new results based on today's Korean dataset update

deathcurve.py

@@ -69,8 +69,8 @@
 
 import numpy as np
 import pandas as pd
-import scipy.special as ss
-from ctypes import *
+from scipy.special import erf
+from ctypes import cdll, c_void_p, c_int
 import matplotlib.pyplot as plt
 from os.path import abspath
 from typing import Tuple
@@ -88,10 +88,10 @@ def internalLogS(x, b0, b1, b2, b3, b4, b5):
         return np.log(np.power(10.0, b0) + np.power(10.0, b1) * x + np.power(10.0, b2) * (x ** 2) + np.power(10.0, b3) * (x ** 3) + np.power(10.0, b4) * (x ** 4) + np.power(10.0, b5) * (x ** 5))
 
     def erf(self, x):
-        return ss.erf(bestFit.internalLogL(x, self.b0, self.b1, self.b2, self.b3, self.b4, self.b5, self.b6, self.b7)) * 0.5 + 0.5
+        return erf(bestFit.internalLogL(x, self.b0, self.b1, self.b2, self.b3, self.b4, self.b5, self.b6, self.b7)) * 0.5 + 0.5
 
     def erfFC(self, x):
-        return ss.erf(bestFit.internalLogS(x, self.b0, self.b1, self.b2, self.b3, self.b4, self.b5)) * (0.5 - self.b7) + 0.5 - self.b7 + self.b6
+        return erf(bestFit.internalLogS(x, self.b0, self.b1, self.b2, self.b3, self.b4, self.b5)) * (0.5 - self.b7) + 0.5 - self.b7 + self.b6
 
     def hyperbTan(self, x):
         return np.tanh(bestFit.internalLogL(x, self.b0, self.b1, self.b2, self.b3, self.b4, self.b5, self.b6, self.b7)) * 0.5 + 0.5
@@ -135,10 +135,10 @@ def xOverAbsFC(self, x):
                       'Algebraic function derived from x over (1 + abs(x)) with floor and ceiling']
 
     testFuncsReports = [
-                        '\n\n\tPython\n\tscipy.special.erf(math.log({0:e} + {1:e}*x + {2:e}*(x**2) + {3:e}*(x**3) + {4:e}*(x**4) + {5:e}*(x**5) + {6:e}*(x**6) + {7:e}*(x**7) )) * 0.5 + 0.5\n\n\t' +
+                        '\n\n\tPython\n\tfrom scipy.special import erf\n\terf(math.log({0:e} + {1:e}*x + {2:e}*(x**2) + {3:e}*(x**3) + {4:e}*(x**4) + {5:e}*(x**5) + {6:e}*(x**6) + {7:e}*(x**7) )) * 0.5 + 0.5\n\n\t' +
                         'Microsoft Excel\n\tERF(LN({0:e} + {1:e}*A1 + {2:e}*(A1^2) + {3:e}*(A1^3) + {4:e}*(A1^4) + {5:e}*(A1^5) + {6:e}*(A1^6) + {7:e}*(A1^7) ))/2 + 0.5\n\n\t' +
                         'WolframAlpha\n\tplot | erf(log({0:e} + {1:e} x + {2:e} x^2 + {3:e} x^3 + {4:e} x^4 + {5:e} x^5 + {6:e} x^6 + {7:e} x^7 ))/2 + 0.5 | x = 0 to 100\n\n',
-                        '\n\n\tPython\n\tscipy.special.erf(math.log({0:e} + {1:e}*x + {2:e}*(x**2) + {3:e}*(x**3) + {4:e}*(x**4) + {5:e}*(x**5) )) * (0.5 - {7:e}) + 0.5 - {7:e} + {6:e}\n\n\t' +
+                        '\n\n\tPython\n\tfrom scipy.special import erf\n\terf(math.log({0:e} + {1:e}*x + {2:e}*(x**2) + {3:e}*(x**3) + {4:e}*(x**4) + {5:e}*(x**5) )) * (0.5 - {7:e}) + 0.5 - {7:e} + {6:e}\n\n\t' +
                         'Microsoft Excel\n\tERF(LN({0:e} + {1:e}*A1 + {2:e}*(A1^2) + {3:e}*(A1^3) + {4:e}*(A1^4) + {5:e}*(A1^5) )) * (0.5 - {7:e}) + 0.5 - {7:e} + {6:e}\n\n\t' +
                         'WolframAlpha\n\tplot | erf(log({0:e} + {1:e} x + {2:e} x^2 + {3:e} x^3 + {4:e} x^4 + {5:e} x^5 )) * (0.5 - {7:e}) + 0.5 - {7:e} + {6:e} | x = 0 to 100\n\n',
                         '\n\n\tPython\n\tmath.tanh(math.log({0:e} + {1:e}*x + {2:e}*(x**2) + {3:e}*(x**3) + {4:e}*(x**4) + {5:e}*(x**5) + {6:e}*(x**6) + {7:e}*(x**7) ))/2 + 0.5\n\n\t' +
@@ -166,7 +166,7 @@ def xOverAbsFC(self, x):
                         'Microsoft Excel\n\t(0.5 - {7:e}) * (LN({0:e} + {1:e}*A1 + {2:e}*(A1^2) + {3:e}*(A1^3) + {4:e}*(A1^4) + {5:e}*(A1^5) + {6:e}*(A1^6) + {7:e}*(A1^7) ) )/(1 + ABS(LN({0:e} + {1:e}*A1 + {2:e}*(A1^2) + {3:e}*(A1^3) + {4:e}*(A1^4) + {5:e}*(A1^5) ))) + 0.5 - {7:e} + {6:e}\n\n\t' +
                         'WolframAlpha\n\tplot |  (0.5 - {7:e}) * log({0:e} + {1:e} x + {2:e} x^2 + {3:e} x^3 + {4:e} x^4 + {5:e} x^5 + {6:e} x^6 + {7:e} x^7)/(1 + abs(log({0:e} + {1:e} x + {2:e} x^2 + {3:e} x^3 + {4:e} x^4 + {5:e} x^5 ))) + 0.5 - {7:e} + {6:e} | x = 0 to 100\n\n' ]
 
-    def function(self, x):
+    def function(self, x) -> float:
         return self.testFuncs[self.best](self, x)
 
     def __init__(self, parameters: np.ndarray, functionNumber: int):
@@ -219,7 +219,7 @@ def reportModel(models: Tuple[bestFit, bestFit]) -> None:
 
     print(text_output)
 
-    #Saving the report text file in the same directory
+    # Saving the report text file in the same directory
     with open('report.txt', 'w') as f:
         f.write(text_output)
 
@@ -263,6 +263,6 @@ def plotModel(models: Tuple[bestFit, bestFit]) -> None:
         subpl.set_title(models[0].bestName)
         subpl.grid()
 
-    #Saving the graph image file in the same directory
+    # Saving the graph image file in the same directory
     fig.savefig("result.png")
     plt.show()

report.txt

@@ -2,11 +2,12 @@
 Best fit is:
 
 	Python
-	scipy.special.erf(math.log(1.581612e-31 + 1.705297e-03*x + 4.506091e-05*(x**2) + 8.379151e-20*(x**3) + 4.658006e-22*(x**4) + 7.664785e-104*(x**5) + 3.756644e-108*(x**6) + 3.756644e-113*(x**7) )) * 0.5 + 0.5
+	from scipy.special import erf
+	erf(math.log(1.623678e-14 + 3.139786e-03*x + 4.001290e-05*(x**2) + 1.648922e-08*(x**3) + 4.791815e-22*(x**4) + 1.750653e-132*(x**5) + 8.580251e-137*(x**6) + 8.580251e-142*(x**7) )) * 0.5 + 0.5
 
 	Microsoft Excel
-	ERF(LN(1.581612e-31 + 1.705297e-03*A1 + 4.506091e-05*(A1^2) + 8.379151e-20*(A1^3) + 4.658006e-22*(A1^4) + 7.664785e-104*(A1^5) + 3.756644e-108*(A1^6) + 3.756644e-113*(A1^7) ))/2 + 0.5
+	ERF(LN(1.623678e-14 + 3.139786e-03*A1 + 4.001290e-05*(A1^2) + 1.648922e-08*(A1^3) + 4.791815e-22*(A1^4) + 1.750653e-132*(A1^5) + 8.580251e-137*(A1^6) + 8.580251e-142*(A1^7) ))/2 + 0.5
 
 	WolframAlpha
-	plot | erf(log(1.581612e-31 + 1.705297e-03 x + 4.506091e-05 x^2 + 8.379151e-20 x^3 + 4.658006e-22 x^4 + 7.664785e-104 x^5 + 3.756644e-108 x^6 + 3.756644e-113 x^7 ))/2 + 0.5 | x = 0 to 100
+	plot | erf(log(1.623678e-14 + 3.139786e-03 x + 4.001290e-05 x^2 + 1.648922e-08 x^3 + 4.791815e-22 x^4 + 1.750653e-132 x^5 + 8.580251e-137 x^6 + 8.580251e-142 x^7 ))/2 + 0.5 | x = 0 to 100