

In [1]:

    
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

from electronfactors import (
    new_reading, calc_and_display, energy_to_reference_depth, 
    initialise)



In [2]:

    
data = dict()

6 MeV



In [3]:

    
energy_to_reference_depth(6)









    Out[3]:





array([13])



In [4]:

    
# Standard insert 10app 6MeV 150MU
np.mean([1.519, 1.519])









    Out[4]:





1.5189999999999999



In [5]:

    
# Standard insert 6app 6MeV 150MU
np.mean([1.342, 1.341])









    Out[5]:





1.3414999999999999



In [6]:

    
key = 'arbit'

data = initialise(
    key=key, data=data,
    reference=1.341,
    energy=6
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.256, 1.256]),
    depth=12
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.252, 1.253]),
    depth=11
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.255, 1.253]),
    depth=13
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.274, 1.274]),
    depth=13
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.274, 1.273]),
    depth=12
)

data[key]['factor'] = calc_and_display(**data[key])









    












    



Cutout factor = 0.950 | -5.0%
Inverse factor = 1.053 | 5.3%



In [7]:

    
# Standard insert 6app 6MeV 150MU
np.mean([1.336, 1.336])









    Out[7]:





1.3360000000000001



In [8]:

    
key = 'arbit'

data = initialise(
    key=key, data=data,
    reference=1.336,
    energy=6
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.270, 1.270]),
    depth=13
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.250]),
    depth=13
)

data[key]['factor'] = calc_and_display(**data[key])









    












    



Cutout factor = 0.951 | -4.9%
Inverse factor = 1.052 | 5.2%



In [9]:

    
# Standard insert 6app 6MeV 150MU
np.mean([1.336, 1.338])









    Out[9]:





1.3370000000000002



In [10]:

    
1.336/1.270









    Out[10]:





1.051968503937008

9 MeV



In [11]:

    
energy_to_reference_depth(9)









    Out[11]:





array([20])



In [12]:

    
# Standard insert 6 app, 9 MeV, 150 MU
np.mean([1.416, 1.418, 1.418])









    Out[12]:





1.4173333333333333



In [13]:

    
key = 'arbit'

data = initialise(
    key=key, data=data,
    reference=1.417,
    energy=9
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.419, 1.420]),
    depth=20
)

data[key]['factor'] = calc_and_display(**data[key])









    



Cutout factor = 1.002 | 0.2%
Inverse factor = 0.998 | -0.2%



In [14]:

    
# Standard insert 6 app, 9 MeV, 150 MU
np.mean([1.416, 1.417])









    Out[14]:





1.4165000000000001



In [15]:

    
1.417 / 1.4195









    Out[15]:





0.9982388164846777



In [ ]: