Added more models

Author: fabriciocravo

for_local_use.py

@@ -3,54 +3,6 @@
 
 if __name__ == '__main__':
 
-    A = BaseSpecies()
-    p1, p2 = ModelParameters([1], [1 / u.hour, 2 / u.hour, 3 / u.hour])
-    A >> Zero[p1 * p2]
-    A(100)
-    S = Simulation(A)
-    S.run(duration=5 * u.hour, plot_data=False, level=-1)
-    print(S.results)
-
-    exit()
-
-    """
-        This is the Tree model from the paper
-        We have a population of Trees
-        The Trees can die, age, have different colors and be in two different forests
-        The colors can change randomly from time to time
-        All old Trees can reproduce, but the Three is born green and young 
-    """
-    Ager, Mortal, Colored, Location = BaseSpecies()
-    Colored.green, Colored.yellow, Colored.brown
-    Location.dense, Location.sparse
-    Ager.young >> Ager.old[1 / 10 / u.year]
-    Mortal >> Zero [lambda r1: 0.1 / u.year if r1.old else 0]
-    Tree = Ager * Colored * Mortal * Location
-
-    # replication
-    Tree.old >> Tree + Tree.green.young[0.1/u.year]
-
-    # competition
-    Tree.dense.old + Tree.dense.young >> Tree.dense.old[1e-10 * u.decimeter ** 2 / u.year]
-
-    # reproduction
-    bf = 1e-10 * u.decimeter ** 2 / u.year
-    rep_r = lambda t1, t2: 5 * bf if (Location(t1) == Location(t2) and Colored(t1) == Colored(t2)) else bf
-    2 * Tree >> 2 * Tree + Tree.yound[rep_r]
-
-    # color cycling
-    colors = ['green', 'yellow', 'brown']
-    for color, next_color in zip(colors, colors[1:] + colors[:1]):
-        Tree.c(color) >> Tree.c(next_color)[10/u.year]
-
-    # initial conditions
-    Tree.dense(50), Tree.dense.old(50), Tree.sparse(50), Tree.sparse.old(50)
-    # initial conditions
-    Tree.dense(50), Tree.dense.old(50), Tree.sparse(50), Tree.sparse.old(50)
-    MySim = Simulation(Tree)
-    MySim.run(volume = 1 * u.meter ** 2, unit_x='year',
-              duration=100 * u.years, repetitions=3, output_concentration=False,
-              simulation_method='stochastic', save_data=False, plot_data=False)
-    MySim.plot_stochastic(Tree.dense, Tree.sparse, Tree.brown)
+    pass
 
 

mobspy/modules/meta_class.py

@@ -471,7 +471,6 @@ def __rshift__(self, other):
         return reaction
 
     # Reacting_Species call
-
     def __call__(self, quantity):
         """
             The call operator here is used to add counts to species non-default state. This stores the characteristics
@@ -1022,14 +1021,17 @@ def __call__(self, quantity):
         # If called within a Any context, add the characteristics of the Any context to the specie called.
         # The specie becomes a reacting specie.
         if len(Species.meta_specie_named_any_context) != 0:
+
             for i in Species.meta_specie_named_any_context:
                 self.c(i)
             quantity_dict = self.add_quantities(Species.meta_specie_named_any_context.copy(), quantity)
 
         # Check if the quantity is a valid type and add the new count to the specie
         elif (type(quantity) == int or type(quantity) == float or isinstance(quantity, Quantity)
               or isinstance(quantity, mp_Mobspy_Parameter)) and not asgi_Assign.check_context():
+
             quantity_dict = self.add_quantities('std$', quantity)
+
         elif asgi_Assign.check_context():
             self.assign(quantity)
         elif isinstance(quantity, me_Specific_Species_Operator):
@@ -1061,6 +1063,7 @@ def __call__(self, quantity):
         else:
             return self
 
+    # Species add_quantities
     def add_quantities(self, characteristics, quantity):
         """
             This function was implemented because Python can't accept sets as dictionary keys