Aim: To calibrate CCD of GigE using LED1050E.

The following table shows some of the specifications for LED1050E as given in Thorlabs datasheet.

 The circuit diagram is given in Attachment 1.

Considering a power supply voltage V_cc = 15V, current I = 20mA & forward voltage of led V_F = 1.25V, resistance in the circuit is calculated as,

R = (V_cc - V_F)/I = 687.5

Attachment 2 gives a plot of resistance (R) vs input voltage (V_cc) when a current of 20mA flows through the circuit. I hope I can proceed with this setup soon.

Today I made the led (1050nm) circuit inside a box as given in my previous elog. Steve drilled a 1mm hole in the box as an aperture for led light.

Resistance (R) used = 665 .

We connected a power supply and IR has been detected using the card.

Later we changed the input voltage and measured the optical power using a powermeter.

Input voltage (Vcc in V)	Optical power
0 (dark reading)	60 nW
15	68 W
18	82.5 W
20	92 W

Since the optical power values are very less, we may need to drill a larger hole.

Now the hole is approximately 7mm from led, therefore aperture angle is approximately 2*tan^-1(0.5/7) = 8deg. From radiometric curve given in the datasheet of LED1050E, most of the power is within 20 deg. So a hole of size 2* tan(10) *7 = 2.5mm may be required.

I have also attached a photo of the led beam spot on the IR detection card.

Aim : To develop a neural network on simulated data.

I developed a python code that generates a 64*64 image of a white Gaussian beam spot at the centre of black background. I gave a sine wave of frequency 0.2Hz that moves the spot vertically (i.e. in pitch). Then I simulated this video at 10 frames/sec for 10 seconds. Then I saved this data into an hdf5 file, reshaped it to a 1D array and gave as input to a neural network. Out of the 100 image frames, 75 were taken as training dataset and 25 as test data. I varied several hyperparameters like learning rate of the optimizer, number of layers, nodes, activation function etc. Finally, I was successful in reducing the mean squared error with the following network model:

• Sequential model of 2 fully connected layers with 256 nodes each and a dropout of 0.1
• loss function = mean squared error, optimizer = RMSprop (learning rate = 0.00001) and activation function that adds nonlinearity = relu
• batch size = 32 and number of epochs = 1000

I have attached the plot of the output of neural network (NN) as well as sine signal applied to simulate the video and their residula error in Attachment 1. The plot of variation in mean squared error (in log scale) as number of epochs increases is given in Attachment 2.

I think this network worked easily since there is no noise in the input. Gautam suggested to try the working of this network on simulated data with a noisy background.

Aim: To measure the optical power from led using a powermeter.

Yesterday Gautam drilled a larger hole of diameter 5mm in the box as an aperture for led (aperture angle is approximately 2*tan^-1(2.5/7) = 39 deg). I repeated the measurements that I had done before (https://nodus.ligo.caltech.edu:8081/40m/13951). The measurents of optical power measured using a powermeter and the corresponding input voltages are listed below.

So we are able to receive optical power close to the value (1.6mW) given in Thorlabs datasheet for LED1050E (https://www.thorlabs.com/drawings/e6da1d5608eefd5c-035CFFE5-C317-209E-7686CA23F717638B/LED1050E-SpecSheet.pdf). I hope we can proceed to BRDF measurements for CCD calibration.

Steve: did you center the LED ?

Yes.

Aim: To track the motion of beam spot in simulated video.

I simulated a video that moves the beam spot at the centre of the image initially by applying a sinusoidal signal of frequency 0.2Hz and amplitude 1 i.e. it moves maximum by 1 pixel. It can be found in this shared google drive link (https://drive.google.com/file/d/1GYxPbsi3o9W0VXybPfPSigZtWnVn7656/view?usp=sharing). I found a program that uses Kernelized Correlation Filter (KCF) to track object motion from the video. In the program we can initially define the bounding box (rectangle) that encloses the object we want to track in the video or select the bounding box by dragging in GUI platform. Then I saved the bounding box parameters in the program (x & y coordinates of the left corner point, width & height) and plotted the variation in the y coordinates. I have yet to figure out how this tracker works since the program reads 64*64 image frames in video as 480*640 frames with 3 colour channels and frame rate also randomly changes. The plot of the output of this tracking program & the applied signal has been attached below. The output is not exactly sinusoidal because it may not be able to track very slight movement especially at the peaks where the slope = 0.

Thanks to a discussion yesterday with Joe Betzweiser, I was able to identify and fix the remaining problem with the LLO GigE camera software. It is working now, currently only on rossa, but can be set up on all the machines. I've started a wiki page with documentation and usage instructions here:

https://wiki-40m.ligo.caltech.edu/Electronics/GigE_Cameras

This page is also linked from the main 40m wiki page under "Electronics."

This software has the ability to both stream live camera feeds and to record feeds as .avi files. It is described more on the wiki page.

Aim:  To find a model that trains the simulated data of Gaussian beam spot moving in a vertical direction by the application of a sinusoidal signal. The data also includes random uniform noise ranging from 0 to 10.

All the attachments are in the zip folder.

I simulated images 128*128 at 10 frames/sec by applying a sine wave of frequency 0.2Hz that moves the beam spot, added random uniform noise ranging from 0 to 10 & resized the image frame using opencv to 64*64. 1000 cycles of this data is taken as train & 300 cycles as test data for the following cases. Optimizer = Nadam (learning rate = 0.001), loss function used = mean squared error, batch size = 32,

Case 1:

Model topology:

                         256 (dropout = 0.1)  ->           256 (dropout = 0.1)   ->       1

Activation :             selu                                         selu

Number of epochs = 240.

Variation in loss value of train & test datasets is given in Attachment 1 of the attached zip folder & the applied signal as well as the output of neural network given in Attachments 2 & 3 (zoomed version of 2).

The model fits well but there is no training since test loss is lower than train loss value. I found in several sites that dropout of some of the nodes during training but retaining them during test could be the probable reason for this (https://stackoverflow.com/questions/48393438/validation-loss-when-using-dropout , http://forums.fast.ai/t/validation-loss-lower-than-training-loss/4581 ). So I removed dropout while training next time.

Case 2:

Model topology:

                         256 (dropout = 0.1)  ->           256 (dropout = 0.1)   ->       1

Activation :             selu                                         selu                          linear

Number of epochs = 200.

Variation in loss value of train & test datasets is given in Attachment 4 of the attached zip folder & the applied signal as well as the output of neural network given in Attachments 5 & 6 (zoomed version of 2).

But still no improvement.

Case 3:

I changed the optimizer to Adam and tried with the same model topology & hyperparameters as case 2 with no success (Attachments 7,8 & 9).

Finally I think this is because I'm training & testing on the same data. So I'm now training with the simulated video but moving it by a maximum of 2 pixels only and testing with a video of ETMY that we had captured earlier.

(Gautam, Pooja)

Aim: To develop medm screen for GigE.

Gautam helped me set up the medm screen through which we can interact with the GigE camera. The steps adopted are as follows:

(i) Copied CUST_CAMERA.adl file from the location /opt/rtcds/userapps/release/cds/common/medm/ to /opt/rtcds/caltech/c1/medm/MISC/.

(ii) Made the following changes by opening CUST_CAMERA.adl in text editor.  

• Changed the name of file to "/cvs/cds/rtcds/caltech/c1/medm/MISC/CUST_CAMERA.adl"
• Replaced all occurences of "/ligo/apps/linux-x86_64/camera/bin/" to "/opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/" & "/ligo/cds/$(site)/$(ifo)/camera/" to "/opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/"

(iii) Added this .adl file as drop-out menu 'GigE' to VIDEO/LIGHTS section of sitemap (circled in Attachment 1) i.e opened Resource Palette of VIDEO/LIGHTS, clicked on Label/Name/Args & defined macros as CAMERA=C1:CAM-ETMX,CONFIG=C1-CAM-ETMX in Arguments box of Related Display Data dialog box (circled in Attachment 2) that appears. In Related Display Data dialog box, Display label is given as GigE and Display File as ./MISC/CUST_CAMERA.adl

(iv) All the channel names can be found in Jigyasa's elog https://nodus.ligo.caltech.edu:8081/40m/13023

(v) Since the slider (circled in Attachment 3) for pixel sum was not moving, changed the high limit value to 10000000 in PV Limits dialog box. This value is set such that the slider reaches the other end on setting the exposure time to maximum.

(vii) Set the Snapshot channel C1:CAM-ETMX_SNAP to off (very important!). Otherwise we cannot interact with the camera.

(vii) GigE camera gstreamer client is run in tmux session.

Now we can change the exposure time and record a video by specifying the filename and its location using medm screen. However, while recording the video, gstream video laucher of GigE stops or is stuck.

Aim: To develop a neural network that resolves mirror motion from video.

I had created a python code to find the combination of hyperparameters that trains the neural network. The code (nn_hyperparam_opt.py) is present in the github repo. It's running in cluster since a few days. In the meanwhile I had just tried some combination of hyperparameters.

These give a low loss value of approximately 1e-5 but there is a large error bar for loss value since it fluctuates a lot even after 1500 epochs. This is unclear.

Input: 64*64 image frames of simulated video by applying beam motion sine wave of frequency 0.2Hz and at 10 frames per sec. This input data is given as an hdf5 file.

Train : 100 cycles,  Test: 300 cycles, Optimizer = Nadam (learning rate = 0.001)

Model topology:

                    256       ->      128    ->       1

Activation :        selu     selu           linear

Case 1: batch size = 48, epochs = 1000, loss function = mean squared error

Plots of output predicted by neural network (NN) & input signal has been shown in 1st graph & variation in loss value with epochs in 2nd graph.

Case 2: batch size = 32, epochs = 1500, loss function = mean squared logarithmic error

Plots of output predicted by neural network (NN) & input signal has been shown in 3rd graph & variation in loss value with epochs in 4th graph.

Aim: To develop a neural network that resolves mirror motion from video.

I tried to reduce the overfitting problem in previous neural network by reducing the number of nodes and layers and by varying the learning rate, beta factors (exponential decay rates of moving first and second moments) of Nadam optimizer assuming error of 5% is reasonable.

Input:

32 * 32 image frames (converted to 1d array & pixel values of 0 to 255 normalized) of simulated video by applying sine signal to move beam spot in pitch with frequency 0.2Hz and at 10 frames per second.

Total: 300 cycles ,           Train: 60 cycles,    Validation: 90 cycles,    Test: 150 cycles

Model topology:

                                          Input               -->                  Hidden layer               -->                    Output layer                                  

                                                                                          4 nodes                                              1 node

Activation function:                                  selu                                             linear

Batch size = 32, Number of epochs = 128, loss function = mean squared error

Optimizer: Nadam

Case 1:

Learning rate = 0.00001,    beta_1 = 0.8 (default value in Keras = 0.9),  beta_2 = 0.85 (default value in Keras = 0.999)

Plot of predicted output by neural network, applied input signal & residual error given in 1st attachment.

Case 2:

Changed number of nodes in hidden layer from 4 to 8. All other parameters same.

These plots show that when residual error increases basically the output of neural network has a smaller amplitude compared to the applied signal. This kind of training error is unclear to me.

When beta parameters of optimizer is changed farther from 1, error increases.

Aim: To develop a neural network that resolves mirror motion from video.

Case 1:

Input : Simulated video of beam spot motion in pitch by applying 4 sine  waves of frquencies 0.2, 0.4, 0.1, 0.3 Hz  and amplitude ratios to frame size to be 0.1, 0.04, 0.05, 0.08

The data has been split into train, validation and test datasets and I tried training on neural network with the same model topology & parameters as in my previous elog (https://nodus.ligo.caltech.edu:8081/40m/14070)

The output of NN and residual error have been shown in Attachment 1. This NN model gives a large error for this. So I think we have to increase the number of nodes and learning rate so that we get a lower error value with a single sine wave simulated video ( but not overfitting) and then try training on linear combination of sine waves.

Case 2 :

Normalized the target sine signal of NN so that it ranges from -1 to 1 and then trained on the same neural network as in my previous elog with simulated video created using single sine wave. This gave comparatively lower error (shown in Attachment 2). But if we train using this network, we can get only the frequency of test mass motion but we can't resolve the amount by which test mass moves. So I'm unclear about whether we can use this.

Aim: To develop a neural network that resolves mirror motion from video.

Since error was high for the same input as in my previous elog http://nodus.ligo.caltech.edu:8080/40m/14089

I modified the network topology by tuning the number of nodes, layers and learning rate so that the model fitted the sum of 4 sine waves efficiently, saved weights of the final epoch and then in a different program, loaded saved weights & tested on simulated video that's produced by moving beam spot from the centre of image by sum of 4 sine waves whose frequencies and amplitudes change with time.

Input : Simulated video of beam spot motion in pitch by applying 4 sine  waves of frquencies 0.2, 0.4, 0.1, 0.3 Hz  and amplitude ratios to frame size to be 0.1, 0.04, 0.05, 0.08. This is divided into train (0.4), validation (0.1) and test (0.5).

Model topology:

                                          Input               -->                  Hidden layer               -->                    Output layer                                  

                                                                                          8 nodes                                              1 node

Activation function:                                  selu                                             linear

Batch size = 32, Number of epochs = 128, loss function = mean squared error

Optimizer: Nadam ( learning rate = 0.00001, beta_1 = 0.8, beta_2 = 0.85)

Normalized the target sine signal of NN by dividing by its maximum value.

Plot of predicted output by neural network, applied input signal & residual error given in 1st attachment. These weights of the model in the final epoch have been saved to h5 file and then loaded & tested with simulated data of 4 sine waves with amplitudes and frequencies changing with time from their initial values by random uniform noise ranging from 0 to 0.05. Plot of predicted output by neural network, target signal of sine waves & residual error given in 2nd attachment. The actual signal can be got from predicted output of NN by multiplication with normalization constant used before. However, even though network fits training  & validation sets efficiently, it gives a comparatively large error on test data of varying amplitude & frequency.

Gautam suggested to try training on this noisy data of varying amplitudes and frequencies. The results using the same model of NN is given in Attachment 3. It was found that tuning the number of nodes, layers or learning rate didn't improve fitting much in this case.

This looks like good progress. Instead of fixed sines or random noise, you should generate now a time series for the motion which is random noise but with a power spectrum similar to what we see for the ETM pitch motion in lock. You can use inverse FFT to get the time series from the open loop OL spectra (being careful about edge effects).

I was thinking a little more about the way we are training the network for the current topology - because the network has no recurrent layers, I guess it has no memory of past samples, and so it doesn't have any sense of the temporal axis. In fact, Keras by default shuffles the training data you give it randomly so the time ordering is lost. So the training amounts to requiring the network to identify the center of the Gaussian beam and output that. So in the training dataset, all we need is good (spatial) coverage of the area in which the spot is most likely to move? Or is the idea to develop some tools to generate video with spot motion close to that on the ETM in lock, so that we can use it with a network topology that has memory? 

Aim: To develop a convolutional neural network that resolves mirror motion from video.

Input : Previous simulated video of beam spot motion in pitch by applying 4 sine  waves of frquencies 0.2, 0.4, 0.1, 0.3 Hz  and amplitude ratios to frame size to be 0.1, 0.04, 0.05, 0.08 where random uniform noise ranging 0.05 has been added to amplitudes and frequencies. This is divided into train (0.4), validation (0.1) and test (0.5).

Model topology:

• Number of filters = 2
• Kernel size = 2
• Size of pooling windows = 2
•                                        ----->         Dense layer of 4 nodes  ---->    Output layer of 1 node 

         Activation:                      selu                                                  linear

Batch size = 32, Number of epochs = 128, loss function = mean squared error

Optimizer: Nadam ( learning rate = 0.00001, beta_1 = 0.8, beta_2 = 0.85)

Plots of CNN output & applied signal given in Attachment 1. The variation in loss value with epochs given in Attachment 2.

This needs to be further analysed with increasing random uniform noise over the pixels and by training CNN on simulated data of varying ampltides and frequencies for sine waves.

I have done the following thus far since elog #14626:

Simulation:

1. Cleaned up Pooja's code for simulating the beam spot. Added extensive comments and made the code modular. Simulated the Gaussian beam spot to exhibit 
   1. Horizontal motion
   2. Vertical motion
   3. motion along both x and y directions:
2. The motion exhibited in any direction in the above videos is the combination of four sinusoids at the frequencies: 0.2, 0.4, 0.1, 0.3 Hz with amplitudes that can be found as defaults in the script ((0.1, 0.04, 0.05, 0.08)*64 for these simulations.). The variation looks as shown in Attachment 1. For the sake of convenience I have created the above video files with only a hundred frames (fps = 10, total time ~ 10s) and this took around 2.4s to write. Longer files need much longer. As I wish to simply perform image processing on these frames immediately, I don't see the need to obtain long video files right away.
3. I have yet to add noise at the image level and randomness to the motion itself.  I intend to do that right away. Currently video 3 will show you that even though the time variation of the coordinates of the center of the beam is sinusoidal, the motion of the beam spot itself is along a line as both x and y motions have the same phase. I intend to add the feature of phase between the motion of x and y coordinates of the center of the beam, but it doesn't seem all too important to me right now. The white margins in the videos generated are annoying and make tracking the beam spot itself slightly difficult as they introduce offset (see below). I shall fix them later if simple cropping doesn't do the trick.
4. I have yet to push the code to git. I will do that once I've incorporated the changes in (3).

Circle detection:

1. If the beam spot intensity variation is indeed Gaussian (as it definitely is in the simulation), then the contours are circular. Consequently, centroid detection of the beam spot reduces to detecting these contours and then finding their centroid (center). I tried this for a simulated video I found in elog post 14005. It was a quick implementation of the following sequence of operations: threshold (arbritrarily set to 127), contour detection (video dependent and needs to be done manually), centroid determination from the required contour.  Its evident that the beam spot is being tracked (green circle in the video). Check #Attachment 2 for the results. However, no other quantitative claims can be made in the absence of other data.
2. Following this, Gautam pointed me to a capture in elog post 13908. Again, the steps mentioned in (1) were followed and the results are presented below in Attachment #3. However, this time the contour is no longer circular but distorted. I didn't pursue this further. This test was just done to check that this approach does extend (even if not seamlessly) to real data. I'm really looking forward to trying this with this real data.
3. So far, the problem has been that there is no source data to compare the tracked centroid with. That ought to be resolved with the use of simulated data that I've generated above. As mentioned before, some matplotlib features such as saving with margins introduce offsets in the tracked beam position. However, I expect to still be able to see the same sinusoidal motion. As a quick test, I'll obtain the fft of the centroid position time series data and check if the expected frequencies are present.

I will wrap up the simulation code today and proceed to going through Gabriele's repo. I will also test if the contour detection method works with the simulated data. During our meeting, it was pointed out that when working with real data, care has to be taken to synchronize the data with the video obtained. However, I wish to put off working on that till later in the pipeline as I think it doesn't affect the algorithm being used. I hope that's alright (?).

On Tuesday, I tried reproducing Pooja's measurements (https://nodus.ligo.caltech.edu:8081/40m/13986). The table below shows the values I got. Pictures of LED circuit, schematic and the setup are attached. The powermeter readings fluctuated quite a bit for input volatges (Vcc) > 8V, therefore, I expect a maximum uncertainity of 50µW to be on a safer side. Though the readings at lower input voltages didn't vary much over time (variation < 2µW), I don't know how relaible the Ophir powermeter is at such low power levels. The optical power output of LED was linear for input voltages 10V to 20V. I'll proceed with the CCD calibration soon.

1. Implemented image level noise for simulation. Added only uniform random noise.
2. Implemented addition of uniform random noise to any sinusoidal motion of beam spot.
3. Implemented motion along y axis according to data in "power_spectrum" file.
4. Impelemented simulation of random motion of beam spot in both x and y directions (done previously by Pooja, but a cleaner version).
5. Created a video file for 10s with motion of beam spot along the y direction as given by Attachment #1. This was created by mixing four sinusoids at different amplitudes (frequencies (0.1, 0.2, 0.4, 0.8) Hz Amplitudes as fractions of N = 64 (0.1 0.09 0.08 0.09). FPS = 10. Total number of frames = 100 for the sake of convenience.  See Attachment #5.
6. Following this, I used the thresholding (threshold = 127, chosen arbitrarily), contour detection and centroid computation sequence (see Attachment #6 for results) to obtain the plot in Attachment 2 for the predicted motion of the y coordinate. As is evident, the centering and scale of values obtained are off and I still haven't figured out how to precisely convert from one to another.
7. Consequently, as a workaround, I simply normalised the values corresponding to each plot by subtracting the mean in each case and dividing the resulting series of values by their maximum. This resulted in the plots in Attachments 3 and 4 which show the normalised values of y coordinate variation and the error between the actual and predicted values between 0 and 1 respectively.

Things yet to be done:

Simulation:

1. I will implement the mean square error function to compute the relativer performance as conditions change.
2. I will add noise both to the image and to the motion (meaning introduce some randomness in the motion) to see how the performance, determined by both the curves such as the ones below and the mean square error, changes.
3. Following this, I will vary the standard deviation of the beam spot along X and Y directions and try to obtain beam spot motion similar to the video in Attachment #2 of elog post 14632.
4. Currently, I have made no effort to carefully tune the parameters associated with contour detection and threshold and have simply used the popular defaults. While this has worked admirably in the case of the simple simulated videos, I suspect much more tweaking will be needed before I can use this on real data.
5. It is an easy step to determine the performance of the algorithm for random, circular and other motions of the beam spot. However, I will defer this till later as I do not see any immediate value in this.
6. Determine noise threshold. In simulation or with real data: obtain a video where the beam spot is ideally motionless (easy to do with simulated data) and then apply the above approach to the video and study the resulting predicted motion. In simulation, I expect the predictions for a motionless beam spot video (without noise) to be constant. Therefore, I shall add some noise to the video and study the prediction of the algorithm.
7. NOTE: the above approach relies on some previous knowledge of what the video data will look like. This is useful in determining which contours to ignore, if any like the four bright regions at the corners in this video.

Real data:

1. Obtaining real data and evaluate if the algorithm is succesful in determining contours which can be used to track the beam spot.
2. Once the kind of video feed this will be used on is decided, use the data generated from such a feed to determine what the best settings of hyperparameters are and detect the beam spot motion.
3. Synchronization of data stream regarding beam spot motion and video.
4. Determine the calibration: anglular motion of the optic to beam spot motion on the camera sensor to video to pixel mapping in the frames being processed.

Other approaches:

1. Review work done by Gabriele with CNNs, implement it and then compare performance with the above method.

1. I used the same motion as defined in the previous elog. I gradually added noise to the images. Noise added was uniform random noise - a 2 dimensinoal array of random numbers between 0 and a predetermined maximum (noise_amp). The previous elog provides the variation of the y coordinate. In this, I am also uploading the effect of noise on the error in the prediction of the x coordinate. As a reminder, the motion of the beam spot center was purely vertical. Attachement #1  is the error for noise_amp = 0, #2 for noise_amp = 20 and #3  for noise_amp = 40. While Attachment #3 does provide the impression of there being a large error, this is not really the case as without normalization, each peak corresponds to a deviation of one pixel about the central value, see Attachement #4 for reference.
2. While the error does increase marginally, adding noise has no significant effect on the prediction of the y coordinate of the centroid as Attachment #5 shows at noise_amp = 40.
3. I am currently running an experiment to obtain the variation of mean square error with different noise amplitudes and will put up the plots soon. Further, I shall vary the resolution of the image frames and the the standard deviation of the Gaussain beam with time and try to obtain simulations very close to the real data available and then determine the performance of the algorithm.
4. The following videos will serve as a quick reference for what the videos and detection look like at
   1. noise_amp = 20
   2. noise_amp = 40
5. I also performed a quick experiment to see how low the amplitude of motion could be before the algorithm falied to detect the motion and found it to occur at 2 orders of magnitude below the values used in the previous post. This is a line of thought I intend to pursue more carefully and I am looking into how opencv and python handle images with floats as coordinates and will provide more details about the previous trial soon. This should give us an idea of what the smallest motion of the beam spot that can be resolved is.

On Friday, I tried calibrating the CCD with the following setup. Here, I present the expected values of scattered power (P_s) at s = 45°, where s is scattering angle (refer figure). The LED box has a hole with an aperture of 5mm and the LED is placed at approximately 7mm from the hole. Thus the aperture angle is 2*tan^-1(2.5/7) ≈ 40° approx. Using this, the spot size of the LED light at a distance 'd' was estimated. The width of the LED holder/stand (approx 4") puts a constraint on the lowest possible s. At this lowest possible s, the distance of CCD/Ophir from the screen is given by . This was taken as the imaging distance for other angles also.

In the table below, P_i is taken to be 1.5mW, and P_s and  were calculated using the following equations:

d (cm)	Estimated spot diameter (cm)	Lowest possible s  (in degrees)	Distance of CCD/Ophir from the screen (in cm)	(in sr)	Expected Ps at   s = 45° (in µW)
1.0	1.2	78.86	5.2	0.1036	34.98
2.0	2.0	68.51	5.5	0.0259	8.74
3.0	2.7	59.44	5.9	0.0115	3.88
4.0	3.4	51.78	6.5	0.0065	2.19
5.0	4.1	45.45	7.1	0.0041	1.38
6.0	4.9	40.25	7.9	0.0029	0.98
7.0	5.6	35.97	8.6	0.0021	0.71
8.0	6.3	32.42	9.5	0.0016	0.54
9.0	7.1	29.44	10.3	0.0013	0.44
10.0	7.8	26.93	11.2	0.0010	0.34

On measuring the scattered power (P_s) using the ophir power meter, I got values of the same order as that of  expected values given the above table. Like Gautam suggested, we could use a photodiode to detect the scattered power as it will offer us better precision or we could calibrate the power meter using the method mentioned in Johannes's post: https://nodus.ligo.caltech.edu:8081/40m/13391.

[Kruthi, Milind]

Yesterday, we were able to capture some images of objects at a distane of approx 60cm (see the attachment), with the GigE mounted onto the telescope. I think, Johannes had used it earlier to image the ETMX (https://nodus.ligo.caltech.edu:8081/40m/13375). His elog entry doesn't say anything about the focal length of the lenses that he had used. The link to the python code he had used to calculate the lens solution wasn't working. After Gautam fixed it, I took a look at it. He has used 150mm (front lens) and 250mm (back lens) as the focal length of lenses for the calculation. Using the lens formula and an image of a nearby light source, with a very rough measurement, I found the focal lengths to be around 14 cm and 23 cm. So, I'm assuming that the lenses in the telescope are of same focal lengths as in his code, i.e 150mm and 250mm.

no BMP files 

The following steps summarize the steps to setting up and interacting with a GigE camera.

Launching the PylonViewerApp:

1. Open a new terminal using Ctrl + Alt + T on the keyboard.
2. Launch the app using the command pylon.

Using setup python scripts to interact with the GigE (a summary of the steps listed here and here)

1. Connect the GigE camera to the ethernet cable and record its IP address. If the IP address is not printed on the GigE, launch the PylonViewerApp and navigate to the "Tools" dropdown menu and select "pylon IP configurator" to be presented with a list of all connected cameras and their IP addresses.
2. To simply observe the camera feed, open a new terminal and run the following commands:
   1. cd /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon
   2. python camera_server.py -c C1-CAM-ETMX.ini  (only one config file is present currently and more will be added as more cameras are set up. The "Camera IP" in the  .ini file must match that determined in step 1). This starts the camera server.
3. Open a new tab (Ctrl + Shift + T on the keyboard) in the terminal. You should still be in the same directory as navigated to in step 2.1. Run the following command.
   1. python camera_client.py -c C1-CAM-ETMX.ini
4. This should bring up a feed from the camera. Close at will.
5. To record a video file, repeat steps 1 and 2. Open a new tab as described in step 3. Then run the following command:
   1. python camera_client_movie.py -c C1-CAM-ETMX.ini
6. Enter the full path to the file where you wish to save the movie in the prompt that appears. Use ./your_file_name_here.avi to save the the video in the working directory. Press Ctrl + C to stop recording. The recording can be played by navigating to the location where the recording is stored and running vlc your_file_name_here.avi.
7. To adjust the exposure setting of the camera, open a new terminal and run the command sitemap . This should bring up the medm display in Attachment #1. Click on the Video/Lights button highlighted in red and select GigE. Adjust the exposure value in the next window using the slider before starting the server in step 1. Adjusting the slider once the server is started causes the program to freeze. Also set the Snapshot channel C1:CAM-ETMX_SNAP to off as mentioned in elog 14037.

Upcoming updates:

1. Automatic script to run the above steps.
2. Pre-determining the time duration of the recorded video.
3. Obtaining snapshots.

Chub and I are trying to figure out a way to co-mount GigE into the existing cylindrical enclosure. I'm attaching a picture of the current setup that is being used for imaging MC2. As of now, I have thought of 3 possible setups (schematics attached); but I don't know how feasible they are. Let us know if you have any other ideas.

Update: The setup 3 would require us to use the 52cm long enclosure. It has a long breadboard welded to it, which makes it very convienient, but the whole setup becomes quite heavy and it's not that safe to install such heavy enclosure on top of the vaccuum system. Also, aligning its components would be more complicated than other setups.

I decided to start with the simple one, therefore, I tried implementing setup 1. Fitting in the analog camera horizontally alongside the telescope turned out to be tricky. Though I did manage to fit it in, it didn't leave any room to change the orientation of the beamsplitter. Like Koji suggested, I'll be trying the setup 2.

Figured out how to get/grab frames by looking at the pypylon documenation as that turned out to be easier than modifying Jon's code. Still not sure about how to modify the exposure time (other than using the pylon app, the only technique I know so far is to adjust the exposure manually on the medm screen and then run the scripts as described in the previous elog). I will figure that out tomorrow and make a script suitable for Kruthi's usage (obtain a bunch of images with different exposure times). I will also try and integrate the video saving and streaming code into this and have a neat little script set up asap.

caget/caput probably does the job.

Thanks! It does indeed do the trick! With that I was able to

1. Obtain current exposure value using the terminal command caget C1:CAM-ETMX_EXP
2. Set exposure value using the terminal command caput C1:CAM-ETMX_EXP <desired_exposure_value>

Further, a quick look at the camera server code in /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/camera_server.py revealed that the script expects the details of "Number of Snapshots" in "Camera Settings" in the configuration file i.e in C1-CAM-ETMX.ini at ( /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/C1-CAM-ETMX.ini) which wasn't present before. Adding this parameter to the config file allows one to take a snapshot using the medm screen. Infact, unlike as described in this elog, I was able to start the server and client as described in elog 14649, and then obtain snapshots using the terminal command  caput C1:CAM-ETMX_SNAP 1.

[Koji, Milind]

Today I ran into the following errors:

1. Inability to access the EPICS channels using the commands caget and caput and thus the generation of a blank medm screen (error in Attachment #1) when simultaneously running the code in camera_server.py (/opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/camera_server.py).
2. Inability to run camera_server.py code with an active medm screen with a "... failed to read <EPICS channel>" error.

Therefore, Koji and I took a look at it and putting our faith in Gautam's hunch from elog 13023, we walked down to rack 1Y1 and keyed it. Following this, all the functionality previously described was restored! Koji then took a look at all the channels handled by this machine and bestowed upon me the permission to key the crate should I lose control of the GigE again.

I managed to fit all the parts into the cylindrical enclosure without having to drill a hole in the enclosure to mount the analog camera (pictures attached); thanks to Koji for helping me find some fancy mechanical components (swivel post clamps, right angle post clamps and brackets). On Thursday, with Chub's help, I took a look at all the current analog camera positions with respect to the cylindrical enclosures. I think this setup gives me enough flexibility to align the components, as necessary, to be able to image the test masses/mirrors in all the cavities. I'll set it up for MC2 tomorrow.

Steps to take snapshots using GigE at different exposures [Instructions for Kruthi]:

1. Setup C1-CAM-ETMX.ini (/opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/C1-CAM-ETMX.ini) appropriately. The parameter Number of Snapshots determines how many snapshots will be taken at any given exposure. Set Name Overlay, Time Overlay, Calculation Overlay, Calculations (if using very low values of exposure) and Auto Exposure to False. Ensure that that the IP address of the Camera in use and that in the configuration file match.
2. Launch a server using the following commands (as described in elog 14649)
   1. cd /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon
   2. python camera_server.py -c C1-CAM-ETMX.ini
3. Open another terminal in the same directory and then run the following command
   1. python exposure_variation.py --minval <minval> --maxval <maxval> --step <step> where
      1. minval: lower bound of range of exposure values, defaults to 150
      2. maxval: upper bound of range of exposure values, defaults to 100000
      3. step: step size of variation in the range [minval, maxval], defaults to 2000

The python script takes in the above parameters and then takes snapshots by setting the exposure to values starting at minval and going upto maxval incrementing by step at each turn. This uses a simple for loop and is nothing elaborate.

A few unrelated updates:

1. On a sidenote, I installed Sublime Text editor on rossa following the instructions at this site (check install using yum section). Further, I have also installed miniconda but did not set it up fully as I was in a rush and did not want to disturb any previously set up environment variables.
2. I have cloned Gabriele's repository and am trying to get it to work on my system. As Gautam has pointed out that the end goal is to get stuff working on the lab machines, I will sharea .yml file with the necessary environment details upon completion.
3. I will upload details of how I am going to construct the two learning tasks that Rana, Gautam and I discussed in a day or two including details of the use of simulation data for training data in the absence of real data (until Kruthi is done setting up the GigE) which Gautam suggested I do to speed things up.

[Kruthi, Milind]

Today, with Milind's help, I installed the analog camera into the MC2 enclosure [picture attached]; but it is not yet focused. We replaced the bulky angular bracket with a simple one, this saved a lot of space inside and it's easier to align other components now. I'll finish setting it up tomorrow.

Telescope design for MC2:  Instead of using two 3" long stackable lens tubes (SM2L30), we can use one 3" lens tube with an adjustable lens tube (SM2V10), as shown in the picture. This gives a flexibility to change the focal plane distance by 1" and also reduces the overall length of telescope from 9 inches to 6-7 inches. I decided to use two 150mm biconvex lens instead of a combination of 150mm and 250mm lenses, as the former combination results in lower focal plane distance for a given distance between the lenses.

Specifications of current telescope system (for future reference):

With the above telescope, assuming the MC2 mirror to be at a distance of approx 75cm, the focal plane distance will range from 7.9cm to 8.1cm. Using the adjustable lens tube, we can further make the fine adjustment.

[Koji, Kruthi]

Yesterday, Koji helped me clean all the optics that are being used for the setup. We tried aligning the cameras with the previous configuration we had, but after connecting the analog camera cables there wasn't much room to align the beam splitter. Today, I tried a different configuration and tested the alignment of analog camera, GigE, beam splitter and the mirror using a laser beam [pictures attached]. But the MC2 isn't locked to test if the whole setup is actually aligned with the mirror inside the vacuum. 

Also, with this setup, just by using posts of different lengths with the middle 90º-post-clamp, we will be able to move all the components. This way, we can easily image the beam spot in all the cavities.

I'm attaching a picture of the screen. I just positioned the enclosure by turning it a bit and I suppose we can see the mirror inside the vacuum now (the MC2 is still not locked). 

Today, Rana asked me to work on improving simulations based on the ideas we discussed last week. As of the previous elog the simulation accomodated only

1. Simulation of Gaussian beam spot.
2. Arbitrary motion.

Today, I added the simulation of point scatterers.

What?

The image on the sensor (camera) is produced in roughly the following steps.

1. Motion of the Gaussian beam on the optic (X,Y coordinates) which is what has been simulated so far.
2. Reflection from the surface of the optic which can be modeled using knowledge of the BRDF has not been included as of this elog as I wish to do a little more reading before doing so.
3. Reflection from point scatterers (dust particles burnt into the optic surface by the laser and so forth) which are characterised as peaks (impulses) in the TIS vs position plot. The laser beam is incident nearly normally on the optic and this behaviour is independent of the angle of observation. This is what has been added to the simulation.

How?

1. Increased the frame resolution to 720 x 480.
2. Defined an array of the same size and set values of at most "num_scatter" number of points at random positions to values determined randomly between 1 and "scatter_amp" + 1 where scatter_amp is non-negative.
3. Multiplied the resulting array by the resulting Gaussian beam. The motivation was to imitate the bright specks obtained on various camera feeds in the lab. Physically, this also implies normal incidence and normal observation which is not the real case at all. I shall add these features in a day or two.

Herewith, in attachments #1, #2, #3 I am attaching videos obtained by varying scattering amplitude and number of scattering points in a vain attempt to reproduce this data. I shall work more on this simulation on Friday.

Scripting stuff:

1. Previous elogs detail how to take gige images at various exposure times. I am still waiting on Kruthi to use the script.
2. Tomorrow I shall work on the scripting software to interact with the GigE and take video for a fixed duration etc. I shall also begin working on a script to autolock the PMC based on what Rana showed me on Monday. I will also take a look at the the contents of this elog and try to pick up from there. I hope to make significant progress by the next lab meeting.

Neural network stuff:

GANs for simulation:

1. Other than putting the physics into simulation i.e the first portion of this elog, GANs can be trained to generate images similar to the original data. I am unfamiliar with training GANs and the various tricks that are used specifically for them. I will do a bit of reading and make an update by Friday. As of now, the data I plan to use is this and I will train it using the GTX 1060 on my machine.

Networks for beam tracking:

1. I will use the architectures suggested in this work with a few modifications. I will use MSE loss function, Adam optimizer and my local GPU for training.

don't need to lock - make sure the 4 OSEMs are centered on the camera field just as we have for the arm cavity mirrors

As directed by Gautam, I have set up one script- interact.py (at /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/interact.py) to perform the following two tasks:

1. View the GigE feed for a fixed period of time.
2. Record the GigE feed for a fixed amount of time.

Steps to view GigE feed for a fixed amount of time:

1. Run the following commands in the terminal to navigate to the concerned directory and then view the feed
   1. cd /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon
   2. python interact.py --path_to_config <path_config> --mode 0 --view_time <viewing_time>, where
      1. path_config: full path to configuration file, defaults to /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/C1-CAM-ETMX.ini if --path_to_config is not used
      2. viewing_time: time in seconds for which the feed is to be displayed. The server is closed  after this time and the window freezes and can be manually closed.
   3. Exiting the feed in between: The script terminates automatically after the specified time. To terminate the feed in between, close the window manually using the x icon the top right. This makes sure that the server is correctly closed. If closed using the Ctrl-C command in the terminal, the server is left running and any attempt to unwittingly set up another results in an error (see Attachment #1). In this case, the server and client processes needs to be identified manually and killed. I have used the following steps
      1. ps -eaf | grep server, then identify the PID for the python camera_server.py process
      2. kill PID
      3. similarly for the client file

Steps to record the GigE feed for a fixed amount of time:

I tried to look for elegant solutions that wouldn't require editing the code that Jon wrote and stumbled upon this useful bit of information but ended up deciding that it was just easier to change the camera_client_movie.py (/opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/camera_client_movie.py). It can still be run as previously described, where video recording is terminated by using Ctrl-C. Steps to record for a fixed period of time are

1. cd /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon
2. python interact.py --path_to_config <path_config> --mode 1 --save_time <recording_time> --file_name filename, where\
   1. path_config: full path to configuration file, defaults to /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon/C1-CAM-ETMX.ini if --path_to_config is not used
   2. recording_time: time in seconds for which the feed is to be saved. No video is displayed during this time.
   3. filename: full path to the file where the video is to be saved. Overwrites any existing files.

I'll make aliases for these to make the whole process more user friendly. I'm halting this for now and will discuss what else needs to be done once Gautam gets back.

Regarding the autolocker: I spoke to Aaron today and as he is in tomorrow, I'll ask him about the burt files and the ideal configuration.

I'm also starting with GANs now.

Today, I tried aligning it further; I'm attaching a picture of it. We are not able to see all the 4 OSEMs yet. In the reference picture I had taken, before taking off the previous analog setup, the OSEMs are not seen. So, I don't really understand what the other 2 spots seen on the current screen are. Are they actually OSEMs?

I need a laptop next to MC2, so that I can have a look at it and make further alignments. So, I tried accessing the GigE attached to the telescope using Paola. The pylon app in it, throws an error, few seconds after running it in continuous shot mode, and disconnects the GigE; everything works fine on Rossa though. I'll put up further details soon.

The analog camera is aligned and we are able to see all the 4 OSEMs (pictures attached). Due to secondary reflection from the beamspiltter (BS1-1064-33-2037-45S), when the MC2 is locked, we are getting a ghost image of the beam spot along with the primary image. 

The pylon app in Paola was reporting an error saying "0xE1000014: The buffer was incompletely grabbed". I followed the instructions given in this site, and changed the 'Packet Size' to 1500 and 'Inter-Packet Delay parameter' to a value greater than 20,000 (µs). This did the trick and I was able to use the continuous shot mode without any interruption. I'm attaching a picture of MC2 that I captured using GigE.

Begun setting up an environment (as mentioned before, on my local machine) and scripts to run experiments with Convolutional networks for beam tracking. All code has been pushed to this folder in the GigEcamera repository. I am presently looking for pre-processing techniques for the video which go beyond the usual "Crop the images! Normalize pixel values! Convert to Grayscale!".

Worked further on this. I skimmed through a few resources to look for details of what pre-processing can be done. Here (am planning to convert all these resources, particularly those I come across for GANs into either a README on the repo or a Wiki soon) are some of the useful things I found during today's reading. The work I skimmed through today mostly pointed to the use of a median filter for pre-processing, if any is to be done. I am presently using the Sequential() API in Keras to set up the neural network. I will train it tomorrow.

In the previous meeting, Koji pointed out (once again) that I should determine if the displacement values and frames are synchronized before training a network. Pooja did the following last time. Koji also suggested that I first predict the motion (a series of x and y coordintates) and then slide resulting plots around until I get the best match for the original motion. This is however not possible with a neural network based approach as the network learns exactly what you show it and therefore it will learn any mismatch between the labels and the frames and predict exactly that. Therefore I came up with what Koji described as "hacky" method to achieve the same using the opencv work described previously in this elog (the only addition being the application of a mask to block out the OSEMs and work only with the beam spot) .

Hacky technique to sync frames and labels:

1. I ran the OpenCV algorithm on the data to obtain a plot for predicted motion depicted in Attachment #2. As is evident, the predicted motion is only an approximate of the actual motion and also displays a shift . However, a plot of the fourier transform of the signal (see Attachment #1) shows that the components present are the same. However, the predominant frequency component is 0.22 Hz rather than 0.2 Hz as stated by Pooja in her elog. I wonder if this is of any consequence. Therefore, this predicted motion can be slid around until it overlaps with the applied sinusoidal dither signal "well".
2. Defining "well": I computed an error signal as the differnece between the predicted signal and the actual motion with each signal being normalized by subtracting the mean and then dividing the resulting signal by the maximum value (see Attachment #3). The lower the power of the resulting signal, the better the synchronization of the predicted and actual signal. Note: To achieve this overlap of signals, datapoints are removed from either the start or end of the signals and this effectively reduces the number of data points available for training by 36 pionts (see Attachment #4, positive and negative shifts merely indicate if the predicted signal is being moved right or left).
3. Attachments #5,#6 show the resutls of shifting the data by 36 samples. it is evident that there is far greater overlap of the prediction and the actual values.
4. Well, what now? I will use the mapping between labels and frames obtained by the above steps to train a neural network.

[Koji, Milind - 21/06/2019]

1. Well, the above is fine, but why is contour detection really necessary? Why not take a weighted sum of all the pixel values (in a rectangular region obtained, say, after blocking out the OSEMs) to see what the centroid motion is? Black areas (0 pixel intensity values) will not contribute to this sum anyway. Perhaps that can be used for the sliding instead of the above (fallible!) approach, specially for cases in which the beam "spot"  is just a collection of random speckles?
   1. Something like this was done by Pooja where she computed the sum of pixel intensities in a rectangular region containing the beam spot. However, she did this for very noisy data and observed intensity variation at a frequency double that of the applied signal.
   2. Results of applying a median filter and doing the same are presented in Attachment #7. Clearly, they can't be used for this sliding task.
   3. Results of computing the weighted sum of all the coordinates (with pixel intensities as the weights are presented in Attachment #8. Clearly, for this data and for this task, the contour approach seems to be a better method. Further, these resutls just serve to prove Rana's point that such simple, unsophisticated, naive approaches will not produce desired results and therefore, shall be presented in this very context in the report that is due.
2. The contour detection technique does not work if the beam spot is just a cokllection of speckles. In that case Koji suggested that we use a bounding convex hull instead of a contour. Alternately, for a bunch of speckles I can perform dilation to reduce it to the same problem.
3. Using gpstime for time stamping: To determine the absolute time which a frame is grabbed. However, the time between the time being recorded and grabbing of frame needs to be determined for this which should be doable using linux/python commands.

Upcoming work (in the order of priority):

1. Data acquisition: With the mode cleaner being locked and Kruthi having focused on to the beam spot, I will obtain data for training both GANs and the convolutional networks. I really hope that some of the work done above can be extended to the new data. Rana suggested that I automate this by writing a script which I will do after a discussion with Gautam tomorrow.
2. Network training for beam spot tracking: I will begin training the convolutional network with the data pre-processed as described above. I will also simultaneously prepare data acquired from the GigE and train networks on that. Note: I planned to experiment with framewize predictions and hence did some of the work described above. However, I will restrict the number of experiments on that and perform more of those that use 3D convolution. Rana also pointed out that it would be interesting to have the network output uncertainity in the predictions. I am not sure how this can be done, but I will look into it.
3. Simulation:
   1. Putting the physics in: Previously, I worked on adding point scatterers. I shall add the effect of surface roughness and incorporate the BRDF next. Just as Gautam did, Rana also reccommended that I go through Hiro Yamamoto's work to improve my understanding of this.
   2. GANs: I will put together a readme (which I will turn into a wiki later) for all the material that I am using to develop my ideas about GAN training. Currently, my understanding of GANs is that they take as input noise vectors which are fed to the generative networks which then produce the fakes. This clearly isn't the only way to do it as GANs are used for several applications such as image generation from text. I am referring to these papers to set up the necessary architecture.
4. PMC autolocker: I will convert the existing autolocker script to python. Rana also suggested that it would be interesting to see what the best settings of the hyperparameters would be to lock the PMC the fastest. I will write a script to do that and plot a 3D surface plot of the average time taken to lock the PMC as a function of the PZT scan speed and the Servo gain to determine the optimal setting of these "hyperparameters".
5. Cleaning up/ formalizing code: Rana pointed out that any code that messes with channel values must return them to the original settings once the script is finished running. I have overlooked this and will add code to do this to all the files I have created thus far. Further, while most of my code is well documented and frequently pushed to Github, I will make sure to push any code that I might have missed to github.
6. Talk to Jon!: Gautam suggested that I speak to Jon about the machine requirements for setting up a dedicated machine for running the camera server and about connecting the GigE to a monitor now that we have a feed. Koji also suggested that I talk to him about somehow figuring out the hardware to ensure that the GigE clock is the same as the rest of the system.

Turns out, focusing the GigE is actually a bit tricky. With pylon, everytime I change the exposure or the focus, I'm running into the error I had mentioned earlier in one of my elogs; so I tried using the python scripts to interact with the GigE. But whenever I try to change the focal plane distance by rotating the lens coupler, the ethernet cable connection becomes loose and the camera server needs to be relaunched every now and then. Also, everytime we want to change the distance between the lenses, the telescope needs to be dismantled and refocused again. I'll try to come up with a better telescope design for this.

Yesterday, I had focused the GigE using a low exposure time and small aperture of iris, to make sure that we are actually seeing a sharp image of the beam spot. I'm attaching a picture of the beam spot I had clicked while focusing it, unfortunately, I forgot to take a picture after I had focused it completely. I'm also attaching a picture of the final setup for future reference. 

Yesterday night, Rana asked me to lock the MC2. I figured that the PSL shutter was closed; I just opened it and was able to see the beam spot on the analog camera screen.

I discussed this with Gautam and he asked me to come up with a list of signals that I would need for my use and then design the data acquisition task at a high level before proceeding. I'm working on that right now. We came up with a very elementary sketch of what the script will do-

1. Check the MC is locked.
2. Choose an exposure value.
3. Choose a frequency and amplitude value for the applied sinusoidal dither (check warning by Gabriele below).
4. Apply sinusoidal dither to optic.
5. Timestamping: Record gpstime, instantaneous channel values and a frame. These frames can later be put together in a sequence and a network can be trained on this. (NEED TO COME UP WITH SOMETHING CLEVERER THAN THIS!)

Tomorrow I will try and prepare a dummy script for this before the meeting at noon. Gautam asked me to familiarize myself with the awg, cdsutils (I have already used ezca before) to write the script. This will also help me do the following two tasks-

1. IFO test scripts that Rana asked me to work on a while ago
2. The PMC autolocker scripts that Rana asked me work on

I got to speak to Gabriele about the project today and he suggested that if I am using Rana's memory based approach, then I had better be careful to ensure that the network does not falsely learn to predict a sinusoid at all points in time and that if I use the frame wise approach I try to somehow incorporate the fact that certain magnitudes and frequencies of motion are simply not physically possible. Something that Rana and Gautam emphasized as well.

I am pushing the code that I wrote for

1. Kruthi's exposure variation - ccd calibration experiment
2. modified camera_client_movie.py code (currently at /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon)
3. interact.py (to interact with the GigE in viewing or recording mode) (currently at /opt/rtcds/caltech/c1/scripts/GigE/SnapPy_pypylon)

to the GigEcamera repository.

Gautam also asked me to look at Jigyasa's report and elog 13443 to come up with the specs of a machine that would accomodate a dedicated camera server.

Yesterday, Rana asked me to look at Hiro Yamamoto's docs on the DCC to improve the simulation. I'm performing a first pass (=> Just skimming through to see if they're relevant, I will go through them more carefully soon!) and putting up stuff here for future reference. @Kruthi's help much appreciated!

The GigE is focused now (judged by eye) and I have closed the lid. I'm attaching a picture of the MC2 beam spot, captured using GigE at an exposure time of 400µs.

What was the solution to resolving the flaky video streaming during the alignment process????

-> I think, the issue was with either the poor wireless network conection or the GigE-PoE ethernet cable.

For the beam spot position tracking, I am wondering if there is any benefit to going for a wider field of view and getting the OSEMs in the frame? It may provide some "anchor points" against which whatever algorithm can calibrate the spot position against. But there are also several point scatterers visible in the current view, and perhaps the Gaussiam beam profile moving over them and tracking the scattered intensity from these point scatterers serves the same function? I don't know of a good solution to have a "switchable" field of view configuration in the already cramped camera enclosure though.

Also, I think it may be useful to have a cron job take a picture of MC2 and archive it (once a week? or daily?) to have some long term diagnostic of how the scattered light received by the camera changes over several months.

And finally, a network is trained!

Result summary (TLDR :-P) : No memory was used. Model trained. Results were garbage. Will tune hyperparameters now. Code pushed to github.

More details of the experiment:

Aim:

1. To train a network to check that training occurs and get a feel for what the learning might be like.
2. To set up the necessary framework to perform mulitple experiments and record results in a manner facilitating comparison.
3. To track beam spot motion.

What I did:

1. Set up a network that learns a framewise mapping as described in here.
2. Training data: 0.9 x 1791 frames. Validation data: 0.1 x 1791 frames. Test data (only prediction): all the 1791 frames
3. Hyperparameters: Attachment #1
4. Did no tuning of hyperparameters.
5. Compiled and fit the models and saved the results.

What I saw

1. Attachment #2: data fed to the network after pre-processing - median blur + crop
2. Attachment #3: learning curves.
3. Attachment #4: true and predicted motion. Nothing great.

What I think is going wrong-

1. No hyperparameter tuning. This was only a first pass but is being reported as it will form the basis of all future experiments.
2. Too little data.
3. Maybe wrong architecture.

Well, what now?

1. Tune hyperparmeters (try to get the network to overfit on the data and then test on that. We'll then know for sure that all we probably need is more data?)
2. Currently the network has around 200k parameters. Maybe reduce that.
3. Set up a network that takes as input (one example corresponding to one forward pass)  a bunch of frames and predicts a vector of position values that can be used as continuous data).

Finding the gain of the Photodiode: The three-position rotary switch of the photodiode being used (PDA520) wasn't working, so I determined its gain by making a comparative measurement between ophir power meter and the photodiode. The photodiode has a responsitivity of 0.34 A/W at 1064 nm (obtained from the responsitivity curve given in the spec sheet using a curve digitizing software). Using the following equation, I determined the gain setting, which turned out to be 20dB.

Setup: Here a 1050nm (closest we have to 1064nm) LED is used as the light source instead of a laser to eliminate the effects caused by coherence of a laser source, which might affect our radiometric calibration. The LED is placed in a box with a hole of diameter 5mm (aperture angle = 40 degrees approx.). Suitable lenses are used to focus the light onto a white paper, which is fixed at an arbitrary angle and serves as a Lambertian scatterer. To make a comparative measurement between the photodiode (PDA520) and GigE, we need to account for their different sensor areas, 8.8mm (aperture diameter) and 3.7mm x 2.8 mm respectively . This can be done by either using an iris with a common aperture so that both the photodiode and GigE receive same amount of light , or by calculating the power incident on GigE using the ratio of sensor areas and power incident on the photodiode (here we are using the fact that power scattered by Lambertian scatterer per unit solid angle is constant). 

Calibration of GigE 152 unit: I took around 50 images, starting with an exposure time of 2000  in steps of 2000, using the exposure_variation.py code. But the code doesn't allow us to take images with an exposure time greater than 100 ms, so I took few more images at higher exposures manually. From each image I subtracted a dark image (not in the sense of usual CCD calibration, but just an image with same exposure time and no LED light). These dark images do the job of usual dark frame + bias frame and also account for stray lights. A plot of pixel sum vs exposure time is attached. From a linear fit for the unsaturated region, I obtained the slope and calculated the calibration factor.

Equations:                          

Result: CF = 1.91x 10^-16 W-sec/counts  Update: I had used a wrong value for the area of photodiode. On using 61.36 mm^2 as the area, I got 2.04 x 10^-15 W-sec/counts.

I'll put the uncertainities soon. I'm also attaching the GigE spectral response curve for future reference.

I wanted to try out the unstick.py script on c1aux but kept running into timeout errors. I was also confronted by a blank GigE screen. Further, couldn't telnet into c1aux using telnet c1aux as described here. Therefore, I went in and keyed the c1aux crate (1Y1).