k = TMASS_K[iBool]
bins = np.arange(-5,22,1.0)
inds = np.digitize(tmass_k, bins)
n = 2048 # Size of an unWISE tile.
array = np.ones((n, n),dtype=int)
# If the number of 2MASS objects is too large, then.
TMASS_num = 50000 # Average number seems to be about 25000
print 'tmass_ra.size: ', tmass_ra.size
if tmass_ra.size > TMASS_num:
return 0
rTolerance = np.array([1000, 800, 500, 350, 180, 110, 90, 70, 40,35,30,20,15,10,8,5,4,3.5,2,2,2])*1.25 # 11/15/2015: This change was made to make the mask sizes larger.
for m in range(3, 24):
r = rTolerance[m-3]
# iBool = degrees_between(ra, dec, tmass_ra[inds==m], tmass_dec[inds==m]) <tol # Not necessary.
# Getting x,y positions of the objects near by the center of the tile.
wcs = Tan(fileaddress)
ok, x, y = wcs.radec2pixelxy(tmass_ra[inds==m], tmass_dec[inds==m])
# This is an insurance
a = np.isnan(x)
b = np.isnan(y)
x[a] = -5000
y[b] = -5000
ibool = ((-r-1)<x)&(x<(2048+r))&((-r-1)<y)&(y<(2048+r)) # This saves so much time!
x -= 1
y -= 1
x=x[ibool]
y=y[ibool]
for (a,b) in zip (y,x):
Y,X = np.ogrid[-a:n-a, -b:n-b] # I guess this doesn't really matter.
mask = X*X + Y*Y <= r*r
array[mask] = 0
print m, '(',bins[m-1],',',bins[m],')', r, x.size #Printing the the bin number, mask radius, the size of x.
if plot:
# Holes filled with the median image.
objs1 = fitsio.FITS(fileaddress)
blockImage =objs1[0][:,:]
plt.subplot(1,2, 1)
plt.imshow(blockImage, cmap='gray', vmin=vmin, vmax=vmax, origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix.
plt.subplot(1,2,2)
filtered_image = np.copy(blockImage)
filtered_image[array==0] = np.median(filtered_image)
plt.imshow(filtered_image, cmap='gray', vmin=vmin, vmax=vmax, origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix.
plt.savefig(tileName+'_masked_compare.eps', bbox_inches='tight',interpolation='nearest')
plt.show()
# Turning up the contrast
plt.imshow(filtered_image, cmap='gray', vmin=filtered_image.min(), vmax=np.percentile(filtered_image,99), origin='lower',interpolation='nearest') # 10/8/2015: Becareful about the orientation of the matrix.
plt.savefig(tileName+'_masked.eps', bbox_inches='tight',interpolation='nearest')
plt.show()
objs1.close()
if plotsave:
fits = fitsio.FITS(get_TMASS_mask_filename(tileName), 'rw', clobber=True) #
fits.write(array)
fits.close()
return array
