jobs for week 11/05

[Emiree123]

add a progress line to say __% completed in accumulate function vectorisation to speed up - instead of for loops masking

Problem A - vectorise the pre-processing

step 1

• read the numpy docs for reshape
• currently rcvData is 3D [Ntx, Nrx, Nt]. It needs to be 2D [Ntx*Nrx, Nt] for vectorisation.

• step 2: new tukey window approach

• create a normal 1D tukey window
• read the numpy docs for tile
• stack the windows into a replicated 2D array (needs to be same size as reshaped rcvData)
• use np,multiply to apply the window to the data

step 3: new envelope approach

• read the docs for hilbert
• pass the entire waveform_win into hilbert to calculate the envelopes all at once

[Emiree123]

turn the 2d matrix back into 3d at the end of the processing function give the new processed data, that has been vectorised into the transmitter/receiver loop (the loop with 4)

[Emiree123]

-change to vectorised function in the tukeywindowCOPY.ipynb file -make new scripts without extra functions/plotting etc -have the big loop (of 4) in the hierarchy of tx, rx, pixelrow, pixelcol, in that order. This ensures that the first transmitter image can first be generated over the loop so that the imshow image will be clearer as the loop goes on

Step 1

• X and Y (image grid) are 2D, these need to be reshaped to 1D

Step 2

• calculate a distance map between the detectors and the image pixels
• `d = sqrt( (Xd - Xp)^2 + (Yd - Yp)^2 )
• Xd is Detector X coordinates
• Xp is image Pixel x coord etc. d will be a 2D 14400 x 256 array

  Step 3 - convert to a time map

[Emiree123]

Parallel computing: -parfor -joblib