def detSources( image, outfile="", verbose=False, sigma=0.0, threshold=2.5, fwhm=5.5,
sharplim=[0.2,1.0], roundlim=[-1.0,1.0], window=None, exts=None,
timing=False, grid=False, rejection=None, ratio=None, drawWindows=False,
dispFrame=1 ):
"""
Performs similar to the source detecting algorithm
'http://idlastro.gsfc.nasa.gov/ftp/pro/idlphot/find.pro'.
This code is heavily influenced by 'http://idlastro.gsfc.nasa.gov/ftp/pro/idlphot/find.pro'.
'find.pro' was written by W. Landsman, STX February, 1987.
This code was converted to Python with areas re-written for optimization by:
River Allen, Gemini Observatory, December 2009. [email protected]
Sources:
[1] - W. Landsman. http://idlastro.gsfc.nasa.gov/ftp/pro/idlphot/find.pro
@param image: The filename of the fits file. It must be in the format N2.fits[1] for the specific
extension. (i.e.) If you want to find objects only in the image extension [1], than you would pass N2.fits[1].
@type filename: String
@param outfile: The name of the file where the output will be written. By default output will not be written (ie if outfile
is left as "", no output file is written).
@type outfile: String
@param verbose: Print out non-critical and debug information.
@type verbose: Boolean
@param sigma: The mean of the background value. If nothing is passed, detSources will run
background() to determine it.
@type sigma: Number
@param threshold: "Threshold intensity for a point source - should generally be 3 or 4 sigma
above background RMS"[1]. It was found that 2.5 works best for IQ source detection.
@type threshold: Number
@param fwhm: "FWHM to be used in the convolve filter"[1]. This ends up playing a factor in
determining the size of the kernel put through the gaussian convolve.
@type fwhm: Number
@param sharplim: "2 element vector giving low and high cutoff for the sharpness statistic (Default: [0.2,1.0] ).
Change this default only if the stars have significantly larger or smaller concentration than a Gaussian"[1]
@type sharplim: 2-Element List of Numbers
@param roundlim: "2 element vector giving low and high cutoff for the roundness statistic (Default: [-1.0,1.0] ).
Change this default only if the stars are significantly elongated."[1]
@type roundlim: 2-Element List of Numbers
@param window: Rectangle regions of the data to process. detSources will only look at the data within
windows passed, if a window is passed. If no window is set, detSources will look at the entire image.
Beware: small objects on the edges of the windows may not be detected.
<pre>
General Coordinate Form:
( x_offset, y_offset, width, height )
(x_offset + width, y_offset + height)
__________ /
| Window |
|__________|
/
(x_offset, y_offset)
Example:
Window=[(0,0,200,200)] ~~ Looks at a window of size 200, 200 in bottom left corner
Window=[(0,0,halfWidth,Height),(halfWidth,0,halfWidth,Height)] ~~ Splits the image in 2, divided vertically
down the middle.
</pre>
@type window: List of 4 dimensional tuples or None
@param timing: If timing is set to true, the return type for detSources will be a tuple. The tuple
is of the form (xyArray, overalltime) where overalltime represents the time it took detSources to
run minus any displaying time. This feature is for engineering purposes.
@type timing: Boolean
@param grid: If no window is set, detSources will run the image in a grid. This is supposed to work in
conjunction with rejection.
@type grid: Boolean
@param rejection: Rejection functions to be run on each grid point. See baseHeuristic() for an example.
@type rejection: A list of rejection functions or None
@param ratio: What the ratio or grid size should be. Ratio of 5 means the image will be split up into a
5x5 grid. Should be modified to take fixe grid size (50,50), for example.
@type ratio: int
@param drawWindows: If this is set to True, will attempt to draw the windows using iraf.tvmark().
Beware: a ds9 must be running.
@type drawWindows: Boolean
@param dispFrame: This works in conjunction with drawWindows.
debug=False, grid=False, rejection=None, ratio=None, drawWindows=False,
dispFrame=1
@return: A List of centroids. For example:
[[ 626.66661222, 178.89720247],
[ 718.1319315 , 2265.69332291],
[ 783.03009601, 13.21621043],
[ 1161.89652591, 2149.35972066],
[ 1228.65067586, 1873.15018455],
[ 1339.96915669, 725.79570466],
[ 1477.96348539, 1107.85307289],
[ 1485.17058871, 2059.1712877 ],
[ 1501.959992 , 227.32708114],
[ 2003.10937888, 572.89806682],
[ 2217.95000197, 763.01713875],
[ 2407.5780915 , 2018.30400873]]
@rtype: 2-D List.
"""
#===========================================================================
# Parameter Checking
#===========================================================================
# image = paramutil.checkParam( image, str, "" )
if image == "":
raise "daoFind requires an image file."
imageName, exts = paramutil.checkFileFitExtension( image )
if verbose:
print "Opening and Loading: %s[%d]"% (imageName,exts)
hdu = pf.open( imageName )
if window is not None:
if type(window) == tuple:
window = [window]
elif type(window) == list:
pass
else:
raise "'window' must be a tuple of length 4, or a list of tuples length 4."
for wind in window:
if type(wind) == tuple:
if len(wind) == 4:
continue
else:
raise 'A window tuple has incorrect information, %s, require x,y,width,height' %(str(wind))
else:
raise 'The window list contains a non-tuple. %s' %(str(wind))
if type( exts ) != int and exts is not None:
raise 'exts must be int or None.'
# outfile = paramutil.checkParam( outfile, str, "" )
writeOutFlag = False
if outfile != "":
writeOutFlag = True
# fwhm = paramutil.checkParam( fwhm, type(0.0), 5.5, 0.0 )
# verbose = paramutil.checkParam( verbose, bool, False )
if len(sharplim) < 2:
raise "Sharplim parameter requires 2 num elements. (i.e. [0.2,1.0])"
if len(roundlim) < 2:
raise "Roundlim parameter requires 2 num elements. (i.e. [-1.0,1.0])"
if verbose:
print "Opened and loaded."
#------------------------------------------------------------------------------
#===========================================================================
# Setup
#===========================================================================
ost = time.time()
maxConvSize = 13 #Maximum size of convolution box in pixels
radius = maximum(0.637 * fwhm, 2.001) #Radius is 1.5 sigma
radiusSQ = radius ** 2
kernelHalfDimension = minimum(array(radius, copy=0).astype(int32), (maxConvSize - 1) / 2)
kernelDimension = 2 * kernelHalfDimension + 1 # Dimension of the kernel or "convolution box"
sigSQ = (fwhm / 2.35482) ** 2
# Mask identifies valid pixels in convolution box
mask = zeros([kernelDimension, kernelDimension], int8)
# g will contain Gaussian convolution kernel
gauss = zeros([kernelDimension, kernelDimension], float32)
row2 = (arange(kernelDimension) - kernelHalfDimension) ** 2
for i in arange(0, (kernelHalfDimension)+(1)):
temp = row2 + i ** 2
gauss[kernelHalfDimension - i] = temp
gauss[kernelHalfDimension + i] = temp
mask = array(gauss <= radiusSQ, copy=0).astype(int32) #MASK is complementary to SKIP in Stetson's Fortran
good = where(ravel(mask))[0] #Value of c are now equal to distance to center
pixels = good.size
# Compute quantities for centroid computations that can be used for all stars
gauss = exp(-0.5 * gauss / sigSQ)
"""
In fitting Gaussians to the marginal sums, pixels will arbitrarily be
assigned weights ranging from unity at the corners of the box to
kernelHalfDimension^2 at the center (e.g. if kernelDimension = 5 or 7, the weights will be
1 2 3 4 3 2 1
1 2 3 2 1 2 4 6 8 6 4 2
2 4 6 4 2 3 6 9 12 9 6 3
3 6 9 6 3 4 8 12 16 12 8 4
2 4 6 4 2 3 6 9 12 9 6 3
1 2 3 2 1 2 4 6 8 6 4 2
1 2 3 4 3 2 1
respectively). This is done to desensitize the derived parameters to
possible neighboring, brighter stars.[1]
"""
xwt = zeros([kernelDimension, kernelDimension], float32)
wt = kernelHalfDimension - abs(arange(kernelDimension).astype(float32) - kernelHalfDimension) + 1
for i in arange(0, kernelDimension):
xwt[i] = wt
ywt = transpose(xwt)
sgx = sum(gauss * xwt, 1)
sumOfWt = sum(wt)
sgy = sum(gauss * ywt, 0)
sumgx = sum(wt * sgy)
sumgy = sum(wt * sgx)
sumgsqy = sum(wt * sgy * sgy)
sumgsqx = sum(wt * sgx * sgx)
vec = kernelHalfDimension - arange(kernelDimension).astype(float32)
dgdx = sgy * vec
dgdy = sgx * vec
sdgdxs = sum(wt * dgdx ** 2)
sdgdx = sum(wt * dgdx)
sdgdys = sum(wt * dgdy ** 2)
sdgdy = sum(wt * dgdy)
sgdgdx = sum(wt * sgy * dgdx)
sgdgdy = sum(wt * sgx * dgdy)
kernel = gauss * mask #Convolution kernel now in c
sumc = sum(kernel)
sumcsq = sum(kernel ** 2) - (sumc ** 2 / pixels)
sumc = sumc / pixels
# The reason for the flatten is because IDL and numpy treat statements like arr[index], where index
# is an array, differently. For example, arr.shape = (100,100), in IDL index=[400], arr[index]
# would work. In numpy you need to flatten in order to get the arr[4][0] you want.
kshape = kernel.shape
kernel = kernel.flatten()
kernel[good] = (kernel[good] - sumc) / sumcsq
kernel.shape = kshape
# Using row2 here is pretty confusing (From IDL code)
# row2 will be something like: [1 2 3 2 1]
c1 = exp(-.5 * row2 / sigSQ)
sumc1 = sum(c1) / kernelDimension
sumc1sq = sum(c1 ** 2) - sumc1
c1 = (c1 - sumc1) / sumc1sq
mask[kernelHalfDimension,kernelHalfDimension] = 0 # From now on we exclude the central pixel
pixels = pixels - 1 # so the number of valid pixels is reduced by 1
# What this operation looks like:
# ravel(mask) = [0 0 1 1 1 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 ...]
# where(ravel(mask)) = (array([ 2, 3, 4, 8, 9, 10, 11, 12, 14, ...]),)
good = where(ravel(mask))[0] # "good" identifies position of valid pixels
# x and y coordinate of valid pixels
xx = (good % kernelDimension) - kernelHalfDimension
# relative to the center
yy = array(good / kernelDimension, copy=0).astype(int32) - kernelHalfDimension
#------------------------------------------------------------------------------
#===========================================================================
# Extension and Window / Grid
#===========================================================================
xyArray = []
outputLines = []
if exts is None:
# May want to include astrodata here to deal with
# all 'SCI' extensions, etc.
exts = 1
sciData = hdu[exts].data
if sigma <= 0.0:
sigma = background( sciData )
if verbose:
print 'Estimated Background:', sigma
hmin = sigma * threshold
if window is None:
# Make the window the entire image
window = [(0,0,sciData.shape[1],sciData.shape[0])]
if grid:
ySciDim, xSciDim = sciData.shape
xgridsize = int(xSciDim / ratio)
ygridsize = int(ySciDim / ratio)
window = []
for ypos in range(ratio):
for xpos in range(ratio):
window.append( (xpos * xgridsize, ypos * ygridsize, xgridsize, ygridsize) )
drawtime = 0
if drawWindows:
drawtime = draw_windows( window, dispFrame, label=True)
if rejection is None:
rejection = []
elif rejection is 'default':
rejection = [baseHeuristic]
windName = 0
for wind in window:
windName += 1
subXYArray = []
##@@TODO check for negative values, check that dimensions don't violate overall dimensions.
yoffset, xoffset, yDimension, xDimension = wind
if verbose:
print 'x,y,w,h: ', xoffset, yoffset, xDimension, yDimension
print '='*50
print 'W' + str(windName)
print '='*50
sciSection = sciData[xoffset:xoffset+xDimension,yoffset:yoffset+yDimension]
#=======================================================================
# Quickly determine if a window is worth processing
#=======================================================================
rejFlag = False
for rejFunc in rejection:
if rejFunc(sciSection, sigma, threshold):
rejFlag = True
break
if rejFlag:
# Reject
continue
#------------------------------------------------------------------------------
#===========================================================================
# Convolve
#===========================================================================
if verbose:
print "Beginning convolution of image"
st = time.time()
h = convolve2d( sciSection, kernel ) # Convolve image with kernel
et = time.time()
if verbose:
print 'Convole Time:', ( et-st )
if not grid:
h[0:kernelHalfDimension,:] = 0
h[xDimension - kernelHalfDimension:xDimension,:] = 0
h[:,0:kernelHalfDimension] = 0
h[:,yDimension - kernelHalfDimension:yDimension] = 0
if verbose:
print "Finished convolution of image"
#------------------------------------------------------------------------------
#===========================================================================
# Filter
#===========================================================================
offset = yy * xDimension + xx
index = where(ravel(h >= hmin))[0] # Valid image pixels are greater than hmin
nfound = index.size
if nfound > 0: # Any maxima found?
h = h.flatten()
for i in arange(pixels):
# Needs to be changed
try:
stars = where(ravel(h[index] >= h[index+ offset[i]]))[0]
except:
break
nfound = stars.size
if nfound == 0: # Do valid local maxima exist?
if verbose:
print "No objects found."
break
index = index[stars]
h.shape = (xDimension, yDimension)
ix = index % yDimension # X index of local maxima
iy = index / yDimension # Y index of local maxima
ngood = index.size
else:
if verbose:
print "No objects above hmin (%s) were found." %(str(hmin))
continue
# Loop over star positions; compute statistics
st = time.time()
for i in arange(ngood):
temp = array(sciSection[iy[i] - kernelHalfDimension:(iy[i] + kernelHalfDimension)+1,
ix[i] - kernelHalfDimension:(ix[i] + kernelHalfDimension)+1])
pixIntensity = h[iy[i],ix[i]] # pixel intensity
# Compute Sharpness statistic
#@@FIXME: This should do proper checking...the issue is an out of range index with kernelhalf and temp
# IndexError: index (3) out of range (0<=index<=0) in dimension 0
try:
sharp1 = (temp[kernelHalfDimension,kernelHalfDimension] - (sum(mask * temp)) / pixels) / pixIntensity
except:
continue
if (sharp1 < sharplim[0]) or (sharp1 > sharplim[1]):
# Reject
# not sharp enough?
continue
dx = sum(sum(temp, 1) * c1)
dy = sum(sum(temp, 0) * c1)
if (dx <= 0) or (dy <= 0):
# Reject
continue
around = 2 * (dx - dy) / (dx + dy) # Roundness statistic
# Reject if not within specified roundness boundaries.
if (around < roundlim[0]) or (around > roundlim[1]):
# Reject
continue
"""
Centroid computation: The centroid computation was modified in Mar 2008 and
now differs from DAOPHOT which multiplies the correction dx by 1/(1+abs(dx)).
The DAOPHOT method is more robust (e.g. two different sources will not merge)
especially in a package where the centroid will be subsequently be
redetermined using PSF fitting. However, it is less accurate, and introduces
biases in the centroid histogram. The change here is the same made in the
IRAF DAOFIND routine (see
http://iraf.net/article.php?story=7211&query=daofind ) [1]
"""
sd = sum(temp * ywt, 0)
sumgd = sum(wt * sgy * sd)
sumd = sum(wt * sd)
sddgdx = sum(wt * sd * dgdx)
hx = (sumgd - sumgx * sumd / sumOfWt) / (sumgsqy - sumgx ** 2 / sumOfWt)
# HX is the height of the best-fitting marginal Gaussian. If this is not
# positive then the centroid does not make sense. [1]
if (hx <= 0):
# Reject
continue
skylvl = (sumd - hx * sumgx) / sumOfWt
dx = (sgdgdx - (sddgdx - sdgdx * (hx * sumgx + skylvl * sumOfWt))) / (hx * sdgdxs / sigSQ)
if abs(dx) >= kernelHalfDimension:
# Reject
continue
xcen = ix[i] + dx #X centroid in original array
# Find Y centroid
sd = sum(temp * xwt, 1)
sumgd = sum(wt * sgx * sd)
sumd = sum(wt * sd)
sddgdy = sum(wt * sd * dgdy)
hy = (sumgd - sumgy * sumd / sumOfWt) / (sumgsqx - sumgy ** 2 / sumOfWt)
if (hy <= 0):
# Reject
continue
skylvl = (sumd - hy * sumgy) / sumOfWt
dy = (sgdgdy - (sddgdy - sdgdy * (hy * sumgy + skylvl * sumOfWt))) / (hy * sdgdys / sigSQ)
if abs(dy) >= kernelHalfDimension:
# Reject
continue
ycen = iy[i] + dy #Y centroid in original array
subXYArray.append( [xcen, ycen] )
et = time.time()
if verbose:
print 'Looping over Stars time:', ( et - st )
subXYArray = averageEachCluster( subXYArray, 10 )
xySize = len(subXYArray)
for i in range( xySize ):
subXYArray[i] = subXYArray[i].tolist()
# I have no idea why the positions are slightly modified. Was done originally in
# iqTool, perhaps for minute correcting.
subXYArray[i][0] += 1
subXYArray[i][1] += 1
subXYArray[i][0] += yoffset
subXYArray[i][1] += xoffset
if writeOutFlag:
outputLines.append( " ".join( [str(subXYArray[i][0]), str(subXYArray[i][1])] )+"\n" )
xyArray.extend(subXYArray)
oet = time.time()
overall_time = (oet-ost-drawtime)
if verbose:
print 'No. of objects detected:', len(xyArray)
print 'Overall time:', overall_time, 'seconds.'
if writeOutFlag:
outputFile = open( outfile, "w" )
outputFile.writelines( outputLines )
outputFile.close()
if timing:
return xyArray, overall_time
else:
return xyArray
def pyDaoFind(filename, scidata, pixelscale, frame=1, debug=False, pymark=True,
display=True, imageSigma='default', saturation=65000, qa=False):
xyArray = []
if display:
# Redirecting the screen prints of stdout so they don't appear when
# the next couple iraf routines are used
SAVEOUT = sys.stdout
capture = StringIO.StringIO()
sys.stdout = capture
# Setting the image buffer to the size of GMOS image
iraf.set(stdimage='imtgmos')
# Displaying the image with iraf.display which sends the image to ds9
iraf.display(filename, frame=frame)
# Return stdout to normal so prints will show on the screen
sys.stdout = SAVEOUT
# Mask out stars to get a good measure of background deviation for daofind
if imageSigma == 'default':
maskedImage = starMask(scidata)
imageSigma = maskedImage.std()
# Use default FWHM of 0.8 arcsec
daoFWHM = 0.8/pixelscale
maxObjects = 100
(fileName, exten) = paramutil.checkFileFitExtension(filename)
#print "IU40:", imageSigma, daoFWHM, saturation
# Start time for detecting the sources in the data frame
st = time.time()
if qa:
# Use a grid of sub-windows for detecting the sources rather than the entire frame at once.
# I will be the first to admit this is very unfriendly and kluged.
# Hopefully, I will have enough time to improve it
ratio = 5
currentGrid = 0
searchPattern = [12,13,11,7,17,6,8,16,18]
gridSize = len(searchPattern)
height, width = scidata.shape
grid_width = width / ratio
grid_height = height / ratio
while len(xyArray) <= maxObjects and currentGrid < gridSize:
grid = searchPattern[currentGrid]
xoffset = (grid % ratio) * grid_width
yoffset = (grid / ratio) * grid_height
xyArray += detectSources.detSources( filename, sigma=imageSigma,
threshold=3.0, fwhm=daoFWHM,
window=[(xoffset,yoffset,grid_width,grid_height)],
exts=exten )
currentGrid += 1
else:
xyArray = detectSources.detSources( filename, sigma=imageSigma,
threshold=2.0, fwhm=daoFWHM,
exts=exten )
# End time for detecting the sources in the data frame
et = time.time()
if debug:
print 'Time detSources took to find the sources:'+str(et-st)
if pymark:
## Mark objects found with daofind with blue 'X'
# Create the temporary file name
tmpFileName = 'tmpCoordFile'+str(os.getpid())+'.temp'
# Creating normal type file with temp name
tmpFile = file(tmpFileName, 'w')
# Write locations of objects found to temp file
for xy in xyArray:
tmpcoo2 = '%5d%6d\n'%(xy[0]+1,xy[1]+1)
tmpFile.write(tmpcoo2)
tmpFile.close()
# Use tvmark to mark the objects listed in the temp file with a blue X
iraf.tvmark(frame=frame,coords=tmpFileName,
mark='cross', color=206, pointsize=6)
# Delete the temp file from the system
filesystem.deleteFile(tmpFileName)
if debug:
#print 'PYEXAM - DAOFIND outputs: '+str(xyArray)
print "Number of Objects = ", len(xyArray)
return xyArray