def test_dump_lsqfit(self):
" MultiFitter.lsqfit "
fitter = MultiFitter(models=self.make_models(ncg=1))
dfit = gv.loads(
gv.dumps(fitter.lsqfit(data=self.data, prior=self.prior)))
self.assertEqual(
self.ref_fit.format(True)[:-2],
dfit.format(True)[:-2])
def test_dump_chained_lsqfit(self):
" MultiFitter.chained_lsqfit(...) "
# sequential fit
fitter = MultiFitter(models=self.make_models(ncg=1))
fit1 = gv.loads(
gv.dumps(fitter.chained_lsqfit(data=self.data, prior=self.prior)))
self.assertTrue(self.agree_ref(fit1.p))
self.assertEqual(list(fit1.chained_fits.keys()), ['l', 'c1', 'c2'])
def fit_and_save(data_file_name, fit_type, save, include_2pt, include_3pt, include_sum, pt2_nstates, pt3_nstates, sum_nstates, sum_tau_cut, id_num=None, pt2_range=[None, None], pt3_A3_range=[None, None], pt3_V4_range=[None, None], sum_A3_range=[None, None], sum_V4_range=[None, None], pt3_tau_dict=None):
if fit_type == "scattered":
pt2_t = np.array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17])
pt3_A3_t = [3, 4, 4, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14]
pt3_A3_tau = [1, 1, 2, 1, 2, 1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5, 6, 2, 3, 4, 5, 6, 2, 3, 4, 5, 6, 7]
pt3_V4_t = pt3_A3_t
pt3_V4_tau = pt3_A3_tau
pt3_A3 = [np.array(pt3_A3_t), np.array(pt3_A3_tau)]
pt3_V4 = [np.array(pt3_V4_t), np.array(pt3_V4_tau)]
sum_A3 = np.array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
sum_V4 = sum_A3
elif fit_type == "continuous":
pt2_t = np.arange(pt2_range[0], pt2_range[1])
pt3_A3_t = []
pt3_A3_tau = []
for t in range(pt3_A3_range[0], pt3_A3_range[1]):
for tau in pt3_tau_dict['A3_tsep'+str(t)]:
pt3_A3_t.append(t)
pt3_A3_tau.append(tau)
pt3_V4_t = []
pt3_V4_tau = []
for t in range(pt3_V4_range[0], pt3_V4_range[1]):
for tau in pt3_tau_dict['V4_tsep'+str(t)]:
pt3_V4_t.append(t)
pt3_V4_tau.append(tau)
pt3_A3 = [np.array(pt3_A3_t), np.array(pt3_A3_tau)]
pt3_V4 = [np.array(pt3_V4_t), np.array(pt3_V4_tau)]
sum_A3 = np.arange(sum_A3_range[0], sum_A3_range[1])
sum_V4 = np.arange(sum_V4_range[0], sum_V4_range[1])
print(pt2_t)
print(pt3_A3)
print(pt3_V4)
print(sum_A3)
print(sum_V4)
data_avg_dict_completed = prepare_data.add_sum_data(data_avg_dict, sum_tau_cut)
if use_p0 == False:
best_p0 = 0
best_p0 = {'E0': 0.49007432827585923, 'log(dE1)': -1.2182574657830274, 'z0': 0.0003202622719326246, 'z1': 0.00033086411929253656, 'z0_ps': 0.003, 'z1_ps': 1.4290420366321432e-21,'log(dE2)': -1.0203037715038503, 'z2': -0.0003420981842067054, 'z2_ps': 4.77757294807751e-19, 'log(dE3)': -0.6763116611503818, 'z3': 0.0006114436301814257, 'z3_ps': 6.18600096063414e-20, 'log(dE4)': 0.1096154276143707, 'z4': 0.00030912500545967415, 'z4_ps': -2.1747716630250984e-21, 'log(dE5)': -1.25, 'z5': -1.5592681273181436e-22, 'z5_ps': 1.3797601616142584e-19,
'log(E_fh)': -1.2512330692654803, 'z_fh_ss': 1.747909186937848e-05, 'z_fh_ps': 4.363143347633416e-21, 'A3_00': 1.2564901354721003, 'V4_00': 1.0231038334230558, 'A3_01': -0.2679609024371228,
'V4_01': -0.004166047120256729, 'A3_11': 0.8761219755499188, 'V4_11': 0.9902000810575383, 'A3_02': -0.1521913122970933, 'V4_02': 0.0014475402638539437, 'A3_12': -0.06373623540219797,
'V4_12': 0.01132917934978911, 'A3_22': 0.7453490951343201, 'V4_22': 1.0632482453026812, 'A3_03': 0.10049661764180362, 'V4_03': 0.08038556296566231, 'A3_13': 0.1361847606270459,
'V4_13': 0.07099939052507677, 'A3_23': 0.25916215890865607, 'V4_23': -0.01133073437078887, 'A3_33': 0.865312735370937, 'V4_33': 0.7748558903412537, 'A3_04': 0.6204441395578857,
'V4_04': 0.3522708502720259, 'A3_14': 0.40763003829174205, 'V4_14': 0.335117716953269, 'A3_24': -1.101075754867819, 'V4_24': -0.3733418857549809, 'A3_34': 0.7190174286711979,
'V4_34': 0.6261439927183979, 'A3_44': -0.25761311272489074, 'V4_44': 0.9973793333624172, 'A3_05': -8.257694779526328e-21, 'V4_05': 4.986761175050056e-20, 'A3_15': -3.247769578767987e-21,
'V4_15': 2.124892576801777e-20, 'A3_25': -5.58683860326533e-20, 'V4_25': 1.6748959838190552e-20, 'A3_35': 7.295422809984337e-21, 'V4_35': 2.214178969103922e-20, 'A3_45': 3.5577787778307475e-20,
'V4_45': -2.5297406179805884e-20, 'A3_55': 1.5137185489740753e-20, 'V4_55': 1.0, 'fh_A3_0': 0.002666305722764564, 'fh_V4_0': 0.01628080958292751, 'd0_ss_A3': -1.5e-06, 'd0_ss_V4': 1.3e-06,
'd0_ps_A3': -9e-06, 'd0_ps_V4': 7.5e-06, 'fh_A3_1': -0.0024127278385662476, 'fh_V4_1': 0.003973560962251952, 'd1_ss_A3': 4.059923399570421e-26, 'd1_ss_V4': -6.166269907949912e-26, 'd1_ps_A3': 2.3105448329306423e-26,
'd1_ps_V4': 4.902765855922857e-26, 'fh_A3_2': -0.022933720252869036, 'fh_V4_2': -0.055108665759743665, 'd2_ss_A3': -5.052876947576615e-26, 'd2_ss_V4': -1.7883348339254526e-27, 'd2_ps_A3': 9.668146857259591e-27,
'd2_ps_V4': -2.664874890850679e-25, 'fh_A3_3': -0.025212744548757944, 'fh_V4_3': -0.09513944044174277, 'd3_ss_A3': -4.139294809856933e-26, 'd3_ss_V4': 3.1366216019351247e-26, 'd3_ps_A3': 6.126928367111804e-26,
'd3_ps_V4': 8.925147564672736e-27, 'fh_A3_4': -0.0007628417809514719, 'fh_V4_4': -0.0026882359647512925, 'd4_ss_A3': -1.8337248632272437e-26, 'd4_ss_V4': 1.0839295795776121e-30, 'd4_ps_A3': -8.40639490383513e-31,
'd4_ps_V4': -1.8447470978983475e-30, 'd5_ss_A3': -4.996925396047227e-31, 'd5_ss_V4': 1.0374204770912712e-30, 'd5_ps_A3': -2.4636016481713015e-30, 'd5_ps_V4': -5.605193857299268e-45, 'fh_A3_5': -9.616184647013097e-06, 'fh_V4_5': 0.9999986504760016}
fitter = Fit(file_name, prior, pt2_nstates, pt3_nstates, sum_nstates, sum_tau_cut, include_2pt, include_3pt, include_sum)
fit_result, hexcode, dr_hex = fitter.fit(data_avg_dict_completed, pt2_t, pt3_A3, pt3_V4, sum_A3, sum_V4, best_p0, save)
best_p0 = {key: fit_result.p[key].mean for key in fit_result.p}
print(fit_result)
print(fit_result.p['A3_00'].mean)
print(fit_result.p['A3_00'].sdev)
if save == True:
tau_dict = gv.dumps(pt3_tau_dict)
tau_dict_hex = tau_dict.hex()
ff_fit_ff_fit, created = ff_fit_FF_fit.objects.get_or_create(
data_file_name=data_file_name, # Store the name of data file
include_3pt=include_3pt, # Whether include 3pt part in fitting
include_sum=include_sum, # Whether include sum part in fitting
data_and_results=dr_hex, # Use gv.dump to package data, prama, chi2 and dof, and turn into…
prior_hexcode=hexcode, # put all priors into a string with fixed order and turn it…
pt2_nstates=pt2_nstates, # Number of states in 2pt fit
pt3_nstates=pt3_nstates, # Number of states in 3pt fit
sum_nstates=sum_nstates, # Number of states in sum tau fit
sum_tau_cut=sum_tau_cut, # Start of sum in sum tau fit
E0=fit_result.p['E0'].mean, # fit result of E0
E0_err=fit_result.p['E0'].sdev, # fit result of E0 err
z0=fit_result.p['z0'].mean, # fit result of z0
z0_err=fit_result.p['z0'].sdev, # fit result of z0 err
A300=fit_result.p['A3_00'].mean, # fit result of A300
A300_err=fit_result.p['A3_00'].sdev, # fit result of A300 err
V400=fit_result.p['V4_00'].mean, # fit result of V400
V400_err=fit_result.p['V4_00'].sdev, # fit result of V400 err
Q_value=fit_result.Q, # Q value of fitting
log_GBF=fit_result.logGBF, # logGBF of fitting
fit_type=fit_type, # "scattered" or "continuous"
include_2pt=include_2pt, # (Optional) Whether include 2pt part in fitting
pt2_tmin=pt2_range[0], # (Optional) Minimum of t in 2pt fit
pt2_tmax=pt2_range[1], # (Optional) Maximum of t in 2pt fit
pt3_A3_tsep_min=pt3_A3_range[0], # (Optional) Minimum of tsep in 3pt fit
pt3_A3_tsep_max=pt3_A3_range[1], # (Optional) Maximum of tsep in 3pt fit
pt3_V4_tsep_min=pt3_V4_range[0], # (Optional) Minimum of tsep in 3pt fit
pt3_V4_tsep_max=pt3_V4_range[1], # (Optional) Maximum of tsep in 3pt fit
pt3_tau_dict=tau_dict_hex, # (Optional) Use gv.dump to package tau dict of 3pt, and turn into hexcode
sum_A3_tsep_min=sum_A3_range[0], # (Optional) Minimum of tsep in sum tau fit
sum_A3_tsep_max=sum_A3_range[1], # (Optional) Maximum of tsep in sum tau fit
sum_V4_tsep_min=sum_V4_range[0], # (Optional) Minimum of tsep in sum tau fit
sum_V4_tsep_max=sum_V4_range[1], # (Optional) Maximum of tsep in sum tau fit
id_num=id_num, # (Optional) used to divide and select
)
def fit(self, data_avg_dict, pt2_t, pt3_A3, pt3_V4, sum_A3, sum_V4, best_p0, save=False, priors=None):
if priors == None:
priors = self.prior
t_tsep_tau = {}
Amp = {}
if self.include_2pt == True:
pt2_amp = []
for t in pt2_t:
pt2_amp.append(data_avg_dict['pt2_tsep_'+str(t)])
t_tsep_tau['pt2'] = pt2_t
Amp['pt2'] = np.array(pt2_amp)
if self.include_3pt == True:
pt3_A3_tsep = pt3_A3[0]
pt3_A3_tau = pt3_A3[1]
pt3_V4_tsep = pt3_V4[0]
pt3_V4_tau = pt3_V4[1]
pt3_A3_amp = []
pt3_V4_amp = []
for i in range(len(pt3_A3[0])):
t = pt3_A3[0][i]
tau = pt3_A3[1][i]
pt3_A3_amp.append((data_avg_dict['pt3_A3_tsep_' + str(t)][tau] + data_avg_dict['pt3_A3_tsep_' + str(t)][t - tau])/2) # average tau=i and tau=tsep-i to make data symmetric
for i in range(len(pt3_V4[0])):
t = pt3_V4[0][i]
tau = pt3_V4[1][i]
pt3_V4_amp.append((data_avg_dict['pt3_V4_tsep_' + str(t)][tau] + data_avg_dict['pt3_V4_tsep_' + str(t)][t - tau])/2) # average tau=i and tau=tsep-i to make data symmetric
t_tsep_tau['pt3_A3'] = [np.array(pt3_A3_tsep), np.array(pt3_A3_tau)]
t_tsep_tau['pt3_V4'] = [np.array(pt3_V4_tsep), np.array(pt3_V4_tau)]
Amp['pt3_A3'] = np.array(pt3_A3_amp)
Amp['pt3_V4'] = np.array(pt3_V4_amp)
if self.include_sum == True:
sum_A3_factor=[]
sum_V4_factor=[]
for t in sum_A3:
sum_A3_factor.append(data_avg_dict['sum_A3_fit_'+str(t)])
for t in sum_V4:
sum_V4_factor.append(data_avg_dict['sum_V4_fit_'+str(t)])
t_tsep_tau['sum_A3'] = sum_A3
t_tsep_tau['sum_V4'] = sum_V4
Amp['sum_A3'] = np.array(sum_A3_factor)
Amp['sum_V4'] = np.array(sum_V4_factor)
#print(t_tsep_tau)
#print(Amp)
if best_p0 == 0:
fit_result = lsf.nonlinear_fit(data=(t_tsep_tau, Amp), prior=priors, fcn=self.fcn, maxit=100000, fitter='scipy_least_squares') # scipy_least_squares # gsl_multifit
else :
fit_result = lsf.nonlinear_fit(data=(t_tsep_tau, Amp), prior=priors, fcn=self.fcn, maxit=100000, fitter='scipy_least_squares', p0=best_p0)
hexcode = None
dr_hex = None
if save == True:
str_of_prior = ''
for key in self.prior:
if key in ['E0', 'log(dE1)', 'log(dE2)', 'log(E_sum)', 'z0', 'z1', 'z2', 'A3_00', 'V4_00', 'A3_01', 'V4_01', 'A3_11', 'V4_11']:
str_of_prior += key
str_of_prior += str(self.prior[key])
hextype = hashlib.md5()
str_of_prior = bytes(str_of_prior, encoding= 'utf-8')
hextype.update(str_of_prior)
hexcode = hextype.hexdigest()
g = dict(data=(t_tsep_tau, Amp), prior=priors, params=fit_result.p, chi2=fit_result.chi2, dof=fit_result.dof)
data_and_results = gv.dumps(g)
dr_hex = data_and_results.hex()
print(hexcode)
return fit_result, hexcode, dr_hex