Merge pull request #12 from s-kuberski/feature/covobs

Feature/covobs

tests/fits_test.py

@@ -46,6 +46,19 @@ def test_least_squares():
     assert((out.fit_parameters[0] - beta[0]).is_zero())
     assert((out.fit_parameters[1] - beta[1]).is_zero())
 
+    oyc = []
+    for i, item in enumerate(x):
+        oyc.append(pe.cov_Obs(y[i], yerr[i]**2, 'cov' + str(i)))
+
+    outc = pe.least_squares(x, oyc, func)
+    betac = outc.fit_parameters
+    
+    for i in range(2):
+        betac[i].gamma_method(S=1.0)
+        assert math.isclose(betac[i].value, popt[i], abs_tol=1e-5)
+        assert math.isclose(pcov[i, i], betac[i].dvalue ** 2, abs_tol=1e-3)
+    assert math.isclose(pe.covariance(betac[0], betac[1]), pcov[0, 1], abs_tol=1e-3)
+
     num_samples = 400
     N = 10
 
@@ -135,6 +148,44 @@ def test_total_least_squares():
     assert(diff / beta[0] < 1e-3 * beta[0].dvalue)
     assert((out.fit_parameters[1] - beta[1]).is_zero())
 
+    oxc = []
+    for i, item in enumerate(x):
+        oxc.append(pe.cov_Obs(x[i], xerr[i]**2, 'covx' + str(i)))
+
+    oyc = []
+    for i, item in enumerate(x):
+        oyc.append(pe.cov_Obs(y[i], yerr[i]**2, 'covy' + str(i)))
+
+    outc = pe.total_least_squares(oxc, oyc, func)
+    betac = outc.fit_parameters
+
+    for i in range(2):
+        betac[i].gamma_method(S=1.0)
+        assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
+        assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
+    assert math.isclose(pe.covariance(betac[0], betac[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
+
+    outc = pe.total_least_squares(oxc, oyc, func, const_par=[betac[1]])
+
+    diffc = outc.fit_parameters[0] - betac[0]
+    assert(diffc / betac[0] < 1e-3 * betac[0].dvalue)
+    assert((outc.fit_parameters[1] - betac[1]).is_zero())
+    
+    outc = pe.total_least_squares(oxc, oy, func)
+    betac = outc.fit_parameters
+
+    for i in range(2):
+        betac[i].gamma_method(S=1.0)
+        assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
+        assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
+    assert math.isclose(pe.covariance(betac[0], betac[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
+
+    outc = pe.total_least_squares(oxc, oy, func, const_par=[betac[1]])
+
+    diffc = outc.fit_parameters[0] - betac[0]
+    assert(diffc / betac[0] < 1e-3 * betac[0].dvalue)
+    assert((outc.fit_parameters[1] - betac[1]).is_zero())
+
 
 def test_odr_derivatives():
     x = []

tests/obs_test.py

@@ -543,3 +543,41 @@ def test_import_jackknife():
     reconstructed_obs = pe.import_jackknife(my_jacks, 'test')
     assert my_obs == reconstructed_obs
 
+
+def test_covobs():
+    val = 1.123124
+    cov = .243423
+    name = 'Covariance'
+    co = pe.cov_Obs(val, cov, name)
+    co.gamma_method()
+    assert (co.dvalue == np.sqrt(cov))
+    assert (co.value == val)
+
+    do = 2 * co
+    assert (do.covobs[name].grad[0] == 2)
+
+    do = co * co
+    assert (do.covobs[name].grad[0] == 2 * val)
+    assert np.array_equal(do.covobs[name].cov, co.covobs[name].cov)
+
+    pi = [16.7457, -19.0475]
+    cov = [[3.49591, -6.07560], [-6.07560, 10.5834]]
+
+    cl = pe.cov_Obs(pi, cov, 'rAP')
+    pl = pe.misc.gen_correlated_data(pi, np.asarray(cov), 'rAPpseudo')
+
+    def rAP(p, g0sq):
+        return -0.0010666 * g0sq * (1 + np.exp(p[0] + p[1] / g0sq))
+
+    for g0sq in [1, 1.5, 1.8]:
+        oc = rAP(cl, g0sq)
+        oc.gamma_method()
+        op = rAP(pl, g0sq)
+        op.gamma_method()
+        assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
+
+    assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
+    assert(pe.covariance2(cl[0], cl[1]) == cov[1][0])
+
+    do = cl[0] * cl[1]
+    assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))