This is the polar equivalent of the CIAO
dmnautilus
tool.
It works by subdividing the image into 4 pie-shaped wedges and checking if the SNR in each wedge meets the threshold criteria. If so, then each wedge is divided in 4 (radii by 2, angle divided by 2) and the process is repeated.
By default, the wedges are circular: pie shapes. Users can also
use elliptical wedges, epanda , or box wedges, bpanda. There is also
a box mode which emulates the behavior of the original dmnautilus tool.
Where does
*pandacome from?
pandais fromSAOImage ds9. It means: pie and annulus.
Users must have CIAO installed and have sourced the appropriate
ciao setup script for their environment (eg bash|tcsh and conda|ciao-install).
Users do not need to have the ciao source code installed.
For internal CXC users, they can use the Makefile.cxc for example in
their views. Make sure that you have built src/da/analysis/dmtools/dmimgio first.
cd /tmp
git clone https://github.com/kglotfelty/dmradar
cd dmradar
mkdir -p .../src/da/analysis/dmtools/dmradar
cp src/* .../src/da/analysis/dmtools/dmradar
cd .../src/da/analysis/dmtools/dmradar
rm Makefile.am Makefile.in
cp Makefile.cxc Makefile
make
make installThe test script, inputs, and save data are in the dmradar/test
directory.
For external users, this tool uses the dmimgio local
library. This library needs to be built into your CIAO installation
git clone https://github.com/kglotfelty/dmimgio
cd dmimgio
./configure --prefix=$ASCDS_INSTALL
make
make installIf you get an error about aclocal then you may need to run
./autogen.shbefore running configure. You will need to have the standard set of
autconfig tools installed.
Then you can install dmradar in the same way
git clone https://github.com/kglotfelty/dmradar
cd dmradar
./configure --prefix=$ASCDS_INSTALL
make
make check
make installIf building into a conda environment you may need to create a fake
readline.pc file
cat <<EOM > $ASCDS_INSTALL/lib/pkgconfig/readline.pc
Name: readline
Description: hack hack hack hack hack. do not use!
Version: 7.0
Requires:
Libs:
Cflags:
EOM
to satisfy the package-config requirements.
The shape=pie is the default output shape.
xcenter=4030.5
ycenter=4223.0
inner_radius=25
punlearn dmradar
dmradar \
infile=img.fits \
outfile=dmradar/pie4.abin \
snr=10 \
xcen=$xcenter ycen=$ycenter\
method=4 \
shape=pie \
rinner=$inner_radius router=1025 \
outmask=dmradar/pie4.map \
mode=h clob+
This figure shows the output from dmradar. (Left) The adaptively binned
counts image. With method=4 all 4 pie-slices must meet the snr=10 threshold
(so all will have at least 100 counts). (Right) The output map (outmaskfile)
file showing which pixels are grouped together.
The outmaskfile contains a REGION block containing pie shapes which
are displayed automatically by ds9.
dmradar \
infile=img.fits \
outfile=dmradar/${shape}4.abin \
snr=10 \
xcen=$xcenter ycen=$ycenter\
method=4 \
shape=${shape} \
rinner=$inner_radius router=1020 rotang=30\
outmask=dmradar/${shape}4.map \
minrad=10 minangl=15 ell=0.7 \
mode=h clob+| shape | image |
|---|---|
| pie | |
| epanda |  |
| bpanda |  |
| box |  |
The bpanda and box used a different starting angle astart=10 which
gives them their rotation.
For epanda and bpanda, the output REGION extension contains regions in the form of
shape_outer * !shape_inner * sector
Three individual shapes, logically ANDed together, to describe each wedge.
Note:
ds9does not recognizesectorshapes and skips them. Users will not see radial lines differentiating the different wedges. The above image was created by taking the map file and computing the magnitude of the image gradient. The gradient is 0 inside the wedge, and non-zero when transitioning between wedges. This is then used as a mask when displaying inds9
epanda and bpanda are MUCH slower than pie and box. pie and box
may only take seconds to run on the same image that epanda and bpanda
take several minutes to complete.
if shape==pie:
region = pie(xcenter,ycenter,inner,outer,start,stop)
elif shape == epanda
region = ellipse(xcenter,ycenter,outer,outer*ellipticity)*
!ellipse(xcenter,ycenter,inner,inner*ellipticity)*
sector(xcenter,ycenter,start,stop)
elif shape == bpanda
region = box(xcenter,ycenter,outer,outer*ellipticity)*
!box(xcenter,ycenter,inner,inner*ellipticity)*
sector(xcenter,ycenter,start,stop)
else shape == box
region = box(x,y,xlen,ylen,start)
Read the infile 2D image.
For shape != box:
draw a region at xcenter,ycenter,inner=0,outer=rstart, rotang=rotang,
start=astart, stop=arange.
All pixels in that region are added the first group, id=1
Draw a region at xcenter, ycenter, inner=rstart, outer=router,
rotang=rotang, start=astart, stop=arange+astart.
Divide that region into 4 quadrants:
let dr_2 = (rstop-rstart)/2.0
let da_2 = arange/2.0
Q1: inner=rstart outer=rstart+dr_2 start=astart stop=astart+da_2
Q2: inner=rstart+dr_2 outer=rstop start=astart stop=astart+da_2
Q3: inner=rstart outer=rstart+dr_2 start=astart+da_2 stop=arange+astart
Q4: inner=rstart+dr_2 outer=rstop start=astart+da_2 stop=arange+astart
For shape == box:
The center of the box is (x,y) is shifted to the center
of the quadrant, eg
Q1: x = rstart+(dr_2)/2 y = astart + (da_2)
Q2: x = rstart+3*(dr_2)/2 y = astart + (da_2)
Q1: x = rstart+(dr_2)/2 y = astart + 3*(da_2)/2
Q2: x = rstart+3*(dr_2)/2 y = astart + 3*(da_2)/2
Compute the SNR in Q1, Q2, Q3, Q4.
If inerrfile is none or blank:
# The Gaussian approximation: snr=N/sqrt(N)=sqrt(N)
SNR=sqrt(SUM(p_q))
where p_q are the infile pixel values inside the quadrant Q region.
NULL values, NaN/Inf values, and pixels outside the image subspace
do not contribute to the sum.
else:
SNR = SUM(p_q)/sqrt(SUM(e_q^2))
where e_q are the pixel values from the input error image in the
quadrant Q region. In other words, the error term is computed
by adding the individual pixel error terms in quadrature.
If method == 0:
If the SNR is for all of the quadrants is BELOW the SNR threshold
then the current region is grouped. Otherwise the current
region is grouped.
elif method == 1:
If the SNR for any one Q1, Q2, Q3, or Q4 is ABOVE the threshold,
then the algorithm will proceed separately with Q1, Q2, Q3, and Q4.
Otherwise the current region is grouped.
elif method == 2
If the SNR for any two quadrants which share a common side are
above the SNR threshold, then the algorithm will proceed with
each quadrant separately. Quadrants that only touch at the corner
(ie diagonally) do not qualify.
elif method == 3:
If the SNR for any 3 quadrants are ABOVE the threshold, then
the individual quadrants are processed.
else method == 4:
If the SNR for all 4 quadrants are ABOVE the threshold, then
the individual quadrants are processed.
The process continues until
. The number of quadrants above|below threshold no longer satisfies the
condition for the method setting.
. Or the radius, dr_2, becomes smaller than the minimum allowed value, minradius parameter.
. Or the angle, da_2, becomes smaller than the minimum allowed value, minangle parameter.
If a quadrant contains only NULL, NaN, Inf, or pixels outside the
data subspace, then the quadrant is treated as if if meets the
acceptance criteria to allow further splitting appropriate for the method.
No special consideration for quadrants with only some Null/NaN/out-of-
subspace pixels.
When pixels in a quadrant are grouped
. The output image arrays for all the pixels in that region are
assigned the SUM(p_q)/AREA(p_q), ie the sum of all the pixels
in the region averaged out over the area of the region.
. The output mask array for all the pixels in that region are assigned
the same mask ID -- a unique integer number starting from 2. ('1' is used for
the first region described above).
. The output snr array for all the pixels in that region are assigned
the SNR value computed for that region.
. The output area array for all the pixel in that region are assigned the
area, defined as the number of pixels, in that region.