CODEWITHSUNDEEP
pythonstate-machine
Rohit Kumar Singh
Rohit Kumar Singh

Reputation: 669

Flowchart logic in python

I have a flowchart that has components like a state machine. I am not able to decode how to start writing this in python. The TMS is an input list as [1,1,1,1,1,0,1,1,0]

enter image description here

I started writing some code, but got stuck in developing this further.

TMS = [1,1,1,1,1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0]

next_step = False
def test_logic_reset():
  if tms == 1:
    print("reset")
  elif tms == 0:
    run_test_idle()

for i in TMS:
  tms = i
  print(tms)
  test_logic_reset()

Can anyone help in understanding the paradigm which I should use?

Upvotes: 1

Views: 740

Answers (2)

DV82XL
DV82XL

Reputation: 6659

This seems conducive to a state machine implementation. You can implement this from scratch, or you can use a Python library like pytransitions. This library has many features, like calling a method before or after a transition, executing a transition only if a condition is met, handling errors for invalid states/transitions, etc. A basic solution would look something like this:

from transitions import Machine

class StateMachine:

    states = ['test_logic_reset', 'run_test_idle', 'another_state']

    def __init__(self):
        self.machine = Machine(model=self, states=StateMachine.states, initial='test_logic_reset')

        # you can also use named arguments: trigger, source, dest
        self.machine.add_transition('input_0', 'test_logic_reset', 'run_test_idle')
        self.machine.add_transition('input_1', 'test_logic_reset', '=')
        self.machine.add_transition('input_0', 'run_test_idle', '=')
        self.machine.add_transition('input_1', 'run_test_idle', 'another_state')

    def trigger(self, value):
        if value == 0:
            self.input_0()
        elif value == 1:
            self.input_1()
        else:
            raise NotImplementedError

You can then call it like this:

TMS = [1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0]
sm = StateMachine()
for i in TMS:
    start_state = sm.state
    sm.trigger(i)
    end_state = sm.state
    print(f'Input: {i}, Transition: {start_state} -> {end_state}')

Upvotes: 1

greenBox
greenBox

Reputation: 552

This may help you along the right path. Based on the part of the diagram that is visible, it might be best to model the finite state machine with a class, where the key required for the machine to reach the next state is determined by the current state.

class FSM:
    TEST_LOGIC_REST = 'TEST_LOGIC_REST'
    RUN_TEST_IDLE = 'RUN_TEST_IDLE'
    FINAL_STATE = 'FINAL_STATE'

    states = {
        TEST_LOGIC_REST: (0, RUN_TEST_IDLE),
        RUN_TEST_IDLE: (1, FINAL_STATE),
        FINAL_STATE: (None, None)
    }

    def __init__(self):
        self.state = FSM.TEST_LOGIC_REST
     

    def input(self, n):
        keyValue, nextState = FSM.states[self.state]
        if keyValue == n:
            self.state = nextState


    def __str__(self):
        return f'FSM(state={self.state})'


if __name__ == '__main__':
    TMS = [1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0]
    fsm = FSM()

    for i in TMS:
        fsm.input(i)
        print(f'input: {i},', fsm)

Upvotes: 1

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