added more functions to utils

requirements.txt

+numpy
+scipy
+matplotlib
+joblib
\ No newline at end of file

setup.py

@@ -15,7 +15,7 @@ test_requirements = ['pytest',]
 setup(
     author="Hossein Rafipoor, Michiel Cottaar, Saad Jbabdi",
     author_email='hossein.rafipoor@ndcn.ox.ac.uk',
-    python_requires='>=3.6',
+    python_requires='>=3.7',
     classifiers=[
         'Programming Language :: Python :: 3',
         'Programming Language :: Python :: 3.6',

whim/hbm.py

@@ -6,7 +6,7 @@ from dataclasses import dataclass
 from . import utils
 
 @dataclass
-class hbm():
+class hbm:
     forward_model: Callable
     param_names: List
     param_priors: Mapping

whim/utils.py

@@ -155,3 +155,27 @@ def generate_acq(n_b0=10, n_dir=64, b=(1, 2, 3)):
         bvecs = np.concatenate([bvecs, fibonacci_sphere(n_dir)])
     return bvals, bvecs
 
+
+def grad(f, p, bounds, dp=1e-6):
+    dp = np.maximum(1e-10, abs(p * dp))
+    g = []
+    for i in range(len(p)):
+        pi = np.zeros(len(p))
+        pi[i] = dp[i]
+
+        u = p + pi
+        if u[i] > bounds[i][1]:
+            u[i] = bounds[i][1]
+
+        l = p - pi
+        if l[i] < bounds[i][0]:
+            l[i] = bounds[i][0]
+
+        g.append((f(u) - f(l)) / (u[i]-l[i]))
+    return np.stack(g, axis=0)
+
+
+def hessian(f, p, bounds, dp=1e-6):
+    g = lambda p_: grad(f, p_, bounds, dp)
+    return grad(g, p, bounds, dp)
+