diff --git a/docs/pyerrors/obs.html b/docs/pyerrors/obs.html
index 6cfd4ea9..4ca2bf3a 100644
--- a/docs/pyerrors/obs.html
+++ b/docs/pyerrors/obs.html
@@ -1765,23 +1765,23 @@
1452 if isinstance(smooth, int):
1453 corr = _smooth_eigenvalues(corr, smooth)
1454
-1455 errors = [o.dvalue for o in obs]
-1456 cov = np.diag(errors) @ corr @ np.diag(errors)
-1457
-1458 eigenvalues = np.linalg.eigh(cov)[0]
-1459 if not np.all(eigenvalues >= 0):
-1460 warnings.warn("Covariance matrix is not positive semi-definite (Eigenvalues: " + str(eigenvalues) + ")", RuntimeWarning)
-1461
-1462 if visualize:
-1463 plt.matshow(corr, vmin=-1, vmax=1)
-1464 plt.set_cmap('RdBu')
-1465 plt.colorbar()
-1466 plt.draw()
-1467
-1468 if correlation is True:
-1469 return corr
-1470 else:
-1471 return cov
+1455 if visualize:
+1456 plt.matshow(corr, vmin=-1, vmax=1)
+1457 plt.set_cmap('RdBu')
+1458 plt.colorbar()
+1459 plt.draw()
+1460
+1461 if correlation is True:
+1462 return corr
+1463
+1464 errors = [o.dvalue for o in obs]
+1465 cov = np.diag(errors) @ corr @ np.diag(errors)
+1466
+1467 eigenvalues = np.linalg.eigh(cov)[0]
+1468 if not np.all(eigenvalues >= 0):
+1469 warnings.warn("Covariance matrix is not positive semi-definite (Eigenvalues: " + str(eigenvalues) + ")", RuntimeWarning)
+1470
+1471 return cov
1472
1473
1474def _smooth_eigenvalues(corr, E):
@@ -5026,23 +5026,23 @@ Currently only works if ensembles are identical (this is not strictly necessary)
1453 if isinstance(smooth, int):
1454 corr = _smooth_eigenvalues(corr, smooth)
1455
-1456 errors = [o.dvalue for o in obs]
-1457 cov = np.diag(errors) @ corr @ np.diag(errors)
-1458
-1459 eigenvalues = np.linalg.eigh(cov)[0]
-1460 if not np.all(eigenvalues >= 0):
-1461 warnings.warn("Covariance matrix is not positive semi-definite (Eigenvalues: " + str(eigenvalues) + ")", RuntimeWarning)
-1462
-1463 if visualize:
-1464 plt.matshow(corr, vmin=-1, vmax=1)
-1465 plt.set_cmap('RdBu')
-1466 plt.colorbar()
-1467 plt.draw()
-1468
-1469 if correlation is True:
-1470 return corr
-1471 else:
-1472 return cov
+1456 if visualize:
+1457 plt.matshow(corr, vmin=-1, vmax=1)
+1458 plt.set_cmap('RdBu')
+1459 plt.colorbar()
+1460 plt.draw()
+1461
+1462 if correlation is True:
+1463 return corr
+1464
+1465 errors = [o.dvalue for o in obs]
+1466 cov = np.diag(errors) @ corr @ np.diag(errors)
+1467
+1468 eigenvalues = np.linalg.eigh(cov)[0]
+1469 if not np.all(eigenvalues >= 0):
+1470 warnings.warn("Covariance matrix is not positive semi-definite (Eigenvalues: " + str(eigenvalues) + ")", RuntimeWarning)
+1471
+1472 return cov