From Won: (the zip file is also on the SVN /users/won/compiled_code/test_HS.zip)

Attached is test_HS.zip file, that contains

- test_HS.prj: project file created by Matlab Compiler. This file is not
 required to run the application but I included it just in case someone's
 insterested.

- test_HS folder contains two subfolders src and distrib, each of which
 contains the standalone application test_HS.

Usage: test_HS <path to the image folder>, for example

 test_HS ~/test_images/

Make sure you create the folder prior to running the application, and the
folder name ends with "/". Running test_HS will take and save 10 images
using the camera (provided the frame grabber applications are installed in
/opt/EDTpdv), averages those 10 images and find centroids, then plots the
result.

As I put in the eLOG, one needs MCRInstaller.bin and run it to install MCR
(probably 2008b 64bit version to test my files). If there are difficulties
getting MCRInstaller, let me know.

Won
<test_HS.zip>

Given below is a brief overview of calculating rms of spot position changes to test the accuracy/precision of the centroiding code. Centroids are obtained by summing over the array of size 30 by 30 around peak pixels, as opposed to the old method of using matlab built-in functions only. Still peak pixel positions were obtained by using builtin matlab function. Plese see the code detect_peaks_bygrid.m for bit more details.

My apologies for codes being well modularised and bit messy...

Please unzip the attached file to find the matlab codes.

The rest of this log is mainly put together by Kathryn.

Won

(EDIT/PS) The attached codes were run with raw image data saved on the hard disk, but it should be relatively easy to edit the script to use images acquired real time. We are yet to play with real-time images, and still operating under Windows XP...

---
When calculating the rms, the code outputs the results of two
different methods. The "old" method is using the built-in matlab
method while the "new" method is one Won constructed and seems to
give a result that is closer to the expected value. In calculating
and plotting the rms, the following codes were used:

- centroid_statics_raw_bygrid.m (main script run to do the analysis)
- process_raw.m (takes raw image data and converts them into 2D array)
- detect_peaks_bygrid.m (returns centroids obtained by old and new methods)
- shuffle.m (used to shuffle the images before averaging)

The reference image frame was obtained by averaging 4000 image frames,
the test image frames were obtained by averaging 1, 2, 5, 10 ... 500,
1000 frames respectively, from the remaining 1000 images.

In order to convert rms values in units of pixels to wavefront
aberration, do the following:

aberration = rms * pixel_width * hole_spacing / lever_arm

pixel_width: 12 micrometer
hole_spacing: about 37*12 micrometer
lever_arm: 0.01 meter

rms of 0.00018 roughly corresponds to lambda over 10000.

Note: In order to get smaller rms values the images had to be shuffled
before taking averages. By setting shuffle_array (in
centroid_statics_raw_bygrid.m) to be false one can
turn off the image array shuffling.

N_av        rms

1     0.004018866673087
2     0.002724680286563
5     0.002319477846009
10    0.001230553835673
20    0.000767638027270
50    0.000432681002432
100   0.000427139665006
200   0.000270955332752
500   0.000226521040455
1000  0.000153760240692

fitted_slope = -0.481436501422376

Here are some plots:

---

Next logs will be about centroid testing with simulated images, and wavefront changes due to the change in the camera temperature!

(PS) I uploaded the same figure twice by accident, and the site does not let me remove a copy!...

Just a note: this board was for the QPD not the Bull's eye detector.

 I installed the pyepics package on princess_sparkle since this is much easier under Ubuntu than under CentOS.

1. sudo apt-get install python-dateutil python-setuptools
2. make sure that LD_LIBRARY_PATH points to EPICS libraries by echo $LD_LIBRARY_PATH
3. sudo ldconfig
4. sudo easy_install -U pyepics

Then I started the following python script ~/start_test_channels.py in the background on princess_sparkle. The EPICS channels are actually in an IOC on tcs_daq. They are all acquired by the frame builder at 16Hz.

Regarding the installation of EDT software, I overlooked a note from the install.pdf  file.

The gist of it is that if the scripts do not run, then remount the CD-ROM by typing the

following:

mount /mnt/cdrom -o remount,exec

which will then allow the scripts to be run. The directory /mnt/cdrom should be changed if

the cdrom is mounted somewhere else. (The note can be found in the page 1 of the file

install.pdf.)

Unfortunately I don't have linux installed at the moment so I cannot test this. My computer was

reinstalled with Windows XP, the previous CentOS system being wiped out. However if this works,

then there is probably no need to copy the files to the hard drive. 

I saw this and tried it when i was installing, but I had more flexibility when I copied the files directly to the hard drive.

 Here is a brief and preliminary summary of rms of centroid displacements calculated at a number of different exposure time values. To get the results I did the following for each value of exposure time:

1. Take a set of images. I took 2000 images for shorter exposure times, 1000 images with exposure times greater than 1 second, and 200 images with exposure time 4.4 second. I tried to keep the maximum pixel count to be roughly the same (about 2430 plus/minus 40).

2. Obtain the centroids for each image frames in a set. I saved centroids as an array of n by m by 2, where n is the number of image frames that I took, m is the number of centroids in each frame, and 2 for x and y coordinates.

Then I iterated through the centroid sets to calculate total rms, using various N_av values. If N_av = 100; then reference centroids were obtained by averaging the centroids of first 50 and the last 50 frames, and remaining 100 frames are averaged to get the other (or non-reference) centroids. I think this method gives a better view of the centroid reproducibility than fixing the number of reference centroids to be, say, first 1000 and last 1000 frames and varying the number of frames to be averaged for non-reference centroid.

Datasets of centroids are labelled as spcdet_I_t, where spcdet stands for "same (maximum) pixel count (with) differen exposure times", I the value of the current that drives the light source, and t the exposure time that I used.

Here are the plots:

It can be seen from these plots that the benefit of averaging multiple frames quickly diminish once we go over 1 second. I am investigating if there is any way to improve the reproducibility while using the same sets of images.

Issues that need further investigation:

  1. Effect of pixels with unusually high pixel count. Dark images that we took show that, with longer exposure times, not only overall dark noise increase (and become less uniform) but also several pixels show unusually high pixel count (even higher than 2000), without a light source on. More investigation is needed to determine how much this affects the centroids calculation and to devise an way to deal with it.

  2. Extra/Duplicate centroids. As exposure time increased, I observed that duplicate centroids start to appear, i.e., HS_Centroids##centroids had duplicate entries. The number of duplicate entries increased as exposure time increased. I believe this is due to the images getting noisier as exposure time increases. So After taking initial reference centroids, I removed duplicate centroid entries before calculating rms. I am thinking about adding a method to do this in HS_Centroids class.

In addition, there were one 'false' centroid when the exposure time was 4.4 seconds. For now I chose to manually remove it myself before calculating rms.

Here's the error:

 Traceback (most recent call last):

 http://nodus.ligo.caltech.edu:8080/AdhikariLab/514

Happy Fourth of July!

The following is a brief overview of how we are analyzing the wavefront aberration and includes the aberration parameters calculated for 9 different temperature differences. So far we are still seeing the cylindrical power even after removing the tape/glue on the Hartmann plate. Attached are the relevant matlab codes and a couple of plots of the wavefront aberration.

We took pictures when the camera was in equilibrium at room temperature and then at each degree increase in camera temperature as we heated the room using the air conditioner. For each degree increase in camera temperature, we compared the spot positions at the increased temperature to the spot positions at room temperature. We used the following codes to generate the aberration parameters and make plots of the wavefront aberration:

-build_M.m (builds 8 by 8 matrix M from centroid displacements)
-wf_aberration_temperature_bygrid.m (main script)
-wf_from_parms.m (generates 2D aberration array from aberation parameters)
-intgrad2.m (generates 2D aberration array from an interpolated array of centroid displacements)

In order to perform the "inverse gradient" method to obtain contours, we first interpolated the centroid displacement vectors to generate a square array. As this array has some NaN (not a number) values, we cropped out some outer region of the array and used array values from (200,200) to (800,800). Sorry we forgot to put that part of the code in wf_aberration_temperature_bygrid.m.

The main script wf_aberration_temperature_bygrid.m needs to be revised so that the sign conventions are less confusing... We will update the code later.

The initial and final temperature values are as follows:

Aberration parameters:

1) Comparing high temp (+10)  with room temp

        p: 1.888906773203923e-004
       al: -0.295042766811686
      phi: 0.195737681653530
        c: -0.001591869846958
        s: -0.003826146141562
        b: 0.098283157674967
       be: -0.376038636781319
        a: 5.967617809296910

2) Comparing +9 with room temp

        p: 1.629083055002727e-004
       al: -0.222506109890745
      phi: 0.193334452094940
        c: -0.001548838746542
        s: -0.003404217451916
        b: 0.091368295953142
       be: -0.351830698303612
        a: 5.764068008962653

3) Comparing +8 with room temp

        p: 1.485283322069376e-004
       al: -0.212605187544093
      phi: 0.206716196097728
        c: -0.001425962488852
        s: -0.003148796701331
        b: 0.089936286297599
       be: -0.363538909377296
        a: 5.546514425485094

4) Comparing +7 with room temp

        p: 1.284124028380585e-004
       al: -0.163672705473379
      phi: 0.229219952949728
        c: -0.001452457146947
        s: -0.002807207555944
        b: 0.084090100490331
       be: -0.379195428095102
        a: 5.289173743478881

5) Comparing +6 with room temp

        p: 1.141756950753851e-004
       al: -0.149439038317734
      phi: 0.240503450300707
        c: -0.001350015836130
        s: -0.002529240946848
        b: 0.078118977034120
       be: -0.326704416216547
        a: 4.847406652448727

6) Comparing +5 with room temp

        p: 8.833496828581757e-005
       al: -0.071871278822766
      phi: 0.263210114512376
        c: -0.001257787180513
        s: -0.002095618522105
        b: 0.069587080420443
       be: -0.335912998511077
        a: 4.542557551218057

7) Comparing +4 with room temp

        p: 6.217428324604411e-005
       al: 0.019965235199575
      phi: 0.250991433584904
        c: -0.001266061216964
        s: -0.001568527823273
        b: 0.058323732750548
       be: -0.289315790283207
        a: 3.957825468583509

8) Comparing +3 with room temp

        p: 4.781068895714900e-005
       al: 0.140720713391208
      phi: 0.270865276786418
        c: -0.001228146894728
        s: -0.001371110045136
        b: 0.052794990899554
       be: -0.273968130963666
        a: 3.591187350052610

9) Comparing +2 with room temp

        p: 2.491163442408281e-005
       al: 0.495136135872766
      phi: 0.220727346409557
        c: -9.897729773516012e-004
        s: -0.001076008621974
        b: 0.048467660428427
       be: -0.280879088681660
        a: 3.315430577872808

10) Comparing +1 with room temp

       p: 8.160828332639811e-006
      al: 1.368853902659128
     phi: 0.116300954280238
       c: -6.149390553733007e-004
       s: -3.621216621887707e-004
       b: 0.025454969698557
      be: -0.242584267252882
       a: 1.809039775332749

The first plot is of the wavefront aberration obtained by integrating the gradient of the aberration and the second plot fits the aberration according to the aberration parameters so is smoother since it is an approximation.

 Here are some pictures of the ring heater segments destined for the H2 Y-arm this year.

 These still need to be put onto ResourceSpace.

For archive purposes, attached is a write-up of all the HWS measurements I've made to date for the SRM CO2 projector mock-up.

 The attached file shows the output of the command. The maximum average frame rate is 57.2Hz when the nominal frame rate was 58Hz:

/opt/EDTpdv/take -f max_frame_rate_image -l 120 -N 4 -d > max_frame_rate_data.txt

 A waveform channel was added to the HWS softIoc on hartmann. This allows arrays of data to be stored in a single channel. It's not clear whether it is storing this data as a set of number or strings. However, the values can be changed by the following command:

caput -a -n C4:TCS-HWS_CENTROIDSX 5 1,2,3,4,5

Although the <no of values> entry doesn't seem to actual enforce anything - you can have more or less values than this in the array and they are still added to the channel. What does seem to be necessary is no spaces between the commas and the values of the array.

This also works:

[controls@fb1 cds]$ caput -a -n C4:TCS-HWS_CENTROIDSX 2 1,2,3n

Old : C4:TCS-HWS_CENTROIDSX          1,2,35.4342 

New : C4:TCS-HWS_CENTROIDSX          1,2,3n 

which suggests that this is really a string variable - even with the -n enforce. The cainfo command suggests this as well. 

Usage: caput [options] <PV name> <PV value>

       caput -a [options] <PV name> <no of values> <PV value> ...

  -h: Help: Print this message

Channel Access options:

  -w <sec>:  Wait time, specifies longer CA timeout, default is 1.000000 second

Format options:

  -t: Terse mode - print only sucessfully written value, without name

Enum format:

  Default: Auto - try value as ENUM string, then as index number

  -n: Force interpretation of values as numbers

  -s: Force interpretation of values as strings

Arrays:

  -a: Put array

      Value format: number of requested values, then list of values

After some discussion with Frank we figured out how to edit the record type in HWS.db so that the waveform/array channel actually behaved like a numerical array and not like a single string. This just involved defining the data type and the element count in the record definition, like so:

and then when the ioc was rebooted, examination of the channel showed the following:

[controls@fb1 cds]$ caput -a -n C4:TCS-HWS_CENTROIDSX 2 1,2,3n

Old : C4:TCS-HWS_CENTROIDSX          1,2,35.4342 

New : C4:TCS-HWS_CENTROIDSX          1,2,3n 

which suggests that this is really a string variable - even with the -n enforce. The cainfo command suggests this as well. 

Caltech Facilities has determined that the walls in the SE corner of the TCS Lab in West Bridge were water damaged during last weekend’s rain. They are going to remove the plaster from the walls and dehumidify the area for a week or so. All tables in the room are going to be covered with plastic for this process. In the short term I’ve shutdown all the equipment in the lab (including FB4). The 2-micron cavity-testing fabrication has been moved next door to the QIL.

The SLED in the cross-sampling experiment produces unpolarized light at 980nm. So I added a PBS after the output and then a HWP (for 1064nm sadly) after that. In this way I produced linearly p-polarized light from the PBS. Then I could rotate it to any angle by rotating the HWP. The only drawback was that the HWP was only close to half a wave of retardation at 980nm. As a result, the output from this plate became slightly elliptically polarized.

The beam then went into another PBS which split it into two beams in a Michelson-type configuration (REFL and TRANS beams) - see attached image. By rotating the HWP I could vary the relative amount of power in the two arms of the Michelson. The two beams were retro-reflected and we then incident onto a HWS.

I measured the power in the REFL beam relative to the total power as a function of the HWP angle. The results are shown in the attached plot.

We fixed the start-up settings on the VME crate to look for a TCS startup file on fb0. The settings on the Baja 4700 are now:

I installed a recycled VME crate in the electronics rack. It currently has a Baja 4700E CPU card in it - and this needs to be configured. We also have the following cards, which are not plugged in right now.

1. ICS-110A-32 Analogue-to-Digital Converter - the jumpers need to be set on this to give it a unique memory address in the VME bus.

2. D000186 LIGO-type Anti Image card.

The CPU card needs to be configured to search it's OS binaries on the network (in this case we're going to store them on the framebuilder in Rana's lab). These settings are accessed by plugging a serial cable into the front of the card and using a terminal window to access the menu system. There are some screen caps of this below. As the card is reset we get the Start-up screen and then we can either do nothing (and a full boot will take place) or we can press a key and access the menu. From there we can restart the boot process by entering "@" or we can change the boot settings by entering "c". These are shown below:

I added a COMSOL model of the aLIGO ITM being heated by an axicon-formed annulus to the 40m SVN. The model assumes a fixed input beam size into an axicon pair and then varies the distance between the axicons. The output is imaged onto the ITM with varying magnitudes. The thermal lens is determined in the ITM and added  to the self-heating thermal lens (assuming 1W absorption, I think - need to check). The power in the annulus is varied until the sum of the two thermal lenses scatters the least amount of power out of the TEM00 mode of the IFO.

https://nodus.ligo.caltech.edu:30889/svn/trunk/comsol/TCS/aLIGO/

The results across the parameter space (axicon separation and post-axicon-magnification) are attached. These were then mapped from this space to the space of annulus thickness vs annulus diameter, (see elog here).

Here are the results in the annulus thickness vs annulus diameter space ...

I changed the HWS code to the new git.ligo HWS version.

• Object files are in ~/hws
• scripts are in ~/hws-server
• utilities are still in old git repo moved to ~/.HWS_code_temporary_home

I've set up some symbolic links to these directories to mimic the old directory structure, so ..

• ~/pyHWS links to new object file directory
• ~/pyHWS/scripts lnks to new scripts directory
• ~/pyHWS/utilities links to old utilities directory

The previous 2-lens setup focused the beam to a tight spot, however due to the divergence angle of the laser beam, a significant amount of power was not being captured by the fiirst lens at a distance of 40 cm from the source. The divergence angle seems to be bigger than 0.06 by a factor of 2, so a f = 20 cm lens was used to collimate the beam and a f = 30 cm lens was used to focus it. A mirror was used to reflect the beam, so we obtain steering control. Additionally, the focusing lens was placed on a small 1-axis stage in order to control the distance of the lens from the CCD, providing control over the focused beam size.

Note: The 30 cm lens was cleaned with methanol, however it still has some residue on the surface. The beam imaged to the Harrtmann Sensor looks good, however the lens will be cleaned by using a different solvent or replaced by a different 30 cm lens. The 3 lenses at the edge of the box will stay inside in order to prevent contamination, however they will not be used in the design.

Purchased from Lesker, for venting of IR Labs Cryostat:

 - P/N C35103000 (KF10 Valve for up-to-air)

 - P/N QF25XQF10 (KF25-KF10 Reducer)

 - P/N QF25-100-T (KF25 Tee)

(to be connected in between the Chamber Tee and the Gate Valve)

Should be in next week!

I'm considering the 86-711 2" 532nm PBS from Edmund Optics for the ETM HWS at the sites.

https://www.edmundoptics.com/optics/polarization/linear-polarizers/532nm-50mm-diameter-high-energy-laser-line-polarizer/

The effect on the transmission through the system, compared to the THorlabs PBS, is shown in the attached plot.

Conclusion: it looks almost as effective as the Thorlabs PBS with the added benefit of being 2" in diameter.

I checked the lab this morning. It was dry and there wall was in the same state as yesterday.

 The 200W Thermopile power head from Thorlabs arrive today. The scanned delivery note and calibration info are attached.

 I measured the prism and displacement of the Gaussian beam on the Hartmann sensor. The beam pointing was modulated at 10mHz using a galvo mirror as illustrated in Attachment 1. The galvo was around 680mm from the Hartmann sensor. The amplitude of the prism modulation was approximately 1E-5 radians. The displacement of the beam was measured using a new algorithm that tries to fit a parabola to the logarithm of the intensity of each Hartmann spot. The amplitude of the displacement modulation was measured at around 42 microns: corresponding to around 6E-5 radians (=42um/680mm).

To resolve the discrepancy between the prism and displacement measurements, I removed the Hartmann plate to simply get a Gaussian beam on the CCD (bottom right image in Figures 2 & 3 - the beam is slightly clipped and there is a ghost beam in the center - I'm not yet certain where this is coming from). I measured the Gaussian beam displacement directly by fitting a Gaussian to the mean horizontal cross-section of the intensity distribution (top right plot in Figures 2 & 3). Using this technique the measured displacement on the CCD had an amplitude of around 0.7 +/- 0.05 pixels = 8.4 +/- 0.6 microns, corresponding to a prism of 12.5E-6 radians (seen in top left plot in Figures 2 and 3). This indicates that there is an error in the Gaussian fitting algorithm using the Hartmann sensor data.

 The second plot simply shows the position modulation of the beam as I increased the amplitude of the signal going to the galvo. 

 Does this work?

Yes.

I noticed that the TCS lab temperature sensor batteries died. Apparently they died two days ago. I swapped in some new batteries this morning.
 

1. Write a Wiki page that describes how to add channels to the Athena Box
2. Write a Wiki page that describes how to add a new computer to the network and mount all the network drives
3. Add an EPICS channel that writes the disk usage to file (to keep track of the total accumulated disk space used by the centroid storage)

 Around a year ago, Phil and I discussed the possibility of using an OPO to possibly generate our own laser beam at ~2 microns for TCS. This was to avoid all of the usual hassle of the 10 micron CO2 laser.

As it turns out, the 1.5-3 micron range doesn't have enough absorption in fused silica: the absorption depth would be basically the whole thickness of the optics and this is not so useful when trying to correct surface heating.

During my recent trip to JILA, Jan Hall mentioned to me that it should be possible to operate instead at ~5 microns, where laser technology may be solid state and where we can use Si:As detectors instead of the inefficient HgCdTe ones which we use now.

JWST, in partnerships with industry, have developed some Si:As detectors:  http://www.jwst.nasa.gov/infrared.html   

Some internet searching shows that there are now several laser technologies for the mid-IR or MWIR range. Some are <1 W, but some are in the ~10 W range.

Of course, its possible that we'll switch to Silicon substrates, in which case we need to re-evaluate the goals and/or existence of TCS.

 I started out the day by taking some images from the CCD with the OLED switched off, to just look at the pattern when it's dark. The images looked like this:

128 images, spot search point (100,100), threshold level 129

 I added some new channels to the Athena DAQ that record the diagnostic channels from the Superlum SLED.

• C4:TCS-ATHENA_I_SET_VOLTS:  - the set current signal in Volts (1V = 1A)
• C4:TCS-ATHENA_I_ACTUAL_VOLTS:   - a signal proportional to the actual current flowing to the SLED (1V = 1A)
• C4:TCS-ATHENA_I_LIM_VOLTS: - the current limit signal in volts (1V = 1A)
• C4:TCS-ATHENA_TEMP_SENS_V:   - the signal from the on-board temperature sensor [thermistor] (1V = 10kOhm ?)
• C4:TCS-ATHENA_PD_VOLTAGE: - the signal from the on-board photodetector (1V = 1A?)

The ioc that handles the EPICS channels is on tcs_daq(10.0.1.34) in /target/TCS_westbridge.db

The channels are added to the frame builder in /cvs/cds/caltech/chans/daq/C4TCS.ini

Currently, the driver for the SLED is ON but the current to the SLED is off. This is to check that the zero value of the PD_VOLTAGE signal doesn't wander.

Also, the input noise of the Athena is around +/- 10 counts (where 2^15 counts = 10V) which is a pretty poor 3mV.

Below is a table summarizing the results of recent thermal defocus experiments. The values are the calculated change in measured defocus per unit temperature change of the sensor:

More detail on these experiments will be available in my second progress report, which will be uploaded to the LIGO DCC by next Monday.

The main purpose of this particular eLog is to summarize what functions I wrote and used to do this data analysis, and how I used them. All relevant code which is referenced here can be found on the SVN; I uploaded my most recent versions earlier today.

Here is a flowchart summarizing the three master functions which were specifically addressed for each experiment:

py4plot.m is probably the most complicated of these three functions, in terms of the amount of data analysis done, so here's a flowchart which shows how the function works and the main subfunctions that it addresses:

Also, here is a step-by-step example of how these functions might be used during a particular experiment:

(1)Suppose that I have an experiment which I have named "73010a", in which I wish to take 40 images of 200 sums. I would open the code for framesumexport2.py and change lines 7, 8 and 17 to read:

7  LoopN=40
8 SumN=200
17 mainoutbase="73010a"

And I would then save the changes. I would double-check that the output basename had indeed been changed to 73010a (it will overwrite existing data files, if you forget to change the basename before running it). I would then let the script run (changing the set temperature of the lab after the first summed image was taken). Note that the total duration of the measurement is a function of how many images are summed and how many summed images are taken (in this example, if I was taking each single image at a rate of 11Hz, data collection would take ~20 seconds and data processing (summing the images) would take ~4 minutes (on the order of ~1 second per image in the sum) (the script isn't very quick at summing images, obviously).

EDIT(7/30 3:40pm):  I just updated framesumexport2.py so that the program prompts you for this information. I also changed enabled execute permissions on the copy of the code on the Hartmann machine located in /users/jkunert/, so going to that directory and typing ./framesumexport2.py then inputting the information when prompted is all you need to do now. No need to go change around the actual code every time, any more.

(2)Once data collection had ceased entirely, I would open MATLAB and enter the following:

[x,y,dx,dy,time,M,centroids]=pyanalyze_uni('73010a',40);

The function would then look for 73010a.raw and 73010a.txt in ./opt/EDTpdv/ and import the 40 images individually and centroid them. The x and y outputs are the centroid locations. If, for example, 952 centroids were located, x and y would be 952x1x40 arrays. M would be a 40x4 array of the form:

[time_before_img_taken      time_after_img_taken      digitizer_temp      sensor_temp]

(3)Once MATLAB had finished the previous function, I would input:

tG=struct;
py4plot('73010a',0,39,x,y,'73010a','200',[1 952],2,tG)

The inputs are, respectively:

(1)python output basename,
(2)first image to analyze (where the first image is image 0),
(3)last image to analyze,
(4)x data (or, rather, data to analyze. to analyze y instead, just flip around "x" and "y" in the input),
(5)y data (or, if you want to analyze the y-direction, "x" would be the entry here),
(6)experiment name,
(7)number of sums in each image (as a string),
(8)range of centroids to include in analysis (if you have 952 centroids, for example, and no ridiculous noise at the edges of the CCD, then [1 952] would be the best entry here),
(9)outlier tolerance (number of standard deviations from initial fit line that a datapoint must be within to be included in the second line fitting, in the dx vs x plot),
(10)exponential fitting structure (input an empty structure unless the temperature/time exponential fit turns out poorly, in which case a better fit parameter guess can be inputted as field tG.guess)

 June 5

-Discussed the actual project outline 

-Installed Comsol on the system

-Learned the basics of Comsol with the help of tutorials available on 40m wiki

and others.

June 6

-Made few simple models in Comsol 

-Studied LIGO GWADW slides for a better understanding of the project.

-Setup SVN to access remote repository.

- Discussed the further project with Dr. Brooks.

-Tried to derive formula for the test mass inside cryogenic shield(infinitely long shield from one side)

 -Continued with the same cryogenic model created and varied the length of  outer shield and studied the temperature variation inside.

-Compared the temperature difference given by COMSOL with manually calculated one.

-Created a COMSOL model for cryogenically shielded test mass with compensation plate.

-Analyzed the behavior of the model in different size configurations.

-Created a COMSOL model for variation of temperature in two mass system.

-Used the above model for cryogenic conditions.

-checked it analytically.

-Derived formula for manual calculation of temperature due to total influx.

-Compared the results by COMSOL and by the formula.

 -Discussed the project outline for next 6 weeks.

-made a write up for the tasks. (attached)

-Analyzed the variation of temperature of the test mass with input power for different lengths of the shield. 

 -Updated 3 week progress report with new additions and deletions.

-Attended LIGO lecture which was very interesting and full of information.

 -Attented LIGO orientation meeting and safety session.

-Prepared 3 week report

 I reattached the Hartmann Sensor to the LENOVO machine that is running Ubuntu and turned it on (it's been disconnected for a couple of months). The /opt/EDTpdv/serial_cmd was able to communicate successfully with the camera.

I noticed that when i ran /opt/EDTpdv/camconfig and selected camera 331, which appeared to be closest to the Dalsa Pantera 1M60 camera, the software loaded the configuration file pantera11m4fr.cfg.

I tried to locate which entry in the camconfig list corresponded to the dalsa_1m60.cfg configuration file, but none of them seemed to. I couldn't select any entry and get it to report that it was using the 1m60 config file.

Next I noticed that there were 659 configuration files in the /opt/EDTpdv/camera_config directory but only 460 configuration options in camconfig. This seemed like 1/3 of the config files were somehow not formatted correctly, including,possibly the 1M60 config file.

By editing the pantera11m4fr.cfg I verified that the name of the camera, as it appears in the camconfig program, is the second line in the configuration file. For that file it was:

# CAMERA_MODEL 	"Dalsa Pantera 12 bit single channel camera link"

where the first line is just a single hash. The dalsa_1m60.cfg file did not have a name formatted in the same way as above: it was originally as shown below:

# Dalsa 1m60 config file (freerun)

so i changed the name in that configuration file to the following and it was suddenly available in the list when ./camconfig was run

# CAMERA_MODEL "Dalsa 1m60 config file (freerun)"

I selected that camera (number 53 in the list). Once this was done I ran pdv_flshow/pdvshow again the image that was displayed from the camera appeared to be correctlty demodulated.

Actually, the very first time i ran pdvshow the image was demodulated correctly but it appeared that the origin was offset and then the image wrapped around a little at the edges. However, every successive time I've run pdvshow since then I've had a perfectly demodulated image.

I ran some test patterns by changing the video mode using the serial communications menu in the camera. I also illuminated the Hartmann sensor with a torch/flashlight and got some spot patterns - see attached images.

Also, I've attached the dalsa_1m60.cfg file.