predator/prey model

demos/predator_prey_1.py

@@ -0,0 +1,79 @@
+from decimal import Decimal
+import numpy as np
+from datetime import timedelta
+
+from SimCAD import configs
+from SimCAD.configuration import Configuration
+from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
+    ep_time_step
+
+seed = {
+}
+
+# Behaviors
+# There are no behaviors in this example
+
+# Mechanisms
+# There are no mechanisms in this example
+
+# Parameters
+alfa = 1.1e-3
+beta = 0.4e-3
+gama = 0.4e-3
+delta = 0.1e-3
+
+# Exogenous States
+def prey_model(step, sL, s, _input):
+    y = 'Prey'
+    x = s['Prey'] + alfa*s['Prey'] - beta*s['Prey']*s['Predator']
+    return (y, x)
+
+def predator_model(step, sL, s, _input):
+    y = 'Predator'
+    x = s['Predator'] + delta*s['Prey']*s['Predator'] - gama*s['Predator']
+    return (y, x)
+
+ts_format = '%Y-%m-%d %H:%M:%S'
+t_delta = timedelta(days=0, minutes=0, seconds=1)
+def time_model(step, sL, s, _input):
+    y = 'timestamp'
+    x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
+    return (y, x)
+
+# Genesis States
+genesis_states = {
+    'Prey': 10,
+    'Predator': 10,
+    'timestamp': '2018-01-01 00:00:00'
+}
+
+# remove `exo_update_per_ts` to update every ts
+exogenous_states = exo_update_per_ts(
+    {
+    'Prey': prey_model,
+    'Predator': predator_model,
+    'timestamp': time_model
+    }
+)
+
+env_processes = {
+}
+
+mechanisms = {
+}
+
+sim_config = {
+    'N': 1,
+    'T': range(50000)
+}
+
+configs.append(
+    Configuration(
+        sim_config=sim_config,
+        state_dict=genesis_states,
+        seed=seed,
+        exogenous_states=exogenous_states,
+        env_processes=env_processes,
+        mechanisms=mechanisms
+    )
+)

demos/predator_prey_2.py

@@ -0,0 +1,79 @@
+from decimal import Decimal
+import numpy as np
+from datetime import timedelta
+
+from SimCAD import configs
+from SimCAD.configuration import Configuration
+from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
+    ep_time_step
+
+seed = {
+}
+
+# Behaviors
+# There are no behaviors in this example
+
+# Mechanisms
+# There are no mechanisms in this example
+
+# Parameters
+alfa = 0.8e-3
+beta = 0.4e-3
+gama = 0.4e-3
+delta = 0.1e-3
+
+# Exogenous States
+def prey_model(step, sL, s, _input):
+    y = 'Prey'
+    x = s['Prey'] + alfa*s['Prey'] - beta*s['Prey']*s['Predator']
+    return (y, x)
+
+def predator_model(step, sL, s, _input):
+    y = 'Predator'
+    x = s['Predator'] + delta*s['Prey']*s['Predator'] - gama*s['Predator']
+    return (y, x)
+
+ts_format = '%Y-%m-%d %H:%M:%S'
+t_delta = timedelta(days=0, minutes=0, seconds=1)
+def time_model(step, sL, s, _input):
+    y = 'timestamp'
+    x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
+    return (y, x)
+
+# Genesis States
+genesis_states = {
+    'Prey': 10,
+    'Predator': 10,
+    'timestamp': '2018-01-01 00:00:00'
+}
+
+# remove `exo_update_per_ts` to update every ts
+exogenous_states = exo_update_per_ts(
+    {
+    'Prey': prey_model,
+    'Predator': predator_model,
+    'timestamp': time_model
+    }
+)
+
+env_processes = {
+}
+
+mechanisms = {
+}
+
+sim_config = {
+    'N': 1,
+    'T': range(50000)
+}
+
+configs.append(
+    Configuration(
+        sim_config=sim_config,
+        state_dict=genesis_states,
+        seed=seed,
+        exogenous_states=exogenous_states,
+        env_processes=env_processes,
+        mechanisms=mechanisms
+    )
+)