def test_no_overlay():
m = Model(name="Overlay", drift=DriftConstant(drift=2), overlay=OverlayNone())
s = m.solve_numerical()
sample = s.resample(10000)
f = fit_model(sample, drift=DriftConstant(drift=Fittable(minval=0, maxval=3)))
plot_compare_solutions(s, f.solve_numerical())
print(f)
_verify_param_match("drift", "drift", m, f)
def test_uniform_overlay():
m = Model(name="Overlay", drift=DriftConstant(drift=2), overlay=OverlayUniformMixture(umixturecoef=.1))
s = m.solve_numerical()
sample = s.resample(10000)
f = fit_model(sample, drift=DriftConstant(drift=Fittable(minval=0, maxval=3)),
overlay=OverlayUniformMixture(umixturecoef=Fittable(minval=.001, maxval=.5)))
plot_compare_solutions(s, f.solve_numerical())
print(f)
_verify_param_match("drift", "drift", m, f)
_verify_param_match("overlay", "umixturecoef", m, f)
def test_fit_simple_ddm():
m = Model(name="DDM", dt=.01,
drift=DriftConstant(drift=2),
noise=NoiseConstant(noise=1),
bound=BoundConstant(B=1))
s = m.solve()
sample = s.resample(10000)
mfit = fit_model(sample, drift=DriftConstant(drift=Fittable(minval=0, maxval=10)))
# Within 10%
if SHOW_PLOTS:
mfit.name = "Fitted solution"
sfit = mfit.solve()
plot_compare_solutions(s, sfit)
plt.show()
_verify_param_match("drift", "drift", m, mfit)
def test_overlay_chain_distribution_integrates_to_1():
m = Model(name="Overlay_test", drift=DriftConstant(drift=2),
overlay=OverlayChain(overlays=[OverlayPoissonMixture(pmixturecoef=.2, rate=2),
OverlayUniformMixture(umixturecoef=.2)]))
s = m.solve_numerical()
distsum = s.prob_correct() + s.prob_error()
assert .98 < distsum < 1.0001, "Distribution doesn't sum to 1"
def test_verify_ddm_analytic_close_to_numeric_params4():
m = Model(dx=.005, dt=.01, T_dur=2,
drift=DriftConstant(drift=.1),
noise=NoiseConstant(noise=1),
bound=BoundConstant(B=.6))
_modeltest_numerical_vs_analytical(m, max_diff=1)