# Minimize the Chi Sq
migrad.minimize()
# Print the resulting parameters
print f.getParameters()
# Add the function to the display of the data
fl = Linear()
disp.addFunction(f)
# Add the function to the factory so it can be used from the GUI
# One might just do this instead of using the minimizers directly
from hippo import FunctionFactory
ff = FunctionFactory.instance()
ff.add(f)
# Create an DataArray with columns not in order expected by the fitter
da2 = DataArray('NTuple') # use NTuple to store data
da2.register('randomized line 2') # name the data source
# Fill the contents by adding named columns in the order expected by
# the fitter (more later)
da2['y'] = y
da2['x'] = x
da2['yerr'] = yerr
da2['xerr'] = xerr
def register(self):
from hippo import FunctionFactory
FunctionFactory.instance().add(self)
# Minimize the Chi Sq migrad.minimize() # Print the resulting parameters print f.getParameters() # Add the function to the display of the data fl = Linear () disp.addFunction ( f ) # Add the function to the factory so it can be used from the GUI # One might just do this instead of using the minimizers directly from hippo import FunctionFactory ff = FunctionFactory.instance() ff.add ( f ) # Create an DataArray with columns not in order expected by the fitter da2 = DataArray ( 'NTuple' ) # use NTuple to store data da2.register ( 'randomized line 2' ) # name the data source # Fill the contents by adding named columns in the order expected by # the fitter (more later) da2['y'] = y da2['x'] = x da2['yerr'] = yerr da2['xerr'] = xerr
def register(self):
from hippo import FunctionFactory
FunctionFactory.instance().add(self)
