Image Analysis with CASA - ESO

CASA> imgstat = imstat()
imgstat is now defined as
a Python dictionary,
containing all keys listed on the right.
Access: imgstat['KEY'][AXIS]
CASA>rms=(imgstat['rms'][0])
print '>> rms: '+str(rms)
peak=(imgstat['max'][0])
print '>> Peak: '+str(peak)
print '>> Dynamic range: '+str(peak/rms)
KEYS
blc ­ absolute PIXEL coordinate of the bottom left corner of
the bounding box surrounding the selected region
blcf ­ Same as blc, but uses WORLD coordinates instead of pixels
trc ­ the absolute PIXEL coordinate of the top right corner
of the bounding box surrounding the selected region
trcf ­ Same as trc, but uses WORLD coordinates instead of pixels
flux ­ the integrated flux density if the beam is defined and
the if brightness units are $Jy/beam$
npts ­ the number of unmasked points used
max ­ the maximum pixel value
min ­ minimum pixel value
maxpos ­ absolute PIXEL coordinate of maximum pixel value
maxposf ­ Same as maxpos, but uses WORLD coordinates instead of pixels
minpos ­ absolute pixel coordinate of minimum pixel value
minposf ­ Same as minpos, but uses WORLD coordinates instead of pixels
sum ­ the sum of the pixel values: $\sum I_i$
sumsq ­ the sum of the squares of the pixel values: $\sum I_i^2$
mean ­ the mean of pixel values:
$ar{I} = \sum I_i / n$
sigma ­ the standard deviation about the mean:
$\sigma^2 = (\sum I_i ­ar{I})^2 / (n­1)$
rms ­ the root mean square:
$\sqrt {\sum I_i^2 / n}$
median ­ the median pixel value (if robust=T)
medabsdevmed ­ the median of the absolute deviations from the
median (if robust=T)
quartile ­ the inter­quartile range (if robust=T). Find the points
which are 25% largest and 75% largest (the median is
50% largest), find their difference and divide that
difference by 2.
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