Add some testing

nifty6/minimization/iteration_controllers.py

@@ -108,7 +108,7 @@ class EnergyHistory(object):
         self._lst = []
 
     @property
-    def timestamps(self):
+    def time_stamps(self):
         return [x for x, _ in self._lst]
 
     @property
@@ -128,6 +128,9 @@ class EnergyHistory(object):
         self._lst += other._lst
         return self
 
+    def __len__(self):
+        return len(self._lst)
+
 
 def append_history(func):
     @functools.wraps(func)

nifty6/plot.py

@@ -288,7 +288,7 @@ def _plot_history(f, ax, **kwargs):
     plt.yscale(kwargs.pop("yscale", "linear"))
     mi, ma = np.inf, -np.inf
     for i, fld in enumerate(f):
-        xcoord = date2num([dt.fromtimestamp(ts) for ts in fld.timestamps])
+        xcoord = date2num([dt.fromtimestamp(ts) for ts in fld.time_stamps])
         ycoord = fld.energy_values
         ax.scatter(xcoord, ycoord, label=label[i], alpha=alpha[i],
                    color=color[i], s=size[i])

test/test_kl.py

@@ -39,7 +39,7 @@ def test_kl(constants, point_estimates, mirror_samples, mf):
     import numpy as np
     lh = ift.GaussianEnergy(domain=op.target, sampling_dtype=np.float64) @ op
     ic = ift.GradientNormController(iteration_limit=5)
-    ic.activate_and_reset_logging()
+    ic.enable_logging()
     h = ift.StandardHamiltonian(lh, ic_samp=ic)
     mean0 = ift.from_random('normal', h.domain)
 
@@ -51,7 +51,14 @@ def test_kl(constants, point_estimates, mirror_samples, mf):
                               point_estimates=point_estimates,
                               mirror_samples=mirror_samples,
                               napprox=0)
-    ic.pop_history()
+    assert_(len(ic.history) > 0)
+    assert_(len(ic.history) == len(ic.history.time_stamps))
+    assert_(len(ic.history) == len(ic.history.energy_values))
+    ic.history.reset()
+    assert_(len(ic.history) == 0)
+    assert_(len(ic.history) == len(ic.history.time_stamps))
+    assert_(len(ic.history) == len(ic.history.energy_values))
+
     locsamp = kl._local_samples
     klpure = ift.MetricGaussianKL(mean0,
                                   h,
@@ -87,8 +94,10 @@ def test_kl(constants, point_estimates, mirror_samples, mf):
 
     # Test constants (after some minimization)
     cg = ift.GradientNormController(iteration_limit=5)
-    minimizer = ift.NewtonCG(cg)
+    minimizer = ift.NewtonCG(cg, activate_logging=True)
     kl, _ = minimizer(kl)
+    if len(constants) != 2:
+        assert_(len(minimizer.inversion_history) > 0)
     diff = (mean0 - kl.position).to_dict()
     for kk in constants:
         assert_allclose(diff[kk].val, 0*diff[kk].val)