Displays statistical information from an image or image region
Many parameters are determined from the specified region of an image.
For this version, the region can be specified by a set of rectangular
pixel coordinates, the channel ranges and the Stokes.
For directed output, run as
myoutput = imstat()
Keyword arguments:
imagename Name of input image
Default: none; Example: imagename='ngc5921_task.im'
axes axes to compute statistics over. -1 => all axes.
region Region of interest. See help par.region.
box A box region specified in pixels on the directional plane
Default: none (whole 2-D plane);
Example: box='10,10,50,50'
box = '10,10,30,30,35,35,50,50' (two boxes)
chans Zero based channel numbers
Range of channel numbers to include in statistics
All spectral windows are included
Default:''= all; Example: chans='3~20'
stokes Stokes parameters to analyze.
Default: all; Example: stokes='IQUV';
Example:stokes='I,Q'
Options: 'I','Q','U','V','RR','RL','LR','LL','XX','YX','XY','YY', ...
listit Print stats and bounding box to logger?
verbose Print additional messages to logger?
mask Mask to use. See help par.mask. Default is none.
stretch Stretch the mask if necessary and possible? See help par.stretch
logfile Name of file to write fit results.
append If logfile exists, append to it (True) or overwrite it (False).
General procedure:
1. Specify inputs, then
2. myoutput = imstat()
or specify inputs directly in calling sequence to task
myoutput = imstat(imagename='image.im', etc)
3. myoutput['KEYS'] will contain the result associated with any
of the keys given below
KEYS CURRENTLY AVAILABLE
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:
$\bar{I} = \sum I_i / n$
sigma - the standard deviation about the mean:
$\sigma^2 = (\sum I_i - \bar{I})^2 / (n-1)$
rms - the root mean square:
$\sqrt {\sum I_i^2 / n}$
median - the median pixel value
medabsdevmed - the median of the absolute deviations from the
median
quartile - the inter-quartile range. Find the points
which are 25% largest and 75% largest (the median is
50% largest), find their difference and divide that
difference by 2.
Additional Examples
# Selected two box region
# box 1, bottom-left coord is 2,3 and top-right coord is 14,15
# box 2, bottom-left coord is 30,31 and top-right coord is 42,43
imstat( 'myImage', box='2,3,14,15;30,31,42,43' )
# Select the same two box regions but only channels 4 and 5
imstat( 'myImage', box='2,3,14,15;30,31,42,43', chan='4~5' )
# Select all channels greater the 20 as well as channel 0.
# Then the mean and standard deviation are printed
results = imstat( 'myImage', chans='>20;0' )
print "Mean is: ", results['mean'], " s.d. ", results['sigma']
# Find statistical information for the Q stokes value only
# then the I stokes values only, and printing out the statistical
# values that we are interested in.
s1 = imstat( 'myimage', stokes='Q' )
s2 = imstat( 'myimage', stokes='I' )
print " | MIN | MAX | MEAN"
print " Q | ",s1['min'][0]," | ",s1['max'][0]," | ",," | ",s1['mean'][0]
print " I | ",s2['min'][0]," | ",s2['max'][0]," | ",," | ",s2['mean'][0]
# evaluate statistics for each spectral plane in an ra x dec x frequency image
ia.fromshape("", [20,30,40])
# give pixels non-zero values
ia.addnoise()
# These are the display axes, the calculation of statistics occurs
# for each (hyper)plane along axes not listed in the axes parameter,
# in this case axis 2 (the frequency axis)
# display the rms for each frequency plane (your mileage will vary with
# the values).
stats = ia.statistics(axes=[0,1])
stats["rms"]
Out[10]:
array([ 0.99576014, 1.03813124, 0.97749186, 0.97587883, 1.04189885,
1.03784776, 1.03371549, 1.03153074, 1.00841606, 0.947155 ,
0.97335404, 0.94389403, 1.0010221 , 0.97151822, 1.03942156,
1.01158476, 0.96957082, 1.04212773, 1.00589049, 0.98696715,
1.00451481, 1.02307892, 1.03102005, 0.97334671, 0.95209879,
1.02088714, 0.96999902, 0.98661619, 1.01039267, 0.96842754,
0.99464947, 1.01536798, 1.02466023, 0.96956468, 0.98090756,
0.9835844 , 0.95698935, 1.05487967, 0.99846411, 0.99634868])
1.8.0