Merge branch 'develop' into documentation

.gitignore

@@ -8,3 +8,4 @@ examples/B1k2_pcac_plateau.p
 examples/Untitled.*
 core.*
 *.swp
+htmlcov

pyerrors/fits.py

@@ -678,7 +678,7 @@ def qqplot(x, o_y, func, p):
     plt.xlabel('Theoretical quantiles')
     plt.ylabel('Ordered Values')
     plt.legend()
-    plt.show()
+    plt.draw()
 
 
 def residual_plot(x, y, func, fit_res):
@@ -707,7 +707,7 @@ def residual_plot(x, y, func, fit_res):
     ax1.set_xlim([xstart, xstop])
     ax1.set_ylabel('Residuals')
     plt.subplots_adjust(wspace=None, hspace=None)
-    plt.show()
+    plt.draw()
 
 
 def covariance_matrix(y):
@@ -782,7 +782,7 @@ def ks_test(obs=None):
     loc_max_diff = np.argmax(np.abs(diffs))
     loc = Xs[loc_max_diff]
     plt.annotate(s='', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
-    plt.show()
+    plt.draw()
 
     print(scipy.stats.kstest(Qs, 'uniform'))
 

pyerrors/obs.py

@@ -1546,70 +1546,6 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
     return dvalue
 
 
-def covariance3(obs1, obs2, correlation=False, **kwargs):
-    """Another alternative implementation of the covariance of two observables.
-
-    covariance2(obs, obs) is equal to obs.dvalue ** 2
-    Currently only works if ensembles are identical.
-    The gamma method has to be applied first to both observables.
-
-    If abs(covariance2(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance
-    is constrained to the maximum value in order to make sure that covariance
-    matrices are positive semidefinite.
-
-    Keyword arguments
-    -----------------
-    correlation -- if true the correlation instead of the covariance is
-                   returned (default False)
-    plot -- if true, the integrated autocorrelation time for each ensemble is
-            plotted.
-    """
-
-    for name in sorted(set(obs1.names + obs2.names)):
-        if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
-            raise Exception('Shapes of ensemble', name, 'do not fit')
-        if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
-            raise Exception('Shapes of ensemble', name, 'do not fit')
-
-    if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
-        raise Exception('The gamma method has to be applied to both Obs first.')
-
-    tau_exp = []
-    S = []
-    for e_name in sorted(set(obs1.e_names + obs2.e_names)):
-        t_1 = obs1.tau_exp.get(e_name)
-        t_2 = obs2.tau_exp.get(e_name)
-        if t_1 is None:
-            t_1 = 0
-        if t_2 is None:
-            t_2 = 0
-        tau_exp.append(max(t_1, t_2))
-        S_1 = obs1.S.get(e_name)
-        S_2 = obs2.S.get(e_name)
-        if S_1 is None:
-            S_1 = Obs.S_global
-        if S_2 is None:
-            S_2 = Obs.S_global
-        S.append(max(S_1, S_2))
-
-    check_obs = obs1 + obs2
-    check_obs.gamma_method(tau_exp=tau_exp, S=S)
-
-    if kwargs.get('plot'):
-        check_obs.plot_tauint()
-        check_obs.plot_rho()
-
-    cov = (check_obs.dvalue ** 2 - obs1.dvalue ** 2 - obs2.dvalue ** 2) / 2
-
-    if np.abs(cov / obs1.dvalue / obs2.dvalue) > 1.0:
-        cov = np.sign(cov) * obs1.dvalue * obs2.dvalue
-
-    if correlation:
-        cov = cov / obs1.dvalue / obs2.dvalue
-
-    return cov
-
-
 def pseudo_Obs(value, dvalue, name, samples=1000):
     """Generate a pseudo Obs with given value, dvalue and name
 

tests/fits_test.py

@@ -29,7 +29,7 @@ def test_least_squares():
         y = a[0] * np.exp(-a[1] * x)
         return y
 
-    out = pe.least_squares(x, oy, func)
+    out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True)
     beta = out.fit_parameters
 
     for i in range(2):
@@ -133,7 +133,7 @@ def test_total_least_squares():
     odr.set_job(fit_type=0, deriv=1)
     output = odr.run()
 
-    out = pe.total_least_squares(ox, oy, func)
+    out = pe.total_least_squares(ox, oy, func, expected_chisquare=True)
     beta = out.fit_parameters
 
     for i in range(2):