Provide option to distribute the detectors along the tube (easy to test)

[Gesner Passos]

Now, the distribution of the detectors along the tube follows a quadratic fit function.

Extend this to allow the user to choose linear and 3rd order polynomial.

[Gesner Passos]

Change Quadratic to Polynomial n2

Changint the fit curve from quadratic to polynomial with n=2 produces exactly the same result. This was seen by saving the calibtable to a file

tb = mtd['CalibTable']
tofile = open('/tmp/quadraticcalibrated.txt','w')
for row in tb:
	print >> tofile, row['Detector ID'], row['Detector Position']
tofile.close()

and comparing the files for the quadratic and for the polynomial n=2, and both files are identical.

re #7501

Changeset: 86e410d05e8e349d12f3fabd5ac34ec9da2def96

[Gesner Passos]

Implement different polynomial fit

It nows allow a polynomial of any order.

re #7501

Changeset: d50c85a7965de4124e7b2edc17aa7dad2c314a09

[Gesner Passos]

Extend to allow fit with polynomial 1,2,3

re #7501

Changeset: 065b9fc2ac3bcb9064af2f810ee0c767ea5aa68f

[Gesner Passos]

Change parameter name from fitPolin to fitPolyn

Correct mispelling and update the documentation removing the 'not yet implemented'

re #7501

Changeset: ed33dedb5cee3bd85e90902aff8a4f943193596b

[Gesner Passos]

Tester:

the attached file is a slightly different example taken from the TubeCalibDemoWish_Simple.py, but using the fitPolyn argument.

You should be able to execute this example changing fitPolyn to 1, 2 or 3. The results will be just slightly different (maybe even hard to see by eyes), when viewing in the instrument view. But, if you compare the value of the y from the calibrationTable from them, you will see that they differ, but not so much.

From my point of view, you can stop on the observation that it work for the 3 values. But, if you are willing to do further investigation on the quality of the answer... (I've done)

I've also attached two pictures that compare the difference of the position of the detectors inside the tube. I've plotted the max difference, the minimum difference, the average and the standard deviation of the difference of the positions. For both, they are around 0, but, while there is not great improvement from 2 to 3, there is a considerable improvement from 1 to 2.

But, it is something that you have to judge.

BTW, if you want to get all the values for the y position from the calibration table, you may use:

v1 = numpy.array([r['Detector Position'].getY() for r in mtd['calibTable']])
v1_r = v1.reshape(152,512) # than you access v1_r[0] the positions for every detector in tube 0.

[Karl Palmen]

Testing

Testing showed a good result for polynomials of degree 2 and 3.

One concern was the slow performance of showing the instrument. I'll investigate this further possibly in unscripted testing.

Code Review

The code seems to be well structured. Some comments need updating and English needs improving.

[Karl Palmen]

Merge remote-tracking branch 'origin/feature/7501_implement_polin'

[Karl Palmen]

The slow performance of show instrument may be just because I had built in 'Debug' instead of 'Release'.

[Stuart Campbell]

This ticket has been transferred to github issue ​8346