updated HPV modules with more fitting naming

lymph/models/__init__.py

@@ -5,4 +5,4 @@
 from lymph.models.midline import Midline
 from lymph.models.unilateral import Unilateral
 
-__all__ = ["Unilateral", "HpvWrapper" "Bilateral", "Midline"]
+__all__ = ["Unilateral", "HpvUnilateral" "Bilateral", "Midline"]

lymph/models/hpv.py

@@ -4,7 +4,6 @@
 
 import logging
 import warnings
-from collections.abc import Iterable
 from typing import Any, Literal
 
 import numpy as np
@@ -16,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-class HpvWrapper(
+class HpvUnilateral(
     diagnosis_times.Composite,
     modalities.Composite,
     types.Model,
@@ -95,7 +94,7 @@ def _init_models(
         self.base_2_key = list(self.HPV.graph.tumors.keys())[0] + "toII"
 
     @classmethod
-    def binary(cls, *args, **kwargs) -> HpvWrapper:
+    def binary(cls, *args, **kwargs) -> HpvUnilateral:
         """Initialize a binary bilateral model.
 
         This is a convenience method that sets the ``allowed_states`` of the
@@ -107,7 +106,7 @@ def binary(cls, *args, **kwargs) -> HpvWrapper:
         return cls(*args, uni_kwargs=uni_kwargs, **kwargs)
 
     @classmethod
-    def trinary(cls, *args, **kwargs) -> HpvWrapper:
+    def trinary(cls, *args, **kwargs) -> HpvUnilateral:
         """Initialize a trinary bilateral model.
 
         This is a convenience method that sets the ``allowed_states`` of the
@@ -478,60 +477,3 @@ def risk(
         )
 
         return self.marginalize(involvement, posterior_state_dist)
-
-    def draw_patients(
-        self,
-        num: int,
-        stage_dist: Iterable[float],
-        rng: np.random.Generator | None = None,
-        seed: int = 42,
-        **_kwargs,
-    ) -> pd.DataFrame:
-        """Draw ``num`` random patients from the parametrized model.
-
-        See Also
-        --------
-            :py:meth:`.diagnosis_times.Distribution.draw_diag_times`
-                Method to draw diagnosis times from a distribution.
-            :py:meth:`.Unilateral.draw_diagnosis`
-                Method to draw individual diagnosis from a unilateral model.
-            :py:meth:`.Unilateral.draw_patients`
-                The unilateral method to draw a synthetic dataset.
-
-        """
-        if rng is None:
-            rng = np.random.default_rng(seed)
-
-        if sum(stage_dist) != 1.0:
-            warnings.warn("Sum of stage distribution is not 1. Renormalizing.")
-            stage_dist = np.array(stage_dist) / sum(stage_dist)
-
-        drawn_t_stages = rng.choice(
-            a=self.t_stages,
-            p=stage_dist,
-            size=num,
-        )
-        drawn_diag_times = [
-            self.get_distribution(t_stage).draw_diag_times(rng=rng)
-            for t_stage in drawn_t_stages
-        ]
-
-        drawn_obs_ipsi = self.ipsi.draw_diagnosis(drawn_diag_times, rng=rng)
-        drawn_obs_noHPV = self.contra.draw_diagnosis(drawn_diag_times, rng=rng)
-        drawn_obs = np.concatenate([drawn_obs_ipsi, drawn_obs_noHPV], axis=1)
-
-        # construct MultiIndex with "ipsi" and "contra" at top level to allow
-        # concatenation of the two separate drawn diagnosis
-        sides = ["ipsi", "contra"]
-        modality_names = list(self.get_all_modalities().keys())
-        lnl_names = list(self.ipsi.graph.lnls.keys())
-        multi_cols = pd.MultiIndex.from_product([sides, modality_names, lnl_names])
-
-        # reorder the column levels and thus also the individual columns to match the
-        # LyProX format without mixing up the data
-        dataset = pd.DataFrame(drawn_obs, columns=multi_cols)
-        dataset = dataset.reorder_levels(order=[1, 0, 2], axis="columns")
-        dataset = dataset.sort_index(axis="columns", level=0)
-        dataset[("tumor", "1", "t_stage")] = drawn_t_stages
-
-        return dataset