detector_parametrisations_old/analysis_efficiency.csv

+logE,any3_gtr3,3phased_2support_gtr3,3power_gtr3,3power_2support_gtr3
+14.7425,0.0027,-0.06004,-0.07107,-0.00011
+14.7532,0.00414,-0.05861,-0.06931,-0.0001
+14.9678,0.03396,-0.03227,-0.03886,1E-05
+15.0107,0.04048,-0.02733,-0.0338,5E-06
+15.1717,0.06801,-0.00937,-0.01774,-5E-05
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+15.397,0.1205,0.01503,-0.00208,0.00075
+15.5043,0.15096,0.0266,0.00313,0.00179
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+15.633,0.18784,0.04061,0.00805,0.00371
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+16.2983,0.31136,0.1082,0.03877,0.02294
+16.309,0.31451,0.11043,0.04017,0.02391
+16.3305,0.32118,0.11521,0.04325,0.02606
+16.3948,0.34242,0.13221,0.05461,0.03402
+16.4807,0.36859,0.15774,0.07268,0.04703
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+19.2811,0.85271,0.79587,0.66037,0.6507
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+19.4635,0.86131,0.80983,0.67352,0.66219
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+19.6996,0.87292,0.82532,0.6907,0.67928
+19.7318,0.87462,0.82768,0.69312,0.68183
+19.7639,0.87633,0.83005,0.69552,0.68435

detector_parametrisations_old/effective_area.csv

+x 50 60 70 80
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+8.2549e+18 1.1396 0.8782 0.9657 -0.0776
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+9.85811e+19 10.9269 11.2378 11.2679 10.8373

detector_parametrisations_old/efficiency_fits.json

+{
+    "3phased_2support_gtr3": [
+        -0.06101332755522515, 
+        0.8906299050281568, 
+        8.50399113140374, 
+        0.9359124867145965
+    ], 
+    "3power_2support_gtr3": [
+        -0.09234690288543035, 
+        0.7383663079013153, 
+        8.723278791843732, 
+        0.8570357474080368
+    ], 
+    "3power_gtr3": [
+        -0.1648019401860514, 
+        0.7685389747837494, 
+        8.469036591701329, 
+        1.0351725210942482
+    ], 
+    "any3_gtr3": [
+        -0.19848464922474787, 
+        0.9254389841644048, 
+        7.423472943972761, 
+        1.2133976987739408
+    ]
+}
\ No newline at end of file

detector_parametrisations_old/gen2_radio_2020.py

+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import interpolate
+
+class efficiency:
+    def __init__(self, filename='analysis_efficiency.csv'):
+        self.df_eff = pd.read_csv(filename, sep=",")
+        self.logE = self.df_eff.logE
+    def efficiency(self, xvals, selection='any3_gtr3'):
+        self.eff = self.df_eff[selection]
+        interp_function = interpolate.interp1d(self.logE, self.eff, kind='linear', bounds_error=False)
+        xvals[xvals<min(self.logE)] = min(self.logE)
+        xvals[xvals>max(self.logE)] = max(self.logE)
+        vals = interp_function(xvals)
+        vals[vals<0] = 0
+        return vals
+    def keys(self):
+        return list(self.df_eff.columns[1:])
+

detector_parametrisations_old/gen2_radio_overview.ipynb

+%% Cell type:markdown id: tags:
+
+# Gen2 analysis for radio - An overview
+JvS framework for sensitivity estimates for Gen2 is based around a 4 dimensional effective volume tuple based on
+- true energy, $E_{\nu}$
+- true cosine zenith angle $\cos(\theta_\nu)$
+- reco energy, $E_\text{reco}$
+- reco space angle between true and reco direction ($\psi$)
+
+
+## efficiency / effective volume / analysis classes
+For radio, we might want to take several efficiencies into account:
+- analysis efficiency: which fraction of triggered events (a function on $E_{\nu}$) can be reconstructed and analysed?
+- station coincidence: naively, effective area of an array is just single station effective area, multiplied by number of stations. Depending on how close stations are put together, the overlap might be significant. The station coincidence could be accounted for, to make it more realistic.
+
+## parametrisations
+- angular resolution (**2d?, cos(zenith) cut?**)
+- energy resolution
+
+## results
+- sensitivity as function of number of stations
+- sensitivity vs declination
+- diffuse sensitivity
+- pointsource sensitivity
+
+%% Cell type:code id: tags:
+
+``` python
+# classification efficiency
+import numpy as np
+import NuRadioMC
+from NuRadioReco.utilities import units
+```
+
+%% Cell type:code id: tags:
+
+``` python
+
+energies = np.logspace(0,10,301)*units.GeV
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def sigmoid(x,a,b,c,d,m):
+    #modified, such that turnover is middle between min and max
+    return ((a-d) / (1 + (np.exp(b*(np.log10(x)-c)))**m)) + d
+```
+
+%% Cell type:code id: tags:
+
+``` python
+ce = sigmoid(energies/units.GeV,
+        1, #min
+        5, #1/width
+        6.0, #turnover / TeV
+        0, #max
+        1)
+
+ce2 = sigmoid(energies/units.GeV,
+        0.2, #min
+        0.5, #1/width
+        0.0, #turnover / TeV
+        1, #max
+        1)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import matplotlib.pyplot as plt
+```
+
+%% Cell type:code id: tags:
+
+``` python
+plt.plot(energies, ce )
+plt.plot(energies, ce2 )
+plt.grid()
+plt.semilogx()
+```
+
+%% Output
+
+    []
+
+
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```