def test_intproj(old_numpy_printing, override_plot_backend):
p = plot.IntervalProjection()
r = p._repr_html_()
check_empty(r, 'IntervalProjection', nsummary=8)
x = np.arange(5, 8, 0.5)
y = np.asarray([2, 3, 4, 5, 4, 3])
dy = y / 2
d = Data1D('n n', x, y, staterror=dy)
m = Const1D()
fit = Fit(d, m, stat=Chi2())
fr = fit.fit()
assert fr.succeeded
p.prepare(min=1, max=6, nloop=10)
p.calc(fit, m.c0)
r = p._repr_html_()
assert r is not None
if plot.backend.name == 'pylab':
assert '<summary>IntervalProjection</summary>' in r
assert '<svg ' in r
return
assert '<summary>IntervalProjection (8)</summary>' in r
assert '<div class="dataname">x</div><div class="dataval">[ 1. 1.555556 2.111111 2.666667 3.222222 3.777778 4.333333 4.888889\n 5.444444 6. ]</div>' in r
assert '<div class="dataname">nloop</div><div class="dataval">10</div>' in r
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_stats_calc_stat_chi2_nostat(usesys):
"""chi-quare statistic with no staterror fails"""
data, model = setup_single(False, usesys)
statobj = Chi2()
with pytest.raises(StatErr) as excinfo:
statobj.calc_stat(data, model)
emsg = 'If you select chi2 as the statistic, all datasets ' + \
'must provide a staterror column'
assert emsg == str(excinfo.value)
def test_data2d_int_eval_model_to_fit(array_sizes_fixture):
from sherpa.fit import Fit
from sherpa.optmethods import LevMar
from sherpa.stats import Chi2
from sherpa.models import Gauss2D
x0, x1, dx, y = array_sizes_fixture
data2 = Data2DInt('name', x0.flatten(), x0.flatten() + dx, x1.flatten(), x1.flatten() + dx, y.flatten(),
staterror=numpy.sqrt(y).flatten())
model2 = Gauss2D()
fitter = Fit(data2, model2, Chi2(), LevMar())
fitter.fit() # Failed in Sherpa 4.11.0
def test_regproj(old_numpy_printing, override_plot_backend):
p = plot.RegionProjection()
r = p._repr_html_()
check_empty(r, 'RegionProjection', nsummary=13)
x = np.arange(5, 8, 0.5)
y = np.asarray([2, 3, 4, 5, 4, 3])
dy = y / 2
d = Data1D('n n', x, y, staterror=dy)
m = Polynom1D()
m.c1.thaw()
fit = Fit(d, m, stat=Chi2())
fr = fit.fit()
assert fr.succeeded
p.prepare(min=(-2, -1), max=(2, 2), nloop=(10, 20))
p.calc(fit, m.c0, m.c1)
r = p._repr_html_()
assert r is not None
if plot_backend_is("pylab"):
assert "<summary>RegionProjection</summary>" in r
assert "<svg " in r
return
assert "<summary>RegionProjection (13)</summary>" in r
# Issue #1372 shows that the numbers here can depend on the platform; as
# this test is not about whether the fit converged to the same solution
# the tests are very basic. An alternative would be to just place
# the values from the fit object into the strings, but then there is
# the problem that this test currently requires old_numpy_printing,
# so the results would not necessarily match.
#
assert '<div class="dataname">parval0</div><div class="dataval">-0.5' in r
assert '<div class="dataname">parval1</div><div class="dataval">0.5' in r
assert '<div class="dataname">sigma</div><div class="dataval">(1, 2, 3)</div>' in r
# These values may depend on the platform so only very-limited check.
#
assert '<div class="dataname">y</div><div class="dataval">[ 30' in r
assert '<div class="dataname">levels</div><div class="dataval">[ 3.6' in r
assert '<div class="dataname">min</div><div class="dataval">[-2, -1]</div>' in r
assert '<div class="dataname">max</div><div class="dataval">[2, 2]</div>' in r
assert '<div class="dataname">nloop</div><div class="dataval">(10, 20)</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_regproj(old_numpy_printing, override_plot_backend):
p = plot.RegionProjection()
r = p._repr_html_()
check_empty(r, 'RegionProjection', nsummary=13)
x = np.arange(5, 8, 0.5)
y = np.asarray([2, 3, 4, 5, 4, 3])
dy = y / 2
d = Data1D('n n', x, y, staterror=dy)
m = Polynom1D()
m.c1.thaw()
fit = Fit(d, m, stat=Chi2())
fr = fit.fit()
assert fr.succeeded
p.prepare(min=(-2, -1), max=(2, 2), nloop=(10, 20))
p.calc(fit, m.c0, m.c1)
r = p._repr_html_()
assert r is not None
if plot.backend.name == 'pylab':
assert '<summary>RegionProjection</summary>' in r
assert '<svg ' in r
return
print(r)
assert '<summary>RegionProjection (13)</summary>' in r
assert '<div class="dataname">parval0</div><div class="dataval">-0.5315772076542427</div>' in r
assert '<div class="dataname">parval1</div><div class="dataval">0.5854611101216837</div>' in r
assert '<div class="dataname">sigma</div><div class="dataval">(1, 2, 3)</div>' in r
assert '<div class="dataname">y</div><div class="dataval">[ 306.854444 282.795953 259.744431 237.699877 216.662291 196.631674\n' in r
assert '<div class="dataname">levels</div><div class="dataval">[ 3.606863 7.491188 13.140272]</div>' in r
assert '<div class="dataname">min</div><div class="dataval">[-2, -1]</div>' in r
assert '<div class="dataname">max</div><div class="dataval">[2, 2]</div>' in r
assert '<div class="dataname">nloop</div><div class="dataval">(10, 20)</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
report("mdl")
from sherpa.plot import ModelPlot
mplot = ModelPlot()
mplot.prepare(d, mdl)
plt.subplot(2, 1, 1)
mplot.plot(clearwindow=False)
plt.subplot(2, 1, 2)
dplot.plot(clearwindow=False)
plt.title('')
savefig("model_data_before_fit.png")
from sherpa.stats import Chi2
from sherpa.fit import Fit
f = Fit(d, mdl, stat=Chi2())
report("f")
print("Starting statistic: {}".format(f.calc_stat()))
fitres = f.fit()
report("fitres.format()")
print("Reduced chi square = {:.2f}".format(fitres.rstat))
mplot.prepare(d, mdl)
dplot.plot()
mplot.overplot()
savefig("model_data_fit1.png")
from sherpa.optmethods import NelderMead