def create_corr():
corr = Correlation()
tau = np.exp(np.linspace(np.log(1e-3),np.log(1e6), 10))
data = corr.fit_model(corr.fit_parameters, tau)
noise = (np.random.random(data.shape[0])-.5)*.0005
data += noise
corr.correlation = np.dstack((tau, data))[0]
return corr
def create_corr():
corr = Correlation()
tau = np.exp(np.linspace(np.log(1e-3), np.log(1e6), 10))
data = corr.fit_model(corr.fit_parameters, tau)
noise = (np.random.random(data.shape[0]) - .5) * .0005
data += noise
corr.correlation = np.dstack((tau, data))[0]
return corr
def fit_single_parameter(modelid, fullparms, parmid, parmval, noise=False):
"""
Use the full parameter set `fullparms` and leave a single parameter
`parmid` variable during the fit.
Returns the fitted value of the parameter with index `parmid`
"""
corr = Correlation(fit_model=modelid, fit_algorithm=FITALG, verbose=0)
tau = np.exp(np.linspace(np.log(TAUMIN),np.log(TAUMAX), TAULEN))
# Create artificial data by using the current fit_model
data = corr.fit_model(fullparms, tau)
if noise:
if noise is True:
deltanoise = (np.max(data)-np.min(data))/20
else:
deltanoise = (np.max(data)-np.min(data))*noise
anoise = (np.random.random(data.shape[0])-.5)*deltanoise
data += anoise
# Add artificial data to data set
corr.correlation = np.dstack((tau, data))[0]
# Set variable parameters
fit_bool = np.zeros(fullparms.shape[0])
fit_bool[parmid] = True
corr.fit_parameters_variable = fit_bool
fullparms_edit = fullparms.copy()
fullparms_edit[parmid] = parmval
corr.fit_parameters = fullparms_edit
Fit(corr)
return corr.fit_parameters[parmid]
def create_corr():
n = 2.4
taud = 10
SP = 3.3
tau = np.exp(np.linspace(np.log(1e-3), np.log(1e6), 10))
corr1 = Correlation(fit_model=6002)
corr1.lag_time = tau
# 0: n
# 1: τ_diff [ms]
p1a = corr1.fit_parameters.copy()
p1b = p1a.copy()
p1b[0] = n
p1b[1] = taud
# write values and return to original
corr1.fit_parameters = p1b
corr1.correlation = corr1.modeled_fit.copy()
corr1.fit_parameters = p1a
corr1.fit_parameters_variable = [True, True, False, False, False]
corr2 = Correlation(fit_model=6011)
corr2.lag_time = tau
# 0: n
# 3: τ_diff [ms]
# 4: SP
p2a = corr2.fit_parameters.copy()
p2b = p2a.copy()
p2b[0] = n
p2b[3] = taud
p2b[4] = SP
# write values and return to original
corr2.fit_parameters = p2b
corr2.correlation = corr2.modeled_fit.copy()
corr2.fit_parameters = p2a
corr2.fit_parameters_variable = [True, False, False, True, True, False]
corrs = [corr1, corr2]
initparms = np.array([n, taud, SP])
return corrs, initparms
def create_corr():
n = 2.4
taud = 10
SP = 3.3
tau = np.exp(np.linspace(np.log(1e-3), np.log(1e6), 10))
corr1 = Correlation(fit_model=6002)
corr1.lag_time = tau
# 0: n
# 1: τ_diff [ms]
p1a = corr1.fit_parameters.copy()
p1b = p1a.copy()
p1b[0] = n
p1b[1] = taud
# write values and return to original
corr1.fit_parameters = p1b
corr1.correlation = corr1.modeled_fit.copy()
corr1.fit_parameters = p1a
corr1.fit_parameters_variable = [True, True, False, False, False]
corr2 = Correlation(fit_model=6011)
corr2.lag_time = tau
# 0: n
# 3: τ_diff [ms]
# 4: SP
p2a = corr2.fit_parameters.copy()
p2b = p2a.copy()
p2b[0] = n
p2b[3] = taud
p2b[4] = SP
# write values and return to original
corr2.fit_parameters = p2b
corr2.correlation = corr2.modeled_fit.copy()
corr2.fit_parameters = p2a
corr2.fit_parameters_variable = [True, False, False, True, True, False]
corrs = [corr1, corr2]
initparms = np.array([n, taud, SP])
return corrs, initparms