Release 1.3.3

CHANGELOG.md

@@ -2,6 +2,24 @@
 
 All notable changes to this project will be documented in this file.
 
+## [1.3.3] - 2025-03-11
+
+### Bug Fixes
+
+- Make `midext_prob` last param in get/set methods
+- Midline musn't use last of `set_param` args.\
+  Instead, it should use the one at `num_dims - 1`.
+
+### Documentation
+
+- Fix broken quickstart link.
+- Add `emcee` to requirements for quickstart notebook.
+- Explain midline extension probability in set/get.
+
+### Testing
+
+- `midext_prob` is last parameter in get/set methods.
+
 ## [1.3.2] - 2025-01-31
 
 ### Bug Fixes
@@ -848,6 +866,7 @@ Almost the entire API has changed. I'd therefore recommend to have a look at the
 - fix pyproject.toml typo
 - add pre-commit hook to check commit msg
 
+[1.3.3]: https://github.com/rmnldwg/lymph/compare/1.3.2...1.3.3
 [1.3.2]: https://github.com/rmnldwg/lymph/compare/1.3.1...1.3.2
 [1.3.1]: https://github.com/rmnldwg/lymph/compare/1.3.0...1.3.1
 [1.3.0]: https://github.com/rmnldwg/lymph/compare/1.2.3...1.3.0

README.rst

@@ -42,7 +42,7 @@ The mathematical details of the models can be found in in our earlier publicatio
 Get started
 ===========
 
-To learn how to use this package, head over to our `documentation <https://lymph-model.readthedocs.io>`_ where we explain the API of the package and also provide a `quickstart guide <https://lymph-model.readthedocs.io/en/latest/quickstart.html>`_.
+To learn how to use this package, head over to our `documentation <https://lymph-model.readthedocs.io>`_ where we explain the API of the package and also provide a `quickstart guide <https://lymph-model.readthedocs.io/stable/quickstart.html>`_.
 
 The implementation is pure-python and has only a few dependencies. However, it is intended to be used with powerful inference algorithms, e.g. the great sampling package `emcee <https://github.com/dfm/emcee>`_, which we used for our results.
 

lymph/models/midline.py

@@ -354,12 +354,13 @@ def get_params(
 
         Includes the spread parameters from the call to :py:meth:`get_spread_params`
         and the distribution parameters from the call to
-        :py:meth:`~.diagnosis_times.Composite.get_distribution_params`.
+        :py:meth:`~.diagnosis_times.Composite.get_distribution_params`. It also appends
+        the probability of midline extension to the end of the returned params.
         """
         params = {}
-        params["midext_prob"] = self.midext_prob
         params.update(self.get_spread_params(as_flat=as_flat))
         params.update(self.get_distribution_params(as_flat=as_flat))
+        params["midext_prob"] = self.midext_prob
 
         if as_flat or not as_dict:
             params = utils.flatten(params)
@@ -482,11 +483,14 @@ def set_params(
         """Set all parameters of the model.
 
         Combines the calls to :py:meth:`.set_spread_params` and
-        :py:meth:`.set_distribution_params`.
+        :py:meth:`.set_distribution_params`. Additionally, it sets the probability
+        for midline extension. Note that this parameter is always the last one that
+        is set after the spread and distribution parameters.
         """
-        first, args = utils.popfirst(args)
-        self.midext_prob = kwargs.get("midext_prob", first) or self.midext_prob
-        args = self.set_spread_params(*args, **kwargs)
+        last_param_idx = self.get_num_dims() - 1
+        before, last, after = utils.popat(args, idx=last_param_idx)
+        self.midext_prob = kwargs.get("midext_prob", last) or self.midext_prob
+        args = self.set_spread_params(*(before + after), **kwargs)
         return self.set_distribution_params(*args, **kwargs)
 
     def load_patient_data(

lymph/utils.py

@@ -279,6 +279,55 @@ def popfirst(seq: Sequence[Any]) -> tuple[Any, Sequence[Any]]:
         return None, seq
 
 
+def poplast(seq: Sequence[Any]) -> tuple[Sequence[Any], Any]:
+    """Return the sequence without the last element and the last element.
+
+    If the sequence is empty, the first element will be ``None`` and the second just
+    the empty sequence. Example:
+
+    >>> poplast([1, 2, 3])
+    ([1, 2], 3)
+    >>> poplast([])
+    ([], None)
+    """
+    first, rest = popfirst(seq[::-1])
+    return rest[::-1], first
+
+
+def popat(seq: Sequence[Any], idx: int) -> tuple[Sequence[Any], Any, Sequence[Any]]:
+    """Return the sequence before, the element at, and the sequence after ``idx``.
+
+    If the sequence is empty, the sequence before and after will be empty and the
+    element at ``idx`` will be ``None``.
+
+    If ``idx`` is too large, the sequence before will be the whole sequence, the element
+    at ``idx`` will be ``None``, and the sequence after will be empty. Example:
+
+    >>> popat([1, 2, 3], 1)
+    ([1], 2, [3])
+    >>> popat([], 0)
+    ([], None, [])
+    >>> popat([1, 2, 3], -1)
+    ([1, 2], 3, [])
+    >>> popat([1, 2, 3], -10)
+    ([], None, [1, 2, 3])
+    >>> popat([1, 2, 3], 10)
+    ([1, 2, 3], None, [])
+    >>> popat((1, 2, 3), 10)
+    ((1, 2, 3), None, ())
+    """
+    if idx < 0:
+        idx += len(seq)
+
+    if idx < 0:
+        return type(seq)(), None, seq
+
+    if idx >= len(seq):
+        return seq, None, type(seq)()
+
+    return seq[:idx], seq[idx], seq[idx + 1 :]
+
+
 def flatten(mapping, parent_key="", sep="_") -> dict:
     """Flatten a nested dictionary.
 

pyproject.toml

@@ -39,6 +39,7 @@ docs = [
     "ipython",
     "matplotlib",
     "scipy",
+    "emcee",
 ]
 
 [project.urls]

tests/binary_midline_test.py

@@ -60,6 +60,21 @@ def test_get_set_params_order(self) -> None:
         self.assertEqual(unused_param, params_to_set[-1])
         self.assertEqual(params_to_set[:-1].tolist(), returned_params)
 
+    def test_midext_prob_is_last(self) -> None:
+        """Check that the midline probability is the last parameter."""
+        expected_midext_prob = self.rng.uniform()
+        self.model.midext_prob = expected_midext_prob
+        self.assertEqual(
+            list(self.model.get_params(as_dict=False))[-1],
+            expected_midext_prob,
+        )
+
+        expected_midext_prob = self.rng.uniform()
+        num_dims = self.model.get_num_dims()
+        params_to_set = [0.0] * (num_dims - 1) + [expected_midext_prob]
+        self.model.set_params(*params_to_set)
+        self.assertEqual(self.model.midext_prob, expected_midext_prob)
+
 
 class MidlineLikelihoodTestCase(
     fixtures.MidlineFixtureMixin,