Gleiches Skript (kafe_fit-from-file.py)
mit anderen Eingabedaten
(Mess-)Werte mit Unsicherheit und
Kovarianzmatrix (angegeben als
Wurzel aus den Nebendiagonalelementen)
aveCorrDat.fit
# Simulated correlated measurements
# - common error between pairs of measurements
# - common error of all measurements
# ----------------------------------------------------------------------
*BASENAME averageCorrDat
*TITLE simulated correlated measurements
*xLabel number of measurement
*yLabel values
#*xData # commented out, as not needed for averaging
*yData_SCOV
# val err syst sqrt. of cov. mat. elements
0.82 0.10 0.15
0.81 0.10 0.15 0.15
1.32 0.10 0.15 0. 0.
1.44 0.10 0.15 0. 0. 0.15
0.93 0.10 0.15 0. 0. 0. 0.
0.99 0.10 0.15 0. 0. 0. 0. 0.15
# common (correlated) error for all
*yAbsCor 0.05
# python code of fit function
*FITLABEL Average
*FitFunction
@ASCII(expression='average')
@LaTeX(name='f', parameter_names=('m',), expression='m')
@FitFunction
def fitf(x, m=1.): # fit an average
~~~~return m
*InitialParameters
1. 1.