def test_errors_with_no_stat():
"""Check we get no errors when stat is None"""
d = Data1D('x', numpy.asarray([2, 4, 10]), numpy.asarray([2, 4, 0]))
dp = sherpaplot.DataPlot()
dp.prepare(d, stat=None)
assert dp.yerr is None
def test_fit(override_plot_backend):
p = plot.FitPlot()
r = p._repr_html_()
assert r is None # note: always None
x = np.arange(5, 8, 0.5)
y = np.ones(x.size)
d = Data1D('n n', x, y)
m = Const1D()
dplot = plot.DataPlot()
dplot.prepare(d)
mplot = plot.ModelPlot()
mplot.prepare(d, m)
p.prepare(dplot, mplot)
r = p._repr_html_()
# different to previous checks
assert r is not None
if plot.backend.name == 'pylab':
assert '<summary>FitPlot</summary>' in r
assert '<svg ' in r
return
assert '<summary>DataPlot (' in r
assert '<summary>ModelPlot (' in r
assert '<div class="dataval">n n</div>' in r
assert '<div class="dataval">Model</div>' in r
def test_data(override_plot_backend):
p = plot.DataPlot()
r = p._repr_html_()
check_full(r, 'DataPlot', 'None', 'None', nsummary=7) # NOT empty
x = np.arange(5, 8, 0.5)
y = np.ones(x.size)
dr = y / 0.1
d = Data1D('n n', x, y, staterror=dr)
p.prepare(d)
r = p._repr_html_()
check_full(r, 'DataPlot', 'y', 'n n', nsummary=7)
print(sim_model)
print([p.fullname for p in sim_model.pars])
s1.ampl = 1.0
s1.pos = 0.0
s1.fwhm = 0.5
s2.ampl = 2.5
s2.pos = 0.5
s2.fwhm = 0.25
x = np.linspace(-1, 1, 200)
y = sim_model(x) + np.random.normal(0., 0.2, x.shape)
d = data.Data1D('simulated', x, y)
dplot = plot.DataPlot()
dplot.prepare(d)
print(">>> dplot.plot()")
print(">>> save to _static/models/combine/model_combine_data.png")
print(sim_model)
print(sim_model.op)
print(repr(sim_model.lhs))
print(repr(sim_model.rhs))
print(sim_model.parts)
for cpt in sim_model.parts:
print(cpt)
print(sim_model([-1.0, 0, 1]))
g1 = models.Gauss1D('g1')
