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')