In [1]:
%pylab
from IPython.display import Image
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")
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In [3]:
Image("bloated.png")
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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.
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])
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']))
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']])
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diff = 1 - oval_factors['cutout_007_weight_1.0'] / factor['007']
print("Weight 1 diff = %0.1f%%" %(diff*100))
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diff = 1 - oval_factors['cutout_007_weight_0.5'] / factor['007']
print("Weight 0.5 diff = %0.1f%%" %(diff*100))
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diff = 1 - oval_factors['cutout_007_weight_3.0'] / factor['007']
print("Weight 3 diff = %0.1f%%" %(diff*100))
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']])
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diff = 1 - oval_factors['cutout_035_weight_1.0'] / factor['035']
print("Weight 1 diff = %0.1f%%" %(diff*100))
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diff = 1 - oval_factors['cutout_035_weight_3.0'] / factor['035']
print("Weight 3 diff = %0.1f%%" %(diff*100))