rename biomass_function to objective_function, fixes issue #82 (#97)

README.md

@@ -256,7 +256,7 @@ You could also set an alternative objective function. For example, to maximize t
 n = model.num_of_reactions()
 obj_fun = np.zeros(n)
 obj_fun[0] = 1
-model.biomass_function(obj_fun)
+model.objective_function(obj_fun)
 
 # apply FVA using the new objective function
 fva_output = model.fva()

dingo/MetabolicNetwork.py

@@ -47,7 +47,7 @@ def __init__(self, tuple_args):
         self._metabolites = tuple_args[3]
         self._reactions = tuple_args[4]
         self._biomass_index = tuple_args[5]
-        self._biomass_function = tuple_args[6]
+        self._objective_function = tuple_args[6]
         self._medium = tuple_args[7]
         self._medium_indices = tuple_args[8]
         self._exchanges = tuple_args[9]
@@ -58,9 +58,9 @@ def __init__(self, tuple_args):
                 or self._lb.size != self._S.shape[1]
                 or len(self._metabolites) != self._S.shape[0]
                 or len(self._reactions) != self._S.shape[1]
-                or self._biomass_function.size != self._S.shape[1]
+                or self._objective_function.size != self._S.shape[1]
                 or (self._biomass_index < 0)
-                or (self._biomass_index > self._biomass_function.size)
+                or (self._biomass_index > self._objective_function.size)
             ):
                 raise Exception(
                     "Wrong tuple format given to initialize a metabolic network object."
@@ -112,25 +112,25 @@ def fva(self):
                 self._lb,
                 self._ub,
                 self._S,
-                self._biomass_function,
+                self._objective_function,
                 self._parameters["opt_percentage"],
             )
         else:
             return slow_fva(
                 self._lb,
                 self._ub,
                 self._S,
-                self._biomass_function,
+                self._objective_function,
                 self._parameters["opt_percentage"],
             )
 
     def fba(self):
         """A member function to apply the FBA method on the metabolic network."""
 
         if self._parameters["fast_computations"]:
-            return fast_fba(self._lb, self._ub, self._S, self._biomass_function)
+            return fast_fba(self._lb, self._ub, self._S, self._objective_function)
         else:
-            return slow_fba(self._lb, self._ub, self._S, self._biomass_function)
+            return slow_fba(self._lb, self._ub, self._S, self._objective_function)
 
     @property
     def lb(self):
@@ -157,8 +157,8 @@ def biomass_index(self):
         return self._biomass_index
 
     @property
-    def biomass_function(self):
-        return self._biomass_function
+    def objective_function(self):
+        return self._objective_function
 
     @property
     def medium(self):
@@ -181,7 +181,7 @@ def get_as_tuple(self):
             self._metabolites,
             self._reactions,
             self._biomass_index,
-            self._biomass_function,
+            self._objective_function,
             self._medium,
             self._inter_medium,
             self._exchanges
@@ -217,9 +217,9 @@ def reactions(self, value):
     def biomass_index(self, value):
         self._biomass_index = value
 
-    @biomass_function.setter
-    def biomass_function(self, value):
-        self._biomass_function = value
+    @objective_function.setter
+    def objective_function(self, value):
+        self._objective_function = value
 
 
     @medium.setter

dingo/PolytopeSampler.py

@@ -77,8 +77,8 @@ def get_polytope(self):
         ):
 
             (
-                max_biomass_flux_vector,
-                max_biomass_objective,
+                max_flux_vector,
+                max_objective,
             ) = self._metabolic_network.fba()
 
             if (
@@ -90,7 +90,7 @@ def get_polytope(self):
                     self._metabolic_network.lb,
                     self._metabolic_network.ub,
                     self._metabolic_network.S,
-                    self._metabolic_network.biomass_function,
+                    self._metabolic_network.objective_function,
                     self._parameters["opt_percentage"],
                 )
             else:
@@ -104,8 +104,8 @@ def get_polytope(self):
                 (
                     min_fluxes,
                     max_fluxes,
-                    max_biomass_flux_vector,
-                    max_biomass_objective,
+                    max_flux_vector,
+                    max_objective,
                 ) = self._metabolic_network.fva()
 
                 A, b, Aeq, beq = get_matrices_of_low_dim_polytope(
@@ -123,11 +123,11 @@ def get_polytope(self):
             ):
                 raise Exception("Preprocess for full dimensional polytope failed.")
 
-            A = np.vstack((A, -self._metabolic_network.biomass_function))
+            A = np.vstack((A, -self._metabolic_network.objective_function))
 
             b = np.append(
                 b,
-                -np.floor(max_biomass_objective / self._parameters["tol"])
+                -np.floor(max_objective / self._parameters["tol"])
                 * self._parameters["tol"]
                 * self._parameters["opt_percentage"]
                 / 100,
@@ -293,8 +293,8 @@ def round_polytope(
     def sample_from_fva_output(
         min_fluxes,
         max_fluxes,
-        biomass_function,
-        max_biomass_objective,
+        objective_function,
+        max_objective,
         S,
         opt_percentage=100,
         ess=1000,
@@ -307,8 +307,8 @@ def sample_from_fva_output(
         Keyword arguments:
         min_fluxes -- minimum values of the fluxes, i.e., a n-dimensional vector
         max_fluxes -- maximum values for the fluxes, i.e., a n-dimensional vector
-        biomass_function -- the biomass objective function
-        max_biomass_objective -- the maximum value of the biomass objective function
+        objective_function -- the objective function
+        max_objective -- the maximum value of the objective function
         S -- stoichiometric matrix
         opt_percentage -- consider solutions that give you at least a certain
                       percentage of the optimal solution (default is to consider
@@ -323,12 +323,12 @@ def sample_from_fva_output(
             S, min_fluxes, max_fluxes, opt_percentage, tol
         )
 
-        A = np.vstack((A, -biomass_function))
+        A = np.vstack((A, -objective_function))
         b = np.append(
             b,
             -(opt_percentage / 100)
             * self._parameters["tol"]
-            * math.floor(max_biomass_objective / self._parameters["tol"]),
+            * math.floor(max_objective / self._parameters["tol"]),
         )
 
         A, b, N, N_shift = get_matrices_of_full_dim_polytope(A, b, Aeq, beq)

tutorials/dingo_tutorial.ipynb

@@ -874,7 +874,7 @@
       },
       "outputs": [],
       "source": [
-        "model.biomass_function"
+        "model.objective_function"
       ]
     },
     {
@@ -1118,7 +1118,7 @@
         "id": "jWEhcWmJm1Qu"
       },
       "source": [
-        "Now we can replace the `biomass_function` parameter of our model with our new objective function"
+        "Now we can replace the `objective_function` parameter of our model with our new objective function"
       ]
     },
     {
@@ -1129,7 +1129,7 @@
       },
       "outputs": [],
       "source": [
-        "model.biomass_function = obj_fun"
+        "model.objective_function = obj_fun"
       ]
     },
     {
@@ -1153,7 +1153,7 @@
       },
       "outputs": [],
       "source": [
-        "model.biomass_function"
+        "model.objective_function"
       ]
     },
     {
@@ -1249,7 +1249,7 @@
         "\n",
         "**Remember!** \n",
         "\n",
-        "In the previous step, we replaced the `biomass_function` of our model. So if we run FVA in our current model, then the first reaction of our model "
+        "In the previous step, we replaced the `objective_function` of our model. So if we run FVA in our current model, then the first reaction of our model "
       ]
     },
     {
@@ -1279,7 +1279,7 @@
         "id": "qGVto8ckp7LS"
       },
       "source": [
-        "We can go with that or if we need to go back in using the actual biomass function of our model, we can either do the same process as before to change again the `biomass_function` or we can just build a new instance of the `MetabolicNetwork` class. "
+        "We can go with that or if we need to go back in using the actual biomass function of our model, we can either do the same process as before to change again the `objective_function` or we can just build a new instance of the `MetabolicNetwork` class. "
       ]
     },
     {