Cornell University
Memo:
TM-FOR02-47
Subject:
WTD Peak-up Procedure
Distribution:
FORCAST Team
Author:
Terry Herter
FORCAST
Date:
21-Jan-02
Version:
0.3
Orig. Date:
18-Jan-02
Posted:
21-Jan-02
This tech memo covers peak-up procedures for the WTD. It is found that the procedure can be
automated, and take very little time via some simple filtering of the data. Both the peak-up procedure and the analysis yielding the procedure are described.
Peak-up Procedure
1. Set all 3 filter wheels at nominal home (stop) positions
2. Set up nominal integration parameters
a. Integration time = 0.5 sec, 1 coadd
3. Set up acquisition boxes at
a. Box 1: Lower left = 1, 88
Size = 41, 81
b. Box 2: Lower left = 10, 225
Size = 21, 21
c. Box 3: Lower left = 10, 11
Size = 21, 21
4. Acquire set of 16 dark frames and compute average image for background subtraction
5. Move to first guess for position the wheels
6. Acquire data in a 7x7x7 grid of wheel positions, stepping by 3 motor steps each.
7. Subtract background from each of the images.
8. Compute for each image
a. Box 1 average
b. Box 1 average – Box 2 average
c. Box 1 average – Box 3 average
9. Remove points which have the follow conditions
a. Box 1 average < 0.9 * Box 1 max
b. Or | Box 1 avg – Box 2 avg | > 0.1 * Box 1 max
c. Or | Box 1 avg – Box 3 avg | > 0.1 * Box 1 max
10. Compute average motor positions for remaining images with fulfill this criteria
11. Set motors to these positions
12. Repeat steps 6-9 with motor step size of 1.
13. Set motor defaults to final motor position average found in step 8.
TM-FOR02-47
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Data Set
The data set obtained on 15-Jan-02 by Bruce Pirger is analyzed.
The dark frames are the average of all frames in files 662 and 2525
The illuminated data is as follows

Scanned about 2.8 mm aperture, 10.3 mm filter, open ND

Files 663 through 2521
The scan increment is one step for each motor. Table 1 contains the scan parameters.
Table 1: Peak-up Grid Parameters
Motor
Number
Steps
Low
High
Filter
1
13
103
115
Aperture
2
11
625
635
N.D.
3
13
353
365
Box 2
Box Definitions
The sample image at right shows the extraction boxes overlaid. The boxes locations are given in Table 2. Each pair of numbers represents x,y.
Table 2: Box Coordinates
Box
Lower left
Upper Right
Size
1.
1, 88
40, 168
41, 81
2.
10, 225
30, 245
21, 21
3.
10, 11
30, 31
21, 21
Box 1
The line indicates where cuts are made through the images for plotting.
Analysis
The boxes are chosen to give a representative average for an image near the center and to measure gradients across the image.
Figure 2 shows the total data set giving the box average for Box 1 and the differences (Box 1 avg) – (Box 2 avg) and (Box 1 avg – Box 3 avg).
This data set is very large, making it hard to find the optimal motor peak-up locations.
Box 3
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WTD Peak-up - Box Signal & Differences (15-Jan-02)
Cornell University
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2000
1500
Box 1
Box 1-2
Box 1-3
1000
Data Number
500
0
-500
-1000
-1500
-2000
600
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
File Number
Figure 2: Extracted box averages or differences after clipping out images with signal < 0.9*max or difWTD Peak-up - Clipped Box Signal & Differences (15-Jan-02)
ferences > 0.1*max.
1600
1400
1200
Box 1
Box 1-2
Box 1-3
Data Number
1000
800
600
400
200
0
-200
-400
1900
2000
2100
2200
2300
2400
2500
File Number
Figure 3: Same as figure 2 but only with those images that have signal > 0.9*max and differences <
0.1*max, where max is the maximum signal for Box 1.
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2600
Cornell University
FORCAST
However, since we would like to “peak” the signal and get uniform illumination, the data can be
filtered using some reasonable criteria.
Figure 3 shows the results of applying a set of filters to the data. This plot is generated with the
requirement that a given image must satisfy the following conditions:
1. Box 1 average < 0.9 * Box 1 max, AND
2. | Box 1 avg – Box 2 avg | > 0.1 * Box 1 max, AND
3. | Box 1 avg – Box 3 avg | > 0.1 * Box 1 max
This was arrived at after several trials, and seemed to work reasonably well. However, it would
be good to try on other data sets. It may be possible to make the criteria even stricter.
After several trials, a nominal filtering requiring that box 1 average is greater than 90% of the
max, and the differences are less than 10% of the max seems to work well. Figure 4 shows the
motors motor positions that pass
clipping
criteria.
WTDthese
Peak-up
- Motor
Positions (15-Jan-02)
8
Motor 1
6
Motor 2
Motor 3
Position - Average
4
2
0
-2
-4
-6
-8
1900
2000
2100
2200
2300
File Number
2400
2500
2600
Motor Averages = 109, 630, 359
Figure 4: Display of differential motor positions that pass the clipping criteria of signal > 0.9*max and
differences < 0.1*max, where max is the maximum signal for Box 1. The original grid center positions for
each motor (1: 109, 2: 630, 3: 359) have been subtracted.
Selecting any of the motor positions in Figure 4 guarantees that the response is within 10% of the
maximum. To better optimize the peak up, we compute the average differential positions in Figure 4. This yields the following nominal motor positions for this data set
TM-FOR02-47
Motor 1
112
Motor 2
626.5
Motor 3
363.5
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Since only integer positions are possible, we need to choose either 626 or 627 or 363 or 364 for
motors 2 and 3 respectively. Figure 5 shows the images for these locations. Again the choice
which image is best is problematic since all are basically the same.
2193
2194
2336
2337
Figure 5: Images for nominal peak-up position(s). The file sequence number is given for each image.
These correspond to the following motor positions (1,2,3): 2193 = (112, 626, 363), 2194 = (112, 627,
363), 2336 = (112, 626, 364), 2337 = (112, 627, 364).
Figure 6 shows the vertical and horizontal plots through each of these images.
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1700
1700
2193
2194
1600
Signal
Signal
1600
1500
1400
1300
1500
1400
-100
-50
0
Pixel
50
1300
100
1700
-100
-50
0
Pixel
50
1700
2336
2337
1600
Signal
Signal
1600
1500
1400
1300
100
1500
1400
-100
-50
0
Pixel
50
1300
100
-100
-50
0
Pixel
50
Figure 6: Vertical and horizontal cuts through for the images in Figure 5. The file sequence number is
given for each plot. These correspond to the following motor positions (1,2,3): 2193 = (112, 626, 363),
2194 = (112, 627, 363), 2336 = (112, 626, 364), 2337 = (112, 627, 364).
Conclusions
Finding the nominal motor positions for each filter wheel seems straightforward but will needed
to be checked on different cooldowns. A different set of clipping limits might be more broadly
applicable.
Two features are evident from Figure 2. It shows that more sparsely sample data (in motor space)
would still cover the signal peak. In addition, a larger search area would be beneficial.
This suggests an iterative technique. First do 7x7x7 grid with a step size of 3, clip the data, and
find the next guest at the motor positions from the filtered data. Next, reduce the step size to 1
and repeat the procedure. This last set need to be checked so that points are clipped on all sides,
or it will not work – a larger grid would have to be done.
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Supporting Files
File
Description
WTD_Peak-up(15-Jan-02).xls
Box average and motor data with plots. Clipping algorithms applied and tested here.
Revision History
Version
Date
Comments/Changes
0.1
18-Jan-02
Initial draft. Chose PowerPoint format because it is easier to deal with
lots of images and plots.
0.3
19-Jan-02
Translated from PowerPoint to Word format for consistency with other
Tech Memos.
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