add more tests for fitting.py

tests/test_fitting.py

@@ -4,7 +4,7 @@
 __author__ = 'Paul Hancock'
 __date__ = ''
 
-from AegeanTools import fitting
+from AegeanTools import fitting, models
 import lmfit
 import numpy as np
 
@@ -25,7 +25,7 @@ def test_elliptical_gaussian():
     x, y = np.indices((3, 3))
     gauss = fitting.elliptical_gaussian(x, y, amp=1, xo=0, yo=1, sx=1, sy=1, theta=0)
     assert not np.any(np.isnan(gauss))
-    gauss = fitting.elliptical_gaussian(x, y, amp=1, xo=0, yo=1, sx=1, sy=1, theta=np.nan)
+    gauss = fitting.elliptical_gaussian(x, y, amp=1, xo=0, yo=1, sx=1, sy=1, theta=np.inf)
     assert np.all(np.isnan(gauss))
 
 
@@ -126,10 +126,82 @@ def test_emp_vs_ana_jacobian():
     assert np.max(diff) < 1e-3
 
 
+def test_emp_vs_ana_hessian():
+    model = lmfit.Parameters()
+    model.add('c0_amp', 1, vary=True)
+    model.add('c0_xo', 5, vary=True)
+    model.add('c0_yo', 5, vary=True)
+    model.add('c0_sx', 2.001, vary=False)
+    model.add('c0_sy', 2, vary=False)
+    model.add('c0_theta', 0, vary=False)
+    model.add('components', 1, vary=False)
+    nvar = 3
+    x, y = np.indices((10, 10))
+    emp_Hij = fitting.emp_hessian(model, x, y)
+    ana_Hij = fitting.hessian(model, x, y)
+    diff = np.abs(ana_Hij - emp_Hij)
+    assert np.max(diff) < 1e-5
+
+    model.add('c1_amp', 1, vary=True)
+    model.add('c1_xo', 5, vary=True)
+    model.add('c1_yo', 5, vary=True)
+    model.add('c1_sx', 2.001, vary=True)
+    model.add('c1_sy', 2, vary=True)
+    model.add('c1_theta', 0, vary=True)
+    nvar = 9
+    model['components'].value = 2
+    emp_Hij = fitting.emp_hessian(model, x, y)
+    ana_Hij = fitting.hessian(model, x, y)
+    diff = np.abs(ana_Hij - emp_Hij)
+    assert np.max(diff) < 1
+
+
 def test_make_ita():
     noise = np.random.random((10, 10))
     ita = fitting.make_ita(noise)
-    assert ita.shape == (100,100)
+    assert ita.shape == (100, 100)
+    noise *= np.nan
+    ita = fitting.make_ita(noise)
+    assert len(ita) == 0
+
+
+def test_RB_bias():
+    data = np.random.random((4, 4))
+    model = make_model()
+    bias = fitting.RB_bias(data, model)
+    assert len(bias) == 3
+
+
+def test_bias_correct():
+    data = np.random.random((4, 4))
+    model = make_model()
+    fitting.bias_correct(model, data)
+
+
+def dont_test_CRB_errs():
+    x, y = np.indices((3, 3))
+    C = fitting.Cmatrix(x.ravel(), y.ravel(), sx=1, sy=2, theta=0)
+    model = make_model()
+    Jij = fitting.jacobian(model, x, y)
+    fitting.CRB_errs(Jij, C)
+    B = fitting.Bmatrix(C)
+    fitting.CRB_errs(Jij, C, B)
+
+
+def test_condon_errs():
+    source = models.OutputSource()
+    source.ra = 0
+    source.dec = 1
+    source.a = 10
+    source.b = 10.
+    source.pa = 0
+    source.local_rms = 0.1
+    source.peak_flux = 1
+    source.int_flux = 1
+    fitting.condon_errors(source, None)
+    assert source.err_a == 0
+    fitting.condon_errors(source, theta_n=8.)
+    assert source.err_a > 0
 
 
 if __name__ == "__main__":