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simul_fit_parents.py
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simul_fit_parents.py
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import numpy as np
from iminuit import Minuit
from fit_parents import mass_bounds, amp_bounds, const_bounds
class sharedmass_amp(object):
"""Parent class for functions which take a mass and an amplitude"""
def __init__(self):
self.starting_guess = self.thisguess
self.bounds = [mass_bounds, amp_bounds, amp_bounds]
self.parameter_names = ["mass", "amp1", "amp2"]
self.subtract = False
self.stride = 1
def thisguess(self, cor, period, *args):
dt = 1
ave = cor.average_sub_vev()
emass = cor.periodic_effective_mass(dt, fast=True, period=period)
mass_guess = np.mean([emass[i[1]-dt-1] for i in self.indexes])
mid1 = (self.indexes[0][0]+self.indexes[0][1])/2
mid2 = (self.indexes[1][0]+self.indexes[1][1])/2
rmid1 = (self.ranges[0][0]+self.ranges[0][1])/2
rmid2 = (self.ranges[1][0]+self.ranges[1][1])/2
amp_guess1 = ave[mid1]*np.exp(mass_guess*(rmid1))
amp_guess2 = ave[mid2]*np.exp(mass_guess*(rmid2))
return [mass_guess, amp_guess1, amp_guess2]
def my_cov_fun(self, mass, amp1, amp2):
vect = self.aoc - self.formula((mass, amp1, amp2), self.times)
return vect.dot(self.inv_cov).dot(vect)
def valid(self, *kargs):
return True
def custom_minuit(self, data, invmatrix, times, guess):
self.aoc = data
self.inv_cov = invmatrix
self.times = times
dof = len(guess)+len(data)
m = Minuit(self.my_cov_fun, mass=guess[0], error_mass=guess[0]*0.1,
amp1=guess[1], error_amp1=guess[1]*0.1,
amp2=guess[2], error_amp2=guess[2]*0.1,
errordef=1.0,
print_level=0, pedantic=True)
return m
class shared_twice_mass_amp(object):
"""Parent class for functions which take a mass and an amplitude"""
def __init__(self):
self.starting_guess = self.thisguess
self.bounds = [mass_bounds, amp_bounds, amp_bounds, mass_bounds, amp_bounds, amp_bounds]
self.parameter_names = ["massa", "amp1a", "amp2a", "massb", "amp1b", "amp2b"]
self.subtract = False
self.stride = 1
def thisguess(self, cor, period, *args):
dt = 1
ave = cor.average_sub_vev()
emass = cor.periodic_effective_mass(dt, fast=False, period=period)
massa_guess = np.mean([emass[i[1]-dt-1] for i in self.indexes])
massb_guess = massa_guess
amp_guess1a = ave[self.indexes[0][0]]*np.exp(massa_guess*(self.ranges[0][0]))
amp_guess2a = ave[self.indexes[1][0]]*np.exp(massa_guess*(self.ranges[1][0]))
amp_guess1b = ave[self.indexes[0][0]]*np.exp(massb_guess*(self.ranges[0][0]))
amp_guess2b = ave[self.indexes[1][0]]*np.exp(massb_guess*(self.ranges[1][0]))
return [massa_guess, amp_guess1a, amp_guess2a, massb_guess, amp_guess1b, amp_guess2b]
def my_cov_fun(self, massa, amp1a, amp2a, massb, amp1b, amp2b):
vect = self.aoc - self.formula((massa, amp1a, amp2a, massb, amp1b, amp2b), self.times)
return vect.dot(self.inv_cov).dot(vect)
def valid(self, *kargs):
return True
def custom_minuit(self, data, invmatrix, times, guess):
self.aoc = data
self.inv_cov = invmatrix
self.times = times
dof = len(guess)+len(data)
m = Minuit(self.my_cov_fun, massa=guess[0], error_massa=guess[0]*0.1,
amp1a=guess[1], error_amp1a=guess[1]*0.1,
amp2a=guess[2], error_amp2a=guess[2]*0.1,
massb=guess[0], error_massb=guess[0]*0.1,
amp1b=guess[1], error_amp1b=guess[1]*0.1,
amp2b=guess[2], error_amp2b=guess[2]*0.1,
errordef=1.0,
print_level=0, pedantic=True)
return m
class multirange(object):
""" Parent class for functions which are periodic and need to know the time extent"""
def setranges(self, ranges):
self.ranges = ranges
indexes = []
prev = 0
for i in ranges:
length = (i[1]-i[0])
indexes.append((prev, prev+length))
prev = prev+length+1
self.indexes = indexes