In [1]:
%pylab

from IPython.display import Image


Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib

Details

In order to work out what would be a valid choice for equivalent oval weighting a variety of weights were chosen for two differing cutouts. Turns out a weighting factor of 1 (no weighting factor) worked the best for both the "bloated" cutout and the "indent" cutout, which is neat. Factor differences between the cutouts and their equivalent oval (with weight 1) was 0.0% for the "bloated" cutout and 0.2% for the "indent" cutout.

Other weights were tested, and differences were noted, for example an equivalent oval with a weight of 3 for the "indent cutout" had a factor that was 0.6% different.

An example of these two cutouts and one particular oval weighting is given in the next two images.


In [2]:
Image("indent.png")


Out[2]:

In [3]:
Image("bloated.png")


Out[3]:

Measurements

These measurements were done on Harry 2694, with a Markus chamber set to +300 V. The sensitivity was $1.398 \times 10^9$. All measurements were done at 100 SSD with a 12 MeV beam and a $10\times10$ cm applicator. Below are the readings recorded in chronological order.

Readings


In [4]:
readings = {}
readings['std_ins_00'] = mean([1.546,1.547,1.548])
readings['cutout_007_00'] = mean([1.538,1.537,1.537])
readings['cutout_007_weight_1.0'] = mean([1.537,1.537,1.535])
readings['cutout_007_weight_0.5'] = mean([1.545,1.543,1.545])
readings['cutout_007_weight_3.0'] = mean([1.538,1.538,1.538])
readings['cutout_007_01'] = mean([1.537,1.537,1.537])
readings['std_ins_01'] = mean([1.548,1.548,1.548])

readings['std_ins_02'] = mean([1.541,1.542,1.542])
readings['cutout_035_posMid'] = mean([1.531,1.531,1.531])
readings['cutout_035_posOffcentre'] = mean([1.530,1.531,1.531])
readings['cutout_035_weight_3.0'] = mean([1.517,1.517,1.517])
readings['cutout_035_weight_1.0'] = mean([1.527,1.527,1.527])
readings['std_ins_03'] = mean([1.542,1.542])

Factors


In [5]:
factor = {}

factor['007'] = (mean([readings['cutout_007_00'],readings['cutout_007_01']]) /
                 mean([readings['std_ins_01'],readings['std_ins_00']]))
print("Cutout factor 007 = %0.3f" %(factor['007']))

factor['035'] = (mean([readings['cutout_035_posMid'],readings['cutout_035_posOffcentre']]) /
                 mean([readings['std_ins_02'],readings['std_ins_03']]))
print("Cutout factor 035 = %0.3f" %(factor['035']))


Cutout factor 007 = 0.993
Cutout factor 035 = 0.993

Results

Cutout 007


In [6]:
oval_factors = {}
oval_factors['cutout_007_weight_1.0'] = readings['cutout_007_weight_1.0'] / mean([readings['std_ins_01'],readings['std_ins_00']])
oval_factors['cutout_007_weight_0.5'] = readings['cutout_007_weight_0.5'] / mean([readings['std_ins_01'],readings['std_ins_00']])
oval_factors['cutout_007_weight_3.0'] = readings['cutout_007_weight_3.0'] / mean([readings['std_ins_01'],readings['std_ins_00']])

In [7]:
diff = 1 - oval_factors['cutout_007_weight_1.0'] / factor['007']
print("Weight 1 diff = %0.1f%%" %(diff*100))


Weight 1 diff = 0.1%

In [8]:
diff = 1 - oval_factors['cutout_007_weight_0.5'] / factor['007']
print("Weight 0.5 diff = %0.1f%%" %(diff*100))


Weight 0.5 diff = -0.5%

In [9]:
diff = 1 - oval_factors['cutout_007_weight_3.0'] / factor['007']
print("Weight 3 diff = %0.1f%%" %(diff*100))


Weight 3 diff = -0.1%

Cutout 035


In [10]:
oval_factors['cutout_035_weight_1.0'] = readings['cutout_035_weight_1.0'] / mean([readings['std_ins_02'],readings['std_ins_03']])
oval_factors['cutout_035_weight_3.0'] = readings['cutout_035_weight_3.0'] / mean([readings['std_ins_02'],readings['std_ins_03']])

In [11]:
diff = 1 - oval_factors['cutout_035_weight_1.0'] / factor['035']
print("Weight 1 diff = %0.1f%%" %(diff*100))


Weight 1 diff = 0.3%

In [12]:
diff = 1 - oval_factors['cutout_035_weight_3.0'] / factor['035']
print("Weight 3 diff = %0.1f%%" %(diff*100))


Weight 3 diff = 0.9%