Warn if gain, bias override intercepts, max_rates

CHANGES.rst

@@ -22,6 +22,13 @@ Release History
 2.7.1 (unreleased)
 ==================
 
+**Added**
+
+- Added a warning when setting ``gain`` and ``bias`` along with either of
+  ``max_rates`` or ``intercepts``, as the latter two parameters are ignored.
+  (`#1431 <https://github.com/nengo/nengo/issues/1431>`_,
+  `#1433 <https://github.com/nengo/nengo/pull/1433>`_)
+
 **Changed**
 
 - Learning rules can now be sliced when providing error input.

nengo/builder/ensemble.py

@@ -8,7 +8,7 @@
 from nengo.builder.operator import Copy, DotInc, Reset
 from nengo.dists import Distribution, get_samples
 from nengo.ensemble import Ensemble
-from nengo.exceptions import BuildError
+from nengo.exceptions import BuildError, NengoWarning
 from nengo.neurons import Direct
 from nengo.utils.builder import default_n_eval_points
 
@@ -88,6 +88,14 @@ def get_gain_bias(ens, rng=np.random):
         bias = get_samples(ens.bias, ens.n_neurons, rng=rng)
         max_rates, intercepts = ens.neuron_type.max_rates_intercepts(
             gain, bias)
+
+        if (ens.max_rates is not Ensemble.max_rates.default or
+                ens.intercepts is not Ensemble.intercepts.default):
+            warnings.warn(NengoWarning(
+                "Specifying the gains and biases for %s imposes a set of "
+                "maximum firing rates and intercepts. Further specifying "
+                "either max_rates or intercepts has no effect." % ens))
+
     elif ens.gain is not None or ens.bias is not None:
         # TODO: handle this instead of error
         raise NotImplementedError("gain or bias set for %s, but not both. "

nengo/tests/test_ensemble.py

@@ -4,7 +4,7 @@
 import nengo
 import nengo.utils.numpy as npext
 from nengo.dists import Choice, Gaussian, UniformHypersphere
-from nengo.exceptions import BuildError
+from nengo.exceptions import BuildError, NengoWarning
 from nengo.processes import WhiteNoise, FilteredNoise
 from nengo.utils.testing import allclose
 
@@ -216,6 +216,18 @@ def test_eval_points_number_warning(Simulator, seed):
     assert np.allclose(sim.data[A].eval_points, [[0.1], [0.2]])
 
 
+def test_gain_bias_warning(Simulator, seed):
+    model = nengo.Network(seed=seed)
+    with model:
+        nengo.Ensemble(1, 1, gain=[1], bias=[1], intercepts=[0.5])
+        nengo.Ensemble(1, 1, gain=[1], bias=[1], max_rates=[200])
+
+    with pytest.warns(NengoWarning):
+        # gain, bias override specified intercepts and max_rates, which warns
+        with Simulator(model):
+            pass
+
+
 @pytest.mark.parametrize('neurons, dims', [
     (10, 1), (392, 1), (2108, 1), (100, 2), (1290, 4), (20, 9)])
 def test_eval_points_heuristic(Simulator, neurons, dims, seed):