dwelltime: enforce minimum observable time

For the discrete variant, the minimum observable time should be no smaller than the line time. Anything shorter cannot be observed under that model.

lumicks/pylake/population/dwelltime.py

@@ -504,6 +504,10 @@ class DwelltimeModel:
     ValueError
         if `min_observation_time` or `max_observation_time` is not a scalar and is not the same
         length as `dwelltimes`.
+    ValueError
+        if `discretization_time` is provided and is bigger than the `min_observation_time`. A
+        minimum observation time shorter than the discretization step does not make sense under
+        this model.
     """
 
     def __init__(
@@ -542,6 +546,20 @@ def __init__(
                         f"the number of dwell times provided ({dwelltimes.size})."
                     )
 
+            # A minimum time shorter than the discretization time makes no sense (cannot be
+            # observed under this model)
+            rel_tolerance = 1e-6
+            if np.any(
+                violations := discretization_timestep > (1.0 + rel_tolerance) * min_observation_time
+            ):
+                dt, min_obs = np.array(
+                    list(np.broadcast(discretization_timestep, min_observation_time))
+                )[violations, :].T
+                raise ValueError(
+                    f"The discretization timestep ({dt.flat[0]}) cannot be larger than the minimum "
+                    f"observable time ({min_obs.flat[0]})."
+                )
+
         self.n_components = n_components
         self.dwelltimes = dwelltimes
         self.use_jacobian = use_jacobian

lumicks/pylake/population/tests/test_dwelltimes.py

@@ -64,7 +64,7 @@ def test_invalid_multi_dwelltime_parameters():
             r"Size of maximum observation time array (4) must be equal to that of dwelltimes (3)"
         )
     ):
-        DwelltimeModel(data, min_observation_time=0, max_observation_time=np.array([1, 2, 3, 4]))
+        DwelltimeModel(data, min_observation_time=1.0, max_observation_time=np.array([1, 2, 3, 4]))
 
     with pytest.raises(
         ValueError,
@@ -73,7 +73,7 @@ def test_invalid_multi_dwelltime_parameters():
             r"discretization timesteps (2) should equal the number of dwell times provided (3)."
         )
     ):
-        DwelltimeModel(data, discretization_timestep=np.array([10, 1]))
+        DwelltimeModel(data, min_observation_time=10, discretization_timestep=np.array([10, 1]))
 
     with pytest.raises(
         ValueError,
@@ -82,7 +82,22 @@ def test_invalid_multi_dwelltime_parameters():
             r"zero as this leads to an invalid probability mass function."
         )
     ):
-        DwelltimeModel(data, discretization_timestep=np.array([10, 0, 10]))
+        DwelltimeModel(data, min_observation_time=10, discretization_timestep=np.array([10, 0, 10]))
+
+    for dt, min_obs in (
+        (np.array([10, 10, 10]), np.array([0.0, 0.0, 0.0])),
+        (10, np.array([0.0, 0.0, 0.0])),
+        (np.array([10, 10, 10]), 0.0),
+        (10, 0.0),
+    ):
+        with pytest.raises(
+            ValueError,
+            match=re.escape(
+                r"The discretization timestep (10.0) cannot be larger than the minimum observable "
+                r"time (0.0)."
+            ),
+        ):
+            DwelltimeModel(data, discretization_timestep=dt, min_observation_time=min_obs)
 
 
 def test_optim_options(exponential_data):
@@ -123,8 +138,13 @@ def test_fit_parameters(exponential_data):
             None,
             (0.3497069, 1.3528150)
         ),
-        (0.1, 1.4, 0.2, (0.375963, 1.482267)),
-        (0.1, 1.4, np.array([0.2, 0.2, 0.4, 0.4]), (0.4263154934247158, 1.4892888434906768)),
+        (0.2, 1.4, 0.2, (0.247748, 1.463575)),
+        (
+            np.array([0.2, 0.2, 0.4, 0.4]),
+            1.4,
+            np.array([0.2, 0.2, 0.4, 0.4]),
+            (0.21762, 1.448516)
+        ),
     ]
 )
 def test_bootstrap_multi(min_obs, max_obs, ref_ci, time_step):
@@ -218,15 +238,13 @@ def test_dwelltime_profiles(exponential_data, exp_name, reference_bounds, reinte
 
     profiles = fit.profile_likelihood(max_chi2_step=0.25)
     for (name, component), ref_interval in reference_bounds:
-        print(profiles.get_interval(name, component))
-        #np.testing.assert_allclose(profiles.get_interval(name, component), ref_interval, rtol=1e-3)
+        np.testing.assert_allclose(profiles.get_interval(name, component), ref_interval, rtol=1e-3)
 
     # Re-interpolated confidence level (different significance level than originally profiled).
     for (name, component, significance), ref_interval in reinterpolated_bounds:
-        print(profiles.get_interval(name, component, significance))
-        #np.testing.assert_allclose(
-        #    profiles.get_interval(name, component, significance), ref_interval, rtol=1e-3
-        #)
+        np.testing.assert_allclose(
+            profiles.get_interval(name, component, significance), ref_interval, rtol=1e-3
+        )
 
     # Significance level cannot be chosen lower than what we profiled.
     with pytest.raises(