Add test for autodiff on initial states

tests/test_grad.py

@@ -0,0 +1,46 @@
+import jax
+
+jax.config.update("jax_enable_x64", True)
+jax.config.update("jax_platform_name", "cpu")
+
+import jax.numpy as jnp
+import pytest
+from jax import value_and_grad
+
+import jaxley as jx
+from jaxley.channels import HH
+
+
+@pytest.mark.parametrize("key", ["HH_m", "voltages"])
+def test_grad_against_finite_diff_initial_state(key):
+    def simulate():
+        return jnp.sum(jx.integrate(comp))
+
+    def simulate_with_params(params):
+        return jnp.sum(jx.integrate(comp, params=params))
+
+    comp = jx.Compartment()
+    comp.insert(HH())
+    comp.record()
+    comp.stimulate(jx.step_current(0.1, 0.2, 0.1, 0.025, 5.0))
+
+    val = 0.2 if key == "HH_m" else -70.0
+    step_size = 0.01
+
+    # Finite differences.
+    comp.set(key, val)
+    r1 = simulate()
+    comp.set(key, val + step_size)
+    r2 = simulate()
+    finitediff_grad = (r2 - r1) / step_size
+
+    # Autodiff gradient.
+    grad_fn = value_and_grad(simulate_with_params)
+    comp.set(key, val)
+    comp.make_trainable(key)
+    params = comp.get_parameters()
+    v, g = grad_fn(params)
+    autodiff_grad = g[0][key]
+
+    # Less than 5% error on the gradient difference.
+    assert jnp.abs(autodiff_grad - finitediff_grad) / autodiff_grad < 0.05