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| <pre>
| | {{imstat}} |
| Help on imstat task:
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| Displays statistical information from an image or image region
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| Many parameters are determined from the specified region of an image.
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| For this version, the region can be specified by a set of rectangular
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| pixel coordinates, the channel ranges and the Stokes.
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| For directed output, run as
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| myoutput = imstat()
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|
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| Keyword arguments:
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| imagename -- Name of input image
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| Default: none; Example: imagename='ngc5921_task.im'
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| region -- File path to an ImageRegion file or name.
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| Use the viewer, then region manager to select regions of
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| the image to process. Similar to box, but graphical
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| Or the name of a region stored with the image,
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| use rg.namesintable()
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| to retrieve the list of names.
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| Default: none
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| Example: region='myimage.im.rgn'
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| region='region1'
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| box -- A box region on the directional plane
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| Only pixel values acceptable at this time.
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| Default: none (whole 2-D plane);
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| Example: box='10,10,50,50'
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| box = '10,10,30,30,35,35,50,50' (two boxes)
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| chans -- channel numbers
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| Range of channel numbers to include in statistics
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| All spectral windows are included
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| Default:''= all; Example: chans='3~20'
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| stokes -- Stokes parameters to analyze.
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| Default: none (all); Example: stokes='IQUV';
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| Example:stokes='I,Q'
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| Options: 'I','Q','U','V','RR','RL','LR','LL','XX','YX','XY','YY', ...
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| General procedure:
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| 1. Specify inputs, then
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| 2. myoutput = imstat()
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| or specify inputs directly in calling sequence to task
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| myoutput = imstat(imagename='image.im', etc)
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| 3. myoutput['KEYS'] will contain the result associated with any
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| of the keys given below
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|
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| KEYS CURRENTLY AVAILABLE
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| blc - absolute PIXEL coordinate of the bottom left corner of
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| the bounding box surrounding the selected region
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| blcf - Same as blc, but uses WORLD coordinates instead of pixels
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| trc - the absolute PIXEL coordinate of the top right corner
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| of the bounding box surrounding the selected region
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| trcf - Same as trc, but uses WORLD coordinates instead of pixels
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| flux - the integrated flux density if the beam is defined and
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| the if brightness units are $Jy/beam$
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| npts - the number of unmasked points used
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| max - the maximum pixel value
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| min - minimum pixel value
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| maxpos - absolute PIXEL coordinate of maximum pixel value
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| maxposf - Same as maxpos, but uses WORLD coordinates instead of pixels
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| minpos - absolute pixel coordinate of minimum pixel value
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| minposf - Same as minpos, but uses WORLD coordinates instead of pixels
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| sum - the sum of the pixel values: $\sum I_i$
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| sumsq - the sum of the squares of the pixel values: $\sum I_i^2$
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| mean - the mean of pixel values:
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| ar{I} = \sum I_i / n$
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| sigma - the standard deviation about the mean:
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| $\sigma^2 = (\sum I_i -ar{I})^2 / (n-1)$
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| rms - the root mean square:
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| $\sqrt {\sum I_i^2 / n}$
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| median - the median pixel value (if robust=T)
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| medabsdevmed - the median of the absolute deviations from the
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| median (if robust=T)
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| quartile - the inter-quartile range (if robust=T). Find the points
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| which are 25% largest and 75% largest (the median is
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| 50% largest), find their difference and divide that
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| difference by 2.
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| Additional Examples
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| # Selected two box region
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| # box 1, bottom-left coord is 2,3 and top-right coord is 14,15
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| # box 2, bottom-left coord is 30,31 and top-right coord is 42,43
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| imstat( 'myImage', box='2,3,14,15;30,31,42,43' )
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| # Select the same two box regions but only channels 4 and 5
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| imstat( 'myImage', box='2,3,14,15;30,31,42,43', chan='4~5' )
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| # Select all channels greater the 20 as well as channel 0.
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| # Then the mean and standard deviation are printed
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| results = imstat( 'myImage', chans='>20;0' )
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| print "Mean is: ", results['mean'], " s.d. ", results['sigma']
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| max - the maximum pixel value
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| min - minimum pixel value
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| maxpos - absolute PIXEL coordinate of maximum pixel value
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| maxposf - Same as maxpos, but uses WORLD coordinates instead of pixels
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| minpos - absolute pixel coordinate of minimum pixel value
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| minposf - Same as minpos, but uses WORLD coordinates instead of pixels
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| sum - the sum of the pixel values: $\sum I_i$
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| sumsq - the sum of the squares of the pixel values: $\sum I_i^2$
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| mean - the mean of pixel values:
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| ar{I} = \sum I_i / n$
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| sigma - the standard deviation about the mean:
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| $\sigma^2 = (\sum I_i -ar{I})^2 / (n-1)$
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| rms - the root mean square:
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| $\sqrt {\sum I_i^2 / n}$
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| median - the median pixel value (if robust=T)
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| medabsdevmed - the median of the absolute deviations from the
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| median (if robust=T)
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| quartile - the inter-quartile range (if robust=T). Find the points
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| which are 25% largest and 75% largest (the median is
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| 50% largest), find their difference and divide that
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| difference by 2.
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| Additional Examples
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| # Selected two box region
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| # box 1, bottom-left coord is 2,3 and top-right coord is 14,15
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| # box 2, bottom-left coord is 30,31 and top-right coord is 42,43
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| imstat( 'myImage', box='2,3,14,15;30,31,42,43' )
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| # Select the same two box regions but only channels 4 and 5
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| imstat( 'myImage', box='2,3,14,15;30,31,42,43', chan='4~5' )
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| # Select all channels greater the 20 as well as channel 0.
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| # Then the mean and standard deviation are printed
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| results = imstat( 'myImage', chans='>20;0' )
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| print "Mean is: ", results['mean'], " s.d. ", results['sigma']
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| # Find statistical information for the Q stokes value only
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| # then the I stokes values only, and printing out the statistical
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| # values that we are interested in.
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| s1 = imstat( 'myimage', stokes='Q' )
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| s2 = imstat( 'myimage', stokes='I' )
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| print " | MIN | MAX | MEAN"
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| print " Q | ",s1['min'][0]," | ",s1['max'][0]," | ",," | ",s1['mean'][0]
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| print " I | ",s2['min'][0]," | ",s2['max'][0]," | ",," | ",s2['mean'][0]
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| </pre>
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