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.