How to use pymoca - 10 common examples

# do not edit, generated by pymoca

from __future__ import print_function, division
import sympy
import sympy.physics.mechanics as mech
from pymoca.backends.sympy.runtime import OdeModel
from sympy import sin, cos, tan


class Quad(OdeModel):

    def __init__(self):

        super(Quad, self).__init__()

        # states
        phi, theta, psi_, P, Q, R, x, y, z, U, V, W = mech.dynamicsymbols('phi, theta, psi_, P, Q, R, x, y, z, U, V, W')
        self.x = sympy.Matrix([phi, theta, psi_, P, Q, R, x, y, z, U, V, W])
        self.x0 = {
            phi : 0,
            theta : 0,
            psi_ : 0,
            P : 0,
            Q : 0,
            R : 0,
            x : 0,

# do not edit, generated by pymoca

from __future__ import print_function, division
import sympy
import sympy.physics.mechanics as mech
from pymoca.backends.sympy.runtime import OdeModel
from sympy import sin, cos, tan


class Aircraft(OdeModel):

    def __init__(self):

        super(Aircraft, self).__init__()

        # states
        body__x, body__v_x = mech.dynamicsymbols('body.x, body.v_x')
        self.x = sympy.Matrix([body__x, body__v_x])
        self.x0 = {
            body__x : 0,
            body__v_x : 0,
            }

        # variables
        body__f_x, body__a_x, accel__a_x, accel__ma_x, accel__b_x__u, accel__b_x__y = mech.dynamicsymbols('body.f_x, body.a_x, accel.a_x, accel.ma_x, accel.b_x.u, accel.b_x.y')
        self.v = sympy.Matrix([body__f_x, body__a_x, accel__a_x, accel__ma_x, accel__b_x__u, accel__b_x__y])

# do not edit, generated by pymoca

from __future__ import print_function, division
import sympy
import sympy.physics.mechanics as mech
from pymoca.backends.sympy.runtime import OdeModel
from sympy import sin, cos, tan


class SpringSystem(OdeModel):

    def __init__(self):

        super(SpringSystem, self).__init__()

        # states
        x, v_x = mech.dynamicsymbols('x, v_x')
        self.x = sympy.Matrix([x, v_x])
        self.x0 = {
            x : 1.0,
            v_x : 1.0,
            }

        # variables
        spring__x, spring__f, damper__v, damper__f = mech.dynamicsymbols('spring.x, spring.f, damper.v, damper.f')
        self.v = sympy.Matrix([spring__x, spring__f, damper__v, damper__f])

# do not edit, generated by pymoca

from __future__ import print_function, division
import sympy
import sympy.physics.mechanics as mech
from pymoca.backends.sympy.runtime import OdeModel
from sympy import sin, cos, tan


class Estimator(OdeModel):

    def __init__(self):

        super(Estimator, self).__init__()

        # states
        x = mech.dynamicsymbols('x')
        self.x = sympy.Matrix([x])
        self.x0 = {
            x : 0,
            }

        # variables
        y = mech.dynamicsymbols('y')
        self.v = sympy.Matrix([y])

def get_derivative(self, s):

        # Case 1: s is a constant, e.g. MX(5)
        if ca.MX(s).is_constant():
            return 0

        # Case 2: s is a symbol, e.g. MX(x)
        elif s.is_symbolic():
            if s.name() not in self.derivative:
                if len(self.for_loops) > 0 and s in self.for_loops[-1].indexed_symbols:
                    # Create a new indexed symbol, referencing to the for loop index inside the vector derivative symbol.
                    for_loop_symbol = self.for_loops[-1].indexed_symbols[s]
                    s_without_index = self.get_mx(ast.ComponentRef(name=for_loop_symbol.tree.name))
                    der_s_without_index = self.get_derivative(s_without_index)
                    if ca.MX(der_s_without_index).is_symbolic():
                        return self.get_indexed_symbol(ast.ComponentRef(name=der_s_without_index.name(), indices=for_loop_symbol.tree.indices), der_s_without_index)
                    else:
                        return 0
                else:
                    der_s = _new_mx("der({})".format(s.name()), s.size())
                    # If the derivative contains an expression (e.g. der(x + y)) this method is
                    # called with MX variables that are the result of a ca.symvar call. This
                    # ca.symvar call strips the _modelica_shape field from the MX variable,
                    # therefore we need to find the original MX to get the modelica shape.
                    der_s._modelica_shape = \
                        self.nodes[self.current_class][s.name()]._modelica_shape
                    self.derivative[s.name()] = der_s
                    self.nodes[self.current_class][der_s.name()] = der_s
                    return der_s
            else:
                return self.derivative[s.name()]

def exitExpression(self, tree: ast.Expression):
        if isinstance(tree.operator, ast.ComponentRef):
            op_name = tree.operator.name
        else:
            op_name = tree.operator
        if len(tree.operands) == 1:
            self.xml[tree] = E(
                'operator', name=op_name,
                *[self.xml[c] for c in tree.operands])
        else:
            self.xml[tree] = E(
                'apply', builtin=op_name,
                *[self.xml[c] for c in tree.operands])

if options.flatten_only:
    # Load folder
    _ast = None
    for root, dir, files in os.walk(model_folder, followlinks=True):
        for item in fnmatch.filter(files, "*.mo"):
            logger.info("Parsing {}".format(item))

            with open(os.path.join(root, item), 'r') as f:
                if _ast is None:
                    _ast = parser.parse(f.read())
                else:
                    _ast.extend(parser.parse(f.read()))

    logger.info("Flattening")

    _ast = tree.flatten(_ast, ast.ComponentRef.from_string(model_name))
    print(_ast)
else:
    # Set CasADi installation folder
    if options.casadi_folder is not None:
        sys.path.append(options.casadi_folder)

    from pymoca.backends.casadi.api import transfer_model
    import casadi as ca

    logger.info("Generating CasADi model")
    
    compiler_options = \
        {'replace_constants': True,
         'replace_parameter_expressions': True,
         'eliminable_variable_expression': r'_\w+',
         'detect_aliases': True,

def generate(ast_tree: ast.Tree, model_name: str):
    """
    :param ast_tree: AST to generate from
    :param model_name: class to generate
    :return: sympy source code for model
    """
    component_ref = ast.ComponentRef.from_string(model_name)
    ast_tree_new = copy.deepcopy(ast_tree)
    ast_walker = TreeWalker()
    flat_tree = flatten(ast_tree_new, component_ref)
    sympy_gen = SympyGenerator()
    ast_walker.walk(sympy_gen, flat_tree)
    return sympy_gen.src[flat_tree]

def get_derivative(self, s):

        # Case 1: s is a constant, e.g. MX(5)
        if ca.MX(s).is_constant():
            return 0

        # Case 2: s is a symbol, e.g. MX(x)
        elif s.is_symbolic():
            if s.name() not in self.derivative:
                if len(self.for_loops) > 0 and s in self.for_loops[-1].indexed_symbols:
                    # Create a new indexed symbol, referencing to the for loop index inside the vector derivative symbol.
                    for_loop_symbol = self.for_loops[-1].indexed_symbols[s]
                    s_without_index = self.get_mx(ast.ComponentRef(name=for_loop_symbol.tree.name))
                    der_s_without_index = self.get_derivative(s_without_index)
                    if ca.MX(der_s_without_index).is_symbolic():
                        return self.get_indexed_symbol(ast.ComponentRef(name=der_s_without_index.name(), indices=for_loop_symbol.tree.indices), der_s_without_index)
                    else:
                        return 0
                else:
                    der_s = _new_mx("der({})".format(s.name()), s.size())
                    # If the derivative contains an expression (e.g. der(x + y)) this method is
                    # called with MX variables that are the result of a ca.symvar call. This
                    # ca.symvar call strips the _modelica_shape field from the MX variable,
                    # therefore we need to find the original MX to get the modelica shape.
                    der_s._modelica_shape = \
                        self.nodes[self.current_class][s.name()]._modelica_shape
                    self.derivative[s.name()] = der_s
                    self.nodes[self.current_class][der_s.name()] = der_s
                    return der_s

def generate(ast_tree: ast.Tree, model_name: str, options: Dict[str, bool]=None) -> Model:
    """
    :param ast_tree: AST to generate from
    :param model_name: class to generate
    :param options: dictionary of generator options
    :return: casadi model
    """
    options = _merge_default_options(options)


    component_ref = ast.ComponentRef.from_string(model_name)
    ast_walker = GeneratorWalker()
    flat_tree = flatten(ast_tree, component_ref)
    component_ref_tuple = component_ref.to_tuple()
    casadi_gen = Generator(flat_tree, component_ref_tuple[-1], options)
    ast_walker.walk(casadi_gen, flat_tree)
    return casadi_gen.model