def test_errresults_limits_interval():
"""Missing an error limit"""
d = Data1D('dx', [1, 2, 3], [4, 2, 2], [1.2, 0.9, 0.9])
m = Const1D()
m.c0 = 3
f = fit.Fit(d, m, stat=Chi2())
er = f.est_errors()
# perhaps should just fake this instead?
assert len(er.parmaxes) == 1
er.parmaxes = ([0.1, 0.2], )
r = er._repr_html_()
assert r is not None
print(r)
assert '<summary>covariance 1σ (68.2689%) bounds</summary>' in r
assert '<summary>Summary (2)' in r
assert '<td>const1d.c0</td>' in r
assert '<div class="dataname">Fitting Method</div><div class="dataval">levmar</div>' in r
assert '<div class="dataname">Statistic</div><div class="dataval">chi2</div>' in r
assert '<tr><td>const1d.c0</td><td> 3</td><td> -0.562226</td><td>(1.000000e-01, 2.000000e-01)</td></tr>' in r
def test_fitresults_multi(method):
"""Fit multiple datasets"""
d1 = Data1D('dx', [1, 2, 3], [4, 2, 2])
d2 = Data1D('dx', [4, 5, 6, 10], [4, 4, 2, 4])
d = DataSimulFit('combined', (d1, d2))
m1 = Const1D()
m1.c0 = 3
m = SimulFitModel('silly', (m1, m1))
fr = fit.Fit(d, m, method=method(), stat=LeastSq()).fit()
fr.datasets = ['ddx', 'ddy']
r = fr._repr_html_()
assert r is not None
assert '<summary>Summary (9)</summary>' in r
assert '<td>const1d.c0</td>' in r
assert '<div class="dataname">Datasets</div><div class="dataval">ddx,ddy</div>' in r
assert '<div class="dataname">Method</div><div class="dataval">{}</div>'.format(fr.methodname) in r
assert '<div class="dataname">Statistic</div><div class="dataval">leastsq</div>' in r
assert '<div class="dataname">Δ statistic</div><div class="dataval">0.142857</div>' in r
assert '<div class="dataname">Number of data points</div><div class="dataval">7</div>' in r
assert '<div class="dataname">Degrees of freedom</div><div class="dataval">6</div>' in r
def test_errresults():
d = Data1D('dx', [1, 2, 3], [4, 2, 2], [1.2, 0.9, 0.9])
m = Const1D()
m.c0 = 3
f = fit.Fit(d, m, stat=Chi2())
er = f.est_errors()
r = er._repr_html_()
assert r is not None
assert '<summary>covariance 1σ (68.2689%) bounds</summary>' in r
assert '<summary>Summary (2)' in r
assert '<td>const1d.c0</td>' in r
assert '<div class="dataname">Fitting Method</div><div class="dataval">levmar</div>' in r
assert '<div class="dataname">Statistic</div><div class="dataval">chi2</div>' in r
assert '<tr><td>const1d.c0</td><td> 3</td><td> -0.562226</td><td> 0.562226</td></tr>' in r
def test_fitresults(method):
d = Data1D('dx', [1, 2, 3], [4, 2, 2])
m = Const1D()
m.c0 = 3
fr = fit.Fit(d, m, method=method(), stat=LeastSq()).fit()
r = fr._repr_html_()
assert r is not None
assert '<summary>Fit parameters</summary>' in r
assert '<summary>Summary (8)' in r
assert '<td>const1d.c0</td>' in r
assert '<div class="dataname">Method</div><div class="dataval">{}</div>'.format(fr.methodname) in r
assert '<div class="dataname">Statistic</div><div class="dataval">leastsq</div>' in r
assert '<div class="dataname">Δ statistic</div><div class="dataval">0.333333</div>' in r
assert '<div class="dataname">Number of data points</div><div class="dataval">3</div>' in r
assert '<div class="dataname">Degrees of freedom</div><div class="dataval">2</div>' in r
def test_fitresults_chisq():
d = Data1D('dx', [1, 2, 3], [4, 2, 2], [1.2, 1.4, 1.4])
m = Const1D()
m.c0 = 3
fr = fit.Fit(d, m, method=LevMar(), stat=Chi2()).fit()
r = fr._repr_html_()
assert r is not None
assert '<summary>Fit parameters</summary>' in r
assert '<summary>Summary (10)</summary>' in r
assert '<td>const1d.c0</td>' in r
assert '<div class="dataname">Method</div><div class="dataval">{}</div>'.format(fr.methodname) in r
assert '<div class="dataname">Final statistic</div><div class="dataval">1.65289</div>' in r
assert '<div class="dataname">Reduced statistic</div><div class="dataval">0.826446</div>' in r
assert '<div class="dataname">Probability (Q-value)</div><div class="dataval">0.437602</div>' in r
assert '<div class="dataname">Number of data points</div><div class="dataval">3</div>' in r
assert '<div class="dataname">Degrees of freedom</div><div class="dataval">2</div>' in r
def test_errresults_multi():
d1 = Data1D('dx', [1, 2, 3], [4, 2, 2], [1.2, 0.9, 0.9])
d2 = Data1D('dx', [10, 11, 12, 13], [4, 4, 2, 4], [0.8, 1.1, 1.1, 0.9])
d = DataSimulFit('combined', (d1, d2))
m1 = Const1D()
m1.c0 = 3
m = SimulFitModel('silly', (m1, m1))
f = fit.Fit(d, m, stat=Chi2())
er = f.est_errors()
r = er._repr_html_()
assert r is not None
assert '<summary>covariance 1σ (68.2689%) bounds</summary>' in r
assert '<summary>Summary (2)' in r
assert '<td>const1d.c0</td>' in r
assert '<div class="dataname">Fitting Method</div><div class="dataval">levmar</div>' in r
assert '<div class="dataname">Statistic</div><div class="dataval">chi2</div>' in r
assert '<tr><td>const1d.c0</td><td> 3</td><td> -0.362415</td><td> 0.362415</td></tr>' in r
g2.pos = g1.pos + 0.5
g1.fwhm = 0.1
g2.fwhm = 0.1
print(mdl)
ystart = mdl(x)
mplot = plot.ModelPlot()
mplot.prepare(d, mdl)
print(">>> dplot.plot()")
print(">>> mplot.plot(overplot=True)")
print(">>> save to _static/models/combine/model_combine_start.png")
f = fit.Fit(d, mdl, stats.LeastSq())
res = f.fit()
print(res.succeeded)
fplot = plot.FitPlot()
mplot.prepare(d, mdl)
fplot.prepare(dplot, mplot)
print(">>> fplot.plot()")
print(">>> plt.plot(x, ystart, label='Start')")
print(">>> plt.legend(loc=2)")
print(">>> save as docs/_static/models/combine/model_combine.png")
print(mdl)
for p in mdl.pars:
if p.link is None: