Merge pull request #28 from pasmopy/develop

Update `test_patient_specific_modeling()`

tests/test_patient_specific_modeling.py

@@ -3,7 +3,7 @@
 import shutil
 import sys
 import time
-from typing import Callable, List
+from typing import Callable, List, Optional
 
 import numpy as np
 from pasmopy import Model, PatientModelAnalyses, PatientModelSimulations, Text2Model
@@ -45,7 +45,7 @@ def test_model_construction():
         from models import erbb_network
     except ImportError:
         print("can't import erbb_network from models.")
-        
+
     model = Model(erbb_network.__package__).create()
     # add weighting factors
     gene_expression = {
@@ -134,7 +134,12 @@ def test_model_construction():
         assert len(model.problem.idx_params) + len(model.problem.idx_initials) == 221
 
 
-def test_patient_model_simulations():
+def test_patient_model_simulations(
+    n_patients: int = 3,
+    dynamical_feature: Optional[List[str]] = None,
+):
+    if not 1<= n_patients <= 6:
+        raise ValueError("`n_patients` must be lie within [1, 6].")
     # Initialization
     for patient in TCGA_ID:
         if patient in os.listdir(PATH_TO_MODELS) and patient != "TCGA_3C_AALK_01A":
@@ -160,32 +165,34 @@ def test_patient_model_simulations():
         if patient != "TCGA_3C_AALK_01A":
             shutil.copytree(path_to_patient("TCGA_3C_AALK_01A"), path_to_patient(f"{patient}"))
     # Execute patient-specific models
-    simulations = PatientModelSimulations(models.breast.__package__, random.sample(TCGA_ID, 3))
+    simulations = PatientModelSimulations(
+        models.breast.__package__,
+        random.sample(TNBC_ID, n_patients),
+    )
     start = time.time()
     assert simulations.run() is None
     elapsed = time.time() - start
-    print(f"Computation time for simulating 3 patients: {elapsed/60:.1f} [min]")
+    print(f"Computation time for simulating {n_patients} patients: {elapsed/60:.1f} [min]")
     # Add new response characteristics
     get_droprate: Callable[[np.ndarray], float] = (
         lambda time_course: -(time_course[-1] - np.max(time_course)) / (len(time_course) - np.argmax(time_course))
     )
     simulations.response_characteristics["droprate"] = get_droprate
     # Extract response characteristics and visualize patient classification
-    try:
-        simulations.subtyping(
-            "subtype_classification.pdf",
-            {
-                "Phosphorylated_Akt": {"EGF": ["AUC", "droprate"], "HRG": ["AUC", "droprate"]},
-                "Phosphorylated_ERK": {"EGF": ["AUC", "droprate"], "HRG": ["AUC", "droprate"]},
-                "Phosphorylated_c-Myc": {"EGF": ["AUC", "droprate"], "HRG": ["AUC", "droprate"]},
-            },
-        )
-        obs_names = ["Phosphorylated_Akt", "Phosphorylated_ERK", "Phosphorylated_c-Myc"]
-        for observable in obs_names:
-            assert os.path.isfile(os.path.join("classification", f"{observable}.csv"))
-        assert os.path.isfile("subtype_classification.pdf")
-    except ValueError:
-        pass
+    if dynamical_feature is None:
+        dynamical_feature = ["AUC", "droprate"]
+    simulations.subtyping(
+        "subtype_classification.pdf",
+        {
+            "Phosphorylated_Akt": {"EGF": dynamical_feature, "HRG": dynamical_feature},
+            "Phosphorylated_ERK": {"EGF": dynamical_feature, "HRG": dynamical_feature},
+            "Phosphorylated_c-Myc": {"EGF": dynamical_feature, "HRG": dynamical_feature},
+        },
+    )
+    obs_names = ["Phosphorylated_Akt", "Phosphorylated_ERK", "Phosphorylated_c-Myc"]
+    for observable in obs_names:
+        assert os.path.isfile(os.path.join("classification", f"{observable}.csv"))
+    assert os.path.isfile("subtype_classification.pdf")
 
 
 def test_patient_model_analyses():