Added the spatial birth death model to testing models.

test/models/birth_death.py

@@ -0,0 +1,50 @@
+# SpatialPy is a Python 3 package for simulation of
+# spatial deterministic/stochastic reaction-diffusion-advection problems
+# Copyright (C) 2019 - 2023 SpatialPy developers.
+
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU GENERAL PUBLIC LICENSE Version 3 as
+# published by the Free Software Foundation.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU GENERAL PUBLIC LICENSE Version 3 for more details.
+
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+''' spatialpy model file for the spatial birth death example. '''
+import spatialpy
+
+def create_birth_death():
+    ''' Create the spatial birth death model. '''
+    model = spatialpy.Model(name='Spatial Birth-Death')
+
+    model.HABITAT = "Habitat"
+
+    domain = spatialpy.Domain.create_2D_domain(
+        xlim=(0, 1), ylim=(0, 1), numx=10, numy=10, type_id=model.HABITAT, fixed=True
+    )
+    model.add_domain(domain)
+
+    rabbits = spatialpy.Species(name='Rabbits', diffusion_coefficient=0.1)
+    model.add_species(rabbits)
+
+    init_rabbit_pop = spatialpy.ScatterInitialCondition(species='Rabbits', count=100)
+    model.add_initial_condition(init_rabbit_pop)
+
+    k_birth = spatialpy.Parameter(name='k_birth', expression=10)
+    k_death = spatialpy.Parameter(name='k_death', expression=0.1)
+    model.add_parameter([k_birth, k_death])
+
+    birth = spatialpy.Reaction(
+        name='birth', reactants={}, products={"Rabbits":1}, rate="k_birth"
+    )
+    death = spatialpy.Reaction(
+        name='death', reactants={"Rabbits":1}, products={}, rate="k_death"
+    )
+    model.add_reaction([birth, death])
+
+    tspan = spatialpy.TimeSpan.linspace(t=10, num_points=11, timestep_size=1)
+    model.timespan(tspan)
+    return model