Change 'induced' to 'inferred'

Author: joseph-hellerstein

README.md

@@ -22,7 +22,7 @@ Below, we summarize the ``pySubnetSB`` API.
 Here we illustrate ``pySubnetSB`` for two small networks using default values in the API call.
 The reference and target models are:
 
-    rerence_model = """
+    reference_model = """
         R1: S2 -> S3; k2*S2
         R2: S1 -> S2; k1*S1
         
@@ -44,6 +44,11 @@ The reference and target models are:
         k3 = 0.2
         """
 
+To use ``pySubnetSB``, execute
+
+    !pip install pySubnetSB
+    from pySubnetSB.api import ModelSpecification findReferenceInTarget, findReferencesInTargets, makeSerializationFile
+
 The API call is
 
     result = findReferenceInTarget(reference_model, target_model)
@@ -80,18 +85,18 @@ The target model is BioModels 695.
             reference_model,
             ModelSpecification(URL, specification_type="sbmlurl"),
             max_num_mapping_pair=1e14,
-            num_process=mp.cpu_count(),
-            identity=cn.ID_WEAK)
+            num_process=2,
+            identity="weak")
 
-As before, the first two arguments of the API call are the reference and target models. Since the target is a URL, ``ModelSpecification`` is used to convert the URL to a model string. ``max_num_mapping_pair`` is used to manage computational demands by limiting the number mapping pairs that are considered. ``num_process`` specifies the number of processes (cores) that are used by ``pySubnetSB``. By default, all cores are used. Last, ``identity`` specifies the kind of subnet to discover.
+As before, the first two arguments of the API call are the reference and target models. Since the target is a URL, ``ModelSpecification`` is used to convert the URL to a model string. ``max_num_mapping_pair`` is used to manage computational demands by limiting the number mapping pairs that are considered. ``num_process`` specifies the number of processes (cores) that are used by ``pySubnetSB``. By default, all cores are used. Last, ``identity`` specifies the kind of subnet to discover - "weak" or "strong" (default).
 
 The ``identity`` argument requires more explanation. A subnet of the target is weakly identical (``cn.ID_WEAK``) to the reference if they have the same stoichiometry matrix. A target subnet is strongly identical (default) if it is just a renaming of the species and reactions in the reference network.
 
 Running the foregoing code takes about 10 minutes on a two core machine. You will see a status bar as the command executes that indicates the number of mapping pairs processed.
 
     mapping pairs: 100%|███████████████████████████████████████████████████████████████████████████████████| 483649090/483649090 [00:29<00:00, 16350750.64it/s]
 
-As before ``result.mapping_pairs`` is a list of mapping pairs. You can display the inferred network form mapping pair 1 using ``result.makeInducedNetwork(1)``. There is some stochasticity to the order of the results. When we ran this study, inferred network 1 was:
+As before ``result.mapping_pairs`` is a list of mapping pairs. You can display the inferred network form mapping pair 1 using ``result.makeInferredNetwork(1)``. There is some stochasticity to the order of the results. When we ran this study, inferred network 1 was:
 
     R_31: xFinal_2 -> xFinal_1
     R_10:  -> xFinal_8

src/pySubnetSB/network.py

@@ -48,13 +48,13 @@ def mapping_pairs(self)->List[AssignmentPair]:
         return self.assignment_pairs
 
     @property
-    def induced_network(self)->'Network':
-        """Induced network from the first assignment pair."""
-        return self.makeInducedNetwork()
+    def inferred_network(self)->'Network':
+        """Inferred network from the first assignment pair."""
+        return self.makeInferredNetwork()
 
-    def makeInducedNetwork(self, assignment_pair_idx:int=0)->'Network':
+    def makeInferredNetwork(self, assignment_pair_idx:int=0)->'Network':
         """
-        Creates an induced network from the assignment pair.
+        Creates an inferred network from the assignment pair.
 
         Args:
             assignment_pair_idx (int): index of the assignment pair
@@ -68,7 +68,7 @@ def makeInducedNetwork(self, assignment_pair_idx:int=0)->'Network':
             msg = f'Assignment pair index {assignment_pair_idx} is out of range.'
             msg += f' Max is {len(self.assignment_pairs)}'
             raise ValueError(msg)
-        return self.network.makeInducedNetwork(self.assignment_pairs[assignment_pair_idx])
+        return self.network.makeInferredNetwork(self.assignment_pairs[assignment_pair_idx])  # type: ignore
 
     def __bool__(self)->bool:
         return len(self.assignment_pairs) > 0

src/pySubnetSB/network_base.py

@@ -924,9 +924,9 @@ def makeRandomReferenceAndTarget(cls,
         )
 
 
-    def makeInducedNetwork(self, assignment_pair:AssignmentPair)->'NetworkBase':
+    def makeInferredNetwork(self, assignment_pair:AssignmentPair)->'NetworkBase':
         """
-        Makes an induced network based on an assignment pair.
+        Makes an inferred network based on an assignment pair.
 
         Args:
             assignment_pair (AssignmentPair): Assignment pair.

src/pySubnetSB_tests/test_network_base.py

@@ -332,7 +332,7 @@ def testMakeInducedNetwork(self):
         species_assignment = np.array(range(self.network.num_species))
         reaction_assignment = np.array(range(self.network.num_reaction))
         assignment_pair = AssignmentPair(species_assignment, reaction_assignment)
-        induced_network = self.network.makeInducedNetwork(assignment_pair)
+        induced_network = self.network.makeInferredNetwork(assignment_pair)
         self.assertTrue(self.network.isEquivalent(induced_network))
 
     def testMakeMatricesForIdentity(self):