added some road signs

tests/test_examples.py

@@ -5,6 +5,8 @@
 from derivative.differentiation import _gen_method
 
 
+# Utilities for tests
+# ===================
 def default_args(kind):
     """ The assumption is that the function will have dt = 1/100 over a range of 1 and not vary much. The goal is to
      to set the parameters such that we obtain effective derivatives under these conditions.
@@ -26,8 +28,7 @@ def default_args(kind):
         return {"sigma": 1, "lmbd": .01, "kernel": "gaussian"}
     else:
         raise ValueError('Unimplemented default args for kind {}.'.format(kind))
-
-
+
 class NumericalExperiment:
     def __init__(self, fn, fn_str, t, kind, args):
         self.fn = fn
@@ -40,7 +41,6 @@ def __init__(self, fn, fn_str, t, kind, args):
     def run(self):
         return dxdt(self.fn(self.t), self.t, self.kind, self.axis, **self.kwargs)
 
-
 def compare(experiment, truth, rel_tol, abs_tol, shape_only=False):
     """ Compare a numerical experiment to theoretical expectations. Issue warnings for derivative methods that fail,
     use asserts for implementation requirements.
@@ -60,8 +60,8 @@ def mean_sq(x):
         assert np.linalg.norm(residual, ord=np.inf) < max(abs_tol, np.linalg.norm(truth, ord=np.inf) * rel_tol)
 
 
-# Check that numbers are returned
-# ===============================
+# Check that only numbers are returned
+# ====================================
 @pytest.mark.parametrize("m", methods)
 def test_notnan(m):
     t = np.linspace(0, 1, 100)
@@ -71,8 +71,8 @@ def test_notnan(m):
     assert not np.any(np.isnan(values)), message
 
 
-# Test some basic functions
-# =========================
+# Test that basic functions are differentiated correctly
+# ======================================================
 funcs_and_derivs = (
     (lambda t: np.ones_like(t), "f(t) = 1", lambda t: np.zeros_like(t), "const1"),
     (lambda t: np.zeros_like(t), "f(t) = 0", lambda t: np.zeros_like(t), "const0"),
@@ -112,6 +112,8 @@ def test_fn(m, func_spec):
     compare(nexp, deriv(t), 1e-1, 1e-1, bad_combo)
 
 
+# Test smoothing for those that do it
+# ===================================
 @pytest.mark.parametrize("kind", ("kalman", "trend_filtered"))
 def test_smoothing_x(kind):
     t = np.linspace(0, 1, 100)
@@ -122,7 +124,6 @@ def test_smoothing_x(kind):
     # MSE
     assert np.linalg.norm(x_est - np.sin(t)) ** 2 / len(t) < 1e-1
 
-
 @pytest.mark.parametrize("kind", ("kalman", "trend_filtered"))
 def test_smoothing_functional(kind):
     t = np.linspace(0, 1, 100)
@@ -133,13 +134,14 @@ def test_smoothing_functional(kind):
     assert np.linalg.norm(x_est - np.sin(t)) ** 2 / len(t) < 1e-1
 
 
+# Test caching of the expensive _gen_method using a dummy
+# =======================================================
 @pytest.fixture
 def clean_gen_method_cache():
     _gen_method.cache_clear()
     yield
     _gen_method.cache_clear()
 
-
 def test_gen_method_caching(clean_gen_method_cache):
     x = np.ones(3)
     t = np.arange(3)
@@ -150,7 +152,6 @@ def test_gen_method_caching(clean_gen_method_cache):
     assert _gen_method.cache_info().currsize == 1
     assert id(expected) == id(result)
 
-
 def test_gen_method_kwarg_caching(clean_gen_method_cache):
     x = np.ones(3)
     t = np.arange(3)
@@ -164,6 +165,8 @@ def test_gen_method_kwarg_caching(clean_gen_method_cache):
     assert id(expected) != id(result)
 
 
+# Test caching of the expensive private _global methods using a dummy
+# ===================================================================
 @pytest.fixture
 def method_inst(request):
     x = np.ones(3)
@@ -173,7 +176,6 @@ def method_inst(request):
     yield x, t, method
     method._global.cache_clear()
 
-
 @pytest.mark.parametrize("method_inst", ["kalman", "trend_filtered", "spectral"], indirect=True)
 def test_dglobal_caching(method_inst):
     # make sure we're not recomputing expensive _global() method
@@ -184,8 +186,7 @@ def test_dglobal_caching(method_inst):
     assert method._global.cache_info().misses == 1
     assert method._global.cache_info().currsize == 1
 
-
-@pytest.mark.parametrize("method_inst", ["kalman", "trend_filtered"], indirect=True)
+@pytest.mark.parametrize("method_inst", ["kalman", "trend_filtered", "spectral"], indirect=True)
 def test_cached_global_order(method_inst):
     x, t, method = method_inst
     x = np.vstack((x, -x))