dwelltime: make standard errors private

changelog.md

@@ -15,7 +15,6 @@
 * Added parameter `titles` to customize title of each subplot in [`Kymo.plot_with_channels()`](https://lumicks-pylake.readthedocs.io/en/latest/_api/lumicks.pylake.kymo.Kymo.html#lumicks.pylake.kymo.Kymo.plot_with_channels).
 * Added [`KymoTrack.sample_from_channel()`](https://lumicks-pylake.readthedocs.io/en/latest/_api/lumicks.pylake.kymotracker.kymotrack.KymoTrack.html#lumicks.pylake.kymotracker.kymotrack.KymoTrack.sample_from_channel) to downsample channel data to the time points of a kymotrack.
 * Added support for file names with spaces in [`lk.download_from_doi()`](https://lumicks-pylake.readthedocs.io/en/latest/_api/lumicks.pylake.download_from_doi.html#lumicks.pylake.download_from_doi).
-* Added `err_amplitudes` and `err_lifetimes` to [`DwelltimeModel`](https://lumicks-pylake.readthedocs.io/en/latest/_api/lumicks.pylake.DwelltimeModel.html). These return asymptotic standard errors and should only be used for well identified models.
 
 #### Improvements
 

lumicks/pylake/kymotracker/tests/test_kymotrack.py

@@ -874,7 +874,6 @@ def test_fit_binding_times_nonzero(blank_kymo, blank_kymo_track_args):
         np.testing.assert_equal(dwelltime_model.dwelltimes, [4, 4, 4, 4])
         np.testing.assert_equal(dwelltime_model._observation_limits[0], 4)
         np.testing.assert_allclose(dwelltime_model.lifetimes[0], [0.4])
-        np.testing.assert_allclose(dwelltime_model.err_lifetimes[0], 0.199994, rtol=1e-5)
 
 
 def test_fit_binding_times_empty():

lumicks/pylake/population/dwelltime.py

@@ -197,7 +197,7 @@ def n_components(self):
         """Number of components in the model."""
         return self.model.n_components
 
-    def plot(self, alpha=None, *, with_stderr=False, **kwargs):
+    def plot(self, alpha=None, **kwargs):
         """Plot the profile likelihoods for the parameters of a model.
 
         Confidence interval is indicated by the region where the profile crosses the chi squared
@@ -209,11 +209,11 @@ def plot(self, alpha=None, *, with_stderr=False, **kwargs):
             Significance level. Confidence intervals are calculated as 100 * (1 - alpha)%. The
             default value of `None` results in using the significance level applied when
             profiling (default: 0.05).
-        with_stderr : bool
-            Also show bounds based on standard errors.
         """
         import matplotlib.pyplot as plt
 
+        with_stderr = kwargs.pop("with_stderr") if "with_stderr" in kwargs else False
+
         std_errs = self.model._std_errs[~np.isnan(self.model._std_errs)]
         if self.n_components == 1:
             next(iter(self.profiles.values())).plot(
@@ -632,34 +632,6 @@ def lifetimes(self):
         """Lifetime parameter (in time units) of each model component."""
         return self._parameters[self.n_components :]
 
-    @property
-    def err_amplitudes(self):
-        """Asymptotic standard error estimate on the model amplitudes.
-
-        Returns an asymptotic standard error on the amplitude parameters. These error estimates
-        are only reliable for estimates where a lot of data is available and the model does
-        not suffer from identifiability issues. To verify that these conditions are met, please
-        use either :meth:`DwelltimeModel.profile_likelihood()` method or
-        :meth:`DwelltimeModel.calculate_bootstrap()`.
-
-        Note that `np.nan` will be returned in case the parameter was either not estimated or no
-        error could be obtained."""
-        return self._std_errs[: self.n_components]
-
-    @property
-    def err_lifetimes(self):
-        """Asymptotic standard error estimate on the model lifetimes.
-
-        Returns an asymptotic standard error on the amplitude parameters. These error estimates
-        are only reliable for estimates where a lot of data is available and the model does
-        not suffer from identifiability issues. To verify that these conditions are met, please
-        use either :meth:`DwelltimeModel.profile_likelihood()` method or
-        :meth:`DwelltimeModel.calculate_bootstrap()`.
-
-        Note that `np.nan` will be returned in case the parameter was either not estimated or no
-        error could be obtained."""
-        return self._std_errs[self.n_components :]
-
     @property
     def rate_constants(self):
         """First order rate constant (units of per time) of each model component."""

lumicks/pylake/population/tests/test_dwelltimes.py

@@ -274,8 +274,6 @@ def test_std_errs(exponential_data, exp_name, n_components, ref_std_errs):
         discretization_timestep=dataset["parameters"].dt,
     )
     np.testing.assert_allclose(fit._std_errs, ref_std_errs, rtol=1e-4)
-    np.testing.assert_allclose(fit.err_amplitudes, ref_std_errs[:n_components], rtol=1e-4)
-    np.testing.assert_allclose(fit.err_lifetimes, ref_std_errs[n_components:], rtol=1e-4)
 
 
 @pytest.mark.parametrize("n_components", [2, 1])