def test_confidence_warnings(data, pars):
"""Make sure the proper warnings are emitted when needed."""
minimizer = lmfit.Minimizer(residual, pars, fcn_args=data)
out = minimizer.minimize(method='leastsq')
with pytest.warns(UserWarning) as record:
lmfit.conf_interval(minimizer, out, maxiter=1)
assert 'maxiter=1 reached and prob' in str(record[0].message)
def test_confidence_sigma_vs_prob(data, pars):
"""Calculate confidence by specifying sigma or probability."""
minimizer = lmfit.Minimizer(residual, pars, fcn_args=(data))
out = minimizer.leastsq()
ci_sigmas = lmfit.conf_interval(minimizer, out, sigmas=[1, 2, 3])
ci_1sigma = lmfit.conf_interval(minimizer, out, sigmas=[1])
ci_probs = lmfit.conf_interval(minimizer, out, sigmas=[0.68269, 0.9545,
0.9973])
assert_allclose(ci_sigmas['a'][0][1], ci_probs['a'][0][1], rtol=0.01)
assert_allclose(ci_sigmas['b'][2][1], ci_probs['b'][2][1], rtol=0.01)
assert len(ci_1sigma['a']) == 3
assert len(ci_probs['a']) == 7
def __init__(self,dt,df,acf,maxf=None,tdif0=None,fdif0=None):
self.tdif0=tdif0
self.fdif0=fdif0
nt,nf = acf.shape
self.facf = acf[nt/2+1,nf/2+1:]
self.fr = df*np.arange(1,self.facf.shape[0]+1)
if maxf:
maxidx = int(maxf/df)-1
self.fr = self.fr[:maxidx]
self.facf = self.facf[:maxidx]
self.tacf = acf[nt/2+1:,nf/2]
self.t = dt*np.arange(1,self.tacf.shape[0]+1)
if maxf is None:
mi = ismfit.simFitAcf(self.t, self.tacf, self.fr, self.facf,tdif0=tdif0,fdif0=fdif0)
else:
mi = ismfit.fitIf3(self.fr, self.facf, fdif0=fdif0)
try:
self.ci,self.trace = lmfit.conf_interval(mi,trace=True)
except:
self.ci = None
self.trace = None
self.params = dict([(x,makePickleableParam(mi.params[x])) for x in mi.params.keys()])
self.fitfacf = ismfit.gammaIf3(self.params,self.fr)
self.offset = self.params['offs'].value
self.scale = self.params['scale'].value
if maxf is None:
self.fittacf = ismfit.gammaIs3(self.params,self.t)
self.fittacfn = self.normalize(self.fittacf)
self.fitfacfn = self.normalize(self.fitfacf)
self.facfn = self.normalize(self.facf)
self.tacfn = self.normalize(self.tacf)
def test_confidence1():
x = np.linspace(0.3,10,100)
np.random.seed(0)
y = 1/(0.1*x)+2+0.1*np.random.randn(x.size)
pars = lmfit.Parameters()
pars.add_many(('a', 0.1), ('b', 1))
minimizer = lmfit.Minimizer(residual, pars, fcn_args=(x, y) )
out = minimizer.leastsq()
# lmfit.report_fit(out)
assert(out.nfev > 5)
assert(out.nfev < 500)
assert(out.chisqr < 3.0)
assert(out.nvarys == 2)
assert_paramval(out.params['a'], 0.1, tol=0.1)
assert_paramval(out.params['b'], -2.0, tol=0.1)
ci = lmfit.conf_interval(minimizer, out)
assert_allclose(ci['b'][0][0], 0.997, rtol=0.01)
assert_allclose(ci['b'][0][1], -2.022, rtol=0.01)
assert_allclose(ci['b'][2][0], 0.674, rtol=0.01)
assert_allclose(ci['b'][2][1], -1.997, rtol=0.01)
assert_allclose(ci['b'][5][0], 0.95, rtol=0.01)
assert_allclose(ci['b'][5][1], -1.96, rtol=0.01)
def test_confidence2():
x = np.linspace(0.3,10,100)
np.random.seed(0)
y = 1/(0.1*x)+2+0.1*np.random.randn(x.size)
pars = lmfit.Parameters()
pars.add_many(('a', 0.1), ('b', 1), ('c', 1.0))
pars['a'].max = 0.25
pars['a'].min = 0.00
pars['a'].value = 0.2
pars['c'].vary = False
minimizer = lmfit.Minimizer(residual2, pars, fcn_args=(x, y) )
out = minimizer.minimize(method='nelder')
out = minimizer.minimize(method='leastsq', params=out.params)
# lmfit.report_fit(out)
assert(out.nfev > 5)
assert(out.nfev < 500)
assert(out.chisqr < 3.0)
assert(out.nvarys == 2)
assert_paramval(out.params['a'], 0.1, tol=0.1)
assert_paramval(out.params['b'], -2.0, tol=0.1)
ci = lmfit.conf_interval(minimizer, out)
assert_allclose(ci['b'][0][0], 0.997, rtol=0.01)
assert_allclose(ci['b'][0][1], -2.022, rtol=0.01)
assert_allclose(ci['b'][2][0], 0.674, rtol=0.01)
assert_allclose(ci['b'][2][1], -1.997, rtol=0.01)
assert_allclose(ci['b'][5][0], 0.95, rtol=0.01)
assert_allclose(ci['b'][5][1], -1.96, rtol=0.01)
lmfit.printfuncs.report_ci(ci)
def calc_ci(self, sigmas=[0.68, 0.90]):
# `conf_interval' requires the fitted results have valid `stderr',
# so we need to re-fit the model with method `leastsq'.
fitted = self.fitter.minimize(method="leastsq",
params=self.fitted.params)
self.ci, self.trace = lmfit.conf_interval(self.fitter, fitted,
sigmas=sigmas, trace=True)
def confidence_intervals(fit_result, sigmas=(1, 2, 3), _larch=None, **kws):
"""calculate the confidence intervals from a fit
for supplied sigma values
wrapper around lmfit.conf_interval
"""
fitter = getattr(fit_result, 'fitter', None)
result = getattr(fit_result, 'fit_details', None)
return conf_interval(fitter, result, sigmas=sigmas, **kws)
def main():
f_tf = open('Ihfit4_taufast_a.p', 'r')
tf = f_tf.load()
p = lmfit.Parameters()
p.add_many(('dc', 0), ('a', 1), ('vh1', -40), ('t1', 100.), ('b', 1), ('vh2', -100), ('t2', 100.))
mi = lmfit.minimize(taucurve, p)
mi.leastsq()
lm.printfuncs.report_fit(mi.params)
ci = lmfit.conf_interval(mi)
lmfit.printfuncs.report_ci(ci)
def test_ci():
np.random.seed(1)
p_true = Parameters()
p_true.add('amp', value=14.0)
p_true.add('period', value=5.33)
p_true.add('shift', value=0.123)
p_true.add('decay', value=0.010)
def residual(pars, x, data=None):
amp = pars['amp']
per = pars['period']
shift = pars['shift']
decay = pars['decay']
if abs(shift) > pi / 2:
shift = shift - np.sign(shift) * pi
model = amp * np.sin(shift + x / per) * np.exp(-x * x * decay * decay)
if data is None:
return model
return model - data
n = 2500
xmin = 0.
xmax = 250.0
noise = np.random.normal(scale=0.7215, size=n)
x = np.linspace(xmin, xmax, n)
data = residual(p_true, x) + noise
fit_params = Parameters()
fit_params.add('amp', value=13.0)
fit_params.add('period', value=4)
fit_params.add('shift', value=0.1)
fit_params.add('decay', value=0.02)
out = minimize(residual, fit_params, args=(x,), kws={'data': data})
fit = residual(fit_params, x)
print( ' N fev = ', out.nfev)
print( out.chisqr, out.redchi, out.nfree)
report_errors(fit_params)
ci, tr = conf_interval(out, sigmas=[0.674], trace=True)
report_ci(ci)
for p in out.params:
diff1 = ci[p][1][1] - ci[p][0][1]
diff2 = ci[p][2][1] - ci[p][1][1]
stderr = out.params[p].stderr
assert(abs(diff1 - stderr) / stderr < 0.05)
assert(abs(diff2 - stderr) / stderr < 0.05)
def test_confidence_with_trace(data, pars):
"""Test calculation of confidence intervals with trace."""
minimizer = lmfit.Minimizer(residual, pars, fcn_args=(data))
out = minimizer.leastsq()
ci, tr = lmfit.conf_interval(minimizer, out, sigmas=[0.6827], trace=True)
for p in out.params:
diff1 = ci[p][1][1] - ci[p][0][1]
diff2 = ci[p][2][1] - ci[p][1][1]
stderr = out.params[p].stderr
assert abs(diff1 - stderr) / stderr < 0.05
assert abs(diff2 - stderr) / stderr < 0.05
assert p in tr.keys()
assert 'prob' in tr[p].keys()
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 fitter(model,
params,
args,
mcmc=False,
pos=None,
nwalkers=100,
steps=1000,
burn=0.2,
progress=True,
get_ci=False,
nan_policy='raise',
max_nfev=None,
thin=10,
is_weighted=True):
# Do fit
maxfev = [0 if max_nfev is None else max_nfev]
maxfev = int(maxfev[0])
func = Minimizer(model,
params,
fcn_args=args,
nan_policy=nan_policy,
maxfev=maxfev)
results = func.minimize()
if mcmc:
func = Minimizer(model, results.params, fcn_args=args)
mcmc_results = func.emcee(nwalkers=nwalkers,
steps=steps,
burn=int(burn * steps),
pos=pos,
is_weighted=is_weighted,
progress=progress,
thin=thin)
results = mcmc_results
if get_ci:
if results.errorbars:
ci = conf_interval(func, results)
else:
ci = ''
return results, ci
else:
return results
def graph_profile(profile, fit, sky_error):
ci = lm.conf_interval(fit)
profile.confs.update(ci)
fig, main, res = prepare_single_figure()
plot_fit(profile, main, fit, sky_error)
plot_residuals(profile, res, fit, False)
# plot_residuals(profile, res, fit, True, 'g.')
plot_ci(profile, fit, ci, 2, main)
plot_ci_residual(profile, fit, ci, 2, res)
main.yaxis.set_major_locator(MaxNLocator(prune='upper'))
main.set_ylabel('$\mu$ [mag arcsec$^{-1}]$')
res.set_xlabel('$R$ [arcsec]')
res.set_ylabel('$\Delta\mu$ [mag arcsec$^{-1}$]')
title = ''
if profile.gal_name: title += profile.gal_name
if profile.cam_name: title += profile.cam_name
if profile.name: title += profile.name
main.set_title(title)
return fig
def fit_quadlimb(time, flux, flux_err, stretch=0.5):
"""
Perform a fit of time/flux data to the quadratic limb darkening model
"""
params = Parameters()
params.add('r_p', value=0.12, min=0)
params.add('r_s', value=1.4, min=0)
params.add('stretch', value=stretch, min=0, max=1)
params.add('shift', value=0, min=-0.9, max=1.1)
mf = ModelFit(len(time), flux, flux_err)
result = minimize(mf.residual, params)
f = mf.model(params)
ci = conf_interval(result, sigmas=[0.95])
rp_err = ci['r_p'][1][1] - ci['r_p'][0][1]
print(params['r_p'], rp_err)
return f, params['r_p'].value, abs(rp_err)
def test_ci_report():
"""test confidence interval report"""
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('amp', value=14.0)
p_true.add('period', value=5.33)
p_true.add('shift', value=0.123)
p_true.add('decay', value=0.010)
n = 2500
xmin = 0.
xmax = 250.0
x = np.linspace(xmin, xmax, n)
data = residual(p_true, x) + np.random.normal(scale=0.7215, size=n)
fit_params = Parameters()
fit_params.add('amp', value=13.0)
fit_params.add('period', value=2)
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
mini = Minimizer(residual,
fit_params,
fcn_args=(x, ),
fcn_kws={'data': data})
out = mini.leastsq()
report = fit_report(out)
assert (len(report) > 500)
ci, tr = conf_interval(mini, out, trace=True)
report = ci_report(ci)
assert (len(report) > 250)
def get_errs(self, res, appr, method='manual', ci_vals=(0.683,), **kwargs):
"""Find dictionary of confidence intervals
Input:
res = lmfit.Minimizer() from fit
appr = 'full' or 'simp'
method = 'manual' (default), 'lmfit', or 'stderr'
**kwargs are passed to relevant functions
method='lmfit': maxiter=200, prob_func=None
(see lmfit.conf_interval docstring)
method='manual': adapt=True, anneal=True, eps=None;
remaining **kwargs sent to brentq
(anneal keyword only if appr='full')
Output:
confidence interval dictionary
"""
self._vprint('Finding {} fit errors; method={}'.format(appr, method))
if method == 'lmfit':
return lmfit.conf_interval(res, sigmas=ci_vals,
verbose=self._verbose, **kwargs)
if method == 'stderr':
def f(conf_intv, res, pstr):
if conf_intv != 0.683:
print ('Warning: requested CI={}. But, stderr only '
'gives 68.3% errors').format(conf_intv)
v, s = res.params[pstr].value, res.params[pstr].stderr
return v - s, v + s
elif method == 'manual':
f = lambda *args: self.get_bounds(*args, appr=appr,
verbose=self._verbose, **kwargs)
else:
raise Exception('ERROR: method must be one of lmfit/stderr/manual')
ci = build_ci_dict(res, f, ci_vals=ci_vals)
if self._verbose:
self._vprint('Finished finding fit errors:')
print lmfit.printfuncs.ci_report(ci)
self._vprint('')
return ci
def confidence_intervals(self, p_names=None, sigmas=(1, 2, 3)):
"""Prints a confidence intervals.
Parameters
----------
p_names : {list, None}, optional
Names of the parameters for which the confidence intervals are calculated. If None (default),
the confidence intervals are calculated for every parameter.
sigmas : {list, tuple}, optional
The sigma-levels to find (default is [1, 2, 3]). See Note below.
Note
----
The values for sigma are taken as the number of standard deviations for a normal distribution
and converted to probabilities. That is, the default sigma=[1, 2, 3] will use probabilities of
0.6827, 0.9545, and 0.9973. If any of the sigma values is less than 1, that will be interpreted
as a probability. That is, a value of 1 and 0.6827 will give the same results, within precision.
"""
ci = conf_interval(self.minimizer, self.result, p_names=p_names, sigmas=sigmas)
report_ci(ci)
def test_ci_report():
"""test confidence interval report"""
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('amp', value=14.0)
p_true.add('period', value=5.33)
p_true.add('shift', value=0.123)
p_true.add('decay', value=0.010)
n = 2500
xmin = 0.
xmax = 250.0
x = np.linspace(xmin, xmax, n)
data = residual(p_true, x) + np.random.normal(scale=0.7215, size=n)
fit_params = Parameters()
fit_params.add('amp', value=13.0)
fit_params.add('period', value=2)
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
mini = Minimizer(residual, fit_params, fcn_args=(x,),
fcn_kws={'data': data})
out = mini.leastsq()
report = fit_report(out)
assert(len(report) > 500)
ci, tr = conf_interval(mini, out, trace=True)
report = ci_report(ci)
assert(len(report) > 250)
def test_confidence_leastsq(data, pars, verbose, capsys):
"""Calculate confidence interval after leastsq minimization."""
minimizer = lmfit.Minimizer(residual, pars, fcn_args=(data))
out = minimizer.leastsq()
assert 5 < out.nfev < 500
assert out.chisqr < 3.0
assert out.nvarys == 2
assert_paramval(out.params['a'], 0.1, tol=0.1)
assert_paramval(out.params['b'], -2.0, tol=0.1)
ci = lmfit.conf_interval(minimizer, out, verbose=verbose)
assert_allclose(ci['b'][0][0], 0.997, rtol=0.01)
assert_allclose(ci['b'][0][1], -2.022, rtol=0.01)
assert_allclose(ci['b'][2][0], 0.683, rtol=0.01)
assert_allclose(ci['b'][2][1], -1.997, rtol=0.01)
assert_allclose(ci['b'][5][0], 0.95, rtol=0.01)
assert_allclose(ci['b'][5][1], -1.96, rtol=0.01)
if verbose:
captured = capsys.readouterr()
assert 'Calculating CI for' in captured.out
def test_confidence_leastsq(data, pars, verbose, capsys):
"""Calculate confidence interval after leastsq minimization."""
minimizer = lmfit.Minimizer(residual, pars, fcn_args=(data))
out = minimizer.leastsq()
assert 5 < out.nfev < 500
assert out.chisqr < 3.0
assert out.nvarys == 2
assert_paramval(out.params['a'], 0.1, tol=0.1)
assert_paramval(out.params['b'], -2.0, tol=0.1)
ci = lmfit.conf_interval(minimizer, out, verbose=verbose)
assert_allclose(ci['b'][0][0], 0.997, rtol=0.01)
assert_allclose(ci['b'][0][1], -2.022, rtol=0.01)
assert_allclose(ci['b'][2][0], 0.683, rtol=0.01)
assert_allclose(ci['b'][2][1], -1.997, rtol=0.01)
assert_allclose(ci['b'][5][0], 0.95, rtol=0.01)
assert_allclose(ci['b'][5][1], -1.96, rtol=0.01)
if verbose:
captured = capsys.readouterr()
assert 'Calculating CI for' in captured.out
def test_confidence_exceptions(data, pars):
"""Make sure the proper exceptions are raised when needed."""
minimizer = lmfit.Minimizer(residual, pars, calc_covar=False,
fcn_args=data)
out = minimizer.minimize(method='nelder')
out_lsq = minimizer.minimize(params=out.params, method='leastsq')
# no uncertainty estimated
msg = 'Cannot determine Confidence Intervals without sensible uncertainty'
with pytest.raises(lmfit.MinimizerException, match=msg):
lmfit.conf_interval(minimizer, out)
# uncertainty is NaN
out_lsq.params['a'].stderr = np.nan
with pytest.raises(lmfit.MinimizerException, match=msg):
lmfit.conf_interval(minimizer, out_lsq)
# only one varying parameter
out_lsq.params['a'].vary = False
msg = r'Cannot determine Confidence Intervals with < 2 variables'
with pytest.raises(lmfit.MinimizerException, match=msg):
lmfit.conf_interval(minimizer, out_lsq)
def test_confidence_bounds_reached(data, pars):
"""Check if conf_interval handles bounds correctly"""
# Should work
pars['a'].max = 0.2
minimizer = lmfit.Minimizer(residual, pars, fcn_args=(data))
out = minimizer.leastsq()
out.params['a'].stderr = 1
lmfit.conf_interval(minimizer, out, verbose=True)
# Should warn (i.e,. limit < para.min)
pars['b'].max = 2.03
pars['b'].min = 1.97
minimizer = lmfit.Minimizer(residual, pars, fcn_args=(data))
out = minimizer.leastsq()
out.params['b'].stderr = 0.005
out.params['a'].stderr = 0.01
with pytest.warns(UserWarning, match="Bound reached"):
lmfit.conf_interval(minimizer, out, verbose=True)
# Should warn (i.e,. limit > para.max)
out.params['b'].stderr = 0.1
with pytest.warns(UserWarning, match="Bound reached"):
lmfit.conf_interval(minimizer, out, verbose=True)
def test_confidence_exceptions(data, pars):
"""Make sure the proper exceptions are raised when needed."""
minimizer = lmfit.Minimizer(residual,
pars,
calc_covar=False,
fcn_args=data)
out = minimizer.minimize(method='nelder')
out_lsq = minimizer.minimize(params=out.params, method='leastsq')
# no uncertainty estimated
msg = 'Cannot determine Confidence Intervals without sensible uncertainty'
with pytest.raises(lmfit.MinimizerException, match=msg):
lmfit.conf_interval(minimizer, out)
# uncertainty is NaN
out_lsq.params['a'].stderr = np.nan
with pytest.raises(lmfit.MinimizerException, match=msg):
lmfit.conf_interval(minimizer, out_lsq)
# only one varying parameter
out_lsq.params['a'].vary = False
msg = r'Cannot determine Confidence Intervals with < 2 variables'
with pytest.raises(lmfit.MinimizerException, match=msg):
lmfit.conf_interval(minimizer, out_lsq)
def fit_v06_model_joint(r, sb_src, sb_src_err, instruments, theta, energy, results_pickle=None):
"""
Fit a v06 x psf model to any combination of instruments via joint
likelihood
Arguments:
"""
# settings
APPLY_PSF = True
DO_ZERO_PAD = True
DO_FIT = True
FIT_METHOD = 'simplex'
# FIT_METHOD = 'leastsq' # 'leastsq' - Levemberg-Markquardt,
# 'simplex' - simplex
CALC_1D_CI = False # in most cases standard error is good
# enough, this is not needed then
CALC_2D_CI = False
PLOT_PROFILE = True
PRINT_FIT_DIAGNOSTICS = True
######################################################################
# modelling is done in 2D and then projected - setup here the 2D
# parameters
size = 2.0 * r.max()
xsize = size
ysize = xsize
xcen = xsize/2
ycen = ysize/2
# imsize = input_im.shape # FIXME: is this necessary? I could just use it inside the model
imsize = (size, size) # FIXME: is this necessary? I could just use it inside the model
xsize_obj = xsize # 100 # if running t1.fits set to 100 else xsize
ysize_obj = xsize_obj
xcen_obj = xsize_obj / 2
ycen_obj = ysize_obj / 2
r_aper = xsize_obj / 2 # aperture for the fitting
# pre-calculate distmatrix for speedup - it is same for all
# instruments
distmatrix = distance_matrix(zeros((imsize[0]-2, imsize[1]-2)), xcen_obj, ycen_obj).astype(int) # need int for bincount
# set the ancilarry parameters
# +1 bc of the central divergence
data = empty(imsize)
distmatrix_input = distance_matrix(data, xcen_obj, ycen_obj).astype('int') + 1
bgrid = unique(distmatrix_input.flat)
# r contains the start of the innermost bin for integration, but not needed for plotting
rplt = r[1:]
######################################################################
# scale the data
# scale_sb_src = {}
# scale_sb_src_err = {}
psf_dict = {}
ndata = 0
for instrument in instruments:
ndata += len(sb_src[instrument])
psf_dict[instrument] = make_2d_king(distmatrix_input, instrument, theta[instrument], energy)
######################################################################
# init beta model
n0 = 1e+0
rc = 20.0
beta = 4.0/3.0
rs = 20.0
alpha = 1.5
gamma = 3.0
epsilon = 1.5
r500_pix = r.max()
# v06 pars lmfit structure
pars = lm.Parameters()
pars.add('rc' , value=rc, vary=True, min=0.05, max=r.max())
pars.add('beta' , value=beta, vary=True, min=0.05, max=2.0)
pars.add('rs' , value=rs, vary=True, min=0.05, max=2*r.max())
pars.add('alpha' , value=alpha, vary=True, min=0.01, max=3.0)
pars.add('epsilon' , value=epsilon, vary=True, min=0.0, max=5.0)
pars.add('gamma' , value=gamma, vary=False)
# FIXME: reasonable initial value and bounds!
for instrument in instruments:
pars.add('n0_'+instrument, value=n0, #value=mean(sb_src[instrument]),
vary=True, min=1.0e-9, max=1.0e3)
# non-fit arguments
nonfit_args = (distmatrix_input, bgrid, r500_pix, instruments, psf_dict,
xcen_obj, ycen_obj, r, sb_src, sb_src_err)
# fit stop criteria
if FIT_METHOD == 'leastsq':
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+0} # debug set; quickest
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+4} # debug set; some evol
leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+7}
# leastsq_kws={'xtol': 1.0e-8, 'ftol': 1.0e-8, 'maxfev': 1.0e+9}
if FIT_METHOD == 'simplex':
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': 1.0e+1} # debug set; quickest
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': 1.0e+4} # debug set; some evol
leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': 1.0e+7}
# leastsq_kws={'xtol': 1.0e-8, 'ftol': 1.0e-8, 'maxfun': 1.0e+9}
######################################################################
# do the fit: beta
if DO_FIT:
print "starting v06 fit with method :: ", FIT_METHOD
t1 = time.clock()
result = lm.minimize(v06_psf_2d_lmfit_profile_joint,
pars,
args=nonfit_args,
method=FIT_METHOD,
**leastsq_kws)
t2 = time.clock()
print
print
print "fitting took: ", t2-t1, " s"
print
print
######################################################################
# get the output model
(r_model, profile_norm_model) = \
v06_psf_2d_lmfit_profile_joint(pars, distmatrix_input, bgrid,
r500_pix, instruments, psf_dict,
xcen_obj, ycen_obj)
######################################################################
# save structures
if results_pickle:
outstrct = lmfit_result_to_dict(result, pars)
with open(results_pickle, 'wb') as output:
pickle.dump(outstrct, output, pickle.HIGHEST_PROTOCOL)
print "results written to:: ", results_pickle
######################################################################
# output
if PRINT_FIT_DIAGNOSTICS:
print_fit_diagnostics(result, t2-t1, ndata, leastsq_kws)
# print_result_tab(pars_true, pars)
lm.printfuncs.report_errors(result.params)
with open(results_pickle+'.txt', 'w') as f:
sys.stdout = f
if PRINT_FIT_DIAGNOSTICS:
print_fit_diagnostics(result, t2-t1, ndata, leastsq_kws)
print
print
lm.printfuncs.report_errors(result.params)
print
print
sys.stdout = sys.__stdout__
print
print "fitting subroutine done!"
######################################################################
# plot beta fit and data profiles
if DO_FIT and PLOT_PROFILE:
for instrument in instruments:
output_figure = results_pickle+'.'+instrument+'.beta_psf.png'
print "result plot :: ", output_figure
# FIXME: implement plotter for joint fits
plot_data_model_resid(rplt, sb_src[instrument],
r_model, profile_norm_model[instrument],
output_figure, sb_src_err[instrument])
######################################################################
# calculate confidence intervals
if DO_FIT and CALC_1D_CI:
print "Calculating 1D confidence intervals"
sigmas = [0.682689492137, 0.954499736104, 0.997300203937]
# sigmas = [0.682689492137, 0.954499736104]
# sigmas = [0.997300203937]
# sigmas = [0.954499736104]
# sigmas = [0.682689492137]
# ci_pars = ['rc', 'beta']
# ci_pars = ['rc']
# ci_pars = ['n0_pn', 'rc']
ci_pars = ['n0_'+instruments[0]]
t1 = time.clock()
ci, trace = lm.conf_interval(result, p_names=ci_pars, sigmas=sigmas,
trace=True, verbose=True, maxiter=1e3)
t2 = time.clock()
# save to file
with open(results_pickle+'.ci', 'wb') as output:
pickle.dump(ci, output, pickle.HIGHEST_PROTOCOL)
print
print "Confidence interval calculation took : ", t2 - t1
lm.printfuncs.report_ci(ci)
######################################################################
# FIXME: not done yet: Calculate 2D confidence intervals
if DO_FIT and CALC_2D_CI:
output_figure = results_pickle+'.2d_like_beta_psf.png'
from timer import Timer
with Timer() as t:
print "Calculating 2D confidence intervals"
x, y, likelihood = lm.conf_interval2d(result,'rcore','beta', 10, 10)
plt_like_surface(x, y, likelihood, output_figure, 'rcore', 'beta')
print "elasped time:", t.secs, " s"
return 0
def process(filename, post, post2_start, fit=True):
kin = [-600, post[1]]
fig_width = 14
fs = 18
meth = 'em' # by default use data generated from this fiting method
def savefig(title, **kwargs):
plt.savefig("figures/%s %s.png" % (filename, title))
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Load Data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
methods, windows, step = _get_methods_windows_step(filename)
bursts = load_bursts_data(filename, windows[1], step)
bursts_pre = bursts[bursts.tstop < 0].copy()
bursts_post = bursts[(bursts.tstart > post[0]) & (bursts.tstart < post[1])].copy()
bursts_post2 = bursts[bursts.tstart > post2_start].copy()
params_all = load_fit_data(filename)
params, params_pre, params_post, params_post2 = partition_fit_data(params_all, kin, post, post2_start)
p = {window: params[meth, window, step] for window in windows}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Number of Bursts
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fig, ax = plt.subplots(figsize=(fig_width, 3))
ax.plot(bursts.tstart, bursts.num_bursts)
ax.axvline(0, color='k', ls='--')
ax.axvline(post[1], color='k', ls='--')
ax.axvline(post2_start, color='k', ls='--')
title = 'Number of Bursts - Full measurement'
ax.set_title(title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Burst Duration
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fig, ax = plt.subplots(figsize=(fig_width, 3))
ax.plot(bursts.tstart, bursts.burst_width)
ax.axvline(0, color='k', ls='--')
ax.axvline(post[1], color='k', ls='--')
ax.axvline(post2_start, color='k', ls='--')
title = 'Burst Duration - Full measurement'
ax.set_title(title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Number of Bursts in PRE, POST, POST2 time ranges
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
for nb, label in zip((bursts_pre, bursts_post, bursts_post2),
('PRE', 'POST', 'POST2')):
slope, intercept, r_value, p_value, std_err = linregress(nb.tstart, nb.num_bursts)
y_model = nb.tstart*slope + intercept
nb_corr = (nb.num_bursts - y_model) + nb.num_bursts.mean()
nb['num_bursts_corr'] = nb_corr
nb['linregress'] = y_model
nbc = nb.num_bursts_corr
nbm = nb.num_bursts.mean()
print("%5s Number of bursts (detrended): %7.1f MEAN, %7.1f VAR, %6.3f VAR/MEAN" %
(label, nbm, nbc.var(), nbc.var()/nbm))
fig, ax = plt.subplots(1, 2, figsize=(fig_width, 4))
ax[0].plot(nb.tstart, nb.num_bursts)
ax[0].plot(nb.tstart, nb.linregress, 'r')
ax[1].plot(nb.tstart, nb.num_bursts_corr)
ax[1].plot(nb.tstart, np.repeat(nbm, nb.shape[0]), 'r')
title = 'Number of bursts - %s-kinetics' % label
fig.text(0.35, 0.95, title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Full Kinetic Curve (Population Fraction)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fig, ax = plt.subplots(figsize=(fig_width, 3))
ax.plot('kinetics', data=params_all[meth, windows[0], step], marker='h', lw=0, color='gray', alpha=0.2)
ax.plot('kinetics', data=params_all[meth, windows[1], step], marker='h', lw=0, alpha=0.5)
ax.axvline(0, color='k', ls='--')
ax.axvline(post[1], color='k', ls='--')
ax.axvline(post2_start, color='k', ls='--')
title = 'Population Fraction - Full measurement'
ax.set_title(title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kinetic Curve Auto-Correlation
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
w = windows[0]
d = np.array(p[w].kinetics.loc[post[0] : post[1]])
delta_t_max = 600 # seconds
corr, t_corr = autocorrelation(d, t_step=step, delta_t_max=delta_t_max)
fig, ax = plt.subplots(figsize=(fig_width, 3))
ax.plot(t_corr, corr, '-o')
ax.set_xlabel(r'$\Delta t$ (seconds)')
title = 'Kinetic Curve Auto-Correlation - window = %d s' % w
ax.set_title(title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kinetic Curve in Stationary Time Ranges
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
for px, label in zip([params_pre, params_post, params_post2], ('PRE', 'POST', 'POST2')):
fig, ax = plt.subplots(1, 2, figsize=(fig_width, 4))
ax[0].plot('kinetics', data=px[meth, windows[0], step], marker='h', lw=0, color='gray', alpha=0.2)
ax[0].plot('kinetics', data=px[meth, windows[1], step], marker='h', lw=0, alpha=0.5)
ax[0].plot('kinetics_linregress', data=px[meth, windows[1], step], color='r')
s1, s2 = slice(None, None, windows[0]//step), slice(None, None, windows[1]//step)
ax[1].plot(px[meth, windows[0], step].index[s1], px[meth, windows[0], step].kinetics[s1],
marker='h', lw=0, color='gray', alpha=0.2)
ax[1].plot(px[meth, windows[1], step].index[s2], px[meth, windows[1], step].kinetics[s2],
marker='h', lw=0, alpha=1)
ax[1].plot('kinetics_linregress', data=px[meth, windows[1], step], color='r')
print('%5s Kinetics 30s: %.3f STD, %.3f STD detrended.' %
(label, (100*px[meth, windows[1], step].kinetics).std(),
(100*px[meth, windows[1], step].kinetics_linregress).std()))
title = 'Population Fraction - %s-kinetics' % label
fig.text(0.40, 0.95, title, fontsize=fs)
savefig(title)
if not fit:
return None, params
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Exploratory Fit
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#method = 'nelder'
decimation = 5
t0_vary = False
model = models.factory_model_exp(t0_vary=t0_vary)
rest0f, tau = {}, {}
for window, px in p.items():
#____ = model.fit(np.array(px.kinetics), t=px.tstart, verbose=False, method=method)
resx = model.fit(np.array(px.kinetics), t=px.tstart, verbose=False)
rest0f[window] = resx
tau[window] = resx.best_values['tau']
tau0, tau1 = tau[windows[0]], tau[windows[1]]
print(' FIT Simple Exp (t0_vary=%s): tau(w=%ds) = %.1fs tau(w=%ds) = %.1fs Delta = %.1f%%' %
(t0_vary, windows[0], tau0, windows[1], tau1, 100*(tau0 - tau1)/tau0), flush=True)
t0_vary = False
reswt0f, tauw = {}, {}
for window, px in p.items():
modelw1 = models.factory_model_expwin(tau=150, t_window=window, decimation=decimation, t0_vary=t0_vary)
#____ = modelw1.fit(np.array(px.kinetics), t=px.tstart, verbose=False, method=method)
resx = modelw1.fit(np.array(px.kinetics), t=px.tstart, verbose=False)
reswt0f[window] = resx
tauw[window] = resx.best_values['tau']
tauw0, tauw1 = tauw[windows[0]], tauw[windows[1]]
print(' FIT Window Exp (t0_vary=%s): tau(w=%ds) = %.1fs tau(w=%ds) = %.1fs Delta = %.1f%%' %
(t0_vary, windows[0], tauw0, windows[1], tauw1, 100*(tauw0 - tauw1)/tauw0), flush=True)
t0_vary = True
model = models.factory_model_exp(t0_vary=t0_vary)
res, tau, ci = {}, {}, {}
for window, px in p.items():
#____ = model.fit(np.array(px.kinetics), t=px.tstart, verbose=False, method=method)
resx = model.fit(np.array(px.kinetics), t=px.tstart, verbose=False)
res[window] = resx
tau[window] = resx.best_values['tau']
ci[window] = lmfit.conf_interval(resx, resx)
tau0, tau1 = tau[windows[0]], tau[windows[1]]
print(' FIT Simple Exp (t0_vary=%s): tau(w=%ds) = %.1fs tau(w=%ds) = %.1fs Delta = %.1f%%' %
(t0_vary, windows[0], tau0, windows[1], tau1, 100*(tau0 - tau1)/tau0), flush=True)
t0_vary = True
resw, tauw, ciw = {}, {}, {}
for window, px in p.items():
modelw1 = models.factory_model_expwin(tau=150, t_window=window, decimation=decimation, t0_vary=t0_vary)
#____ = modelw1.fit(np.array(px.kinetics), t=px.tstart, verbose=False, method=method)
resx = modelw1.fit(np.array(px.kinetics), t=px.tstart, verbose=False)
resw[window] = resx
tauw[window] = resx.best_values['tau']
ciw[window] = lmfit.conf_interval(resx, resx)
tauw0, tauw1 = tauw[windows[0]], tauw[windows[1]]
print(' FIT Window Exp (t0_vary=%s): tau(w=%ds) = %.1fs tau(w=%ds) = %.1fs Delta = %.1f%%' %
(t0_vary, windows[0], tauw0, windows[1], tauw1, 100*(tauw0 - tauw1)/tauw0), flush=True)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kinetic Curve During Transient
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
t = params[meth, windows[0], step].tstart
fig, ax = plt.subplots(1, 2, figsize=(fig_width, 6))
ax[0].plot('tstart', 'kinetics', data=params[meth, windows[0], step], marker='h', lw=0, color='gray', alpha=0.2)
ax[0].plot('tstart', 'kinetics', data=params[meth, windows[1], step], marker='h', lw=0, alpha=0.5)
ax[0].plot(t, models.expwindec_func(t, **resw[windows[1]].best_values), 'm')
ax[0].set_xlim(kin[0], kin[1])
s1, s2 = slice(None, None, windows[0]//step), slice(None, None, windows[1]//step)
ax[1].plot(params[meth, windows[0], step].index[s1], params[meth, windows[0], step].kinetics[s1],
marker='h', lw=0, color='gray', alpha=0.2)
ax[1].plot(params[meth, windows[1], step].index[s2], params[meth, windows[1], step].kinetics[s2],
marker='h', lw=0, alpha=1)
ax[1].plot(t, models.expwindec_func(t, **resw[windows[1]].best_values), 'm')
title = 'Population Fraction - kinetics'
fig.text(0.40, 0.95, title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Plot Fitted Kinetic Curves
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fitcycler = cycler('color', ('k', 'm', red))
datacycler = cycler('color', ('grey', blue, green)) + cycler('alpha', (0.2, 0.5, 0.5))
fig, ax = plt.subplots(2, 1, figsize=(fig_width, 8), sharex=True)
tau_str = r'$\tau_{%ds} = %.1f s (%.1f, %.1f)$'
for i, ((w, px), fitsty, datsty) in enumerate(zip(sorted(p.items()), fitcycler, datacycler)):
if i == 2: break
ax[0].plot('tstart', 'kinetics', 'o', data=px, label='', **datsty)
label = tau_str % (w, tau[w], ci[w]['tau'][2][1], ci[w]['tau'][4][1])
ax[0].plot(px.tstart, models.exp_func(px.tstart, **res[w].best_values),
label=label, **fitsty)
ax[0].legend(loc='lower right', fontsize=fs)
w = windows[1]
ax[1].plot(p[w].tstart, p[w].kinetics - models.exp_func(p[w].tstart, **res[w].best_values),
'o', color=purple)
ax[1].set_title('Residuals - $\chi_\mathrm{red}^2 = %.4f \, 10^{-3}$' %
(res[w].redchi*1e3), fontsize=fs)
ax[0].set_xlim(kin[0], kin[1])
title = 'Kinetics Fit - Simple Exponential (t0_vary=%s)' % t0_vary
ax[0].set_title(title, fontsize=fs)
savefig(title)
fig, ax = plt.subplots(2, 1, figsize=(fig_width, 8), sharex=True)
for i, ((w, px), fitsty, datsty) in enumerate(zip(sorted(p.items()), fitcycler, datacycler)):
if i == 2: break
ax[0].plot('tstart', 'kinetics', 'o', data=px, label='', **datsty)
label = tau_str % (w, tauw[w], ciw[w]['tau'][2][1], ciw[w]['tau'][4][1])
ax[0].plot(px.tstart, models.expwindec_func(px.tstart, **resw[w].best_values),
label=label, **fitsty)
ax[0].legend(loc='lower right', fontsize=fs)
w = windows[1]
ax[1].plot(p[w].tstart, p[w].kinetics - models.exp_func(p[w].tstart, **res[w].best_values),
'o', color=purple)
ax[1].set_title('Residuals - $\chi_\mathrm{red}^2 = %.4f \, 10^{-3}$' %
(resw[w].redchi*1e3), fontsize=fs)
ax[0].set_xlim(kin[0], kin[1])
title = 'Kinetics Fit - Integrated Exponential (t0_vary=%s)' % t0_vary
ax[0].set_title(title, fontsize=fs)
savefig(title)
return (res, resw, rest0f, reswt0f, ci, ciw), params
def thindiskcurve_fitter(xsep,
velo,
error=None,
mguess=20 * u.M_sun,
rinner=20 * u.au,
router=50 * u.au,
fixedmass=False,
conf_int=False,
**kwargs):
parameters = lmfit.Parameters()
parameters.add(
'mass',
value=u.Quantity(mguess, u.M_sun).value,
min=min([10, mguess.value]),
max=25,
vary=not fixedmass,
)
parameters.add('rinner',
value=u.Quantity(rinner, u.au).value,
min=3,
max=50)
parameters.add('delta', value=20, min=10, max=50)
parameters.add('router',
value=u.Quantity(router, u.au).value,
min=20,
max=100,
expr='rinner+delta')
parameters.add('vcen', value=vcen.value, min=3.5, max=7.5)
fcn_kws = kwargs
fcn_kws.update({
'xsep': u.Quantity(xsep, u.au),
'velo': u.Quantity(velo, u.km / u.s),
'error': error
})
minimizer = lmfit.Minimizer(thindiskcurve_residual,
parameters,
epsfcn=0.005,
fcn_kws=fcn_kws)
result = minimizer.minimize()
result.params.pretty_print()
if fixedmass:
assert parameters['mass'].value == mguess.value
if conf_int:
lmfit.report_fit(result.params, min_correl=0.5)
ci, trace = lmfit.conf_interval(minimizer,
result,
sigmas=[1, 2],
trace=True,
verbose=False)
lmfit.printfuncs.report_ci(ci)
return result, ci, trace, minimizer
return result
def residual(p):
v = p.valuesdict()
return v['a1'] * np.exp(-x / v['t1']) + v['a2'] * np.exp(
-(x - 0.1) / v['t2']) - y
# first solve with Nelder-Mead
mi = lmfit.minimize(residual, p, method='Nelder')
mi = lmfit.minimize(residual, p)
lmfit.printfuncs.report_fit(mi.params, min_correl=0.5)
ci, trace = lmfit.conf_interval(mi,
sigmas=[0.68, 0.95],
trace=True,
verbose=False)
lmfit.printfuncs.report_ci(ci)
plot_type = 3
if plot_type == 0:
plt.plot(x, y)
plt.plot(x, residual(p) + y)
elif plot_type == 1:
cx, cy, grid = lmfit.conf_interval2d(mi, 'a2', 't2', 30, 30)
plt.contourf(cx, cy, grid, np.linspace(0, 1, 11))
plt.xlabel('a2')
plt.colorbar()
plt.ylabel('t2')
fit_params = Parameters()
fit_params.add('amp', value=13.0)
fit_params.add('period', value=2)
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
out = minimize(residual, fit_params, args=(x, ), kws={'data': data})
fit = residual(fit_params, x)
print(' N fev = ', out.nfev)
print(out.chisqr, out.redchi, out.nfree)
report_fit(fit_params)
#ci=calc_ci(out)
ci, tr = conf_interval(out, trace=True)
report_ci(ci)
if HASPYLAB:
names = fit_params.keys()
i = 0
gs = pylab.GridSpec(4, 4)
sx = {}
sy = {}
for fixed in names:
j = 0
for free in names:
if j in sx and i in sy:
ax = pylab.subplot(gs[i, j], sharex=sx[j], sharey=sy[i])
elif i in sy:
ax = pylab.subplot(gs[i, j], sharey=sy[i])
# # standard error is sigma/sqrt(n)
# se = numpy.std(y)/numpy.sqrt(len(y))
# return out.params['gradient'] /
params = Parameters()
params.add('gradient', value=1)
params.add('intercept', value=1)
mini = lmfit.Minimizer(residual, params, args=(x, y))
out = mini.minimize() #residual, params, args=(x,y))
print(fit_report(out))
print('Residual Standard Error = reduced chi-sqr sqrt',
np.sqrt(out.redchi)) # RSE, redchi has N_values - N_DOF in denominator
print('R-squared', rsqr(out, y))
modm = out.params['gradient']
modc = out.params['intercept']
moddata = modm * x + modc
print('testing gradient standard error', out.params['gradient'].stderr)
print('manually this is', np.sqrt(np.std(y)**2 / np.sum((x - np.mean(x))**2)))
import matplotlib.pyplot as plt
# plt.figure()
# plt.scatter(x,y)
# plt.plot(x,moddata)
# plt.show()
ci, trace = lmfit.conf_interval(out, sigmas=[1, 2], trace=True)
lmfit.printfuncs.report_ci(ci)
fit_params = Parameters()
fit_params.add('amp', value=13.0)
fit_params.add('period', value=2)
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
out = minimize(residual, fit_params, args=(x,), kws={'data':data})
fit = residual(fit_params, x)
print( ' N fev = ', out.nfev)
print( out.chisqr, out.redchi, out.nfree)
report_fit(fit_params)
#ci=calc_ci(out)
ci, tr = conf_interval(out, trace=True)
report_ci(ci)
if HASPYLAB:
names=fit_params.keys()
i=0
gs=pylab.GridSpec(4,4)
sx={}
sy={}
for fixed in names:
j=0
for free in names:
if j in sx and i in sy:
ax=pylab.subplot(gs[i,j],sharex=sx[j],sharey=sy[i])
elif i in sy:
ax=pylab.subplot(gs[i,j],sharey=sy[i])
import lmfit
import numpy as np
x = np.linspace(0.3,10,100)
np.random.seed(0)
y = 1/(0.1*x)+2+0.1*np.random.randn(x.size)
p = lmfit.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
mi = lmfit.minimize(residual, p)
lmfit.printfuncs.report_fit(mi.params)
ci = lmfit.conf_interval(mi)
lmfit.printfuncs.report_ci(ci)
#!/usr/bin/env python
# <examples/doc_confidence_basic.py>
import numpy as np
import lmfit
x = np.linspace(0.3, 10, 100)
np.random.seed(0)
y = 1/(0.1*x) + 2 + 0.1*np.random.randn(x.size)
pars = lmfit.Parameters()
pars.add_many(('a', 0.1), ('b', 1))
def residual(p):
return 1/(p['a']*x) + p['b'] - y
mini = lmfit.Minimizer(residual, pars)
result = mini.minimize()
print(lmfit.fit_report(result.params))
ci = lmfit.conf_interval(mini, result)
lmfit.printfuncs.report_ci(ci)
# <end examples/doc_confidence_basic.py>
print '='*70
print '='*70
# create parameter container
pars=lm.Parameters()
pars.add_many(('a', 2.0),('b', 3.0))
FIT_METHOD='leastsq'
leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+7}
result = do_fit()
# confidence interval
# sigmas = [0.682689492137]
sigmas = [0.682689492137, 0.954499736104, 0.997300203937]
ci_pars = ['a', 'b']
ci, trace = lm.conf_interval(result, p_names=ci_pars, sigmas=sigmas,
trace=True, verbose=True, maxiter=1e3)
lm.printfuncs.report_ci(ci)
print
######################################################################
# do the fitting: simplex
print
print
print '='*70
print '='*70
# create parameter container
pars=lm.Parameters()
pars.add_many(('a', 2.0),('b', 3.0))
minus = []
for i in range(nParameters):
plus.append(np.percentile(pSamples[i], 97.5) - mean[i])
minus.append(mean[i] - np.percentile(pSamples[i], 2.5))
# Note that the limits are not symmetric
print('Computed 95 percent percentiles from the Monte Carlo run:')
for i in range(nParameters):
print(toFit[i] + ': ', mean[i], "+/- ", plus[i], minus[i])
print('Time for monte carlo = ', time.time() - to)
print('Number of simulations = ', nsims)
if False:
to = time.time()
ci = lmfit.conf_interval(minimizer, result)
print('Time for conf Interval = ', time.time() - to)
lmfit.printfuncs.report_ci(ci)
#
cx, cy, grid = lmfit.conf_interval2d(minimizer, result, toFit[0], toFit[1],
100, 100)
plt.contourf(cx, cy, grid, np.linspace(0, 1, 11))
plt.xlabel(toFit[0])
plt.colorbar()
plt.ylabel(toFit[1])
if False:
np.set_printoptions(precision=4, linewidth=150)
print(result.covar)
def OBS(kic):
path = 'TEMP/' + str(kic) + '/'
color = "#ff7f0e"
c1 = "#00ff00"
c2 = '#66cccc'
c3 = '#cc00ff'
c4 = '#ee0000'
labels = {
"l0": '$l=0$',
"l1": '$l=1$',
"l2": '$l=2$',
"lz": 'Lorentz peak'
}
############################################################################################################
def separacao(freq, psd):
"""This function will calculate the distance between two frequencies
It is good to automate the calculation of large and small separations"""
global X
X = [], []
fig = plt.figure(figsize=(17, 5), dpi=130)
plt.plot(freq, psd, 'k-', lw=0.5, alpha=0.5)
plt.title("Select the high frequency region")
plt.xlim(min(freq), max(freq))
plt.ylim(min(psd), max(psd))
def onclick(event):
global X
x = event.xdata
X = np.append(X, x)
if len(X) == 1:
plt.plot((x, x), (min(psd), max(psd)),
'r--',
alpha=0.5,
lw=0.8)
plt.xlim(min(freq), max(freq))
plt.ylim(min(psd), max(psd))
plt.draw()
if len(X) == 2:
plt.plot((X[-1], X[-1]), (min(psd), max(psd)),
'r--',
alpha=0.5,
lw=0.8)
plt.fill_betweenx((min(psd), max(psd)), (X[-2], X[-2]),
(X[-1], X[-1]),
color='red',
alpha=0.2)
plt.xlim(min(freq), max(freq))
plt.ylim(min(psd), max(psd))
plt.draw()
if len(X) > 2:
plt.clf()
plt.plot(freq, psd, 'k', lw=0.5, alpha=0.5)
plt.plot((X[-2], X[-2]), (min(psd), max(psd)),
'r--',
alpha=0.5,
lw=0.8)
plt.plot((X[-1], X[-1]), (min(psd), max(psd)),
'r--',
alpha=0.5,
lw=0.8)
plt.fill_betweenx((min(psd), max(psd)), (X[-2], X[-2]),
(X[-1], X[-1]),
color='red',
alpha=0.2)
plt.xlim(min(freq), max(freq))
plt.ylim(min(psd), max(psd))
plt.draw()
print('Ultimo click: x = ', x)
fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()
plt.clf()
return abs(X[-2] - X[-1]), X[-2], X[-1]
def filtragem(x, y, ajuste):
plt.subplots_adjust(bottom=0.2)
box_kernel = Box1DKernel(25)
yha = convolve(y, box_kernel)
yhat = ((y - yha)**2)
yhat = (yhat / yhat.mean())
yoriginal = copy.copy(yhat)
original, = plt.plot(x, yoriginal, 'b-', alpha=0.3)
l, = plt.plot(x, yhat, 'k-', alpha=0.8)
tresh = 0.05
j = peakutils.indexes(yhat, thres=tresh)
inf = min(yhat[j])
linha, = plt.plot((min(x), max(x)), (inf, inf),
ls='--',
color='blue',
lw=0.8)
k = peakutils.indexes(yhat, thres=tresh)
xx = x[k]
yy = yhat[k]
s = Lorentz1D(xx, yy, fwhm=0.025)
freqs = np.array(s.amplitude)
psds = np.array(s.x_0)
modos, = plt.plot(freqs,
psds,
'x',
color='green',
alpha=0.4,
label=labels['lz'])
plt.annotate(np.str(tresh), xy=(np.mean(xx), max(yy) / 2))
class Index(object):
ajuste = 25
ajuste2 = 0.05
def next(self, event):
global yhat
self.ajuste += 5
print(self.ajuste)
box_kernel = Box1DKernel(self.ajuste)
yha = convolve(y, box_kernel)
yhat = ((y - yha)**2)
yhat = (yhat / yhat.mean())
original.set_ydata(yoriginal)
l.set_ydata(yhat)
plt.draw()
def prev(self, event):
global yhat
self.ajuste -= 5
if self.ajuste < 1:
self.ajuste = 5
print(self.ajuste)
box_kernel = Box1DKernel(self.ajuste)
yha = convolve(y, box_kernel)
yhat = ((y - yha)**2)
yhat = (yhat / yhat.mean())
original.set_ydata(yoriginal)
l.set_ydata(yhat)
plt.draw()
def up(self, event):
global inf, limite, yhat
self.ajuste2 += 0.01
j = peakutils.indexes(yhat, thres=self.ajuste2)
inf = min(yhat[j])
infinito = (inf, inf)
k = peakutils.indexes(yhat, thres=self.ajuste2)
xx = x[k]
yy = yhat[k]
s = Lorentz1D(xx, yy, fwhm=0.025)
freqs = np.array(s.amplitude)
psds = np.array(s.x_0)
print('With tresh: ', np.round(self.ajuste2, 3), ' Found ',
len(psds), ' mods')
original.set_ydata(yoriginal)
linha.set_ydata(infinito)
modos.set_ydata(psds)
modos.set_xdata(freqs)
plt.annotate(np.str(self.ajuste2),
xy=(np.mean(xx), max(yy) / 2))
limite = self.ajuste2
plt.draw()
def down(self, event):
global inf, limite, yhat
self.ajuste2 -= 0.01
if self.ajuste2 < 0:
self.ajuste2 = 0
j = peakutils.indexes(yhat, thres=self.ajuste2)
inf = min(yhat[j])
infinito = (inf, inf)
k = peakutils.indexes(yhat, thres=self.ajuste2)
xx = x[k]
yy = yhat[k]
s = Lorentz1D(xx, yy, fwhm=0.025)
freqs = np.array(s.amplitude)
psds = np.array(s.x_0)
print('With tresh ', np.round(self.ajuste2, 3), ' found ',
len(psds), ' mods')
original.set_ydata(yoriginal)
linha.set_ydata(infinito)
modos.set_ydata(psds)
modos.set_xdata(freqs)
limite = self.ajuste2
plt.draw()
callback = Index()
axprev = plt.axes([0.7, 0.05, 0.1, 0.075])
axnext = plt.axes([0.81, 0.05, 0.1, 0.075])
axup = plt.axes([0.2, 0.05, 0.1, 0.075])
axdown = plt.axes([0.31, 0.05, 0.1, 0.075])
bnext = Button(axnext, 'more')
bnext.on_clicked(callback.next)
bprev = Button(axprev, 'less')
bprev.on_clicked(callback.prev)
bup = Button(axup, 'up')
bup.on_clicked(callback.up)
down = Button(axdown, 'down')
down.on_clicked(callback.down)
plt.show()
return x, yhat, limite
f = np.loadtxt(str(path) + 'PS_' + str(kic) + '.txt')
freq = f[:, 0]
psd = f[:, 1]
diff, primeiro, segundo = separacao(freq, psd)
l = (primeiro < freq) & (freq < segundo)
x, y = freq[l], psd[l]
mod = GaussianModel(prefix='gauss_')
pars = mod.guess(y, x=x)
out = mod.fit(y, pars, x=x)
data = out.fit_report()
print(data)
result = out.minimize(method='leastsq')
ci = conf_interval(out,
result,
p_names=['gauss_center', 'gauss_amplitude'],
sigmas=(1, 2, 3, 5))
report = lmfit.printfuncs.report_ci(ci)
print(report)
numax = np.array(out.params['gauss_center'])
plt.annotate('',
xy=(primeiro, 0.8 * max(psd)),
xytext=(primeiro, max(psd)),
arrowprops=dict(shrink=0.025,
alpha=0.8,
fc=c1,
ec='k',
headwidth=5))
plt.annotate('',
xy=(segundo, 0.8 * max(psd)),
xytext=(segundo, max(psd)),
arrowprops=dict(shrink=0.025,
alpha=0.8,
fc=c1,
ec='k',
headwidth=5))
plt.annotate(r'$\nu_{max}$',
xy=(numax, 0.7 * max(psd)),
xytext=(numax, max(psd)),
arrowprops=dict(alpha=0.5,
fc='r',
ec='r',
headwidth=2.4,
width=2))
plt.grid(alpha=0.4)
plt.ylabel('PSD[Amplitude Units]')
plt.xlabel('Frequency [$\mu$Hz]')
plt.title('KIC {}'.format(kic))
plt.plot(freq, psd, 'k-', lw=0.5, alpha=0.5)
plt.tight_layout()
plt.show()
plt.clf()
plt.close()
deltanu = 135 * ((numax / 3050)**0.8)
np.savetxt(str(path) + 'Data_asteroseismic',
np.c_[deltanu, numax],
header='DeltaNu and Numax')
print('DeltaNu: {}\nNumax: {}'.format(deltanu, numax))
j = peakutils.indexes(y, thres=0.015)
inf = min(y[j])
plt.axhline(inf, xmin=0.045, xmax=0.95, ls='--', color='blue', lw=0.8)
x, yhat, tresh = filtragem(x, y, 25)
print(tresh)
j = peakutils.indexes(yhat, thres=tresh)
inf = min(yhat[j])
plt.axhline(inf, xmin=0.045, xmax=0.95, ls='--', color='blue', lw=0.8)
plt.plot(freq[l], yhat, 'k-', lw=0.5)
k = peakutils.indexes(yhat, thres=tresh)
xx = x[k]
yy = yhat[k]
s = Lorentz1D(xx, yy, fwhm=0.025)
freqs = np.array(s.amplitude)
psds = np.array(s.x_0)
erro = s.fwhm * psds
dados_lorenz = np.c_[freqs, psds, erro]
Freq_Region = np.c_[freq[l], yhat]
np.savetxt(
str(path) + str(kic) + '_data_modos_obs.txt',
dados_lorenz,
header=
'all peaks (frequencies) of modes detected by lorentz profile, power, error'
)
np.savetxt(str(path) + 'High_Freq_Region_' + str(kic) + '.txt',
Freq_Region,
header='High-frequency region with filter Box1DKernel')
for i in range(0, len(freqs)):
plt.plot([freqs[i]], [psds[i]],
'x',
color='green',
alpha=0.4,
label=labels['lz'])
labels['lz'] = "_nolegend_"
plt.tight_layout()
plt.legend(loc='best', scatterpoints=1)
plt.savefig(str(path) + 'peak_ident_KIC' + str(kic) + '_oversample.png',
dpi=170)
idx = np.argmax(psd[l])
print('Greater power in the Gaussian region: {}'.format(x[idx]))
plt.show()
def main():
# sets up command line argument parser
args = init_argparse()
# The file format is:
# X values of size n
# Number of replicates
# A values
# Y values with n values on each line
if (args.input): # has i flag, read from specified file
f = open(args.input, 'r')
x = array([float(val) for val in f.readline().split()])
num_rep = int(f.readline())
a = array([float(val) for val in f.readline().split()])
y = zeros((len(a), len(x)))
stddev = zeros((len(a), len(x)))
for i in range(len(a)):
all_y = zeros((num_rep, len(x)))
for j in range(num_rep):
all_y[j] = array([float(val) for val in f.readline().split()])
y[i] = average(all_y, axis=0)
stddev[i] = std(all_y, axis=0)
f.close()
else: #read from cmdline
x = array([float(val) for val in raw_input().split()])
num_rep = int(raw_input())
a = array([float(val) for val in raw_input().split()])
y = zeros((len(a), len(x)))
stddev = zeros((len(a), len(x)))
for i in range(len(a)):
all_y = zeros((num_rep, len(x)))
for j in range(num_rep):
all_y[j] = array([float(val) for val in raw_input().split()])
y[i] = average(all_y, axis=0)
stddev[i] = std(all_y, axis=0)
#adding parameters, initial guesses, and constraints
params = Parameters()
params.add('Kd', value=1, min=0)
params.add('EmFRETMAX', value=1, min=0)
return_data = {}
ci = []
emfretmax = []
emfretmax_error = []
#run fitting procedure and display results
#this needs to be repeated for each A value. Note that A[i] corresponds to Y[i]
#X is assumed to hold constant across all of these, so it remains unchanged across iterations
for i in range(len(a)):
result = minimize(residuals, params, args=(x, y[i], a[i]))
ci.append(conf_interval(result, maxiter=1000))
emfretmax.append(params['EmFRETMAX'].value)
emfretmax_error.append(params['EmFRETMAX'].stderr)
# generate table of results with tabulate
return_data[a[i]] = [
a[i],
round(params['Kd'].value, 4),
round(params['Kd'].stderr, 4),
round(params['EmFRETMAX'].value, 4),
round(params['EmFRETMAX'].stderr, 4),
round(1 - result.residual.var() / var(y), 4)
]
# plots data and curve on graph and displays if output file is given
if (args.scatter):
create_scatter(args.scatter, result, x, y[i], a[i], stddev[i], i,
args.unit)
if (args.bar):
create_bar(args.bar, emfretmax, emfretmax_error, a)
print(json.dumps(return_data))
import lmfit
import numpy as np
x = np.linspace(0.3,10,100)
np.random.seed(0)
y = 1/(0.1*x)+2+0.1*np.random.randn(x.size)
p = lmfit.Parameters()
p.add_many(('a', 0.1), ('b', 1))
def residual(p):
return 1/(p['a']*x)+p['b']-y
minimizer = lmfit.Minimizer(residual, p)
out = minimizer.leastsq()
lmfit.printfuncs.report_fit(out.params)
ci = lmfit.conf_interval(minimizer, out)
lmfit.printfuncs.report_ci(ci)
# <examples/doc_confidence_basic.py>
import numpy as np
import lmfit
x = np.linspace(0.3, 10, 100)
np.random.seed(0)
y = 1/(0.1*x) + 2 + 0.1*np.random.randn(x.size)
pars = lmfit.Parameters()
pars.add_many(('a', 0.1), ('b', 1))
def residual(p):
return 1/(p['a']*x) + p['b'] - y
mini = lmfit.Minimizer(residual, pars)
result = mini.minimize()
print(lmfit.fit_report(result.params))
ci = lmfit.conf_interval(mini, result)
lmfit.printfuncs.report_ci(ci)
# <end examples/doc_confidence_basic.py>
plot_q_u_p(qspec, sqspec, uspec, suspec, p, sp, thetp, sthetp, polarnu, polarwave, plotout, qmjd, oldq, oldu, restnu)
normflux = 10.0**np.fix(np.log10(np.mean(polarflux)))
normnu = 10.0**np.fix(np.log10(np.mean(polarnu)))
#################### Fit the polarized flux with a power-law####################
##### use the lmfit package to determine alpha##################################
plaw_params = Parameters()
initialnormal = np.float64(np.median(polarflux)*np.median(polarnu))/normflux
plaw_params.add('norm', value=initialnormal, min=np.float64(0.0))
plaw_params.add('alpha', value=np.float64(1.5))
output = minimize(find_plaw_resid, plaw_params, args=(polarnu, polarflux/normflux, 4.0*spflux/normflux))
model = find_plaw(plaw_params, polarnu)*normflux
lmfit.printfuncs.report_fit(output.params)
name='justplaw'
plot_polarized_flux_fit(polarflux, spflux, polarnu, qmjd, plotout, restnu, oldq, oldu, nuorig, model, name)
ci = lmfit.conf_interval(output, maxiter=1000)
#################################################################################
########### Fit the polarized flux with a modified power-law###################
########## F = (A * nu + B) * Norm * nu^(-alpha)##############################
########## Start with fitting the polarization with P = (A nu + B)##############
linepol_params = Parameters()
linepol_params.add('slope', value = 1/gom(np.median(restnu)))
linepol_params.add('intercept', value = 1.0)
linepol_output = minimize(find_line_resid, linepol_params, args=(polarnu, p, sp))
linepol_model = find_line(linepol_params, polarnu)
### Use the Slope and Intercept as fixed paramters for the modified power-law
modified_plaw_params = Parameters()
minslope = linepol_params['slope'].value - 2*linepol_params['slope'].stderr
maxslope = linepol_params['slope'].value + 2*linepol_params['slope'].stderr
minint = linepol_params['intercept'].value - 2*linepol_params['intercept'].stderr
fit_params.add('period', value=2)
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
###############################################################################
# Set-up the minimizer and perform the fit using ``leastsq`` algorithm, and
# show the report:
mini = Minimizer(residual, fit_params, fcn_args=(x, ), fcn_kws={'data': data})
out = mini.leastsq()
fit = residual(out.params, x)
report_fit(out)
###############################################################################
# Calculate the confidence intervals for parameters and display the results:
ci, tr = conf_interval(mini, out, trace=True)
report_ci(ci)
names = out.params.keys()
i = 0
gs = plt.GridSpec(4, 4)
sx = {}
sy = {}
for fixed in names:
j = 0
for free in names:
if j in sx and i in sy:
ax = plt.subplot(gs[i, j], sharex=sx[j], sharey=sy[i])
elif i in sy:
ax = plt.subplot(gs[i, j], sharey=sy[i])
def process(filename, post, post2_start, fit=True):
kin = [-600, post[1]]
fig_width = 14
fs = 18
meth = 'em' # by default use data generated from this fiting method
def savefig(title, **kwargs):
plt.savefig("figures/%s %s.png" % (filename, title))
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Load Data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
methods, windows, step = _get_methods_windows_step(filename)
bursts = load_bursts_data(filename, windows[1], step)
bursts_pre = bursts[bursts.tstop < 0].copy()
bursts_post = bursts[(bursts.tstart > post[0])
& (bursts.tstart < post[1])].copy()
bursts_post2 = bursts[bursts.tstart > post2_start].copy()
params_all = load_fit_data(filename)
params, params_pre, params_post, params_post2 = partition_fit_data(
params_all, kin, post, post2_start)
p = {window: params[meth, window, step] for window in windows}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Number of Bursts
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fig, ax = plt.subplots(figsize=(fig_width, 3))
ax.plot(bursts.tstart, bursts.num_bursts)
ax.axvline(0, color='k', ls='--')
ax.axvline(post[1], color='k', ls='--')
ax.axvline(post2_start, color='k', ls='--')
title = 'Number of Bursts - Full measurement'
ax.set_title(title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Burst Duration
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fig, ax = plt.subplots(figsize=(fig_width, 3))
ax.plot(bursts.tstart, bursts.burst_width)
ax.axvline(0, color='k', ls='--')
ax.axvline(post[1], color='k', ls='--')
ax.axvline(post2_start, color='k', ls='--')
title = 'Burst Duration - Full measurement'
ax.set_title(title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Number of Bursts in PRE, POST, POST2 time ranges
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
for nb, label in zip((bursts_pre, bursts_post, bursts_post2),
('PRE', 'POST', 'POST2')):
slope, intercept, r_value, p_value, std_err = linregress(
nb.tstart, nb.num_bursts)
y_model = nb.tstart * slope + intercept
nb_corr = (nb.num_bursts - y_model) + nb.num_bursts.mean()
nb['num_bursts_corr'] = nb_corr
nb['linregress'] = y_model
nbc = nb.num_bursts_corr
nbm = nb.num_bursts.mean()
print(
"%5s Number of bursts (detrended): %7.1f MEAN, %7.1f VAR, %6.3f VAR/MEAN"
% (label, nbm, nbc.var(), nbc.var() / nbm))
fig, ax = plt.subplots(1, 2, figsize=(fig_width, 4))
ax[0].plot(nb.tstart, nb.num_bursts)
ax[0].plot(nb.tstart, nb.linregress, 'r')
ax[1].plot(nb.tstart, nb.num_bursts_corr)
ax[1].plot(nb.tstart, np.repeat(nbm, nb.shape[0]), 'r')
title = 'Number of bursts - %s-kinetics' % label
fig.text(0.35, 0.95, title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Full Kinetic Curve (Population Fraction)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fig, ax = plt.subplots(figsize=(fig_width, 3))
ax.plot('kinetics',
data=params_all[meth, windows[0], step],
marker='h',
lw=0,
color='gray',
alpha=0.2)
ax.plot('kinetics',
data=params_all[meth, windows[1], step],
marker='h',
lw=0,
alpha=0.5)
ax.axvline(0, color='k', ls='--')
ax.axvline(post[1], color='k', ls='--')
ax.axvline(post2_start, color='k', ls='--')
title = 'Population Fraction - Full measurement'
ax.set_title(title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kinetic Curve Auto-Correlation
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
w = windows[0]
d = np.array(p[w].kinetics.loc[post[0]:post[1]])
delta_t_max = 600 # seconds
corr, t_corr = autocorrelation(d, t_step=step, delta_t_max=delta_t_max)
fig, ax = plt.subplots(figsize=(fig_width, 3))
ax.plot(t_corr, corr, '-o')
ax.set_xlabel(r'$\Delta t$ (seconds)')
title = 'Kinetic Curve Auto-Correlation - window = %d s' % w
ax.set_title(title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kinetic Curve in Stationary Time Ranges
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
for px, label in zip([params_pre, params_post, params_post2],
('PRE', 'POST', 'POST2')):
fig, ax = plt.subplots(1, 2, figsize=(fig_width, 4))
ax[0].plot('kinetics',
data=px[meth, windows[0], step],
marker='h',
lw=0,
color='gray',
alpha=0.2)
ax[0].plot('kinetics',
data=px[meth, windows[1], step],
marker='h',
lw=0,
alpha=0.5)
ax[0].plot('kinetics_linregress',
data=px[meth, windows[1], step],
color='r')
s1, s2 = slice(None, None,
windows[0] // step), slice(None, None,
windows[1] // step)
ax[1].plot(px[meth, windows[0], step].index[s1],
px[meth, windows[0], step].kinetics[s1],
marker='h',
lw=0,
color='gray',
alpha=0.2)
ax[1].plot(px[meth, windows[1], step].index[s2],
px[meth, windows[1], step].kinetics[s2],
marker='h',
lw=0,
alpha=1)
ax[1].plot('kinetics_linregress',
data=px[meth, windows[1], step],
color='r')
print('%5s Kinetics 30s: %.3f STD, %.3f STD detrended.' %
(label, (100 * px[meth, windows[1], step].kinetics).std(),
(100 * px[meth, windows[1], step].kinetics_linregress).std()))
title = 'Population Fraction - %s-kinetics' % label
fig.text(0.40, 0.95, title, fontsize=fs)
savefig(title)
if not fit:
return None, params
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Exploratory Fit
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#method = 'nelder'
decimation = 5
t0_vary = False
model = models.factory_model_exp(t0_vary=t0_vary)
rest0f, tau = {}, {}
for window, px in p.items():
#____ = model.fit(np.array(px.kinetics), t=px.tstart, verbose=False, method=method)
resx = model.fit(np.array(px.kinetics), t=px.tstart, verbose=False)
rest0f[window] = resx
tau[window] = resx.best_values['tau']
tau0, tau1 = tau[windows[0]], tau[windows[1]]
print(
' FIT Simple Exp (t0_vary=%s): tau(w=%ds) = %.1fs tau(w=%ds) = %.1fs Delta = %.1f%%'
% (t0_vary, windows[0], tau0, windows[1], tau1, 100 *
(tau0 - tau1) / tau0),
flush=True)
t0_vary = False
reswt0f, tauw = {}, {}
for window, px in p.items():
modelw1 = models.factory_model_expwin(tau=150,
t_window=window,
decimation=decimation,
t0_vary=t0_vary)
#____ = modelw1.fit(np.array(px.kinetics), t=px.tstart, verbose=False, method=method)
resx = modelw1.fit(np.array(px.kinetics), t=px.tstart, verbose=False)
reswt0f[window] = resx
tauw[window] = resx.best_values['tau']
tauw0, tauw1 = tauw[windows[0]], tauw[windows[1]]
print(
' FIT Window Exp (t0_vary=%s): tau(w=%ds) = %.1fs tau(w=%ds) = %.1fs Delta = %.1f%%'
% (t0_vary, windows[0], tauw0, windows[1], tauw1, 100 *
(tauw0 - tauw1) / tauw0),
flush=True)
t0_vary = True
model = models.factory_model_exp(t0_vary=t0_vary)
res, tau, ci = {}, {}, {}
for window, px in p.items():
#____ = model.fit(np.array(px.kinetics), t=px.tstart, verbose=False, method=method)
resx = model.fit(np.array(px.kinetics), t=px.tstart, verbose=False)
res[window] = resx
tau[window] = resx.best_values['tau']
ci[window] = lmfit.conf_interval(resx, resx)
tau0, tau1 = tau[windows[0]], tau[windows[1]]
print(
' FIT Simple Exp (t0_vary=%s): tau(w=%ds) = %.1fs tau(w=%ds) = %.1fs Delta = %.1f%%'
% (t0_vary, windows[0], tau0, windows[1], tau1, 100 *
(tau0 - tau1) / tau0),
flush=True)
t0_vary = True
resw, tauw, ciw = {}, {}, {}
for window, px in p.items():
modelw1 = models.factory_model_expwin(tau=150,
t_window=window,
decimation=decimation,
t0_vary=t0_vary)
#____ = modelw1.fit(np.array(px.kinetics), t=px.tstart, verbose=False, method=method)
resx = modelw1.fit(np.array(px.kinetics), t=px.tstart, verbose=False)
resw[window] = resx
tauw[window] = resx.best_values['tau']
ciw[window] = lmfit.conf_interval(resx, resx)
tauw0, tauw1 = tauw[windows[0]], tauw[windows[1]]
print(
' FIT Window Exp (t0_vary=%s): tau(w=%ds) = %.1fs tau(w=%ds) = %.1fs Delta = %.1f%%'
% (t0_vary, windows[0], tauw0, windows[1], tauw1, 100 *
(tauw0 - tauw1) / tauw0),
flush=True)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kinetic Curve During Transient
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
t = params[meth, windows[0], step].tstart
fig, ax = plt.subplots(1, 2, figsize=(fig_width, 6))
ax[0].plot('tstart',
'kinetics',
data=params[meth, windows[0], step],
marker='h',
lw=0,
color='gray',
alpha=0.2)
ax[0].plot('tstart',
'kinetics',
data=params[meth, windows[1], step],
marker='h',
lw=0,
alpha=0.5)
ax[0].plot(t, models.expwindec_func(t, **resw[windows[1]].best_values),
'm')
ax[0].set_xlim(kin[0], kin[1])
s1, s2 = slice(None, None,
windows[0] // step), slice(None, None, windows[1] // step)
ax[1].plot(params[meth, windows[0], step].index[s1],
params[meth, windows[0], step].kinetics[s1],
marker='h',
lw=0,
color='gray',
alpha=0.2)
ax[1].plot(params[meth, windows[1], step].index[s2],
params[meth, windows[1], step].kinetics[s2],
marker='h',
lw=0,
alpha=1)
ax[1].plot(t, models.expwindec_func(t, **resw[windows[1]].best_values),
'm')
title = 'Population Fraction - kinetics'
fig.text(0.40, 0.95, title, fontsize=fs)
savefig(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Plot Fitted Kinetic Curves
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fitcycler = cycler('color', ('k', 'm', red))
datacycler = cycler('color', ('grey', blue, green)) + cycler(
'alpha', (0.2, 0.5, 0.5))
fig, ax = plt.subplots(2, 1, figsize=(fig_width, 8), sharex=True)
tau_str = r'$\tau_{%ds} = %.1f s (%.1f, %.1f)$'
for i, ((w, px), fitsty,
datsty) in enumerate(zip(sorted(p.items()), fitcycler,
datacycler)):
if i == 2: break
ax[0].plot('tstart', 'kinetics', 'o', data=px, label='', **datsty)
label = tau_str % (w, tau[w], ci[w]['tau'][2][1], ci[w]['tau'][4][1])
ax[0].plot(px.tstart,
models.exp_func(px.tstart, **res[w].best_values),
label=label,
**fitsty)
ax[0].legend(loc='lower right', fontsize=fs)
w = windows[1]
ax[1].plot(p[w].tstart,
p[w].kinetics -
models.exp_func(p[w].tstart, **res[w].best_values),
'o',
color=purple)
ax[1].set_title('Residuals - $\chi_\mathrm{red}^2 = %.4f \, 10^{-3}$' %
(res[w].redchi * 1e3),
fontsize=fs)
ax[0].set_xlim(kin[0], kin[1])
title = 'Kinetics Fit - Simple Exponential (t0_vary=%s)' % t0_vary
ax[0].set_title(title, fontsize=fs)
savefig(title)
fig, ax = plt.subplots(2, 1, figsize=(fig_width, 8), sharex=True)
for i, ((w, px), fitsty,
datsty) in enumerate(zip(sorted(p.items()), fitcycler,
datacycler)):
if i == 2: break
ax[0].plot('tstart', 'kinetics', 'o', data=px, label='', **datsty)
label = tau_str % (w, tauw[w], ciw[w]['tau'][2][1],
ciw[w]['tau'][4][1])
ax[0].plot(px.tstart,
models.expwindec_func(px.tstart, **resw[w].best_values),
label=label,
**fitsty)
ax[0].legend(loc='lower right', fontsize=fs)
w = windows[1]
ax[1].plot(p[w].tstart,
p[w].kinetics -
models.exp_func(p[w].tstart, **res[w].best_values),
'o',
color=purple)
ax[1].set_title('Residuals - $\chi_\mathrm{red}^2 = %.4f \, 10^{-3}$' %
(resw[w].redchi * 1e3),
fontsize=fs)
ax[0].set_xlim(kin[0], kin[1])
title = 'Kinetics Fit - Integrated Exponential (t0_vary=%s)' % t0_vary
ax[0].set_title(title, fontsize=fs)
savefig(title)
return (res, resw, rest0f, reswt0f, ci, ciw), params
# create Minimizer
mini = lmfit.Minimizer(residual, p, nan_policy='omit')
# first solve with Nelder-Mead
out1 = mini.minimize(method='Nelder')
# then solve with Levenberg-Marquardt using the
# Nelder-Mead solution as a starting point
out2 = mini.minimize(method='leastsq', params=out1.params)
lmfit.report_fit(out2.params, min_correl=0.5)
ci, trace = lmfit.conf_interval(mini,
out2,
sigmas=[1, 2],
trace=True,
verbose=False)
lmfit.printfuncs.report_ci(ci)
plot_type = 2
if plot_type == 0:
plt.plot(x, y)
plt.plot(x, residual(out2.params) + y)
elif plot_type == 1:
cx, cy, grid = lmfit.conf_interval2d(mini, out2, 'a2', 't2', 30, 30)
plt.contourf(cx, cy, grid, np.linspace(0, 1, 11))
plt.xlabel('a2')
plt.colorbar()
-(x - 0.1) / p['t2']) - y
# create Minimizer
mini = lmfit.Minimizer(residual, p, nan_policy='propagate')
# first solve with Nelder-Mead algorithm
out1 = mini.minimize(method='Nelder')
# then solve with Levenberg-Marquardt using the
# Nelder-Mead solution as a starting point
out2 = mini.minimize(method='leastsq', params=out1.params)
lmfit.report_fit(out2.params, min_correl=0.5)
ci, trace = lmfit.conf_interval(mini, out2, sigmas=[1, 2], trace=True)
lmfit.printfuncs.report_ci(ci)
# plot data and best fit
plt.figure()
plt.plot(x, y, 'b')
plt.plot(x, residual(out2.params) + y, 'r-')
# plot confidence intervals (a1 vs t2 and a2 vs t2)
fig, axes = plt.subplots(1, 2, figsize=(12.8, 4.8))
cx, cy, grid = lmfit.conf_interval2d(mini, out2, 'a1', 't2', 30, 30)
ctp = axes[0].contourf(cx, cy, grid, np.linspace(0, 1, 11))
fig.colorbar(ctp, ax=axes[0])
axes[0].set_xlabel('a1')
axes[0].set_ylabel('t2')
def residual(p):
return p['a1']*np.exp(-x/p['t1']) + p['a2']*np.exp(-(x-0.1)/p['t2'])-y
# create Minimizer
mini = lmfit.Minimizer(residual, p)
# first solve with Nelder-Mead
out1 = mini.minimize(method='Nelder')
# then solve with Levenberg-Marquardt using the
# Nelder-Mead solution as a starting point
out2 = mini.minimize(method='leastsq', params=out1.params)
lmfit.report_fit(out2.params, min_correl=0.5)
ci, trace = lmfit.conf_interval(mini, out2, sigmas=[0.68,0.95],
trace=True, verbose=False)
lmfit.printfuncs.report_ci(ci)
plot_type = 2
if plot_type == 0:
plt.plot(x, y)
plt.plot(x, residual(out2.params)+y )
elif plot_type == 1:
cx, cy, grid = lmfit.conf_interval2d(mini, out2, 'a2','t2',30,30)
plt.contourf(cx, cy, grid, np.linspace(0,1,11))
plt.xlabel('a2')
plt.colorbar()
plt.ylabel('t2')
elif plot_type == 2:
import lmfit
import numpy as np
x = np.linspace(0.3, 10, 100)
np.random.seed(0)
y = 1 / (0.1 * x) + 2 + 0.1 * np.random.randn(x.size)
p = lmfit.Parameters()
p.add_many(('a', 0.1), ('b', 1))
def residual(p):
return 1 / (p['a'] * x) + p['b'] - y
minimizer = lmfit.Minimizer(residual, p)
out = minimizer.leastsq()
lmfit.printfuncs.report_fit(out.params)
ci = lmfit.conf_interval(minimizer, out)
lmfit.printfuncs.report_ci(ci)
np.save('2014_04_13_freq.npy',y)
## assume quantum projection noise
yerr = np.arcsin(1/np.sqrt(4*100)/0.35)/(2*np.pi*ramsey_time)
params = lmfit.Parameters()
params.add('A', value = 0, vary = True) ##cos ### -3 cXZ sin 2 chi: 7 +- 23 mHz
params.add('B', value = 0, vary = True) ##sin ### -3 cYZ sin 2 chi: 32 +- 56 mHz
params.add('C', value = 0, vary = True) ##cos2 ### -1.5 (cXX-cYY) sin^2 chi: 15 +- 22 mHz
params.add('D', value = 0, vary = True) ##sin2 ### -3 cXY sin^2 chi: 8 +- 20 mHz
params.add('offset', value = 0.069818)
result = lmfit.minimize(cosine_fit, params, args = (x, y, yerr))
fit_values = y + result.residual
lmfit.report_errors(params)
print "Reduced chi-squared = ", result.redchi
ci = lmfit.conf_interval(result)
lmfit.report_ci(ci)
x_plot = np.linspace(x.min(),x.max()+100000,1000)
figure = pyplot.figure(1)
figure.clf()
pyplot.plot(x,y,'o')
pyplot.plot(x_plot,cosine_model(params,x_plot),linewidth = 3.0)
pyplot.show()
def fit_plots(x, y, minimizer, minimizer_result, residual,
model, title='',
contour_level=0.6827, cmap=plt.cm.coolwarm,
xlabel='x', ylabel='y', xlim=None, ylim=None):
params = minimizer_result.params
ci, trace = lmfit.conf_interval(minimizer, minimizer_result, trace=True)
param_names = list(params.keys())
figs = []
##################################
# Figure 1: Fit Parameters as text
##################################
fig = plt.figure()
s = '%s\n\n' % model
if title:
s += title + '\n'
for ndx, k in enumerate(param_names):
s += '%s: %.3f ± %.3f' % (k, minimizer_result.params[k].value, minimizer_result.params[k].stderr)
s += '\n'
plt.text(0.5, 0.5, s, fontsize=12, ha='center', va='center')
plt.axis('off')
figs.append(fig)
#############################
# Figure 2: Fit and residuals
#############################
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [3, 1]})
ax1.plot(x, y, '.')
xs = np.linspace(x[0], x[-1])
ax1.plot(xs, residual(minimizer_result.params, xs), '-')
ax1.set_ylabel(ylabel)
if ylim:
ax1.set_ylim(ylim)
r = residual(minimizer_result.params, x, data=y)
ax2.plot(x, r)
ax2.axhline(y=0, color='k', linestyle=':')
mx = np.max(np.abs(r))
ax2.set_ylim([-mx, mx])
ax2.set_ylabel('R')
ax2.set_xlabel(xlabel)
if xlim:
ax2.set_xlim(xlim)
figs.append(fig)
#############################
# Figure 3: Probability plots
#############################
contours = {}
fig, axs = plt.subplots(len(param_names), len(param_names))
for ndx1 in range(len(param_names)):
for ndx2 in range(len(param_names)):
ax = axs[ndx2][ndx1]
if ndx1 > ndx2:
ax.set_axis_off()
continue
if ndx1 == ndx2:
x = trace[param_names[ndx1]][param_names[ndx1]]
y = trace[param_names[ndx1]]['prob']
t, s = np.unique(x, True)
f = interp1d(t, y[s], 'slinear')
xn = np.linspace(x.min(), x.max(), 50)
ax.plot(xn, f(xn), 'g', lw=1)
contours[ndx1] = (x, y)
else:
x, y, m = lmfit.conf_interval2d(minimizer, minimizer_result, param_names[ndx1], param_names[ndx2], 20, 20)
ax.contourf(x, y, m, np.linspace(0, 1, 10), cmap=cmap)
ch = QuadContourGenerator.from_rectilinear(x, y, m, numpy_formatter)
contours[(ndx1, ndx2)] = ch.contour(contour_level)
if ndx1 == 0:
if ndx2 > 0:
ax.set_ylabel(param_names[ndx2])
else:
ax.set_ylabel('prob')
else:
ax.set_yticks([])
if ndx2 == len(param_names) - 1:
ax.set_xlabel(param_names[ndx1])
else:
ax.set_xticks([])
figs.append(fig)
return figs, contours
def conf_int(self):
"""Return confidence intervals."""
result = lmfit.conf_interval(self.lm_model, self._result)
return result
def fit_beta_model_joint(r, sb_src, sb_src_err, instruments, theta, energy, results_pickle=None):
"""
Fit a beta x psf model to any combination of instruments via joint
likelihood
Arguments:
"""
# settings
APPLY_PSF = True
DO_ZERO_PAD = True
DO_FIT = True
# FIT_METHOD = 'simplex'
FIT_METHOD = 'leastsq' # 'leastsq' - Levemberg-Markquardt,
# 'simplex' - simplex
CALC_1D_CI = False # in most cases standard error is good
# enough, this is not needed then
CALC_2D_CI = False
PLOT_PROFILE = True
PRINT_FIT_DIAGNOSTICS = True
######################################################################
# modelling is done in 2D and then projected - setup here the 2D
# parameters
# FIXME:
# 2013-07-11 - lot of vars are deprecated, but it works ok, just
# redundant
size = 2.0 * r.max()
xsize = size
ysize = xsize
xcen = xsize/2
ycen = ysize/2
# imsize = input_im.shape # FIXME: is this necessary? I could just use it inside the model
imsize = (size, size) # FIXME: is this necessary? I could just use it inside the model
xsize_obj = xsize # 100 # if running t1.fits set to 100 else xsize
ysize_obj = xsize_obj
xcen_obj = xsize_obj / 2
ycen_obj = ysize_obj / 2
r_aper = xsize_obj / 2 # aperture for the fitting
# pre-calculate distmatrix for speedup - it is same for all
# instruments
distmatrix = distance_matrix(zeros((imsize[0]-2, imsize[1]-2)), xcen_obj, ycen_obj).astype(int) # need int for bincount
# r contains the start of the innermost bin for integration, but not needed for plotting
rplt = r[1:]
######################################################################
# scale the data - not really necessary? do val-min/(max-min)
# scale_sb_src = {}
# scale_sb_src_err = {}
ndata = 0
psf_dict = {}
for instrument in instruments:
# scale_sb_src[instrument] = median(sb_src[instrument])
# sb_src[instrument] = sb_src[instrument] / scale_sb_src[instrument]
# sb_src_err[instrument] = sb_src_err[instrument] / scale_sb_src[instrument]
ndata += len(sb_src[instrument])
# calculate the PSF
# could be changed to the newer function make_2d_king but need small changes
# to the centering/size - not worth at the moment
psf_dict[instrument] = make_2d_king_old(imsize, xcen, ycen, instrument, theta[instrument], energy)
######################################################################
# init beta model
pars = lm.Parameters()
pars.add('rcore', value=5.0, vary=True, min=0.05, max=80.0) # [rcore]
pars.add('beta', value=0.67, vary=True, min=0.1, max=2.0)
pars.add('xcen', value=xcen_obj, vary=False) # fitting not fully suported yet [imcoord]
pars.add('ycen', value=ycen_obj, vary=False) # fitting not fully suported yet [imcoord]
for instrument in instruments:
pars.add('norm_'+instrument, value=mean(sb_src[instrument]),
vary=True, min=min(sb_src[instrument]),
max=sum(abs(sb_src[instrument])))
nonfit_args = (imsize, xsize_obj, ysize_obj, distmatrix, instruments,
psf_dict, APPLY_PSF, DO_ZERO_PAD, r, sb_src,
sb_src_err)
# fit stop criteria
if FIT_METHOD == 'leastsq':
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+0} # debug set; quickest
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+4} # debug set; some evol
leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': int(1.0e+7)}
# leastsq_kws={'xtol': 1.0e-8, 'ftol': 1.0e-8, 'maxfev': 1.0e+9}
if FIT_METHOD == 'simplex':
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': 1.0e+0} # debug set; quickest
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': 1.0e+4} # debug set; some evol
leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': int(1.0e+7)}
# leastsq_kws={'xtol': 1.0e-8, 'ftol': 1.0e-8, 'maxfun': 1.0e+9}
######################################################################
# do the fit: beta
if DO_FIT:
print "starting beta fit"
t1 = time.clock()
result = lm.minimize(beta_psf_2d_lmfit_profile_joint,
pars,
args=nonfit_args,
method=FIT_METHOD,
**leastsq_kws)
t2 = time.clock()
print
print
print "fitting took: ", t2-t1, " s"
print
print
######################################################################
# scale the data back
# for instrument in instruments:
# sb_src[instrument] = sb_src[instrument] * scale_sb_src[instrument]
# sb_src_err[instrument] = sb_src_err[instrument] * scale_sb_src[instrument]
# # scale also the fitted norm
# pars['norm_'+instrument].value = pars['norm_'+instrument].value * scale_sb_src[instrument]
# pars['norm_'+instrument].stderr = pars['norm_'+instrument].stderr * scale_sb_src[instrument]
# pars['norm_'+instrument].max = pars['norm_'+instrument].max * scale_sb_src[instrument]
# pars['norm_'+instrument].min = pars['norm_'+instrument].min * scale_sb_src[instrument]
######################################################################
# get the output model
(r_model, profile_norm_model) = \
beta_psf_2d_lmfit_profile_joint(pars, imsize,
xsize_obj, ysize_obj,
distmatrix,
instruments,
psf_dict,
APPLY_PSF, DO_ZERO_PAD)
######################################################################
# save structures
if results_pickle:
outstrct = lmfit_result_to_dict(result, pars)
with open(results_pickle, 'wb') as output:
pickle.dump(outstrct, output, pickle.HIGHEST_PROTOCOL)
print "results written to:: ", results_pickle
######################################################################
# output
if PRINT_FIT_DIAGNOSTICS:
print_fit_diagnostics(result, t2-t1, ndata, leastsq_kws)
# print_result_tab(pars_true, pars)
lm.printfuncs.report_errors(result.params)
with open(results_pickle+'.txt', 'w') as f:
sys.stdout = f
if PRINT_FIT_DIAGNOSTICS:
print_fit_diagnostics(result, t2-t1, ndata, leastsq_kws)
print
print
lm.printfuncs.report_errors(result.params)
print
print
sys.stdout = sys.__stdout__
print
print "fitting subroutine done!"
######################################################################
# plot beta fit and detcprofiles
if DO_FIT and PLOT_PROFILE:
for instrument in instruments:
output_figure = results_pickle+'.'+instrument+'.beta_psf.png'
print "result plot :: ", output_figure
# FIXME: implement plotter for joint fits
plot_data_model_resid(rplt, sb_src[instrument],
r_model, profile_norm_model[instrument],
output_figure, sb_src_err[instrument])
######################################################################
# FIXME: not yet ported, confidence intervals
if DO_FIT and CALC_1D_CI:
print "Calculating 1D confidence intervals"
# sigmas = [0.682689492137, 0.954499736104, 0.997300203937]
# sigmas = [0.682689492137, 0.954499736104]
# sigmas = [0.997300203937]
sigmas = [0.954499736104]
# sigmas = [0.682689492137]
# ci_pars = ['rc', 'beta']
# ci_pars = ['rc']
# ci_pars = ['norm_pn', 'rc']
ci_pars = ['norm_'+instruments[0]]
t1 = time.clock()
ci, trace = lm.conf_interval(result, p_names=ci_pars, sigmas=sigmas,
trace=True, verbose=True, maxiter=1e3)
t2 = time.clock()
# save to file
with open(results_pickle+'.ci', 'wb') as output:
pickle.dump(ci, output, pickle.HIGHEST_PROTOCOL)
print
print "Confidence interval calculation took : ", t2 - t1
lm.printfuncs.report_ci(ci)
return 0
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
mini = Minimizer(residual, fit_params, fcn_args=(x,),
fcn_kws={'data':data})
out = mini.leastsq()
fit = residual(out.params, x)
print( ' N fev = ', out.nfev)
print( out.chisqr, out.redchi, out.nfree)
report_fit(out)
#ci=calc_ci(out)
ci, tr = conf_interval(mini, out, trace=True)
report_ci(ci)
if HASPYLAB:
names=out.params.keys()
i=0
gs=pylab.GridSpec(4,4)
sx={}
sy={}
for fixed in names:
j=0
for free in names:
if j in sx and i in sy:
ax=pylab.subplot(gs[i,j],sharex=sx[j],sharey=sy[i])
elif i in sy:
ax=pylab.subplot(gs[i,j],sharey=sy[i])
def fit_v06_model(r, sb_src, sb_src_err, instrument, theta, energy, results_pickle=None):
"""
Fit of v06 model with psf convolution
"""
# settings
APPLY_PSF = True
DO_ZERO_PAD = True
DO_FIT = True
FIT_METHOD = 'simplex'
# FIT_METHOD = 'leastsq' # 'leastsq' - Levemberg-Markquardt,
# 'simplex' - simplex
CALC_1D_CI = True # in most cases standard error is good
# enough, this is not needed then
CALC_2D_CI = False
PLOT_PROFILE = True
PRINT_FIT_DIAGNOSTICS = True
######################################################################
# modelling is done in 2D and then projected - setup here the 2D
# parameters
size = 2.0 * r.max()
xsize = size
ysize = xsize
xcen = xsize/2
ycen = ysize/2
# imsize = input_im.shape # FIXME: is this necessary? I could just use it inside the model
imsize = (size, size) # FIXME: is this necessary? I could just use it inside the model
xsize_obj = xsize # 100 # if running t1.fits set to 100 else xsize
ysize_obj = xsize_obj
xcen_obj = xsize_obj / 2
ycen_obj = ysize_obj / 2
r_aper = xsize_obj / 2 # aperture for the fitting
######################################################################
# init model
n0 = 1.0
rc = 20.0
beta = 4.0/3.0
rs = 20.0
alpha = 1.5
gamma = 3.0
epsilon = 1.5
# rmax = 2*r500_pix
r500_pix = r.max()
ndata = len(sb_src)
# v06 pars lmfit structure
pars = lm.Parameters()
pars.add('n0_'+instrument, value=n0, vary=True, min=1.0e-9, max=1.0e3)
pars.add('rc' , value=rc, vary=True, min=0.05, max=r.max())
pars.add('beta' , value=beta, vary=True, min=0.05, max=2.0)
pars.add('rs' , value=rs, vary=True, min=0.05, max=2*r.max())
pars.add('alpha' , value=alpha, vary=True, min=0.01, max=3.0)
pars.add('epsilon' , value=epsilon, vary=True, min=0.0, max=5.0)
pars.add('gamma' , value=gamma, vary=False)
# set the ancilarry parameters
# +1 bc of the central divergence
data = empty(imsize)
distmatrix_input = distance_matrix(data, xcen_obj, ycen_obj).astype('int') + 1
bgrid = unique(distmatrix_input.flat)
######################################################################
# do the fit: v06
nonfit_args = (distmatrix_input, bgrid, r500_pix, instrument, theta, energy,
xcen_obj, ycen_obj, r, sb_src, sb_src_err)
# fit stop criteria
if FIT_METHOD == 'leastsq':
leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+0} # debug set; quickest
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+4} # debug set; some evol
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfev': 1.0e+7}
# leastsq_kws={'xtol': 1.0e-8, 'ftol': 1.0e-8, 'maxfev': 1.0e+9}
if FIT_METHOD == 'simplex':
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': 1.0e+0} # debug set; quickest
# leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': 1.0e+4} # debug set; some evol
leastsq_kws={'xtol': 1.0e-7, 'ftol': 1.0e-7, 'maxfun': 1.0e+7}
# leastsq_kws={'xtol': 1.0e-8, 'ftol': 1.0e-8, 'maxfun': 1.0e+9}
######################################################################
# do the actual fitting
if DO_FIT:
print "starting v06 fit with method :: ", FIT_METHOD
t1 = time.clock()
result = lm.minimize(v06_psf_2d_lmfit_profile,
pars,
args=nonfit_args,
method=FIT_METHOD,
**leastsq_kws)
t2 = time.clock()
print "fitting took: ", t2-t1, " s"
# get the output model
(r_model, profile_norm_model) = v06_psf_2d_lmfit_profile(pars,
distmatrix_input,
bgrid,
r500_pix,
instrument, theta, energy,
xcen_obj,
ycen_obj)
######################################################################
# save structures
if results_pickle:
outstrct = lmfit_result_to_dict(result, pars)
with open(results_pickle, 'wb') as output:
pickle.dump(outstrct, output, pickle.HIGHEST_PROTOCOL)
print "results written to:: ", results_pickle
######################################################################
# output
if PRINT_FIT_DIAGNOSTICS:
print_fit_diagnostics(result, t2-t1, ndata, leastsq_kws)
# print_result_tab(pars_true, pars)
lm.printfuncs.report_errors(result.params)
with open(results_pickle+'.txt', 'w') as f:
sys.stdout = f
if PRINT_FIT_DIAGNOSTICS:
print_fit_diagnostics(result, t2-t1, ndata, leastsq_kws)
print
print
lm.printfuncs.report_errors(result.params)
print
print
sys.stdout = sys.__stdout__
print
print "fitting subroutine done!"
######################################################################
# plot v06 fit and data profiles
if DO_FIT and PLOT_PROFILE:
output_figure = results_pickle+'.'+instrument+'.v06_psf.png'
print "result plot :: ", output_figure
# FIXME: implement plotter for joint fits
plot_data_model_resid(r, sb_src,
r_model, profile_norm_model,
output_figure, sb_src_err)
######################################################################
# FIXME: not yet ported, confidence intervals
if DO_FIT and CALC_1D_CI:
print "Calculating 1D confidence intervals"
# sigmas = [0.682689492137, 0.954499736104, 0.997300203937]
sigmas = [0.682689492137, 0.954499736104]
# just an example
ci_pars = ['rc', 'beta']
ci, trace = lm.conf_interval(result, p_names=ci_pars, sigmas=sigmas,
trace=True, verbose=True, maxiter=1e3)
lm.printfuncs.report_ci(ci)
# FIXME: this seems to fail for beta and rc (i think problem is
# due to parameter degeneracy no code issue)
if DO_FIT and CALC_2D_CI:
output_figure = results_pickle+'.2d_like_v06_psf.png'
from timer import Timer
with Timer() as t:
print "Calculating 2D confidence intervals"
x, y, likelihood = lm.conf_interval2d(result,'rc','beta', 10, 10)
plt_like_surface(x, y, likelihood, output_figure, 'rc', 'beta')
print "elasped time:", t.secs, " s"
# import IPython
# IPython.embed()
return 0
x=T_min_plus1,
weights=weight_mp1) #±U Ts luminosity
resultn4 = model1.fit(lum_min_plus2 / 1e50,
par3,
x=T_min_plus2,
weights=weight_mp2) #±30 percent lum
norma3, normN3 = resultn3.best_values[
'pow_exponent'], resultn3.best_values[
'pow_amplitude'] #±U Ts luminosity params
norma4, normN4 = resultn4.best_values[
'pow_exponent'], resultn4.best_values[
'pow_amplitude'] #±30 percent lum params
#finding the errors on the best fit
ci1 = lmfit.conf_interval(result1, result1, sigmas=[0.68])
ci2 = lmfit.conf_interval(result2, result2, sigmas=[0.68])
ci3 = lmfit.conf_interval(result3, result3, sigmas=[0.68])
ci4 = lmfit.conf_interval(result4, result4, sigmas=[0.68])
normci5 = lmfit.conf_interval(resultn5,
resultn5,
sigmas=[0.68])
normci3 = lmfit.conf_interval(resultn3,
resultn3,
sigmas=[0.68])
normci4 = lmfit.conf_interval(resultn4,
resultn4,
sigmas=[0.68])
print(normci4)
def run_PSF_analysis(sel_PNe,
PNe_spectra,
obj_err,
wavelength,
x_fit,
y_fit,
z,
n_pixels=9.,
run_CI=False):
"""Fits multiple PNe simultaneously to evaluate the Point Spread Function (PSF) and Line Spread Function,
of the galaxy (pointing dependant).
Parameters
----------
sel_PNe : list
list of selected PNe, by index, for simultaneous PSF fitting.
PNe_spectra : list / array
residual data containing the emission lines of PNe. A list of minicubes, one for each PNe to be fitted for the PSF.
obj_err : list / array
objective function error, made during the spaxel-by-spaxel fitting stage.
wavelength : array
Wavelength array
x_fit : list /array
x array of matrix sized n_pixel x n_pixel.
y_fit : list / array
y array of matrix sized n_pixel x n_pixel.
z : float
Redshift value
n_pixels : int, optional
pixel width of PNe minicubes, by default 9.
Returns
-------
PSF_results,
LMfit minimisation results, dictionary object.
PSF_ci,
PSF confidence intervals, from LMfit.
"""
print("\n")
print("################################################################")
print("########################## Fitting PSF #########################")
print("################################################################")
print("\n")
print(f"Using PNe: {sel_PNe}")
selected_PNe = PNe_spectra[sel_PNe]
selected_PNe_err = obj_err[sel_PNe]
# Create parameters for each PNe: one set of entries per PN
PSF_params = generate_PSF_params(sel_PNe,
amp=750.0,
mean=5006.77 * (1 + z))
# Add in the PSF and LSF paramters
PSF_params.add('FWHM', value=4.0, min=0.01, vary=True)
PSF_params.add("beta", value=2.5, min=1.00, vary=True)
PSF_params.add("LSF", value=3.0, min=0.01, vary=True)
# minimisation of the PSF functions
PSF_min = lmfit.Minimizer(PSF_residuals_3D,
PSF_params,
fcn_args=(wavelength, x_fit, y_fit, selected_PNe,
selected_PNe_err, z),
nan_policy="propagate")
PSF_results = PSF_min.minimize()
# use LMfits confidence interval functionality to map out the errors between the 3 different parameters.
print("Calculating Confidence Intervals for FWHM, beta and LSF")
if run_CI == True:
PSF_ci = lmfit.conf_interval(PSF_min,
PSF_results,
p_names=["FWHM", "beta", "LSF"],
sigmas=[1, 2])
else:
PSF_ci = []
print(
"FWHM: ", round(PSF_results.params["FWHM"].value, 4), "+/-",
round(PSF_results.params["FWHM"].stderr, 4), "(",
round(
PSF_results.params["FWHM"].stderr /
PSF_results.params["FWHM"].value, 4) * 100, "%)")
print(
"Beta: ", round(PSF_results.params["beta"].value, 4), "+/-",
round(PSF_results.params["beta"].stderr, 4), "(",
round(
PSF_results.params["beta"].stderr /
PSF_results.params["beta"].value, 4) * 100, "%)")
print(
"LSF: ", round(PSF_results.params["LSF"].value, 4), "+/-",
round(PSF_results.params["LSF"].stderr, 4), "(",
round(
PSF_results.params["LSF"].stderr / PSF_results.params["LSF"].value,
4) * 100, "%)")
return PSF_results, PSF_ci
## assume quantum projection noise
yerr = np.arcsin(1 / np.sqrt(4 * 100) / 0.35) / (2 * np.pi * ramsey_time)
params = lmfit.Parameters()
params.add('A', value=0, vary=True) ##cos ### -3 cXZ sin 2 chi: 7 +- 23 mHz
params.add('B', value=0, vary=True) ##sin ### -3 cYZ sin 2 chi: 32 +- 56 mHz
params.add('C', value=0,
vary=True) ##cos2 ### -1.5 (cXX-cYY) sin^2 chi: 15 +- 22 mHz
params.add('D', value=0, vary=True) ##sin2 ### -3 cXY sin^2 chi: 8 +- 20 mHz
params.add('offset', value=0.069818)
result = lmfit.minimize(cosine_fit, params, args=(x, y, yerr))
fit_values = y + result.residual
lmfit.report_errors(params)
print "Reduced chi-squared = ", result.redchi
ci = lmfit.conf_interval(result)
lmfit.report_ci(ci)
x_plot = np.linspace(x.min(), x.max() + 100000, 1000)
figure = pyplot.figure(1)
figure.clf()
pyplot.plot(x, y, 'o')
pyplot.plot(x_plot, cosine_model(params, x_plot), linewidth=3.0)
pyplot.show()
import lmfit
import numpy as np
x = np.linspace(0.3, 10, 100)
np.random.seed(0)
y = 1 / (0.1 * x) + 2 + 0.1 * np.random.randn(x.size)
p = lmfit.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
mi = lmfit.minimize(residual, p)
lmfit.printfuncs.report_fit(mi.params)
ci = lmfit.conf_interval(mi)
lmfit.printfuncs.report_ci(ci)
def confidence_intervals(minout, sigmas=(1, 2, 3), **kws):
"""explicitly calculate the confidence intervals from a fit
for supplied sigma values"""
return conf_interval(minout, sigmas=sigmas, **kws)