Merge branch 'develop' into documentation

CHANGELOG.md

@@ -2,10 +2,14 @@
 
 All notable changes to this project will be documented in this file.
 
+## [2.1.2] - 2022-06-10
+### Fixed
+- Bug in `Corr.matrix_symmetric` fixed which appeared when a time slice contained an array with `None` entries.
+
 ## [2.1.1] - 2022-06-06
 ### Fixed
 - Bug in error propagation of correlated least square fits fixed.
-- Fit_result.gamma_method can now be called with kwargs
+- `Fit_result.gamma_method` can now be called with kwargs
 
 ## [2.1.0] - 2022-05-31
 ### Added

pyerrors/version.py

@@ -1 +1 @@
-__version__ = "2.2.0+dev"
+__version__ = "2.1.2"

setup.py

@@ -4,7 +4,7 @@ this_directory = Path(__file__).parent
 long_description = (this_directory / "README.md").read_text()
 
 setup(name='pyerrors',
-      version='2.2.0+dev',
+      version='2.1.2',
       description='Error analysis for lattice QCD',
       long_description=long_description,
       long_description_content_type='text/markdown',
@@ -18,7 +18,7 @@ setup(name='pyerrors',
       license="MIT",
       packages=find_packages(),
       python_requires='>=3.6.0',
-      install_requires=['numpy>=1.16', 'autograd>=1.4', 'numdifftools', 'matplotlib>=3.3', 'scipy>=1', 'iminuit>=2', 'h5py', 'lxml', 'python-rapidjson'],
+      install_requires=['numpy>=1.16', 'autograd>=1.4', 'numdifftools', 'matplotlib>=3.3', 'scipy>=1', 'iminuit>=2', 'h5py>=3', 'lxml>=4', 'python-rapidjson>=1'],
       classifiers=[
           'Development Status :: 5 - Production/Stable',
           'Intended Audience :: Science/Research',
@@ -28,6 +28,6 @@ setup(name='pyerrors',
           'Programming Language :: Python :: 3.8',
           'Programming Language :: Python :: 3.9',
           'Programming Language :: Python :: 3.10',
-          'Topic :: Scientific/Engineering'
+          'Topic :: Scientific/Engineering :: Physics'
       ],
      )

tests/fits_test.py

@@ -184,6 +184,26 @@ def test_fit_corr_independent():
         assert (out[1] - out_corr[1]).is_zero(atol=1e-5)
 
 
+def test_linear_fit_guesses():
+    for err in [10, 0.1, 0.001]:
+        xvals = []
+        yvals = []
+        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))
+        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];
+        with pytest.raises(Exception):
+            pe.least_squares(xvals, yvals, lin_func, initial_guess=[5])
+
+        bad_guess = pe.least_squares(xvals, yvals, lin_func, initial_guess=[999, 999])
+        good_guess = pe.least_squares(xvals, yvals, lin_func, initial_guess=[0, 1])
+        assert np.isclose(bad_guess.chisquare, good_guess.chisquare, atol=1e-8)
+        assert np.all([(go - ba).is_zero(atol=1e-6) for (go, ba) in zip(good_guess, bad_guess)])
+
+
 def test_total_least_squares():
     dim = 10 + int(30 * np.random.rand())
     x = np.arange(dim) + np.random.normal(0.0, 0.15, dim)
@@ -223,7 +243,7 @@ def test_total_least_squares():
         beta[i].gamma_method(S=1.0)
         assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
         assert math.isclose(output.cov_beta[i, i], beta[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(beta[i].dvalue ** 2)
-    assert math.isclose(pe.covariance([beta[0], beta[1]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
+    assert math.isclose(pe.covariance([beta[0], beta[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
 
     out = pe.total_least_squares(ox, oy, func, const_par=[beta[1]])
 
@@ -246,7 +266,7 @@ def test_total_least_squares():
         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]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
+    assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
 
     outc = pe.total_least_squares(oxc, oyc, func, const_par=[betac[1]])
 
@@ -261,7 +281,7 @@ def test_total_least_squares():
         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]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
+    assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
 
     outc = pe.total_least_squares(oxc, oy, func, const_par=[betac[1]])
 

tests/obs_test.py

@@ -736,6 +736,23 @@ def test_covariance_factorizing():
     assert np.isclose(pe.covariance([mt0, tt[1]])[0, 1], -pe.covariance(tt)[0, 1])
 
 
+def test_covariance_smooth_eigenvalues():
+    for c_coeff in range(0, 14, 2):
+        length = 14
+        sm = 5
+        t_fac = 1.5
+        tt = pe.misc.gen_correlated_data(np.zeros(length), 1 - 0.1 ** c_coeff * np.ones((length, length)) + 0.1 ** c_coeff * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=200)
+        [o.gamma_method() for o in tt]
+        full_corr = pe.covariance(tt, correlation=True)
+        cov = pe.covariance(tt, smooth=sm, correlation=True)
+
+        full_evals = np.linalg.eigh(full_corr)[0]
+        sm_length = np.where(full_evals < np.mean(full_evals[:-sm]))[0][-1]
+
+        evals = np.linalg.eigh(cov)[0]
+        assert np.all(np.isclose(evals[:sm_length], evals[0], atol=1e-8))
+
+
 def test_covariance_alternation():
     length = 12
     t_fac = 2.5