def make_gauss(folder):
df = pd.read_csv(folder + "data.csv")
print(df)
# print(df['20.0'])
df.drop(["Unnamed: 0", "t"], axis=1, inplace=True)
# print(df)
xs = np.array([float(x) for x in df.columns])
ys = np.array([sum(df[x]) for x in df.columns])
def f(x, sigma, amp, mu, base):
return base + amp * np.exp(-((x - mu) / sigma)**2 / 2)
gmodel = models.Model(f)
# params = gmodel.guess(ys, x=xs)
params = Parameters()
params.add_many(('sigma', 5, True, 1), ('amp', 1000, True, 1),
('mu', 35, True, 1), ('base', 200, True, 1))
fit_result = gmodel.fit(ys, params=params, x=xs)
print(folder)
print(fit_result.fit_report())
print()
ax.plot(xs, ys, label=folder[:-1])
ax.plot(xs, fit_result.eval(), label=folder[:-1] + 'fit')
def _setup_params_from(self, initial_guess, data): # 5 ms
params = Parameters()
x, y, z = self._make_center_priors(data, initial_guess) # 4.47 ms
min_index = data.medium_index * 1.001
n = Parameter(name='n',
value=initial_guess['n'],
min=min_index,
max=2.33)
r = Parameter(name='r', value=initial_guess['r'], min=0.05, max=5)
params.add_many(x, y, z, n, r)
if self.theory == 'mieonly':
alpha_val = self._alpha_guess(initial_guess)
params.add(name='alpha', value=alpha_val, min=0.05, max=1.0)
elif self.theory == 'mielens':
angle_val = self._lens_guess(initial_guess)
params.add(name='lens_angle', value=angle_val, min=0.05, max=1.1)
if self.theory == 'mielensalpha':
alpha_val = self._alpha_guess(initial_guess)
angle_val = self._lens_guess(initial_guess)
params.add(name='alpha', value=alpha_val, min=0.05, max=1.0)
params.add(name='lens_angle', value=angle_val, min=0.05, max=1.1)
if 'illum_wavelen' in initial_guess:
wavelength = initial_guess['illum_wavelen']
params.add(name='illum_wavelen',
value=wavelength,
min=.1,
max=2.000)
return params
def calc_A_unbinned(data, bg_params, sig_params):
'''Given input data and the true distribution parameters, calculate the 95% UL for the unbinned
data. The bg and signal parameters are held fixed. The best-fit A value is determined first,
then the 95% UL is determined by scanning for the correct value of A that leads to a p-value of
0.05. This procedure must be run many times and averaged to get the mean UL value and error
bands.'''
mu = sig_params[0]
sigma = sig_params[1]
alpha = bg_params[0]
beta = bg_params[1]
gamma = bg_params[2]
params = Parameters()
params.add_many(
('C' , 0.01 , True , 0 , 1 , None , None) ,
('mu' , mu , False , None , None , None , None) ,
('sigma' , sigma , False , None , None , None , None) ,
('alpha' , alpha , False , None , None , None , None) ,
('beta' , beta , False , None , None , None , None) ,
('gamma' , gamma , False , None , None , None , None)
)
bg_sig_model = Model(bg_sig_pdf, params)
# Obtain the best fit value for A
mle_fitter = NLLFitter(bg_sig_model)
mle_res = mle_fitter.fit(np.asarray(data), calculate_corr=False,
verbose=False)
return mle_res.x[0]
def fitz_star(spec, zguess, fluxpoly=True):
flux_median = np.median(spec['flux'])
parameters = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
parameters.add_many(('z', zguess, True, None, None, None),
('eigen1', flux_median*0.1, True, None, None, None),
('eigen2', flux_median*0.1, True, None, None, None),
('eigen3', flux_median*0.1, True, None, None, None),
('eigen4', flux_median*0.1, True, None, None, None),
('eigen5', flux_median*0.1, True, None, None, None),
('eigen6', flux_median*0.1, True, None, None, None),
('eigen7', flux_median*0.1, True, None, None, None),
('eigen8', flux_median*0.1, True, None, None, None),
('eigen9', flux_median*0.1, True, None, None, None),
('eigen10', flux_median*0.1, True, None, None, None),
('eigen11', flux_median*0.1, True, None, None, None),
('fluxcal0', 0.0, fluxpoly, None, None, None),
('fluxcal1', 0.0, fluxpoly, None, None, None),
('fluxcal2', 0.0, fluxpoly, None, None, None))
star_model = Model(eigensum_star, missing='drop')
result = star_model.fit(spec['flux'], wave=spec['wave'], weights=1/spec['error'],
params=parameters, missing='drop')
return result
def params_creating(filename, w):
"""
Returns Parameters object with initial guesses
Parameters:
----------
filename: str
Name of file. May be various text formats, data structure example is in file init_guess.init
w: 1darray(dtype=float)
array with experminetal frequencies
Return:
--------
parameter: Parameters()
"""
parameter = Parameters()
e0, gamma, A = read_from_file(filename, w)
n = len(e0)
for i in range(n):
str1 = 'E0_' + str(i + 1)
str2 = 'gamma_' + str(i + 1)
str3 = 'A_' + str(i + 1)
parameter.add_many((str1, e0[i, 0], True, e0[i, 1], e0[i, 2]),
(str2, gamma[i, 0], True, gamma[i, 1], gamma[i, 2]),
(str3, A[i, 0], True, A[i, 1], A[i, 2]))
return parameter
def approximate_angles(goal,
side_lengths,
angle_guesses=(90, 90, 90),
report=True,
iter_cb=None):
"""Given the desired end effector location (goal), the lengths of the arm segments, and the initial guesses of the
angles, approximate the angles the servos must be set to."""
# The base servo angle can be calculated and fixed.
base = np.rad2deg(np.arctan(goal[0] / goal[2]))
# define the starting values of the parameters of the minimized function
base = Parameter('base', base, vary=False) # base is exact
shoulder = Parameter('shoulder', angle_guesses[0], min=0, max=180)
elbow = Parameter('elbow', angle_guesses[1], min=0, max=180)
wrist = Parameter('wrist', angle_guesses[2], min=0, max=180)
params = Parameters()
params.add_many(base, shoulder, elbow, wrist)
result = minimize(end_effector,
params,
args=(goal, side_lengths),
method="leastsq",
iter_cb=iter_cb)
if report:
print(fit_report(result.params))
return result
def parameters(ini_values):
from lmfit import Parameters
k1, k2, k3, k4, k5, k6, k7, k8, k9, k10, k11, UA, mu_0, E, q, prim_stab_0, LDH_0 = ini_values
p = Parameters()
# ( Name, Value, Vary, Min, Max)
p.add_many(( 'k1', k1, True, 1.6, 2.1),
( 'k2', k2, True, 8.0, 46.0),
( 'k3', k3, True, 0.0, 6.0),
( 'k4', k4, True, 0.0, 2.1),
( 'k5', k5, True, 0.0, 0.03),
( 'k6', k6, True, 0.0, 39.0),
( 'k7', k7, True, 0.0, 2.7),
( 'k8', k8, True, 0.0, 7.9),
( 'k9', k9, True, 0.0, 13.1),
( 'k10', k10, True, 0.7, 10.9),
( 'k11', k11, True, 2.0, 3.6),
( 'UA', UA, True, 275.0, 402.0),
( 'mu_0', mu_0, False, 0.0, 0.1),
( 'E', E, False, 5000.0, None),
( 'q', q, False, 0.0, 17.0),
('prim_stab_0', prim_stab_0, False, 0.5, 1.3),
( 'LDH_0', LDH_0, False, None, None))
return p
def mle_ou(t, s):
"""Maximum-likelihood estimator for standard OU"""
def log_likelihood(q):
"""Calculates log likelihood of a standard OU path"""
K = q["kappa"]
theta = q["theta"]
sigma = q["sigma"]
dt = np.diff(t)
mu = VasicekModel.mean(s[:-1], dt, K, theta)
sigma = VasicekModel.std(dt, K, sigma)
return -np.sum(scipy.stats.norm.logpdf(s[1:], loc=mu, scale=sigma))
params = Parameters()
params.add_many(
("kappa", 0.5, True, 1e-6, None),
("theta", np.mean(s), True, 0.0, None),
("sigma", est_sigma_quadratic_variation(t, s), True, 1e-8, None),
)
result = minimize(
log_likelihood,
params,
method="L-BFGS-B",
options={
"maxiter": 500,
"disp": False
},
)
return result
def heartrate_model(heartrate, power, **kwargs):
"""
Source:
De Smet et al., Heart rate modelling as a potential physical finess assessment for runners and cyclists.
http://ceur-ws.org/Vol-1842/paper_13.pdf
"""
# Initial model parameters
model_params = Parameters()
model_params.add_many(
('hr_rest', kwargs.get('hr_rest', 75)),
('hr_max', kwargs.get('hr_max', 200)),
('dhr', kwargs.get('dhr', 0.30)),
('tau_rise', kwargs.get('tau_rise', 24)),
('tau_fall', kwargs.get('tau_fall', 30)),
('hr_drift', kwargs.get('hr_drift', 3 * 10**-5)),
)
model = minimize(
fcn=_heartrate_model_residuals,
params=model_params,
method='nelder', # Nelder-Mead
args=(power, heartrate))
predictions = _heartrate_model_predict(model.params, power)
return model, predictions
def confidence_interval():
"""Return the result of the confidence interval (ci) calculation."""
def residual(pars, x, data=None):
argu = (x * pars['decay'])**2
shift = pars['shift']
if abs(shift) > np.pi / 2:
shift = shift - np.sign(shift) * np.pi
model = pars['amp'] * np.sin(shift +
x / pars['period']) * np.exp(-argu)
if data is None:
return model
return model - data
p_true = Parameters()
p_true.add_many(('amp', 14.0), ('period', 5.33), ('shift', 0.123),
('decay', 0.010))
x = np.linspace(0.0, 250.0, 2500)
data = residual(p_true, x) + np.random.normal(scale=0.7215, size=x.size)
fit_params = Parameters()
fit_params.add_many(('amp', 13.0), ('period', 2), ('shift', 0.0),
('decay', 0.02))
mini = Minimizer(residual,
fit_params,
fcn_args=(x, ),
fcn_kws={'data': data})
out = mini.leastsq()
ci = conf_interval(mini, out)
return ci
def generate_q0_via_nll_unbinned_constrained(bg, data, bg_params):
'''Perform two nll fits to data, one for bg+signal, one for bg-only.
Use these values to create the q0 statistic.'''
data = np.asarray(data)
bg = np.asarray(bg)
_bg_params = bg_params.copy()
for p in _bg_params:
_bg_params[p].vary = False
bg_model = Model(bg_pdf, _bg_params)
mc_bg_only_fitter = NLLFitter(bg_model, verbose=False)
mc_bg_only_fitter.fit(bg, calculate_corr=False)
bg_nll = bg_model.calc_nll(None, data)
_sig_params = Parameters()
_sig_params.add_many(
('C', 0.1, True, 0, 1, None, None),
('mu', 125.77, False, 120, 130, None, None),
('sigma', 2.775, False, 1, 4, None, None),
('a1', _bg_params['a1'].value, False, -1, 1, None, None),
('a2', _bg_params['a2'].value, False, -1, 1, None, None),
('a3', _bg_params['a3'].value, False, -1, 1, None, None))
bg_sig_model = Model(bg_sig_pdf, _sig_params)
mc_bg_sig_fitter = NLLFitter(bg_sig_model, verbose=False)
mc_bg_sig_result = mc_bg_sig_fitter.fit(data, calculate_corr=False)
bg_sig_nll = mc_bg_sig_result.fun
q0 = 2 * max(bg_nll - bg_sig_nll, 0)
return q0
def assess(self):
# TODO: mutually exclusive args
# TODO: check for when best and worst are not extrema
if self.optimal_fit:
points_params = Parameters()
worst = Parameter("worst", value=self.worst, vary=False)
lower_middle = Parameter("lower_middle")
middle = Parameter("middle")
upper_middle = Parameter("upper_middle")
best = Parameter("best", value=self.best, vary=False)
for param in [lower_middle, middle, upper_middle]:
param.set(min=self.data.min(), max=self.data.max())
points_params.add_many(worst, lower_middle, middle, upper_middle,
best)
def objective(params):
v = params.valuesdict()
u = Utility(name="u", points=list(v.values()))
u.fit()
chisqr = u.result.chisqr
return chisqr
self.optimal_points = minimize(objective,
points_params,
method=self.method)
v = self.optimal_points.params.valuesdict()
self.points = np.array(list(v.values()))
return self.points
def build_params_linear():
params = Parameters()
params.add_many(
('a0', 1.),
('a1', 1.),
)
return params
def test_fittheory(self):
"""
Test the fitting of the available analytical functions
"""
testspec = generate_cdespectrum()
testspec = testspec.subspectrum(2500., 3700., clone=True)
testfitter = Fitter.fromspectrum(testspec)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(
('H', 0.1, True, 0.0, None, None, None),
('xR', 3000., True, 3000. - 50., 3000. + 50., None, None),
('w', 50.0, True, 0.0, None, None, None),
('tau', 50.0, True, 0.0, None, None, None))
testfitter.add_analytical('flipped_egh',
testpars,
funcname='test fEGH')
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(
('peak', 1.0, True, 0.0, None, None, None),
('pos', 3300., True, 3300. - 300., 3300. + 300., None, None),
('fwhm', 500.0, True, 0.0, None, None, None),
)
testfitter.add_analytical('gaussian',
testpars,
funcname='test gaussian')
testfitter.perform_fit()
def Fit_frvsT(temp, freq, fitpara):
# create a set of Parameters
params = Parameters()
print fitpara
params.add_many(
('CPWC', fitpara[0], False, None, None, None),
('CPWG', fitpara[1], False, None, None, None),
('thick', fitpara[2], False, None, None, None),
('BCS', fitpara[3], False, None, None, None),
('Tc', fitpara[4], True, None, None, None),
('f0', fitpara[5], False, None, None, None),
('sigman', fitpara[6], False, None, None, None),
('A', fitpara[7], True, fitpara[7] * 0.9, fitpara[7] * 1.1, None))
# do fit, here with leastsq model
result = minimize(Fit_frvsT_Func, params, args=(temp, freq))
# calculate final result
residual = result.residual
Tc = result.params['Tc'].value
Tc_err = np.abs(result.params['Tc'].stderr / Tc)
A = result.params['A'].value
A_err = np.abs(result.params['A'].stderr / A)
print fit_report(result)
return Tc, Tc_err, A, A_err, fit_report(result)
def generate_initial_params(data_bg_mul2, data_bg_mul8, seed=5):
# fit to the data distributions
bg_params = Parameters()
bg_params.add_many(
('alpha', -1.80808e+01, True, 1e-20, 20, None, None),
('beta', -8.21174e-02, True, -10, -1e-20, None, None),
('gamma', 8.06289e-01, True, 1e-20, 10, None, None)
)
bg_model = Model(bg_pdf, bg_params)
bg_fitter = NLLFitter(bg_model)
bg_result = bg_fitter.fit(data_bg_mul2, calculate_corr=False)
n_bg = len(data_bg_mul8)
gRandom.SetSeed(seed)
# Set up bg sampling
bg_pdf_ROOT = functools.partial(bg_pdf, doROOT=True)
tf1_bg_pdf = TF1("tf1_bg_pdf", bg_pdf_ROOT, 2800, 13000, 3)
tf1_bg_pdf.SetParameters(*bg_result.x)
mc_bg = [tf1_bg_pdf.GetRandom() for i in range(n_bg)]
be_bg = bayesian_blocks(mc_bg, p0=0.02)
be_bg[-1] += 0.1
be_bg = np.append(be_bg, [13000])
be_bg[0] = 2800
# print be_bg
# hist(data_bg_mul8, bins=be_bg, scale='binwidth')
# plt.show()
return bg_result, n_bg, be_bg
def fitting_bb_data(all_data):
""" Fits the black body portion of the spectrum for each datafile.
This definition goes through all of the Run classes for a given dataset and filters out the
peaks to fit a black body curve to the filtered data. The LMFIT fitting routine is used as a wrapper for the SCIPY optmize tools to fit the Planck's Black Body curve. This function feeds in initial guesses for the parameters and returns a best fitted parameters for the curve. Keyword Arguments:
all_data -- List of Run classes
"""
filtered_data = []
for dat in all_data:
counts = np.asarray(dat.calibrated_counts)
lam = np.asarray(dat.calibrated_lam)
bb = sig.medfilt(counts, kernel_size=81)
p = Parameters()
# (Name , Value, Vary, Min, Max, Expr)
p.add_many(('T', 5000., True, None, None, None),
('scale', 1E-15, True, None, None, None),
('shift', 0.0, False, None, None, None))
func = Model(pbb)
result = func.fit(bb, lam=lam, params=p)
dat.bb = bb
dat.bb_err = 0.0
dat.temp = result.params['T'].value
dat.temp_err = result.params['T'].stderr
dat.aic = result.aic
filtered_data.append(dat)
return filtered_data
def main():
#Input the name of the file to be read from the command line
filename = str(sys.argv[1])
#Read out the temperatures and times associated with them
time, temps = np.loadtxt(filename, skiprows=1, usecols=(0, 1), unpack=True)
plt.plot(time, temps, 'x', label="Heating Data")
plt.legend()
plt.xlabel("Time (min)")
plt.ylabel("Temperature (C)")
plt.show()
exponentialModel = Model(exponentialFunction)
p = Parameters()
#The parameters in the equation are coupled, and so we can't solve for any of their actual values without knowing one
#We choose an estimate of 30000 J/min for the heating rate
p.add_many(
('h', 30000, False, None, None, None),
('k', 6000, True, 0, 10000, None),
('c', 600000, True, 0, 1000000, None),
('Tout', 16, False, None, None, None),
)
#Perform a least-squares regression to the data using the model function and parameters
exponentialFit = exponentialModel.fit(temps, x=time, params=p)
print(exponentialFit.fit_report())
#Plot data and save the resulting figure.
plt.plot(time, temps, 'x', label="Heating Data")
plt.plot(time, exponentialFit.best_fit, label='Fit to Data')
plt.legend()
plt.xlabel("Time (min)")
plt.ylabel("Temperature (C)")
plt.grid()
plt.suptitle("Fitting of Room Heating")
plt.savefig("Room_Temp_Fitting.png")
plt.show()
def Fit_frvsT(temp, freq, fitpara):
# create a set of Parameters
params = Parameters()
print fitpara
params.add_many(('CPWC', fitpara[0], False, None, None, None),
('CPWG', fitpara[1], False, None, None, None),
('thick', fitpara[2], False, None, None, None),
('BCS', fitpara[3], False, None, None, None),
('Tc', fitpara[4], True, None, None, None),
('f0', fitpara[5], False, None, None, None),
('sigman',fitpara[6], False, None, None, None),
('A', fitpara[7], True, fitpara[7]*0.9, fitpara[7]*1.1, None))
# do fit, here with leastsq model
result = minimize(Fit_frvsT_Func, params, args=(temp, freq))
# calculate final result
residual = result.residual
Tc = result.params['Tc'].value
Tc_err = np.abs(result.params['Tc'].stderr/Tc)
A = result.params['A'].value
A_err = np.abs(result.params['A'].stderr/A)
print fit_report(result)
return Tc, Tc_err, A, A_err, fit_report(result)
def smooth_and_remove_step(x_lst, y_lst, x_min_flt,x_max_flt,rmv_step_bool):
'''
Takes entire data set, x and y
cuts down the spectra s.t x_min < x < x_max
THEN
Removes a step function from y_lst
'''
# Restrict the fit
x_fit = []
y_fit = []
top_lst = []
bottom_lst = []
for x,y in zip(x_lst, y_lst):
# Restrict the fitting region
if x_min_flt < x < x_max_flt:
x_fit.append(float(x))
y_fit.append(float(y))
# Find top and bottom of step
if x < x_min_flt + 7:
bottom_lst.append(float(y))
elif x > x_max_flt - 7:
top_lst.append(float(y))
x_fit = np.asarray(x_fit)
y_fit = np.asarray(y_fit)
top = np.mean(np.asarray(top_lst))
bottom = np.mean(np.asarray(bottom_lst))
delta = top-bottom
if (rmv_step_bool):
# Step Parameters
step_at = 100
step_width = 1
pp = Parameters()
pp.add_many(
('amplitude',delta),
('sigma',step_width),
('center',step_at)
)
step = StepModel(form = 'erf', prefix='', independent_vars=['x'])
y_fit = np.asarray([yy-bottom-step.eval(x=xx, params=pp) for xx,yy in zip(x_fit,y_fit)])
# rest is the same as smooth_the_data
# now we find the parameters using the - d^2/dx^2
ysmooth = interp.interp1d(x_fit, y_fit, kind='cubic')
# differentiate x 2
yp = np.gradient(ysmooth(x_fit))
ypp = np.gradient(yp)
# we want the peaks of -d2/dx2
ypp = np.asarray([-x for x in ypp])
return x_fit, y_fit, ysmooth, yp, ypp
def parameters(ini_values):
from lmfit import Parameters
from Adjust_Kinetics import params
p = Parameters()
pi = params(ini_values)
for i,tup in enumerate(pi):
p.add_many(tup)
return p
def build_params():
params = Parameters()
params.add_many(
('a0', 1.),
('a1', 1.),
('a2', 0.1),
)
return params
def test_add_many_params(self):
# test that we can add many parameters, but only parameters are added.
a = Parameter('a', 1)
b = Parameter('b', 2)
p = Parameters()
p.add_many(a, b)
assert_(list(p.keys()) == ['a', 'b'])
def sipm_phdfit(x, y, npk, nz_pe=0):
"""
Function to fit a SiPM phd array with a Generalized Poisson
Parameters
----------
x : float array
Typically the pulse heigth in mV
y : int array
The number of events per bin
npk : int scalar
The number of distinguishable peaks in the PHD - for initial parameter guess
nz_pe : If 0 - include pedestal in PHD
If 1 - exclude pedestal
Returns
-------
result : lmfit result object
Including initial guess, best fit, and all fit parameters
"""
ymax = y.max()
#find peaks in the PHD to npk
for i in range(50):
peaks, p_prop = find_peaks(y,
prominence=ymax * (1 - i / 50),
height=ymax / 10)
if len(peaks) >= npk: break
#now estimate the initial fit parameters
mu = np.sum(p_prop['peak_heights'] *
np.arange(nz_pe, npk + nz_pe)) / np.sum(p_prop['peak_heights'])
nev = np.sum(y)
xtalk = 0.5 #based on CHEC-S devices
v_pe = np.mean(np.diff(x[peaks]))
xoff = x[peaks[0]] - v_pe * nz_pe
v_n = v_pe * 0.2
v_gain = v_pe * 0.1
thresh = 0
smod = Model(sipm_fitfunc)
pars = Parameters()
# parameter constraints
# (Name, Value, Vary, Min, Max, Expr, Brute Step)
pars.add_many(('xoff', xoff, True, -50.0, 50.0, None, None),
('mu', mu, True, 0.01, 50.0, None, None),
('nev', nev, True, 1, 1e8, None, None),
('xtalk', xtalk, True, 0.0, 0.75, None, None),
('v_pe', v_pe, True, 0.0, 50.0, None, None),
('v_n', v_n, True, 0.0, 50.0, None, None),
('v_gain', v_gain, True, 0.0, 50.0, None, None),
('thresh', thresh, False, 0, 500, None, None),
('nz_pe', nz_pe, False, 0, 1, None, None))
#solve
result = smod.fit(y, params=pars, x=x, method='leastsq')
return result
def test_resample():
p = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
p.add_many(('amplitude', 100, True, None, None, None),
('decay' , 6e-7, True, None, None, None),
('nu' , 1.0, True, None, None, None))
x = linspace(0,500,50)
y = strExp(x,p)
y_err = abs(y - (y * normal(0.0,0.1,len(y))))
return resample(x,y,yerr=None,max_replacement=100)
def test_fitlab(self):
"""
Test the fitting of lab data
"""
testspec = generate_cdespectrum()
testfitter = Fitter(testspec.x.value,2.*testspec.y.value)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(('mul', 1.0, True, 0.0, None, None))
testfitter.add_empirical(testspec,testpars)
testfitter.perform_fit()
def test_peakfit():
from lmfit.utilfuncs import gauss
def residual(pars, x, data=None):
g1 = gauss(x, pars['a1'].value, pars['c1'].value, pars['w1'].value)
g2 = gauss(x, pars['a2'].value, pars['c2'].value, pars['w2'].value)
model = g1 + g2
if data is None:
return model
return (model - data)
n = 601
xmin = 0.
xmax = 15.0
noise = np.random.normal(scale=.65, size=n)
x = np.linspace(xmin, xmax, n)
org_params = Parameters()
org_params.add_many(('a1', 12.0, True, None, None, None),
('c1', 5.3, True, None, None, None),
('w1', 1.0, True, None, None, None),
('a2', 9.1, True, None, None, None),
('c2', 8.1, True, None, None, None),
('w2', 2.5, True, None, None, None))
data = residual(org_params, x) + noise
fit_params = Parameters()
fit_params.add_many(('a1', 8.0, True, None, 14., None),
('c1', 5.0, True, None, None, None),
('w1', 0.7, True, None, None, None),
('a2', 3.1, True, None, None, None),
('c2', 8.8, True, None, None, None))
fit_params.add('w2', expr='2.5*w1')
myfit = Minimizer(residual,
fit_params,
fcn_args=(x, ),
fcn_kws={'data': data})
myfit.prepare_fit()
init = residual(fit_params, x)
myfit.leastsq()
print(' N fev = ', myfit.nfev)
print(myfit.chisqr, myfit.redchi, myfit.nfree)
report_fit(fit_params)
fit = residual(fit_params, x)
check_paras(fit_params, org_params)
def FD2p(x, data, T): #generate parameters for FD2
params = FD2Guess(x,data,T)
p = Parameters()
p.add_many(('EF', params[0], True, x[0], x[-1], None),
('T', params[1], False, None, None, None),
('sig', params[2], True, 0.0, None, None),
('A', params[3], True, 0.0, None, None),
('dy1', params[4], True, None, None, None),
('dy2', params[5], False, None, None, None),
('c', params[6], True, None, None, None))
return p
def on_fit_data_triggered(self):
try:
print("start fitting")
import operator
# list_of_models = [Model(v.getModelFunction(),prefix=k, nan_policy="propagate") for k,v in self.noise_components.items()]
list_of_models = list()
list_of_params = list()
for name, component in self.noise_components.items():
if not component.enabled:
continue
model, param = component.getFittingModelAndParams(
logMode=False)
list_of_models.append(model)
parameters = list(param.values())
list_of_params.extend(parameters)
# print()
# list_of_models = [v.getFittingModelAndParams for k,v in self.noise_components.items()]
# print("list of parameters")
print(list_of_params)
fit_model = reduce(operator.add, list_of_models)
fit_parameters = Parameters()
fit_parameters.add_many(*list_of_params)
print()
print(fit_parameters)
data = self._displayData
result = fit_model.fit(data, fit_parameters, f=self._displayFreq)
print(result.fit_report())
# print("best values")
# print(result.params)
# print(result.init_fit)
# print("init fit")
# print(result.init_fit)
# print("best fit")
# print(result.best_fit)
# freq, data_converted = self.coordinate_transform.convert(self._displayFreq, result.init_fit)
# self._initFitDataCurve.setData(freq, data_converted)
# freq, data_converted = self.coordinate_transform.convert(self._displayFreq, result.best_fit)
# self._bestFitDataCurve.setData(freq, data_converted)
for name, param in result.best_values.items(): #items():
try:
component_name, param_name = name.split(
BaseNoiseComponent.PREFIX_SPLITTER)
component = self.noise_components[component_name]
setattr(component, param_name, param)
except Exception as e:
print("exception while setting fitted values")
print(e)
except Exception as e:
print("Exception occured while fitting data")
print(str(e))
def test_fitlab(self):
"""
Test the fitting of lab data
"""
testspec = generate_cdespectrum()
testfitter = Fitter(testspec.x.value, 2. * testspec.y.value)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(('mul', 1.0, True, 0.0, None, None))
testfitter.add_empirical(testspec, testpars)
testfitter.perform_fit()
def fitting_bb_data(all_data):
""" Fits the black body portion of the spectrum for each datafile.
This definition goes through all of the Run classes for a given dataset and filters out the
peaks to fit a black body curve to the filtered data. The LMFIT fitting routine is used as a wrapper for the SCIPY optmize tools to fit the Planck's Black Body curve. This function feeds in initial guesses for the parameters and returns a best fitted parameters for the curve. Keyword Arguments:
all_data -- List of Run classes
"""
#cal_data = np.loadtxt(os.getcwd()+'/suspect_calibration_data/CalibrationFile.txt')
filtered_data = []
#count = 0
for dat in all_data:
counts = np.asarray(dat.calibrated_counts)
lam = np.asarray(dat.calibrated_lam)
bb = sig.medfilt(counts, kernel_size=81)
p = Parameters()
# (Name , Value, Vary, Min, Max, Expr)
p.add_many(('T', 5000., True, None, None, None),
('scale', 1E-11, True, None, None, None),
('shift', 0.0, False, None, None, None))
func = Model(pbb)
result = func.fit(bb, lam=lam, params=p)
#print(dat.filename)
#print(result.fit_report())
dat.bb = bb
dat.temp = result.params['T'].value
dat.temp_err = result.params['T'].stderr
dat.aic = result.aic
filtered_data.append(dat)
# pp = PdfPages('Filter_Test.pdf')
# plt.figure()
# plt.plot(lam,counts, 'k-', label='Raw Data')
# plt.plot(lam[1::], corrected_counts, 'm-', label='Corrected Data')
# ##plt.plot(cal_lam[1::], deconc_counts[1], 'm-', label='Deconc Data')
# ##plt.plot(cal_lam[1::], rebinned_counts, 'c--', label='Deconc Data')
# ##plt.plot(lam[1::], bb, 'r-', label='Filtered Data')
# plt.plot(lam[1::], result.best_fit, 'b-', label='Fit Filtered Data')
# plt.legend()
# plt.savefig(pp, format='pdf')
# plt.figure()
## plt.plot(lamp_det_lam[1::], cal_calculated, 'ko', label='Calculated')
# plt.plot(cal_lam, cal_counts, 'b*', label='From File')
# plt.legend()
# plt.savefig(pp, format='pdf')
#pp.close()
return filtered_data
def sineFITwLINEARbackground(param1, param2, **kwargs):
"""
Bounded LS minimisation for the fitting of a sinefunction to a given dataset *with a linear background instead of a constant background*. It is assumed that you can represent param2 as a function of param1. The lmfit package is used to perform the bounded LS minimisation.
DANGER this routine assumes you have a fixed frequency of 1 [unit**-1]. For example:
- if param1 is time and param2 is flux, you will have a sine with a frequency of 1 c/d.
- if param1 is position and param2 is flux, you will have a sine with a frequency of 1 c/pix.
Returns: The optimal lmfit parameter class.
TODO provide options through kwargs to set the boundaries
Returns: The optimal lmfit parameter class.
@param param1: param1 measurements [???]
@type param1: numpy array of length N
@param param2: param2 measurements [???]
@type param2: numpy array of length N
@return: paramSINE
@rtype: lmfit parameter class
@kwargs: show_ME: Boolean to indicate if you want to see the report_fit - Default is False [Boolean]
"""
show_ME = kwargs.get('show_ME', False)
# Determination of the guesses to start your bounded LS fit with.
constantGUESS = np.median(param2) # param2
amplitudeGUESS = np.max(np.abs(constantGUESS - param2)) / 2. # param2
frequencyGUESS = 1. # DANGER Here is the frequency assumption assumption.
phaseGUESS = 0.1 # Using the param1[np.where(param2==np.max(param2))]-param1[0]%1-0.5 is best, when there is no scatter on param2
slopeGUESS, constantGUESS = np.polyfit(param1, param2, 1)
paramSINE = Parameters()
#Make a params class for lmfit.
# (Name, Value, Vary, Min, Max, Expr)
paramSINE.add_many(
('amplitude', amplitudeGUESS, True, amplitudeGUESS * 0.1,
amplitudeGUESS * 1.2, None),
(
'frequency', frequencyGUESS, False, frequencyGUESS - 0.05,
frequencyGUESS + 0.05, None
), # DANGER Here is the frequency assumption assumption. (It is set to non-vary.)
('constant', constantGUESS, True, -abs(constantGUESS) * 1.5,
abs(constantGUESS) * 1.5, None),
('phase', phaseGUESS, True, 0., 1., None),
('slope', slopeGUESS, True, -2 * slopeGUESS, +2 * slopeGUESS, None))
resultSIN = minimize(BRITE.fitfunctions.ff_lmfitlmfit_sinslope_vs_data,
paramSINE,
args=(param1, param2))
if show_ME:
report_fit(paramsSIN, show_correl=False)
return paramSINE
def test_fitres(self):
"""
Test the returning of fit results
"""
testspec = generate_cdespectrum()
testfitter = Fitter(testspec.x.value,2.*testspec.y.value)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(('mul', 1.0, True, 0.0, None, None))
testfitter.add_empirical(testspec,testpars)
testfitter.perform_fit()
res = testfitter.fit_results()
def test_fitres(self):
"""
Test the returning of fit results
"""
testspec = generate_cdespectrum()
testfitter = Fitter(testspec.x.value, 2. * testspec.y.value)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(('mul', 1.0, True, 0.0, None, None))
testfitter.add_empirical(testspec, testpars)
testfitter.perform_fit()
res = testfitter.fit_results()
def gaussian_peak_fitting(all_data, peak_wavelength):
all_data = fitting_bb_data(all_data)
for dat in all_data:
counts = np.asarray(dat.corrected_counts)
lam = np.asarray(dat.wavelengths)
peak_indexes = [
i for i in range(np.size(lam))
if lam[i] > (peak_wavelength - 10.) and lam[i] < (peak_wavelength +
10.)
]
### Before BB substraction
peak_lam = lam[peak_indexes[0]:peak_indexes[-1]]
peak_counts = counts[peak_indexes[0]:peak_indexes[-1]]
p = Parameters()
# (Name , Value, Vary, Min, Max, Expr)
p.add_many(('amp', 15000, True, None, None, None),
('cen', peak_wavelength, True, None, None, None),
('wid', 3.0, True, None, None, None),
('scale', 0.0, True, None, None, None))
func = Model(gaussian)
result = func.fit(peak_counts, x=peak_lam, params=p)
print(result.fit_report())
### After BB subtraction
peak_bb_sub_counts = peak_counts - np.asarray(
dat.bb[peak_indexes[0]:peak_indexes[-1]])
p = Parameters()
# (Name , Value, Vary, Min, Max, Expr)
p.add_many(('amp', 14000., True, None, None, None),
('cen', peak_wavelength, True, None, None, None),
('wid', 3.0, True, None, None, None),
('scale', 0.0, True, None, None, None))
func = Model(gaussian)
result_bb = func.fit(peak_bb_sub_counts, x=peak_lam, params=p)
print("With Black Body Subtraction")
print(result_bb.fit_report())
pp = PdfPages('Gaussian_Fit.pdf')
plt.figure()
#plt.plot(lam,counts, 'k-', label='Raw Data')
plt.plot(peak_lam, peak_counts, 'ro', label='Peak Data')
plt.plot(peak_lam, result.best_fit, 'b-', label='Best Fit Peak Data')
plt.plot(peak_lam, peak_bb_sub_counts, 'ko', label='Peak BB Sub Data')
plt.plot(peak_lam,
result_bb.best_fit,
'm-',
label='Best Fit Peak BB Sub Data')
plt.legend()
plt.savefig(pp, format='pdf')
pp.close()
def test_peakfit():
from lmfit.utilfuncs import gaussian
def residual(pars, x, data=None):
g1 = gaussian(x, pars['a1'].value, pars['c1'].value, pars['w1'].value)
g2 = gaussian(x, pars['a2'].value, pars['c2'].value, pars['w2'].value)
model = g1 + g2
if data is None:
return model
return (model - data)
n = 601
xmin = 0.
xmax = 15.0
noise = np.random.normal(scale=.65, size=n)
x = np.linspace(xmin, xmax, n)
org_params = Parameters()
org_params.add_many(('a1', 12.0, True, None, None, None),
('c1', 5.3, True, None, None, None),
('w1', 1.0, True, None, None, None),
('a2', 9.1, True, None, None, None),
('c2', 8.1, True, None, None, None),
('w2', 2.5, True, None, None, None))
data = residual(org_params, x) + noise
fit_params = Parameters()
fit_params.add_many(('a1', 8.0, True, None, 14., None),
('c1', 5.0, True, None, None, None),
('w1', 0.7, True, None, None, None),
('a2', 3.1, True, None, None, None),
('c2', 8.8, True, None, None, None))
fit_params.add('w2', expr='2.5*w1')
myfit = Minimizer(residual, fit_params,
fcn_args=(x,), fcn_kws={'data':data})
myfit.prepare_fit()
init = residual(fit_params, x)
myfit.leastsq()
print(' N fev = ', myfit.nfev)
print(myfit.chisqr, myfit.redchi, myfit.nfree)
report_fit(fit_params)
fit = residual(fit_params, x)
check_paras(fit_params, org_params)
def fit_experimental(time, YI, initial, x0, constants):
def obj_func(params, time, YI):
# unpack parameters to be fitted
v = params.valuesdict()
# create vector for lstsq solution
constant_vector = array(
(v['a'], v['b'], v['c'], v['d'], v['e'], v['f'], v['g']),
dtype=float)
# join each fit into a single residual array
all_YI = array([])
all_fits = array([])
for i in range(len(time)):
print 'Busy with fit %d' % (i)
# simulate the Metrastat
concentrations = odeint(metrastat,
initial[i],
time[i],
args=(v['k12'], constants[i]))
# use the simulation to estimate the YI
fit = dot(array(concentrations), constant_vector)
# update residual function
all_YI = one_row(YI[i], all_YI)
all_fits = one_row(fit, all_fits)
print '\n\n\nNext iteration:\n\n'
return all_YI - all_fits
# set initial parameter values
params = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
params.add_many(('a', x0[0], True, None, None, None),
('b', x0[1], True, None, None, None),
('c', x0[2], True, None, None, None),
('d', x0[3], True, None, None, None),
('e', x0[4], True, None, None, None),
('f', x0[5], True, None, None, None),
('g', x0[6], True, None, None, None),
('k12', x0[7], True, None, None, None))
# do fit with leastsq model
minimize(obj_func, params, args=(time, YI))
return params.valuesdict()
def test_plotfit(self):
"""
Test the plotting of fit results
"""
testspec = generate_cdespectrum()
testfitter = Fitter(testspec.x.value,2.*testspec.y.value)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(('mul', 1.0, True, 0.0, None, None))
testfitter.add_empirical(testspec,testpars)
testfitter.perform_fit()
fig = plt.figure()
ax = fig.add_subplot(111)
testfitter.plot_fitresults(ax)
plt.close()
def time_confinterval(self):
np.random.seed(0)
x = np.linspace(0.3,10,100)
y = 1/(0.1*x)+2+0.1*np.random.randn(x.size)
p = Parameters()
p.add_many(('a', 0.1), ('b', 1))
def residual(p):
a = p['a'].value
b = p['b'].value
return 1/(a*x)+b-y
minimizer = Minimizer(residual, p)
out = minimizer.leastsq()
return conf_interval(minimizer, out)
def sineFITwLINEARbackground(param1, param2, **kwargs):
"""
Bounded LS minimisation for the fitting of a sinefunction to a given dataset *with a linear background instead of a constant background*. It is assumed that you can represent param2 as a function of param1. The lmfit package is used to perform the bounded LS minimisation.
DANGER this routine assumes you have a fixed frequency of 1 [unit**-1]. For example:
- if param1 is time and param2 is flux, you will have a sine with a frequency of 1 c/d.
- if param1 is position and param2 is flux, you will have a sine with a frequency of 1 c/pix.
Returns: The optimal lmfit parameter class.
TODO provide options through kwargs to set the boundaries
Returns: The optimal lmfit parameter class.
@param param1: param1 measurements [???]
@type param1: numpy array of length N
@param param2: param2 measurements [???]
@type param2: numpy array of length N
@return: paramSINE
@rtype: lmfit parameter class
@kwargs: show_ME: Boolean to indicate if you want to see the report_fit - Default is False [Boolean]
"""
show_ME = kwargs.get('show_ME', False)
# Determination of the guesses to start your bounded LS fit with.
constantGUESS = np.median(param2) # param2
amplitudeGUESS = np.max(np.abs(constantGUESS-param2))/2. # param2
frequencyGUESS = 1. # DANGER Here is the frequency assumption assumption.
phaseGUESS = 0.1 # Using the param1[np.where(param2==np.max(param2))]-param1[0]%1-0.5 is best, when there is no scatter on param2
slopeGUESS, constantGUESS = np.polyfit(param1, param2, 1)
paramSINE = Parameters()
#Make a params class for lmfit.
# (Name, Value, Vary, Min, Max, Expr)
paramSINE.add_many(('amplitude', amplitudeGUESS, True, amplitudeGUESS*0.1, amplitudeGUESS*1.2, None),
('frequency', frequencyGUESS, False, frequencyGUESS-0.05, frequencyGUESS+0.05, None), # DANGER Here is the frequency assumption assumption. (It is set to non-vary.)
('constant', constantGUESS, True, -abs(constantGUESS)*1.5, abs(constantGUESS)*1.5, None),
('phase', phaseGUESS, True, 0., 1., None),
('slope', slopeGUESS, True, -2*slopeGUESS, +2*slopeGUESS, None))
resultSIN = minimize(BRITE.fitfunctions.ff_lmfitlmfit_sinslope_vs_data, paramSINE, args=(param1, param2))
if show_ME:
report_fit(paramsSIN, show_correl=False)
return paramSINE
def test_fitres_tofile(self):
"""
Test the dumping of fit results to a file
"""
testspec = generate_cdespectrum()
testfitter = Fitter(testspec.x.value,2.*testspec.y.value)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(('mul', 1.0, True, 0.0, None, None))
testfitter.add_empirical(testspec,testpars)
testpars=Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(
('peak', 1.0, True, 0.0, None, None),
('pos', 3000., True, 3000.-200., 3000.+200., None),
('fwhm', 50.0, True, 0.0, None, None),
)
testfitter.add_analytical('gaussian',testpars,funcname='test gaussian')
testfitter.perform_fit()
res = testfitter.fitresults_tofile('testfile')
def test_fittheory(self):
"""
Test the fitting of the available analytical functions
"""
testspec = generate_cdespectrum()
testfitter = Fitter.fromspectrum(testspec)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(('lor1', 1.67, False, None, None, None),
('lor2', 195., False, None, None, None),
('lor3', 1.5, False, None, None, None),
('peak', 0.05, True, 0.0, 0.1, None),
('pos', 2139.9, True, 2129.9, 2149.9, None))
testfitter.add_analytical('cde_lorentzian',testpars,funcname='test lorentzian')
testpars=Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(
('H', 1.0, True, 0.0, None, None),
('xR', 3000., True, 3000.-50., 3000.+50., None),
('w', 50.0, True, 0.0, None, None),
('tau', 50.0, True, 0.0, None, None)
)
testfitter.add_analytical('flipped_egh',testpars,funcname='test fEGH')
testpars=Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(
('peak', 1.0, True, 0.0, None, None),
('pos', 3000., True, 3000.-200., 3000.+200., None),
('fwhm', 50.0, True, 0.0, None, None),
)
testfitter.add_analytical('gaussian',testpars,funcname='test gaussian')
testfitter.perform_fit()
def test_peakfit():
def residual(pars, x, data=None):
g1 = gaussian(x, pars['a1'], pars['c1'], pars['w1'])
g2 = gaussian(x, pars['a2'], pars['c2'], pars['w2'])
model = g1 + g2
if data is None:
return model
return (model - data)
n = 601
xmin = 0.
xmax = 15.0
noise = np.random.normal(scale=.65, size=n)
x = np.linspace(xmin, xmax, n)
org_params = Parameters()
org_params.add_many(('a1', 12.0, True, None, None, None),
('c1', 5.3, True, None, None, None),
('w1', 1.0, True, None, None, None),
('a2', 9.1, True, None, None, None),
('c2', 8.1, True, None, None, None),
('w2', 2.5, True, None, None, None))
data = residual(org_params, x) + noise
fit_params = Parameters()
fit_params.add_many(('a1', 8.0, True, None, 14., None),
('c1', 5.0, True, None, None, None),
('w1', 0.7, True, None, None, None),
('a2', 3.1, True, None, None, None),
('c2', 8.8, True, None, None, None))
fit_params.add('w2', expr='2.5*w1')
myfit = Minimizer(residual, fit_params, fcn_args=(x,),
fcn_kws={'data': data})
myfit.prepare_fit()
out = myfit.leastsq()
check_paras(out.params, org_params)
class MinimizerClassSuite:
"""
Benchmarks for the Minimizer class
"""
def setup(self):
self.x = np.linspace(1, 10, 250)
np.random.seed(0)
self.y = (3.0 * np.exp(-self.x / 2)
- 5.0 * np.exp(-(self.x - 0.1) / 10.)
+ 0.1 * np.random.randn(len(self.x)))
self.p = Parameters()
self.p.add_many(('a1', 4., True, 0., 10.),
('a2', 4., True, -10., 10.),
('t1', 3., True, 0.01, 10.),
('t2', 3., True, 0.01, 20.))
self.p_emcee = deepcopy(self.p)
self.p_emcee.add('noise', 0.2, True, 0.001, 1.)
self.mini_de = Minimizer(Minimizer_Residual,
self.p,
fcn_args=(self.x, self.y),
kws={'seed': 1,
'polish': False,
'maxiter': 100})
self.mini_emcee = Minimizer(Minimizer_lnprob,
self.p_emcee,
fcn_args=(self.x, self.y))
def time_differential_evolution(self):
return self.mini_de.minimize(method='differential_evolution')
def time_emcee(self):
return self.mini_emcee.emcee(self.p_emcee, steps=100, seed=1)
def test_fittheory_convolved(self):
"""
Test the fitting of the available analytical functions with convolution enabled
"""
testspec = generate_cdespectrum()
testspec1 = testspec.subspectrum(2000.,2300.,clone=True)
testpsf1 = convolution.Gaussian1DKernel(5)
print testspec1
testfitter1 = Fitter.fromspectrum(testspec1,psf=testpsf1)
testpars = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(('lor1', 1.67, False, None, None, None),
('lor2', 195., False, None, None, None),
('lor3', 1.5, False, None, None, None),
('peak', 0.05, True, 0.0, 0.1, None),
('pos', 2139.9, True, 2129.9, 2149.9, None))
testfitter1.add_analytical('cde_lorentzian',testpars,funcname='test lorentzian')
testfitter1.perform_fit()
testspec2 = testspec.subspectrum(2500.,3700.,clone=True)
testpsf2 = convolution.Gaussian1DKernel(5)
testfitter2 = Fitter.fromspectrum(testspec2,psf=testpsf2)
testpars=Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(
('H', 1.0, True, 0.0, None, None),
('xR', 3000., True, 3000.-50., 3000.+50., None),
('w', 50.0, True, 0.0, None, None),
('tau', 50.0, True, 0.0, None, None)
)
testfitter2.add_analytical('flipped_egh',testpars,funcname='test fEGH')
testfitter2.perform_fit()
testfitter3 = Fitter.fromspectrum(testspec2,psf=testpsf2)
testpars=Parameters()
# (Name, Value, Vary, Min, Max, Expr)
testpars.add_many(
('peak', 1.0, True, 0.0, None, None),
('pos', 3000., True, 3000.-200., 3000.+200., None),
('fwhm', 50.0, True, 0.0, None, None),
)
testfitter3.add_analytical('gaussian',testpars,funcname='test gaussian')
testfitter3.perform_fit()
from lmfit import minimize, Minimizer, Parameters
# EXAMPLE 1 #
# taken from: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.brute.html
# create a set of Parameters
params = Parameters()
params.add_many(
('a', 2, False, None, None, None),
('b', 3, False, None, None, None),
('c', 7, False, None, None, None),
('d', 8, False, None, None, None),
('e', 9, False, None, None, None),
('f', 10, False, None, None, None),
('g', 44, False, None, None, None),
('h', -1, False, None, None, None),
('i', 2, False, None, None, None),
('j', 26, False, None, None, None),
('k', 1, False, None, None, None),
('l', -2, False, None, None, None),
('scale', 0.5, False, None, None, None),
('x', 0.0, True, -4.0, 4.0, None, 0.25),
('y', 0.0, True, -4.0, 4.0, None, 0.25),
)
# define functions
def f1(p):
par = p.valuesdict()
return (par['a'] * par['x']**2 + par['b'] * par['x'] * par['y'] +
par['c'] * par['y']**2 + par['d']*par['x'] + par['e']*par['y'] + par['f'])
class DecompositionFitter(object):
combinations = [(dist_1_key, dist_2_key) for dist_1_key in _keys for dist_2_key in _keys]
def __init__(self, relations):
self.data = relations.to_vector()
self.params = Parameters()
self.params.add_many(
("before_dist_1_beginning_dist_2_beginning", 0.5, True, 0.0, 1.0, None),
("similarity_dist_1_beginning_dist_2_beginning", 0.5, True, 0.0, 1.0, None),
(
"after_dist_1_beginning_dist_2_beginning",
None,
False,
None,
None,
"1 - before_dist_1_beginning_dist_2_beginning",
),
("before_dist_1_beginning_dist_2_ending", 0.5, True, 0.0, 1.0, None),
("similarity_dist_1_beginning_dist_2_ending", 0.5, True, 0.0, 1.0, None),
(
"after_dist_1_beginning_dist_2_ending",
None,
False,
None,
None,
"1 - before_dist_1_beginning_dist_2_ending",
),
("before_dist_1_ending_dist_2_beginning", 0.5, True, 0.0, 1.0, None),
("similarity_dist_1_ending_dist_2_beginning", 0.5, True, 0.0, 1.0, None),
(
"after_dist_1_ending_dist_2_beginning",
None,
False,
None,
None,
"1 - before_dist_1_ending_dist_2_beginning",
),
("before_dist_1_ending_dist_2_ending", 0.5, True, 0.0, 1.0, None),
("similarity_dist_1_ending_dist_2_ending", 0.5, True, 0.0, 1.0, None),
("after_dist_1_ending_dist_2_ending", None, False, None, None, "1 - before_dist_1_ending_dist_2_ending"),
)
minimize(self.fitness, self.params)
for param_key in self.params:
self.params[param_key].value = round(self.params[param_key].value, 6)
def fitness(self, params):
model = numpy.zeros(13)
before_dist_1_beginning_dist_2_beginning = params["before_dist_1_beginning_dist_2_beginning"].value
similarity_dist_1_beginning_dist_2_beginning = params["similarity_dist_1_beginning_dist_2_beginning"].value
after_dist_1_beginning_dist_2_beginning = params["after_dist_1_beginning_dist_2_beginning"].value
before_dist_1_beginning_dist_2_ending = params["before_dist_1_beginning_dist_2_ending"].value
similarity_dist_1_beginning_dist_2_ending = params["similarity_dist_1_beginning_dist_2_ending"].value
after_dist_1_beginning_dist_2_ending = params["after_dist_1_beginning_dist_2_ending"].value
before_dist_1_ending_dist_2_beginning = params["before_dist_1_ending_dist_2_beginning"].value
similarity_dist_1_ending_dist_2_beginning = params["similarity_dist_1_ending_dist_2_beginning"].value
after_dist_1_ending_dist_2_beginning = params["after_dist_1_ending_dist_2_beginning"].value
before_dist_1_ending_dist_2_ending = params["before_dist_1_ending_dist_2_ending"].value
similarity_dist_1_ending_dist_2_ending = params["similarity_dist_1_ending_dist_2_ending"].value
after_dist_1_ending_dist_2_ending = params["after_dist_1_ending_dist_2_ending"].value
same_dist_1_beginning_dist_2_beginning = similarity_dist_1_beginning_dist_2_beginning * (
1 - fabs(before_dist_1_beginning_dist_2_beginning - after_dist_1_beginning_dist_2_beginning)
)
same_dist_1_beginning_dist_2_ending = similarity_dist_1_beginning_dist_2_ending * (
1 - fabs(before_dist_1_beginning_dist_2_ending - after_dist_1_beginning_dist_2_ending)
)
same_dist_1_ending_dist_2_beginning = similarity_dist_1_ending_dist_2_beginning * (
1 - fabs(before_dist_1_ending_dist_2_beginning - after_dist_1_ending_dist_2_beginning)
)
same_dist_1_ending_dist_2_ending = similarity_dist_1_ending_dist_2_ending * (
1 - fabs(before_dist_1_ending_dist_2_ending - after_dist_1_ending_dist_2_ending)
)
model[0] = (
before_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* before_dist_1_ending_dist_2_beginning
* before_dist_1_ending_dist_2_ending
)
model[1] = (
before_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* same_dist_1_ending_dist_2_beginning
* before_dist_1_ending_dist_2_ending
)
model[2] = (
before_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* before_dist_1_ending_dist_2_ending
)
model[3] = (
before_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* same_dist_1_ending_dist_2_ending
)
model[4] = (
before_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* after_dist_1_ending_dist_2_ending
)
model[5] = (
same_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* before_dist_1_ending_dist_2_ending
)
model[6] = (
same_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* same_dist_1_ending_dist_2_ending
)
model[7] = (
same_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* after_dist_1_ending_dist_2_ending
)
model[8] = (
after_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* before_dist_1_ending_dist_2_ending
)
model[9] = (
after_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* same_dist_1_ending_dist_2_ending
)
model[10] = (
after_dist_1_beginning_dist_2_beginning
* before_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* after_dist_1_ending_dist_2_ending
)
model[11] = (
after_dist_1_beginning_dist_2_beginning
* same_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* after_dist_1_ending_dist_2_ending
)
model[12] = (
after_dist_1_beginning_dist_2_beginning
* after_dist_1_beginning_dist_2_ending
* after_dist_1_ending_dist_2_beginning
* after_dist_1_ending_dist_2_ending
)
return model - self.data
def compare(self, dist_1_key="beginning", dist_2_key="beginning"):
before = self.params["before_dist_1_" + dist_1_key + "_dist_2_" + dist_2_key].value
after = self.params["after_dist_1_" + dist_1_key + "_dist_2_" + dist_2_key].value
similarity = self.params["similarity_dist_1_" + dist_1_key + "_dist_2_" + dist_2_key].value
# before, similarity, after = round(before, 6), round(similarity, 6), round(after, 6)
same = similarity * (1 - fabs(before - after))
return before, same, after
def get_composition_data(self):
data = []
for key in self.combinations:
before, same, after = self.compare(*key)
data.append(before)
data.append(same)
return data
def check(self):
from spatiotemporal.temporal_events import FormulaCreator
print self.data
print FormulaCreator(self).calculate_relations().to_vector()
print
class TestParameters(unittest.TestCase):
def setUp(self):
self.params = Parameters()
self.params.add_many(('a', 1., True, None, None, None),
('b', 2., True, None, None, None),
('c', 3., True, None, None, '2. * a'))
def test_expr_was_evaluated(self):
self.params.update_constraints()
assert_almost_equal(self.params['c'].value,
2 * self.params['a'].value)
def test_deepcopy(self):
# check that a simple copy works
b = deepcopy(self.params)
assert_(self.params == b)
# check that we can add a symbol to the interpreter
self.params['b'].expr = 'sin(1)'
self.params['b'].value = 10
assert_almost_equal(self.params['b'].value, np.sin(1))
assert_almost_equal(self.params._asteval.symtable['b'], np.sin(1))
# check that the symbols in the interpreter are still the same after
# deepcopying
b = deepcopy(self.params)
unique_symbols_params = self.params._asteval.user_defined_symbols()
unique_symbols_b = self.params._asteval.user_defined_symbols()
assert_(unique_symbols_b == unique_symbols_params)
for unique_symbol in unique_symbols_b:
if self.params._asteval.symtable[unique_symbol] is np.nan:
continue
assert_(self.params._asteval.symtable[unique_symbol]
==
b._asteval.symtable[unique_symbol])
def test_add_many_params(self):
# test that we can add many parameters, but only parameters are added.
a = Parameter('a', 1)
b = Parameter('b', 2)
p = Parameters()
p.add_many(a, b)
assert_(list(p.keys()) == ['a', 'b'])
def test_expr_and_constraints_GH265(self):
# test that parameters are reevaluated if they have bounds and expr
# see GH265
p = Parameters()
p['a'] = Parameter('a', 10, True)
p['b'] = Parameter('b', 10, True, 0, 20)
assert_equal(p['b'].min, 0)
assert_equal(p['b'].max, 20)
p['a'].expr = '2 * b'
assert_almost_equal(p['a'].value, 20)
p['b'].value = 15
assert_almost_equal(p['b'].value, 15)
assert_almost_equal(p['a'].value, 30)
p['b'].value = 30
assert_almost_equal(p['b'].value, 20)
assert_almost_equal(p['a'].value, 40)
def test_pickle_parameter(self):
# test that we can pickle a Parameter
p = Parameter('a', 10, True, 0, 1)
pkl = pickle.dumps(p)
q = pickle.loads(pkl)
assert_(p == q)
def test_pickle_parameters(self):
# test that we can pickle a Parameters object
p = Parameters()
p.add('a', 10, True, 0, 100)
p.add('b', 10, True, 0, 100, 'a * sin(1)')
p.update_constraints()
p._asteval.symtable['abc'] = '2 * 3.142'
pkl = pickle.dumps(p, -1)
q = pickle.loads(pkl)
q.update_constraints()
assert_(p == q)
assert_(not p is q)
# now test if the asteval machinery survived
assert_(q._asteval.symtable['abc'] == '2 * 3.142')
def test_isclose(self):
assert_(isclose(1., 1+1e-5, atol=1e-4, rtol=0))
assert_(not isclose(1., 1+1e-5, atol=1e-6, rtol=0))
assert_(isclose(1e10, 1.00001e10, rtol=1e-5, atol=1e-8))
assert_(not isclose(0, np.inf))
assert_(not isclose(-np.inf, np.inf))
assert_(isclose(np.inf, np.inf))
assert_(not isclose(np.nan, np.nan))
class TestParameters(unittest.TestCase):
def setUp(self):
self.params = Parameters()
self.params.add_many(('a', 1., True, None, None, None),
('b', 2., True, None, None, None),
('c', 3., True, None, None, '2. * a'))
def test_expr_was_evaluated(self):
self.params.update_constraints()
assert_almost_equal(self.params['c'].value,
2 * self.params['a'].value)
def test_copy(self):
# check simple Parameters.copy() does not fail
# on non-trivial Parameters
p1 = Parameters()
p1.add('t', 2.0, min=0.0, max=5.0)
p1.add('x', 10.0)
p1.add('y', expr='x*t + sqrt(t)/3.0')
p2 = p1.copy()
assert(isinstance(p2, Parameters))
assert('t' in p2)
assert('y' in p2)
assert(p2['t'].max < 6.0)
assert(np.isinf(p2['x'].max) and p2['x'].max > 0)
assert(np.isinf(p2['x'].min) and p2['x'].min < 0)
assert('sqrt(t)' in p2['y'].expr )
assert(p2._asteval is not None)
assert(p2._asteval.symtable is not None)
assert((p2['y'].value > 20) and (p2['y'].value < 21))
def test_deepcopy(self):
# check that a simple copy works
b = deepcopy(self.params)
assert_(self.params == b)
# check that we can add a symbol to the interpreter
self.params['b'].expr = 'sin(1)'
self.params['b'].value = 10
assert_almost_equal(self.params['b'].value, np.sin(1))
assert_almost_equal(self.params._asteval.symtable['b'], np.sin(1))
# check that the symbols in the interpreter are still the same after
# deepcopying
b = deepcopy(self.params)
unique_symbols_params = self.params._asteval.user_defined_symbols()
unique_symbols_b = self.params._asteval.user_defined_symbols()
assert_(unique_symbols_b == unique_symbols_params)
for unique_symbol in unique_symbols_b:
if self.params._asteval.symtable[unique_symbol] is np.nan:
continue
assert_(self.params._asteval.symtable[unique_symbol]
==
b._asteval.symtable[unique_symbol])
def test_add_many_params(self):
# test that we can add many parameters, but only parameters are added.
a = Parameter('a', 1)
b = Parameter('b', 2)
p = Parameters()
p.add_many(a, b)
assert_(list(p.keys()) == ['a', 'b'])
def test_expr_and_constraints_GH265(self):
# test that parameters are reevaluated if they have bounds and expr
# see GH265
p = Parameters()
p['a'] = Parameter('a', 10, True)
p['b'] = Parameter('b', 10, True, 0, 20)
assert_equal(p['b'].min, 0)
assert_equal(p['b'].max, 20)
p['a'].expr = '2 * b'
assert_almost_equal(p['a'].value, 20)
p['b'].value = 15
assert_almost_equal(p['b'].value, 15)
assert_almost_equal(p['a'].value, 30)
p['b'].value = 30
assert_almost_equal(p['b'].value, 20)
assert_almost_equal(p['a'].value, 40)
def test_pickle_parameter(self):
# test that we can pickle a Parameter
p = Parameter('a', 10, True, 0, 1)
pkl = pickle.dumps(p)
q = pickle.loads(pkl)
assert_(p == q)
def test_pickle_parameters(self):
# test that we can pickle a Parameters object
p = Parameters()
p.add('a', 10, True, 0, 100)
p.add('b', 10, True, 0, 100, 'a * sin(1)')
p.update_constraints()
p._asteval.symtable['abc'] = '2 * 3.142'
pkl = pickle.dumps(p, -1)
q = pickle.loads(pkl)
q.update_constraints()
assert_(p == q)
assert_(not p is q)
# now test if the asteval machinery survived
assert_(q._asteval.symtable['abc'] == '2 * 3.142')
# check that unpickling of Parameters is not affected by expr that
# refer to Parameter that are added later on. In the following
# example var_0.expr refers to var_1, which is a Parameter later
# on in the Parameters OrderedDict.
p = Parameters()
p.add('var_0', value=1)
p.add('var_1', value=2)
p['var_0'].expr = 'var_1'
pkl = pickle.dumps(p)
q = pickle.loads(pkl)
def test_isclose(self):
assert_(isclose(1., 1+1e-5, atol=1e-4, rtol=0))
assert_(not isclose(1., 1+1e-5, atol=1e-6, rtol=0))
assert_(isclose(1e10, 1.00001e10, rtol=1e-5, atol=1e-8))
assert_(not isclose(0, np.inf))
assert_(not isclose(-np.inf, np.inf))
assert_(isclose(np.inf, np.inf))
assert_(not isclose(np.nan, np.nan))
spcorr[:,0] = spforcorr[:,0]
spcorr[:,1] = interpolate.splev(spforcorr[:,0],tck,der=0)
if sptarget is None: #in such case we return the corrected spectrum
return spcorr
return (spcorr[:,1] - sptarget[:,1])
###########################################################################
# Now we choose the portion of spectra to fit
DiamondtoFit = corrdiamond[np.where((corrdiamond[:,0]> 655) & (corrdiamond[:,0] < 670))]
SampletoFit = corrsample[np.where((corrsample[:,0]> 655) & (corrsample[:,0] < 670))]
# Now we enter the model parameters
params = Parameters()
params.add_many(('xshift', 1, True, -15, 15, None),
('yshift', 0, True, None, None, None),
('yshiftcarr', 1e-2, True, None, None, None))
# Now we chose the algorithm and run the optimization
algorithm = "leastsq"
result = minimize(residual, params,method = algorithm, args=(DiamondtoFit, SampletoFit))
cds = residual(result.params,corrdiamond)
# To apply the correction for x shift, we need to interpolate to create new datasets
tck = interpolate.splrep(corrsample[:,0],corrsample[:,1],s=0)
tck2 = interpolate.splrep(cds[:,0],cds[:,1],s=0)
# The following rows contain the spectra corrected from x and y shifts
diamondfinal = np.zeros((len(x),2))
samplefinal = np.zeros((len(x),2))
spcorr[:,0] = spforcorr[:,0]
spcorr[:,1] = interpolate.splev(spcorr[:,0],tck,der=0)
if sptarget is None: #in such case we return the corrected spectrum
return spcorr
return (spcorr[:,1] - sptarget[:,1])
#######################################################################
# Now we choose the portion of spectra to fit
DiamondtoFit = corrdiamond[np.where((corrdiamond[:,0]> dconvfit[0,0]) & (corrdiamond[:,0] < dconvfit[0,1]))]
SampletoFit = corrsample[np.where((corrsample[:,0]> dconvfit[0,0]) & (corrsample[:,0] < dconvfit[0,1]))]
# Now we enter the model parameters
params = Parameters()
params.add_many(('xshift', 1, True, -15, 15, None),
('yshift', 1e-1, True, None, None, None),
('yshiftcarr', 1e-5, True, None, None, None))
# Now we chose the algorithm and run the optimization
algorithm = "leastsq"
result = minimize(residual, params,method = algorithm, args=(DiamondtoFit, SampletoFit))
cds = residual(params,corrdiamond)
# To apply the correction for x shift, we need to interpolate to create new datasets
tck = interpolate.splrep(corrsample[:,0],corrsample[:,1],s=0)
tck2 = interpolate.splrep(cds[:,0],cds[:,1],s=0)
# The following rows contain the spectra corrected from x and y shifts
diamondfinal = np.zeros((len(x),2))
samplefinal = np.zeros((len(x),2))
class TestParameters(unittest.TestCase):
def setUp(self):
self.params = Parameters()
self.params.add_many(('a', 1., True, None, None, None),
('b', 2., True, None, None, None),
('c', 3., True, None, None, '2. * a'))
def test_expr_was_evaluated(self):
self.params.update_constraints()
assert_almost_equal(self.params['c'].value,
2 * self.params['a'].value)
def test_copy(self):
# check simple Parameters.copy() does not fail
# on non-trivial Parameters
p1 = Parameters()
p1.add('t', 2.0, min=0.0, max=5.0)
p1.add('x', 10.0)
p1.add('y', expr='x*t + sqrt(t)/3.0')
p2 = p1.copy()
assert(isinstance(p2, Parameters))
assert('t' in p2)
assert('y' in p2)
assert(p2['t'].max < 6.0)
assert(np.isinf(p2['x'].max) and p2['x'].max > 0)
assert(np.isinf(p2['x'].min) and p2['x'].min < 0)
assert('sqrt(t)' in p2['y'].expr)
assert(p2._asteval is not None)
assert(p2._asteval.symtable is not None)
assert((p2['y'].value > 20) and (p2['y'].value < 21))
def test_copy_function(self):
# check copy(Parameters) does not fail
p1 = Parameters()
p1.add('t', 2.0, min=0.0, max=5.0)
p1.add('x', 10.0)
p1.add('y', expr='x*t + sqrt(t)/3.0')
p2 = copy(p1)
assert(isinstance(p2, Parameters))
# change the 'x' value in the original
p1['x'].value = 4.0
assert(p2['x'].value > 9.8)
assert(p2['x'].value < 10.2)
assert(np.isinf(p2['x'].max) and p2['x'].max > 0)
assert('t' in p2)
assert('y' in p2)
assert(p2['t'].max < 6.0)
assert(np.isinf(p2['x'].min) and p2['x'].min < 0)
assert('sqrt(t)' in p2['y'].expr)
assert(p2._asteval is not None)
assert(p2._asteval.symtable is not None)
assert((p2['y'].value > 20) and (p2['y'].value < 21))
assert(p1['y'].value < 10)
def test_deepcopy(self):
# check that a simple copy works
b = deepcopy(self.params)
assert_(self.params == b)
# check that we can add a symbol to the interpreter
self.params['b'].expr = 'sin(1)'
self.params['b'].value = 10
assert_almost_equal(self.params['b'].value, np.sin(1))
assert_almost_equal(self.params._asteval.symtable['b'], np.sin(1))
# check that the symbols in the interpreter are still the same after
# deepcopying
b = deepcopy(self.params)
unique_symbols_params = self.params._asteval.user_defined_symbols()
unique_symbols_b = self.params._asteval.user_defined_symbols()
assert_(unique_symbols_b == unique_symbols_params)
for unique_symbol in unique_symbols_b:
if self.params._asteval.symtable[unique_symbol] is np.nan:
continue
assert_(self.params._asteval.symtable[unique_symbol]
==
b._asteval.symtable[unique_symbol])
def test_add_many_params(self):
# test that we can add many parameters, but only parameters are added.
a = Parameter('a', 1)
b = Parameter('b', 2)
p = Parameters()
p.add_many(a, b)
assert_(list(p.keys()) == ['a', 'b'])
def test_expr_and_constraints_GH265(self):
# test that parameters are reevaluated if they have bounds and expr
# see GH265
p = Parameters()
p['a'] = Parameter('a', 10, True)
p['b'] = Parameter('b', 10, True, 0, 20)
assert_equal(p['b'].min, 0)
assert_equal(p['b'].max, 20)
p['a'].expr = '2 * b'
assert_almost_equal(p['a'].value, 20)
p['b'].value = 15
assert_almost_equal(p['b'].value, 15)
assert_almost_equal(p['a'].value, 30)
p['b'].value = 30
assert_almost_equal(p['b'].value, 20)
assert_almost_equal(p['a'].value, 40)
def test_pickle_parameter(self):
# test that we can pickle a Parameter
p = Parameter('a', 10, True, 0, 1)
pkl = pickle.dumps(p)
q = pickle.loads(pkl)
assert_(p == q)
def test_pickle_parameters(self):
# test that we can pickle a Parameters object
p = Parameters()
p.add('a', 10, True, 0, 100)
p.add('b', 10, True, 0, 100, 'a * sin(1)')
p.update_constraints()
p._asteval.symtable['abc'] = '2 * 3.142'
pkl = pickle.dumps(p, -1)
q = pickle.loads(pkl)
q.update_constraints()
assert_(p == q)
assert_(p is not q)
# now test if the asteval machinery survived
assert_(q._asteval.symtable['abc'] == '2 * 3.142')
# check that unpickling of Parameters is not affected by expr that
# refer to Parameter that are added later on. In the following
# example var_0.expr refers to var_1, which is a Parameter later
# on in the Parameters OrderedDict.
p = Parameters()
p.add('var_0', value=1)
p.add('var_1', value=2)
p['var_0'].expr = 'var_1'
pkl = pickle.dumps(p)
q = pickle.loads(pkl)
def test_params_usersyms(self):
# test passing usersymes to Parameters()
def myfun(x):
return x**3
params = Parameters(usersyms={"myfun": myfun})
params.add("a", value=2.3)
params.add("b", expr="myfun(a)")
xx = np.linspace(0, 1, 10)
yy = 3 * xx + np.random.normal(scale=0.002, size=len(xx))
model = Model(lambda x, a: a * x)
result = model.fit(yy, params=params, x=xx)
assert_(np.isclose(result.params['a'].value, 3.0, rtol=0.025))
assert_(result.nfev > 3)
assert_(result.nfev < 300)
def test_set_symtable(self):
# test that we use Parameter.set(value=XXX) and have
# that new value be used in constraint expressions
pars = Parameters()
pars.add('x', value=1.0)
pars.add('y', expr='x + 1')
assert_(np.isclose(pars['y'].value, 2.0))
pars['x'].set(value=3.0)
assert_(np.isclose(pars['y'].value, 4.0))
def test_dumps_loads_parameters(self):
# test that we can dumps() and then loads() a Parameters
pars = Parameters()
pars.add('x', value=1.0)
pars.add('y', value=2.0)
pars['x'].expr = 'y / 2.0'
dumps = pars.dumps()
newpars = Parameters().loads(dumps)
newpars['y'].value = 100.0
assert_(np.isclose(newpars['x'].value, 50.0))
def test_isclose(self):
assert_(np.isclose(1., 1+1e-5, atol=1e-4, rtol=0))
assert_(not np.isclose(1., 1+1e-5, atol=1e-6, rtol=0))
assert_(np.isclose(1e10, 1.00001e10, rtol=1e-5, atol=1e-8))
assert_(not np.isclose(0, np.inf))
assert_(not np.isclose(-np.inf, np.inf))
assert_(np.isclose(np.inf, np.inf))
assert_(not np.isclose(np.nan, np.nan))
def test_expr_with_bounds(self):
"test an expression with bounds, without value"
pars = Parameters()
pars.add('c1', value=0.2)
pars.add('c2', value=0.2)
pars.add('c3', value=0.2)
pars.add('csum', value=0.8)
# this should not raise TypeError:
pars.add('c4', expr='csum-c1-c2-c3', min=0, max=1)
assert_(np.isclose(pars['c4'].value, 0.2))
def test_invalid_expr_exceptions(self):
"test if an exception is raised for invalid expressions (GH486)"""
p1 = Parameters()
p1.add('t', 2.0, min=0.0, max=5.0)
p1.add('x', 10.0)
with self.assertRaises(SyntaxError):
p1.add('y', expr='x*t + sqrt(t)/')
assert(len(p1['y']._expr_eval.error) > 0)
p1.add('y', expr='x*t + sqrt(t)/3.0')
p1['y'].set(expr='x*3.0 + t**2')
assert('x*3' in p1['y'].expr)
assert(len(p1['y']._expr_eval.error) == 0)
with self.assertRaises(SyntaxError):
p1['y'].set(expr='t+')
assert(len(p1['y']._expr_eval.error) > 0)
assert_almost_equal(p1['y'].value, 34.0)
def test_eval(self):
# check that eval() works with usersyms and parameter values
def myfun(x):
return 2.0 * x
p = Parameters(usersyms={"myfun": myfun})
p.add("a", value=4.0)
p.add("b", value=3.0)
assert_almost_equal(p.eval("myfun(2.0) * a"), 16)
assert_almost_equal(p.eval("b / myfun(3.0)"), 0.5)
delta_time = numpy.zeros(samples)
delta_time [0] = 1.0
x_freq = numpy.linspace(0.0, sample_rate/2, (samples/2)+1)
pre1_filters = '-eadb:-{adb} -el:RThighshelf,{RThighshelf_A},{RThighshelf_f0},{RThighshelf_Q} -el:RTlowshelf,{RTlowshelf_A},{RTlowshelf_f0},{RTlowshelf_Q}'
fmin = 20
fmax = 22000
pre1 = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
pre1.add_many( ('adb', 9.28136887, True, 0, None, None),
('RThighshelf_A', 4.38211718, True, 0, None, None),
('RThighshelf_f0', 134.645409, True, 100, 200, None),
('RThighshelf_Q', 0.48178881, True, 0.1, 3, None),
('RTlowshelf_A', 3.42318251, True, 0, None, None),
('RTlowshelf_f0', 1747.22195, True, 1600, 1800, None),
('RTlowshelf_Q', 0.48331721, True, 0.1, 3, None),
('sample_rate', sample_rate, False, None, None, None))
#out = minimize(residual, pre1, args=(pre1_filters, x_freq, fmin, fmax, delta_time), kws={'data':pre1_reference}, method='nelder')
#pre1 = out.params
#print(fit_report(out))
pre1_matched = residual(pre1, pre1_filters, x_freq, fmin, fmax, delta_time)
plotFilter("pre1.png", "Pre 1", fmin, fmax, x_freq, pre1_reference, pre1_matched)
from stress_to_spike import stress_to_group_current, stress_to_fr_inst
from gen_function import stress_to_current, get_interp_stress
import cy_lif_model as lif_model
from model_constants import MC_GROUPS, REF_DISPL, REF_STIM
from fit_model import get_data_dicts, get_single_residual
TEST_DATA_PATH = './data/test/'
# Commonly used constants
params = {
'tau_arr': np.array([8, 500, 1000, np.inf]),
'k_arr': np.array([1.35, 2, .15, 1.5])}
lmpars = Parameters()
lmpars.add_many(('tau1', 8), ('tau2', 200), ('tau3', 1832), ('tau4', np.inf),
('k1', 0.782), ('k2', 0.304), ('k3', 0.051), ('k4', 0.047))
interp_static_displ = .35
extrap_static_displ = .65
def load_test_csv(vname_list):
data = {}
for vname in vname_list:
data[vname] = np.genfromtxt('%s%s.csv' % (TEST_DATA_PATH, vname),
delimiter=',')
return data
def save_test_csv(data):
for key, item in data.items():
np.savetxt('%s%s.csv' % (TEST_DATA_PATH, key), item, delimiter=',')
interestspectra = sample[np.where((sample[:,0] > lb)&(sample[:,0] < hb))]
ese0 = interestspectra[:,2]/abs(interestspectra[:,1]) #take ese as a percentage, we assume that the treatment was made correctly for error determination... if not, please put sigma = None
interestspectra[:,1] = interestspectra[:,1]/np.amax(interestspectra[:,1])*100 # normalise spectra to maximum, easier to handle after
sigma = abs(ese0*interestspectra[:,1]) #calculate good ese
#sigma = None # you can activate that if you are not sure about the errors
xfit = interestspectra[:,0] # region to be fitted
data = interestspectra[:,1] # region to be fitted
params = Parameters()
####################### FOR MELT:
####################### COMMENT IF NOT WANTED
# (Name, Value, Vary, Min, Max, Expr)
params.add_many(('a1', 1, True, 0, None, None),
('f1', 5200, True, 750, None, None),
('l1', 1, True, 0, None, None),
('a2', 1, True, 0, None, None),
('f2', 5400, True, None, None, None),
('l2', 1, True, None, None, None))
result = minimize(residual_melt, params, args=(xfit, data)) # fit data with leastsq model from scipy
model = fit_report(params) # the report
yout, peak1,peak2,= residual_melt(params,xfit) # the different peaks
#### We just calculate the different areas up to 4700 cmm-1 and those of the gaussians
# Select interest areas for calculating the areas of OH and H2Omol peaks
intarea45 = sample[np.where((sample[:,0]> 4100) & (sample[:,0]<4700))]
area4500 = np.trapz(intarea45[:,1],intarea45[:,0])
esearea4500 = 1/sqrt(area4500) # We assume that RELATIVE errors on areas are globally equal to 1/sqrt(Area)
# now for the gaussians
# unpack parameters:
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
ax1.set_title('RT')
ax2.set_title(temperature[lg])
ax1.plot(inputRT[:,0], inputRT[:,1],'k-')
ax2.plot(inputHT[:,0],inputHT[:,1],'r-')
params = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
params.add_many(('a1', 10, True, 0, None, None),
('f1', 1278, True, 1259, 1300, None),
('l1', 5, True, 0, 50, None),
('e1', 0.5, True, 0, 1, None),
('a2', 100, True, 0, None, None),
('f2', 1332, True, 1300, 1400, None),
('l2', 5, True, 0, 50, None),
('e2', 0.5, True, 0, 1, None),
('a3', 2, True, 0, None, None),
('f3', 1882, True, 1800, 1920, None),
('l3', 4, True, 0, 50, None),
('e3', 0.5, True, 0, 1, None))
paramsHT = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
paramsHT.add_many(('a1', 10, True, 0, None, None),
('f1', 1261, True, 1249, 1280, None),
('l1', 5, True, 0, 50, None),
('e1', 0.5, True, 0, 1, None),
('a2', 100, True, 0, None, None),
('f2', 1304, True, 1300, 1330, None),
('l2', 5, True, 0, 50, None),
g2 = gauss(x, pars['a2'].value, pars['c2'].value, pars['w2'].value)
model = g1 + g2
if data is None:
return model
return (model - data)
n = 601
xmin = 0.
xmax = 15.0
noise = random.normal(scale=.65, size=n)
x = linspace(xmin, xmax, n)
fit_params = Parameters()
fit_params.add_many(('a1', 12.0, True, None, None, None),
('c1', 5.3, True, None, None, None),
('w1', 1.0, True, None, None, None),
('a2', 9.1, True, None, None, None),
('c2', 8.1, True, None, None, None),
('w2', 2.5, True, None, None, None))
data = residual(fit_params, x) + noise
pylab.plot(x, data, 'r+')
fit_params = Parameters()
fit_params.add_many(('a1', 8.0, True, None, 14., None),
('c1', 5.0, True, None, None, None),
('w1', 0.7, True, None, None, None),
('a2', 3.1, True, None, None, None),
('c2', 8.8, True, None, None, None))
fit_params.add('w2', expr='2.5*w1')
def lame_lmfit_gaussian_centering(imageCube, yguess=15, xguess=15, subArraySize=10, init_params=None, nSig=False, useMoments=False, method='leastsq'):
"""Class methods are similar to regular functions.
Note:
Do not include the `self` parameter in the ``Args`` section.
Args:
param1: The first parameter.
param2: The second parameter.
Returns:
True if successful, False otherwise.
"""
imageSize = imageCube.shape[1]
nparams = 6
if init_params is None:
useMoments = True
init_params = moments(imageCube[0])
ihg, iyc, ixc, iyw, ixw, ibg = arange(nparams)
lmfit_init_params = Parameters()
lmfit_init_params.add_many(
('height' , init_params[ihg], True , 0.0 , inf ),
('center_y', init_params[iyc], True , 0.0 , imageSize),
('center_x', init_params[ixc], True , 0.0 , imageSize),
('width_y' , init_params[iyw], True , 0.0 , imageSize),
('width_x' , init_params[ixw], True , 0.0 , imageSize),
('offset' , init_params[ibg], True))
gfit_model = Model(gaussian, independent_vars=['yy', 'xx'])
yy0, xx0 = indices(imageCube[0].shape)
npix = subArraySize//2
ylower = yguess - npix
yupper = yguess + npix
xlower = xguess - npix
xupper = xguess + npix
ylower, xlower, yupper, xupper = int32([ylower, xlower, yupper, xupper])
yy = yy0[ylower:yupper, xlower:xupper]
xx = xx0[ylower:yupper, xlower:xupper]
heights, ycenters, xcenters, ywidths, xwidths, offsets = zeros((nparams, nFrames))
for k, image in enumerate(imageCube):
subFrameNow = image[ylower:yupper, xlower:xupper]
subFrameNow[isnan(subFrameNow)] = median(subFrameNow)
subFrameNow = gaussianFilter(subFrameNow, nSig) if not isinstance(nSig, bool) else subFrameNow
init_params = moments(subFrameNow) if useMoments else init_params
gfit_res = gfit_model.fit(subFrameNow, params=lmfit_init_params, xx=xx, yy=yy, method=method)
heights[k] = gfit_res.best_values['height']
ycenters[k] = gfit_res.best_values['center_y']
xcenters[k] = gfit_res.best_values['center_x']
ywidths[k] = gfit_res.best_values['width_y']
xwidths[k] = gfit_res.best_values['width_x']
offsets[k] = gfit_res.best_values['offset']
return heights, ycenters, xcenters, ywidths, xwidths, offsets
if data is None:
return model
return model - data
n = 601
xmin = 0.0
xmax = 15.0
noise = random.normal(scale=0.65, size=n)
x = linspace(xmin, xmax, n)
fit_params = Parameters()
fit_params.add_many(
("a1", 12.0, True, None, None, None),
("c1", 5.3, True, None, None, None),
("w1", 1.0, True, None, None, None),
("a2", 9.1, True, None, None, None),
("c2", 8.1, True, None, None, None),
("w2", 2.5, True, None, None, None),
)
data = residual(fit_params, x) + noise
if HASPYLAB:
pylab.plot(x, data, "r+")
fit_params = Parameters()
fit_params.add_many(
("a1", 8.0, True, None, 14.0, None),
("c1", 5.0, True, None, None, None),
("w1", 0.7, True, None, None, None),
("a2", 3.1, True, None, None, None),
