Simulation_Execution.md

Simulation Execution

System Simulations are executed with the execution engine executor (cadCAD.engine.Executor) given System Model Configurations. There are multiple simulation Execution Modes and Execution Contexts.

Steps:

1. Choose Execution Mode:

   • Simulation Execution Modes:

     cadCAD executes a process per System Model Configuration and a thread per System Simulation.

   Class: cadCAD.engine.ExecutionMode

   Attributes:

   • Single Process: A single process Execution Mode for a single System Model Configuration (Example: cadCAD.engine.ExecutionMode().single_proc).
   • Multi-Process: Multiple process Execution Mode for System Model Simulations which executes on a thread per given System Model Configuration (Example: cadCAD.engine.ExecutionMode().multi_proc).

2. Create Execution Context using Execution Mode:

from cadCAD.engine import ExecutionMode, ExecutionContext
exec_mode = ExecutionMode()
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)

3. Create Simulation Executor

from cadCAD.engine import Executor
from cadCAD import configs
simulation = Executor(exec_context=single_proc_ctx, configs=configs)

4. Execute Simulation: Produce System Event Dataset

A Simulation execution produces a System Event Dataset and the Tensor Field applied to initial states used to create it.

import pandas as pd
raw_system_events, tensor_field = simulation.execute()

# Simulation Result Types:
# raw_system_events: List[dict] 
# tensor_field: pd.DataFrame

# Result System Events DataFrame
simulation_result = pd.DataFrame(raw_system_events)

Example Tensor Field

+----+-----+--------------------------------+--------------------------------+
|    |   m | b1                             | s1                             |
|----+-----+--------------------------------+--------------------------------|
|  0 |   1 | <function p1m1 at 0x10c458ea0> | <function s1m1 at 0x10c464510> |
|  1 |   2 | <function p1m2 at 0x10c464048> | <function s1m2 at 0x10c464620> |
|  2 |   3 | <function p1m3 at 0x10c464400> | <function s1m3 at 0x10c464730> |
+----+-----+--------------------------------+--------------------------------+

Example Result: System Events DataFrame

+----+-------+------------+-----------+------+-----------+
|    |   run |   timestep |   substep |   s1 | s2        |
|----+-------+------------+-----------+------+-----------|
|  0 |     1 |          0 |         0 |    0 | 0.0       |
|  1 |     1 |          1 |         1 |    1 | 4         |
|  2 |     1 |          1 |         2 |    2 | 6         |
|  3 |     1 |          1 |         3 |    3 | [ 30 300] |
|  4 |     2 |          0 |         0 |    0 | 0.0       |
|  5 |     2 |          1 |         1 |    1 | 4         |
|  6 |     2 |          1 |         2 |    2 | 6         |
|  7 |     2 |          1 |         3 |    3 | [ 30 300] |
+----+-------+------------+-----------+------+-----------+

Execution Examples:

Single Simulation Execution (Single Process Execution)

Example System Model Configurations:

• System Model A: /documentation/examples/sys_model_A.py
• System Model B: /documentation/examples/sys_model_B.py Example Simulation Executions:
• System Model A: /documentation/examples/sys_model_A_exec.py
• System Model B: /documentation/examples/sys_model_B_exec.py

import pandas as pd
from tabulate import tabulate
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from documentation.examples import sys_model_A
from cadCAD import configs

exec_mode = ExecutionMode()

# Single Process Execution using a Single System Model Configuration:
# sys_model_A
sys_model_A = [configs[0]] # sys_model_A
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
sys_model_A_simulation = Executor(exec_context=single_proc_ctx, configs=sys_model_A)

sys_model_A_raw_result, sys_model_A_tensor_field = sys_model_A_simulation.execute()
sys_model_A_result = pd.DataFrame(sys_model_A_raw_result)
print()
print("Tensor Field: sys_model_A")
print(tabulate(sys_model_A_tensor_field, headers='keys', tablefmt='psql'))
print("Result: System Events DataFrame")
print(tabulate(sys_model_A_result, headers='keys', tablefmt='psql'))
print()

Multiple Simulation Execution

• Multi Process Execution

Documentation: Simulation Execution Example Simulation Executions:: /documentation/examples/sys_model_AB_exec.py Example System Model Configurations:

• System Model A: /documentation/examples/sys_model_A.py
• System Model B: /documentation/examples/sys_model_B.py

import pandas as pd
from tabulate import tabulate
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from documentation.examples import sys_model_A, sys_model_B
from cadCAD import configs

exec_mode = ExecutionMode()

# # Multiple Processes Execution using Multiple System Model Configurations:
# # sys_model_A & sys_model_B
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
sys_model_AB_simulation = Executor(exec_context=multi_proc_ctx, configs=configs)

i = 0
config_names = ['sys_model_A', 'sys_model_B']
for sys_model_AB_raw_result, sys_model_AB_tensor_field in sys_model_AB_simulation.execute():
    sys_model_AB_result = pd.DataFrame(sys_model_AB_raw_result)
    print()
    print(f"Tensor Field: {config_names[i]}")
    print(tabulate(sys_model_AB_tensor_field, headers='keys', tablefmt='psql'))
    print("Result: System Events DataFrame:")
    print(tabulate(sys_model_AB_result, headers='keys', tablefmt='psql'))
    print()
    i += 1

• Parameter Sweep

Documentation: System Model Parameter Sweep Example: /documentation/examples/param_sweep.py

import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from documentation.examples import param_sweep
from cadCAD import configs

exec_mode = ExecutionMode()
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run = Executor(exec_context=multi_proc_ctx, configs=configs)

for raw_result, tensor_field in run.execute():
    result = pd.DataFrame(raw_result)
    print()
    print("Tensor Field:")
    print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
    print("Output:")
    print(tabulate(result, headers='keys', tablefmt='psql'))
    print()