Change ThinLayer missing dependency handling to give RuntimeError

pyenzyme/thinlayers/TL_Copasi.py

@@ -16,11 +16,18 @@
 from pyenzyme import EnzymeMLDocument
 from pyenzyme.thinlayers import BaseThinLayer
 import os
-
-import COPASI
 import pandas as pd
 from builtins import enumerate
 
+_COPASI_IMPORT_ERROR = None
+try:
+    import COPASI
+except ModuleNotFoundError as e:
+    _COPASI_IMPORT_ERROR = """
+    ThinLayerCopasi is not available. 
+    To use it, please install the following dependencies:
+    {}
+    """.format(e)
 
 class ThinLayerCopasi(BaseThinLayer):
 
@@ -36,6 +43,9 @@ def __init__(self, path, outdir,
         :param measurement_ids: the measurement ids or all
         :param init_file: optional initialization file for fit items
         """
+        # check dependencies
+        if _COPASI_IMPORT_ERROR:
+            raise RuntimeError(_COPASI_IMPORT_ERROR)
 
         # initialize base class, let it do the reading
         BaseThinLayer.__init__(

pyenzyme/thinlayers/TL_Pysces.py

@@ -10,13 +10,20 @@
 import numpy as np
 import pandas as pd
 import os
-import pysces
-import lmfit
-import copy
 
 from typing import Union, Optional
 from pyenzyme.thinlayers.TL_Base import BaseThinLayer
 
+_PYSCES_IMPORT_ERROR = None
+try:
+    import pysces
+    import lmfit
+except ModuleNotFoundError as e:
+    _PYSCES_IMPORT_ERROR = """
+    ThinLayerPysces is not available. 
+    To use it, please install the following dependencies:
+    {}
+    """.format(e)
 
 class ThinLayerPysces(BaseThinLayer):
 
@@ -28,6 +35,10 @@ def __init__(
         init_file: Optional[str] = None
     ):
 
+        # check dependencies
+        if _PYSCES_IMPORT_ERROR:
+            raise RuntimeError(_PYSCES_IMPORT_ERROR)
+
         super().__init__(path, measurement_ids, init_file)
 
         # Convert model to PSC and get experimental data
@@ -187,7 +198,7 @@ def _get_pysces_model(self, model_dir: str):
         # Finally, load the PSC model
         self.model = pysces.model(sbmlfile_name, dir=model_dir)
 
-    def _calculate_residual(self, parameters: lmfit.Parameters) -> np.ndarray:
+    def _calculate_residual(self, parameters) -> np.ndarray:
         """Function that will be optimized"""
 
         simulated_data = self._simulate_experiment(parameters)
@@ -197,7 +208,7 @@ def _calculate_residual(self, parameters: lmfit.Parameters) -> np.ndarray:
 
         return np.array(self.experimental_data - simulated_data)
 
-    def _simulate_experiment(self, parameters: lmfit.Parameters):
+    def _simulate_experiment(self, parameters):
         """Performs simulation based on the PySCeS model"""
 
         self.model.SetQuiet()

pyenzyme/thinlayers/__init__.py

@@ -1,21 +1,4 @@
-import warnings
-
 from .TL_Base import BaseThinLayer
 from .TL_Strenda import ThinLayerStrendaML
-
-try:
-    from .TL_Pysces import ThinLayerPysces
-except ModuleNotFoundError as e:
-    print(
-        "%s - ThinLayerPysces now returns 'None', please provide the dependencies to use this module." % str(e)
-    )
-    ThinLayerPysces = None
-
-try:
-    from .TL_Copasi import ThinLayerCopasi
-except ModuleNotFoundError as e:
-    print(
-        "%s - ThinLayerCopasi now returns 'None', please provide the dependencies to use this module." % str(e)
-    )
-    ThinLayerCopasi = None
-
+from .TL_Pysces import ThinLayerPysces
+from .TL_Copasi import ThinLayerCopasi