Merge branch 'develop' into documentation

CHANGELOG.md

@@ -2,6 +2,15 @@
 
 All notable changes to this project will be documented in this file.
 
+## [2.x.x] - 2022-xx-xx
+### Added
+- `Obs.reweight` method added in analogy to `Corr.reweight` which allows for a more convenient reweighting of individual observables.
+- `Corr.show` now has the additional argument `title` which allows to add a title to the figure. Figures are now saved with `bbox_inches='tight'`.
+
+### Fixed
+- `Corr.m_eff` can now deal with correlator entries which are exactly zero.
+- Minor bugs in `input.dobs` fixed.
+
 ## [2.1.3] - 2022-06-13
 ### Fixed
 - Further bugs in connection with correlator objects which have arrays with None entries as content fixed.

pyerrors/fits.py

@@ -177,13 +177,15 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
     if not callable(func):
         raise TypeError('func has to be a function.')
 
-    for i in range(25):
+    for i in range(42):
         try:
             func(np.arange(i), x.T[0])
         except Exception:
-            pass
+            continue
         else:
             break
+    else:
+        raise RuntimeError("Fit function is not valid.")
 
     n_parms = i
     if not silent:
@@ -321,9 +323,11 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
         try:
             func(np.arange(i), 0)
         except Exception:
-            pass
+            continue
         else:
             break
+    else:
+        raise RuntimeError("Fit function is not valid.")
 
     n_parms = i
 
@@ -442,13 +446,15 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
     if not callable(func):
         raise TypeError('func has to be a function.')
 
-    for i in range(25):
+    for i in range(42):
         try:
             func(np.arange(i), x.T[0])
         except Exception:
-            pass
+            continue
         else:
             break
+    else:
+        raise RuntimeError("Fit function is not valid.")
 
     n_parms = i
 

tests/correlators_test.py

@@ -249,9 +249,9 @@ def test_matrix_corr():
 
 def test_corr_none_entries():
     a = pe.pseudo_Obs(1.0, 0.1, 'a')
-    l = np.asarray([[a, a], [a, a]])
+    la = np.asarray([[a, a], [a, a]])
     n = np.asarray([[None, None], [None, None]])
-    x = [l, n]
+    x = [la, n]
     matr = pe.Corr(x)
     matr.projected(np.asarray([1.0, 0.0]))
 

tests/fits_test.py

@@ -194,7 +194,7 @@ def test_linear_fit_guesses():
         lin_func = lambda a, x: a[0] + a[1] * x
         with pytest.raises(Exception):
             pe.least_squares(xvals, yvals, lin_func)
-        [o.gamma_method() for o in yvals];
+        [o.gamma_method() for o in yvals]
         with pytest.raises(Exception):
             pe.least_squares(xvals, yvals, lin_func, initial_guess=[5])
 
@@ -414,7 +414,7 @@ def test_fit_vs_jackknife():
 
     for i, cov in enumerate([cov1, cov2, cov3]):
         dat = pe.misc.gen_correlated_data(np.arange(1, 4), cov, 'test', 0.5, samples=samples)
-        [o.gamma_method(S=0) for o in dat];
+        [o.gamma_method(S=0) for o in dat]
         func = lambda a, x: a[0] + a[1] * x
         fr = pe.least_squares(np.arange(1, 4), dat, func)
         fr.gamma_method(S=0)
@@ -448,8 +448,7 @@ def test_correlated_fit_vs_jackknife():
     x_val = np.arange(1, 6, 2)
     for i, cov in enumerate([cov1, cov2, cov3]):
         dat = pe.misc.gen_correlated_data(x_val + x_val ** 2 + np.random.normal(0.0, 0.1, 3), cov, 'test', 0.5, samples=samples)
-        [o.gamma_method(S=0) for o in dat];
+        [o.gamma_method(S=0) for o in dat]
-        dat
         func = lambda a, x: a[0] * x + a[1] * x ** 2
         fr = pe.least_squares(x_val, dat, func, correlated_fit=True, silent=True)
         [o.gamma_method(S=0) for o in fr]
@@ -496,6 +495,31 @@ def test_fit_no_autograd():
         pe.total_least_squares(oy, oy, func)
 
 
+def test_invalid_fit_function():
+    def func1(a, x):
+        return a[0] + a[1] * x + a[2] * anp.sinh(x) + a[199]
+
+    def func2(a, x, y):
+        return a[0] + a[1] * x
+
+    def func3(x):
+        return x
+
+    xvals =[]
+    yvals =[]
+    err = 0.1
+
+    for x in range(1, 8, 2):
+        xvals.append(x)
+        yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 100, 'test2', samples=87))
+    [o.gamma_method() for o in yvals]
+    for func in [func1, func2, func3]:
+        with pytest.raises(Exception):
+            pe.least_squares(xvals, yvals, func)
+        with pytest.raises(Exception):
+            pe.total_least_squares(yvals, yvals, func)
+
+
 def test_singular_correlated_fit():
     obs1 = pe.pseudo_Obs(1.0, 0.1, 'test')
     with pytest.raises(Exception):

tests/linalg_test.py

@@ -258,8 +258,8 @@ def test_complex_matrix_inverse():
     inverse_matrix = np.linalg.inv(matrix)
     inverse_obs_matrix = pe.linalg.inv(obs_matrix)
     for (n, m), entry in np.ndenumerate(inverse_matrix):
-        assert np.isclose(inverse_matrix[n, m].real,  inverse_obs_matrix[n, m].real.value)
+        assert np.isclose(inverse_matrix[n, m].real, inverse_obs_matrix[n, m].real.value)
-        assert np.isclose(inverse_matrix[n, m].imag,  inverse_obs_matrix[n, m].imag.value)
+        assert np.isclose(inverse_matrix[n, m].imag, inverse_obs_matrix[n, m].imag.value)
 
 
 def test_matrix_functions():