Education: what do I/we mean by it?

I wish to write more details about how to orient an undergraduate teaching program in a liberal arts setting. Given the influence of Harvard's "Peer Instruction" and how radical schools (Summerhill and Tagore's Shantiniketan) approach school teaching, I would like to remodel the undergraduate program along similar lines. I will illustrate details using Physics/Astronomy examples, but I believe the principles will apply to other science/social-science streams as well. 

Here is an outline of my future posts: the color codes indicate various groupings under which I will attempt to address the various questions. Perhaps the answers clubbed in this way will allow more systematic thought into this set of problems.

1. What does a man/woman learn?
2. What is learning?
3. What use is learning?
4. Learner, society, industry...
5. How to learn?
6. How can a 'learner' be helped best?
7. How does 'skill-gap' occur?
8. What use of a structured program for learning?
9. How to bridge skill gap through structured degree programs?
10. What is the right model of higher education?
11. Economics of higher education.

Daily, prose-bound, routine remembering...

गणपती बाप्पा मोरया !

This week I experienced the lines from poem by Adrienne Rich, who passed away recently:

Freedom. It isn’t once, to walk out
under the Milky Way, feeling the rivers
of light, the fields of dark—
freedom is daily, prose-bound, routine
remembering. Putting together, inch by inch
the starry worlds. From all the lost collections.

      —“For Memory,” from A Wild Patience Has Taken Me This Far


Most of my efforts have gone into :

• Reading a binary output file produced by a FORTRAN program. Realizing that it was an output on a 32-bit machine, I (i) Installed a 32-bit virtual machine; (ii)Installed the FORTRAN code package with gfortran; (iii) Installed Python environment and a code to read the output file on the virtual machine
• Applying for jobs,
• Meeting umpteen people to hunt for suitable positions,
• Trying to focus on image processing with huge cold, cough, fever keeping me indoors.
• Fixing my travel to Delhi, my talks, etc.
• Fixing my talk within IUCAA as well.
• Avoiding my relatives, friends, and even my own family.

The actual science output has been fairly limited, must I say that?

Educating India: Colleges

Colleges will train teachers for

1. for democratic pedagogy: It is not easy to grant rights to minors and listen to their valid demands.
2. for life-oriented learning: Teachers should become innovative and mix different techniques to make learning enjoyable and worthy of hard-work. This means that learning has to be oriented towards lives of students.
3. to remove teachers' own previous biases and background: We all are humans, and carry our 'culture' with us. It is important to unlearn our habits and past karma, at least to certain degree become aware of them.

Several schools embody these principles (and more), JK Foundation runs 'Rishi Valley Schools', then there is 'Geniekids', or 'Srujan Anand'. However, relatively very few schools also go on to train more teachers in a more systematic way. Geniekids, and now Azim Premji University aspires to do this. This has to grow as a movement, spread as a contagion and take roots as a Banyan tree...

Text User Interface using PythonDialog2.7

I have been struggling to have a simple text user interface to display information, and then change the variables/ settings in a program from command line (while running the program). This all with a user menu and other tools.

It now works with PythonDialog 2.7, with only 4 days of complete work ! Here are a couple of screenshots, this is pretty cool.

First the default read of the data file and its parameters: Page 1



I can change the telescope name (to correct value "WSRT")



And once I press RETURN key in the window above, the file is modified:



More code and pics will come soon. This is the time to take this program forward...


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PPV maps details

Equations for the position to velocity (PPV) cubes were computed for a 3-D cube of density with its center (point P) at a given distance (d) from Sun (S). Here, by data convention, Y axis is along the line of site (SP).

For any pixel in the cube (point Q), the line of site SQ would then subtend an angle wrt the center (SP). Let Q' be the projection of Q on X-Y plane, therefore, SQ' has projections of x & y along the two axes.

We can then relate R0 (= CS = distance of Sun from Galaxy's center), R ( = distance CQ'), distance d (SP), and distance d' (SQ') through other quantities and angles (such as longitude= angle CSP).

The projection of relative velocity between S & Q' (due to galactic rotation) is added to the projections of the pixel velocities (vxx, vyy and vzz). Doing this for each pixel creates the cube "v_los". We sort the pixel values falling in different velocity bins, and make velocity maps of width 1 km/s.

Simulations: PPV maps ready

1. Testing with 10x10x20 cubes
2. PPV maps seem to be all right.
3. Will now test on the desktop with full limits put in.
   

MHD Simulations: PPV maps

1. Read density, vxx, vyy,vzz cubes
2. Compute pixel (radial) velocities due galactic rotation
3. Add components of vxx,vyy,vzz from individual pixel values.
   
4. ERROR in writing the files in PPV files

MHD simulations

Miguel has asked for the latest in MHD simulation cubes. so, i have read data with Python, instead of C++. it is a lot faster to write the code and test it. it is also to process and update.

1. Read the density array: split the lines in parts
2. Store the data in density_data[] and reshape it.
3. plot select slices along z, and they are okay.
4. Now read density and velocities.

flucatuation analysis: easyGUI

1. The program menu now has a comprehensive logical structure. There will be text files holding menu data.
2. Program reads menu (text) files and records pulsar parameters as read from the data file. These can be later used for various analyses.
   
3. The main menu leading to average profile in a sub-menu "fold menu". There wiill be plot menu on all such sub-menus.
4. I can now display pulse sequence and zoom in on the chosen sequence area.

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fluctuation analysis: EasyGUI

EasyGUI is god-sent

1. I can display various options in a menu window and ask the user to click and choose.
2. I can choose the data file and read it.
   
3. I can plot the average profile

All this in 3 days work. Python is getting better every day.

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fluctuation spectral analysis

1. We can now read PPR data from Gauribidanur and UTR-2, YEY!!!
2. The problems in LRFLUC are ignored for the moment.
3. We will now consider to write a suitable GUI. The only candidate (simplest to work with, and most basic to be found on all Python installations) is 'easyGUI'

easygui.sourceforge.net
you try it too.


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fluctuation spectral analysis

1. We read Arecibo data
2. We obtain a 2-D zoomed single pulse data as an image (intensity on 3rd axis)
3. We take 2-D FFT
4. We fold the FFT to obtain an equivalent of LRFLUC, and it does not work properly.

fluctuation analysis

1. Average profile zoom works:

we use Matplotlib window (ginput) to read click location.
two clicks are used to mark pulse window.

fluctuation spectrum analysis

I am writing Python code to analyse single-pulse data from radio pulsars. The idea is to make polar maps from fluctuation analysis.

1. I can read data from Arecibo
2. display average profile, and
3. plot single-pulse sequences from the file.
   

Interference Mitigation

Vaishnavi worked on Median-based interference identification and removal. The basic idea is to treat the time-freq map of a given baseline as a 2-D array. See the above diagram for such an example, where the red arrow indicates one interference location.

We then take a small section of this 2-D array (say 32x32 matrix), where the 3rd axis is the intensity (or amplitude). We compute the median and standard deviation of the section (32x32 matrix). We then put some criterion of (median+7*sigma) for genuine data.

Amplitude > (median + 7*sigma) is treated as interference. This appears to identify interference quite reasonably. Check the following image, where black pixel indicates interference. Compare that with the top image, where most of the interference is identified.

There is some data loss due to over-correction. Even so, total data flagged is about 15%, which is quite good.

Fractals and image characterisation

Some links are in order

1. Fractal Dimension: Wikipedia
   
2. A course on Fractals in Yale U
3. A course on Fractal dimension from images: Munich U
4. Fractal Dimension explained
   

So, once you know about fractal dimensions, come to read the stuff on the right (Conti, 2001)


One can treat the image 3-D object. Compute the total number of occupied boxes in X-Y-I dimension box, as a function of size of the box. D = ln(number)/ ln(radius).

It is a little bit more complicated. Check the paper by Conci, a PPT talk can also be found.

How to distinguish between landscape and portrait pictures?

1. Perhaps we can search for a large number of pixels with same natural colors: green, blue and black (shadows). look if a large fraction of pixels contain the same 'Hue' and 'Saturation'.
2. Another try: Look at the Fourier spectra of images, and mark radii of 60%, 90%, 99%, 99.9% power. they should be distinct for landscape images and facial portraits or nearby objects.
   
3. Human objects have a lot more symmetry than the natural objects. In fact, there could be some fractal pattern seen over the different length scales of an image of a natural scenery. Try to capture 'fractal' properties of pixels.

speaking of the last one: one could look at fractal dimension of a picture pixel values. How? Perhaps in the next blog post...

AIPS : initial calib idea

Initial Calibration steps in AIPS

1. INDXR
   
2. First clip source for arbitrarily high points (100 KJy), due to correlator errors. (CLIPM)


3. SETJY to set fluxes of prim calib sources in SU table
   
4. CALIB on prim calib to find antenna solutions, SN tab 1
   
5. CALIB on second calib : SN tab 2
   
6. GETJY to calculate second calib fluxes to second calib (using SN and SU tables)
   
7. CLCAL apply second calib source calibration to target sources: CL tab 2
   

Now, one is free to excise interference. once one has cleaned all the data, return to step 4 above to redo the calibration process (after deleting all SN and CL tables generated above).

fruitful day!

things done:

1. AIPS processing tutorial by Chiranjeevi, it has given a new perspective about GMRT interference removal and calibration.


2. ms for SAX 1808 paper is ready for desh's view.



3. there has been tons of discussion about radio telescope.
   
   
   • the 2-bit samples from 4 elements could combined in one byte per sample rate.
   
   
   • Cross-correlations of each two-element pairs (total 6 pairs, called 'baselines') can be done for N delays. Resultant array of N correlations can be passed over to FFT, resulting in amplitudes and phases for N/2 spectral channels. (This job can be simplified by using tabulated results for all 8-byte combinations).
   
   
   • The value N above is decided by delta-u and delta-v that we can have, using 1/imsize for our map (possibly 1/60 deg).
   
   
   • We need to keep computing such cross-correlations over a large time, integrating amplitude and phases over a large time (about 8 seconds or so).
   
   
   • These integrated amp and phases are the output (dynamic spectrum) of an effective software correlator.
   
   
   
   
   

MHD simulations

I have been working with MHD simulations of my collaborator, Miguel de Avillez. My C++ program still sticks like a sore thumb, even after 6 months of work. There is still a bug in reading/plotting the data. However, I think I am on top of it for now. The next step is to create position to velocity maps for comparison with Miguel's own column density plots.