def __init__(self, parent=None, size=(-1, -1)):
wx.Frame.__init__(self,
parent,
-1,
'Parameter Panel Test',
size=size,
style=wx.DEFAULT_FRAME_STYLE)
panel = GridPanel(self)
param1 = Parameter(value=99.0,
vary=True,
min=0,
name='peak1_amplitude')
param2 = Parameter(value=110.2,
vary=True,
min=100,
max=120,
name='peak1_center')
param3 = Parameter(value=1.23,
vary=True,
min=0.5,
max=2.0,
name='peak1_sigma')
panel.Add(ParameterPanel(panel, param1, show_name=True), style=LEFT)
# panel.NewRow()
panel.Add(ParameterPanel(panel, param2, show_name=True), style=LEFT)
panel.Add(ParameterPanel(panel, param3, show_name=True), style=LEFT)
panel.pack()
self.createMenus()
self.SetSize((700, 200))
self.Show()
self.Raise()
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 gaussians_to_population(y_array, x_array, xl, xr):
"""
Fit y_array with two Gaussian distributions with the initial guess of their center
coordinates xl and xr.
Returns a Parameter object representing the ratio of the left population.
Example:
left_populations = data.new_data('Left_populations', -1)
for idx in np.ndindex(left_populations.shape):
left_populations[idx] = gaussian_to_population(data['Digitizer_count'][idx], data['Volt'], 0.12, 0.16).value
"""
def dual_gaussian(x, ratio, sigma, center_left, sep):
gauss = lambda m, s, x: 1.0/(s*sqrt(2*pi)) * np.exp(-(x-m)**2/(2*s**2))
return ratio * gauss(center_left, sigma, x) + (1.0-ratio) * gauss(center_left + sep, sigma, x)
# Normalize distribution so that the integration by x gives one
norm = y_array.sum() * (x_array[1] - x_array[0])
model = lmfit.Model(dual_gaussian, independent_vars=['x'])
result = model.fit(y_array / norm, x=x_array,
ratio=Parameter(value=0.5, min=0.0, max=1.0),
sigma=Parameter(value=(xr-xl)/2, max=(xr-xl)),
center_left=xl,
sep=Parameter(value=xr-xl, min=(xr-xl)/2))
return result.params['ratio']
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 epw_single_species_settings_params():
"""
Standard input for the spectral_density_model function
Includes both settings and params: separated by the function
spectral_density_model_settings_params
"""
probe_wavelength = 532 * u.nm
scattering_angle = np.deg2rad(63)
scatter_vec = np.array(
[np.cos(scattering_angle),
np.sin(scattering_angle), 0])
kwargs = {}
kwargs["probe_wavelength"] = probe_wavelength.to(u.m).value
kwargs["probe_vec"] = np.array([1, 0, 0])
kwargs["scatter_vec"] = scatter_vec
kwargs["ions"] = ["H+"]
kwargs["n"] = Parameter("n",
value=2e17 * 1e6,
vary=True,
min=8e16 * 1e6,
max=6e17 * 1e6)
kwargs["T_e_0"] = Parameter("T_e_0", value=10, vary=True, min=5, max=20)
kwargs["T_i_0"] = Parameter("T_i_0", value=20, vary=False, min=5, max=70)
w0 = probe_wavelength.value
kwargs["wavelengths"] = ((np.linspace(w0 - 40, w0 + 40, num=512) *
u.nm).to(u.m).value)
return kwargs
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 factory(Flux_Density=1, Spectral_Index=2, Pivot_Energy=1):
from lmfit import Parameter
pars = [
Parameter(name='Flux_Density', value=Flux_Density),
Parameter(name='Spectral_Index', value=Spectral_Index),
Parameter(name='Pivot_Energy', value=Pivot_Energy)
]
return PowerLaw(*pars)
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 fit(fun, x0=None, args=None, check_fun=None, debug=False,
cons_meth=methods_cons_good, uncons_meth=methods_uncons_good):
from lmfit import Parameter, Parameters, minimize
best_result = None
best_coeffs = None
best_err = 1e300
N = len(x0)
for restart in (False, True):
if restart:
if debug:
print('-'*10 + 'RESTARTING WITH BEST SOLUTION' + '-'*10)
best_coeffs_restart = best_coeffs
for method_list, bounded in zip([uncons_meth, cons_meth], [False, True]):
for method in method_list:
fit_params = Parameters() # Ordered dict
for i in range(N):
if restart:
v = best_coeffs_restart[i]
else:
try:
v = x0[-i]
except:
v = 1e-20
if bounded:
if restart:
l, h = -abs(best_coeffs_restart[i])*1e3, abs(best_coeffs_restart[i])*1e3
else:
l, h = -1e5, 1e5
fit_params['c' + str(i)] = Parameter(value=v, min=l, max=h, vary=True)
else:
fit_params['c' + str(i)] = Parameter(value=v, vary=True)
try:
print('starting', [i.value for i in fit_params.values()])
result = minimize(fun, fit_params, args=args, method=method)
except Exception as e:
if debug:
print(e)
continue
result = postproc_lmfit(result)
fit = [i.value for i in result.params.values()]
if result.aard < best_err and (check_fun is None or check_fun(fit)):
best_err = result.aard
best_coeffs = fit
best_result = result
if debug:
if check_fun is None:
print(result.aard, method)
else:
print(result.aard, check_fun(fit), method)
return best_coeffs, best_result
def line(data_obj, opt_kwargs={}):
d, f, t = data_obj.dft
_line = Model(linear)
params = Parameters()
params['m'] = Parameter(name='m', value=np.median(f))
params['c'] = Parameter(name='c', value=0, min=-10 * np.median(f))
fitted = _line.fit(f, params, x=d)
return fitted, (d, f, fitted.best_fit)
def run_kropff_low_lambda(self, update_table_ui=False):
gmodel = Model(kropff_low_lambda,
missing='drop',
independent_vars=['lda'])
lda = self.xaxis_to_fit
o_gui = GuiUtility(parent=self.parent)
ahkl_init = np.float(str(self.parent.kropff_low_lda_ahkl_init.text()))
bhkl_init = np.float(str(self.parent.kropff_low_lda_bhkl_init.text()))
for _row, yaxis in enumerate(self.list_yaxis_to_fit):
_entry = self.parent.fitting_input_dictionary['rois'][_row][
'fitting']['kropff']['high']
a0 = np.float(_entry['a0'])
b0 = np.float(_entry['b0'])
yaxis = -np.log(yaxis)
_result = gmodel.fit(yaxis,
lda=lda,
a0=Parameter('a0', value=a0, vary=False),
b0=Parameter('b0', value=b0, vary=False),
ahkl=ahkl_init,
bhkl=bhkl_init)
ahkl = _result.params['ahkl'].value
ahkl_error = _result.params['ahkl'].stderr
bhkl = _result.params['bhkl'].value
bhkl_error = _result.params['bhkl'].stderr
yaxis_fitted = kropff_low_lambda(lda, a0, b0, ahkl, bhkl)
result_dict = {
'ahkl': ahkl,
'bhkl': bhkl,
'ahkl_error': ahkl_error,
'bhkl_error': bhkl_error,
'xaxis_to_fit': lda,
'yaxis_fitted': yaxis_fitted
}
self.parent.fitting_input_dictionary['rois'][_row]['fitting'][
'kropff']['low'] = deepcopy(result_dict)
if update_table_ui:
o_gui.update_kropff_low_lambda_table_ui(row=_row,
ahkl=ahkl,
bhkl=bhkl,
ahkl_error=ahkl_error,
bhkl_error=bhkl_error)
def __init__(self, config):
defaultRkpVary = True
defaultRkpMin = float("-inf")
defaultRkpMax = float("inf")
params = Parameters()
params['s1'] = Parameter('s1',
value=config['compounds'][0]['solubility'],
vary=False)
params['s2'] = Parameter('s2',
value=config['compounds'][1]['solubility'],
vary=False)
rkp12Conf = config['binary']['rkp']['x1-x2']
params['rkp12_1'] = \
Parameter('rkp12_1', \
value=rkp12Conf[0]['value'],
vary=(rkp12Conf[0]['vary'] if 'vary' in rkp12Conf[0] else defaultRkpVary), \
min=(rkp12Conf[0]['min'] if 'min' in rkp12Conf[0] else defaultRkpMin), \
max=(rkp12Conf[0]['max'] if 'max' in rkp12Conf[0] else defaultRkpMax))
params['rkp12_2'] = \
Parameter('rkp12_2', \
value=rkp12Conf[1]['value'],
vary=(rkp12Conf[1]['vary'] if 'vary' in rkp12Conf[1] else defaultRkpVary), \
min=(rkp12Conf[1]['min'] if 'min' in rkp12Conf[1] else defaultRkpMin), \
max=(rkp12Conf[1]['max'] if 'max' in rkp12Conf[1] else defaultRkpMax))
params['rkp12_3'] = \
Parameter('rkp12_3', \
value=rkp12Conf[2]['value'],
vary=(rkp12Conf[2]['vary'] if 'vary' in rkp12Conf[2] else defaultRkpVary), \
min=(rkp12Conf[2]['min'] if 'min' in rkp12Conf[2] else defaultRkpMin), \
max=(rkp12Conf[2]['max'] if 'max' in rkp12Conf[2] else defaultRkpMax))
# data
rawdata = np.array(config['data'])
rawdataLength = len(rawdata)
data = np.delete(rawdata, 4, 1)
soly = np.delete(rawdata, [0, 1, 2, 3], 1).reshape((rawdataLength, ))
self.params = params
self.data = data
self.soly = soly
def dofit(self):
minVidx = find_nearest(self.d[:,0],float(self.entryminV.get()))
maxVidx = find_nearest(self.d[:,0],float(self.entrymaxV.get()))
self.vdata = self.d[minVidx:maxVidx,0]
idata = self.d[minVidx:maxVidx,1]
self.popt['i0'] = Parameter(value=float(self.entryi0.get()),vary=self.usei0.get())
self.popt['nvt'] = Parameter(value=float(self.entrynvt.get()), vary =self.usenvt.get() )
self.popt['R'] = Parameter(value=float(self.entryR.get()), vary = self.useR.get())
minimize(fnc2min, self.popt, args=(self.vdata,idata))
self.newfit=1
self.refreshFigure()
def fit_g_smd(ser_smd, ser_gs, wp_smd):
from lmfit import Model, Parameter, Parameters
model_g_smd = Model(
cal_g_smd,
independent_vars=["smd"],
param_names=["g_smd", "wp_smd"],
)
prms = Parameters()
prm_g_smd = Parameter("g_smd", 0.02, vary=True, min=1e-4, max=0.5)
prm_wp_smd = Parameter("wp_smd", wp_smd, vary=False)
prms.add_many(prm_g_smd, prm_wp_smd)
res_fit = model_g_smd.fit(ser_gs / ser_gs.max(), smd=ser_smd, params=prms)
return res_fit
def fit_polynomial(self, max_degree: int = 7) -> List[float]:
"""
Fit the highest degree polynomial possible with the available energy data. (0 degree is actually 1st degree with 0 shift)
Parameters:
max_degree (int): Max degree of polynomial to try and fit. (Max possible: 7)
Returns:
(List[Float]): The polynomial coefficients from highest degree to the constant term
"""
energies = self.refined_energies if self.refined_energies else self.energies
if not energies:
raise ValueError("No known energies found for strip {}".format(
self.number))
degree = min(
len(energies) -
1, max_degree) if max_degree is not None else len(energies) - 1
model = PolynomialModel(max(degree, 1))
x, y = [*zip(*energies)]
pars = model.guess(y, x=x)
if degree == 0:
pars["c0"] = Parameter("c0", value=0, vary=False)
out = model.fit(y, pars, x=x)
self.polynomial_coefficients = list(
reversed([p[1].value for p in out.params.items()]))
return self.polynomial_coefficients
def test_vary_false(self):
guess = self.guess()
guess['center'] = Parameter(value=1.3, vary=False)
true_values = self.true_values()
true_values['center'] = 1.3
result = self.model.fit(self.data, x=self.x, **guess)
assert_results_close(result.values, true_values, rtol=0.05)
def parameters(self):
"""List of parameters defining the function."""
if self._parameters is None:
self._parameters = [
Parameter(name) for name in self.module.parameters
]
return self._parameters
def fit(self):
if not self.params:
self.params = Parameters()
a = Parameter(name="a", value=1)
b = Parameter(name="b", value=1)
c = Parameter(name="c", value=1)
self.params.add_many(a, b, c)
self.result = minimize(
self.residuals,
self.params,
args=(self.points, np.linspace(0, 1, 5)),
nan_policy="propagate",
)
return self.result
def test_bounding(self):
guess = self.guess()
guess['center'] = Parameter(value=2, min=1.3)
true_values = self.true_values()
true_values['center'] = 1.3 # as close as it's allowed to get
result = self.model.fit(self.data, x=self.x, **guess)
assert_results_close(result.values, true_values, rtol=0.05)
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 hertz_model(data_obj, opt_kwargs={}):
d, f, t = data_obj.dft
_hertz = Model(hertz)
params = Parameters()
params['a1'] = Parameter(name='a1', value=np.median(f))
params['b1'] = Parameter(name='b1', value=0, min=-10 * np.median(f))
params['cp'] = Parameter(name='cp', value=d.max() * 0.7)
params['k'] = Parameter(name='k', value=20)
params['bead_radius'] = Parameter(name='bead_radius',
value=data_obj.bead_radius,
vary=False)
fitted = _hertz.fit(f, params, x=d)
return fitted, (d, f, fitted.best_fit)
def fit_g_ta(ser_ta, ser_gs):
from lmfit import Model, Parameter, Parameters
model_g_ta = Model(
cal_g_ta,
independent_vars=["ta_c"],
param_names=["g_ta", "tl", "th"],
)
prms = Parameters()
prm_g_ta = Parameter(
"g_ta", ser_ta.median(), vary=True, min=ser_ta.min(), max=ser_ta.max()
)
prm_tl = Parameter("tl", min=ser_ta.min(), vary=False)
prm_th = Parameter("th", ser_ta.max(), vary=False)
prms.add_many(prm_g_ta, prm_tl, prm_th)
res_fit = model_g_ta.fit(ser_gs / ser_gs.max(), ta_c=ser_ta, params=prms)
return res_fit
def fit_g_dq(ser_dq, ser_gs):
from lmfit import Model, Parameter, Parameters
model_g_dq = Model(
cal_g_dq,
independent_vars=["dq"],
param_names=["g_dq_base", "g_dq_shape"],
)
prms = Parameters()
prm_g_dq_base = Parameter(
"g_dq_base",
ser_gs.min() / ser_gs.max(),
vary=False,
)
prm_g_dq_shape = Parameter("g_dq_shape", 0.1, vary=True, min=0.01, max=0.95)
prms.add_many(prm_g_dq_base, prm_g_dq_shape)
res_fit = model_g_dq.fit(ser_gs / ser_gs.max(), dq=ser_dq, params=prms)
return res_fit
def convert_to_parameter(obj, name):
"""
Converts a numeric parameter to an lmfit Parameter
"""
return Parameter(name = name,
value = obj.value,
vary = obj.vary,
min = obj.minimum,
max = obj.maximum,
expr = obj.expr)
def fit_Ecal_onedip(xdata, ydata, c1, guessed_sigma=.01):
'''
This just fits one dip, a PseudoVoigt with inverse amplitude.
Useful for getting peak COM.
'''
# just fit the first
# theta-tth factor
factor = 1
# TODO : a1, a2 the number of peaks (this makes 2*2 = 4)
# TODO : Make a Model
#def dips(x, c0, wavelength, a1, a2, sigma):
def dips(x, c1, a1, sigma):
sign = -1
result = voigt(x=x, amplitude=sign * a1, center=c1, sigma=sigma)
return result
model = Model(dips) + LinearModel()
guessed_average = np.mean(ydata)
guessed_amplitude = np.abs(np.min(ydata) - guessed_average)
# Fill out initial guess.
init_guess = {
'c1': Parameter('intercept', value=c1),
'sigma': Parameter('sigma', value=guessed_sigma),
'a1': Parameter('a1', guessed_amplitude, min=0),
'intercept': Parameter("intercept", 0),
'slope': Parameter("slope", guessed_average),
}
params = Parameters(init_guess)
# fit_kwargs = dict(ftol=1)
fit_kwargs = dict()
result = model.fit(ydata, x=xdata, params=params, fit_kwargs=fit_kwargs)
print("Found center to be at theta : {}".format(result.best_values['c1']))
plt.figure(2)
plt.clf()
plt.plot(xdata, ydata, linewidth=0, marker='o', color='b', label="data")
plt.plot(xdata, result.best_fit, color='r', label="best fit")
plt.legend()
return result
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 fit_g_kd(ser_kd, ser_gs):
from lmfit import Model, Parameter, Parameters
model_g_kd = Model(
cal_g_kd,
independent_vars=["kd"],
param_names=["g_kd"],
)
prms = Parameters()
prm_g_kd = Parameter("g_kd", 100, vary=True, min=10, max=300)
prms.add(prm_g_kd)
res_fit = model_g_kd.fit(ser_gs / ser_gs.max(), kd=ser_kd, params=prms)
return res_fit
def __init__(self,
name=None,
value=None,
vary=True,
min=-np.inf,
max=np.inf,
expr=None,
brute_step=None,
user_data=None):
if name is None:
name = randstr(8)
self.name = name
self.user_data = user_data
self.init_value = value
self.min = min
self.max = max
self.brute_step = brute_step
self.vary = vary
self._expr = expr
self._expr_ast = None
self._expr_eval = None
self._expr_deps = []
self._delay_asteval = False
self.stderr = None
self.correl = None
self.from_internal = lambda val: val
self._val = value
self._init_bounds()
lmfitParameter.__init__(self,
name,
value=value,
vary=vary,
min=min,
max=max,
expr=expr,
brute_step=brute_step,
user_data=user_data)
def singlet_probabilities(dist_arrays, x_array, sep, x_pos=None):
"""
Fit n-dimensional array of the double Gaussian distribution dist_arrays
and returns (n-1)-dimensional singlet-return probabilities by thresholding.
The probability of the peak positioned at x_pos is returned.
If x_pos is None (default), the initial guess is given by np.argmax of the first histogram.
Example:
ps = data.new_data('Singlet_probabilities', -1)
ps[:] = singlet_probabilities(data['Digitizer_count'], data['Volt'], 0.010)
"""
integral_dist = np.sum(dist_arrays, axis=tuple(range(len(dist_arrays.shape)-1)), dtype=np.float64)
out_shape = dist_arrays.shape[:-1]
def dual_gaussian(x, ratio, sigma, x_pos, sep):
gauss = lambda m, s, x: 1.0/(s*sqrt(2*pi)) * np.exp(-(x-m)**2/(2*s**2))
return ratio * gauss(x_pos, sigma, x) + (1.0-ratio) * gauss(x_pos + sep, sigma, x)
# Normalize distribution so that the integration by x gives one
full_count = integral_dist.sum()
integral_dist = integral_dist / (full_count * (x_array[1] - x_array[0]))
model = lmfit.Model(dual_gaussian, independent_vars=['x'])
result = model.fit(integral_dist, x=x_array,
ratio=Parameter(value=0.5, min=0.0, max=1.0),
sigma=Parameter(value=abs(sep)/2, max=abs(sep)),
x_pos=x_pos,
sep=Parameter(value=sep, min=sep*(2**copysign(1, -sep)), max=sep*(2**copysign(1, sep))))
threshold = result.params['x_pos'].value + result.params['sep'].value / 2
singlet_slice = slice_for_range(x_array, x_array[0], threshold)
count = full_count / np.product(out_shape)
ps = np.empty(out_shape, dtype=np.float64)
for idx in np.ndindex(out_shape):
ps[idx] = np.sum(dist_arrays[idx][singlet_slice], dtype=np.float64) / count
return ps
def param(*args, **kws):
"create a fitting Parameter as a Variable"
if len(args) > 0:
a0 = args[0]
if isinstance(a0, six.string_types):
kws.update({'expr': a0})
elif isinstance(a0, (int, float)):
kws.update({'value': a0})
else:
raise ValueError(
"first argument to param() must be string or number")
args = args[1:]
if '_larch' in kws:
kws.pop('_larch')
return Parameter(*args, **kws)
def guess(xdata, ydata, sigma=None):
'''
sigma is often hard to guess, allow it to be externally guessed
'''
g_average = np.average(ydata)
# choose between a peak or dip
dip_amp = np.min(ydata) - g_average
peak_amp = np.max(ydata) - g_average
if np.abs(dip_amp) > peak_amp:
print("found a dip")
g_amp = dip_amp
else:
print("found a peak")
g_amp = peak_amp
if sigma is None:
# guess fwhm
w, = np.where(np.abs(ydata - g_average) < np.abs(g_amp / 2.))
g_fwhm = np.abs(xdata[w[-1]] - xdata[w[0]])
# guess...
g_sigma = g_fwhm / 2.
else:
g_sigma = sigma
x0 = xdata[np.argmax(np.abs(ydata - g_average))]
init_guess = {
'amplitude': Parameter('amplitude', value=g_amp, vary=True),
'sigma': Parameter('sigma', min=0, value=g_sigma, vary=True),
'x0': Parameter('x0', value=x0, vary=True),
'intercept': Parameter('intercept', value=g_average, vary=True),
'slope': Parameter('slope', value=0, vary=True),
}
params = Parameters(init_guess)
return params
def __init__(self, glbl = False, *args, **kwargs):
Parameter.__init__(self, *args, **kwargs)
#super().__init__(self, *args, **kwargs)
self.glbl = glbl
def addModel(self, event=None, model=None, prefix=None, isbkg=False):
if model is None and event is not None:
model = event.GetString()
if model is None or model.startswith('<'):
return
self.models_peaks.SetSelection(0)
self.models_other.SetSelection(0)
if prefix is None:
p = model[:5].lower()
curmodels = ["%s%i_" % (p, i+1) for i in range(1+len(self.fit_components))]
for comp in self.fit_components:
if comp in curmodels:
curmodels.remove(comp)
prefix = curmodels[0]
label = "%s(prefix='%s')" % (model, prefix)
title = "%s: %s " % (prefix[:-1], model)
title = prefix[:-1]
mclass_kws = {'prefix': prefix}
if 'step' in model.lower():
form = model.lower().replace('step', '').strip()
if form.startswith('err'): form = 'erf'
label = "Step(form='%s', prefix='%s')" % (form, prefix)
title = "%s: Step %s" % (prefix[:-1], form[:3])
mclass = lm_models.StepModel
mclass_kws['form'] = form
minst = mclass(form=form, prefix=prefix)
else:
if model in ModelFuncs:
mclass = getattr(lm_models, ModelFuncs[model])
else:
mclass = getattr(lm_models, model+'Model')
minst = mclass(prefix=prefix)
panel = GridPanel(self.mod_nb, ncols=2, nrows=5, pad=2, itemstyle=CEN)
def SLabel(label, size=(80, -1), **kws):
return SimpleText(panel, label,
size=size, style=wx.ALIGN_LEFT, **kws)
usebox = Check(panel, default=True, label='Use in Fit?', size=(100, -1))
bkgbox = Check(panel, default=False, label='Is Baseline?', size=(125, -1))
if isbkg:
bkgbox.SetValue(1)
delbtn = Button(panel, 'Delete Component', size=(125, -1),
action=partial(self.onDeleteComponent, prefix=prefix))
pick2msg = SimpleText(panel, " ", size=(125, -1))
pick2btn = Button(panel, 'Pick Values from Data', size=(150, -1),
action=partial(self.onPick2Points, prefix=prefix))
# SetTip(mname, 'Label for the model component')
SetTip(usebox, 'Use this component in fit?')
SetTip(bkgbox, 'Label this component as "background" when plotting?')
SetTip(delbtn, 'Delete this model component')
SetTip(pick2btn, 'Select X range on Plot to Guess Initial Values')
panel.Add(SLabel(label, size=(275, -1), colour='#0000AA'),
dcol=3, style=wx.ALIGN_LEFT, newrow=True)
panel.Add(usebox, dcol=1)
panel.Add(bkgbox, dcol=2, style=LCEN)
panel.Add(delbtn, dcol=1, style=wx.ALIGN_LEFT)
panel.Add(pick2btn, dcol=2, style=wx.ALIGN_LEFT, newrow=True)
panel.Add(pick2msg, dcol=2, style=wx.ALIGN_RIGHT)
# panel.Add((10, 10), newrow=True)
# panel.Add(HLine(panel, size=(150, 3)), dcol=4, style=wx.ALIGN_CENTER)
panel.Add(SLabel("Parameter "), style=wx.ALIGN_LEFT, newrow=True)
panel.AddMany((SLabel(" Value"), SLabel(" Type"), SLabel(' Bounds'),
SLabel(" Min", size=(60, -1)),
SLabel(" Max", size=(60, -1)), SLabel(" Expression")))
parwids = OrderedDict()
parnames = sorted(minst.param_names)
for a in minst._func_allargs:
pname = "%s%s" % (prefix, a)
if (pname not in parnames and
a in minst.param_hints and
a not in minst.independent_vars):
parnames.append(pname)
for pname in parnames:
sname = pname[len(prefix):]
hints = minst.param_hints.get(sname, {})
par = Parameter(name=pname, value=0, vary=True)
if 'min' in hints:
par.min = hints['min']
if 'max' in hints:
par.max = hints['max']
if 'value' in hints:
par.value = hints['value']
if 'expr' in hints:
par.expr = hints['expr']
pwids = ParameterWidgets(panel, par, name_size=100, expr_size=125,
float_size=80, prefix=prefix,
widgets=('name', 'value', 'minval',
'maxval', 'vary', 'expr'))
parwids[par.name] = pwids
panel.Add(pwids.name, newrow=True)
panel.AddMany((pwids.value, pwids.vary, pwids.bounds,
pwids.minval, pwids.maxval, pwids.expr))
for sname, hint in minst.param_hints.items():
pname = "%s%s" % (prefix, sname)
if 'expr' in hint and pname not in parnames:
par = Parameter(name=pname, value=0, expr=hint['expr'])
pwids = ParameterWidgets(panel, par, name_size=100, expr_size=225,
float_size=80, prefix=prefix,
widgets=('name', 'value', 'expr'))
parwids[par.name] = pwids
panel.Add(pwids.name, newrow=True)
panel.Add(pwids.value)
panel.Add(pwids.expr, dcol=4, style=wx.ALIGN_RIGHT)
pwids.value.Disable()
fgroup = Group(prefix=prefix, title=title, mclass=mclass,
mclass_kws=mclass_kws, usebox=usebox, panel=panel,
parwids=parwids, float_size=65, expr_size=150,
pick2_msg=pick2msg, bkgbox=bkgbox)
self.fit_components[prefix] = fgroup
panel.pack()
self.mod_nb.AddPage(panel, title, True)
sx,sy = self.GetSize()
self.SetSize((sx, sy+1))
self.SetSize((sx, sy))
def addModel(self, event=None, model=None, is_step=False):
if model is None and event is not None:
model = event.GetString()
if model is None or model.startswith("<"):
return
curmodels = ["p%i_" % (i + 1) for i in range(1 + len(self.fit_components))]
for comp in self.fit_components:
if comp in curmodels:
curmodels.remove(comp)
prefix = curmodels[0]
label = "%s(prefix='%s')" % (model, prefix)
title = "%s: %s" % (prefix[:-1], (model + " " * 4)[:5])
mclass_kws = {"prefix": prefix}
if is_step:
form = model.lower()
if form.startswith("err"):
form = "erf"
label = "Step(form='%s', prefix='%s')" % (form, prefix)
title = "%s: Step %s" % (prefix[:-1], form[:3])
mclass = lm_models.StepModel
mclass_kws["form"] = form
minst = mclass(form=form, prefix=prefix)
else:
mclass = getattr(lm_models, model + "Model")
minst = mclass(prefix=prefix)
panel = GridPanel(self.mod_nb, ncols=1, nrows=1, pad=1, itemstyle=CEN)
def SLabel(label, size=(75, -1), **kws):
return SimpleText(panel, label, size=size, style=wx.ALIGN_LEFT, **kws)
usebox = Check(panel, default=True, label="Use?", size=(75, -1))
delbtn = Button(panel, "Delete Model", size=(120, -1), action=partial(self.onDeleteComponent, prefix=prefix))
pick2msg = SimpleText(panel, " ", size=(75, -1))
pick2btn = Button(panel, "Pick Data Range", size=(125, -1), action=partial(self.onPick2Points, prefix=prefix))
# SetTip(mname, 'Label for the model component')
SetTip(usebox, "Use this component in fit?")
SetTip(delbtn, "Delete this model component")
SetTip(pick2btn, "Select X range on Plot to Guess Initial Values")
panel.Add(HLine(panel, size=(520, 3)), style=wx.ALIGN_CENTER, dcol=6)
panel.Add(SLabel(label, size=(200, -1), colour="#0000AA"), dcol=3, newrow=True)
panel.AddMany((usebox, pick2msg, pick2btn))
panel.Add(SLabel("Parameter"), newrow=True)
panel.AddMany((SLabel("Value"), SLabel("Type"), SLabel("Min"), SLabel("Max"), SLabel("Expression")))
parwids = OrderedDict()
parnames = sorted(minst.param_names)
for a in minst._func_allargs:
pname = "%s%s" % (prefix, a)
if pname not in parnames and a in minst.param_hints and a not in minst.independent_vars:
parnames.append(pname)
for pname in parnames:
sname = pname[len(prefix) :]
hints = minst.param_hints.get(sname, {})
par = Parameter(name=pname, value=0, vary=True)
if "min" in hints:
par.min = hints["min"]
if "max" in hints:
par.max = hints["max"]
if "value" in hints:
par.value = hints["value"]
if "expr" in hints:
par.expr = hints["expr"]
pwids = ParameterWidgets(
panel,
par,
name_size=80,
expr_size=150,
float_size=70,
prefix=prefix,
widgets=("name", "value", "minval", "maxval", "vary", "expr"),
)
parwids[par.name] = pwids
panel.Add(pwids.name, newrow=True)
panel.AddMany((pwids.value, pwids.vary, pwids.minval, pwids.maxval, pwids.expr))
for sname, hint in minst.param_hints.items():
pname = "%s%s" % (prefix, sname)
if "expr" in hint and pname not in parnames:
par = Parameter(name=pname, value=0, expr=hint["expr"])
pwids = ParameterWidgets(
panel,
par,
name_size=80,
expr_size=275,
float_size=70,
prefix=prefix,
widgets=("name", "value", "vary", "expr"),
)
parwids[par.name] = pwids
panel.Add(pwids.name, newrow=True)
panel.AddMany((pwids.value, pwids.vary))
panel.Add(pwids.expr, dcol=3, style=wx.ALIGN_RIGHT)
pwids.value.Disable()
pwids.vary.Disable()
panel.Add(HLine(panel, size=(90, 3)), style=wx.ALIGN_CENTER, newrow=True)
panel.Add(delbtn, dcol=2)
panel.Add(HLine(panel, size=(250, 3)), dcol=3, style=wx.ALIGN_CENTER)
fgroup = Group(
prefix=prefix,
title=title,
mclass=mclass,
mclass_kws=mclass_kws,
usebox=usebox,
panel=panel,
parwids=parwids,
float_size=65,
expr_size=150,
pick2_msg=pick2msg,
)
self.fit_components[prefix] = fgroup
panel.pack()
self.mod_nb.AddPage(panel, title, True)
sx, sy = self.GetSize()
self.SetSize((sx, sy + 1))
self.SetSize((sx, sy))
