def test_memory_limit():
"Check that the memory limit works for too large analyses"
# Request one million samples, approx. 80GB
yfit = np.linspace(0, 1, int(1e4))
yexp = yfit + whitegaussnoise(yfit, 0.01)
def bootfcn(y):
return y
with pytest.raises(MemoryError):
paruq = bootstrap_analysis(bootfcn, yexp, yfit, 1e6)
def test_complex_values():
# ======================================================================
"Check the functionality of the bootstrapping with complex-valued outputs"
t = np.linspace(0, 5, 200)
r = np.linspace(2, 6, 300)
P = dd_gauss(r, 4, 0.8)
K = dipolarkernel(t, r)
Vexp = K @ P + whitegaussnoise(t, 0.01, seed=1)
Vexp = Vexp + 1j * whitegaussnoise(t, 0.01, seed=1)
par0 = [3, 0.5]
Vmodel = lambda par: K @ dd_gauss(r, *par) + 1j * np.zeros_like(t)
fit = snlls(Vexp, Vmodel, par0)
Vfit = fit.model
def bootfcn(V):
fit = snlls(V, Vmodel, par0)
return fit.nonlin
paruq = bootstrap_analysis(bootfcn, Vexp, Vfit, 3)
assert all(abs(paruq.mean - fit.nonlin) < 1.5e-2)
def test_parallelization():
# ======================================================================
"Check that bootstrap_analysis can run with multiple cores"
t = np.linspace(0, 5, 200)
r = np.linspace(2, 6, 300)
P = dd_gauss(r, 4, 0.8)
K = dipolarkernel(t, r)
Vexp = K @ P + whitegaussnoise(t, 0.01)
par0 = [3, 0.5]
Vmodel = lambda par: K @ dd_gauss(r, *par)
fit = snlls(Vexp, Vmodel, par0)
Vfit = fit.model
def bootfcn(V):
fit = snlls(V, Vmodel, par0)
return fit.nonlin
paruq = bootstrap_analysis(bootfcn, Vexp, Vfit, 10, cores=-1)
assert all(abs(paruq.mean - fit.nonlin) < 1.5e-2)