Merge branch 'develop' into documentation

.github/workflows/binder.yml

@@ -0,0 +1,15 @@
+name: binder
+on:
+  push:
+    branches:
+      - develop
+
+jobs:
+  Create-MyBinderOrg-Cache:
+    runs-on: ubuntu-latest
+    steps:
+    - name: cache binder build on mybinder.org
+      uses: jupyterhub/repo2docker-action@master
+      with:
+        NO_PUSH: true
+        MYBINDERORG_TAG: ${{ github.event.ref }} # This builds the container on mybinder.org with the branch that was pushed on.

pyerrors/fits.py

@@ -264,9 +264,19 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
 
     fitp = out.beta
     try:
-        hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel()))))
+        hess = jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel())))
     except TypeError:
         raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
+    condn = np.linalg.cond(hess)
+    if condn > 1e8:
+        warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.\n \
+                       Maybe try rescaling the problem such that all parameters are of O(1).".format(condn), RuntimeWarning)
+    try:
+        hess_inv = np.linalg.inv(hess)
+    except np.linalg.LinAlgError:
+        raise Exception("Cannot invert hessian matrix.")
+    except Exception:
+        raise Exception("Unkown error in connection with Hessian inverse.")
 
     def odr_chisquare_compact_x(d):
         model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
@@ -542,9 +552,19 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
 
     fitp = fit_result.x
     try:
-        hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(fitp))
+        hess = jacobian(jacobian(chisqfunc))(fitp)
     except TypeError:
         raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
+    condn = np.linalg.cond(hess)
+    if condn > 1e8:
+        warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.\n \
+                       Maybe try rescaling the problem such that all parameters are of O(1).".format(condn), RuntimeWarning)
+    try:
+        hess_inv = np.linalg.inv(hess)
+    except np.linalg.LinAlgError:
+        raise Exception("Cannot invert hessian matrix.")
+    except Exception:
+        raise Exception("Unkown error in connection with Hessian inverse.")
 
     if kwargs.get('correlated_fit') is True:
         def chisqfunc_compact(d):