Merge branch 'stable-0.7' which is tagged as v0.7.4

Author: basnijholt

adaptive/learner/learner1D.py

@@ -129,7 +129,7 @@ def curvature_loss_function(area_factor=1, euclid_factor=0.02, horizontal_factor
     @uses_nth_neighbors(1)
     def curvature_loss(xs, ys):
         xs_middle = xs[1:3]
-        ys_middle = xs[1:3]
+        ys_middle = ys[1:3]
 
         triangle_loss_ = triangle_loss(xs, ys)
         default_loss_ = default_loss(xs_middle, ys_middle)
@@ -654,7 +654,8 @@ def _get_data(self):
         return self.data
 
     def _set_data(self, data):
-        self.tell_many(*zip(*data.items()))
+        if data:
+            self.tell_many(*zip(*data.items()))
 
 
 def loss_manager(x_scale):
@@ -670,7 +671,7 @@ def finite_loss(ival, loss, x_scale):
     sort intervals that have infinite loss."""
     # If the loss is infinite we return the
     # distance between the two points.
-    if math.isinf(loss):
+    if math.isinf(loss) or math.isnan(loss):
         loss = (ival[1] - ival[0]) / x_scale
         if len(ival) == 3:
             # Used when constructing quals. Last item is

adaptive/learner/learnerND.py

@@ -757,7 +757,8 @@ def _get_data(self):
         return self.data
 
     def _set_data(self, data):
-        self.tell_many(*zip(*data.items()))
+        if data:
+            self.tell_many(*zip(*data.items()))
 
     def _get_iso(self, level=0.0, which='surface'):
         if which == 'surface':

adaptive/tests/test_learner1d.py

@@ -363,3 +363,15 @@ def f(x):
     learner = Learner1D(f, (-1, 1), loss_per_interval=loss)
     simple(learner, goal=lambda l: l.npoints > 100)
     assert learner.npoints > 100
+
+
+def test_NaN_loss():
+    # see https://github.com/python-adaptive/adaptive/issues/145
+    def f(x):
+        a = 0.01
+        if random.random() < 0.2:
+            return np.NaN
+        return x + a**2 / (a**2 + x**2)
+
+    learner = Learner1D(f, bounds=(-1, 1))
+    simple(learner, lambda l: l.npoints > 100)