

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()

15 & 18 MeV



In [3]:

    
corrrection_ion = 0.991 / 0.983



In [ ]:



In [4]:

    
energy_to_reference_depth(15)









    Out[4]:





array([27])



In [5]:

    
energy_to_reference_depth(18)









    Out[5]:





array([30])



In [6]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.065, 1.065, 1.066])









    Out[6]:





1.0653333333333332



In [7]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.096, 1.096, 1.098, 1.098, 1.099, 1.099, 1.099])









    Out[7]:





1.0978571428571429



In [8]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.067, 1.068, 1.069])









    Out[8]:





1.0679999999999998



In [9]:

    
# Standard insert 10app 15MeV 1000MU
10.67 / 10









    Out[9]:





1.067



In [10]:

    
# Standard insert 10app 18MeV 1000MU
np.mean([11.04, 11.06, 11.07]) / 10









    Out[10]:





1.1056666666666666



In [11]:

    
# Standard insert 10app 15MeV 1000MU
np.mean([10.68, 10.69]) / 10









    Out[11]:





1.0684999999999998



In [12]:

    
# Standard insert 10app 18MeV 1000MU
np.mean([11.09, 11.10]) / 10









    Out[12]:





1.1094999999999999



In [13]:

    
key = '9.5cm_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.069 * corrrection_ion,
    energy=15
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.078, 1.079, 1.080]) * corrrection_ion,
    depth=27 #corrected from 30
)

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









    



Cutout factor = 1.009 | 0.9%
Inverse factor = 0.991 | -0.9%



In [14]:

    
key = '9.5cm_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.103,
    energy=18
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.110, 1.112, 1.112],
    depth=30
)

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









    



Cutout factor = 1.008 | 0.8%
Inverse factor = 0.993 | -0.7%



In [15]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.103, 1.103])









    Out[15]:





1.103



In [16]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.069, 1.071])









    Out[16]:





1.0699999999999998



In [17]:

    
key = '8.3cm_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.070 * corrrection_ion,
    energy=15
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.075, 1.076]) * corrrection_ion,
    depth=27 # corrected from 30
)

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









    



Cutout factor = 1.005 | 0.5%
Inverse factor = 0.995 | -0.5%



In [18]:

    
key = '8.3cm_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.1025,
    energy=18
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.105, 1.107],
    depth=30
)

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









    



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



In [19]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.102, 1.103])









    Out[19]:





1.1025



In [20]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.069, 1.070])









    Out[20]:





1.0695000000000001



In [21]:

    
key = '8.5x10.9cm_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.0695 * corrrection_ion,
    energy=15
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.074, 1.075]) * corrrection_ion,
    depth=27 # Corrected from 30
)

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









    



Cutout factor = 1.005 | 0.5%
Inverse factor = 0.995 | -0.5%



In [22]:

    
key = '8.5x10.9cm_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.102,
    energy=18
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.106, 1.107],
    depth=30
)

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









    



Cutout factor = 1.004 | 0.4%
Inverse factor = 0.996 | -0.4%



In [23]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.101, 1.103])









    Out[23]:





1.1019999999999999



In [24]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.070, 1.070])









    Out[24]:





1.0700000000000001



In [25]:

    
key = '5cm_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.0675 * corrrection_ion,
    energy=15
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.027, 1.028],
    depth=30
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.036, 1.036],
    depth=29
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.041, 1.041],
    depth=28
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.045, 1.045],
    depth=27
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.048, 1.048],
    depth=26
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.050, 1.051],
    depth=25
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.047, 1.047],
    depth=26
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.053],
    depth=24
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.056],
    depth=23
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.058, 1.059],
    depth=22
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.058, 1.059],
    depth=22
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.042, 1.043],
    depth=27
)

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









    












    



Cutout factor = 0.975 | -2.5%
Inverse factor = 1.025 | 2.5%



In [26]:

    
# 1.067, 1.068



In [27]:

    
key = '5cm_18MeV_depth_search'

data = initialise(
    key=key, data=data,
    reference=1.102,
    energy=18
)

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

data = new_reading(
    key=key, data=data,
    ionisation=[1.077, 1.079],
    depth=29
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.082, 1.083],
    depth=28
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.084, 1.085],
    depth=27
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.086],
    depth=26
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.092],
    depth=22
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.081, 1.082],
    depth=27
)

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









    












    



Cutout factor = 0.980 | -2.0%
Inverse factor = 1.021 | 2.1%



In [28]:

    
key = '5cm_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.096,
    energy=18
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.081, 1.082, 1.080],
    depth=27
)

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









    



Cutout factor = 0.982 | -1.8%
Inverse factor = 1.019 | 1.9%



In [29]:

    
# 18MeV 1.095 1.097



In [30]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.093, 1.095])









    Out[30]:





1.0939999999999999



In [31]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.068, 1.068])









    Out[31]:





1.0680000000000001



In [32]:

    
key = '6.1cm_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.066 * corrrection_ion,
    energy=18
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.056, 1.058],
    depth=27
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.058, 1.058],
    depth=26
)

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









    












    



Cutout factor = 0.979 | -2.1%
Inverse factor = 1.022 | 2.2%



In [33]:

    
key = '6.1cm_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.095,
    energy=18
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.086, 1.087],
    depth=27
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.087, 1.089],
    depth=26
)

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









    












    



Cutout factor = 0.988 | -1.2%
Inverse factor = 1.013 | 1.3%



In [34]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.066, 1.066])









    Out[34]:





1.0660000000000001



In [35]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.094, 1.096])









    Out[35]:





1.0950000000000002



In [36]:

    
key = '5.3x12.5cm_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.0935,
    energy=18
)

# data = new_reading(
#     key=key, data=data,
#     ionisation=[1.091, 1.094, 1.095],
#     depth=27
# )

data = new_reading(
    key=key, data=data,
    ionisation=[1.090],
    depth=26
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.084],
    depth=30
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.089, 1.091],
    depth=28
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.092, 1.094],
    depth=27
)

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









    












    



Cutout factor = 0.995 | -0.5%
Inverse factor = 1.005 | 0.5%



In [ ]:



In [37]:

    
key = '5.3x12.5cm_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.065 * corrrection_ion,
    energy=15
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.065, 1.065],
    depth=27
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.067, 1.068],
    depth=26
)

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









    












    



Cutout factor = 0.992 | -0.8%
Inverse factor = 1.008 | 0.8%



In [38]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.064, 1.066])









    Out[38]:





1.0649999999999999



In [39]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.092, 1.095])









    Out[39]:





1.0935000000000001



In [40]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.065, 1.064])









    Out[40]:





1.0645



In [41]:

    
key = '6.7x12cm_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.064 * corrrection_ion,
    energy=15
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.074, 1.074],
    depth=30
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.079, 1.080],
    depth=29
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.082, 1.082]) * 99.1/98.8,
    depth=27 # Corrected from 28
)

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









    












    



Cutout factor = 1.012 | 1.2%
Inverse factor = 0.988 | -1.2%



In [42]:

    
key = '6.7x12cm_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.0935,
    energy=18
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.105, 1.106],
    depth=30
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.108, 1.109],
    depth=29
)

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









    












    



Cutout factor = 1.012 | 1.2%
Inverse factor = 0.988 | -1.2%



In [43]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.063, 1.065])









    Out[43]:





1.0640000000000001



In [44]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.094, 1.095])









    Out[44]:





1.0945



In [45]:

    
key = '7.25cm_18MeV'

data = initialise(
    key=key, data=data,
    reference=1.0945,
    energy=18
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.095, 1.096],
    depth=30
)

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









    



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



In [46]:

    
key = '7.25cm_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.065 * corrrection_ion,
    energy=15
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.064, 1.065],
    depth=30
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.068, 1.070],
    depth=29
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.073, 1.074])* 99.1/98.8,
    depth=27 # Corrected from 28
)

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









    












    



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



In [47]:

    
# Standard insert 10app 15MeV 100MU
np.mean([1.065, 1.065])









    Out[47]:





1.0649999999999999



In [48]:

    
# Standard insert 10app 18MeV 100MU
np.mean([1.094, 1.095])









    Out[48]:





1.0945



In [49]:

    
key = 'standard_insert_15MeV'

data = initialise(
    key=key, data=data,
    reference=1.065 * corrrection_ion,
    energy=15
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.065, 1.065],
    depth=30
)

data = new_reading(
    key=key, data=data,
    ionisation=[1.068, 1.068],
    depth=29
)

data = new_reading(
    key=key, data=data,
    ionisation=np.array([1.072, 1.073]) * 99.1/98.8,
    depth=27 # Corrected from 28
)

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









    












    



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



In [50]:

    
np.array([1.072, 1.073]) * 99.1/98.8









    Out[50]:





array([ 1.07525506,  1.0762581 ])



In [51]:

    
1.065 * corrrection_ion









    Out[51]:





1.0736673448626652