- print("===> End of evaluation, without deactivating debug\n")
- #
- for i in range(self._parameters["NumberOfRepetition"]):
- print(" %s\n"%("-"*75,))
- if self._parameters["NumberOfRepetition"] > 1:
- print("===> Repetition step number %i on a total of %i\n"%(i+1,self._parameters["NumberOfRepetition"]))
- #
- Yn = Ys[i]
- msgs = ("===> Information after evaluation:\n")
- msgs += ("\n Characteristics of simulated output vector Y=H(X), to compare to others:\n")
- msgs += (" Type...............: %s\n")%type( Yn )
- msgs += (" Lenght of vector...: %i\n")%max(numpy.matrix( Yn ).shape)
- msgs += (" Minimum value......: %."+str(_p)+"e\n")%numpy.min( Yn )
- msgs += (" Maximum value......: %."+str(_p)+"e\n")%numpy.max( Yn )
- msgs += (" Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Yn, dtype=mfp )
- msgs += (" Standard error.....: %."+str(_p)+"e\n")%numpy.std( Yn, dtype=mfp )
- msgs += (" L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Yn )
- print(msgs)
- if self._toStore("SimulatedObservationAtCurrentState"):
- self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(Yn) )
- #
- if self._parameters["NumberOfRepetition"] > 1:
- msgs = (" %s\n"%("-"*75,))
- msgs += ("\n===> Statistical analysis of the outputs obtained through parallel repeated evaluations\n")
- msgs += ("\n (Remark: numbers that are (about) under %.0e represent 0 to machine precision)\n"%mpr)
+ if __r > 1:
+ msgs += (__flech + "End of repeated evaluation, without deactivating debug\n")
+ else:
+ msgs += (__flech + "End of evaluation, without deactivating debug\n")
+ #
+ if __s or self._toStore("SimulatedObservationAtCurrentState"):
+ for i in range(self._parameters["NumberOfRepetition"]):
+ if __s:
+ msgs += ("\n")
+ msgs += (__marge + "%s\n\n"%("-"*75,))
+ if self._parameters["NumberOfRepetition"] > 1:
+ msgs += (__flech + "Repetition step number %i on a total of %i\n"%(i+1,self._parameters["NumberOfRepetition"]))
+ #
+ Yn = Ys[i]
+ if __s:
+ msgs += ("\n")
+ msgs += (__flech + "Information after evaluation:\n")
+ msgs += ("\n")
+ msgs += (__marge + "Characteristics of simulated output vector Y=F(X), to compare to others:\n")
+ msgs += (__marge + " Type...............: %s\n")%type( Yn )
+ msgs += (__marge + " Length of vector...: %i\n")%max(numpy.ravel( Yn ).shape)
+ msgs += (__marge + " Minimum value......: %."+str(__p)+"e\n")%numpy.min( Yn )
+ msgs += (__marge + " Maximum value......: %."+str(__p)+"e\n")%numpy.max( Yn )
+ msgs += (__marge + " Mean of vector.....: %."+str(__p)+"e\n")%numpy.mean( Yn, dtype=mfp )
+ msgs += (__marge + " Standard error.....: %."+str(__p)+"e\n")%numpy.std( Yn, dtype=mfp )
+ msgs += (__marge + " L2 norm of vector..: %."+str(__p)+"e\n")%numpy.linalg.norm( Yn )
+ #
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(Yn) )
+ #
+ msgs += ("\n")
+ msgs += (__marge + "%s\n"%("-"*75,))
+ #
+ if __r > 1:
+ msgs += ("\n")
+ msgs += (__flech + "Launching statistical summary calculation for %i states\n"%__r)
+ msgs += ("\n")
+ msgs += (__marge + "%s\n"%("-"*75,))
+ msgs += ("\n")
+ msgs += (__flech + "Statistical analysis of the outputs obtained through parallel repeated evaluations\n")
+ msgs += ("\n")
+ msgs += (__marge + "(Remark: numbers that are (about) under %.0e represent 0 to machine precision)\n"%mpr)
+ msgs += ("\n")