fix: Error handling for fits and root finding with numpy instead of autograd.numpy

improved. Tests added.

pyerrors/fits.py

@@ -260,7 +260,10 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
                   output.chisquare_by_expected_chisquare)
 
     fitp = out.beta
+    try:
         hess_inv = np.linalg.pinv(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
 
     def odr_chisquare_compact_x(d):
         model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
@@ -537,7 +540,10 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
                       output.chisquare_by_expected_chisquare)
 
     fitp = fit_result.x
+    try:
         hess_inv = np.linalg.pinv(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
 
     if kwargs.get('correlated_fit') is True:
         def chisqfunc_compact(d):

pyerrors/roots.py

@@ -31,7 +31,10 @@ def find_root(d, func, guess=1.0, **kwargs):
 
     # Error propagation as detailed in arXiv:1809.01289
     dx = jacobian(func)(root[0], d.value)
+    try:
         da = jacobian(lambda u, v: func(v, u))(d.value, root[0])
+    except TypeError:
+        raise Exception("It is required to use autograd.numpy instead of numpy within root functions, see the documentation for details.") from None
     deriv = - da / dx
 
     res = derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (d.value + np.finfo(np.float64).eps) * root[0], [d], man_grad=[deriv])

tests/fits_test.py

@@ -1,4 +1,5 @@
-import autograd.numpy as np
+import numpy as np
+import autograd.numpy as anp
 import math
 import scipy.optimize
 from scipy.odr import ODR, Model, RealData
@@ -41,12 +42,12 @@ def test_least_squares():
         oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
 
     def f(x, a, b):
-        return a * np.exp(-b * x)
+        return a * anp.exp(-b * x)
 
     popt, pcov = scipy.optimize.curve_fit(f, x, y, sigma=[o.dvalue for o in oy], absolute_sigma=True)
 
     def func(a, x):
-        y = a[0] * np.exp(-a[1] * x)
+        y = a[0] * anp.exp(-a[1] * x)
         return y
 
     out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True)
@@ -95,7 +96,7 @@ def test_alternative_solvers():
         oy.append(pe.pseudo_Obs(y[i], yerr[i], 'test'))
 
     def func(a, x):
-        y = a[0] * np.exp(-a[1] * x)
+        y = a[0] * anp.exp(-a[1] * x)
         return y
 
     chisquare_values = []
@@ -145,7 +146,7 @@ def test_correlated_fit():
                 return p[1] + p[0] * x
         else:
             def fitf(p, x):
-                return p[1] * np.exp(-p[0] * x)
+                return p[1] * anp.exp(-p[0] * x)
 
         fitp = pe.least_squares(x, data, fitf, expected_chisquare=True)
 
@@ -172,10 +173,10 @@ def test_total_least_squares():
         oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
 
     def f(x, a, b):
-        return a * np.exp(-b * x)
+        return a * anp.exp(-b * x)
 
     def func(a, x):
-        y = a[0] * np.exp(-a[1] * x)
+        y = a[0] * anp.exp(-a[1] * x)
         return y
 
     data = RealData([o.value for o in ox], [o.value for o in oy], sx=[o.dvalue for o in ox], sy=[o.dvalue for o in oy])
@@ -336,6 +337,27 @@ def test_error_band():
     pe.fits.error_band(x, f, fitp.fit_parameters)
 
 
+def test_fit_no_autograd():
+    dim = 10
+    x = np.arange(dim)
+    y = 2 * np.exp(-0.08 * x) + np.random.normal(0.0, 0.15, dim)
+    yerr = 0.1 + 0.1 * np.random.rand(dim)
+
+    oy = []
+    for i, item in enumerate(x):
+        oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
+
+    def func(a, x):
+        y = a[0] * np.exp(-a[1] * x)
+        return y
+
+    with pytest.raises(Exception):
+        pe.least_squares(x, oy, func)
+
+    with pytest.raises(Exception):
+        pe.total_least_squares(oy, oy, func)
+
+
 def test_ks_test():
     def f(a, x):
         y = a[0] + a[1] * x

tests/roots_test.py

@@ -30,3 +30,15 @@ def test_root_linear_idl():
 
     difference = my_obs - my_root
     assert difference.is_zero()
+
+
+def test_root_no_autograd():
+
+    def root_function(x, d):
+        return x - np.log(np.exp(d))
+
+    value = np.random.normal(0, 100)
+    my_obs = pe.pseudo_Obs(value, 0.1, 't')
+
+    with pytest.raises(Exception):
+        my_root = pe.roots.find_root(my_obs, root_function)