

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()

12 MeV



In [3]:

    
energy_to_reference_depth(12)









    Out[3]:





array([25])



In [4]:

    
# Standard insert 10app 12MeV
np.mean([1.031, 1.031])









    Out[4]:





1.0309999999999999



In [5]:

    
key = 'P3_12MeV'

data = initialise(
    key=key, data=data,
    reference=1.031,
    energy=12    
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.030, 1.031, 1.030],
    depth=25    
)

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









    



Cutout factor = 0.999 | -0.1%
Inverse factor = 1.001 | 0.1%



In [6]:

    
# Standard insert 10app 12MeV
np.mean([1.031])









    Out[6]:





1.0309999999999999

6 MeV



In [7]:

    
energy_to_reference_depth(6)









    Out[7]:





array([13])



In [8]:

    
# Standard insert 10app 6MeV
np.mean([0.9976, 0.9996, 0.999])









    Out[8]:





0.99873333333333336



In [9]:

    
key = 'P3_6MeV'

data = initialise(
    key=key, data=data,
    reference=0.999,
    energy=6    
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.001, 1.003, 1.001],
    depth=13    
)

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









    



Cutout factor = 1.003 | 0.3%
Inverse factor = 0.997 | -0.3%



In [10]:

    
# Standard insert 10app 6MeV
np.mean([0.9986])









    Out[10]:





0.99860000000000004

9 MeV



In [11]:

    
energy_to_reference_depth(9)









    Out[11]:





array([20])



In [12]:

    
# Standard insert 10app 9MeV
np.mean([1.011, 1.012, 1.012])









    Out[12]:





1.0116666666666665



In [13]:

    
key = 'P3_9MeV'

data = initialise(
    key=key, data=data,
    reference=1.0115,
    energy=9  
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.013, 1.013],
    depth=20    
)

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









    



Cutout factor = 1.001 | 0.1%
Inverse factor = 0.999 | -0.1%



In [14]:

    
# Standard insert 10app 9MeV
np.mean([1.011, 1.012])









    Out[14]:





1.0114999999999998

15 MeV



In [15]:

    
energy_to_reference_depth(15)









    Out[15]:





array([27])



In [16]:

    
# Standard insert 10app 15MeV
np.mean([1.046, 1.046])









    Out[16]:





1.046



In [17]:

    
key = 'P3_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.046,
    energy=15  
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.043, 1.044, 1.043],
    depth=30    
)

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









    



Cutout factor = 1.003 | 0.3%
Inverse factor = 0.997 | -0.3%



In [18]:

    
# Standard insert 10app 15MeV
np.mean([1.046])









    Out[18]:





1.046

18 MeV



In [19]:

    
energy_to_reference_depth(18)









    Out[19]:





array([30])



In [20]:

    
key = 'P3_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.0755,
    energy=15  
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.073, 1.075, 1.076, 1.077, 1.075],
    depth=30    
)

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









    



Cutout factor = 1.006 | 0.6%
Inverse factor = 0.994 | -0.6%



In [21]:

    
# Standard insert 10app 18MeV
np.mean([1.075, 1.076])









    Out[21]:





1.0754999999999999