fix: corrected bug that prevented combined fits with multiple x-obs i…

…n some cases (fjosw#241)

* fix: corrected bug that prevented combined fits with multiple x-obs in some cases

* made test more complex

* [Fix] Slightly increase tolerance for matrix function test.

* Adapt test_merge_idx to compare lists

---------

Co-authored-by: Simon Kuberski <[email protected]>
Co-authored-by: Fabian Joswig <[email protected]>

pyerrors/fits.py

@@ -256,7 +256,7 @@ def func_b(a, x):
     if sorted(list(funcd.keys())) != key_ls:
         raise ValueError('x and func dictionaries do not contain the same keys.')
 
-    x_all = np.concatenate([np.array(xd[key]) for key in key_ls])
+    x_all = np.concatenate([np.array(xd[key]).transpose() for key in key_ls]).transpose()
     y_all = np.concatenate([np.array(yd[key]) for key in key_ls])
 
     y_f = [o.value for o in y_all]

tests/fits_test.py

@@ -1082,6 +1082,20 @@ def test_combined_resplot_qqplot():
     fr = pe.least_squares(xd, yd, fd, resplot=True, qqplot=True)
     plt.close('all')
 
+def test_combined_fit_xerr():
+    fitd = {
+        'a' : lambda p, x: p[0] * x[0] + p[1] * x[1],
+        'b' : lambda p, x: p[0] * x[0] + p[2] * x[1],
+        'c' : lambda p, x: p[0] * x[0] + p[3] * x[1],
+    }
+    yd = {
+        'a': [pe.cov_Obs(3 + .1 * np.random.uniform(), .1**2, 'a' + str(i)) for i in range(5)],
+        'b': [pe.cov_Obs(1 + .1 * np.random.uniform(), .1**2, 'b' + str(i)) for i in range(6)],
+        'c': [pe.cov_Obs(3 + .1 * np.random.uniform(), .1**2, 'c' + str(i)) for i in range(3)],
+    }
+    xd = {k: np.transpose([[1 + .01 * np.random.uniform(), 2] for i in range(len(yd[k]))]) for k in fitd}
+    pe.fits.least_squares(xd, yd, fitd)
+
 
 def test_x_multidim_fit():
     x1 = np.arange(1, 10)

tests/linalg_test.py

@@ -276,10 +276,10 @@ def test_matrix_functions():
     for (i, j), entry in np.ndenumerate(check_inv):
         entry.gamma_method()
         if(i == j):
-            assert math.isclose(entry.value, 1.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
+            assert math.isclose(entry.value, 1.0, abs_tol=2e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
         else:
-            assert math.isclose(entry.value, 0.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
-        assert math.isclose(entry.dvalue, 0.0, abs_tol=1e-9), 'dvalue ' + str(i) + ',' + str(j) + ' ' + str(entry.dvalue)
+            assert math.isclose(entry.value, 0.0, abs_tol=2e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
+        assert math.isclose(entry.dvalue, 0.0, abs_tol=2e-9), 'dvalue ' + str(i) + ',' + str(j) + ' ' + str(entry.dvalue)
 
     # Check Cholesky decomposition
     sym = np.dot(matrix, matrix.T)

tests/obs_test.py

@@ -554,11 +554,11 @@ def test_merge_idx():
 
     for j in range(5):
         idll = [range(1, int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)]
-        assert pe.obs._merge_idx(idll) == sorted(set().union(*idll))
+        assert list(pe.obs._merge_idx(idll)) == sorted(set().union(*idll))
 
     for j in range(5):
         idll = [range(int(round(np.random.uniform(1, 28))), int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)]
-        assert pe.obs._merge_idx(idll) == sorted(set().union(*idll))
+        assert list(pe.obs._merge_idx(idll)) == sorted(set().union(*idll))
 
     idl = [list(np.arange(1, 14)) + list(range(16, 100, 4)), range(4, 604, 4), [2, 4, 5, 6, 8, 9, 12, 24], range(1, 20, 1), range(50, 789, 7)]
     new_idx = pe.obs._merge_idx(idl)
@@ -1457,4 +1457,4 @@ def test_missing_replica():
 
     for op in [[O1O2, O1O2b], [O1O2O3, O1O2O3b]]:
         assert np.isclose(op[1].value, op[0].value)
-        assert np.isclose(op[1].dvalue, op[0].dvalue, atol=0, rtol=5e-2)
+        assert np.isclose(op[1].dvalue, op[0].dvalue, atol=0, rtol=5e-2)